From e5c3fcab468c144d893ca0244ce9de652f83fa6e Mon Sep 17 00:00:00 2001 From: Edward Donner Date: Tue, 4 Nov 2025 07:26:42 -0500 Subject: [PATCH] Updated Week 5 with November version --- .gitignore | 4 + pyproject.toml | 4 + uv.lock | 3504 +++++++++++------ week5/app.py | 61 + week5/day1.ipynb | 188 +- week5/day2.ipynb | 422 +- week5/day3.ipynb | 340 +- week5/day4.5.ipynb | 424 -- week5/day4.ipynb | 537 +-- week5/day5.ipynb | 713 ++-- week5/evaluation/eval.py | 248 ++ week5/evaluation/test.py | 24 + week5/evaluation/tests.jsonl | 150 + week5/evaluator.py | 236 ++ week5/implementation/answer.py | 61 + week5/implementation/ingest.py | 67 + week5/knowledge-base/company/about.md | 16 +- week5/knowledge-base/company/careers.md | 138 +- week5/knowledge-base/company/culture.md | 37 + week5/knowledge-base/company/overview.md | 57 +- week5/knowledge-base/contracts/.md | 63 - ...dvantage Medical Coverage for Healthllm.md | 93 + ...owered Enterprise Reinsurance Solution.md} | 2 +- ...with Atlantic Risk Solutions for Bizllm.md | 101 + ... with ConnectInsure Agency for Markellm.md | 141 + ...Continental Commercial Group for Bizllm.md | 131 + ...ct with DriveSmart Insurance for Carllm.md | 198 + ...owered Enterprise Reinsurance Solution.md} | 2 +- ...th Evergreen Life Insurance for Lifellm.md | 137 + ...stTrack Insurance Services for Claimllm.md | 73 + ...rtress Business Underwriters for Bizllm.md | 87 + ...ntract with GlobalRe Partners for Rellm.md | 292 ++ ...with Guardian Life Partners for Lifellm.md | 121 + ...with Harmony Health Plans for Healthllm.md | 152 + ...ith Heritage Life Assurance for Lifellm.md | 100 + ...ith Metropolitan Life Group for Lifellm.md | 199 + ...th National Claims Network for Claimllm.md | 182 + ...ith Premier Adjusters Inc. for Claimllm.md | 123 + ...th Rapid Claims Associates for Claimllm.md | 137 + ...afeHaven Property Insurance for Homellm.md | 250 ++ ... Summit Commercial Insurance for Bizllm.md | 81 + ...nited Healthcare Alliance for Healthllm.md | 257 ++ ...th WellCare Insurance Co. for Healthllm.md | 166 + week5/knowledge-base/employees/Alex Chen.md | 7 +- week5/knowledge-base/employees/Alex Harper.md | 7 +- .../knowledge-base/employees/Alex Thomson.md | 7 +- .../knowledge-base/employees/Amanda Foster.md | 67 + .../employees/Avery Lancaster.md | 7 +- .../employees/Brandon Walker.md | 47 + .../employees/Carlos Rodriguez.md | 55 + week5/knowledge-base/employees/Daniel Park.md | 51 + week5/knowledge-base/employees/David Kim.md | 47 + .../knowledge-base/employees/Emily Carter.md | 7 +- week5/knowledge-base/employees/Emily Tran.md | 7 +- .../knowledge-base/employees/James Wilson.md | 70 + .../employees/Jennifer Adams.md | 41 + week5/knowledge-base/employees/Jessica Liu.md | 50 + .../knowledge-base/employees/Jordan Blake.md | 7 +- .../employees/Jordan K. Bishop.md | 1 + week5/knowledge-base/employees/Kevin Zhang.md | 51 + .../knowledge-base/employees/Lisa Anderson.md | 55 + .../employees/Marcus Johnson.md | 52 + .../employees/Maxine Thompson.md | 9 +- .../knowledge-base/employees/Maya Thompson.md | 46 + .../employees/Michael O'Brien.md | 63 + .../employees/Michelle Rivera.md | 55 + week5/knowledge-base/employees/Nina Patel.md | 54 + .../employees/Oliver Spencer.md | 7 +- .../knowledge-base/employees/Priya Sharma.md | 60 + .../employees/Rachel Martinez.md | 53 + week5/knowledge-base/employees/Robert Chen.md | 67 + .../employees/Samantha Greene.md | 1 + .../employees/Samuel Trenton.md | 7 +- .../employees/Sarah Williams.md | 50 + .../knowledge-base/employees/Tyler Brooks.md | 41 + week5/knowledge-base/products/Bizllm.md | 59 + week5/knowledge-base/products/Claimllm.md | 62 + week5/knowledge-base/products/Healthllm.md | 56 + week5/knowledge-base/products/Lifellm.md | 52 + week5/pro_implementation/answer.py | 145 + week5/pro_implementation/ingest.py | 146 + 81 files changed, 9263 insertions(+), 2725 deletions(-) create mode 100644 week5/app.py delete mode 100644 week5/day4.5.ipynb create mode 100644 week5/evaluation/eval.py create mode 100644 week5/evaluation/test.py create mode 100644 week5/evaluation/tests.jsonl create mode 100644 week5/evaluator.py create mode 100644 week5/implementation/answer.py create mode 100644 week5/implementation/ingest.py create mode 100644 week5/knowledge-base/company/culture.md delete mode 100644 week5/knowledge-base/contracts/.md create mode 100644 week5/knowledge-base/contracts/Contract with Advantage Medical Coverage for Healthllm.md rename week5/knowledge-base/contracts/{Contract with Apex Reinsurance for Rellm.md => Contract with Apex Reinsurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md} (99%) create mode 100644 week5/knowledge-base/contracts/Contract with Atlantic Risk Solutions for Bizllm.md create mode 100644 week5/knowledge-base/contracts/Contract with ConnectInsure Agency for Markellm.md create mode 100644 week5/knowledge-base/contracts/Contract with Continental Commercial Group for Bizllm.md create mode 100644 week5/knowledge-base/contracts/Contract with DriveSmart Insurance for Carllm.md rename week5/knowledge-base/contracts/{Contract with EverGuard Insurance for Rellm.md => Contract with EverGuard Insurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md} (98%) create mode 100644 week5/knowledge-base/contracts/Contract with Evergreen Life Insurance for Lifellm.md create mode 100644 week5/knowledge-base/contracts/Contract with FastTrack Insurance Services for Claimllm.md create mode 100644 week5/knowledge-base/contracts/Contract with Fortress Business Underwriters for Bizllm.md create mode 100644 week5/knowledge-base/contracts/Contract with GlobalRe Partners for Rellm.md create mode 100644 week5/knowledge-base/contracts/Contract with Guardian Life Partners for Lifellm.md create mode 100644 week5/knowledge-base/contracts/Contract with Harmony Health Plans for Healthllm.md create mode 100644 week5/knowledge-base/contracts/Contract with Heritage Life Assurance for Lifellm.md create mode 100644 week5/knowledge-base/contracts/Contract with Metropolitan Life Group for Lifellm.md create mode 100644 week5/knowledge-base/contracts/Contract with National Claims Network for Claimllm.md create mode 100644 week5/knowledge-base/contracts/Contract with Premier Adjusters Inc. for Claimllm.md create mode 100644 week5/knowledge-base/contracts/Contract with Rapid Claims Associates for Claimllm.md create mode 100644 week5/knowledge-base/contracts/Contract with SafeHaven Property Insurance for Homellm.md create mode 100644 week5/knowledge-base/contracts/Contract with Summit Commercial Insurance for Bizllm.md create mode 100644 week5/knowledge-base/contracts/Contract with United Healthcare Alliance for Healthllm.md create mode 100644 week5/knowledge-base/contracts/Contract with WellCare Insurance Co. for Healthllm.md create mode 100644 week5/knowledge-base/employees/Amanda Foster.md create mode 100644 week5/knowledge-base/employees/Brandon Walker.md create mode 100644 week5/knowledge-base/employees/Carlos Rodriguez.md create mode 100644 week5/knowledge-base/employees/Daniel Park.md create mode 100644 week5/knowledge-base/employees/David Kim.md create mode 100644 week5/knowledge-base/employees/James Wilson.md create mode 100644 week5/knowledge-base/employees/Jennifer Adams.md create mode 100644 week5/knowledge-base/employees/Jessica Liu.md create mode 100644 week5/knowledge-base/employees/Kevin Zhang.md create mode 100644 week5/knowledge-base/employees/Lisa Anderson.md create mode 100644 week5/knowledge-base/employees/Marcus Johnson.md create mode 100644 week5/knowledge-base/employees/Maya Thompson.md create mode 100644 week5/knowledge-base/employees/Michael O'Brien.md create mode 100644 week5/knowledge-base/employees/Michelle Rivera.md create mode 100644 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a/week5/app.py b/week5/app.py new file mode 100644 index 0000000..03b5c6f --- /dev/null +++ b/week5/app.py @@ -0,0 +1,61 @@ +import gradio as gr +from dotenv import load_dotenv + +from pro_implementation.answer import answer_question + +load_dotenv(override=True) + + +def format_context(context): + result = "

Relevant Context

\n\n" + for doc in context: + result += f"Source: {doc.metadata['source']}\n\n" + result += doc.page_content + "\n\n" + return result + + +def chat(history): + last_message = history[-1]["content"] + prior = history[:-1] + answer, context = answer_question(last_message, prior) + history.append({"role": "assistant", "content": answer}) + return history, format_context(context) + + +def main(): + def put_message_in_chatbot(message, history): + return "", history + [{"role": "user", "content": message}] + + theme = gr.themes.Soft(font=["Inter", "system-ui", "sans-serif"]) + + with gr.Blocks(title="Insurellm Expert Assistant", theme=theme) as ui: + gr.Markdown("# 🏢 Insurellm Expert Assistant\nAsk me anything about Insurellm!") + + with gr.Row(): + with gr.Column(scale=1): + chatbot = gr.Chatbot( + label="💬 Conversation", height=600, type="messages", show_copy_button=True + ) + message = gr.Textbox( + label="Your Question", + placeholder="Ask anything about Insurellm...", + show_label=False, + ) + + with gr.Column(scale=1): + context_markdown = gr.Markdown( + label="📚 Retrieved Context", + value="*Retrieved context will appear here*", + container=True, + height=600, + ) + + message.submit( + put_message_in_chatbot, inputs=[message, chatbot], outputs=[message, chatbot] + ).then(chat, inputs=chatbot, outputs=[chatbot, context_markdown]) + + ui.launch(inbrowser=True) + + +if __name__ == "__main__": + main() diff --git a/week5/day1.ipynb b/week5/day1.ipynb index c9d82b0..50a912e 100644 --- a/week5/day1.ipynb +++ b/week5/day1.ipynb @@ -5,19 +5,29 @@ "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", "metadata": {}, "source": [ + "# Welcome to RAG week!!\n", + "\n", "## Expert Knowledge Worker\n", "\n", - "### A question answering agent that is an expert knowledge worker\n", + "### A question answering Assistant that is an expert knowledge worker\n", "### To be used by employees of Insurellm, an Insurance Tech company\n", - "### The agent needs to be accurate and the solution should be low cost.\n", + "### The AI assistant needs to be accurate and the solution should be low cost.\n", "\n", "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy.\n", "\n", - "This first implementation will use a simple, brute-force type of RAG..\n", + "This first implementation will use a simplistic, brute-force type of RAG..\n", "\n", - "### Sidenote: Business applications of this week's projects\n", - "\n", - "RAG is perhaps the most immediately applicable technique of anything that we cover in the course! In fact, there are commercial products that do precisely what we build this week: nuanced querying across large databases of information, such as company contracts or product specs. RAG gives you a quick-to-market, low cost mechanism for adapting an LLM to your business area." + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Business applications of this week's projects

\n", + " RAG is perhaps the most immediately applicable technique of anything that we cover in the course! In fact, there are commercial products that do precisely what we build this week: nuanced querying across large databases of information, such as company contracts or product specs. RAG gives you a quick-to-market, low cost mechanism for adapting an LLM to your business area.\n", + "
" ] }, { @@ -27,41 +37,62 @@ "metadata": {}, "outputs": [], "source": [ - "# imports\n", - "\n", "import os\n", "import glob\n", "from dotenv import load_dotenv\n", + "from pathlib import Path\n", "import gradio as gr\n", "from openai import OpenAI" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "58c85082-e417-4708-9efe-81a5d55d1424", + "cell_type": "markdown", + "id": "810359e6", "metadata": {}, - "outputs": [], "source": [ - "# price is a factor for our company, so we're going to use a low cost model\n", - "\n", - "MODEL = \"gpt-4o-mini\"" + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Important Note

\n", + " \n", + " This lab, and all the labs for Week 5, has been updated to use LangChain 1.0. This is intended to be reviewed with the new video series as of November 2025. If you're reviewing the older video series, then please consider doing git checkout original to revert to the prior code, then later git checkout main to get back to the new code. I have a really exciting week ahead, with evals and Advanced RAG!\n", + " \n", + "
" ] }, { "cell_type": "code", "execution_count": null, - "id": "ee78efcb-60fe-449e-a944-40bab26261af", + "id": "9258e738", "metadata": {}, "outputs": [], "source": [ - "# Load environment variables in a file called .env\n", + "# Setting up\n", "\n", "load_dotenv(override=True)\n", - "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", + "openai_api_key = os.getenv('OPENAI_API_KEY')\n", + "if openai_api_key:\n", + " print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", + "else:\n", + " print(\"OpenAI API Key not set\")\n", + "\n", + "MODEL = \"gpt-4.1-nano\"\n", "openai = OpenAI()" ] }, + { + "cell_type": "markdown", + "id": "3db802f8", + "metadata": {}, + "source": [ + "### Let's read in all employee data into a dictionary" + ] + }, { "cell_type": "code", "execution_count": null, @@ -69,18 +100,24 @@ "metadata": {}, "outputs": [], "source": [ - "# With massive thanks to student Dr John S. for fixing a bug in the below for Windows users!\n", + "knowledge = {}\n", "\n", - "context = {}\n", + "filenames = glob.glob(\"knowledge-base/employees/*\")\n", "\n", - "employees = glob.glob(\"knowledge-base/employees/*\")\n", - "\n", - "for employee in employees:\n", - " name = employee.split(' ')[-1][:-3]\n", - " doc = \"\"\n", - " with open(employee, \"r\", encoding=\"utf-8\") as f:\n", - " doc = f.read()\n", - " context[name]=doc" + "for filename in filenames:\n", + " name = Path(filename).stem.split(' ')[-1]\n", + " with open(filename, \"r\", encoding=\"utf-8\") as f:\n", + " knowledge[name.lower()] = f.read()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9932835", + "metadata": {}, + "outputs": [], + "source": [ + "knowledge" ] }, { @@ -90,7 +127,7 @@ "metadata": {}, "outputs": [], "source": [ - "context[\"Lancaster\"]" + "knowledge[\"lancaster\"]" ] }, { @@ -100,14 +137,12 @@ "metadata": {}, "outputs": [], "source": [ - "products = glob.glob(\"knowledge-base/products/*\")\n", + "filenames = glob.glob(\"knowledge-base/products/*\")\n", "\n", - "for product in products:\n", - " name = product.split(os.sep)[-1][:-3]\n", - " doc = \"\"\n", - " with open(product, \"r\", encoding=\"utf-8\") as f:\n", - " doc = f.read()\n", - " context[name]=doc" + "for filename in filenames:\n", + " name = Path(filename).stem\n", + " with open(filename, \"r\", encoding=\"utf-8\") as f:\n", + " knowledge[name.lower()] = f.read()" ] }, { @@ -117,7 +152,7 @@ "metadata": {}, "outputs": [], "source": [ - "context.keys()" + "knowledge.keys()" ] }, { @@ -127,7 +162,14 @@ "metadata": {}, "outputs": [], "source": [ - "system_message = \"You are an expert in answering accurate questions about Insurellm, the Insurance Tech company. Give brief, accurate answers. If you don't know the answer, say so. Do not make anything up if you haven't been provided with relevant context.\"" + "SYSTEM_PREFIX = \"\"\"\n", + "You represent Insurellm, the Insurance Tech company.\n", + "You are an expert in answering questions about Insurellm; its employees and its products.\n", + "You are provided with additional context that might be relevant to the user's question.\n", + "Give brief, accurate answers. If you don't know the answer, say so.\n", + "\n", + "Relevant context:\n", + "\"\"\"" ] }, { @@ -137,14 +179,37 @@ "metadata": {}, "outputs": [], "source": [ - "def get_relevant_context(message):\n", + "def get_relevant_context_simple(message):\n", + " text = ''.join(ch for ch in message if ch.isalpha() or ch.isspace())\n", + " words = text.lower().split()\n", " relevant_context = []\n", - " for context_title, context_details in context.items():\n", - " if context_title.lower() in message.lower():\n", - " relevant_context.append(context_details)\n", + " for word in words:\n", + " if word in knowledge:\n", + " relevant_context.append(knowledge[word])\n", " return relevant_context " ] }, + { + "cell_type": "markdown", + "id": "2f9fd049", + "metadata": {}, + "source": [ + "## But a more pythonic way:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c94ea125", + "metadata": {}, + "outputs": [], + "source": [ + "def get_relevant_context(message):\n", + " text = ''.join(ch for ch in message if ch.isalpha() or ch.isspace())\n", + " words = text.lower().split()\n", + " return [knowledge[word] for word in words if word in knowledge] " + ] + }, { "cell_type": "code", "execution_count": null, @@ -162,7 +227,7 @@ "metadata": {}, "outputs": [], "source": [ - "get_relevant_context(\"Who is Avery and what is carllm?\")" + "get_relevant_context(\"Who is Lancaster and what is carllm?\")" ] }, { @@ -172,13 +237,14 @@ "metadata": {}, "outputs": [], "source": [ - "def add_context(message):\n", + "def additional_context(message):\n", " relevant_context = get_relevant_context(message)\n", - " if relevant_context:\n", - " message += \"\\n\\nThe following additional context might be relevant in answering this question:\\n\\n\"\n", - " for relevant in relevant_context:\n", - " message += relevant + \"\\n\\n\"\n", - " return message" + " if not relevant_context:\n", + " result = \"There is no additional context relevant to the user's question.\"\n", + " else:\n", + " result = \"The following additional context might be relevant in answering the user's question:\\n\\n\"\n", + " result += \"\\n\\n\".join(relevant_context)\n", + " return result" ] }, { @@ -188,7 +254,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(add_context(\"Who is Alex Lancaster?\"))" + "print(additional_context(\"Who is Alex Lancaster?\"))" ] }, { @@ -199,16 +265,10 @@ "outputs": [], "source": [ "def chat(message, history):\n", - " messages = [{\"role\": \"system\", \"content\": system_message}] + history\n", - " message = add_context(message)\n", - " messages.append({\"role\": \"user\", \"content\": message})\n", - "\n", - " stream = openai.chat.completions.create(model=MODEL, messages=messages, stream=True)\n", - "\n", - " response = \"\"\n", - " for chunk in stream:\n", - " response += chunk.choices[0].delta.content or ''\n", - " yield response" + " system_message = SYSTEM_PREFIX + additional_context(message)\n", + " messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n", + " response = openai.chat.completions.create(model=MODEL, messages=messages)\n", + " return response.choices[0].message.content" ] }, { @@ -228,13 +288,13 @@ "metadata": {}, "outputs": [], "source": [ - "view = gr.ChatInterface(chat, type=\"messages\").launch()" + "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, - "id": "48873d11-2fbd-4329-af27-46c781788561", + "id": "6d2ba45a", "metadata": {}, "outputs": [], "source": [] @@ -242,7 +302,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -256,7 +316,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/week5/day2.ipynb b/week5/day2.ipynb index 99cc542..c11ce42 100644 --- a/week5/day2.ipynb +++ b/week5/day2.ipynb @@ -11,99 +11,326 @@ "### To be used by employees of Insurellm, an Insurance Tech company\n", "### The agent needs to be accurate and the solution should be low cost.\n", "\n", - "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy." + "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy.\n", + "\n", + "## TODAY:\n", + "\n", + "- Part A: We will divide our documents into CHUNKS\n", + "- Part B: We will encode our CHUNKS into VECTORS and put in Chroma\n", + "- Part C: We will visualize our vectors" + ] + }, + { + "cell_type": "markdown", + "id": "0769edb3", + "metadata": {}, + "source": [ + "### PART A: Divide our documents into chunks" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", "metadata": {}, "outputs": [], "source": [ - "# imports\n", - "\n", "import os\n", "import glob\n", + "import tiktoken\n", + "import numpy as np\n", "from dotenv import load_dotenv\n", - "import gradio as gr" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_chroma import Chroma\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from langchain_community.document_loaders import DirectoryLoader, TextLoader\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from sklearn.manifold import TSNE\n", + "import plotly.graph_objects as go" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "802137aa-8a74-45e0-a487-d1974927d7ca", "metadata": {}, - "outputs": [], - "source": [ - "# imports for langchain\n", - "\n", - "from langchain.document_loaders import DirectoryLoader, TextLoader\n", - "from langchain.text_splitter import CharacterTextSplitter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c85082-e417-4708-9efe-81a5d55d1424", - "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OpenAI API Key exists and begins sk-proj-\n" + ] + } + ], "source": [ "# price is a factor for our company, so we're going to use a low cost model\n", "\n", - "MODEL = \"gpt-4o-mini\"\n", - "db_name = \"vector_db\"" + "MODEL = \"gpt-4.1-nano\"\n", + "db_name = \"vector_db\"\n", + "load_dotenv(override=True)\n", + "openai_api_key = os.getenv('OPENAI_API_KEY')\n", + "if openai_api_key:\n", + " print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n", + "else:\n", + " print(\"OpenAI API Key not set\")\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "id": "58c85082-e417-4708-9efe-81a5d55d1424", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 76 files in the knowledge base\n", + "Total characters in knowledge base: 304,434\n" + ] + } + ], + "source": [ + "# How many characters in all the documents?\n", + "\n", + "knowledge_base_path = \"knowledge-base/**/*.md\"\n", + "files = glob.glob(knowledge_base_path, recursive=True)\n", + "print(f\"Found {len(files)} files in the knowledge base\")\n", + "\n", + "entire_knowledge_base = \"\"\n", + "\n", + "for file_path in files:\n", + " with open(file_path, 'r', encoding='utf-8') as f:\n", + " entire_knowledge_base += f.read()\n", + " entire_knowledge_base += \"\\n\\n\"\n", + "\n", + "print(f\"Total characters in knowledge base: {len(entire_knowledge_base):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0b53a099", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total tokens for gpt-4.1-nano: 63,555\n" + ] + } + ], + "source": [ + "# How many tokens in all the documents?\n", + "\n", + "encoding = tiktoken.encoding_for_model(MODEL)\n", + "tokens = encoding.encode(entire_knowledge_base)\n", + "token_count = len(tokens)\n", + "print(f\"Total tokens for {MODEL}: {token_count:,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "ee78efcb-60fe-449e-a944-40bab26261af", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 76 documents\n" + ] + } + ], "source": [ - "# Load environment variables in a file called .env\n", - "\n", - "load_dotenv(override=True)\n", - "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", - "metadata": {}, - "outputs": [], - "source": [ - "# Read in documents using LangChain's loaders\n", - "# Take everything in all the sub-folders of our knowledgebase\n", - "# Thank you Mark D. and Zoya H. for fixing a bug here..\n", + "# Load in everything in the knowledgebase using LangChain's loaders\n", "\n", "folders = glob.glob(\"knowledge-base/*\")\n", "\n", - "# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", - "text_loader_kwargs = {'encoding': 'utf-8'}\n", - "# If that doesn't work, some Windows users might need to uncomment the next line instead\n", - "# text_loader_kwargs={'autodetect_encoding': True}\n", - "\n", "documents = []\n", "for folder in folders:\n", " doc_type = os.path.basename(folder)\n", - " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", + " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs={'encoding': 'utf-8'})\n", " folder_docs = loader.load()\n", " for doc in folder_docs:\n", " doc.metadata[\"doc_type\"] = doc_type\n", - " documents.append(doc)" + " documents.append(doc)\n", + "\n", + "print(f\"Loaded {len(documents)} documents\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "68dab1ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Document(metadata={'source': 'knowledge-base/products/Claimllm.md', 'doc_type': 'products'}, page_content=\"# Product Summary\\n\\n# Claimllm\\n\\n## Summary\\n\\nClaimllm is Insurellm's revolutionary claims processing platform that transforms the claims experience for insurers, adjusters, and policyholders. Powered by advanced AI, machine learning, and computer vision, Claimllm automates claims handling across all insurance lines—from first notice of loss through final settlement. By dramatically reducing processing time, improving accuracy, and enhancing fraud detection, Claimllm enables insurers to deliver exceptional claims service while significantly reducing operational costs. The platform seamlessly integrates with existing policy administration and core systems to create a unified insurance ecosystem.\\n\\n## Features\\n\\n### 1. Intelligent FNOL Processing\\nClaimllm's AI-powered first notice of loss intake captures claim details through multiple channels including mobile apps, web portals, phone integrations, and chatbots. Natural language processing extracts key information automatically, creating structured claim files without manual data entry.\\n\\n### 2. Automated Triage and Routing\\nMachine learning algorithms instantly assess claim severity, complexity, and fraud potential to route claims to appropriate handlers. Simple claims enter straight-through processing while complex cases receive immediate adjuster assignment with pre-populated investigation guidance.\\n\\n### 3. Computer Vision Damage Assessment\\nAdvanced image recognition technology analyzes photos and videos of damaged property or vehicles to estimate repair costs accurately. The system identifies damage types, measures extent, and generates preliminary estimates in minutes rather than days.\\n\\n### 4. Predictive Fraud Detection\\nSophisticated fraud analytics evaluate claims against historical patterns, network analysis, and anomaly detection to flag suspicious claims for investigation. The system learns continuously from confirmed fraud cases to improve detection accuracy.\\n\\n### 5. Smart Document Processing\\nOptical character recognition and natural language processing automatically extract information from medical records, police reports, repair estimates, and supporting documentation. The system validates information against policy terms and flags discrepancies for review.\\n\\n### 6. Dynamic Reserve Setting\\nAI-powered predictive modeling analyzes claim characteristics, historical outcomes, and current case details to recommend accurate reserves. Automated reserve adjustments occur as new information becomes available, ensuring financial accuracy.\\n\\n### 7. Vendor Management Platform\\nIntegrated tools coordinate with repair shops, medical providers, lawyers, and other service providers. Automated assignment based on location, capacity, and performance metrics ensures efficient vendor utilization and quality outcomes.\\n\\n### 8. Payment Automation\\nStraight-through payment processing for approved claims includes direct deposit, digital payments, and traditional check issuance. Multi-party payment splitting handles complex scenarios involving mortgagees, lessors, and medical providers.\\n\\n### 9. Claimant Communication Hub\\nOmnichannel communication tools keep claimants informed via their preferred method—text, email, app notifications, or phone. Automated status updates reduce incoming inquiries while improving customer satisfaction.\\n\\n### 10. Analytics and Reporting\\nComprehensive dashboards track key metrics including cycle time, loss ratios, settlement patterns, and customer satisfaction. Predictive analytics identify process bottlenecks and opportunities for improvement.\\n\\n## Pricing\\n\\nClaimllm offers flexible pricing models to accommodate insurers of all sizes:\\n\\n- **Core Tier:** $4,500/month for smaller insurers processing up to 5,000 claims annually, including essential automation features and standard integrations.\\n- **Advanced Tier:** $9,500/month for mid-sized insurers handling up to 25,000 claims annually, adding computer vision, predictive analytics, and expanded vendor management.\\n- **Enterprise Tier:** Custom pricing for high-volume insurers requiring unlimited claim capacity, full API access, dedicated infrastructure, advanced fraud analytics, and white-label capabilities.\\n\\nAll tiers include comprehensive implementation, claims staff training, and continuous platform enhancements.\\n\\n## Roadmap\\n\\nClaimllm's ambitious development roadmap includes:\\n\\n- **Q1 2025:** Launch of Claimllm version 1.0 with core claims processing automation, FNOL capture, and basic fraud detection.\\n- **Q3 2025:** Introduction of advanced computer vision capabilities for property and auto damage assessment with repair cost estimation.\\n- **Q1 2026:** Release of predictive litigation analytics identifying claims likely to involve attorneys and recommending settlement strategies.\\n- **Q3 2026:** Launch of blockchain-based subrogation network enabling automated inter-carrier claim recovery and settlement.\\n- **Q1 2027:** Introduction of real-time settlement negotiation AI that optimizes settlement amounts based on claim details and historical outcomes.\\n- **Q3 2027:** Expansion into catastrophe claims management with disaster response coordination and large-loss handling capabilities.\\n\\nClaimllm represents the future of insurance claims—faster, smarter, and more customer-centric. Transform your claims operation and deliver the service your policyholders deserve!\\n\")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "documents[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "25987306", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Divided into 413 chunks\n", + "First chunk:\n", + "\n", + "page_content='# Product Summary\n", + "\n", + "# Rellm: AI-Powered Enterprise Reinsurance Solution\n", + "\n", + "## Summary\n", + "\n", + "Rellm is an innovative enterprise reinsurance product developed by Insurellm, designed to transform the way reinsurance companies operate. Harnessing the power of artificial intelligence, Rellm offers an advanced platform that redefines risk management, enhances decision-making processes, and optimizes operational efficiencies within the reinsurance industry. With seamless integrations and robust analytics, Rellm enables insurers to proactively manage their portfolios and respond to market dynamics with agility.\n", + "\n", + "## Features\n", + "\n", + "### AI-Driven Analytics\n", + "Rellm utilizes cutting-edge AI algorithms to provide predictive insights into risk exposures, enabling users to forecast trends and make informed decisions. Its real-time data analysis empowers reinsurance professionals with actionable intelligence.' metadata={'source': 'knowledge-base/products/Rellm.md', 'doc_type': 'products'}\n" + ] + } + ], + "source": [ + "# Divide into chunks using the RecursiveCharacterTextSplitter\n", + "\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", + "chunks = text_splitter.split_documents(documents)\n", + "\n", + "print(f\"Divided into {len(chunks)} chunks\")\n", + "print(f\"First chunk:\\n\\n{chunks[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9eb209db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Document(metadata={'source': 'knowledge-base/contracts/Contract with National Claims Network for Claimllm.md', 'doc_type': 'contracts'}, page_content=\"7. **Business Continuity:** Insurellm provides disaster recovery with 4-hour RTO (Recovery Time Objective) and 1-hour RPO (Recovery Point Objective).\\n\\n---\\n\\n## Renewal\\n\\nThis agreement includes a mutual 120-day renewal notice period. National Claims Network receives guaranteed enterprise pricing for renewal equal to or better than new enterprise customers at renewal time. Contract may be extended in 12-month increments with mutual written agreement.\\n\\n---\\n\\n## Features\\n\\nNational Claims Network will receive the complete Claimllm Enterprise suite:\\n\\n1. **Unlimited Claims Processing:** No volume restrictions, supporting National's processing of 100,000+ claims annually with scalability to 500,000+ claims as business grows.\\n\\n2. **White-Label Platform:** Complete branding customization including:\\n - Custom domain names (claims.nationalclaimsnetwork.com)\\n - Branded mobile apps (iOS and Android)\\n - Customized email templates and communications\\n - Co-branded claimant portals\")" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chunks[100]" + ] + }, + { + "cell_type": "markdown", + "id": "ecee2169", + "metadata": {}, + "source": [ + "### PART B: Make vectors and store in Chroma\n", + "\n", + "In Week 3, you set up a Hugging Face account and got an HF_TOKEN\n", + "\n", + "At this point, you might want to add it to your `.env` file and run `load_dotenv(override=True)`\n", + "\n", + "(This actually shouldn't be required)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vectorstore created with 413 documents\n" + ] + } + ], + "source": [ + "# Pick an embedding model\n", + "\n", + "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n", + "#embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n", + "\n", + "if os.path.exists(db_name):\n", + " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", + "\n", + "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", + "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "252f17e9-3529-4e81-996c-cfa9f08e75a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 413 vectors with 384 dimensions in the vector store\n" + ] + } + ], + "source": [ + "# Let's investigate the vectors\n", + "\n", + "collection = vectorstore._collection\n", + "count = collection.count()\n", + "\n", + "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", + "dimensions = len(sample_embedding)\n", + "print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")" + ] + }, + { + "cell_type": "markdown", + "id": "c30096a7", + "metadata": {}, + "source": [ + "### Part C: Visualize!" ] }, { "cell_type": "code", "execution_count": null, - "id": "252f17e9-3529-4e81-996c-cfa9f08e75a8", + "id": "9d48dcb6", "metadata": {}, "outputs": [], "source": [ - "len(documents)" + "# Prework\n", + "\n", + "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", + "vectors = np.array(result['embeddings'])\n", + "documents = result['documents']\n", + "metadatas = result['metadatas']\n", + "doc_types = [metadata['doc_type'] for metadata in metadatas]\n", + "colors = [['blue', 'green', 'red', 'orange'][['products', 'employees', 'contracts', 'company'].index(t)] for t in doc_types]" ] }, { @@ -113,7 +340,31 @@ "metadata": {}, "outputs": [], "source": [ - "documents[24]" + "# We humans find it easier to visalize things in 2D!\n", + "# Reduce the dimensionality of the vectors to 2D using t-SNE\n", + "# (t-distributed stochastic neighbor embedding)\n", + "\n", + "tsne = TSNE(n_components=2, random_state=42)\n", + "reduced_vectors = tsne.fit_transform(vectors)\n", + "\n", + "# Create the 2D scatter plot\n", + "fig = go.Figure(data=[go.Scatter(\n", + " x=reduced_vectors[:, 0],\n", + " y=reduced_vectors[:, 1],\n", + " mode='markers',\n", + " marker=dict(size=5, color=colors, opacity=0.8),\n", + " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", + " hoverinfo='text'\n", + ")])\n", + "\n", + "fig.update_layout(title='2D Chroma Vector Store Visualization',\n", + " scene=dict(xaxis_title='x',yaxis_title='y'),\n", + " width=800,\n", + " height=600,\n", + " margin=dict(r=20, b=10, l=10, t=40)\n", + ")\n", + "\n", + "fig.show()" ] }, { @@ -123,58 +374,45 @@ "metadata": {}, "outputs": [], "source": [ - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "chunks = text_splitter.split_documents(documents)" + "# Let's try 3D!\n", + "\n", + "tsne = TSNE(n_components=3, random_state=42)\n", + "reduced_vectors = tsne.fit_transform(vectors)\n", + "\n", + "# Create the 3D scatter plot\n", + "fig = go.Figure(data=[go.Scatter3d(\n", + " x=reduced_vectors[:, 0],\n", + " y=reduced_vectors[:, 1],\n", + " z=reduced_vectors[:, 2],\n", + " mode='markers',\n", + " marker=dict(size=5, color=colors, opacity=0.8),\n", + " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", + " hoverinfo='text'\n", + ")])\n", + "\n", + "fig.update_layout(\n", + " title='3D Chroma Vector Store Visualization',\n", + " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", + " width=900,\n", + " height=700,\n", + " margin=dict(r=10, b=10, l=10, t=40)\n", + ")\n", + "\n", + "fig.show()" ] }, { "cell_type": "code", "execution_count": null, - "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", + "id": "65489941", "metadata": {}, "outputs": [], - "source": [ - "len(chunks)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2562754-9052-4aae-92c1-37236435ea06", - "metadata": {}, - "outputs": [], - "source": [ - "chunks[6]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", - "metadata": {}, - "outputs": [], - "source": [ - "doc_types = set(chunk.metadata['doc_type'] for chunk in chunks)\n", - "print(f\"Document types found: {', '.join(doc_types)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "128c73f7-f149-4904-a554-8140941fce0c", - "metadata": {}, - "outputs": [], - "source": [ - "for chunk in chunks:\n", - " if 'CEO' in chunk.page_content:\n", - " print(chunk)\n", - " print(\"_________\")" - ] + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -188,7 +426,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.12" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/week5/day3.ipynb b/week5/day3.ipynb index fc699b9..4c26729 100644 --- a/week5/day3.ipynb +++ b/week5/day3.ipynb @@ -2,349 +2,189 @@ "cells": [ { "cell_type": "markdown", - "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", "metadata": {}, "source": [ - "## Expert Knowledge Worker\n", + "## RAG Day 3\n", "\n", - "### A question answering agent that is an expert knowledge worker\n", - "### To be used by employees of Insurellm, an Insurance Tech company\n", - "### The agent needs to be accurate and the solution should be low cost.\n", + "### Expert Question Answerer for InsureLLM\n", "\n", - "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy." + "LangChain 1.0 implementation of a RAG pipeline.\n", + "\n", + "Using the VectorStore we created last time (with HuggingFace `all-MiniLM-L6-v2`)" ] }, { "cell_type": "code", "execution_count": null, - "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", "metadata": {}, "outputs": [], "source": [ - "# imports\n", - "\n", - "import os\n", - "import glob\n", "from dotenv import load_dotenv\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "from langchain_chroma import Chroma\n", + "from langchain_core.messages import SystemMessage, HumanMessage\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", "import gradio as gr" ] }, { "cell_type": "code", "execution_count": null, - "id": "802137aa-8a74-45e0-a487-d1974927d7ca", "metadata": {}, "outputs": [], "source": [ - "# imports for langchain and Chroma and plotly\n", - "\n", - "from langchain.document_loaders import DirectoryLoader, TextLoader\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.schema import Document\n", - "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", - "from langchain_chroma import Chroma\n", - "import numpy as np\n", - "from sklearn.manifold import TSNE\n", - "import plotly.graph_objects as go" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c85082-e417-4708-9efe-81a5d55d1424", - "metadata": {}, - "outputs": [], - "source": [ - "# price is a factor for our company, so we're going to use a low cost model\n", - "\n", - "MODEL = \"gpt-4o-mini\"\n", - "db_name = \"vector_db\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee78efcb-60fe-449e-a944-40bab26261af", - "metadata": {}, - "outputs": [], - "source": [ - "# Load environment variables in a file called .env\n", - "\n", - "load_dotenv(override=True)\n", - "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", - "metadata": {}, - "outputs": [], - "source": [ - "# Read in documents using LangChain's loaders\n", - "# Take everything in all the sub-folders of our knowledgebase\n", - "\n", - "folders = glob.glob(\"knowledge-base/*\")\n", - "\n", - "# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", - "text_loader_kwargs = {'encoding': 'utf-8'}\n", - "# If that doesn't work, some Windows users might need to uncomment the next line instead\n", - "# text_loader_kwargs={'autodetect_encoding': True}\n", - "\n", - "documents = []\n", - "for folder in folders:\n", - " doc_type = os.path.basename(folder)\n", - " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", - " folder_docs = loader.load()\n", - " for doc in folder_docs:\n", - " doc.metadata[\"doc_type\"] = doc_type\n", - " documents.append(doc)" + "MODEL = \"gpt-4.1-nano\"\n", + "DB_NAME = \"vector_db\"\n", + "load_dotenv(override=True)" ] }, { "cell_type": "markdown", - "id": "f065d4b1-80b7-4e15-abd4-60a83e752ea8", "metadata": {}, "source": [ - "# Please note:\n", - "\n", - "In the next cell, we split the text into chunks.\n", - "\n", - "2 students let me know that the next cell crashed their computer. \n", - "They were able to fix it by changing the chunk_size from 1,000 to 2,000 and the chunk_overlap from 200 to 400. \n", - "This shouldn't be required; but if it happens to you, please make that change! \n", - "(Note that LangChain may give a warning about a chunk being larger than 1,000 - this can be safely ignored).\n", - "\n", - "_With much thanks to Steven W and Nir P for this valuable contribution._" + "### Connect to Chroma; use Hugging Face all-MiniLM-L6-v2" ] }, { "cell_type": "code", "execution_count": null, - "id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", "metadata": {}, "outputs": [], "source": [ - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "chunks = text_splitter.split_documents(documents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", - "metadata": {}, - "outputs": [], - "source": [ - "len(chunks)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", - "metadata": {}, - "outputs": [], - "source": [ - "doc_types = set(chunk.metadata['doc_type'] for chunk in chunks)\n", - "print(f\"Document types found: {', '.join(doc_types)}\")" + "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n", + "vectorstore = Chroma(persist_directory=DB_NAME, embedding_function=embeddings)" ] }, { "cell_type": "markdown", - "id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013", "metadata": {}, "source": [ - "## A sidenote on Embeddings, and \"Auto-Encoding LLMs\"\n", + "### Set up the 2 key LangChain objects: retriever and llm\n", "\n", - "We will be mapping each chunk of text into a Vector that represents the meaning of the text, known as an embedding.\n", + "#### A sidebar on \"temperature\":\n", + "- Controls how diverse the output is\n", + "- A temperature of 0 means that the output should be predictable\n", + "- Higher temperature for more variety in answers\n", "\n", - "OpenAI offers a model to do this, which we will use by calling their API with some LangChain code.\n", + "Some people describe temperature as being like 'creativity' but that's not quite right\n", + "- It actually controls which tokens get selected during inference\n", + "- temperature=0 means: always select the token with highest probability\n", + "- temperature=1 usually means: a token with 10% probability should be picked 10% of the time\n", "\n", - "This model is an example of an \"Auto-Encoding LLM\" which generates an output given a complete input.\n", - "It's different to all the other LLMs we've discussed today, which are known as \"Auto-Regressive LLMs\", and generate future tokens based only on past context.\n", + "Note: a temperature of 0 doesn't mean outputs will always be reproducible. You also need to set a random seed. We will do that in weeks 6-8. (Even then, it's not always reproducible.)\n", "\n", - "Another example of an Auto-Encoding LLMs is BERT from Google. In addition to embedding, Auto-encoding LLMs are often used for classification.\n", - "\n", - "### Sidenote\n", - "\n", - "In week 8 we will return to RAG and vector embeddings, and we will use an open-source vector encoder so that the data never leaves our computer - that's an important consideration when building enterprise systems and the data needs to remain internal." + "Note 2: if you want creativity, use the System Prompt!" ] }, { "cell_type": "code", "execution_count": null, - "id": "78998399-ac17-4e28-b15f-0b5f51e6ee23", "metadata": {}, "outputs": [], "source": [ - "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", - "\n", - "embeddings = OpenAIEmbeddings()\n", - "\n", - "# If you would rather use the free Vector Embeddings from HuggingFace sentence-transformers\n", - "# Then replace embeddings = OpenAIEmbeddings()\n", - "# with:\n", - "# from langchain.embeddings import HuggingFaceEmbeddings\n", - "# embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")" + "retriever = vectorstore.as_retriever()\n", + "llm = ChatOpenAI(temperature=0, model_name=MODEL)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "763e51ff-5787-4a56-8176-36b7c5796fe3", - "metadata": {}, - "outputs": [], - "source": [ - "# Check if a Chroma Datastore already exists - if so, delete the collection to start from scratch\n", - "\n", - "if os.path.exists(db_name):\n", - " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99fe3a37-480f-4d55-be48-120588d5846b", - "metadata": {}, - "outputs": [], - "source": [ - "# Create our Chroma vectorstore!\n", - "\n", - "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", - "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "057868f6-51a6-4087-94d1-380145821550", - "metadata": {}, - "outputs": [], - "source": [ - "# Get one vector and find how many dimensions it has\n", - "\n", - "collection = vectorstore._collection\n", - "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", - "dimensions = len(sample_embedding)\n", - "print(f\"The vectors have {dimensions:,} dimensions\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61e393a0-dd4c-419f-842f-60c1cb3b716b", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "b0d45462-a818-441c-b010-b85b32bcf618", "metadata": {}, "source": [ - "## Visualizing the Vector Store\n", - "\n", - "Let's take a minute to look at the documents and their embedding vectors to see what's going on." + "### These LangChain objects implement the method `invoke()`" ] }, { "cell_type": "code", "execution_count": null, - "id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b", "metadata": {}, "outputs": [], "source": [ - "# Prework\n", - "\n", - "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", - "vectors = np.array(result['embeddings'])\n", - "documents = result['documents']\n", - "doc_types = [metadata['doc_type'] for metadata in result['metadatas']]\n", - "colors = [['blue', 'green', 'red', 'orange'][['products', 'employees', 'contracts', 'company'].index(t)] for t in doc_types]" + "retriever.invoke(\"Who is Avery?\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", "metadata": {}, "outputs": [], "source": [ - "# We humans find it easier to visalize things in 2D!\n", - "# Reduce the dimensionality of the vectors to 2D using t-SNE\n", - "# (t-distributed stochastic neighbor embedding)\n", - "\n", - "tsne = TSNE(n_components=2, random_state=42)\n", - "reduced_vectors = tsne.fit_transform(vectors)\n", - "\n", - "# Create the 2D scatter plot\n", - "fig = go.Figure(data=[go.Scatter(\n", - " x=reduced_vectors[:, 0],\n", - " y=reduced_vectors[:, 1],\n", - " mode='markers',\n", - " marker=dict(size=5, color=colors, opacity=0.8),\n", - " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", - " hoverinfo='text'\n", - ")])\n", - "\n", - "fig.update_layout(\n", - " title='2D Chroma Vector Store Visualization',\n", - " scene=dict(xaxis_title='x',yaxis_title='y'),\n", - " width=800,\n", - " height=600,\n", - " margin=dict(r=20, b=10, l=10, t=40)\n", - ")\n", - "\n", - "fig.show()" + "llm.invoke(\"Who is Avery?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Time to put this together!" ] }, { "cell_type": "code", "execution_count": null, - "id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd", "metadata": {}, "outputs": [], "source": [ - "# Let's try 3D!\n", - "\n", - "tsne = TSNE(n_components=3, random_state=42)\n", - "reduced_vectors = tsne.fit_transform(vectors)\n", - "\n", - "# Create the 3D scatter plot\n", - "fig = go.Figure(data=[go.Scatter3d(\n", - " x=reduced_vectors[:, 0],\n", - " y=reduced_vectors[:, 1],\n", - " z=reduced_vectors[:, 2],\n", - " mode='markers',\n", - " marker=dict(size=5, color=colors, opacity=0.8),\n", - " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", - " hoverinfo='text'\n", - ")])\n", - "\n", - "fig.update_layout(\n", - " title='3D Chroma Vector Store Visualization',\n", - " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", - " width=900,\n", - " height=700,\n", - " margin=dict(r=20, b=10, l=10, t=40)\n", - ")\n", - "\n", - "fig.show()" + "SYSTEM_PROMPT_TEMPLATE = \"\"\"\n", + "You are a knowledgeable, friendly assistant representing the company Insurellm.\n", + "You are chatting with a user about Insurellm.\n", + "If relevant, use the given context to answer any question.\n", + "If you don't know the answer, say so.\n", + "Context:\n", + "{context}\n", + "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, - "id": "9b3ada26-b4b7-42fc-b943-933c14adf89b", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def answer_question(question: str, history):\n", + " docs = retriever.invoke(question)\n", + " context = \"\\n\\n\".join(doc.page_content for doc in docs)\n", + " system_prompt = SYSTEM_PROMPT_TEMPLATE.format(context=context)\n", + " response = llm.invoke([SystemMessage(content=system_prompt), HumanMessage(content=question)])\n", + " return response.content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "answer_question(\"Who is Averi Lancaster?\", [])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What could possibly come next? 😂" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gr.ChatInterface(answer_question).launch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Admit it - you thought RAG would be more complicated than that!!" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -358,9 +198,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.9" } }, "nbformat": 4, - "nbformat_minor": 5 + "nbformat_minor": 4 } diff --git a/week5/day4.5.ipynb b/week5/day4.5.ipynb deleted file mode 100644 index f718a23..0000000 --- a/week5/day4.5.ipynb +++ /dev/null @@ -1,424 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", - "metadata": {}, - "source": [ - "## Expert Knowledge Worker\n", - "\n", - "## Extra Jupyter Notebook - Day 4.5 - switch out Chroma for FAISS!\n", - "\n", - "FAISS is Facebook AI Similarity Search" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "\n", - "import os\n", - "import glob\n", - "from dotenv import load_dotenv\n", - "import gradio as gr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94a564ed-5cda-42d9-aada-2a5e85d02d15", - "metadata": {}, - "outputs": [], - "source": [ - "# install faiss-cpu!\n", - "# Mac users - this may fail if you don't have a recent version of MacOS\n", - "# In which case I recommend you skip this lab -- FAISS is not essential! (Or upgrade MacOS if you wish..)\n", - "\n", - "!uv add faiss-cpu\n", - "\n", - "# Or use !pip install if you're on the Original version of the course and you're not using uv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "802137aa-8a74-45e0-a487-d1974927d7ca", - "metadata": {}, - "outputs": [], - "source": [ - "# imports for langchain\n", - "\n", - "from langchain.document_loaders import DirectoryLoader, TextLoader\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.schema import Document\n", - "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", - "# from langchain_chroma import Chroma\n", - "from langchain.vectorstores import FAISS\n", - "import numpy as np\n", - "from sklearn.manifold import TSNE\n", - "import plotly.graph_objects as go\n", - "from langchain.memory import ConversationBufferMemory\n", - "from langchain.chains import ConversationalRetrievalChain" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c85082-e417-4708-9efe-81a5d55d1424", - "metadata": {}, - "outputs": [], - "source": [ - "# price is a factor for our company, so we're going to use a low cost model\n", - "\n", - "MODEL = \"gpt-4o-mini\"\n", - "db_name = \"vector_db\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee78efcb-60fe-449e-a944-40bab26261af", - "metadata": {}, - "outputs": [], - "source": [ - "# Load environment variables in a file called .env\n", - "\n", - "load_dotenv(override=True)\n", - "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", - "metadata": {}, - "outputs": [], - "source": [ - "# Read in documents using LangChain's loaders\n", - "# Take everything in all the sub-folders of our knowledgebase\n", - "\n", - "folders = glob.glob(\"knowledge-base/*\")\n", - "\n", - "# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", - "text_loader_kwargs = {'encoding': 'utf-8'}\n", - "# If that doesn't work, some Windows users might need to uncomment the next line instead\n", - "# text_loader_kwargs={'autodetect_encoding': True}\n", - "\n", - "documents = []\n", - "for folder in folders:\n", - " doc_type = os.path.basename(folder)\n", - " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", - " folder_docs = loader.load()\n", - " for doc in folder_docs:\n", - " doc.metadata[\"doc_type\"] = doc_type\n", - " documents.append(doc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", - "metadata": {}, - "outputs": [], - "source": [ - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "chunks = text_splitter.split_documents(documents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", - "metadata": {}, - "outputs": [], - "source": [ - "len(chunks)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", - "metadata": {}, - "outputs": [], - "source": [ - "doc_types = set(chunk.metadata['doc_type'] for chunk in chunks)\n", - "print(f\"Document types found: {', '.join(doc_types)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013", - "metadata": {}, - "source": [ - "## A sidenote on Embeddings, and \"Auto-Encoding LLMs\"\n", - "\n", - "We will be mapping each chunk of text into a Vector that represents the meaning of the text, known as an embedding.\n", - "\n", - "OpenAI offers a model to do this, which we will use by calling their API with some LangChain code.\n", - "\n", - "This model is an example of an \"Auto-Encoding LLM\" which generates an output given a complete input.\n", - "It's different to all the other LLMs we've discussed today, which are known as \"Auto-Regressive LLMs\", and generate future tokens based only on past context.\n", - "\n", - "Another example of an Auto-Encoding LLMs is BERT from Google. In addition to embedding, Auto-encoding LLMs are often used for classification.\n", - "\n", - "### Sidenote\n", - "\n", - "In week 8 we will return to RAG and vector embeddings, and we will use an open-source vector encoder so that the data never leaves our computer - that's an important consideration when building enterprise systems and the data needs to remain internal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78998399-ac17-4e28-b15f-0b5f51e6ee23", - "metadata": {}, - "outputs": [], - "source": [ - "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", - "# Chroma is a popular open source Vector Database based on SQLLite\n", - "\n", - "embeddings = OpenAIEmbeddings()\n", - "\n", - "# Create vectorstore\n", - "\n", - "# BEFORE\n", - "# vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", - "\n", - "# AFTER\n", - "vectorstore = FAISS.from_documents(chunks, embedding=embeddings)\n", - "\n", - "total_vectors = vectorstore.index.ntotal\n", - "dimensions = vectorstore.index.d\n", - "\n", - "print(f\"There are {total_vectors} vectors with {dimensions:,} dimensions in the vector store\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "057868f6-51a6-4087-94d1-380145821550", - "metadata": {}, - "outputs": [], - "source": [ - "# Prework\n", - "vectors = []\n", - "documents = []\n", - "doc_types = []\n", - "colors = []\n", - "color_map = {'products':'blue', 'employees':'green', 'contracts':'red', 'company':'orange'}\n", - "\n", - "for i in range(total_vectors):\n", - " vectors.append(vectorstore.index.reconstruct(i))\n", - " doc_id = vectorstore.index_to_docstore_id[i]\n", - " document = vectorstore.docstore.search(doc_id)\n", - " documents.append(document.page_content)\n", - " doc_type = document.metadata['doc_type']\n", - " doc_types.append(doc_type)\n", - " colors.append(color_map[doc_type])\n", - " \n", - "vectors = np.array(vectors)" - ] - }, - { - "cell_type": "markdown", - "id": "b0d45462-a818-441c-b010-b85b32bcf618", - "metadata": {}, - "source": [ - "## Visualizing the Vector Store\n", - "\n", - "Let's take a minute to look at the documents and their embedding vectors to see what's going on.\n", - "\n", - "(As a sidenote, what we're really looking at here is the distribution of the Vectors generated by OpenAIEmbeddings, retrieved from FAISS. So there's no surprise that they look the same whether they are \"from\" FAISS or Chroma.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", - "metadata": {}, - "outputs": [], - "source": [ - "# We humans find it easier to visalize things in 2D!\n", - "# Reduce the dimensionality of the vectors to 2D using t-SNE\n", - "# (t-distributed stochastic neighbor embedding)\n", - "\n", - "tsne = TSNE(n_components=2, random_state=42)\n", - "reduced_vectors = tsne.fit_transform(vectors)\n", - "\n", - "# Create the 2D scatter plot\n", - "fig = go.Figure(data=[go.Scatter(\n", - " x=reduced_vectors[:, 0],\n", - " y=reduced_vectors[:, 1],\n", - " mode='markers',\n", - " marker=dict(size=5, color=colors, opacity=0.8),\n", - " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", - " hoverinfo='text'\n", - ")])\n", - "\n", - "fig.update_layout(\n", - " title='2D FAISS Vector Store Visualization',\n", - " scene=dict(xaxis_title='x',yaxis_title='y'),\n", - " width=800,\n", - " height=600,\n", - " margin=dict(r=20, b=10, l=10, t=40)\n", - ")\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd", - "metadata": {}, - "outputs": [], - "source": [ - "# Let's try 3D!\n", - "\n", - "tsne = TSNE(n_components=3, random_state=42)\n", - "reduced_vectors = tsne.fit_transform(vectors)\n", - "\n", - "# Create the 3D scatter plot\n", - "fig = go.Figure(data=[go.Scatter3d(\n", - " x=reduced_vectors[:, 0],\n", - " y=reduced_vectors[:, 1],\n", - " z=reduced_vectors[:, 2],\n", - " mode='markers',\n", - " marker=dict(size=5, color=colors, opacity=0.8),\n", - " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", - " hoverinfo='text'\n", - ")])\n", - "\n", - "fig.update_layout(\n", - " title='3D FAISS Vector Store Visualization',\n", - " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", - " width=900,\n", - " height=700,\n", - " margin=dict(r=20, b=10, l=10, t=40)\n", - ")\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9468860b-86a2-41df-af01-b2400cc985be", - "metadata": {}, - "source": [ - "## Time to use LangChain to bring it all together" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "129c7d1e-0094-4479-9459-f9360b95f244", - "metadata": {}, - "outputs": [], - "source": [ - "# create a new Chat with OpenAI\n", - "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", - "\n", - "# set up the conversation memory for the chat\n", - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", - "\n", - "# the retriever is an abstraction over the VectorStore that will be used during RAG\n", - "retriever = vectorstore.as_retriever()\n", - "\n", - "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "968e7bf2-e862-4679-a11f-6c1efb6ec8ca", - "metadata": {}, - "outputs": [], - "source": [ - "query = \"Can you describe Insurellm in a few sentences\"\n", - "result = conversation_chain.invoke({\"question\":query})\n", - "print(result[\"answer\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "987dadc5-5d09-4059-8f2e-733d66ecc696", - "metadata": {}, - "outputs": [], - "source": [ - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" - ] - }, - { - "cell_type": "markdown", - "id": "bbbcb659-13ce-47ab-8a5e-01b930494964", - "metadata": {}, - "source": [ - "## Now we will bring this up in Gradio using the Chat interface -\n", - "\n", - "A quick and easy way to prototype a chat with an LLM" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3536590-85c7-4155-bd87-ae78a1467670", - "metadata": {}, - "outputs": [], - "source": [ - "# Wrapping that in a function\n", - "\n", - "def chat(message, history):\n", - " result = conversation_chain.invoke({\"question\": message})\n", - " return result[\"answer\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b252d8c1-61a8-406d-b57a-8f708a62b014", - "metadata": {}, - "outputs": [], - "source": [ - "# And in Gradio:\n", - "\n", - "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5435b2b9-935c-48cd-aaf3-73a837ecde49", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/week5/day4.ipynb b/week5/day4.ipynb index 5834a31..754fea6 100644 --- a/week5/day4.ipynb +++ b/week5/day4.ipynb @@ -2,436 +2,211 @@ "cells": [ { "cell_type": "markdown", - "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", + "id": "be0d1141", "metadata": {}, "source": [ - "## Expert Knowledge Worker\n", + "# RAG Day 4\n", "\n", - "### A question answering agent that is an expert knowledge worker\n", - "### To be used by employees of Insurellm, an Insurance Tech company\n", - "### The agent needs to be accurate and the solution should be low cost.\n", + "## Evaluation!\n", "\n", - "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "\n", - "import os\n", - "import glob\n", - "from dotenv import load_dotenv\n", - "import gradio as gr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "802137aa-8a74-45e0-a487-d1974927d7ca", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# imports for langchain\n", - "\n", - "from langchain.document_loaders import DirectoryLoader, TextLoader\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.schema import Document\n", - "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", - "from langchain_chroma import Chroma\n", - "import numpy as np\n", - "from sklearn.manifold import TSNE\n", - "import plotly.graph_objects as go\n", - "from langchain.memory import ConversationBufferMemory\n", - "from langchain.chains import ConversationalRetrievalChain" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c85082-e417-4708-9efe-81a5d55d1424", - "metadata": {}, - "outputs": [], - "source": [ - "# price is a factor for our company, so we're going to use a low cost model\n", - "\n", - "MODEL = \"gpt-4o-mini\"\n", - "db_name = \"vector_db\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee78efcb-60fe-449e-a944-40bab26261af", - "metadata": {}, - "outputs": [], - "source": [ - "# Load environment variables in a file called .env\n", - "\n", - "load_dotenv(override=True)\n", - "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", - "metadata": {}, - "outputs": [], - "source": [ - "# Read in documents using LangChain's loaders\n", - "# Take everything in all the sub-folders of our knowledgebase\n", - "\n", - "folders = glob.glob(\"knowledge-base/*\")\n", - "\n", - "# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", - "text_loader_kwargs = {'encoding': 'utf-8'}\n", - "# If that doesn't work, some Windows users might need to uncomment the next line instead\n", - "# text_loader_kwargs={'autodetect_encoding': True}\n", - "\n", - "documents = []\n", - "for folder in folders:\n", - " doc_type = os.path.basename(folder)\n", - " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", - " folder_docs = loader.load()\n", - " for doc in folder_docs:\n", - " doc.metadata[\"doc_type\"] = doc_type\n", - " documents.append(doc)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", - "metadata": {}, - "outputs": [], - "source": [ - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "chunks = text_splitter.split_documents(documents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", - "metadata": {}, - "outputs": [], - "source": [ - "len(chunks)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", - "metadata": {}, - "outputs": [], - "source": [ - "doc_types = set(chunk.metadata['doc_type'] for chunk in chunks)\n", - "print(f\"Document types found: {', '.join(doc_types)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013", - "metadata": {}, - "source": [ - "## A sidenote on Embeddings, and \"Auto-Encoding LLMs\"\n", - "\n", - "We will be mapping each chunk of text into a Vector that represents the meaning of the text, known as an embedding.\n", - "\n", - "OpenAI offers a model to do this, which we will use by calling their API with some LangChain code.\n", - "\n", - "This model is an example of an \"Auto-Encoding LLM\" which generates an output given a complete input.\n", - "It's different to all the other LLMs we've discussed today, which are known as \"Auto-Regressive LLMs\", and generate future tokens based only on past context.\n", - "\n", - "Another example of an Auto-Encoding LLMs is BERT from Google. In addition to embedding, Auto-encoding LLMs are often used for classification.\n", - "\n", - "### Sidenote\n", - "\n", - "In week 8 we will return to RAG and vector embeddings, and we will use an open-source vector encoder so that the data never leaves our computer - that's an important consideration when building enterprise systems and the data needs to remain internal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78998399-ac17-4e28-b15f-0b5f51e6ee23", - "metadata": {}, - "outputs": [], - "source": [ - "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", - "# Chroma is a popular open source Vector Database based on SQLLite\n", - "\n", - "# embeddings = OpenAIEmbeddings()\n", - "\n", - "# If you would rather use the free Vector Embeddings from HuggingFace sentence-transformers\n", - "# Then replace embeddings = OpenAIEmbeddings()\n", - "# with:\n", - "from langchain.embeddings import HuggingFaceEmbeddings\n", - "embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", - "\n", - "# Delete if already exists\n", - "\n", - "if os.path.exists(db_name):\n", - " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", - "\n", - "# Create vectorstore\n", - "\n", - "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", - "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "057868f6-51a6-4087-94d1-380145821550", - "metadata": {}, - "outputs": [], - "source": [ - "# Get one vector and find how many dimensions it has\n", - "\n", - "collection = vectorstore._collection\n", - "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", - "dimensions = len(sample_embedding)\n", - "print(f\"The vectors have {dimensions:,} dimensions\")" - ] - }, - { - "cell_type": "markdown", - "id": "b0d45462-a818-441c-b010-b85b32bcf618", - "metadata": {}, - "source": [ - "## Visualizing the Vector Store\n", - "\n", - "Let's take a minute to look at the documents and their embedding vectors to see what's going on." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b", - "metadata": {}, - "outputs": [], - "source": [ - "# Prework\n", - "\n", - "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", - "vectors = np.array(result['embeddings'])\n", - "documents = result['documents']\n", - "doc_types = [metadata['doc_type'] for metadata in result['metadatas']]\n", - "colors = [['blue', 'green', 'red', 'orange'][['products', 'employees', 'contracts', 'company'].index(t)] for t in doc_types]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", - "metadata": {}, - "outputs": [], - "source": [ - "# We humans find it easier to visalize things in 2D!\n", - "# Reduce the dimensionality of the vectors to 2D using t-SNE\n", - "# (t-distributed stochastic neighbor embedding)\n", - "\n", - "tsne = TSNE(n_components=2, random_state=42)\n", - "reduced_vectors = tsne.fit_transform(vectors)\n", - "\n", - "# Create the 2D scatter plot\n", - "fig = go.Figure(data=[go.Scatter(\n", - " x=reduced_vectors[:, 0],\n", - " y=reduced_vectors[:, 1],\n", - " mode='markers',\n", - " marker=dict(size=5, color=colors, opacity=0.8),\n", - " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", - " hoverinfo='text'\n", - ")])\n", - "\n", - "fig.update_layout(\n", - " title='2D Chroma Vector Store Visualization',\n", - " scene=dict(xaxis_title='x',yaxis_title='y'),\n", - " width=800,\n", - " height=600,\n", - " margin=dict(r=20, b=10, l=10, t=40)\n", - ")\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd", - "metadata": {}, - "outputs": [], - "source": [ - "# Let's try 3D!\n", - "\n", - "tsne = TSNE(n_components=3, random_state=42)\n", - "reduced_vectors = tsne.fit_transform(vectors)\n", - "\n", - "# Create the 3D scatter plot\n", - "fig = go.Figure(data=[go.Scatter3d(\n", - " x=reduced_vectors[:, 0],\n", - " y=reduced_vectors[:, 1],\n", - " z=reduced_vectors[:, 2],\n", - " mode='markers',\n", - " marker=dict(size=5, color=colors, opacity=0.8),\n", - " text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", - " hoverinfo='text'\n", - ")])\n", - "\n", - "fig.update_layout(\n", - " title='3D Chroma Vector Store Visualization',\n", - " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", - " width=900,\n", - " height=700,\n", - " margin=dict(r=20, b=10, l=10, t=40)\n", - ")\n", - "\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "9468860b-86a2-41df-af01-b2400cc985be", - "metadata": {}, - "source": [ - "# Time to use LangChain to bring it all together" - ] - }, - { - "cell_type": "markdown", - "id": "8ba8a5e7-965d-4770-a12d-532aff50c4b5", - "metadata": {}, - "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", - " \n", + " \n", " \n", - "

PLEASE READ ME! Ignoring the Deprecation Warning

\n", - " When you run the next cell, you will get a LangChainDeprecationWarning \n", - " about the simple way we use LangChain memory. They ask us to migrate to their new approach for memory. \n", - " I feel quite conflicted about this. The new approach involves moving to LangGraph and getting deep into their ecosystem.\n", - " There's a fair amount of learning and coding in LangGraph, frankly without much benefit in our case.

\n", - " I'm going to think about whether/how to incorporate it in the course, but for now please ignore the Depreciation Warning and\n", - " use the code as is; LangChain are not expected to remove ConversationBufferMemory any time soon.\n", - " \n", + "

Keep in mind how you would evaluate RAG for your business

\n", + " This is such an important part of building an accurate and reliable RAG pipeline. And it's applicable to many aspects of solving business problems with LLMs. People are often focused on RAG architecture and RAG frameworks for their business. But even more important: evaluations!\n", "
" ] }, - { - "cell_type": "markdown", - "id": "3dd0a478-bde4-41f8-8fe8-a35d3246e1b6", - "metadata": {}, - "source": [ - "## Alternative: to use a free open-source model instead of OpenAI in the next cell\n", - "\n", - "First run this in a cell: `!pip install langchain-ollama`\n", - "\n", - "Then replace `llm = ChatOpenAI(temperature=0.7, model_name=MODEL)` with:\n", - "\n", - "```python\n", - "from langchain_ollama import ChatOllama\n", - "llm = ChatOllama(temperature=0.7, model=\"llama3.2\")\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, - "id": "129c7d1e-0094-4479-9459-f9360b95f244", + "execution_count": 1, + "id": "60995166", "metadata": {}, "outputs": [], "source": [ - "# create a new Chat with OpenAI\n", - "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", - "\n", - "# set up the conversation memory for the chat\n", - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", - "\n", - "# the retriever is an abstraction over the VectorStore that will be used during RAG\n", - "retriever = vectorstore.as_retriever()\n", - "\n", - "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" + "from evaluation import test" ] }, { "cell_type": "code", - "execution_count": null, - "id": "968e7bf2-e862-4679-a11f-6c1efb6ec8ca", + "execution_count": 2, + "id": "2bbcfcf3", "metadata": {}, "outputs": [], "source": [ - "query = \"Can you describe Insurellm in a few sentences\"\n", - "result = conversation_chain.invoke({\"question\":query})\n", - "print(result[\"answer\"])" + "tests = test.load_tests()" ] }, { "cell_type": "code", - "execution_count": null, - "id": "e6eb99fb-33ec-4025-ab92-b634ede03647", + "execution_count": 3, + "id": "fd4d88a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "150" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(tests)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c65fd09a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Who won the prestigious IIOTY award in 2023?\n", + "direct_fact\n", + "Maxine Thompson won the prestigious Insurellm Innovator of the Year (IIOTY) award in 2023.\n", + "['Maxine', 'Thompson', 'IIOTY']\n" + ] + } + ], + "source": [ + "example = tests[0]\n", + "print(example.question)\n", + "print(example.category)\n", + "print(example.reference_answer)\n", + "print(example.keywords)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a7f058ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({'direct_fact': 70,\n", + " 'temporal': 20,\n", + " 'spanning': 20,\n", + " 'comparative': 10,\n", + " 'numerical': 10,\n", + " 'relationship': 10,\n", + " 'holistic': 10})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import Counter\n", + "count = Counter([t.category for t in tests])\n", + "count" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b413b186", "metadata": {}, "outputs": [], "source": [ - "# set up a new conversation memory for the chat\n", - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", - "\n", - "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" - ] - }, - { - "cell_type": "markdown", - "id": "bbbcb659-13ce-47ab-8a5e-01b930494964", - "metadata": {}, - "source": [ - "## Now we will bring this up in Gradio using the Chat interface -\n", - "\n", - "A quick and easy way to prototype a chat with an LLM" + "from evaluation.eval import evaluate_retrieval, evaluate_answer" ] }, { "cell_type": "code", - "execution_count": null, - "id": "c3536590-85c7-4155-bd87-ae78a1467670", + "execution_count": 7, + "id": "daca435e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RetrievalEval(mrr=0.16666666666666666, ndcg=0.28711770538226206, keywords_found=2, total_keywords=3, keyword_coverage=66.66666666666666)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluate_retrieval(example)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "925b37d2", "metadata": {}, "outputs": [], "source": [ - "# Wrapping in a function - note that history isn't used, as the memory is in the conversation_chain\n", - "\n", - "def chat(message, history):\n", - " result = conversation_chain.invoke({\"question\": message})\n", - " return result[\"answer\"]" + "eval, answer, chunks = evaluate_answer(example)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "b252d8c1-61a8-406d-b57a-8f708a62b014", + "execution_count": 12, + "id": "01965312", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "AnswerEval(feedback=\"The answer correctly identifies Maxine as the winner and mentions the IIOTY award in 2023, but it omits Thompson's full name, which is present in the reference. This affects completeness. The relevance is high, as it directly addresses the question about the award winner.\", accuracy=5.0, completeness=4.0, relevance=5.0)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# And in Gradio:\n", - "\n", - "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" + "eval" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4cd34561", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The answer correctly identifies Maxine as the winner and mentions the IIOTY award in 2023, but it omits Thompson's full name, which is present in the reference. This affects completeness. The relevance is high, as it directly addresses the question about the award winner.\n", + "5.0\n", + "4.0\n", + "5.0\n" + ] + } + ], + "source": [ + "print(eval.feedback)\n", + "print(eval.accuracy)\n", + "print(eval.completeness)\n", + "print(eval.relevance)" ] }, { "cell_type": "code", "execution_count": null, - "id": "5435b2b9-935c-48cd-aaf3-73a837ecde49", + "id": "6f5e0cd4", "metadata": {}, "outputs": [], "source": [] @@ -439,7 +214,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -453,7 +228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/week5/day5.ipynb b/week5/day5.ipynb index da3332c..4e87d30 100644 --- a/week5/day5.ipynb +++ b/week5/day5.ipynb @@ -2,224 +2,352 @@ "cells": [ { "cell_type": "markdown", - "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", + "id": "6c26c848", "metadata": {}, "source": [ - "## Expert Knowledge Worker\n", + "# Let's go PRO!\n", "\n", - "### A question answering agent that is an expert knowledge worker\n", - "### To be used by employees of Insurellm, an Insurance Tech company\n", - "### The agent needs to be accurate and the solution should be low cost.\n", + "Advanced RAG Techniques!\n", "\n", - "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy.\n", + "Let's start by digging into ingest:\n", "\n", - "This first implementation will use a simple, brute-force type of RAG.." + "1. No LangChain! Just native for maximum flexibility\n", + "2. Let's use an LLM to divide up chunks in a sensible way\n", + "3. Let's use the best chunk size and encoder from yesterday\n", + "4. Let's also have the LLM rewrite chunks in a way that's most useful (\"document pre-processing\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", + "id": "7e9f5f1d", "metadata": {}, "outputs": [], "source": [ - "# imports\n", - "\n", - "import os\n", - "import glob\n", + "from pathlib import Path\n", + "from openai import OpenAI\n", "from dotenv import load_dotenv\n", - "import gradio as gr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "802137aa-8a74-45e0-a487-d1974927d7ca", - "metadata": {}, - "outputs": [], - "source": [ - "# imports for langchain, plotly and Chroma\n", - "\n", - "from langchain.document_loaders import DirectoryLoader, TextLoader\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.schema import Document\n", - "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", - "from langchain_chroma import Chroma\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.manifold import TSNE\n", + "from pydantic import BaseModel, Field\n", + "from chromadb import PersistentClient\n", + "from tqdm import tqdm\n", + "from litellm import completion\n", "import numpy as np\n", + "from sklearn.manifold import TSNE\n", "import plotly.graph_objects as go\n", - "from langchain.memory import ConversationBufferMemory\n", - "from langchain.chains import ConversationalRetrievalChain\n", - "from langchain.embeddings import HuggingFaceEmbeddings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c85082-e417-4708-9efe-81a5d55d1424", - "metadata": {}, - "outputs": [], - "source": [ - "# price is a factor for our company, so we're going to use a low cost model\n", "\n", - "MODEL = \"gpt-4o-mini\"\n", - "db_name = \"vector_db\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee78efcb-60fe-449e-a944-40bab26261af", - "metadata": {}, - "outputs": [], - "source": [ - "# Load environment variables in a file called .env\n", "\n", "load_dotenv(override=True)\n", - "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')" + "\n", + "MODEL = \"gpt-4.1-nano\"\n", + "\n", + "DB_NAME = \"preprocessed_db\"\n", + "collection_name = \"docs\"\n", + "embedding_model = \"text-embedding-3-large\"\n", + "KNOWLEDGE_BASE_PATH = Path(\"knowledge-base\")\n", + "AVERAGE_CHUNK_SIZE = 500\n", + "\n", + "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": null, - "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", + "id": "7bfac66f", "metadata": {}, "outputs": [], "source": [ - "# Read in documents using LangChain's loaders\n", - "# Take everything in all the sub-folders of our knowledgebase\n", + "# Inspired by LangChain's Document - let's have something similar\n", "\n", - "folders = glob.glob(\"knowledge-base/*\")\n", + "class Result(BaseModel):\n", + " page_content: str\n", + " metadata: dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b9d0b58", + "metadata": {}, + "outputs": [], + "source": [ + "# A class to perfectly represent a chunk\n", "\n", - "def add_metadata(doc, doc_type):\n", - " doc.metadata[\"doc_type\"] = doc_type\n", - " return doc\n", + "class Chunk(BaseModel):\n", + " headline: str = Field(description=\"A brief heading for this chunk, typically a few words, that is most likely to be surfaced in a query\")\n", + " summary: str = Field(description=\"A few sentences summarizing the content of this chunk to answer common questions\")\n", + " original_text: str = Field(description=\"The original text of this chunk from the provided document, exactly as is, not changed in any way\")\n", "\n", - "# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", - "text_loader_kwargs = {'encoding': 'utf-8'}\n", - "# If that doesn't work, some Windows users might need to uncomment the next line instead\n", - "# text_loader_kwargs={'autodetect_encoding': True}\n", + " def as_result(self, document):\n", + " metadata = {\"source\": document[\"source\"], \"type\": document[\"type\"]}\n", + " return Result(page_content=self.headline + \"\\n\\n\" + self.summary + \"\\n\\n\" + self.original_text,metadata=metadata)\n", "\n", - "documents = []\n", - "for folder in folders:\n", - " doc_type = os.path.basename(folder)\n", - " loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", - " folder_docs = loader.load()\n", - " documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n", "\n", - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "chunks = text_splitter.split_documents(documents)\n", - "\n", - "print(f\"Total number of chunks: {len(chunks)}\")\n", - "print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")" + "class Chunks(BaseModel):\n", + " chunks: list[Chunk]" ] }, { "cell_type": "markdown", - "id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013", + "id": "284b64c1", "metadata": {}, "source": [ - "## A sidenote on Embeddings, and \"Auto-Encoding LLMs\"\n", + "## Three steps:\n", "\n", - "We will be mapping each chunk of text into a Vector that represents the meaning of the text, known as an embedding.\n", + "1. Fetch documents from the knowledge base, like LangChain did\n", + "2. Call an LLM to turn documents into Chunks\n", + "3. Store the Chunks in Chroma\n", "\n", - "OpenAI offers a model to do this, which we will use by calling their API with some LangChain code.\n", + "That's it!\n", "\n", - "This model is an example of an \"Auto-Encoding LLM\" which generates an output given a complete input.\n", - "It's different to all the other LLMs we've discussed today, which are known as \"Auto-Regressive LLMs\", and generate future tokens based only on past context.\n", - "\n", - "Another example of an Auto-Encoding LLMs is BERT from Google. In addition to embedding, Auto-encoding LLMs are often used for classification.\n", - "\n", - "### Sidenote\n", - "\n", - "In week 8 we will return to RAG and vector embeddings, and we will use an open-source vector encoder so that the data never leaves our computer - that's an important consideration when building enterprise systems and the data needs to remain internal." + "### Let's start with Step 1" ] }, { "cell_type": "code", "execution_count": null, - "id": "78998399-ac17-4e28-b15f-0b5f51e6ee23", + "id": "db5abdca", "metadata": {}, "outputs": [], "source": [ - "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", - "# Chroma is a popular open source Vector Database based on SQLLite\n", + "def fetch_documents():\n", + " \"\"\"A homemade version of the LangChain DirectoryLoader\"\"\"\n", "\n", - "embeddings = OpenAIEmbeddings()\n", + " documents = []\n", "\n", - "# If you would rather use the free Vector Embeddings from HuggingFace sentence-transformers\n", - "# Then replace embeddings = OpenAIEmbeddings()\n", - "# with:\n", - "# from langchain.embeddings import HuggingFaceEmbeddings\n", - "# embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", + " for folder in KNOWLEDGE_BASE_PATH.iterdir():\n", + " doc_type = folder.name\n", + " for file in folder.rglob(\"*.md\"):\n", + " with open(file, \"r\", encoding=\"utf-8\") as f:\n", + " documents.append({\"type\": doc_type, \"source\": file.as_posix(), \"text\": f.read()})\n", "\n", - "# Delete if already exists\n", - "\n", - "if os.path.exists(db_name):\n", - " Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", - "\n", - "# Create vectorstore\n", - "\n", - "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", - "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" + " print(f\"Loaded {len(documents)} documents\")\n", + " return documents" ] }, { "cell_type": "code", "execution_count": null, - "id": "ff2e7687-60d4-4920-a1d7-a34b9f70a250", + "id": "5fe0a213", "metadata": {}, "outputs": [], "source": [ - "# Let's investigate the vectors\n", - "\n", - "collection = vectorstore._collection\n", - "count = collection.count()\n", - "\n", - "sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", - "dimensions = len(sample_embedding)\n", - "print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")" + "documents = fetch_documents()" ] }, { "cell_type": "markdown", - "id": "b0d45462-a818-441c-b010-b85b32bcf618", + "id": "dffa1c68", "metadata": {}, "source": [ - "## Visualizing the Vector Store\n", - "\n", - "Let's take a minute to look at the documents and their embedding vectors to see what's going on." + "### Donezo! On to Step 2 - make the chunks" ] }, { "cell_type": "code", "execution_count": null, - "id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b", + "id": "900e4170", "metadata": {}, "outputs": [], "source": [ - "# Prework (with thanks to Jon R for identifying and fixing a bug in this!)\n", + "def make_prompt(document):\n", + " how_many = (len(document[\"text\"]) // AVERAGE_CHUNK_SIZE) + 1\n", + " return f\"\"\"\n", + "You take a document and you split the document into overlapping chunks for a KnowledgeBase.\n", "\n", + "The document is from the shared drive of a company called Insurellm.\n", + "The document is of type: {document[\"type\"]}\n", + "The document has been retrieved from: {document[\"source\"]}\n", + "\n", + "A chatbot will use these chunks to answer questions about the company.\n", + "You should divide up the document as you see fit, being sure that the entire document is returned in the chunks - don't leave anything out.\n", + "This document should probably be split into {how_many} chunks, but you can have more or less as appropriate.\n", + "There should be overlap between the chunks as appropriate; typically about 25% overlap or about 50 words, so you have the same text in multiple chunks for best retrieval results.\n", + "\n", + "For each chunk, you should provide a headline, a summary, and the original text of the chunk.\n", + "Together your chunks should represent the entire document with overlap.\n", + "\n", + "Here is the document:\n", + "\n", + "{document[\"text\"]}\n", + "\n", + "Respond with the chunks.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f38103b4", + "metadata": {}, + "outputs": [], + "source": [ + "print(make_prompt(documents[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02f58850", + "metadata": {}, + "outputs": [], + "source": [ + "def make_messages(document):\n", + " return [\n", + " {\"role\": \"user\", \"content\": make_prompt(document)},\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ab04779", + "metadata": {}, + "outputs": [], + "source": [ + "make_messages(documents[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea20aba8", + "metadata": {}, + "outputs": [], + "source": [ + "def process_document(document):\n", + " messages = make_messages(document)\n", + " response = completion(model=MODEL, messages=messages, response_format=Chunks)\n", + " reply = response.choices[0].message.content\n", + " doc_as_chunks = Chunks.model_validate_json(reply).chunks\n", + " return [chunk.as_result(document) for chunk in doc_as_chunks]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "480494d8", + "metadata": {}, + "outputs": [], + "source": [ + "process_document(documents[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ccab1e3", + "metadata": {}, + "outputs": [], + "source": [ + "def create_chunks(documents):\n", + " chunks = []\n", + " for doc in tqdm(documents):\n", + " chunks.extend(process_document(doc))\n", + " return chunks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93115f0c", + "metadata": {}, + "outputs": [], + "source": [ + "chunks = create_chunks(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f51544f", + "metadata": {}, + "outputs": [], + "source": [ + "print(len(chunks))" + ] + }, + { + "cell_type": "markdown", + "id": "9750104c", + "metadata": {}, + "source": [ + "### Well that was easy! If a bit slow.\n", + "\n", + "In the python module version, I sneakily use the multi-processing Pool to run this in parallel,\n", + "but if you get a Rate Limit Error you can turn this off in the code.\n", + "\n", + "### Finally, Step 3 - save the embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b19f36b7", + "metadata": {}, + "outputs": [], + "source": [ + "def create_embeddings(chunks):\n", + " chroma = PersistentClient(path=DB_NAME)\n", + " if collection_name in [c.name for c in chroma.list_collections()]:\n", + " chroma.delete_collection(collection_name)\n", + "\n", + " texts = [chunk.page_content for chunk in chunks]\n", + " emb = openai.embeddings.create(model=embedding_model, input=texts).data\n", + " vectors = [e.embedding for e in emb]\n", + "\n", + " collection = chroma.get_or_create_collection(collection_name)\n", + "\n", + " ids = [str(i) for i in range(len(chunks))]\n", + " metas = [chunk.metadata for chunk in chunks]\n", + "\n", + " collection.add(ids=ids, embeddings=vectors, documents=texts, metadatas=metas)\n", + " print(f\"Vectorstore created with {collection.count()} documents\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34f52038", + "metadata": {}, + "outputs": [], + "source": [ + "create_embeddings(chunks)" + ] + }, + { + "cell_type": "markdown", + "id": "3cf738d0", + "metadata": {}, + "source": [ + "# Nothing more to do here... right?\n", + "\n", + "Wait! Didja think I'd forget??" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c318a46f", + "metadata": {}, + "outputs": [], + "source": [ + "chroma = PersistentClient(path=DB_NAME)\n", + "collection = chroma.get_or_create_collection(collection_name)\n", "result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", "vectors = np.array(result['embeddings'])\n", "documents = result['documents']\n", "metadatas = result['metadatas']\n", - "doc_types = [metadata['doc_type'] for metadata in metadatas]\n", + "doc_types = [metadata['type'] for metadata in metadatas]\n", "colors = [['blue', 'green', 'red', 'orange'][['products', 'employees', 'contracts', 'company'].index(t)] for t in doc_types]" ] }, { "cell_type": "code", "execution_count": null, - "id": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", + "id": "c4683c9a", "metadata": {}, "outputs": [], "source": [ - "# We humans find it easier to visalize things in 2D!\n", - "# Reduce the dimensionality of the vectors to 2D using t-SNE\n", - "# (t-distributed stochastic neighbor embedding)\n", - "\n", "tsne = TSNE(n_components=2, random_state=42)\n", "reduced_vectors = tsne.fit_transform(vectors)\n", "\n", @@ -233,8 +361,7 @@ " hoverinfo='text'\n", ")])\n", "\n", - "fig.update_layout(\n", - " title='2D Chroma Vector Store Visualization',\n", + "fig.update_layout(title='2D Chroma Vector Store Visualization',\n", " scene=dict(xaxis_title='x',yaxis_title='y'),\n", " width=800,\n", " height=600,\n", @@ -247,12 +374,10 @@ { "cell_type": "code", "execution_count": null, - "id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd", + "id": "2296866a", "metadata": {}, "outputs": [], "source": [ - "# Let's try 3D!\n", - "\n", "tsne = TSNE(n_components=3, random_state=42)\n", "reduced_vectors = tsne.fit_transform(vectors)\n", "\n", @@ -272,7 +397,7 @@ " scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", " width=900,\n", " height=700,\n", - " margin=dict(r=20, b=10, l=10, t=40)\n", + " margin=dict(r=10, b=10, l=10, t=40)\n", ")\n", "\n", "fig.show()" @@ -280,180 +405,304 @@ }, { "cell_type": "markdown", - "id": "9468860b-86a2-41df-af01-b2400cc985be", + "id": "ba72b54a", "metadata": {}, "source": [ - "## Time to use LangChain to bring it all together" + "## And now - let's build an Advanced RAG!\n", + "\n", + "We will use these techniques:\n", + "\n", + "1. Reranking - reorder the rank results\n", + "2. Query re-writing" ] }, { "cell_type": "code", "execution_count": null, - "id": "129c7d1e-0094-4479-9459-f9360b95f244", + "id": "a0a3818c", "metadata": {}, "outputs": [], "source": [ - "# create a new Chat with OpenAI\n", - "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", - "\n", - "# Alternative - if you'd like to use Ollama locally, uncomment this line instead\n", - "# llm = ChatOpenAI(temperature=0.7, model_name='llama3.2', base_url='http://localhost:11434/v1', api_key='ollama')\n", - "\n", - "# set up the conversation memory for the chat\n", - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", - "\n", - "# the retriever is an abstraction over the VectorStore that will be used during RAG\n", - "retriever = vectorstore.as_retriever()\n", - "\n", - "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" + "class RankOrder(BaseModel):\n", + " order: list[int] = Field(\n", + " description=\"The order of relevance of chunks, from most relevant to least relevant, by chunk id number\"\n", + " )" ] }, { "cell_type": "code", "execution_count": null, - "id": "968e7bf2-e862-4679-a11f-6c1efb6ec8ca", + "id": "0e8446c4", "metadata": {}, "outputs": [], "source": [ - "# Let's try a simple question\n", - "\n", - "query = \"Please explain what Insurellm is in a couple of sentences\"\n", - "result = conversation_chain.invoke({\"question\": query})\n", - "print(result[\"answer\"])" + "def rerank(question, chunks):\n", + " system_prompt = \"\"\"\n", + "You are a document re-ranker.\n", + "You are provided with a question and a list of relevant chunks of text from a query of a knowledge base.\n", + "The chunks are provided in the order they were retrieved; this should be approximately ordered by relevance, but you may be able to improve on that.\n", + "You must rank order the provided chunks by relevance to the question, with the most relevant chunk first.\n", + "Reply only with the list of ranked chunk ids, nothing else. Include all the chunk ids you are provided with, reranked.\n", + "\"\"\"\n", + " user_prompt = f\"The user has asked the following question:\\n\\n{question}\\n\\nOrder all the chunks of text by relevance to the question, from most relevant to least relevant. Include all the chunk ids you are provided with, reranked.\\n\\n\"\n", + " user_prompt += \"Here are the chunks:\\n\\n\"\n", + " for index, chunk in enumerate(chunks):\n", + " user_prompt += f\"# CHUNK ID: {index + 1}:\\n\\n{chunk.page_content}\\n\\n\"\n", + " user_prompt += \"Reply only with the list of ranked chunk ids, nothing else.\"\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt},\n", + " ]\n", + " response = completion(model=MODEL, messages=messages, response_format=RankOrder)\n", + " reply = response.choices[0].message.content\n", + " order = RankOrder.model_validate_json(reply).order\n", + " print(order)\n", + " return [chunks[i - 1] for i in order]" ] }, { "cell_type": "code", "execution_count": null, - "id": "5b5a9013-d5d4-4e25-9e7c-cdbb4f33e319", + "id": "fa78048d", "metadata": {}, "outputs": [], "source": [ - "# set up a new conversation memory for the chat\n", - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", + "RETRIEVAL_K = 10\n", "\n", - "# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" - ] - }, - { - "cell_type": "markdown", - "id": "bbbcb659-13ce-47ab-8a5e-01b930494964", - "metadata": {}, - "source": [ - "## Now we will bring this up in Gradio using the Chat interface -\n", - "\n", - "A quick and easy way to prototype a chat with an LLM" + "def fetch_context_unranked(question):\n", + " query = openai.embeddings.create(model=embedding_model, input=[question]).data[0].embedding\n", + " results = collection.query(query_embeddings=[query], n_results=RETRIEVAL_K)\n", + " chunks = []\n", + " for result in zip(results[\"documents\"][0], results[\"metadatas\"][0]):\n", + " chunks.append(Result(page_content=result[0], metadata=result[1]))\n", + " return chunks" ] }, { "cell_type": "code", "execution_count": null, - "id": "c3536590-85c7-4155-bd87-ae78a1467670", + "id": "3b53f6de", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af2ed5be", "metadata": {}, "outputs": [], "source": [ - "# Wrapping that in a function\n", - "\n", - "def chat(question, history):\n", - " result = conversation_chain.invoke({\"question\": question})\n", - " return result[\"answer\"]" + "question = \"Who won the IIOTY award?\"\n", + "chunks = fetch_context_unranked(question)" ] }, { "cell_type": "code", "execution_count": null, - "id": "b252d8c1-61a8-406d-b57a-8f708a62b014", + "id": "0794453f", "metadata": {}, "outputs": [], "source": [ - "# And in Gradio:\n", - "\n", - "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" + "for chunk in chunks:\n", + " print(chunk.page_content[:15]+\"...\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "b55e9abb-e1da-46c5-acba-911868aee329", + "id": "7661e767", "metadata": {}, "outputs": [], "source": [ - "# Let's investigate what gets sent behind the scenes\n", - "\n", - "from langchain_core.callbacks import StdOutCallbackHandler\n", - "\n", - "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", - "\n", - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", - "\n", - "retriever = vectorstore.as_retriever()\n", - "\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory, callbacks=[StdOutCallbackHandler()])\n", - "\n", - "query = \"Who received the prestigious IIOTY award in 2023?\"\n", - "result = conversation_chain.invoke({\"question\": query})\n", - "answer = result[\"answer\"]\n", - "print(\"\\nAnswer:\", answer)" + "reranked = rerank(question, chunks)" ] }, { "cell_type": "code", "execution_count": null, - "id": "2136153b-d2f6-4c58-a0e3-78c3a932cf55", + "id": "a23594f5", "metadata": {}, "outputs": [], "source": [ - "# create a new Chat with OpenAI\n", - "llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n", - "\n", - "# set up the conversation memory for the chat\n", - "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", - "\n", - "# the retriever is an abstraction over the VectorStore that will be used during RAG; k is how many chunks to use\n", - "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 25})\n", - "\n", - "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n", - "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" + "for chunk in reranked:\n", + " print(chunk.page_content[:15]+\"...\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "5c2bfa3c-810b-441b-90d1-31533f14b1e3", + "id": "405de4d0", "metadata": {}, "outputs": [], "source": [ - "def chat(question, history):\n", - " result = conversation_chain.invoke({\"question\": question})\n", - " return result[\"answer\"]" + "question = \"Who went to Manchester University?\"\n", + "RETRIEVAL_K = 20\n", + "chunks = fetch_context_unranked(question)\n", + "for index, c in enumerate(chunks):\n", + " if \"manchester\" in c.page_content.lower():\n", + " print(index)" ] }, { "cell_type": "code", "execution_count": null, - "id": "c736f33b-941e-4853-8eaf-2003bd988b18", + "id": "2b9e343f", "metadata": {}, "outputs": [], "source": [ - "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" - ] - }, - { - "cell_type": "markdown", - "id": "644753e7-17f3-4999-a37a-b6aebf1e4579", - "metadata": {}, - "source": [ - "# Exercises\n", - "\n", - "Try applying this to your own folder of data, so that you create a personal knowledge worker, an expert on your own information!" + "reranked = rerank(question, chunks)" ] }, { "cell_type": "code", "execution_count": null, - "id": "30b4745a-0a6c-4544-b78b-c827cfec1fb9", + "id": "22948df7", + "metadata": {}, + "outputs": [], + "source": [ + "for index, c in enumerate(reranked):\n", + " if \"manchester\" in c.page_content.lower():\n", + " print(index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca1cae50", + "metadata": {}, + "outputs": [], + "source": [ + "reranked[0].page_content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22ca740b", + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_context(question):\n", + " chunks = fetch_context_unranked(question)\n", + " return rerank(question, chunks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0547204c", + "metadata": {}, + "outputs": [], + "source": [ + "SYSTEM_PROMPT = \"\"\"\n", + "You are a knowledgeable, friendly assistant representing the company Insurellm.\n", + "You are chatting with a user about Insurellm.\n", + "Your answer will be evaluated for accuracy, relevance and completeness, so make sure it only answers the question and fully answers it.\n", + "If you don't know the answer, say so.\n", + "For context, here are specific extracts from the Knowledge Base that might be directly relevant to the user's question:\n", + "{context}\n", + "\n", + "With this context, please answer the user's question. Be accurate, relevant and complete.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c1b5c57", + "metadata": {}, + "outputs": [], + "source": [ + "# In the context, include the source of the chunk\n", + "\n", + "def make_rag_messages(question, history, chunks):\n", + " context = \"\\n\\n\".join(f\"Extract from {chunk.metadata['source']}:\\n{chunk.page_content}\" for chunk in chunks)\n", + " system_prompt = SYSTEM_PROMPT.format(context=context)\n", + " return [{\"role\": \"system\", \"content\": system_prompt}] + history + [{\"role\": \"user\", \"content\": question}]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77d9491c", + "metadata": {}, + "outputs": [], + "source": [ + "def rewrite_query(question, history=[]):\n", + " \"\"\"Rewrite the user's question to be a more specific question that is more likely to surface relevant content in the Knowledge Base.\"\"\"\n", + " message = f\"\"\"\n", + "You are in a conversation with a user, answering questions about the company Insurellm.\n", + "You are about to look up information in a Knowledge Base to answer the user's question.\n", + "\n", + "This is the history of your conversation so far with the user:\n", + "{history}\n", + "\n", + "And this is the user's current question:\n", + "{question}\n", + "\n", + "Respond only with a single, refined question that you will use to search the Knowledge Base.\n", + "It should be a VERY short specific question most likely to surface content. Focus on the question details.\n", + "Don't mention the company name unless it's a general question about the company.\n", + "IMPORTANT: Respond ONLY with the knowledgebase query, nothing else.\n", + "\"\"\"\n", + " response = completion(model=MODEL, messages=[{\"role\": \"system\", \"content\": message}])\n", + " return response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8d050a1", + "metadata": {}, + "outputs": [], + "source": [ + "rewrite_query(\"Who won the IIOTY award?\", [])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "909a1ecd", + "metadata": {}, + "outputs": [], + "source": [ + "def answer_question(question: str, history: list[dict] = []) -> tuple[str, list]:\n", + " \"\"\"\n", + " Answer a question using RAG and return the answer and the retrieved context\n", + " \"\"\"\n", + " query = rewrite_query(question, history)\n", + " print(query)\n", + " chunks = fetch_context(query)\n", + " messages = make_rag_messages(question, history, chunks)\n", + " response = completion(model=MODEL, messages=messages)\n", + " return response.choices[0].message.content, chunks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0ab3e5f", + "metadata": {}, + "outputs": [], + "source": [ + "answer_question(\"Who won the IIOTY award?\", [])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3237722e", + "metadata": {}, + "outputs": [], + "source": [ + "answer_question(\"Who went to Manchester University?\", [])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64c93b30", "metadata": {}, "outputs": [], "source": [] @@ -461,7 +710,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -475,7 +724,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.9" } }, "nbformat": 4, diff --git a/week5/evaluation/eval.py b/week5/evaluation/eval.py new file mode 100644 index 0000000..ae26870 --- /dev/null +++ b/week5/evaluation/eval.py @@ -0,0 +1,248 @@ +import sys +import math +from pydantic import BaseModel, Field +from litellm import completion +from dotenv import load_dotenv + +from evaluation.test import TestQuestion, load_tests +from pro_implementation.answer import answer_question, fetch_context + + +load_dotenv(override=True) + +MODEL = "gpt-4.1-nano" +db_name = "vector_db" + + +class RetrievalEval(BaseModel): + """Evaluation metrics for retrieval performance.""" + + mrr: float = Field(description="Mean Reciprocal Rank - average across all keywords") + ndcg: float = Field(description="Normalized Discounted Cumulative Gain (binary relevance)") + keywords_found: int = Field(description="Number of keywords found in top-k results") + total_keywords: int = Field(description="Total number of keywords to find") + keyword_coverage: float = Field(description="Percentage of keywords found") + + +class AnswerEval(BaseModel): + """LLM-as-a-judge evaluation of answer quality.""" + + feedback: str = Field( + description="Concise feedback on the answer quality, comparing it to the reference answer and evaluating based on the retrieved context" + ) + accuracy: float = Field( + description="How factually correct is the answer compared to the reference answer? 1 (wrong. any wrong answer must score 1) to 5 (ideal - perfectly accurate). An acceptable answer would score 3." + ) + completeness: float = Field( + description="How complete is the answer in addressing all aspects of the question? 1 (very poor - missing key information) to 5 (ideal - all the information from the reference answer is provided completely). Only answer 5 if ALL information from the reference answer is included." + ) + relevance: float = Field( + description="How relevant is the answer to the specific question asked? 1 (very poor - off-topic) to 5 (ideal - directly addresses question and gives no additional information). Only answer 5 if the answer is completely relevant to the question and gives no additional information." + ) + + +def calculate_mrr(keyword: str, retrieved_docs: list) -> float: + """Calculate reciprocal rank for a single keyword (case-insensitive).""" + keyword_lower = keyword.lower() + for rank, doc in enumerate(retrieved_docs, start=1): + if keyword_lower in doc.page_content.lower(): + return 1.0 / rank + return 0.0 + + +def calculate_dcg(relevances: list[int], k: int) -> float: + """Calculate Discounted Cumulative Gain.""" + dcg = 0.0 + for i in range(min(k, len(relevances))): + dcg += relevances[i] / math.log2(i + 2) # i+2 because rank starts at 1 + return dcg + + +def calculate_ndcg(keyword: str, retrieved_docs: list, k: int = 10) -> float: + """Calculate nDCG for a single keyword (binary relevance, case-insensitive).""" + keyword_lower = keyword.lower() + + # Binary relevance: 1 if keyword found, 0 otherwise + relevances = [ + 1 if keyword_lower in doc.page_content.lower() else 0 for doc in retrieved_docs[:k] + ] + + # DCG + dcg = calculate_dcg(relevances, k) + + # Ideal DCG (best case: keyword in first position) + ideal_relevances = sorted(relevances, reverse=True) + idcg = calculate_dcg(ideal_relevances, k) + + return dcg / idcg if idcg > 0 else 0.0 + + +def evaluate_retrieval(test: TestQuestion, k: int = 10) -> RetrievalEval: + """ + Evaluate retrieval performance for a test question. + + Args: + test: TestQuestion object containing question and keywords + k: Number of top documents to retrieve (default 10) + + Returns: + RetrievalEval object with MRR, nDCG, and keyword coverage metrics + """ + # Retrieve documents using shared answer module + retrieved_docs = fetch_context(test.question) + + # Calculate MRR (average across all keywords) + mrr_scores = [calculate_mrr(keyword, retrieved_docs) for keyword in test.keywords] + avg_mrr = sum(mrr_scores) / len(mrr_scores) if mrr_scores else 0.0 + + # Calculate nDCG (average across all keywords) + ndcg_scores = [calculate_ndcg(keyword, retrieved_docs, k) for keyword in test.keywords] + avg_ndcg = sum(ndcg_scores) / len(ndcg_scores) if ndcg_scores else 0.0 + + # Calculate keyword coverage + keywords_found = sum(1 for score in mrr_scores if score > 0) + total_keywords = len(test.keywords) + keyword_coverage = (keywords_found / total_keywords * 100) if total_keywords > 0 else 0.0 + + return RetrievalEval( + mrr=avg_mrr, + ndcg=avg_ndcg, + keywords_found=keywords_found, + total_keywords=total_keywords, + keyword_coverage=keyword_coverage, + ) + + +def evaluate_answer(test: TestQuestion) -> tuple[AnswerEval, str, list]: + """ + Evaluate answer quality using LLM-as-a-judge (async). + + Args: + test: TestQuestion object containing question and reference answer + + Returns: + Tuple of (AnswerEval object, generated_answer string, retrieved_docs list) + """ + # Get RAG response using shared answer module + generated_answer, retrieved_docs = answer_question(test.question) + + # LLM judge prompt + judge_messages = [ + { + "role": "system", + "content": "You are an expert evaluator assessing the quality of answers. Evaluate the generated answer by comparing it to the reference answer. Only give 5/5 scores for perfect answers.", + }, + { + "role": "user", + "content": f"""Question: +{test.question} + +Generated Answer: +{generated_answer} + +Reference Answer: +{test.reference_answer} + +Please evaluate the generated answer on three dimensions: +1. Accuracy: How factually correct is it compared to the reference answer? Only give 5/5 scores for perfect answers. +2. Completeness: How thoroughly does it address all aspects of the question, covering all the information from the reference answer? +3. Relevance: How well does it directly answer the specific question asked, giving no additional information? + +Provide detailed feedback and scores from 1 (very poor) to 5 (ideal) for each dimension. If the answer is wrong, then the accuracy score must be 1.""", + }, + ] + + # Call LLM judge with structured outputs (async) + judge_response = completion(model=MODEL, messages=judge_messages, response_format=AnswerEval) + + answer_eval = AnswerEval.model_validate_json(judge_response.choices[0].message.content) + + return answer_eval, generated_answer, retrieved_docs + + +def evaluate_all_retrieval(): + """Evaluate all retrieval tests.""" + tests = load_tests() + total_tests = len(tests) + for index, test in enumerate(tests): + result = evaluate_retrieval(test) + progress = (index + 1) / total_tests + yield test, result, progress + + +def evaluate_all_answers(): + """Evaluate all answers to tests using batched async execution.""" + tests = load_tests() + total_tests = len(tests) + for index, test in enumerate(tests): + result = evaluate_answer(test)[0] + progress = (index + 1) / total_tests + yield test, result, progress + + +def run_cli_evaluation(test_number: int): + """Run evaluation for a specific test (async helper for CLI).""" + # Load tests + tests = load_tests("tests.jsonl") + + if test_number < 0 or test_number >= len(tests): + print(f"Error: test_row_number must be between 0 and {len(tests) - 1}") + sys.exit(1) + + # Get the test + test = tests[test_number] + + # Print test info + print(f"\n{'=' * 80}") + print(f"Test #{test_number}") + print(f"{'=' * 80}") + print(f"Question: {test.question}") + print(f"Keywords: {test.keywords}") + print(f"Category: {test.category}") + print(f"Reference Answer: {test.reference_answer}") + + # Retrieval Evaluation + print(f"\n{'=' * 80}") + print("Retrieval Evaluation") + print(f"{'=' * 80}") + + retrieval_result = evaluate_retrieval(test) + + print(f"MRR: {retrieval_result.mrr:.4f}") + print(f"nDCG: {retrieval_result.ndcg:.4f}") + print(f"Keywords Found: {retrieval_result.keywords_found}/{retrieval_result.total_keywords}") + print(f"Keyword Coverage: {retrieval_result.keyword_coverage:.1f}%") + + # Answer Evaluation + print(f"\n{'=' * 80}") + print("Answer Evaluation") + print(f"{'=' * 80}") + + answer_result, generated_answer, retrieved_docs = evaluate_answer(test) + + print(f"\nGenerated Answer:\n{generated_answer}") + print(f"\nFeedback:\n{answer_result.feedback}") + print("\nScores:") + print(f" Accuracy: {answer_result.accuracy:.2f}/5") + print(f" Completeness: {answer_result.completeness:.2f}/5") + print(f" Relevance: {answer_result.relevance:.2f}/5") + print(f"\n{'=' * 80}\n") + + +def main(): + """CLI to evaluate a specific test by row number.""" + if len(sys.argv) != 2: + print("Usage: uv run eval.py ") + sys.exit(1) + + try: + test_number = int(sys.argv[1]) + except ValueError: + print("Error: test_row_number must be an integer") + sys.exit(1) + + run_cli_evaluation(test_number) + + +if __name__ == "__main__": + main() diff --git a/week5/evaluation/test.py b/week5/evaluation/test.py new file mode 100644 index 0000000..ed66d9b --- /dev/null +++ b/week5/evaluation/test.py @@ -0,0 +1,24 @@ +import json +from pathlib import Path +from pydantic import BaseModel, Field + +TEST_FILE = str(Path(__file__).parent / "tests.jsonl") + + +class TestQuestion(BaseModel): + """A test question with expected keywords and reference answer.""" + + question: str = Field(description="The question to ask the RAG system") + keywords: list[str] = Field(description="Keywords that must appear in retrieved context") + reference_answer: str = Field(description="The reference answer for this question") + category: str = Field(description="Question category (e.g., direct_fact, spanning, temporal)") + + +def load_tests() -> list[TestQuestion]: + """Load test questions from JSONL file.""" + tests = [] + with open(TEST_FILE, "r", encoding="utf-8") as f: + for line in f: + data = json.loads(line.strip()) + tests.append(TestQuestion(**data)) + return tests diff --git a/week5/evaluation/tests.jsonl b/week5/evaluation/tests.jsonl new file mode 100644 index 0000000..d8e2e5d --- /dev/null +++ b/week5/evaluation/tests.jsonl @@ -0,0 +1,150 @@ +{"question": "Who won the prestigious IIOTY award in 2023?", "keywords": ["Maxine", "Thompson", "IIOTY"], "reference_answer": "Maxine Thompson won the prestigious Insurellm Innovator of the Year (IIOTY) award in 2023.", "category": "direct_fact"} +{"question": "When was Insurellm founded?", "keywords": ["2015", "founded"], "reference_answer": "Insurellm was founded in 2015.", "category": "direct_fact"} +{"question": "Who founded Insurellm?", "keywords": ["Avery", "Lancaster"], "reference_answer": "Avery Lancaster founded Insurellm in 2015.", "category": "direct_fact"} +{"question": "How many employees does Insurellm currently have?", "keywords": ["32", "employees"], "reference_answer": "Insurellm currently operates with 32 employees as of 2025.", "category": "direct_fact"} +{"question": "What was Insurellm's first product?", "keywords": ["Markellm", "first"], "reference_answer": "Insurellm's first product was Markellm, the marketplace connecting consumers with insurance providers.", "category": "direct_fact"} +{"question": "How many active contracts does Insurellm manage?", "keywords": ["32", "contracts"], "reference_answer": "Insurellm manages 32 active contracts spanning all eight product lines.", "category": "direct_fact"} +{"question": "Where is Insurellm's headquarters located?", "keywords": ["San Francisco", "headquarters"], "reference_answer": "Insurellm's headquarters is located in San Francisco.", "category": "direct_fact"} +{"question": "How many office locations does Insurellm maintain?", "keywords": ["offices in", "5"], "reference_answer": "Insurellm maintains 5 office locations: San Francisco, New York, Austin, Chicago, and Denver.", "category": "direct_fact"} +{"question": "What is Insurellm's vision statement?", "keywords": ["revolutionize", "insurance", "technology"], "reference_answer": "Insurellm's vision is to revolutionize the insurance industry through innovative technology that makes insurance accessible, transparent, and effortless.", "category": "direct_fact"} +{"question": "How many Bizllm contracts has Insurellm secured?", "keywords": ["7", "Bizllm"], "reference_answer": "Insurellm has secured 7 commercial insurance contracts for Bizllm.", "category": "direct_fact"} +{"question": "How many Claimllm contracts does Insurellm have?", "keywords": ["7", "Claimllm"], "reference_answer": "Insurellm has 7 contracts for Claimllm, ranging from independent adjusting firms to enterprise claims networks.", "category": "direct_fact"} +{"question": "How many life insurance contracts has Lifellm secured?", "keywords": ["6", "Lifellm"], "reference_answer": "Lifellm has secured 6 life insurance contracts serving carriers from small regional providers to major national groups.", "category": "direct_fact"} +{"question": "How many health insurance contracts does Healthllm have?", "keywords": ["6", "Healthllm"], "reference_answer": "Healthllm has 6 health insurance contracts with plans from regional insurers to multi-state healthcare alliances.", "category": "direct_fact"} +{"question": "What is the monthly cost of Carllm's Basic Tier?", "keywords": ["$1,000", "Basic", "Carllm"], "reference_answer": "Carllm's Basic Tier costs $1,000 per month.", "category": "direct_fact"} +{"question": "What is the monthly cost of Carllm's Enterprise Tier?", "keywords": ["$5,000", "Enterprise", "Carllm"], "reference_answer": "Carllm's Enterprise Tier costs $5,000 per month.", "category": "direct_fact"} +{"question": "What is the monthly cost of Homellm's Standard Tier?", "keywords": ["$10,000", "Standard", "Homellm"], "reference_answer": "Homellm's Standard Tier costs $10,000 per month for medium-sized insurers.", "category": "direct_fact"} +{"question": "What is the monthly cost of Bizllm's Professional Tier?", "keywords": ["$12,000", "Professional", "Bizllm"], "reference_answer": "Bizllm's Professional Tier costs $12,000 per month for multi-line carriers.", "category": "direct_fact"} +{"question": "What is the monthly cost of Claimllm's Core Tier?", "keywords": ["$4,500", "Core", "Claimllm"], "reference_answer": "Claimllm's Core Tier costs $4,500 per month for smaller insurers processing up to 5,000 claims annually.", "category": "direct_fact"} +{"question": "What is the monthly cost of Healthllm's Essential Tier?", "keywords": ["$8,000", "Essential", "Healthllm"], "reference_answer": "Healthllm's Essential Tier costs $8,000 per month for regional health plans.", "category": "direct_fact"} +{"question": "What is the monthly cost of Lifellm's Starter Tier?", "keywords": ["$3,500", "Starter", "Lifellm"], "reference_answer": "Lifellm's Starter Tier costs $3,500 per month for small insurers.", "category": "direct_fact"} +{"question": "What is the monthly cost of Rellm's Basic Plan?", "keywords": ["$5,000", "Basic", "Rellm"], "reference_answer": "Rellm's Basic Plan costs $5,000 per month.", "category": "direct_fact"} +{"question": "What is Maxine Thompson's current job title?", "keywords": ["Senior Data Engineer", "Maxine"], "reference_answer": "Maxine Thompson's current job title is Senior Data Engineer.", "category": "direct_fact"} +{"question": "What is Maxine Thompson's current salary?", "keywords": ["$120,000", "Maxine"], "reference_answer": "Maxine Thompson's current salary is $120,000.", "category": "direct_fact"} +{"question": "Where is Maxine Thompson located?", "keywords": ["Austin", "Texas", "Maxine"], "reference_answer": "Maxine Thompson is located in Austin, Texas.", "category": "direct_fact"} +{"question": "What is Avery Lancaster's job title?", "keywords": ["CEO", "Avery", "Lancaster"], "reference_answer": "Avery Lancaster is the Co-Founder and Chief Executive Officer (CEO) of Insurellm.", "category": "direct_fact"} +{"question": "What is Avery Lancaster's current salary?", "keywords": ["$225,000", "Avery"], "reference_answer": "Avery Lancaster's current salary is $225,000.", "category": "direct_fact"} +{"question": "What is James Wilson's job title?", "keywords": ["CTO", "James", "Wilson"], "reference_answer": "James Wilson is the Chief Technology Officer (CTO) of Insurellm.", "category": "direct_fact"} +{"question": "What is James Wilson's current salary?", "keywords": ["$285,000", "James", "Wilson"], "reference_answer": "James Wilson's current salary is $285,000.", "category": "direct_fact"} +{"question": "What is Priya Sharma's job title?", "keywords": ["Senior Data Scientist", "Priya"], "reference_answer": "Priya Sharma is a Senior Data Scientist at Insurellm.", "category": "direct_fact"} +{"question": "What is Priya Sharma's current salary?", "keywords": ["$145,000", "Priya"], "reference_answer": "Priya Sharma's current salary is $145,000.", "category": "direct_fact"} +{"question": "What is Robert Chen's job title?", "keywords": ["Senior Full Stack Engineer", "Robert"], "reference_answer": "Robert Chen is a Senior Full Stack Engineer at Insurellm.", "category": "direct_fact"} +{"question": "What is Robert Chen's current salary?", "keywords": ["$152,000", "Robert"], "reference_answer": "Robert Chen's current salary is $152,000.", "category": "direct_fact"} +{"question": "Which product does Robert Chen serve as technical lead for?", "keywords": ["Homellm", "Robert", "Chen"], "reference_answer": "Robert Chen is the technical lead for Homellm home insurance portal.", "category": "direct_fact"} +{"question": "What is David Kim's job title?", "keywords": ["DevOps Engineer", "David", "Kim"], "reference_answer": "David Kim is a DevOps Engineer at Insurellm.", "category": "direct_fact"} +{"question": "What is Sarah Williams's job title?", "keywords": ["UX Designer", "Sarah"], "reference_answer": "Sarah Williams is a UX Designer at Insurellm.", "category": "direct_fact"} +{"question": "Which product does Sarah Williams lead design for?", "keywords": ["Homellm", "Sarah"], "reference_answer": "Sarah Williams leads design for the Homellm home insurance portal.", "category": "direct_fact"} +{"question": "What is Marcus Johnson's job title?", "keywords": ["Customer Success Manager", "Marcus"], "reference_answer": "Marcus Johnson is a Customer Success Manager at Insurellm.", "category": "direct_fact"} +{"question": "What is Marcus Johnson's client retention rate?", "keywords": ["95%", "retention", "Marcus"], "reference_answer": "Marcus Johnson achieved a 95% client retention rate over the past three years.", "category": "direct_fact"} +{"question": "What is Lisa Anderson's job title?", "keywords": ["Marketing Manager", "Lisa"], "reference_answer": "Lisa Anderson is a Marketing Manager at Insurellm.", "category": "direct_fact"} +{"question": "What is the size of Lisa Anderson's annual marketing budget?", "keywords": ["$2M", "Lisa"], "reference_answer": "Lisa Anderson manages a $2M annual marketing budget.", "category": "direct_fact"} +{"question": "What is Emily Carter's job title?", "keywords": ["Account Executive", "Emily"], "reference_answer": "Emily Carter is an Account Executive at Insurellm.", "category": "direct_fact"} +{"question": "What is the contract number for DriveSmart Insurance's Carllm agreement?", "keywords": ["CR-2025-E-0078", "DriveSmart"], "reference_answer": "The contract number for DriveSmart Insurance's Carllm agreement is CR-2025-E-0078.", "category": "direct_fact"} +{"question": "What is the duration of the DriveSmart Insurance Carllm contract?", "keywords": ["36 months", "DriveSmart"], "reference_answer": "The DriveSmart Insurance Carllm contract is effective for a period of 36 months.", "category": "direct_fact"} +{"question": "What is the total contract value for DriveSmart Insurance's Carllm agreement?", "keywords": ["$702,000", "DriveSmart"], "reference_answer": "The total contract value for DriveSmart Insurance's Carllm agreement is $702,000 over the 36-month term.", "category": "direct_fact"} +{"question": "How many active auto policies does DriveSmart Insurance have?", "keywords": ["85,000", "DriveSmart", "policies"], "reference_answer": "DriveSmart Insurance currently has 85,000 active auto policies across 8 states.", "category": "direct_fact"} +{"question": "What is the uptime SLA guarantee for DriveSmart Insurance?", "keywords": ["99.9%", "uptime", "DriveSmart"], "reference_answer": "Insurellm guarantees 99.9% platform uptime measured monthly for DriveSmart Insurance.", "category": "direct_fact"} +{"question": "Who signed the DriveSmart Insurance contract on behalf of Insurellm?", "keywords": ["Jennifer Rodriguez", "DriveSmart"], "reference_answer": "Jennifer Rodriguez, CEO of Insurellm, signed the DriveSmart Insurance contract.", "category": "direct_fact"} +{"question": "What is the monthly payment for Greenstone Insurance's Homellm contract?", "keywords": ["$10,000", "Greenstone"], "reference_answer": "Greenstone Insurance pays $10,000 per month for the Standard Tier of the Homellm service.", "category": "direct_fact"} +{"question": "What tier did Harmony Health Plans subscribe to for Healthllm?", "keywords": ["Professional", "Harmony"], "reference_answer": "Harmony Health Plans subscribed to the Professional Tier of Healthllm.", "category": "direct_fact"} +{"question": "What is the monthly cost for Harmony Health Plans' Healthllm subscription?", "keywords": ["$15,000", "Harmony"], "reference_answer": "Harmony Health Plans pays $15,000 per month for the Professional Tier of Healthllm.", "category": "direct_fact"} +{"question": "How many covered members does Harmony Health Plans have?", "keywords": ["38,000", "Harmony"], "reference_answer": "Harmony Health Plans currently covers 38,000 members across 3 states.", "category": "direct_fact"} +{"question": "Who signed the Harmony Health Plans contract on behalf of Insurellm?", "keywords": ["Sarah Chen", "Harmony"], "reference_answer": "Sarah Chen, Vice President of Sales at Insurellm, signed the Harmony Health Plans contract.", "category": "direct_fact"} +{"question": "What is the contract number for Metropolitan Life Group's Lifellm agreement?", "keywords": ["LF-2025-E-0087", "Metropolitan"], "reference_answer": "The contract number for Metropolitan Life Group's Lifellm agreement is LF-2025-E-0087.", "category": "direct_fact"} +{"question": "What is the total contract value for Metropolitan Life Group's Lifellm agreement?", "keywords": ["$1,098,000", "Metropolitan"], "reference_answer": "The total contract value for Metropolitan Life Group's Lifellm agreement is $1,098,000 over the 36-month term.", "category": "direct_fact"} +{"question": "How many active policies does Metropolitan Life Group manage?", "keywords": ["50,000", "Metropolitan"], "reference_answer": "Metropolitan Life Group manages 50,000+ active policies.", "category": "direct_fact"} +{"question": "Who signed the Metropolitan Life Group contract on behalf of the client?", "keywords": ["Richard Thompson", "Metropolitan"], "reference_answer": "Richard Thompson, Chairman & Chief Executive Officer, signed the contract on behalf of Metropolitan Life Group.", "category": "direct_fact"} +{"question": "What tier did FastTrack Insurance Services subscribe to for Claimllm?", "keywords": ["Advanced", "FastTrack"], "reference_answer": "FastTrack Insurance Services subscribed to the Advanced Tier of Claimllm.", "category": "direct_fact"} +{"question": "What is the monthly payment for FastTrack Insurance Services' Claimllm contract?", "keywords": ["$9,500", "FastTrack"], "reference_answer": "FastTrack Insurance Services pays $9,500 per month for Claimllm Advanced Tier.", "category": "direct_fact"} +{"question": "What is the duration of the FastTrack Insurance Services contract?", "keywords": ["18 months", "FastTrack"], "reference_answer": "The FastTrack Insurance Services contract has a duration of 18 months.", "category": "direct_fact"} +{"question": "Who signed the FastTrack Insurance Services contract on behalf of Insurellm?", "keywords": ["Sarah Chen", "FastTrack"], "reference_answer": "Sarah Chen, VP of Sales, signed the FastTrack Insurance Services contract on behalf of Insurellm.", "category": "direct_fact"} +{"question": "What tier did Atlantic Risk Solutions subscribe to for Bizllm?", "keywords": ["Professional", "Atlantic"], "reference_answer": "Atlantic Risk Solutions subscribed to the Professional Tier of Bizllm.", "category": "direct_fact"} +{"question": "What is the monthly cost for Atlantic Risk Solutions' Bizllm subscription?", "keywords": ["$12,000", "Atlantic"], "reference_answer": "Atlantic Risk Solutions pays $12,000 per month for Bizllm Professional Tier.", "category": "direct_fact"} +{"question": "How many user licenses are included in Atlantic Risk Solutions' contract?", "keywords": ["35", "Atlantic"], "reference_answer": "Atlantic Risk Solutions' contract includes 35 named user licenses.", "category": "direct_fact"} +{"question": "Who signed the Atlantic Risk Solutions contract on behalf of Insurellm?", "keywords": ["Michael Torres", "Atlantic"], "reference_answer": "Michael Torres, Chief Revenue Officer, signed the Atlantic Risk Solutions contract on behalf of Insurellm.", "category": "direct_fact"} +{"question": "What is the monthly fee for Belvedere Insurance's Markellm listing?", "keywords": ["$199", "Belvedere"], "reference_answer": "Belvedere Insurance pays a Basic Listing Fee of $199 per month for accessing the Markellm platform.", "category": "direct_fact"} +{"question": "When did Maxine Thompson join Insurellm?", "keywords": ["January 2017", "Maxine"], "reference_answer": "Maxine Thompson joined Insurellm in January 2017 as a Junior Data Engineer.", "category": "temporal"} +{"question": "When did James Wilson join Insurellm?", "keywords": ["January 2017", "James", "Wilson"], "reference_answer": "James Wilson joined Insurellm as Chief Technology Officer in January 2017.", "category": "temporal"} +{"question": "When did Insurellm reach its peak of 200 employees?", "keywords": ["2020", "200"], "reference_answer": "Insurellm reached a peak of 200 employees in 2020.", "category": "temporal"} +{"question": "When did Insurellm undergo strategic restructuring?", "keywords": ["2022-2023", "restructuring"], "reference_answer": "Insurellm underwent strategic restructuring in 2022-2023 to focus on profitability and sustainable growth.", "category": "temporal"} +{"question": "When is Carllm's telematics-based pricing feature scheduled to launch?", "keywords": ["Q2 2025", "telematics"], "reference_answer": "Carllm's telematics-based pricing is scheduled to launch in Q2 2025.", "category": "temporal"} +{"question": "When is Homellm version 2.0 scheduled for release?", "keywords": ["Q1 2025", "Homellm"], "reference_answer": "Homellm version 2.0 is scheduled for release in Q1 2025.", "category": "temporal"} +{"question": "When is Bizllm version 1.0 scheduled to launch?", "keywords": ["Q2 2025", "Bizllm"], "reference_answer": "Bizllm version 1.0 is scheduled to launch in Q2 2025 with core multi-line underwriting, quoting, and policy administration.", "category": "temporal"} +{"question": "When is Claimllm version 1.0 scheduled to launch?", "keywords": ["Q1 2025", "Claimllm"], "reference_answer": "Claimllm version 1.0 is scheduled to launch in Q1 2025 with core claims processing automation, FNOL capture, and basic fraud detection.", "category": "temporal"} +{"question": "When is Lifellm version 1.0 scheduled to launch?", "keywords": ["Q2 2025", "Lifellm"], "reference_answer": "Lifellm version 1.0 is scheduled to launch in Q2 2025 with core AI underwriting and policy management capabilities.", "category": "temporal"} +{"question": "When is Healthllm version 1.0 scheduled to launch?", "keywords": ["Q1 2025", "Healthllm"], "reference_answer": "Healthllm version 1.0 is scheduled to launch in Q1 2025 featuring core claims processing, eligibility verification, and member portal.", "category": "temporal"} +{"question": "When was the DriveSmart Insurance contract for Carllm signed?", "keywords": ["March 20, 2025", "DriveSmart"], "reference_answer": "The DriveSmart Insurance contract for Carllm was signed on March 20, 2025.", "category": "temporal"} +{"question": "When was the Harmony Health Plans Healthllm contract effective?", "keywords": ["January 25, 2025", "Harmony"], "reference_answer": "The Harmony Health Plans Healthllm contract became effective on January 25, 2025.", "category": "temporal"} +{"question": "When was the Metropolitan Life Group Lifellm contract signed?", "keywords": ["April 5, 2025", "Metropolitan"], "reference_answer": "The Metropolitan Life Group Lifellm contract was signed on April 5, 2025.", "category": "temporal"} +{"question": "When was the FastTrack Insurance Services Claimllm contract signed?", "keywords": ["May 10, 2025", "FastTrack"], "reference_answer": "The FastTrack Insurance Services Claimllm contract was signed on May 10, 2025.", "category": "temporal"} +{"question": "When was the Atlantic Risk Solutions Bizllm contract effective?", "keywords": ["January 15, 2025", "Atlantic"], "reference_answer": "The Atlantic Risk Solutions Bizllm contract became effective on January 15, 2025.", "category": "temporal"} +{"question": "How much did Priya Sharma's recommendation engine increase conversion by for Marketllm?", "keywords": ["28%", "Priya", "Marketllm"], "reference_answer": "Priya Sharma built a recommendation engine for Marketllm that increased conversion by 28%.", "category": "comparative"} +{"question": "By what percentage did David Kim reduce deployment time with CI/CD pipelines?", "keywords": ["60%", "David", "Kim"], "reference_answer": "David Kim implemented CI/CD pipelines that reduced deployment time by 60%.", "category": "comparative"} +{"question": "By what percentage did Sarah Williams improve user satisfaction scores?", "keywords": ["35%", "Sarah"], "reference_answer": "Sarah Williams conducted comprehensive user research that resulted in a 35% improvement in user satisfaction scores.", "category": "comparative"} +{"question": "By what percentage has Lisa Anderson increased qualified leads since 2021?", "keywords": ["65%", "Lisa"], "reference_answer": "Lisa Anderson increased qualified leads by 65% since 2021.", "category": "comparative"} +{"question": "By what percentage did Emily Carter exceed her annual sales target in 2022?", "keywords": ["30%", "Emily", "Carter"], "reference_answer": "Emily Carter exceeded her annual sales target by 30% in 2022.", "category": "comparative"} +{"question": "What is the performance-based pricing for Belvedere Insurance's Markellm contract?", "keywords": ["$25", "Belvedere"], "reference_answer": "Belvedere Insurance pays $25 per lead generated through Markellm on a performance-based pricing model.", "category": "comparative"} +{"question": "What is the monthly cost for Stellar Insurance Co.'s Rellm subscription?", "keywords": ["$10,000", "Stellar"], "reference_answer": "Stellar Insurance Co. pays $10,000 per month for the Professional Plan of Rellm.", "category": "comparative"} +{"question": "What are Insurellm's four core values?", "keywords": ["Innovation First", "Customer Obsession", "Collaborative Excellence"], "reference_answer": "Insurellm's four core values are Innovation First, Customer Obsession, Integrity & Transparency, and Collaborative Excellence.", "category": "comparative"} +{"question": "What award did Alex Harper win in 2022?", "keywords": ["SDR of the Year", "Alex", "Harper"], "reference_answer": "Alex Harper was awarded SDR of the Year in 2022 for outstanding contributions.", "category": "comparative"} +{"question": "When was the Stellar Insurance Co. Rellm contract effective?", "keywords": ["January 1, 2024", "Stellar"], "reference_answer": "The Stellar Insurance Co. Rellm contract became effective on January 1, 2024.", "category": "comparative"} +{"question": "How many claims does FastTrack Insurance Services project for year 1?", "keywords": ["22,000", "FastTrack"], "reference_answer": "FastTrack Insurance Services projects 22,000 claims in year 1.", "category": "numerical"} +{"question": "How much annual revenue does Marcus Johnson's portfolio generate?", "keywords": ["$5M", "Marcus"], "reference_answer": "Marcus Johnson manages a portfolio of 25 enterprise clients generating $5M in annual revenue.", "category": "numerical"} +{"question": "What percentage of quota did Jennifer Adams achieve in 2023?", "keywords": ["78%", "Jennifer"], "reference_answer": "Jennifer Adams achieved 78% of her lead generation quota in 2023.", "category": "numerical"} +{"question": "How many products does Insurellm offer?", "keywords": ["8", "products"], "reference_answer": "Insurellm offers 8 insurance software products across multiple insurance lines.", "category": "numerical"} +{"question": "What is the monthly cost of Carllm's Professional Tier?", "keywords": ["$2,500", "Professional", "Carllm"], "reference_answer": "Carllm's Professional Tier costs $2,500 per month.", "category": "numerical"} +{"question": "What are the core insurance portals offered by Insurellm?", "keywords": ["Core Insurance Portals", "Carllm"], "reference_answer": "Insurellm's core insurance portals are Carllm (auto), Homellm (home), Lifellm (life), Healthllm (health), and Bizllm (commercial).", "category": "relationship"} +{"question": "What are the marketplace and infrastructure products offered by Insurellm?", "keywords": ["Marketplace & Infrastructure", "Markellm"], "reference_answer": "Insurellm's marketplace and infrastructure products are Markellm (marketplace), Claimllm (claims processing), and Rellm (reinsurance).", "category": "relationship"} +{"question": "Which product does Jessica Liu develop for?", "keywords": ["Rellm", "Jessica"], "reference_answer": "Jessica Liu develops user interfaces for the Rellm reinsurance platform using React.", "category": "relationship"} +{"question": "What is Tyler Brooks's job title?", "keywords": ["Junior Backend Developer", "Tyler"], "reference_answer": "Tyler Brooks is a Junior Backend Developer at Insurellm.", "category": "relationship"} +{"question": "Which product does Tyler Brooks work on?", "keywords": ["Carllm", "Tyler"], "reference_answer": "Tyler Brooks develops backend services for the Carllm auto insurance portal.", "category": "relationship"} +{"question": "What product does the IIOTY award winner work on?", "keywords": ["Maxine", "Thompson", "Senior Data Engineer", "IIOTY"], "reference_answer": "Maxine Thompson, who won the IIOTY award in 2023, works as a Senior Data Engineer.", "category": "spanning"} +{"question": "Who is the technical lead for the product that costs $10,000/month for Standard Tier?", "keywords": ["Robert", "Chen", "Homellm", "$10,000"], "reference_answer": "Robert Chen is the technical lead for Homellm, which costs $10,000/month for the Standard Tier.", "category": "spanning"} +{"question": "What is the salary of the CTO who joined in January 2017?", "keywords": ["James", "Wilson", "$285,000", "January 2017"], "reference_answer": "James Wilson, who joined as CTO in January 2017, has a current salary of $285,000.", "category": "spanning"} +{"question": "What is the current salary of the founder of Insurellm?", "keywords": ["Avery", "Lancaster", "$225,000", "founder"], "reference_answer": "Avery Lancaster, the founder of Insurellm, has a current salary of $225,000.", "category": "spanning"} +{"question": "Which product did the Senior Data Scientist build a recommendation engine for that increased conversion by 28%?", "keywords": ["Priya", "Sharma", "Marketllm", "28%"], "reference_answer": "Priya Sharma built a recommendation engine for Marketllm that increased conversion by 28%.", "category": "spanning"} +{"question": "Which product does the UX Designer who improved user satisfaction by 35% lead design for?", "keywords": ["Sarah", "Williams", "Homellm", "35%"], "reference_answer": "Sarah Williams, who improved user satisfaction by 35%, leads design for Homellm.", "category": "spanning"} +{"question": "What is the annual marketing budget managed by the Marketing Manager in Austin?", "keywords": ["Lisa", "Anderson", "$2M", "Austin"], "reference_answer": "Lisa Anderson, the Marketing Manager in Austin, manages a $2M annual marketing budget.", "category": "spanning"} +{"question": "Who signed the contract on behalf of Insurellm for the client that pays $18,000 per month for Carllm?", "keywords": ["Jennifer", "Rodriguez", "DriveSmart", "$18,000"], "reference_answer": "Jennifer Rodriguez, CEO of Insurellm, signed the contract for DriveSmart Insurance which pays $18,000 per month for Carllm.", "category": "spanning"} +{"question": "What tier did the client with 35 user licenses subscribe to for Bizllm?", "keywords": ["Atlantic", "Risk", "Solutions", "Professional", "35"], "reference_answer": "Atlantic Risk Solutions, which has 35 named user licenses, subscribed to the Professional Tier of Bizllm.", "category": "spanning"} +{"question": "How many covered members does the client who subscribed to Healthllm Professional Tier for $15,000/month have?", "keywords": ["Harmony", "Health", "Plans", "38,000", "$15,000"], "reference_answer": "Harmony Health Plans, which subscribed to Healthllm Professional Tier for $15,000/month, has 38,000 covered members.", "category": "spanning"} +{"question": "Who signed the Metropolitan Life Group contract that totals $1,098,000?", "keywords": ["Jennifer", "Rodriguez", "Richard", "Thompson", "$1,098,000"], "reference_answer": "Jennifer Rodriguez signed for Insurellm and Richard Thompson signed for Metropolitan Life Group on the contract totaling $1,098,000.", "category": "spanning"} +{"question": "What is the total contract value for the client paying $12,000 per month for Bizllm Professional Tier?", "keywords": ["Atlantic", "$144,000", "12 months", "$12,000"], "reference_answer": "Atlantic Risk Solutions pays $12,000/month for 12 months, totaling $144,000 for Bizllm Professional Tier.", "category": "spanning"} +{"question": "By what percentage did the DevOps Engineer in New York reduce deployment time?", "keywords": ["David", "Kim", "60%", "New York"], "reference_answer": "David Kim, the DevOps Engineer in New York, reduced deployment time by 60% with CI/CD pipelines.", "category": "spanning"} +{"question": "What is the annual revenue generated by the portfolio managed by the Customer Success Manager in New York?", "keywords": ["Marcus", "Johnson", "$5M", "New York"], "reference_answer": "Marcus Johnson, the Customer Success Manager in New York, manages a portfolio generating $5M in annual revenue.", "category": "spanning"} +{"question": "By what percentage did the Account Executive in Austin exceed their annual sales target in 2022?", "keywords": ["Emily", "Carter", "30%", "Austin", "2022"], "reference_answer": "Emily Carter, the Account Executive in Austin, exceeded her annual sales target by 30% in 2022.", "category": "spanning"} +{"question": "When is the telematics-based pricing feature scheduled to launch for the product that costs $1,000/month for Basic Tier?", "keywords": ["Carllm", "Q2 2025", "telematics", "$1,000"], "reference_answer": "Carllm, which costs $1,000/month for Basic Tier, has telematics-based pricing scheduled to launch in Q2 2025.", "category": "spanning"} +{"question": "What is the monthly cost for the Basic Listing Fee on the marketplace that was Insurellm's first product?", "keywords": ["Markellm", "$199", "first product"], "reference_answer": "Markellm, Insurellm's first product, has a Basic Listing Fee of $199/month.", "category": "spanning"} +{"question": "How many core values does the company founded by Avery Lancaster have?", "keywords": ["four", "core values", "Avery", "Lancaster", "founded"], "reference_answer": "Insurellm, founded by Avery Lancaster, has four core values: Innovation First, Customer Obsession, Integrity & Transparency, and Collaborative Excellence.", "category": "spanning"} +{"question": "What tier of Healthllm costs $15,000 per month and includes Advanced Medication Management?", "keywords": ["Professional", "Healthllm", "$15,000", "Advanced Medication Management"], "reference_answer": "The Professional Tier of Healthllm costs $15,000 per month and includes Advanced Medication Management features.", "category": "spanning"} +{"question": "How many active policies does the client who pays $28,000 per month for Lifellm manage?", "keywords": ["Metropolitan", "Life", "Group", "50,000", "$28,000"], "reference_answer": "Metropolitan Life Group, which pays $28,000 per month for Lifellm, manages 50,000+ active policies.", "category": "spanning"} +{"question": "What is Alex Chen's current job title?", "keywords": ["Backend Software Engineer", "Alex Chen"], "reference_answer": "Alex Chen is a Backend Software Engineer at Insurellm.", "category": "direct_fact"} +{"question": "What is Carlos Rodriguez's current salary?", "keywords": ["$125,000", "Carlos"], "reference_answer": "Carlos Rodriguez's current salary is $125,000.", "category": "direct_fact"} +{"question": "What is the monthly cost of the Bizllm Business Tier?", "keywords": ["$6,000", "Business", "Bizllm"], "reference_answer": "The Bizllm Business Tier costs $6,000 per month.", "category": "direct_fact"} +{"question": "What is Nina Patel's job title?", "keywords": ["Business Intelligence Analyst", "Nina"], "reference_answer": "Nina Patel is a Business Intelligence Analyst at Insurellm.", "category": "direct_fact"} +{"question": "What is the contract number for the GreenValley Insurance Homellm agreement?", "keywords": ["HV-2023-0458", "GreenValley"], "reference_answer": "The contract number for the GreenValley Insurance Homellm agreement is HV-2023-0458.", "category": "direct_fact"} +{"question": "When did Alex Chen join Insurellm?", "keywords": ["April 2020", "Alex Chen"], "reference_answer": "Alex Chen joined Insurellm in April 2020 as a Junior Backend Developer.", "category": "temporal"} +{"question": "When was the GreenValley Insurance Homellm contract signed?", "keywords": ["October 6, 2023", "GreenValley"], "reference_answer": "The GreenValley Insurance Homellm contract was signed on October 6, 2023.", "category": "temporal"} +{"question": "When was the Summit Commercial Insurance Bizllm contract effective?", "keywords": ["March 15, 2025", "Summit"], "reference_answer": "The Summit Commercial Insurance Bizllm contract became effective on March 15, 2025.", "category": "temporal"} +{"question": "When was the Roadway Insurance Carllm contract effective?", "keywords": ["January 1, 2025", "Roadway"], "reference_answer": "The Roadway Insurance Carllm contract became effective on January 1, 2025.", "category": "temporal"} +{"question": "When was the Evergreen Life Insurance Lifellm contract signed?", "keywords": ["January 20, 2025", "Evergreen"], "reference_answer": "The Evergreen Life Insurance Lifellm contract was signed on January 20, 2025.", "category": "temporal"} +{"question": "How many employees did Insurellm have at its peak in 2020?", "keywords": ["200", "2020", "peak"], "reference_answer": "Insurellm reached a peak of 200 employees in 2020.", "category": "numerical"} +{"question": "What is the monthly cost for Apex Reinsurance's Rellm subscription?", "keywords": ["$10,000", "Apex"], "reference_answer": "Apex Reinsurance pays $10,000 per month for the Rellm solution.", "category": "numerical"} +{"question": "How many active policies does Evergreen Life Insurance currently manage?", "keywords": ["1,400", "Evergreen"], "reference_answer": "Evergreen Life Insurance currently manages 1,400 active policies.", "category": "numerical"} +{"question": "How many user licenses are provided for onboarding training in the Summit Commercial Insurance contract?", "keywords": ["15", "Summit"], "reference_answer": "Summit Commercial Insurance receives onboarding training for up to 15 staff members.", "category": "numerical"} +{"question": "How much new business did Michael O'Brien generate over the past 3 years?", "keywords": ["$8.2M", "Michael", "O'Brien"], "reference_answer": "Michael O'Brien generated $8.2M in new business over the past 3 years.", "category": "numerical"} +{"question": "Which product does Rachel Martinez lead product strategy for?", "keywords": ["Carllm", "Rachel", "Martinez"], "reference_answer": "Rachel Martinez leads product strategy for Carllm, the auto insurance portal.", "category": "relationship"} +{"question": "Which mobile app does Kevin Zhang lead iOS development for?", "keywords": ["Marketllm", "Kevin", "Zhang"], "reference_answer": "Kevin Zhang leads iOS development for the Marketllm consumer mobile app.", "category": "relationship"} +{"question": "Which tier of Bizllm did Summit Commercial Insurance subscribe to?", "keywords": ["Business", "Summit"], "reference_answer": "Summit Commercial Insurance subscribed to the Business Tier of Bizllm.", "category": "relationship"} +{"question": "What award did Carlos Rodriguez win in 2023?", "keywords": ["Solutions Engineer of the Year", "Carlos"], "reference_answer": "Carlos Rodriguez won Solutions Engineer of the Year 2023.", "category": "relationship"} +{"question": "Who signed the Summit Commercial Insurance contract on behalf of Insurellm?", "keywords": ["Michael Torres", "Summit"], "reference_answer": "Michael Torres, Chief Revenue Officer, signed the Summit Commercial Insurance contract on behalf of Insurellm.", "category": "relationship"} +{"question": "How many employees at Insurellm have a current salary under $80,000?", "keywords": ["salary", "employees"], "reference_answer": "Based on the employee records, there are several employees with salaries under $80,000, including Tyler Brooks ($75,000) and Alex Harper ($75,000).", "category": "holistic"} +{"question": "What is the total contract value of all Healthllm contracts combined?", "keywords": ["Healthllm", "contract value"], "reference_answer": "The total contract value of all Healthllm contracts is $3,504,000, combining United Healthcare Alliance ($2,784,000), Harmony Health Plans ($360,000), and other Healthllm clients.", "category": "holistic"} +{"question": "Which product line has the most active contracts according to the company overview?", "keywords": ["contracts", "products"], "reference_answer": "According to the company overview, both Bizllm and Claimllm are tied with the most active contracts at 7 contracts each.", "category": "holistic"} +{"question": "How many total employees work at Insurellm across all locations?", "keywords": ["employees", "total"], "reference_answer": "Insurellm has 32 employees operating primarily remotely across the US, with offices in San Francisco, New York, Austin, Chicago, and Denver.", "category": "holistic"} +{"question": "What is the average monthly subscription cost across all Carllm contracts?", "keywords": ["Carllm", "monthly cost"], "reference_answer": "The average monthly subscription cost across Carllm contracts varies, with examples including Roadway Insurance at $2,500/month for Professional Tier, though specific averages require aggregating all Carllm contract data.", "category": "holistic"} +{"question": "How many different insurance product lines does Insurellm offer in total?", "keywords": ["products", "insurance lines"], "reference_answer": "Insurellm offers 8 different insurance software products: Carllm, Homellm, Lifellm, Healthllm, Bizllm, Markellm, Claimllm, and Rellm.", "category": "holistic"} +{"question": "Which product has the fewest active contracts according to the portfolio breakdown?", "keywords": ["contracts", "fewest"], "reference_answer": "According to the portfolio breakdown, both Markellm and Rellm are tied with the fewest active contracts at 2 contracts each.", "category": "holistic"} +{"question": "What is the total number of active contracts across all Insurellm products?", "keywords": ["contracts", "total"], "reference_answer": "Insurellm has 32 active contracts across all product lines, serving clients ranging from regional insurers to national carriers and global reinsurance partners.", "category": "holistic"} +{"question": "How many states does United Healthcare Alliance operate in according to their contract?", "keywords": ["United Healthcare", "states"], "reference_answer": "United Healthcare Alliance operates across 12 states according to their Healthllm contract, supporting 250,000+ members.", "category": "holistic"} +{"question": "What is the longest contract duration among all Insurellm contracts?", "keywords": ["contract", "duration"], "reference_answer": "The longest contract duration is 48 months (4 years) for the United Healthcare Alliance Healthllm contract, representing Insurellm's most strategic long-term healthcare partnership.", "category": "holistic"} diff --git a/week5/evaluator.py b/week5/evaluator.py new file mode 100644 index 0000000..f1b184d --- /dev/null +++ b/week5/evaluator.py @@ -0,0 +1,236 @@ +import gradio as gr +import pandas as pd +from collections import defaultdict +from dotenv import load_dotenv + +from evaluation.eval import evaluate_all_retrieval, evaluate_all_answers + +load_dotenv(override=True) + +# Color coding thresholds - Retrieval +MRR_GREEN = 0.9 +MRR_AMBER = 0.75 +NDCG_GREEN = 0.9 +NDCG_AMBER = 0.75 +COVERAGE_GREEN = 90.0 +COVERAGE_AMBER = 75.0 + +# Color coding thresholds - Answer (1-5 scale) +ANSWER_GREEN = 4.5 +ANSWER_AMBER = 4.0 + + +def get_color(value: float, metric_type: str) -> str: + """Get color based on metric value and type.""" + if metric_type == "mrr": + if value >= MRR_GREEN: + return "green" + elif value >= MRR_AMBER: + return "orange" + else: + return "red" + elif metric_type == "ndcg": + if value >= NDCG_GREEN: + return "green" + elif value >= NDCG_AMBER: + return "orange" + else: + return "red" + elif metric_type == "coverage": + if value >= COVERAGE_GREEN: + return "green" + elif value >= COVERAGE_AMBER: + return "orange" + else: + return "red" + elif metric_type in ["accuracy", "completeness", "relevance"]: + if value >= ANSWER_GREEN: + return "green" + elif value >= ANSWER_AMBER: + return "orange" + else: + return "red" + return "black" + + +def format_metric_html( + label: str, + value: float, + metric_type: str, + is_percentage: bool = False, + score_format: bool = False, +) -> str: + """Format a metric with color coding.""" + color = get_color(value, metric_type) + if is_percentage: + value_str = f"{value:.1f}%" + elif score_format: + value_str = f"{value:.2f}/5" + else: + value_str = f"{value:.4f}" + return f""" +
+
{label}
+
{value_str}
+
+ """ + + +def run_retrieval_evaluation(progress=gr.Progress()): + """Run retrieval evaluation and yield updates.""" + total_mrr = 0.0 + total_ndcg = 0.0 + total_coverage = 0.0 + category_mrr = defaultdict(list) + count = 0 + + for test, result, prog_value in evaluate_all_retrieval(): + count += 1 + total_mrr += result.mrr + total_ndcg += result.ndcg + total_coverage += result.keyword_coverage + + category_mrr[test.category].append(result.mrr) + + # Update progress bar only + progress(prog_value, desc=f"Evaluating test {count}...") + + # Calculate final averages + avg_mrr = total_mrr / count + avg_ndcg = total_ndcg / count + avg_coverage = total_coverage / count + + # Create final summary metrics HTML + final_html = f""" +
+ {format_metric_html("Mean Reciprocal Rank (MRR)", avg_mrr, "mrr")} + {format_metric_html("Normalized DCG (nDCG)", avg_ndcg, "ndcg")} + {format_metric_html("Keyword Coverage", avg_coverage, "coverage", is_percentage=True)} +
+ ✓ Evaluation Complete: {count} tests +
+
+ """ + + # Create final bar chart data + category_data = [] + for category, mrr_scores in category_mrr.items(): + avg_cat_mrr = sum(mrr_scores) / len(mrr_scores) + category_data.append({"Category": category, "Average MRR": avg_cat_mrr}) + + df = pd.DataFrame(category_data) + + return final_html, df + + +def run_answer_evaluation(progress=gr.Progress()): + """Run answer evaluation and yield updates (async).""" + total_accuracy = 0.0 + total_completeness = 0.0 + total_relevance = 0.0 + category_accuracy = defaultdict(list) + count = 0 + + for test, result, prog_value in evaluate_all_answers(): + count += 1 + total_accuracy += result.accuracy + total_completeness += result.completeness + total_relevance += result.relevance + + category_accuracy[test.category].append(result.accuracy) + + # Update progress bar only + progress(prog_value, desc=f"Evaluating test {count}...") + + # Calculate final averages + avg_accuracy = total_accuracy / count + avg_completeness = total_completeness / count + avg_relevance = total_relevance / count + + # Create final summary metrics HTML + final_html = f""" +
+ {format_metric_html("Accuracy", avg_accuracy, "accuracy", score_format=True)} + {format_metric_html("Completeness", avg_completeness, "completeness", score_format=True)} + {format_metric_html("Relevance", avg_relevance, "relevance", score_format=True)} +
+ ✓ Evaluation Complete: {count} tests +
+
+ """ + + # Create final bar chart data + category_data = [] + for category, accuracy_scores in category_accuracy.items(): + avg_cat_accuracy = sum(accuracy_scores) / len(accuracy_scores) + category_data.append({"Category": category, "Average Accuracy": avg_cat_accuracy}) + + df = pd.DataFrame(category_data) + + return final_html, df + + +def main(): + """Launch the Gradio evaluation app.""" + theme = gr.themes.Soft(font=["Inter", "system-ui", "sans-serif"]) + + with gr.Blocks(title="RAG Evaluation Dashboard", theme=theme) as app: + gr.Markdown("# 📊 RAG Evaluation Dashboard") + gr.Markdown("Evaluate retrieval and answer quality for the Insurellm RAG system") + + # RETRIEVAL SECTION + gr.Markdown("## 🔍 Retrieval Evaluation") + + retrieval_button = gr.Button("Run Evaluation", variant="primary", size="lg") + + with gr.Row(): + with gr.Column(scale=1): + retrieval_metrics = gr.HTML( + "
Click 'Run Evaluation' to start
" + ) + + with gr.Column(scale=1): + retrieval_chart = gr.BarPlot( + x="Category", + y="Average MRR", + title="Average MRR by Category", + y_lim=[0, 1], + height=400, + ) + + # ANSWERING SECTION + gr.Markdown("## 💬 Answer Evaluation") + + answer_button = gr.Button("Run Evaluation", variant="primary", size="lg") + + with gr.Row(): + with gr.Column(scale=1): + answer_metrics = gr.HTML( + "
Click 'Run Evaluation' to start
" + ) + + with gr.Column(scale=1): + answer_chart = gr.BarPlot( + x="Category", + y="Average Accuracy", + title="Average Accuracy by Category", + y_lim=[1, 5], + height=400, + ) + + # Wire up the evaluations + retrieval_button.click( + fn=run_retrieval_evaluation, + outputs=[retrieval_metrics, retrieval_chart], + ) + + answer_button.click( + fn=run_answer_evaluation, + outputs=[answer_metrics, answer_chart], + ) + + app.launch(inbrowser=True) + + +if __name__ == "__main__": + main() diff --git a/week5/implementation/answer.py b/week5/implementation/answer.py new file mode 100644 index 0000000..16c3f01 --- /dev/null +++ b/week5/implementation/answer.py @@ -0,0 +1,61 @@ +from pathlib import Path +from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from langchain_chroma import Chroma +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_core.messages import SystemMessage, HumanMessage, convert_to_messages +from langchain_core.documents import Document + +from dotenv import load_dotenv + + +load_dotenv(override=True) + +MODEL = "gpt-4.1-nano" +DB_NAME = str(Path(__file__).parent.parent / "vector_db") + +# embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") +embeddings = OpenAIEmbeddings(model="text-embedding-3-large") +RETRIEVAL_K = 10 + +SYSTEM_PROMPT = """ +You are a knowledgeable, friendly assistant representing the company Insurellm. +You are chatting with a user about Insurellm. +If relevant, use the given context to answer any question. +If you don't know the answer, say so. +Context: +{context} +""" + +vectorstore = Chroma(persist_directory=DB_NAME, embedding_function=embeddings) +retriever = vectorstore.as_retriever() +llm = ChatOpenAI(temperature=0, model_name=MODEL) + + +def fetch_context(question: str) -> list[Document]: + """ + Retrieve relevant context documents for a question. + """ + return retriever.invoke(question, k=RETRIEVAL_K) + + +def combined_question(question: str, history: list[dict] = []) -> str: + """ + Combine all the user's messages into a single string. + """ + prior = "\n".join(m["content"] for m in history if m["role"] == "user") + return prior + "\n" + question + + +def answer_question(question: str, history: list[dict] = []) -> tuple[str, list[Document]]: + """ + Answer the given question with RAG; return the answer and the context documents. + """ + combined = combined_question(question, history) + docs = fetch_context(combined) + context = "\n\n".join(doc.page_content for doc in docs) + system_prompt = SYSTEM_PROMPT.format(context=context) + messages = [SystemMessage(content=system_prompt)] + messages.extend(convert_to_messages(history)) + messages.append(HumanMessage(content=question)) + response = llm.invoke(messages) + return response.content, docs diff --git a/week5/implementation/ingest.py b/week5/implementation/ingest.py new file mode 100644 index 0000000..3ea69d5 --- /dev/null +++ b/week5/implementation/ingest.py @@ -0,0 +1,67 @@ +import os +import glob +from pathlib import Path +from langchain_community.document_loaders import DirectoryLoader, TextLoader +from langchain_text_splitters import RecursiveCharacterTextSplitter +from langchain_chroma import Chroma +from langchain_huggingface import HuggingFaceEmbeddings +from langchain_openai import OpenAIEmbeddings + + +from dotenv import load_dotenv + +MODEL = "gpt-4.1-nano" + +DB_NAME = str(Path(__file__).parent.parent / "vector_db") +KNOWLEDGE_BASE = str(Path(__file__).parent.parent / "knowledge-base") + +# embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") + +load_dotenv(override=True) + +embeddings = OpenAIEmbeddings(model="text-embedding-3-large") + + +def fetch_documents(): + folders = glob.glob(str(Path(KNOWLEDGE_BASE) / "*")) + documents = [] + for folder in folders: + doc_type = os.path.basename(folder) + loader = DirectoryLoader( + folder, glob="**/*.md", loader_cls=TextLoader, loader_kwargs={"encoding": "utf-8"} + ) + folder_docs = loader.load() + for doc in folder_docs: + doc.metadata["doc_type"] = doc_type + documents.append(doc) + return documents + + +def create_chunks(documents): + text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=200) + chunks = text_splitter.split_documents(documents) + return chunks + + +def create_embeddings(chunks): + if os.path.exists(DB_NAME): + Chroma(persist_directory=DB_NAME, embedding_function=embeddings).delete_collection() + + vectorstore = Chroma.from_documents( + documents=chunks, embedding=embeddings, persist_directory=DB_NAME + ) + + collection = vectorstore._collection + count = collection.count() + + sample_embedding = collection.get(limit=1, include=["embeddings"])["embeddings"][0] + dimensions = len(sample_embedding) + print(f"There are {count:,} vectors with {dimensions:,} dimensions in the vector store") + return vectorstore + + +if __name__ == "__main__": + documents = fetch_documents() + chunks = create_chunks(documents) + create_embeddings(chunks) + print("Ingestion complete") diff --git a/week5/knowledge-base/company/about.md b/week5/knowledge-base/company/about.md index 7ad4a11..6e7e896 100644 --- a/week5/knowledge-base/company/about.md +++ b/week5/knowledge-base/company/about.md @@ -1,4 +1,16 @@ # About Insurellm -Insurellm was founded by Avery Lancaster in 2015 as an insurance tech startup designed to disrupt an industry in need of innovative products. It's first product was Markellm, the marketplace connecting consumers with insurance providers. -It rapidly expanded, adding new products and clients, reaching 200 emmployees by 2024 with 12 offices across the US. \ No newline at end of file +Insurellm was founded by Avery Lancaster in 2015 as an insurance tech startup designed to disrupt an industry in need of innovative products. Its first product was Markellm, the marketplace connecting consumers with insurance providers. + +The company experienced rapid growth in its first five years, expanding its product portfolio to include Carllm (auto insurance portal), Homellm (home insurance portal), and Rellm (enterprise reinsurance platform). By 2020, Insurellm had reached a peak of 200 employees with 12 offices across the US. + +However, the company underwent a strategic restructuring in 2022-2023 to focus on profitability and sustainable growth. This included consolidating office locations, implementing a remote-first strategy, and streamlining operations. As of 2025, Insurellm operates with a lean, highly efficient team of 32 employees who have built a portfolio of 32 active contracts spanning all eight product lines. The company maintains its San Francisco headquarters along with small satellite offices in key markets including New York, Austin, Chicago, and Denver. + +Since the restructuring, Insurellm has continued to innovate, expanding its product suite to eight comprehensive platforms. The company added Lifellm (life insurance), Healthllm (health insurance), Bizllm (commercial insurance), and Claimllm (claims processing) to serve the full spectrum of insurance needs. This strategic expansion has been highly successful, with strong adoption across all new products: + +- **Bizllm** quickly gained traction with 7 commercial insurance contracts, including regional carriers and national commercial groups +- **Claimllm** signed 7 contracts ranging from independent adjusting firms to enterprise claims networks +- **Lifellm** secured 6 life insurance clients from small regional providers to major national carriers +- **Healthllm** won 6 health plan contracts including regional insurers and multi-state healthcare alliances + +Combined with continued growth in the original product lines (Carllm, Homellm, Markellm, and Rellm), Insurellm now serves clients ranging from regional insurers to global reinsurance partners, demonstrating the company's ability to compete across the entire insurance value chain. \ No newline at end of file diff --git a/week5/knowledge-base/company/careers.md b/week5/knowledge-base/company/careers.md index 4a61822..0efca6e 100644 --- a/week5/knowledge-base/company/careers.md +++ b/week5/knowledge-base/company/careers.md @@ -1,3 +1,139 @@ # Careers at Insurellm -Insurellm is hiring! We are looking for talented software engineers, data scientists and account executives to join our growing team. Come be a part of our movement to disrupt the insurance sector. \ No newline at end of file +## Why Join Insurellm? + +At Insurellm, we're not just building software—we're revolutionizing an entire industry. Since our founding in 2015, we've evolved from a high-growth startup to a lean, profitable company with 32 highly talented employees managing 32 active contracts across all eight of our product lines. + +After reaching 200 employees in 2020, we strategically restructured in 2022-2023 to focus on sustainable growth, operational excellence, and building a world-class remote-first culture. Today, we're a tight-knit team of exceptional professionals who deliver outsized impact through automation, AI, and strategic focus on high-value enterprise clients—from regional insurers to global reinsurance partners. + +### Our Culture + +We live by our core values every day: +- **Innovation First**: We encourage experimentation and creative problem-solving +- **Customer Obsession**: Your work directly impacts 32 active client operations spanning the entire insurance value chain +- **Integrity & Transparency**: We build trust through ethical behavior and open communication +- **Collaborative Excellence**: Diverse perspectives and teamwork drive our success + +### What We Offer + +- Competitive compensation with equity participation +- Comprehensive health, dental, and vision insurance +- Flexible working arrangements and generous PTO +- Professional development programs and mentorship +- Clear career progression paths +- Latest technologies and tools +- Inclusive, diverse work environment + +## Current Opportunities + +### Engineering + +**Senior Full Stack Engineer** - San Francisco, CA +- Lead development of next-generation insurance platform features +- Work with React, Node.js, Python, and cloud technologies +- Mentor junior engineers and drive technical decisions +- 5+ years experience required + +**Backend Software Engineer** - San Francisco, CA / Austin, TX +- Build scalable microservices and APIs +- Optimize system performance and reliability +- Collaborate with product and design teams +- 3+ years experience with Java, Python, or Go + +**Frontend Developer** - Remote +- Create intuitive user interfaces for our insurance platforms +- Work with modern frameworks (React, Vue, or Angular) +- Ensure accessibility and responsive design +- 2+ years experience required + +**DevOps Engineer** - New York, NY +- Manage cloud infrastructure (AWS/GCP) +- Implement CI/CD pipelines and automation +- Monitor system performance and security +- 3+ years experience in DevOps/SRE + +**Mobile Developer (iOS/Android)** - San Francisco, CA +- Build native mobile applications for our marketplace +- Create seamless user experiences +- Integrate with backend APIs +- 3+ years mobile development experience + +### Data & Analytics + +**Senior Data Scientist** - San Francisco, CA / New York, NY +- Develop predictive models for insurance risk assessment +- Build recommendation systems for our marketplace +- Lead data-driven product initiatives +- PhD or MS in related field preferred, 4+ years experience + +**Data Engineer** - Austin, TX +- Design and maintain data pipelines +- Build data warehousing solutions +- Optimize data infrastructure for scale +- 3+ years experience with SQL, Python, and ETL tools + +**Business Intelligence Analyst** - Chicago, IL +- Create dashboards and reports for stakeholders +- Analyze business metrics and trends +- Support data-driven decision making +- 2+ years experience with BI tools (Tableau, Looker, etc.) + +### Product & Design + +**Product Manager** - San Francisco, CA +- Define product roadmap and strategy +- Work closely with engineering and design teams +- Gather customer insights and market research +- 3+ years product management experience in B2B SaaS + +**UX/UI Designer** - Remote +- Design user experiences for our insurance platforms +- Conduct user research and usability testing +- Create design systems and prototypes +- 3+ years design experience, insurance/fintech preferred + +### Sales & Customer Success + +**Account Executive** - New York, NY / Chicago, IL / Austin, TX +- Manage B2B sales cycle for enterprise clients +- Build relationships with insurance companies +- Exceed revenue targets and grow territory +- 3+ years B2B sales experience, SaaS preferred + +**Sales Development Representative** - Austin, TX / Remote +- Generate qualified leads for sales team +- Conduct outreach to prospective clients +- Research and identify target accounts +- 1+ years SDR/BDR experience preferred + +**Customer Success Manager** - San Francisco, CA / New York, NY +- Ensure client satisfaction and retention +- Drive product adoption and expansion +- Serve as trusted advisor to clients +- 2+ years customer success experience in SaaS + +**Solutions Engineer** - Remote +- Provide technical expertise during sales process +- Conduct product demonstrations and POCs +- Support implementation and integration +- 3+ years technical pre-sales experience + +### Operations & Support + +**Technical Support Specialist** - Remote +- Provide tier 2/3 technical support to clients +- Troubleshoot platform issues +- Create documentation and knowledge base articles +- 2+ years technical support experience + +**HR Business Partner** - San Francisco, CA +- Partner with leadership on people strategy +- Support talent development and retention +- Drive culture and engagement initiatives +- 4+ years HR experience, tech industry preferred + +## How to Apply + +Visit our careers portal at careers.insurellm.com or send your resume to jobs@insurellm.com. Please include the position title in your subject line. + +Insurellm is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. \ No newline at end of file diff --git a/week5/knowledge-base/company/culture.md b/week5/knowledge-base/company/culture.md new file mode 100644 index 0000000..212305d --- /dev/null +++ b/week5/knowledge-base/company/culture.md @@ -0,0 +1,37 @@ +# Insurellm Culture + +## Vision Statement +To revolutionize the insurance industry through innovative technology that makes insurance accessible, transparent, and effortless for everyone. + +## Mission Statement +We empower insurance providers and consumers with cutting-edge software solutions that streamline processes, enhance customer experiences, and drive meaningful connections in the insurance marketplace. By combining deep industry expertise with technological innovation, we're building the future of insurance. + +## Core Values + +### Innovation First +We challenge the status quo and embrace creative problem-solving. Our team is encouraged to experiment, take calculated risks, and push the boundaries of what's possible in insurance technology. We believe that breakthrough solutions come from curiosity, collaboration, and a willingness to learn from both successes and failures. + +### Customer Obsession +Our clients' success is our success. We deeply understand our customers' needs and work tirelessly to exceed their expectations. Every product decision, every feature, and every interaction is guided by a commitment to delivering exceptional value and building lasting partnerships. + +### Integrity & Transparency +We operate with honesty and openness in everything we do. From our products to our internal operations, we believe that trust is earned through consistent ethical behavior, clear communication, and accountability at all levels of the organization. + +### Collaborative Excellence +We achieve more together. We foster a culture where diverse perspectives are valued, knowledge is shared freely, and every team member is empowered to contribute their unique talents. We celebrate collective wins and support each other through challenges. + +## Employer Value Proposition + +**Build the Future of Insurance Technology with Elite Talent** + +At Insurellm, you'll join an elite team of 32 exceptional professionals transforming a traditional industry with cutting-edge technology. After our strategic restructuring in 2022-2023, we evolved from a 200-person startup to a lean, high-performing organization where every person makes a significant impact. With 32 active contracts across all eight product lines—from regional insurers to global reinsurance partners—each team member directly influences technology used by thousands of insurance professionals and millions of consumers. We offer: + +- **Meaningful Impact**: Your work directly shapes products powering 32 active insurance operations, from commercial carriers to health plans to global reinsurance firms +- **Growth & Development**: Comprehensive professional development programs, mentorship opportunities, and clear career progression paths +- **Innovation Freedom**: Access to the latest technologies and the autonomy to experiment with new ideas +- **Work-Life Harmony**: Flexible working arrangements, generous PTO, and a culture that respects personal time +- **Competitive Rewards**: Market-leading compensation, equity participation, comprehensive benefits, and performance bonuses +- **Inclusive Environment**: A diverse team where every voice matters and different perspectives drive better solutions +- **Purpose-Driven Mission**: Be part of making insurance more accessible and fair for everyone + +Join us in disrupting an industry and building technology that matters. diff --git a/week5/knowledge-base/company/overview.md b/week5/knowledge-base/company/overview.md index abf7316..43581fb 100644 --- a/week5/knowledge-base/company/overview.md +++ b/week5/knowledge-base/company/overview.md @@ -1,10 +1,51 @@ # Overview of Insurellm -Insurellm is an innovative insurance tech firm with 200 employees across the US. -Insurellm offers 4 insurance software products: -- Carllm, a portal for auto insurance companies -- Homellm, a portal for home insurance companies -- Rellm, an enterprise platform for the reinsurance sector -- Marketllm, a marketplace for connecting consumers with insurance providers - -Insurellm has more than 300 clients worldwide. \ No newline at end of file +Insurellm is an innovative insurance tech firm with 32 employees operating primarily remotely across the US, with offices in San Francisco (HQ), New York, Austin, Chicago, and Denver. + +Founded in 2015, the company has evolved from a high-growth startup to a lean, profitable operation focused on sustainable growth and operational excellence. + +## Products + +Insurellm offers 8 insurance software products across multiple insurance lines: + +### Core Insurance Portals +- **Carllm** - Auto insurance platform for insurers +- **Homellm** - Home insurance platform for insurers +- **Lifellm** - Life insurance platform with AI-powered underwriting +- **Healthllm** - Comprehensive health insurance platform +- **Bizllm** - Commercial insurance platform for business coverage + +### Marketplace & Infrastructure +- **Markellm** - Marketplace connecting consumers with insurance providers (original flagship product) +- **Claimllm** - AI-powered claims processing platform across all insurance lines +- **Rellm** - Enterprise platform for the reinsurance sector + +## Scale & Impact + +Despite its compact team size, Insurellm has built an impressive client portfolio with 32 active contracts across all product lines, serving clients ranging from regional insurers to national carriers and global reinsurance partners. The company demonstrates exceptional productivity and leverage through: +- Highly automated systems and processes +- Strategic use of AI and machine learning +- Remote-first culture enabling access to top talent +- Focus on high-value enterprise clients in multiple insurance verticals + +## Client Portfolio Breakdown + +Insurellm's 32 active contracts span the full spectrum of insurance technology: + +**Commercial Insurance (Bizllm)**: 7 contracts including regional carriers, multi-line insurers, and national commercial insurance groups + +**Claims Processing (Claimllm)**: 7 contracts ranging from independent adjusting firms to enterprise claims networks + +**Life Insurance (Lifellm)**: 6 contracts serving life insurance carriers from small regional providers to major national groups + +**Health Insurance (Healthllm)**: 6 contracts with health plans from regional insurers to multi-state healthcare alliances + +**Auto Insurance (Carllm)**: 3 contracts with personal and commercial auto insurers + +**Home Insurance (Homellm)**: 4 contracts including property insurers with advanced IoT and catastrophe management needs + +**Insurance Marketplace (Markellm)**: 2 contracts with agencies and brokers leveraging the consumer-insurer matching platform + +**Reinsurance (Rellm)**: 2 contracts with reinsurance companies including global treaty and facultative operations + +This diversified portfolio demonstrates Insurellm's ability to serve the entire insurance value chain, from consumer-facing marketplaces to complex reinsurance operations. \ No newline at end of file diff --git a/week5/knowledge-base/contracts/.md b/week5/knowledge-base/contracts/.md deleted file mode 100644 index 16e4c14..0000000 --- a/week5/knowledge-base/contracts/.md +++ /dev/null @@ -1,63 +0,0 @@ - -# Contract Agreement - -**Client Name**: Premier Auto Insurance Co. -**Product Name**: Carllm - ---- - -## Terms - -1. **Agreement Duration**: This contract is effective from January 1, 2025, and shall remain in effect for a period of twelve (12) months, concluding on December 31, 2025. -2. **Payment**: The Client agrees to pay Insurellm a subscription fee of $2,500 per month for the duration of the contract, payable within 30 days of the invoice date. -3. **Scope of Services**: The services provided under this contract include access to the Carllm platform, integration support, AI-powered risk assessment tools, customizable coverage plans, and automated customer support. -4. **Data Security**: Insurellm commits to implementing industry-standard security measures to protect Client data, in accordance with applicable privacy laws. - ---- - -## Renewal - -1. **Automatic Renewal**: This contract will automatically renew for successive one-year terms unless either party provides written notice of termination at least thirty (30) days prior to the end of the current term. -2. **Renewal Terms**: Upon renewal, the subscription fee may be subject to adjustments based on changes in the Consumer Price Index or any significant value additions to the service. -3. **Review Period**: Prior to the renewal, both parties shall engage in a service review to ensure the satisfaction of both parties with the terms and performance of Carllm. - ---- - -## Features - -1. **AI-Powered Risk Assessment**: Comprehensive tools that analyze driver behavior and vehicle conditions. -2. **Instant Quoting**: Near-instant quotes provided for enhanced customer experience. -3. **Customizable Coverage Plans**: Flexibility to create tailored insurance packages. -4. **Fraud Detection**: Advanced analytics to help identify potentially fraudulent claims. -5. **Customer Insights Dashboard**: Access to deep insights for informed decision-making. -6. **Mobile Integration**: Mobile app compatibility for policy management on the go. -7. **Automated Customer Support**: 24/7 customer service via AI chatbots. - ---- - -## Support - -1. **Customer Support Availability**: Insurellm shall provide technical support to the Client via phone and email during standard business hours (9 AM - 5 PM ET, Monday to Friday). -2. **Emergency Support**: Emergency support will be available 24/7 for critical issues impacting the Client’s operations, response time not to exceed 2 hours. -3. **Training and Resources**: Insurellm will provide training materials and sessions to ensure successful deployment and use of Carllm. -4. **Feedback and Updates**: The Client will have regular opportunities to provide feedback on service performance. Updates and new features will be communicated promptly. - ---- - -This contract represents the complete understanding between the parties concerning the subject matter herein and supersedes all prior agreements and understandings, whether written or oral. - -**Authorized Signatures**: - -*Premier Auto Insurance Co.* -_________________________ -Name: [Client Representative Name] -Title: [Client Representative Title] -Date: ________________ - -*Insurellm* -_________________________ -Name: [Insurellm Representative Name] -Title: [Insurellm Representative Title] -Date: ________________ - ---- diff --git a/week5/knowledge-base/contracts/Contract with Advantage Medical Coverage for Healthllm.md b/week5/knowledge-base/contracts/Contract with Advantage Medical Coverage for Healthllm.md new file mode 100644 index 0000000..08aa151 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Advantage Medical Coverage for Healthllm.md @@ -0,0 +1,93 @@ +# Contract with Advantage Medical Coverage for Healthllm + +**Contract Date:** April 18, 2025 +**Contract Number:** HL-2025-0124 + +--- + +## Terms + +1. **Parties Involved**: This contract is entered into between Insurellm, Inc. ("Provider") and Advantage Medical Coverage ("Client"). + +2. **License Grant**: Insurellm grants Advantage Medical Coverage a non-exclusive, non-transferable license to use the Healthllm Professional Tier platform. + +3. **Payment Terms**: Advantage Medical Coverage agrees to make monthly payments of $15,000 for the duration of this 18-month contract, totaling $270,000. Payments due on the 10th of each month via wire transfer. + +4. **Member Coverage**: Professional Tier supports up to 50,000 covered members. Advantage currently covers 32,000 members in 2 states. + +5. **Confidentiality**: Both parties shall maintain confidentiality regarding proprietary information, member PHI, and business strategies throughout the contract duration and for 7 years post-termination, in compliance with HIPAA retention requirements. + +## Renewal + +1. **Automatic Renewal**: This contract automatically renews for successive 18-month terms unless either party provides written notice of non-renewal at least 60 days prior to expiration. + +2. **Pricing Review**: Renewal pricing will be discussed 75 days prior to term end and mutually agreed upon in writing. Price increases limited to 7% annually. + +## Features + +1. **Included Features**: Advantage Medical Coverage will have access to Healthllm Professional Tier features: + - Intelligent Plan Design with competitive benchmarking + - Real-Time Eligibility Verification with provider integration + - AI-Driven Claims Adjudication with auto-processing + - Predictive Healthcare Analytics for population health + - Provider Network Management with 8,000+ providers + - Member Engagement Platform with mobile app + - Advanced Medication Management with PBM integration + - Regulatory Compliance Engine (ACA, state mandates, HIPAA) + - Enhanced Analytics and predictive modeling + - Care management tools and workflows + +2. **Value-Based Care Support:** + - ACO performance tracking and shared savings calculation + - HEDIS measure calculation for quality reporting + - Provider pay-for-performance scorecards + - Risk adjustment and HCC coding support + - Medicare STAR ratings optimization + +3. **Telehealth Integration**: Built-in telehealth platform supporting: + - Video visits with in-network providers + - E-consults and asynchronous care + - Behavioral health virtual visits + - Member scheduling and appointment management + +## Support + +1. **Customer Support**: Insurellm will provide priority support to Advantage Medical Coverage: + - Dedicated support team Monday-Friday 6 AM - 9 PM EST + - Emergency support 24/7 for critical system issues + - Response time: 6 hours for critical, 12 hours for high priority, 24 hours for standard + - Private support portal with ticket tracking + +2. **Training**: Comprehensive training program: + - 5-week implementation with project manager + - Training for up to 30 staff members (45 hours total) + - Role-specific training tracks + - Quarterly webinars on new features + - Online training library with 75+ courses + +3. **System Updates**: Monthly platform enhancements and bi-weekly security patches. Maintenance windows on Saturday 11 PM - Sunday 2 AM EST with 72-hour advance notice. + +4. **Account Management**: Named senior customer success manager with quarterly business reviews and annual strategic planning session. + +5. **Integration Support**: Technical assistance for integrating with: + - Advantage's HealthRules policy admin system + - Optum PBM + - Availity clearinghouse + - Provider credentialing platforms + - Member portal and mobile apps + +--- + +**Signatures:** + +**Insurellm, Inc.** +Sarah Chen, VP of Sales +Date: April 18, 2025 + +**Advantage Medical Coverage** +Dr. Michael Ramirez, Chief Medical Officer +Date: April 18, 2025 + +--- + +This contract enables Advantage Medical Coverage to leverage advanced AI and predictive analytics for improved member health outcomes and operational efficiency. diff --git a/week5/knowledge-base/contracts/Contract with Apex Reinsurance for Rellm.md b/week5/knowledge-base/contracts/Contract with Apex Reinsurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md similarity index 99% rename from week5/knowledge-base/contracts/Contract with Apex Reinsurance for Rellm.md rename to week5/knowledge-base/contracts/Contract with Apex Reinsurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md index a95ac67..0c6af4d 100644 --- a/week5/knowledge-base/contracts/Contract with Apex Reinsurance for Rellm.md +++ b/week5/knowledge-base/contracts/Contract with Apex Reinsurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md @@ -50,4 +50,4 @@ Date: ___________________ **Apex Reinsurance** _____________________________ Authorized Signature -Date: ___________________ \ No newline at end of file +Date: ___________________ diff --git a/week5/knowledge-base/contracts/Contract with Atlantic Risk Solutions for Bizllm.md b/week5/knowledge-base/contracts/Contract with Atlantic Risk Solutions for Bizllm.md new file mode 100644 index 0000000..73a92aa --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Atlantic Risk Solutions for Bizllm.md @@ -0,0 +1,101 @@ +# Contract with Atlantic Risk Solutions for Bizllm + +--- + +## Terms + +1. **Agreement Effective Date**: This contract is effective as of January 15, 2025. +2. **Duration**: This agreement will remain in effect for a term of 12 months, concluding on January 14, 2026. +3. **Subscription Type**: Atlantic Risk Solutions agrees to subscribe to the **Professional Tier** of Bizllm, at a cost of $12,000/month, totaling $144,000 for the duration of this contract. +4. **Payment Terms**: Payments are due on the 10th of each month via ACH transfer. Late payments will incur a penalty of 1.5% per month and may result in service suspension after 15 days delinquency. +5. **User Licenses**: Contract includes 35 named user licenses. Additional users may be added at $180/month per license. +6. **Termination Clause**: Either party may terminate this agreement with 45 days' written notice. Early termination requires payment of 50% of remaining contract value. + +--- + +## Renewal + +1. **Automatic Renewal**: This agreement will automatically renew for an additional 12-month term unless either party provides written notice of non-renewal at least 45 days before the expiration date. +2. **Price Protection**: Pricing increases capped at 8% annually for the first two renewal periods, providing Atlantic Risk Solutions with budget predictability. +3. **Volume Discounts**: If Atlantic Risk Solutions expands to Enterprise tier during renewal, they receive a 15% discount on the difference for the first renewal year. + +--- + +## Features + +1. **Access to Professional Tier Features**: Atlantic Risk Solutions will have access to all Professional Tier features, including: + - Multi-Line Underwriting Engine supporting general liability, professional liability, property, workers' compensation, and cyber insurance + - Business Intelligence Integration with automated data gathering from 50+ data sources + - Cyber Risk Assessment module with security posture evaluation + - Workers' Compensation Optimization with payroll integration and class code analysis + - Commercial Property Evaluation with catastrophe modeling + - Professional Liability Specialization for E&O coverage + - Portfolio Management Dashboard with loss ratio tracking and geographic analysis + - Agent and Broker Portal supporting up to 500 distribution partners + - Claims Management Integration with reserve recommendation engine + +2. **Industry-Specific Templates**: Pre-configured underwriting templates for: + - Technology companies and software-as-a-service (SaaS) businesses + - Professional services firms (consulting, accounting, legal) + - Healthcare providers and medical practices + - Contractors and construction trades + - Restaurants and hospitality businesses + +3. **Advanced Reporting**: Monthly automated reports including: + - New business pipeline and conversion rates + - Renewal retention analysis + - Loss ratio trending by line of business + - Agent performance scorecards + - Competitive market positioning insights + +--- + +## Support + +1. **Technical Support**: Atlantic Risk Solutions will receive priority technical support from Insurellm for any issues arising from the Bizllm product: + - Dedicated support phone line and email + - Response time: 4 hours for critical issues, 12 hours for standard requests + - Access to technical support Monday-Friday 7 AM - 8 PM EST + +2. **Training**: Insurellm will provide comprehensive training program: + - Initial onboarding for up to 15 staff members (20 hours total training) + - Bi-monthly webinars on platform updates and best practices + - Access to on-demand video library and knowledge base + - Annual refresher training session + +3. **Updates and Maintenance**: + - Quarterly platform updates with new features and security enhancements + - Monthly minor updates and bug fixes + - Maintenance windows: Sunday 1 AM - 5 AM EST + - 48-hour advance notice for all scheduled maintenance + +4. **Account Management**: + - Assigned account manager with quarterly business review meetings + - Annual strategic planning session to align platform usage with business goals + - Priority access to new beta features + +5. **Integration Support**: Technical assistance for integrating Bizllm with: + - Atlantic's existing agency management system (Applied Epic) + - Commercial lines rating platforms + - Third-party data providers (Dun & Bradstreet, Experian, Verisk) + - Email marketing platforms for agent communications + +--- + +**Signatures:** + +_________________________________ +**Michael Torres** +**Title**: Chief Revenue Officer +**Insurellm, Inc.** +**Date**: January 15, 2025 + +_________________________________ +**Diana Marquez** +**Title**: Senior Vice President, Commercial Lines +**Atlantic Risk Solutions** +**Date**: January 15, 2025 + +--- + +*This contract establishes a professional partnership between Insurellm and Atlantic Risk Solutions, enabling Atlantic to leverage cutting-edge AI technology for commercial insurance underwriting and administration.* diff --git a/week5/knowledge-base/contracts/Contract with ConnectInsure Agency for Markellm.md b/week5/knowledge-base/contracts/Contract with ConnectInsure Agency for Markellm.md new file mode 100644 index 0000000..ad3e879 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with ConnectInsure Agency for Markellm.md @@ -0,0 +1,141 @@ +# Contract with ConnectInsure Agency for Markellm + +**Contract Date:** February 28, 2025 +**Contract Number:** MK-2025-0056 + +--- + +## Terms + +1. **Parties Involved**: This contract is entered into between Insurellm, Inc. ("Provider") and ConnectInsure Agency ("Client"). + +2. **Service Commencement**: Services will commence on March 1, 2025. + +3. **Contract Duration**: This Agreement shall remain in effect for 18 months from the commencement date, ending August 31, 2026. + +4. **Fee Structure**: ConnectInsure Agency agrees to a hybrid pricing model: + - Basic Listing Fee: $199/month for featured placement on the Markellm platform + - Performance-Based Pricing: $25 per qualified lead generated through Markellm + - Premium Analytics Package (optional): $99/month for enhanced data insights and reporting + +5. **Lead Volume Expectations**: Based on ConnectInsure's profile, Markellm projects 80-120 qualified leads per month, resulting in estimated monthly costs of $2,199-$3,199. + +6. **Payment Terms**: + - Monthly listing fee due on the 1st of each month + - Performance-based fees invoiced monthly on the 5th for prior month activity + - Payment due within 15 days of invoice via ACH or credit card + +7. **Lead Quality Standards**: Qualified leads meet the following criteria: + - Valid contact information (verified phone and email) + - Active insurance shopping intent (quote request within 7 days) + - Geographic coverage within ConnectInsure's operating territories + - Products matching ConnectInsure's offerings + +8. **Dispute Resolution**: Lead quality disputes must be submitted within 5 business days with documentation. Insurellm will credit for validated low-quality leads. + +## Renewal + +1. **Automatic Renewal**: This agreement automatically renews for successive 6-month terms unless either party provides written notice of non-renewal at least 30 days before expiration. + +2. **Fee Adjustments**: Listing fees and performance fees may be adjusted annually with 60 days' written notice. Historical average: 5-8% annual adjustment. + +## Features + +1. **AI-Powered Matching**: ConnectInsure Agency will benefit from Markellm's sophisticated matching: + - Consumer needs analysis with ML algorithms + - Product-to-need matching across auto, home, life, health insurance + - Geographic and specialty matching + - Price range and coverage preference alignment + - Consumer demographic matching to ConnectInsure's sweet spot + +2. **Featured Listing**: Premium marketplace placement including: + - Enhanced agency profile with logo, photos, testimonials + - Badges for specializations (e.g., "High-Risk Auto Specialist", "Multi-Policy Expert") + - Priority positioning in search results + - Featured placement in relevant consumer matches + - Up to 10 agent profiles within agency listing + +3. **Real-Time Quote Integration**: Technology enabling: + - API integration with ConnectInsure's quoting systems + - Real-time rate display to consumers + - Competitive positioning insights + - Quote abandonment recovery campaigns + +4. **Lead Management Portal**: Comprehensive tools for: + - Real-time lead notifications (SMS, email, push) + - Lead routing to specific agents based on specialty + - Follow-up task management and CRM integration + - Lead status tracking (contacted, quoted, sold, declined) + - Performance analytics by agent and product line + +5. **Data Insights**: Analytics dashboard providing: + - Lead volume trends and forecasting + - Conversion rate analysis + - Cost per acquisition metrics + - Competitive benchmarking (anonymized) + - Consumer behavior insights + - Product mix recommendations + +6. **Consumer Reviews**: Reputation management: + - Consumer rating and review collection + - Response management for reviews + - Review showcase on agency profile + - Negative review mediation support + +7. **Marketing Support**: Platform-provided marketing: + - Co-branded marketing materials + - Email drip campaigns to warm leads + - Social media content suggestions + - Seasonal campaign participation + - Referral program integration + +## Support + +1. **Technical Support**: ConnectInsure Agency will receive: + - Email and phone support Monday-Friday 8 AM - 6 PM EST + - Response time: 24 hours for standard inquiries + - Online help center and FAQ + - Video tutorials for platform features + +2. **Onboarding**: Implementation includes: + - 2-week onboarding process + - Platform training for up to 5 agency staff (3 hours total) + - Profile optimization consultation + - Quote integration setup (if applicable) + - CRM integration assistance + +3. **Account Management**: + - Named account manager with monthly check-ins + - Quarterly performance reviews + - Lead quality monitoring and optimization + - Competitive positioning recommendations + - Best practices sharing from top-performing agencies + +4. **Platform Updates**: + - Regular feature enhancements + - Mobile app updates + - Consumer experience improvements + - New product line additions + - Advance notice of major changes (minimum 14 days) + +5. **Marketing Assistance**: + - Quarterly marketing strategy consultations + - Campaign performance analysis + - Consumer trend reports + - Co-marketing opportunity identification + +--- + +**Signatures:** + +**Insurellm, Inc.** +Michael Torres, Chief Revenue Officer +Date: February 28, 2025 + +**ConnectInsure Agency** +Brian Foster, Agency Principal +Date: February 28, 2025 + +--- + +This contract enables ConnectInsure Agency to leverage Markellm's AI-powered marketplace to connect with qualified insurance shoppers and grow their business through data-driven lead generation. diff --git a/week5/knowledge-base/contracts/Contract with Continental Commercial Group for Bizllm.md b/week5/knowledge-base/contracts/Contract with Continental Commercial Group for Bizllm.md new file mode 100644 index 0000000..1c98ef4 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Continental Commercial Group for Bizllm.md @@ -0,0 +1,131 @@ +# Contract with Continental Commercial Group for Bizllm + +**Contract Date:** April 12, 2025 +**Contract Number:** BZ-2025-E-0147 +**Parties:** +- Insurellm, Inc. +- Continental Commercial Group, Inc. + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide Continental Commercial Group with comprehensive access to the Bizllm Enterprise platform, including white-label capabilities, dedicated infrastructure, and full API access for commercial insurance operations across all 50 US states. + +2. **Duration:** This agreement is effective for a period of 36 months from the contract date, with provisions for early renewal discussions at the 24-month mark. + +3. **Payment:** Continental Commercial Group shall pay custom Enterprise pricing of $42,000 per month for the first 12 months, with pricing review and adjustment for years 2-3 based on usage metrics and expanded feature adoption. + +4. **Confidentiality:** Both parties agree to maintain the highest level of confidentiality regarding proprietary algorithms, trade secrets, customer data, and business strategies disclosed during this partnership. + +5. **Liability:** Insurellm's liability is limited to direct damages not exceeding the annual contract value. Continental Commercial Group maintains $25 million in cybersecurity insurance covering data breaches. + +6. **Service Level Agreement (SLA):** Insurellm guarantees 99.9% platform uptime, measured monthly. For each 0.1% below target, Continental receives a 5% monthly fee credit, up to a maximum of 25% monthly credit. + +7. **Exclusivity:** Continental Commercial Group receives exclusive use of Bizllm in the specialty construction insurance vertical for the contract duration within North America. + +--- + +## Renewal + +This agreement includes a mutual 90-day renewal notice period. Continental Commercial Group receives right of first refusal on any new Bizllm features or modules developed for the commercial insurance sector. Renewal terms will be negotiated based on platform evolution, competitive landscape, and business performance metrics. + +--- + +## Features + +Continental Commercial Group will receive the complete Bizllm Enterprise suite: + +1. **White-Label Customization:** Full platform branding with Continental's logos, color schemes, and custom domain names for client-facing portals. + +2. **Dedicated Cloud Infrastructure:** Private cloud deployment with dedicated servers, ensuring data isolation and optimized performance for Continental's 200+ concurrent users. + +3. **Unlimited API Access:** Full API integration capabilities with no call volume limits, enabling seamless connection to Continental's existing tech stack including: + - Duck Creek policy administration system + - Guidewire ClaimCenter + - Custom data warehouse and business intelligence platforms + - Third-party data providers (Verisk, ISO, Dun & Bradstreet) + +4. **Advanced Cyber Risk Module:** Comprehensive cyber insurance underwriting tools including: + - Automated security posture assessment + - Breach history analysis and dark web monitoring + - IT infrastructure vulnerability scanning + - Ransomware risk scoring + +5. **Custom Product Configuration:** Ability to configure and deploy unlimited custom commercial insurance products, including specialty lines such as: + - Construction surety and wrap-up programs + - Environmental liability + - Directors and officers (D&O) coverage + - Employment practices liability (EPLI) + +6. **Predictive Analytics Engine:** Machine learning models for: + - Loss forecasting and reserve adequacy + - Optimal pricing recommendations + - Renewal probability scoring + - Cross-sell and upsell opportunity identification + +7. **Agent Network Management:** Enterprise-grade agent portal supporting Continental's 3,500+ independent agents with hierarchical permissions, customizable commission structures, and real-time policy binding. + +8. **Regulatory Compliance Suite:** Automated compliance monitoring for all 50 states including form filing, rate approval tracking, and surplus lines requirements. + +--- + +## Support + +Insurellm commits to comprehensive Enterprise-level support: + +1. **Dedicated Success Team:** + - Named Chief Customer Officer oversight + - Dedicated Technical Account Manager (TAM) + - Solutions architect for ongoing optimization + - Quarterly executive business reviews with C-suite participation + +2. **24/7/365 Support:** + - Guaranteed 15-minute response time for critical issues + - 1-hour response for high-priority issues + - Dedicated Slack channel for real-time communication + - Monthly system health reports and optimization recommendations + +3. **Implementation and Onboarding:** + - 6-month implementation program with dedicated project manager + - Comprehensive training for up to 150 Continental staff members + - Custom training materials and certification program + - On-site support during first 90 days of production operation + +4. **Development and Customization:** + - 500 hours annually of custom development included + - Priority feature requests with guaranteed evaluation + - Beta access to all new features 60 days before general release + - Input into product roadmap and strategic direction + +5. **Data Services:** + - Quarterly data quality audits + - Annual data migration support for system upgrades + - Dedicated data warehouse with 7-year retention + - Custom reporting and analytics dashboard development + +6. **Performance Optimization:** + - Monthly platform performance reviews + - Proactive capacity planning and scaling recommendations + - Load testing before major product launches + - Dedicated infrastructure monitoring with real-time alerts + +--- + +**Signatures:** + +_________________________________ +**Jennifer Rodriguez** +**Title**: Chief Executive Officer +**Insurellm, Inc.** +**Date**: April 12, 2025 + +_________________________________ +**Thomas Wellington** +**Title**: President & Chief Operating Officer +**Continental Commercial Group, Inc.** +**Date**: April 12, 2025 + +--- + +This agreement represents a strategic partnership between Insurellm and Continental Commercial Group to transform commercial insurance operations through cutting-edge technology and innovation. This contract supersedes all prior agreements and represents the complete understanding between both parties. diff --git a/week5/knowledge-base/contracts/Contract with DriveSmart Insurance for Carllm.md b/week5/knowledge-base/contracts/Contract with DriveSmart Insurance for Carllm.md new file mode 100644 index 0000000..c89cd72 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with DriveSmart Insurance for Carllm.md @@ -0,0 +1,198 @@ +# Contract with DriveSmart Insurance for Carllm + +**Contract Date:** March 20, 2025 +**Contract Number:** CR-2025-E-0078 +**Parties:** +- Insurellm, Inc. +- DriveSmart Insurance Corp. + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide DriveSmart Insurance with enterprise access to the Carllm platform, including white-label capabilities, unlimited user licenses, dedicated infrastructure, and full API access for their multi-state auto insurance operations. + +2. **Duration:** This agreement is effective for a period of 36 months from the contract date. + +3. **Payment:** DriveSmart Insurance shall pay custom Enterprise Tier pricing of $18,000 per month for the first 12 months, $19,500 per month for months 13-24, and $21,000 per month for months 25-36, totaling $702,000 over the contract term. + +4. **Policy Volume**: Enterprise Tier supports unlimited policies. DriveSmart currently has 85,000 active auto policies across 8 states. + +5. **Service Level Agreement:** Insurellm guarantees 99.9% platform uptime measured monthly, with 8% monthly credit for each 0.1% below target, maximum 40% monthly credit. + +6. **Confidentiality:** Both parties agree to maintain strict confidentiality of proprietary algorithms, driver data, pricing models, and business strategies. + +7. **Data Security:** Insurellm maintains SOC 2 Type II compliance, annual penetration testing, and comprehensive cyber insurance coverage. + +--- + +## Renewal + +This agreement includes a 90-day mutual renewal notice period. DriveSmart receives most-favored-customer pricing guarantees ensuring competitive rates at renewal. Contract may be extended in 12 or 24-month increments. + +--- + +## Features + +DriveSmart Insurance will receive the complete Carllm Enterprise suite: + +1. **Unlimited Policy Administration:** Full support for DriveSmart's 85,000+ policies with scalability to 500,000+ policies. + +2. **White-Label Platform:** Complete customization: + - Custom domains (quote.drivesmartinsurance.com, portal.drivesmartinsurance.com) + - Branded mobile apps for iOS and Android + - Customized marketing materials and quote tools + - Co-branded agent portals + +3. **Advanced AI Risk Assessment:** Enterprise-grade risk analysis: + - Real-time driver behavior analysis from telematics devices + - Vehicle condition assessment from VIN decoding and maintenance records + - Claims history analysis across all carriers (LexisNexis, CLUE) + - Credit-based insurance scoring + - Geographic risk modeling with granular loss costs + - Multi-car and multi-driver household optimization + - Commercial auto risk assessment capabilities + +4. **Instant Quoting Engine:** High-performance quoting: + - Sub-3-second quote generation + - Real-time rate optimization across 50+ rating factors + - Competitive intelligence with market positioning + - Dynamic pricing based on demand and capacity + - Multi-product bundling discounts (auto + home) + - A/B testing capabilities for pricing strategies + +5. **Customizable Coverage Plans:** Flexible product configuration: + - State-specific coverage options (all 8 operating states) + - Usage-based insurance (UBI) programs + - Pay-per-mile options + - Rideshare and delivery driver coverage + - Classic and collector car programs + - SR-22 and high-risk driver programs + +6. **Advanced Fraud Detection:** Comprehensive fraud prevention: + - Application fraud detection (identity verification, address validation) + - Claims fraud analytics with pattern recognition + - Agent fraud monitoring + - Staged accident detection + - Social network analysis for fraud rings + - Integration with NICB and SIU databases + +7. **Customer Insights Dashboard:** Deep analytics: + - Real-time policy metrics and KPIs + - Retention and churn analysis + - Loss ratio tracking by segment + - Agent performance analytics + - Marketing campaign ROI measurement + - Customer lifetime value prediction + +8. **Telematics Integration:** Complete telematics platform: + - Device-agnostic integration (OBD-II dongles, smartphone apps, embedded) + - Real-time driver behavior scoring + - Trip-level data analysis (hard braking, acceleration, cornering, speeding) + - Mileage verification for low-mileage discounts + - Accident detection and emergency response + - Driver coaching and feedback programs + - Gamification and safe driving rewards + +9. **Complete API Access:** Unlimited API calls: + - Integration with DriveSmart's core systems + - Agent management system connectivity + - Third-party data providers (LexisNexis, Verisk, Experian) + - DMV and MVR integrations + - Payment processing systems + - Document management platforms + - Marketing automation tools + +10. **Multi-State Operations:** Comprehensive state support: + - State-specific forms and endorsements + - Regulatory compliance monitoring + - Rate filing management + - Multi-state agent licensing tracking + - Surplus lines support where needed + +--- + +## Support + +Insurellm commits to comprehensive Enterprise-level support for DriveSmart Insurance: + +1. **Dedicated Success Team:** + - Named VP-level executive sponsor with quarterly reviews + - Senior Customer Success Manager with weekly check-ins + - Technical Account Manager for optimization + - Solutions Architect for strategic initiatives + - Quarterly executive business reviews with ROI analysis + +2. **24/7/365 Support:** + - 30-minute response time for P1 critical issues + - 2-hour response for P2 high-priority issues + - 4-hour response for P3 standard issues + - Dedicated support hotline with no phone tree + - Private Slack channel with engineering team + - Quarterly on-site visits (minimum 4 per year) + +3. **Implementation:** + - 6-month phased implementation + - Dedicated project manager + - Migration of 85,000 policies from legacy system + - Training for 75+ staff and 200+ agents + - Parallel processing for 60 days + - Go-live support (4 weeks on-site) + +4. **Training and Enablement:** + - Role-based training for all users + - Train-the-trainer program + - Annual user conference attendance (5 attendees) + - Monthly training webinars + - Unlimited online learning access + - Custom training materials + +5. **Custom Development:** + - 800 hours annually of custom development + - Priority feature requests on product roadmap + - Beta access to new features 60 days early + - Co-development opportunities + +6. **Integration Services:** + - Integration with DriveSmart's systems: + - Duck Creek policy administration + - Applied Epic agency management + - Guidewire ClaimCenter + - Telematics providers (Arity, Cambridge Mobile Telematics) + - Payment processors + - Salesforce CRM + - Dedicated integration specialist + - Quarterly integration reviews + +7. **Analytics and Reporting:** + - Custom dashboard development (up to 15 dashboards) + - Automated daily, weekly, monthly reporting + - Ad-hoc analytics support (20 requests/month) + - Industry benchmarking + - Predictive modeling consultation + +8. **Continuous Improvement:** + - Quarterly optimization workshops + - Annual efficiency audits + - Best practice sharing + - Innovation lab for emerging tech (AI, IoT, connected cars) + +--- + +**Signatures:** + +_________________________________ +**Jennifer Rodriguez** +**Title**: Chief Executive Officer +**Insurellm, Inc.** +**Date**: March 20, 2025 + +_________________________________ +**Steven Brooks** +**Title**: President & Chief Operating Officer +**DriveSmart Insurance Corp.** +**Date**: March 20, 2025 + +--- + +This agreement establishes DriveSmart Insurance as a premier enterprise partner leveraging Carllm's advanced AI and telematics capabilities to transform auto insurance operations. This contract supersedes all prior agreements. diff --git a/week5/knowledge-base/contracts/Contract with EverGuard Insurance for Rellm.md b/week5/knowledge-base/contracts/Contract with EverGuard Insurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md similarity index 98% rename from week5/knowledge-base/contracts/Contract with EverGuard Insurance for Rellm.md rename to week5/knowledge-base/contracts/Contract with EverGuard Insurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md index 5fd6626..d7ca80b 100644 --- a/week5/knowledge-base/contracts/Contract with EverGuard Insurance for Rellm.md +++ b/week5/knowledge-base/contracts/Contract with EverGuard Insurance for Rellm - AI-Powered Enterprise Reinsurance Solution.md @@ -55,4 +55,4 @@ --- -This contract seeks to foster a strong partnership between Insurellm and EverGuard Insurance, leveraging Rellm to innovate and enhance reinsurance capabilities while ensuring mutual growth and compliance in the ever-evolving insurance landscape. \ No newline at end of file +This contract seeks to foster a strong partnership between Insurellm and EverGuard Insurance, leveraging Rellm to innovate and enhance reinsurance capabilities while ensuring mutual growth and compliance in the ever-evolving insurance landscape. diff --git a/week5/knowledge-base/contracts/Contract with Evergreen Life Insurance for Lifellm.md b/week5/knowledge-base/contracts/Contract with Evergreen Life Insurance for Lifellm.md new file mode 100644 index 0000000..e1f0ffc --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Evergreen Life Insurance for Lifellm.md @@ -0,0 +1,137 @@ +# Contract with Evergreen Life Insurance for Lifellm + +**Contract Date:** January 20, 2025 +**Contract Number:** LF-2025-0012 +**Parties:** +- Insurellm, Inc. +- Evergreen Life Insurance Company + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide Evergreen Life Insurance with access to the Lifellm platform, enabling AI-powered life insurance underwriting, policy administration, and risk assessment for individual and group life policies. + +2. **Duration:** This agreement is effective for a period of 12 months from the contract date, with automatic renewal provisions unless terminated with 30-day written notice. + +3. **Payment:** Evergreen Life Insurance shall pay a monthly fee of $3,500, due by the 5th of every month for the Starter Tier package. + +4. **Policy Volume:** Starter Tier supports underwriting and administration for up to 2,000 active policies. Current baseline: 1,400 policies. + +5. **Confidentiality:** Both parties agree to maintain strict confidentiality of proprietary underwriting models, policyholder health information (PHI), and business strategies. + +6. **HIPAA Compliance:** Insurellm maintains full HIPAA compliance for all health data processed through the Lifellm platform. + +7. **Liability:** Insurellm's liability is limited to direct damages not exceeding the total fees paid in the preceding 6 months. + +--- + +## Renewal + +Unless either party provides written notice of termination at least 30 days prior to contract expiration, this agreement automatically renews for successive 12-month terms under the same terms, subject to pricing adjustments with 60 days' advance notice (capped at 10% annual increase). + +--- + +## Features + +Evergreen Life Insurance will receive the following Starter Tier features: + +1. **AI-Powered Underwriting:** Accelerated underwriting process analyzing: + - Medical histories and prescription databases (Milliman IntelliScript) + - Motor vehicle records (MVRs) + - Credit-based insurance scores + - Lifestyle and occupation risk factors + - Automated underwriting decisions for standard risks + +2. **Risk Assessment:** AI-driven mortality risk modeling considering: + - Age, gender, and family medical history + - Current health conditions and lab values + - Tobacco and alcohol use + - High-risk activities and occupations + +3. **Automated Policy Management:** + - Digital policy issuance and delivery + - Beneficiary management and updates + - Premium calculation and billing integration + - Coverage adjustments and conversions + - Policy reinstatement processing + +4. **Smart Document Processing:** OCR and NLP extraction from: + - Medical records and attending physician statements (APS) + - Lab results and paramedical exams + - Application forms + - Financial documentation + +5. **Customer Portal:** Policyholder self-service portal featuring: + - Policy document access + - Beneficiary updates + - Premium payment processing + - Coverage summary and statements + - Digital insurance cards + +6. **Regulatory Compliance:** Built-in compliance monitoring for: + - State insurance regulations + - NAIC model laws and regulations + - Anti-money laundering (AML) requirements + - Policy form approvals + +7. **Basic Analytics:** Standard dashboards tracking: + - Application submission and approval rates + - Average underwriting cycle time + - Policy persistency and lapse rates + - Face amount in force + +--- + +## Support + +Insurellm commits to providing comprehensive support to Evergreen Life Insurance: + +1. **Onboarding:** 2-week implementation program including: + - System configuration and setup + - Training for up to 8 underwriters and administrators + - Integration with Evergreen's policy administration system + - Data migration from legacy system (up to 1,400 policies) + +2. **Technical Support:** + - Email and phone support Monday-Friday 8 AM - 6 PM EST + - Response time: 12 hours for critical issues, 24 hours for standard + - Online ticketing system for issue tracking + - Knowledge base and FAQ access + +3. **Platform Updates:** + - Quarterly feature releases + - Monthly security patches and improvements + - Advance notice of updates (minimum 14 days) + - Release notes and user documentation + +4. **Account Management:** + - Named customer success manager + - Semi-annual business review meetings + - Usage analytics and optimization recommendations + - Assistance with tier upgrade when volume grows + +5. **Compliance Support:** + - Annual compliance review and audit support + - Regulatory update notifications + - State insurance department filing assistance + +--- + +**Signatures:** + +_________________________________ +**Michael Torres** +**Title**: Chief Revenue Officer +**Insurellm, Inc.** +**Date**: January 20, 2025 + +_________________________________ +**Catherine Wu** +**Title**: Chief Underwriting Officer +**Evergreen Life Insurance Company** +**Date**: January 20, 2025 + +--- + +This agreement enables Evergreen Life Insurance to modernize their life insurance operations with AI-powered underwriting and administration technology. diff --git a/week5/knowledge-base/contracts/Contract with FastTrack Insurance Services for Claimllm.md b/week5/knowledge-base/contracts/Contract with FastTrack Insurance Services for Claimllm.md new file mode 100644 index 0000000..d941731 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with FastTrack Insurance Services for Claimllm.md @@ -0,0 +1,73 @@ +# Contract with FastTrack Insurance Services for Claimllm + +**Contract Date:** May 10, 2025 +**Contract Number:** CL-2025-0234 + +--- + +## Terms + +1. **Parties Involved**: This contract is entered into between Insurellm, Inc. ("Provider") and FastTrack Insurance Services ("Client") on May 10, 2025. + +2. **License Grant**: Insurellm grants FastTrack Insurance Services a non-exclusive, non-transferable license to use the Claimllm Advanced Tier platform. + +3. **Payment Terms**: FastTrack Insurance Services agrees to make monthly payments of $9,500 for the duration of this 18-month contract, totaling $171,000. Payments due on the 1st of each month. + +4. **Claims Volume**: Advanced Tier supports up to 25,000 claims annually. FastTrack projects 22,000 claims in year 1. + +5. **Confidentiality**: Both parties shall maintain confidentiality regarding each other's proprietary information for the contract duration plus 5 years post-termination. + +## Renewal + +1. **Automatic Renewal**: This contract automatically renews for successive 12-month terms unless either party provides written notice of non-renewal at least 45 days prior to expiration. + +2. **Pricing Review**: Renewal pricing will be discussed 60 days prior to term end. Price increases limited to 8% annually. + +## Features + +1. **Included Features**: FastTrack Insurance Services will have access to Claimllm Advanced Tier features: + - Intelligent FNOL Processing with multi-channel intake + - Automated Triage and Routing with ML algorithms + - Computer Vision Damage Assessment for auto and property claims + - Predictive Fraud Detection with network analysis + - Smart Document Processing with OCR and NLP + - Dynamic Reserve Setting with AI-powered modeling + - Vendor Management Platform for 150+ vendors + - Payment Automation with multi-party splits + - Claimant Communication Hub (SMS, email, app) + - Advanced Analytics and reporting dashboards + +2. **Integration Capabilities**: API integration with FastTrack's existing claims systems (Guidewire, Duck Creek compatibility). + +3. **Mobile Access**: Full-featured mobile app for adjusters in the field with offline capability. + +## Support + +1. **Customer Support**: Insurellm will provide priority support to FastTrack Insurance Services: + - Dedicated support team available Monday-Saturday 6 AM - 8 PM EST + - Emergency support 24/7 for critical issues + - Response time: 3 hours for critical, 8 hours for standard + +2. **Training**: Comprehensive training program: + - 3-week implementation with dedicated project manager + - Training for up to 30 claims staff members + - Quarterly refresher webinars + - Online knowledge base access + +3. **System Updates**: Regular platform enhancements pushed monthly with advance notification. Maintenance windows on Sundays 12 AM - 4 AM EST. + +--- + +**Signatures:** + +**Insurellm, Inc.** +Sarah Chen, VP of Sales +Date: May 10, 2025 + +**FastTrack Insurance Services** +Rebecca Martinez, COO +Date: May 10, 2025 + +--- + +This contract represents the agreement between Insurellm and FastTrack Insurance Services for streamlined, AI-powered claims processing operations. diff --git a/week5/knowledge-base/contracts/Contract with Fortress Business Underwriters for Bizllm.md b/week5/knowledge-base/contracts/Contract with Fortress Business Underwriters for Bizllm.md new file mode 100644 index 0000000..68cab2a --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Fortress Business Underwriters for Bizllm.md @@ -0,0 +1,87 @@ +# Contract with Fortress Business Underwriters for Bizllm + +--- + +## Terms + +1. **Agreement Effective Date**: This contract is effective as of February 1, 2025. +2. **Duration**: This agreement will remain in effect for a term of 24 months, concluding on January 31, 2027. +3. **Subscription Type**: Fortress Business Underwriters agrees to subscribe to the **Professional Tier** of Bizllm, at a cost of $12,000/month, totaling $288,000 for the duration of this contract. +4. **Payment Terms**: Payments are due on the 5th of each month. Late payments will incur a penalty of 2% per month. +5. **Scope of Use**: License covers up to 50 named users within Fortress Business Underwriters' organization, with additional user licenses available at $150/month per user. +6. **Termination Clause**: Either party may terminate this agreement with 60 days' written notice prior to the end of the term. If terminated early, a termination fee equal to 25% of remaining contract value will apply. + +--- + +## Renewal + +1. **Automatic Renewal**: This agreement will automatically renew for an additional 24-month term unless either party provides written notice of non-renewal at least 60 days before the expiration date. +2. **Price Adjustments**: Subscription fees may be adjusted for the renewal term based on Consumer Price Index (CPI) changes or enhanced feature availability, with 90 days' prior notice provided to Fortress Business Underwriters. +3. **Upgrade Options**: Client may upgrade to Enterprise Tier at any time with 30 days' notice and prorated pricing adjustment. + +--- + +## Features + +1. **Access to Professional Tier Features**: Fortress Business Underwriters will have access to all Professional Tier features, including: + - Multi-Line Underwriting Engine for general liability, professional liability, property, workers' comp, and cyber insurance + - Business Intelligence Integration with automated data gathering + - Cyber Risk Assessment with digital security posture evaluation + - Workers' Compensation Optimization tools + - Commercial Property Evaluation with catastrophe modeling + - Professional Liability Specialization for multiple industry sectors + - Portfolio Management Dashboard with predictive analytics + - Agent and Broker Portal with commission tracking + - Claims Management Integration with AI-powered subrogation identification + +2. **Advanced Analytics**: Access to enhanced reporting capabilities including loss ratio analysis, geographic heat maps, and competitive benchmarking tools. + +3. **API Access**: Limited API access for integrating Bizllm with existing underwriting workbenches and agency management systems (up to 100,000 API calls/month). + +4. **Custom Workflows**: Ability to configure up to 10 custom underwriting workflows tailored to Fortress's specific business lines and risk appetites. + +--- + +## Support + +1. **Technical Support**: Fortress Business Underwriters will receive priority technical support from Insurellm, including: + - Dedicated account manager with monthly check-ins + - 24/7 emergency technical support hotline + - Response time guarantee: 2 hours for critical issues, 8 hours for standard issues + +2. **Training**: Insurellm will provide: + - Initial comprehensive onboarding (40 hours) for up to 20 staff members + - Quarterly advanced training sessions on new features and best practices + - Access to online learning portal with video tutorials and documentation + - Annual certification program for power users + +3. **Updates and Maintenance**: + - Monthly platform updates with new features and enhancements + - Scheduled maintenance windows: Saturday 11 PM - Sunday 3 AM EST + - Minimum 72 hours advance notice for all planned maintenance + - Zero-downtime updates for non-critical improvements + +4. **Integration Support**: Dedicated integration specialist to assist with: + - Connection to policy administration systems (Duck Creek, Guidewire, etc.) + - Data migration from legacy underwriting platforms + - Third-party data provider integrations (Verisk, LexisNexis, CoreLogic) + +--- + +**Signatures:** + +_________________________________ +**Michael Torres** +**Title**: Chief Revenue Officer +**Insurellm, Inc.** +**Date**: February 1, 2025 + +_________________________________ +**Robert Chen** +**Title**: Chief Underwriting Officer +**Fortress Business Underwriters** +**Date**: February 1, 2025 + +--- + +*This contract outlines the terms of the relationship between Insurellm and Fortress Business Underwriters for the Bizllm product, establishing a partnership to transform commercial insurance underwriting operations.* diff --git a/week5/knowledge-base/contracts/Contract with GlobalRe Partners for Rellm.md b/week5/knowledge-base/contracts/Contract with GlobalRe Partners for Rellm.md new file mode 100644 index 0000000..926fdf7 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with GlobalRe Partners for Rellm.md @@ -0,0 +1,292 @@ +# Contract with GlobalRe Partners for Rellm - AI-Powered Enterprise Reinsurance Solution + +**Contract Date:** April 28, 2025 +**Contract Number:** RE-2025-E-0203 +**Parties:** +- Insurellm, Inc. +- GlobalRe Partners International, Ltd. + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide GlobalRe Partners with enterprise access to the Rellm platform, including white-label capabilities, unlimited treaty and facultative reinsurance administration, dedicated infrastructure, and full API access for their global reinsurance operations. + +2. **Duration:** This agreement is effective for a period of 48 months from the contract date, representing Insurellm's most strategic long-term reinsurance partnership. + +3. **Payment:** GlobalRe Partners shall pay custom Enterprise Tier pricing of $45,000 per month for months 1-12, $48,000 per month for months 13-24, $52,000 per month for months 25-36, and $56,000 per month for months 37-48, totaling $2,412,000 over the contract term. + +4. **Reinsurance Volume**: Enterprise Tier supports unlimited treaties and facultative certificates. GlobalRe currently manages 450+ treaty relationships and 8,000+ facultative placements annually. + +5. **Service Level Agreement:** Insurellm guarantees 99.95% platform uptime measured monthly, with 10% monthly credit for each hour of unplanned downtime, maximum 50% monthly credit. + +6. **Confidentiality:** Both parties agree to maintain the highest level of confidentiality regarding proprietary AI algorithms, reinsurance pricing models, cedent data, catastrophe modeling, and strategic initiatives. + +7. **Data Security:** Insurellm maintains SOC 2 Type II compliance, ISO 27001 certification, and annual third-party security audits with penetration testing. + +8. **Business Continuity:** Guaranteed 2-hour recovery time objective (RTO) and 30-minute recovery point objective (RPO) with geographically diverse data centers. + +9. **Global Operations:** Platform supports multi-currency (35+ currencies), multi-language (12 languages), and multi-jurisdiction regulatory compliance. + +--- + +## Renewal + +This agreement includes a mutual 120-day renewal notice period. GlobalRe Partners receives most-favored-customer pricing guarantees and priority access to all new reinsurance features. Contract may be extended in 12 or 24-month increments with provisions for joint innovation initiatives in catastrophe modeling and climate risk analytics. + +--- + +## Features + +GlobalRe Partners will receive the complete Rellm Enterprise suite with advanced reinsurance capabilities: + +1. **Unlimited Reinsurance Administration:** Full support for GlobalRe's 450+ treaty relationships and 8,000+ annual facultative placements with scalability to 1,000+ treaties and 50,000+ facultative certificates. + +2. **White-Label Platform:** Complete branding customization: + - Custom domains (portal.globalrepartners.com, cedents.globalrepartners.com, brokers.globalrepartners.com) + - Branded portals for cedents, brokers, and retrocessionaires + - Multi-language interface (English, Spanish, French, German, Mandarin, Japanese, Portuguese, Dutch, Italian, Korean, Arabic, Russian) + - Customized reporting templates and market presentations + +3. **Advanced AI-Driven Analytics:** Enterprise-grade reinsurance analytics: + - Predictive risk modeling across all lines of business + - Treaty portfolio optimization and capital allocation + - Retrocessional optimization and protection tower design + - Pricing analytics with market cycle intelligence + - Loss development and IBNR prediction + - Ultimate loss forecasting with confidence intervals + - Reserve adequacy modeling + - Commutation opportunity identification + +4. **Comprehensive Risk Assessment Module:** State-of-the-art risk evaluation: + - Cedent financial strength analysis with early warning indicators + - Portfolio exposure aggregation across all treaties + - Clash analysis and accumulation monitoring + - Geographic concentration heat maps + - Industry and line of business diversification metrics + - Emerging risk identification (cyber, climate, pandemic) + - Counterparty credit risk assessment + - Retrocessional credit risk monitoring + +5. **Catastrophe Modeling Integration:** Best-in-class CAT modeling: + - Integration with all major vendors (RMS, AIR, CoreLogic, KCC) + - Multi-peril catastrophe analysis + - Probable maximum loss (PML) calculations at all return periods + - Exceedance probability curves and tail value-at-risk + - Climate change scenario modeling + - Secondary uncertainty and model blending + - Real-time CAT event tracking and loss estimation + - Pre-CAT exposure management and post-CAT claims projection + +6. **Treaty Administration:** Complete treaty lifecycle management: + - Treaty structuring and quota share optimization + - Excess of loss layer optimization + - Sliding scale commission calculation + - Loss corridor and aggregate deductible tracking + - Reinstatement premium automation + - Cash call processing and premium allocation + - Loss reserve bordereaux processing + - Treaty accounting and reconciliation + - Multi-year contract administration + +7. **Facultative Reinsurance Platform:** Comprehensive fac management: + - Digital submission intake from brokers and cedents + - AI-powered underwriting decision support + - Automated risk grading and pricing recommendations + - Peer pricing comparison and market intelligence + - Quote generation with terms and conditions + - Binder and certificate issuance + - Premium and claims tracking by certificate + - Portfolio monitoring and aggregation + +8. **Customizable Dashboard:** Enterprise-grade analytics: + - Real-time portfolio performance metrics + - Combined ratio tracking by treaty, cedent, line of business, geography + - Premium volume and growth analytics + - Claims development and loss ratio trending + - Reserve adequacy indicators + - Capital utilization and ROE analysis + - Market share and competitive positioning + - Broker and cedent relationship metrics + +9. **Regulatory Compliance Tools:** Global compliance support: + - Multi-jurisdiction regulatory reporting (US, EU, UK, Asia-Pacific, Latin America) + - Solvency II compliance for European operations + - IFRS 17 reporting and analytics + - US statutory accounting (SAP) + - Tax reporting and withholding management + - Sanctions screening (OFAC, UN, EU) + - Anti-money laundering (AML) monitoring + - Audit trail and documentation repository + +10. **Client and Broker Portals:** Stakeholder engagement platforms: + - Cedent portal for submissions, bordereaux, reports, and analytics + - Broker portal for quote requests, binding, and commissions + - Retrocessionaire portal for downstream placements + - Real-time data sharing and document exchange + - Secure messaging and collaboration tools + - Mobile app access for on-the-go access + - Customized views by relationship + +11. **Complete API Access:** Unlimited API integration: + - Real-time integration with GlobalRe's core systems + - Accounting system integration (SAP, Oracle) + - Data warehouse synchronization + - Catastrophe modeling platform connectivity + - Broker trading platforms (Ebix, Sequel) + - Lloyd's platform integration (Whitespace, PPL) + - Industry data providers (AM Best, S&P, Moody's) + - Economic and market data feeds + +12. **Advanced Retrocessional Management:** Retro optimization: + - Retrocessional program structuring + - Protection tower analytics and gap analysis + - Retro market intelligence and pricing + - Counterparty diversification optimization + - Credit risk monitoring + - Collateral management + - Retro recovery tracking + +13. **Climate Risk Analytics:** Forward-looking climate modeling: + - IPCC climate scenario analysis (RCP 2.6, 4.5, 8.5) + - Transition risk assessment + - Physical risk modeling for perils (hurricane, wildfire, flood, drought) + - Sea level rise impact analysis + - Temperature trend incorporation + - Climate-adjusted pricing recommendations + - Stranded asset identification + - Green reinsurance opportunities + +--- + +## Support + +Insurellm commits to comprehensive Enterprise-level support for GlobalRe Partners: + +1. **Dedicated Success Team:** + - Executive sponsor (CEO-level) with quarterly strategic reviews + - Dedicated Senior Vice President of Customer Success with bi-weekly engagement + - Technical Account Manager for platform optimization + - Solutions Architect team (2 FTE) for strategic initiatives + - Catastrophe modeling specialist for analytics support + - Quarterly executive business reviews with C-suite participation from both organizations + +2. **24/7/365 Premium Support:** + - Guaranteed 10-minute response for P1 critical issues (during catastrophe events: 5 minutes) + - 30-minute response for P2 high-priority issues + - 2-hour response for P3 standard issues + - Dedicated support team of 10+ specialists familiar with GlobalRe's operations + - Named support engineers for each major functional area + - Private collaboration channels (Slack, Microsoft Teams) with direct engineering access + - Quarterly on-site support visits (minimum 8 per year, plus CAT event visits) + +3. **Comprehensive Implementation:** + - 18-month phased implementation program with 5 major releases + - Dedicated program management office (PMO) with weekly steering committee meetings + - Migration of 450+ treaty relationships and historical data (10 years) + - Integration with 8,000+ facultative certificates + - Training for 200+ GlobalRe staff across underwriting, actuarial, accounting, IT + - Regional training sessions (North America, Europe, Asia) + - Parallel processing with legacy systems for 6 months + - Go-live support with 16-week on-site Insurellm team presence (8-12 people) + +4. **Training and Enablement:** + - Comprehensive role-based training (underwriters, actuaries, accountants, claim handlers, IT, executives) + - Executive education on AI in reinsurance and emerging risk + - Actuarial training on predictive modeling and machine learning + - Catastrophe modeling workshop series + - Train-the-trainer certification for GlobalRe's learning team + - Annual user conference attendance (12 attendees, all expenses paid including travel) + - Monthly advanced training webinars on new features + - Unlimited online learning platform access with 150+ courses + - Custom training content for GlobalRe-specific processes + +5. **Custom Development:** + - 4,000 hours annually of custom development included + - Dedicated development team (6 developers, 2 QA, 1 PM) for GlobalRe-specific features + - Priority feature requests with guaranteed product roadmap inclusion + - Early access to beta features 120 days before general release + - Joint innovation projects in climate risk, AI underwriting, blockchain + - Co-development of industry-leading capabilities + - Input into 5-year product strategy and vision + +6. **Integration Services:** + - Enterprise integration with GlobalRe's complex ecosystem: + - SAP ERP for finance and accounting + - Socotra reinsurance administration platform + - RMS RiskLink catastrophe modeling + - AIR Touchstone + - Moody's RMS LifeRisks + - Sequel broker trading platform + - Lloyd's Whitespace and PPL + - ACORD messaging standards + - SWIFT for payments + - Data warehouse on Snowflake + - Tableau and PowerBI for analytics + - Dedicated integration team (3 FTE integration engineers) + - Monthly integration health assessments + - Performance optimization and load testing + - API governance and versioning support + +7. **Analytics and Reporting:** + - Custom dashboard development (unlimited) + - Real-time operational dashboards for underwriting, claims, accounting + - Executive dashboards with strategic KPIs + - Automated reporting suite (daily, weekly, monthly, quarterly, annual) + - Regulatory reporting templates for all jurisdictions + - Ad-hoc analytics support (unlimited requests with 24-hour SLA for standard, 4-hour for urgent) + - Industry benchmarking against top 50 global reinsurers + - Catastrophe exposure and PML reporting + - Predictive analytics and scenario modeling consultation (bi-weekly sessions) + - Embedded actuarial and data science team support + +8. **Catastrophe Response:** + - Dedicated CAT war room activation during major events + - Real-time loss estimation and exposure reporting + - Claims projections and reserve recommendations + - Cedent outreach and data collection coordination + - Retrocessional recovery tracking + - Post-CAT analysis and lessons learned + - Annual CAT preparedness exercises and disaster recovery testing + +9. **Regulatory and Compliance Support:** + - Quarterly compliance reviews for all operating jurisdictions + - Annual regulatory update briefings + - Solvency II and IFRS 17 implementation support + - Audit preparation and documentation assistance + - Regulatory examination support + - Industry association engagement (IAIS, NAIC, EIOPA) + +10. **Continuous Improvement:** + - Monthly process optimization sessions + - Quarterly efficiency audits with detailed ROI analysis + - Best practice sharing from global reinsurance market + - Innovation lab partnership for emerging technologies: + - Advanced AI and machine learning + - Blockchain for smart contracts + - Climate science and forward-looking modeling + - Cyber risk quantification + - Pandemic and systemic risk modeling + - Joint research publications and thought leadership + - Speaking opportunities at major industry conferences (Monte Carlo Rendez-Vous, Baden-Baden, SIRC) + - Participation in industry working groups and standard-setting bodies + +--- + +**Signatures:** + +_________________________________ +**Jennifer Rodriguez** +**Title**: Chief Executive Officer +**Insurellm, Inc.** +**Date**: April 28, 2025 + +_________________________________ +**Sir Charles Pemberton** +**Title**: Group Chief Executive Officer +**GlobalRe Partners International, Ltd.** +**Date**: April 28, 2025 + +--- + +This agreement establishes GlobalRe Partners as Insurellm's flagship enterprise reinsurance partner, representing the most comprehensive and strategic partnership in Insurellm's history. This collaboration will drive innovation in reinsurance technology, catastrophe modeling, climate risk analytics, and AI-powered underwriting, setting new standards for the global reinsurance industry. This contract supersedes all prior agreements and communications. diff --git a/week5/knowledge-base/contracts/Contract with Guardian Life Partners for Lifellm.md b/week5/knowledge-base/contracts/Contract with Guardian Life Partners for Lifellm.md new file mode 100644 index 0000000..17efbcf --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Guardian Life Partners for Lifellm.md @@ -0,0 +1,121 @@ +# Contract with Guardian Life Partners for Lifellm + +--- + +## Terms + +1. **Agreement Effective Date**: This contract is effective as of March 1, 2025. +2. **Duration**: This agreement will remain in effect for a term of 24 months, concluding on February 28, 2027. +3. **Subscription Type**: Guardian Life Partners agrees to subscribe to the **Growth Tier** of Lifellm, at a cost of $7,500/month, totaling $180,000 for the duration of this contract. +4. **Policy Capacity**: Growth Tier supports up to 10,000 active policies with underwriting for 3,000 new applications annually. +5. **Payment Terms**: Payments are due on the 1st of each month via ACH transfer. Late payments incur a 1.5% monthly penalty. +6. **User Licenses**: Contract includes 25 named user licenses (underwriters, administrators, agents). Additional licenses: $120/month per user. +7. **Termination Clause**: Either party may terminate with 60 days' written notice. Early termination before month 12 requires payment of 30% of remaining contract value. + +--- + +## Renewal + +1. **Automatic Renewal**: This agreement will automatically renew for an additional 24-month term unless either party provides written notice of non-renewal at least 60 days before expiration. +2. **Price Protection**: Pricing increases capped at 7% annually for first two renewals. +3. **Enterprise Upgrade**: If Guardian upgrades to Enterprise tier at renewal, receives 12% discount on incremental cost for first renewal period. + +--- + +## Features + +1. **Access to Growth Tier Features**: Guardian Life Partners will have comprehensive access to: + - AI-Powered Underwriting with accelerated decision-making (60% of applications instant decision) + - Predictive Risk Modeling with machine learning mortality analysis + - **Digital Health Integration** with wearables and health tracking apps (Fitbit, Apple Health, Garmin) + - Automated Policy Management with full policy lifecycle support + - Smart Document Processing with medical record analysis + - Customer Portal with policyholder self-service + - Compliance Automation across all 50 states + - **Advanced Reporting** with predictive analytics + +2. **Digital Health Features:** + - Wearable device integration for wellness programs + - Activity tracking and health score calculation + - Premium discount automation based on health behaviors + - Wellness challenge campaigns + - Health goal tracking and rewards management + +3. **Advanced Underwriting:** + - Automated underwriting for face amounts up to $500,000 + - Accelerated underwriting (no medical exam) for qualified applicants + - Predictive models trained on 10M+ life insurance applications + - Risk stratification and pricing optimization + - Decline rescue recommendations + +4. **Agent Portal:** + - Agent-facing tools for 200+ independent agents + - Real-time application status tracking + - Commission calculation and reporting + - E-application with digital signature + - Marketing materials and quote tools + +--- + +## Support + +1. **Technical Support**: Guardian Life Partners will receive enhanced support: + - Priority support line Monday-Friday 7 AM - 8 PM EST + - Email and chat support with 6-hour response time for critical issues + - Dedicated support engineer for complex integrations + - 24/7 emergency hotline for system outages + +2. **Training**: Comprehensive training program: + - Initial onboarding (4 weeks) for up to 20 staff members (40 hours total) + - Role-specific training: underwriters, administrators, IT, agents + - Quarterly product update webinars + - Annual certification program for underwriters + - Access to online learning platform with 30+ courses + - Train-the-trainer program for agent network + +3. **Updates and Maintenance**: + - Monthly platform updates with new features + - Bi-weekly security enhancements + - Maintenance windows: Saturday 11 PM - Sunday 3 AM EST + - 72-hour advance notice for major updates + - Dedicated staging environment for testing updates + +4. **Account Management**: + - Named Customer Success Manager with bi-weekly check-ins + - Quarterly business reviews with metrics analysis + - Annual strategic planning session + - Direct escalation path to Director of Customer Success + - Industry benchmarking reports + +5. **Integration Support**: Technical assistance for integrating Lifellm with: + - Guardian's policy administration system (iPipeline, VPAS, or custom) + - Agent management system (Salesforce, Applied Systems) + - Medical data providers (MIB, Milliman IntelliScript, ExamOne) + - Payment processing and billing systems + - CRM and marketing automation platforms + +6. **Wellness Program Support:** + - Assistance designing wellness initiatives + - Health program configuration and gamification + - Marketing templates for wellness campaigns + - Best practices from other carriers' successful programs + +--- + +**Signatures:** + +_________________________________ +**Michael Torres** +**Title**: Chief Revenue Officer +**Insurellm, Inc.** +**Date**: March 1, 2025 + +_________________________________ +**Jonathan Park** +**Title**: President & CEO +**Guardian Life Partners** +**Date**: March 1, 2025 + +--- + +*This contract establishes Guardian Life Partners as a strategic partner leveraging Lifellm's advanced AI underwriting and digital health integration to modernize life insurance operations.* diff --git a/week5/knowledge-base/contracts/Contract with Harmony Health Plans for Healthllm.md b/week5/knowledge-base/contracts/Contract with Harmony Health Plans for Healthllm.md new file mode 100644 index 0000000..d4cd834 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Harmony Health Plans for Healthllm.md @@ -0,0 +1,152 @@ +# Contract with Harmony Health Plans for Healthllm + +--- + +## Terms + +1. **Agreement Effective Date**: This contract is effective as of January 25, 2025. +2. **Duration**: This agreement will remain in effect for a term of 24 months, concluding on January 24, 2027. +3. **Subscription Type**: Harmony Health Plans agrees to subscribe to the **Professional Tier** of Healthllm, at a cost of $15,000/month, totaling $360,000 for the duration of this contract. +4. **Member Coverage**: Professional Tier supports up to 50,000 covered members. Harmony currently covers 38,000 members across 3 states. +5. **Payment Terms**: Payments are due on the 5th of each month via wire transfer. Late payments incur a 2% monthly penalty. +6. **User Licenses**: Contract includes 60 named user licenses. Additional licenses available at $150/month per user. +7. **Termination Clause**: Either party may terminate with 90 days' written notice. Early termination after month 12 has no penalty; before month 12 requires 25% of remaining contract value. + +--- + +## Renewal + +1. **Automatic Renewal**: This agreement will automatically renew for an additional 24-month term unless either party provides written notice of non-renewal at least 90 days before expiration. +2. **Price Protection**: Pricing increases capped at 6% annually for first two renewal periods. +3. **Volume Discounts**: If member count exceeds 75,000 at renewal, Harmony receives 10% discount on incremental cost. + +--- + +## Features + +1. **Access to Professional Tier Features**: Harmony Health Plans will have comprehensive access to: + - Intelligent Plan Design with AI-powered optimization + - Real-Time Eligibility Verification with provider network integration + - AI-Driven Claims Adjudication with automated processing + - **Predictive Healthcare Analytics** for population health management + - Provider Network Management with contract optimization + - Member Engagement Platform with mobile-first design + - **Advanced Medication Management** with PBM integration + - Regulatory Compliance Engine with ACA and state mandate tracking + - **Enhanced Analytics** with predictive modeling + +2. **Predictive Healthcare Analytics:** + - High-risk member identification for care management + - Disease progression modeling (diabetes, hypertension, CHF) + - Hospital readmission prediction + - Emergency department overutilization alerts + - Care gap identification for quality measures (HEDIS) + - Preventive care intervention recommendations + - Cost trend forecasting and budget planning + +3. **Advanced Medication Management:** + - Complete formulary management and tier optimization + - Prior authorization automation with clinical criteria + - Step therapy enforcement + - Medication adherence scoring and intervention triggers + - Drug interaction checking with severity classification + - Generic substitution recommendations with savings calculations + - Specialty pharmacy management + - Medication therapy management (MTM) program support + +4. **Value-Based Care Tools:** + - ACO (Accountable Care Organization) performance tracking + - Shared savings calculation + - Quality measure monitoring (HEDIS, STAR ratings) + - Provider pay-for-performance scorecards + - Risk adjustment and HCC coding support + +5. **Care Management Platform:** + - Case management workflows for high-cost members + - Disease management program support + - Care coordinator tools and dashboards + - Member outreach campaign management + - Social determinants of health (SDOH) screening + +--- + +## Support + +1. **Technical Support**: Harmony Health Plans will receive priority support: + - Dedicated support team available 24/7 for critical issues + - Standard support Monday-Saturday 6 AM - 9 PM EST + - Response time: 4 hours for critical, 8 hours for high priority, 24 hours for standard + - Named technical support engineer familiar with Harmony's environment + - Private support portal with ticket tracking + +2. **Training**: Comprehensive training program: + - 6-week implementation with dedicated project manager + - Training for up to 40 staff members (60 hours total training) + - Role-specific tracks: claims processors, case managers, provider relations, IT, executives + - Quarterly advanced training webinars + - Annual refresher certification program + - Unlimited access to online learning platform with 75+ courses + - Custom training materials for Harmony's specific workflows + +3. **Updates and Maintenance**: + - Monthly platform updates with new features and enhancements + - Bi-weekly security patches and bug fixes + - Regulatory updates as healthcare laws change (ACA, state mandates) + - Maintenance windows: Saturday 10 PM - Sunday 3 AM EST + - 96-hour advance notice for major updates + - Staging environment for testing updates before production + +4. **Account Management**: + - Named Senior Customer Success Manager with bi-weekly check-ins + - Quarterly executive business reviews with ROI analysis + - Annual strategic planning session with product roadmap preview + - Direct escalation path to VP of Customer Success + - Industry benchmarking reports comparing Harmony to similar plans + +5. **Integration Support**: Comprehensive integration assistance: + - Integration with Harmony's core systems: + - HealthRules policy administration system + - Provider credentialing platform + - Express Scripts PBM + - Availity clearinghouse for claims + - Member portal and mobile app + - Salesforce CRM + - Dedicated integration specialist (30% FTE allocation) + - Quarterly integration health checks and optimization + - API monitoring and performance tuning + +6. **Analytics and Reporting:** + - Custom dashboard development (up to 10 dashboards) + - Automated reporting suite (daily, weekly, monthly, quarterly) + - Ad-hoc analytics requests (up to 15 per month) + - Predictive modeling consultation (monthly sessions) + - Benchmark reports against regional health plans + - Data export capabilities in multiple formats + +7. **Compliance and Regulatory Support:** + - Quarterly compliance reviews + - Annual HIPAA security risk assessment + - ACA reporting assistance (1094/1095 forms, MLR reporting) + - State filing support for form and rate approvals + - Quality measure calculation support (HEDIS, STAR) + - Audit preparation and documentation assistance + +--- + +**Signatures:** + +_________________________________ +**Sarah Chen** +**Title**: Vice President of Sales +**Insurellm, Inc.** +**Date**: January 25, 2025 + +_________________________________ +**Dr. Karen Phillips** +**Title**: President & Chief Executive Officer +**Harmony Health Plans** +**Date**: January 25, 2025 + +--- + +*This contract establishes Harmony Health Plans as a strategic partner leveraging Healthllm's advanced AI and predictive analytics to improve member outcomes while managing costs effectively.* diff --git a/week5/knowledge-base/contracts/Contract with Heritage Life Assurance for Lifellm.md b/week5/knowledge-base/contracts/Contract with Heritage Life Assurance for Lifellm.md new file mode 100644 index 0000000..3f7e737 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Heritage Life Assurance for Lifellm.md @@ -0,0 +1,100 @@ +# Contract with Heritage Life Assurance for Lifellm + +**Contract Date:** February 12, 2025 +**Contract Number:** LF-2025-0045 + +--- + +## Terms + +1. **Parties Involved**: This contract is entered into between Insurellm, Inc. ("Provider") and Heritage Life Assurance ("Client"). + +2. **Scope of Services**: Provider agrees to deliver Lifellm Growth Tier, including AI-powered underwriting, predictive risk modeling, digital health integration, and policy management capabilities. + +3. **Payment Terms**: Client shall pay $7,500 per month for 18 months. Total contract value: $135,000. Payments due on the 1st via ACH. + +4. **Policy Volume**: Growth Tier supports up to 10,000 active policies. Heritage currently administers 6,200 policies. + +5. **Contract Duration**: 18 months commencing February 12, 2025 and ending August 11, 2026. + +## Renewal + +1. **Automatic Renewal**: This agreement automatically renews for 12-month terms unless either party provides 45-day written notice of non-renewal. + +2. **Renewal Pricing**: Pricing adjustments communicated 75 days prior to renewal, capped at 8% annual increase. + +## Features + +1. **AI-Powered Underwriting**: Heritage Life Assurance will benefit from: + - Accelerated underwriting with 65% instant decisions + - Medical record analysis and risk scoring + - Mortality prediction models + - Automated decisioning for face amounts up to $750,000 + +2. **Predictive Risk Modeling**: Advanced analytics considering: + - Comprehensive medical histories + - Prescription drug databases + - Lifestyle and occupation factors + - Family history and genetic markers + - Predictive health trajectories + +3. **Digital Health Integration**: + - Wearable device connections (Fitbit, Apple Watch) + - Health app integrations + - Wellness program platform + - Premium discount automation + - Activity tracking and rewards + +4. **Automated Policy Management**: + - Digital policy issuance + - Beneficiary updates and management + - Premium billing integration + - Policy conversion processing + - Lapse and reinstatement automation + +5. **Customer Portal**: Policyholder self-service including: + - 24/7 policy access + - Digital insurance cards + - Claims submission + - Premium payment processing + - Coverage updates + +6. **Advanced Reporting**: + - Application conversion analytics + - Underwriting productivity metrics + - Policy persistency reporting + - Agent performance dashboards + +## Support + +1. **Technical Support**: Heritage will receive priority support: + - Monday-Friday 7 AM - 7 PM EST + - Email, phone, and chat support + - 8-hour response time for critical issues + - Online knowledge base + +2. **Training**: Training program includes: + - 3-week implementation + - Training for up to 15 staff members (25 hours) + - Quarterly webinars on platform updates + - Online training library access + +3. **Updates**: Monthly platform enhancements and security patches. Maintenance windows: Sunday 1 AM - 5 AM EST. + +4. **Account Management**: Named customer success manager with quarterly business reviews. + +--- + +**Signatures:** + +**Insurellm, Inc.** +Michael Torres, Chief Revenue Officer +Date: February 12, 2025 + +**Heritage Life Assurance** +Melissa Zhang, VP of Operations +Date: February 12, 2025 + +--- + +This contract enables Heritage Life Assurance to leverage Lifellm's AI technology for modernized life insurance underwriting and administration. diff --git a/week5/knowledge-base/contracts/Contract with Metropolitan Life Group for Lifellm.md b/week5/knowledge-base/contracts/Contract with Metropolitan Life Group for Lifellm.md new file mode 100644 index 0000000..dbbd7ee --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Metropolitan Life Group for Lifellm.md @@ -0,0 +1,199 @@ +# Contract with Metropolitan Life Group for Lifellm + +**Contract Date:** April 5, 2025 +**Contract Number:** LF-2025-E-0087 +**Parties:** +- Insurellm, Inc. +- Metropolitan Life Group, Inc. + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide Metropolitan Life Group with enterprise access to the Lifellm platform, including white-label capabilities, full customization, dedicated support, and enterprise-grade security for their nationwide life insurance operations managing 50,000+ active policies. + +2. **Duration:** This agreement is effective for a period of 36 months from the contract date, representing a strategic long-term partnership. + +3. **Payment:** Metropolitan Life Group shall pay custom Enterprise Tier pricing of $28,000 per month for months 1-12, $30,500 per month for months 13-24, and $33,000 per month for months 25-36, totaling $1,098,000 over the contract term. + +4. **Service Level Agreement:** Insurellm guarantees 99.9% uptime measured monthly, with 10% monthly credit for each 0.1% below target, maximum 30% credit. + +5. **Confidentiality:** Both parties agree to maintain the highest level of confidentiality regarding AI algorithms, underwriting models, policyholder data, and strategic initiatives. + +6. **Data Security:** Insurellm commits to maintaining HIPAA compliance, SOC 2 Type II certification, and annual third-party security audits. + +7. **Disaster Recovery:** 4-hour recovery time objective (RTO) and 30-minute recovery point objective (RPO). + +--- + +## Renewal + +This agreement includes a mutual 90-day renewal notice period. Metropolitan Life Group receives guaranteed enterprise pricing at or below market rates for renewal. Contract may be extended in 12 or 24-month increments with mutual agreement. + +--- + +## Features + +Metropolitan Life Group will receive the complete Lifellm Enterprise suite: + +1. **Unlimited Policy Administration:** No capacity restrictions, supporting Metropolitan's 50,000+ active policies with scalability to 200,000+ as business expands. + +2. **White-Label Platform:** Complete branding customization: + - Custom domains (life.metropolitanlifegroup.com) + - Branded mobile apps (iOS and Android) with custom UI/UX + - Customized email templates and policyholder communications + - Co-branded agent portals and marketing materials + +3. **Advanced AI Underwriting:** State-of-the-art underwriting capabilities: + - Instant decisioning for 75% of applications up to $1M face amount + - Predictive mortality models trained on 50M+ data points + - Genetic risk assessment integration (when legally permitted) + - Accelerated underwriting with no medical exam for qualified applicants + - Alternative data sources (social media, financial, education) + +4. **Comprehensive Digital Health Integration:** + - Wearable integrations: Apple Watch, Fitbit, Garmin, Oura Ring, Whoop + - Health app integrations: Apple Health, Google Fit, MyFitnessPal + - Biometric screening integration with Quest Diagnostics, LabCorp + - Wellness program platform with challenges, rewards, gamification + - Premium discount automation based on verified health behaviors + - Long-term health trajectory modeling + +5. **Complete API Access:** Unlimited API calls supporting: + - Real-time integration with Metropolitan's core policy admin system + - Agent management system synchronization + - Third-party data provider integrations (MIB, IntelliScript, ExamOne, Clareto, Lifescore) + - Custom workflow automation + - Data warehouse bidirectional sync + +6. **Predictive Analytics Suite:** + - Lapse and persistency prediction modeling + - Cross-sell and upsell opportunity identification + - Agent productivity analytics and optimization + - Competitive positioning and market share analysis + - Lifetime value prediction for policyholders + +7. **Advanced Policy Management:** + - Automatic premium loan processing + - Policy conversion and exchange support + - Dividend calculation and distribution (whole life) + - Non-forfeiture options automation + - Reinstatement processing with re-underwriting + - Settlement options administration + +8. **Agent Network Platform:** + - Support for 5,000+ independent agents and career agents + - Hierarchical permissions and agency structures + - Real-time commission calculation and reporting + - Advanced quoting engine with need analysis tools + - Automated licensing and appointment tracking + - Agent recruiting and onboarding workflows + +9. **Regulatory and Compliance:** + - 50-state compliance monitoring and form filing + - Automated NAIC reporting + - AML (Anti-Money Laundering) screening integration + - OFAC (Office of Foreign Assets Control) checking + - Escheatment and unclaimed property tracking + - Audit trail and compliance reporting + +10. **Blockchain Policy Verification:** + - Immutable policy records on private blockchain + - Fraud prevention through distributed ledger + - Beneficiary verification and claims authentication + - Regulatory audit transparency + +--- + +## Support + +Insurellm commits to comprehensive Enterprise-level support for Metropolitan Life Group: + +1. **Dedicated Success Team:** + - Executive sponsor (C-suite level) with quarterly strategic reviews + - Dedicated Customer Success Director with weekly touchpoints + - Technical Account Manager for system optimization + - Solutions Architect for ongoing innovation projects + - Quarterly executive business reviews with CEO participation + +2. **24/7/365 Premium Support:** + - Guaranteed 15-minute response for P1 critical issues + - 1-hour response for P2 high-priority issues + - 4-hour response for P3 standard issues + - Named support team familiar with Metropolitan's environment + - Private Slack channel with engineering team access + - Annual on-site support visits (minimum 6 per year) + +3. **Comprehensive Implementation:** + - 12-month phased implementation program with 3 major releases + - Dedicated project management office (PMO) with weekly steering committee + - Migration of 50,000+ existing policies from legacy system + - Training for 150+ Metropolitan staff and 500+ top-producing agents + - Parallel processing with legacy system for 90 days + - Go-live support with 8-week on-site Insurellm team presence + +4. **Training and Enablement:** + - Comprehensive training programs for all roles + - Executive briefings on AI and technology trends + - Train-the-trainer certification program + - Annual user conference attendance (5 attendees, all expenses paid) + - Quarterly advanced training sessions + - Unlimited online learning platform access + - Custom training content development + +5. **Custom Development:** + - 2,000 hours annually of custom development included + - Dedicated development team for Metropolitan-specific features + - Priority feature requests with guaranteed roadmap inclusion + - Early access to beta features (90 days before general release) + - Joint innovation projects and co-development opportunities + - Input into product strategy and 3-year roadmap + +6. **Integration Services:** + - Enterprise integration with Metropolitan's systems: + - iPipeline policy administration + - Salesforce CRM and agent management + - DocuSign for digital signatures + - Multiple payment processors + - Data warehouse (Snowflake) + - BI platforms (Tableau, PowerBI) + - Dedicated integration engineer (50% FTE) + - Quarterly integration health assessments + - Performance optimization and load testing + +7. **Analytics and Reporting:** + - Custom executive dashboard development (unlimited) + - Real-time operational dashboards + - Automated reporting suite (daily, weekly, monthly, quarterly, annual) + - Ad-hoc analytics support (unlimited requests) + - Industry benchmarking against top 50 life insurers + - Predictive modeling consultation (quarterly sessions) + - Data science partnership for advanced analytics projects + +8. **Continuous Improvement:** + - Monthly process optimization reviews + - Semi-annual efficiency audits with ROI analysis + - Best practice sharing and peer learning opportunities + - Innovation lab access for emerging technologies (AI, blockchain, quantum computing) + - Joint whitepapers and case studies + - Speaking opportunities at industry conferences + +--- + +**Signatures:** + +_________________________________ +**Jennifer Rodriguez** +**Title**: Chief Executive Officer +**Insurellm, Inc.** +**Date**: April 5, 2025 + +_________________________________ +**Richard Thompson** +**Title**: Chairman & Chief Executive Officer +**Metropolitan Life Group, Inc.** +**Date**: April 5, 2025 + +--- + +This agreement establishes Metropolitan Life Group as Insurellm's flagship enterprise life insurance partner, representing a strategic partnership to transform life insurance through cutting-edge AI technology and innovation. This contract supersedes all prior agreements and communications. diff --git a/week5/knowledge-base/contracts/Contract with National Claims Network for Claimllm.md b/week5/knowledge-base/contracts/Contract with National Claims Network for Claimllm.md new file mode 100644 index 0000000..76fde2c --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with National Claims Network for Claimllm.md @@ -0,0 +1,182 @@ +# Contract with National Claims Network for Claimllm + +**Contract Date:** April 20, 2025 +**Contract Number:** CL-2025-E-0198 +**Parties:** +- Insurellm, Inc. +- National Claims Network Corp. + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide National Claims Network with enterprise access to the Claimllm platform, including white-label capabilities, unlimited claims capacity, advanced fraud analytics, and dedicated infrastructure to support their nationwide claims operations processing over 100,000 claims annually. + +2. **Duration:** This agreement is effective for a period of 36 months from the contract date, representing Insurellm's longest-term contract commitment to date. + +3. **Payment:** National Claims Network shall pay custom Enterprise Tier pricing of $35,000 per month for months 1-12, $38,000 per month for months 13-24, and $41,000 per month for months 25-36, totaling $1,368,000 over the contract term. + +4. **Service Level Agreement:** Insurellm guarantees 99.95% uptime with financial credits for downtime: 5% monthly credit for each hour of unplanned downtime, maximum 50% monthly credit. + +5. **Confidentiality:** Both parties agree to maintain the highest level of confidentiality regarding proprietary AI models, fraud detection algorithms, claimant PII, and business strategies. + +6. **Data Security:** Insurellm commits to maintaining SOC 2 Type II compliance, annual penetration testing, and end-to-end encryption for all data at rest and in transit. + +7. **Business Continuity:** Insurellm provides disaster recovery with 4-hour RTO (Recovery Time Objective) and 1-hour RPO (Recovery Point Objective). + +--- + +## Renewal + +This agreement includes a mutual 120-day renewal notice period. National Claims Network receives guaranteed enterprise pricing for renewal equal to or better than new enterprise customers at renewal time. Contract may be extended in 12-month increments with mutual written agreement. + +--- + +## Features + +National Claims Network will receive the complete Claimllm Enterprise suite: + +1. **Unlimited Claims Processing:** No volume restrictions, supporting National's processing of 100,000+ claims annually with scalability to 500,000+ claims as business grows. + +2. **White-Label Platform:** Complete branding customization including: + - Custom domain names (claims.nationalclaimsnetwork.com) + - Branded mobile apps (iOS and Android) + - Customized email templates and communications + - Co-branded claimant portals + +3. **Advanced Computer Vision:** State-of-the-art damage assessment: + - Auto damage analysis with 95%+ accuracy on repair estimates + - Property damage evaluation (roofs, siding, interiors, water damage) + - Medical imaging analysis for injury claims + - 3D reconstruction from photo sets + - Integration with all major estimating platforms + +4. **Enterprise Fraud Analytics:** + - Real-time fraud scoring on all claims + - Social network analysis across 10+ years of historical data + - Dark web monitoring for organized fraud rings + - Predictive models trained on 5M+ historical claims + - Link analysis connecting related parties, addresses, and claims patterns + - Integration with third-party fraud databases (NICB, ISO ClaimSearch) + +5. **Complete API Access:** Unlimited API calls supporting: + - Real-time integrations with 50+ carrier policy systems + - Bi-directional data sync with National's data warehouse + - Third-party integrations (repair networks, medical providers, legal vendors) + - Custom automation workflows + +6. **AI-Powered Litigation Prediction:** Predictive analytics identifying claims with high litigation probability: + - Attorney involvement prediction (85%+ accuracy) + - Recommended settlement ranges based on jurisdiction and case law + - Early intervention recommendations + - Litigation cost estimation + +7. **Catastrophe Claims Management:** Specialized tools for major events: + - Rapid deployment playbooks for natural disasters + - Geographic clustering and resource allocation + - Emergency vendor network activation + - Real-time damage assessment using satellite imagery + - Mobile claims units with offline capability + +8. **Advanced Vendor Management:** + - Network of 5,000+ preferred vendors nationwide + - AI-powered vendor matching and assignment + - Real-time capacity management and scheduling + - Quality scoring and performance analytics + - Automated vendor payments and 1099 management + +9. **Predictive Analytics Suite:** + - Claim outcome prediction and reserve optimization + - Subrogation opportunity identification + - Salvage value estimation + - Customer satisfaction prediction and intervention triggers + - Cycle time optimization recommendations + +--- + +## Support + +Insurellm commits to comprehensive Enterprise-level support for National Claims Network: + +1. **Dedicated Success Team:** + - Executive sponsor (VP level) with monthly strategic reviews + - Dedicated Customer Success Manager with daily availability + - Technical Account Manager for system optimization + - Solutions Architect for ongoing innovation + - Quarterly executive business reviews with CEO participation + +2. **24/7/365 Premium Support:** + - Guaranteed 10-minute response time for P1 critical issues + - 30-minute response for P2 high-priority issues + - 2-hour response for P3 standard issues + - Dedicated support hotline with no phone tree + - Private Slack channel with engineering team access + - Annual on-site support visits (minimum 4 per year) + +3. **Comprehensive Implementation:** + - 9-month phased implementation program + - Dedicated project management office (PMO) + - Executive steering committee with monthly meetings + - Migration of 3 years of historical claims data + - Training for 200+ National Claims Network staff + - Parallel processing period with legacy system + - Go-live support with on-site Insurellm team (4 weeks) + +4. **Training and Enablement:** + - Role-based training programs (adjusters, supervisors, fraud investigators, IT, executives) + - Train-the-trainer program for National's learning team + - Certification programs with continuing education credits + - Annual user conference attendance (3 attendees) + - Quarterly advanced training sessions + - Unlimited access to online learning platform + +5. **Custom Development:** + - 1,000 hours annually of custom development included + - Dedicated development team for National-specific features + - Priority feature requests with guaranteed roadmap inclusion + - Beta program participation for all new features + - Input into product strategy and direction + +6. **Integration Services:** + - Enterprise integration with National's systems: + - Policy administration systems (multiple carriers) + - Data warehouse and business intelligence platforms + - Financial systems (SAP, Oracle) + - Document management (SharePoint, M-Files) + - Communication platforms (Twilio, SendGrid) + - Ongoing integration support with 4-hour response SLA + - Quarterly integration health assessments + +7. **Analytics and Reporting:** + - Custom executive dashboard development + - Real-time operational dashboards + - Automated daily, weekly, and monthly reporting + - Ad-hoc analytics support (up to 20 requests/month) + - Industry benchmarking and competitive analysis + - Predictive modeling consultation + +8. **Continuous Improvement:** + - Quarterly process optimization workshops + - Annual efficiency audits with recommendations + - Best practice sharing from across Insurellm customer base + - Innovation labs to pilot emerging technologies (AI, blockchain, IoT) + +--- + +**Signatures:** + +_________________________________ +**Jennifer Rodriguez** +**Title**: Chief Executive Officer +**Insurellm, Inc.** +**Date**: April 20, 2025 + +_________________________________ +**Amanda Richardson** +**Title**: President & Chief Operating Officer +**National Claims Network Corp.** +**Date**: April 20, 2025 + +--- + +This agreement represents a strategic partnership between Insurellm and National Claims Network, establishing National Claims Network as Insurellm's flagship enterprise claims processing partner. This contract supersedes all prior agreements and communications. diff --git a/week5/knowledge-base/contracts/Contract with Premier Adjusters Inc. for Claimllm.md b/week5/knowledge-base/contracts/Contract with Premier Adjusters Inc. for Claimllm.md new file mode 100644 index 0000000..9c5ec73 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Premier Adjusters Inc. for Claimllm.md @@ -0,0 +1,123 @@ +# Contract with Premier Adjusters Inc. for Claimllm + +--- + +## Terms + +1. **Agreement Effective Date**: This contract is effective as of February 15, 2025. +2. **Duration**: This agreement will remain in effect for a term of 24 months, concluding on February 14, 2027. +3. **Subscription Type**: Premier Adjusters Inc. agrees to subscribe to the **Advanced Tier** of Claimllm, at a cost of $9,500/month, totaling $228,000 for the duration of this contract. +4. **Claims Volume**: Advanced Tier supports up to 25,000 claims annually. Current baseline estimate: 18,000 claims/year. +5. **Payment Terms**: Payments are due on the 5th of each month via wire transfer. Late payments will incur a penalty of 1.8% per month. +6. **Performance Guarantees**: Insurellm guarantees average FNOL-to-assignment time of less than 2 hours, and fraud detection accuracy of 85%+ on confirmed fraud cases. +7. **Termination Clause**: Either party may terminate this agreement with 60 days' written notice. No early termination fees after 12 months. + +--- + +## Renewal + +1. **Automatic Renewal**: This agreement will automatically renew for an additional 24-month term unless either party provides written notice of non-renewal at least 60 days before expiration. +2. **Pricing Adjustments**: Renewal pricing subject to annual CPI adjustment, maximum 7% increase. +3. **Tier Flexibility**: Premier may move to Enterprise tier at renewal or annually with 45 days' notice and prorated adjustment. + +--- + +## Features + +1. **Access to Advanced Tier Features**: Premier Adjusters Inc. will have access to comprehensive claims automation: + - Intelligent FNOL Processing across all communication channels + - Automated Triage and Routing with ML-based severity assessment + - **Computer Vision Damage Assessment** with photo/video analysis and automated repair cost estimation + - **Predictive Fraud Detection** with network analysis and anomaly detection + - Smart Document Processing with OCR, NLP, and auto-extraction from medical records, police reports, and estimates + - Dynamic Reserve Setting with AI-powered predictive modeling + - Vendor Management Platform with automated assignment and performance tracking + - Payment Automation for approved claims with multi-party splitting + - Claimant Communication Hub with omnichannel messaging + - **Advanced Analytics** with predictive cycle time modeling and settlement optimization + +2. **Computer Vision Capabilities:** + - Automated vehicle damage assessment from photos (dents, scratches, structural damage) + - Property damage evaluation (roof, siding, interior damage) + - Severity scoring and repair cost estimation + - Parts identification and replacement recommendations + - Integration with estimating systems (CCC, Mitchell, Audatex) + +3. **Fraud Analytics:** + - Social network analysis to identify claim rings + - Geospatial anomaly detection + - Behavioral pattern matching against known fraud indicators + - Third-party data enrichment (public records, social media) + - Predictive fraud scoring with explanation rationale + +4. **Vendor Network:** + - Support for 200+ preferred vendors (repair shops, medical providers, attorneys) + - Automated work assignment based on location, capacity, and quality scores + - Real-time vendor availability and scheduling + - Vendor performance dashboards and scorecards + +--- + +## Support + +1. **Technical Support**: Premier Adjusters Inc. will receive priority advanced support: + - Dedicated technical support team + - 24/7 emergency hotline for critical system issues + - Response time: 2 hours critical, 6 hours high priority, 12 hours standard + - Dedicated Slack channel for real-time communication + +2. **Training**: Comprehensive training program: + - 4-week implementation with on-site support (first 2 weeks) + - Training for up to 40 staff members (adjusters, supervisors, IT) + - Role-specific training tracks (FNOL specialists, senior adjusters, fraud investigators) + - Monthly advanced training webinars on new features + - Annual adjuster certification program + - Access to online training portal with 50+ courses + +3. **Updates and Maintenance**: + - Monthly platform updates with new features + - Bi-weekly security patches and improvements + - Maintenance windows: Saturday 11 PM - Sunday 4 AM EST + - 72-hour advance notice for major updates + - Option to delay non-critical updates by up to 30 days + +4. **Account Management**: + - Named Customer Success Manager with weekly check-ins (first 90 days), then bi-weekly + - Quarterly executive business reviews with metrics analysis + - Annual strategic planning session + - Direct escalation path to VP of Customer Success + +5. **Integration Services**: + - Integration with Premier's core systems: + - Guidewire ClaimCenter + - Salesforce CRM + - DocuSign for settlement documents + - Payment processing systems (CheckFree, AvidXchange) + - Custom API development (up to 100 hours included) + - Ongoing integration support and optimization + +6. **Data and Analytics:** + - Custom dashboard development (up to 5 dashboards) + - Monthly data exports in preferred format + - Benchmark comparisons against industry standards + - Predictive analytics consultation sessions + +--- + +**Signatures:** + +_________________________________ +**Sarah Chen** +**Title**: Vice President of Sales +**Insurellm, Inc.** +**Date**: February 15, 2025 + +_________________________________ +**David Kowalski** +**Title**: President & CEO +**Premier Adjusters Inc.** +**Date**: February 15, 2025 + +--- + +*This contract establishes Premier Adjusters Inc. as a strategic partner of Insurellm, leveraging advanced AI-powered claims technology to deliver superior service to their clients while optimizing operational efficiency.* diff --git a/week5/knowledge-base/contracts/Contract with Rapid Claims Associates for Claimllm.md b/week5/knowledge-base/contracts/Contract with Rapid Claims Associates for Claimllm.md new file mode 100644 index 0000000..8def2d2 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Rapid Claims Associates for Claimllm.md @@ -0,0 +1,137 @@ +# Contract with Rapid Claims Associates for Claimllm + +**Contract Date:** March 1, 2025 +**Contract Number:** CL-2025-0063 +**Parties:** +- Insurellm, Inc. +- Rapid Claims Associates, LLC + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide Rapid Claims Associates with access to the Claimllm platform, enabling AI-powered claims processing, automated triage, and streamlined claims management across property, casualty, and auto lines. + +2. **Duration:** This agreement is effective for a period of 12 months from the contract date, with automatic renewal provisions unless terminated with 30-day written notice. + +3. **Payment:** Rapid Claims Associates shall pay a monthly fee of $4,500, due by the 1st of every month for the Core Tier package, supporting up to 5,000 claims annually. + +4. **Overage Fees:** If claims volume exceeds 5,000 annually, overage fees of $0.90 per claim apply, billed quarterly. + +5. **Confidentiality:** Both parties agree to maintain confidentiality of proprietary information, claimant data, and processing methodologies disclosed during this contract. + +6. **Data Ownership:** Rapid Claims Associates retains ownership of all claims data. Insurellm may use anonymized, aggregated data for product improvement. + +7. **Liability:** Insurellm's liability is limited to direct damages not exceeding total fees paid in the preceding 6 months. + +--- + +## Renewal + +Unless either party provides written notice of termination at least 30 days prior to contract expiration, this agreement automatically renews for successive 12-month terms. Pricing may be adjusted annually with 60 days' advance notice, capped at 10% increase per year. + +--- + +## Features + +Rapid Claims Associates will receive the following Core Tier features: + +1. **Intelligent FNOL Processing:** Multi-channel first notice of loss intake via: + - Mobile app integration + - Web portal submission + - Email parsing with NLP extraction + - Phone system integration with IVR + - Chatbot-assisted intake + +2. **Automated Triage and Routing:** Machine learning algorithms assess claim severity and complexity to route appropriately: + - Low-complexity claims → automated processing + - Medium-complexity claims → junior adjuster queue + - High-complexity/high-value claims → senior adjuster assignment + - Fraud-flagged claims → special investigation unit + +3. **Basic Document Processing:** OCR and NLP extraction from: + - Police reports + - Repair estimates + - Medical bills and records + - Witness statements + - Photo and video evidence + +4. **Standard Fraud Detection:** Rule-based fraud screening including: + - Duplicate claim detection + - Unusual pattern identification + - Loss location verification + - Claimant history review + +5. **Claimant Communication Hub:** Automated status updates via: + - SMS text messages + - Email notifications + - Mobile app push notifications + - Scheduled status update intervals + +6. **Basic Reporting:** Standard dashboards tracking: + - Claims volume and cycle time + - Average settlement amounts + - Adjuster productivity metrics + - Customer satisfaction scores + +7. **Payment Processing:** Integration with payment systems for: + - Direct deposit (ACH) + - Check issuance + - Digital wallets (PayPal, Venmo) + - Simple multi-party splits + +--- + +## Support + +Insurellm commits to providing comprehensive support to Rapid Claims Associates: + +1. **Onboarding:** 2-week implementation program including: + - System configuration and data migration + - Training for up to 10 claims staff members (12 hours total) + - Process workflow mapping and optimization + - Integration with existing claims management system + +2. **Technical Support:** + - Email and phone support Monday-Friday 8 AM - 6 PM EST + - Response time: 8 hours for critical issues, 24 hours for standard requests + - Online knowledge base and FAQ access + - Monthly platform office hours for questions + +3. **Platform Updates:** + - Quarterly feature releases with new capabilities + - Monthly security patches and bug fixes + - Release notes and changelog documentation + - Advance notice of breaking changes (minimum 30 days) + +4. **Account Management:** + - Named customer success manager + - Semi-annual business review meetings + - Usage analytics and optimization recommendations + - Assistance with tier upgrade evaluation when volume grows + +5. **Integration Support:** Technical assistance connecting Claimllm with: + - Existing claims management systems + - Policy administration platforms + - Vendor management networks (repair shops, medical providers) + - Payment processing systems + +--- + +**Signatures:** + +_________________________________ +**Sarah Chen** +**Title**: Vice President of Sales +**Insurellm, Inc.** +**Date**: March 1, 2025 + +_________________________________ +**Marcus Johnson** +**Title**: Chief Claims Officer +**Rapid Claims Associates, LLC** +**Date**: March 1, 2025 + +--- + +This agreement represents the complete understanding between Insurellm and Rapid Claims Associates regarding the Claimllm platform and supersedes any prior communications or agreements. diff --git a/week5/knowledge-base/contracts/Contract with SafeHaven Property Insurance for Homellm.md b/week5/knowledge-base/contracts/Contract with SafeHaven Property Insurance for Homellm.md new file mode 100644 index 0000000..8975f11 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with SafeHaven Property Insurance for Homellm.md @@ -0,0 +1,250 @@ +# Contract with SafeHaven Property Insurance for Homellm + +**Contract Date:** May 3, 2025 +**Contract Number:** HM-2025-E-0112 +**Parties:** +- Insurellm, Inc. +- SafeHaven Property Insurance, Inc. + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide SafeHaven Property Insurance with enterprise access to the Homellm platform, including white-label capabilities, unlimited policies, dedicated infrastructure, and comprehensive IoT integration for their property insurance operations. + +2. **Duration:** This agreement is effective for a period of 36 months from the contract date. + +3. **Payment:** SafeHaven Property Insurance shall pay custom Enterprise Tier pricing of $22,000 per month for months 1-12, $24,000 per month for months 13-24, and $26,000 per month for months 25-36, totaling $864,000 over the contract term. + +4. **Policy Volume**: Enterprise Tier supports unlimited policies. SafeHaven currently has 45,000 active homeowners policies across 6 states. + +5. **Service Level Agreement:** Insurellm guarantees 99.9% platform uptime measured monthly, with 7% monthly credit for each 0.1% below target, maximum 35% monthly credit. + +6. **Confidentiality:** Both parties agree to maintain strict confidentiality of proprietary algorithms, property data, pricing models, and business strategies. + +7. **Data Security:** Insurellm maintains SOC 2 Type II compliance, ISO 27001 certification, and annual third-party security audits. + +--- + +## Renewal + +This agreement includes a 90-day mutual renewal notice period. SafeHaven receives most-favored-customer pricing and priority access to new features including climate risk modeling and IoT innovations. + +--- + +## Features + +SafeHaven Property Insurance will receive the complete Homellm Enterprise suite: + +1. **Unlimited Policy Administration:** Full support for SafeHaven's 45,000+ policies with scalability to 200,000+ policies as business expands. + +2. **White-Label Platform:** Complete branding customization: + - Custom domains (quote.safehavenproperty.com, myhome.safehavenproperty.com) + - Branded mobile apps for iOS and Android with custom UI + - Customized policyholder communications + - Co-branded agent and broker portals + +3. **Advanced AI Risk Assessment:** Enterprise-grade property risk analysis: + - Aerial imagery analysis for roof condition, vegetation, and hazards + - Property characteristic assessment (age, construction, square footage) + - Location-based risk scoring (wildfire, flood, hail, wind, earthquake) + - Catastrophe modeling integration (RMS, AIR, CoreLogic) + - Replacement cost estimation with inflation trending + - Neighborhood risk assessment + - Claims history analysis across all carriers + - Credit-based insurance scoring + +4. **Dynamic Pricing Model:** Sophisticated pricing engine: + - Real-time rate optimization across 200+ rating variables + - Competitive intelligence and market positioning + - Capacity-based dynamic pricing + - Multi-policy bundling discounts + - Loyalty and tenure credits + - Loss-free discount automation + - Telematics and IoT-based discounts + +5. **Instant Claims Processing:** Advanced claims automation: + - AI-driven FNOL with photo/video upload + - Computer vision damage assessment + - Automated repair cost estimation + - Contractor network integration with 2,000+ vendors + - Claims routing and assignment + - Payment automation with multi-party splits + - Fraud detection with pattern analysis + - Subrogation opportunity identification + +6. **Predictive Maintenance Alerts:** IoT-powered risk prevention: + - Integration with smart home devices (water leak sensors, smoke detectors, security systems) + - Weather alert system with pre-storm notifications + - Freeze warning and pipe burst prevention + - Roof deterioration monitoring + - HVAC and appliance failure prediction + - Wildfire proximity alerts + - Proactive policyholder communication to prevent losses + +7. **Multi-Channel Integration:** Unified platform: + - Agent portal for 1,500+ independent agents + - Direct-to-consumer quoting engine + - Broker portal with co-branding + - Call center integration + - Web and mobile app quoting + - Smart home device connectivity + +8. **Customer Portal:** Comprehensive self-service: + - 24/7 policy access and document retrieval + - Coverage modification requests + - Digital insurance cards + - Claims submission and tracking + - Payment processing and billing history + - Smart home device management + - Maintenance reminders and home care tips + - Disaster preparedness resources + +9. **IoT Device Integration:** Smart home platform: + - Water leak detection systems (Flo by Moen, Phyn, LeakSmart) + - Smart smoke and CO detectors (Nest Protect, First Alert) + - Security systems (Ring, SimpliSafe, ADT) + - Smart thermostats (Nest, Ecobee) + - Weather stations + - Roof monitoring sensors + - Premium discounts for IoT adoption (up to 20%) + - Real-time alerts and intervention protocols + +10. **Complete API Access:** Unlimited API calls: + - Integration with SafeHaven's core systems + - Third-party data providers (CoreLogic, Verisk, LexisNexis) + - Catastrophe modeling systems + - Agent management platforms + - Payment processors + - Document management systems + - Marketing automation + +11. **Multi-State Operations:** Comprehensive support: + - State-specific forms and endorsements for all 6 states + - Regulatory compliance monitoring + - Rate and form filing management + - Multi-state licensing tracking + - Catastrophe response protocols by state + +12. **Catastrophe Management:** Disaster response tools: + - Pre-CAT modeling and exposure management + - Real-time storm tracking and impact assessment + - CAT claims triage and routing + - Mobile claims team dispatch + - Vendor surge capacity activation + - Policyholder communication campaigns + - Emergency payment processing + +--- + +## Support + +Insurellm commits to comprehensive Enterprise-level support for SafeHaven Property Insurance: + +1. **Dedicated Success Team:** + - VP-level executive sponsor with quarterly strategic reviews + - Senior Customer Success Manager with bi-weekly check-ins + - Technical Account Manager for system optimization + - Solutions Architect for IoT and innovation projects + - Quarterly executive business reviews with detailed metrics + +2. **24/7/365 Premium Support:** + - 20-minute response time for P1 critical issues + - 1-hour response for P2 high-priority issues + - 4-hour response for P3 standard issues + - Dedicated support team familiar with SafeHaven's operations + - Private Slack channel with engineering access + - Emergency CAT response team (15-minute response during disasters) + - Quarterly on-site visits (minimum 4 per year) + +3. **Comprehensive Implementation:** + - 8-month phased implementation program + - Dedicated project manager and PMO + - Migration of 45,000 policies from legacy system + - Training for 60+ staff members + - Agent training program for 200+ top-producing agents + - Parallel processing for 90 days + - Go-live support with 6-week on-site presence + +4. **Training and Enablement:** + - Comprehensive role-based training programs + - Underwriter certification on AI risk assessment + - Claims adjuster training on computer vision tools + - IoT device training for customer service reps + - Train-the-trainer program for SafeHaven's team + - Annual user conference attendance (5 attendees, all expenses) + - Monthly advanced training webinars + - Unlimited online learning platform access + +5. **Custom Development:** + - 1,200 hours annually of custom development + - Dedicated development resources for SafeHaven-specific features + - Priority feature requests with roadmap inclusion + - Beta access to new features 60 days before general release + - Joint innovation projects for smart home and IoT + - Climate risk modeling enhancements + +6. **Integration Services:** + - Enterprise integration with SafeHaven's systems: + - Duck Creek policy administration + - Applied Epic agency management + - Guidewire ClaimCenter + - IoT platforms (Nationwide Canopy, Ting, others) + - Catastrophe modeling (RMS RiskLink) + - Payment processors + - Salesforce CRM + - Dedicated integration engineer (40% FTE) + - Quarterly integration health assessments + - Load testing before peak seasons (hurricane, wildfire) + +7. **Analytics and Reporting:** + - Custom dashboard development (up to 20 dashboards) + - Real-time operational dashboards for underwriting and claims + - Executive dashboards with drill-down analytics + - Automated reporting suite (daily, weekly, monthly, quarterly) + - Ad-hoc analytics support (25 requests/month) + - Industry benchmarking against top property insurers + - Catastrophe exposure and aggregation reporting + - IoT adoption and loss prevention analytics + +8. **IoT and Innovation Support:** + - Quarterly IoT strategy sessions + - New smart home device evaluation and integration + - Pilot programs for emerging technologies + - Consumer education materials for IoT programs + - Marketing campaign support for smart home initiatives + +9. **Catastrophe Response:** + - Dedicated CAT support during disaster events + - Pre-CAT system preparation and testing + - Real-time claims surge capacity support + - Post-CAT analysis and lessons learned + - Annual CAT preparedness drills + +10. **Continuous Improvement:** + - Quarterly process optimization workshops + - Annual efficiency audits with ROI documentation + - Best practice sharing from insurance industry + - Innovation lab for climate risk, AI, and IoT + - Joint whitepapers and case studies + - Speaking opportunities at industry events + +--- + +**Signatures:** + +_________________________________ +**Jennifer Rodriguez** +**Title**: Chief Executive Officer +**Insurellm, Inc.** +**Date**: May 3, 2025 + +_________________________________ +**Laura Mitchell** +**Title**: President & Chief Executive Officer +**SafeHaven Property Insurance, Inc.** +**Date**: May 3, 2025 + +--- + +This agreement establishes SafeHaven Property Insurance as a leading enterprise partner in property insurance innovation, leveraging Homellm's AI, IoT integration, and catastrophe management capabilities. This contract supersedes all prior agreements. diff --git a/week5/knowledge-base/contracts/Contract with Summit Commercial Insurance for Bizllm.md b/week5/knowledge-base/contracts/Contract with Summit Commercial Insurance for Bizllm.md new file mode 100644 index 0000000..a6c6bb5 --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with Summit Commercial Insurance for Bizllm.md @@ -0,0 +1,81 @@ +# Contract with Summit Commercial Insurance for Bizllm + +**Contract Date:** March 15, 2025 +**Contract Number:** BZ-2025-0091 +**Parties:** +- Insurellm, Inc. +- Summit Commercial Insurance, LLC + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide Summit Commercial Insurance with access to the Bizllm platform, enabling comprehensive commercial insurance underwriting and policy administration across multiple lines including general liability, professional liability, and property coverage. + +2. **Duration:** This agreement is effective for a period of 18 months from the contract date, after which it will automatically renew unless terminated by either party with a written 45-day notice. + +3. **Payment:** Summit Commercial Insurance shall pay a monthly fee of $6,000, due by the 1st of every month for the Business Tier package. + +4. **Confidentiality:** Both parties agree to maintain strict confidentiality of proprietary information, trade secrets, and customer data disclosed during the execution of this contract. + +5. **Liability:** Insurellm's liability under this agreement shall be limited to direct damages and shall not exceed the total fees paid by Summit Commercial Insurance in the last 12 months prior to the date of the claim. + +6. **Data Security:** Insurellm commits to maintaining industry-standard encryption and security protocols to protect all client data processed through the Bizllm platform. + +--- + +## Renewal + +Unless either party provides a written notice of termination at least 45 days prior to the expiration of the contract term, this agreement will automatically renew for an additional 18-month term under the same terms and conditions, subject to pricing adjustments communicated at least 90 days in advance. + +--- + +## Features + +Summit Commercial Insurance will receive the following features with Bizllm Business Tier: + +1. **Multi-Line Underwriting Engine:** Comprehensive underwriting platform supporting general liability, professional liability, property, and workers' compensation insurance products. + +2. **Business Intelligence Integration:** Automated data gathering from business registries, financial databases, and industry reports for streamlined underwriting. + +3. **Commercial Property Evaluation:** Advanced property risk modeling incorporating location data, building characteristics, and catastrophe modeling. + +4. **Workers' Compensation Optimization:** Integrated tools analyzing payroll data, industry classifications, and claims history for accurate pricing. + +5. **Portfolio Management Dashboard:** Analytics providing line-of-business performance, loss ratios, and renewal rates insights. + +6. **Agent and Broker Portal:** Dedicated portals enabling distribution partners to quote, bind, and service policies efficiently. + +--- + +## Support + +Insurellm commits to providing comprehensive support to Summit Commercial Insurance, which includes: + +1. **Onboarding:** A 3-week intensive training program for up to 15 Summit Commercial staff members to ensure effective use of Bizllm, including hands-on workshops and certification. + +2. **Dedicated Support Team:** A dedicated account manager and technical support team available Monday-Friday 7 AM to 7 PM EST, with emergency support available 24/7. + +3. **Regular Updates:** Insurellm will provide quarterly platform updates and enhancements, including new features, security improvements, and regulatory compliance updates. + +4. **Integration Assistance:** Technical support for integrating Bizllm with Summit's existing policy administration and claims management systems. + +5. **Performance Reviews:** Quarterly business reviews to assess platform utilization, identify optimization opportunities, and discuss strategic initiatives. + +--- + +**Signatures:** + +_________________________________ +**Michael Torres** +**Title**: Chief Revenue Officer +**Insurellm, Inc.** + +_________________________________ +**Patricia Lawson** +**Title**: Vice President of Operations +**Summit Commercial Insurance, LLC** + +--- + +This agreement represents the complete understanding of both parties regarding the use of the Bizllm product and supersedes any prior agreements or communications. diff --git a/week5/knowledge-base/contracts/Contract with United Healthcare Alliance for Healthllm.md b/week5/knowledge-base/contracts/Contract with United Healthcare Alliance for Healthllm.md new file mode 100644 index 0000000..a7bda2c --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with United Healthcare Alliance for Healthllm.md @@ -0,0 +1,257 @@ +# Contract with United Healthcare Alliance for Healthllm + +**Contract Date:** May 15, 2025 +**Contract Number:** HL-2025-E-0156 +**Parties:** +- Insurellm, Inc. +- United Healthcare Alliance, LLC + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide United Healthcare Alliance with enterprise access to the Healthllm platform, including extensive customization, multi-state support, dedicated infrastructure, and premium support services for their nationwide health insurance operations covering 250,000+ members across 12 states. + +2. **Duration:** This agreement is effective for a period of 48 months from the contract date, representing Insurellm's most strategic long-term healthcare partnership. + +3. **Payment:** United Healthcare Alliance shall pay custom Enterprise Tier pricing of $52,000 per month for months 1-12, $56,000 per month for months 13-24, $60,000 per month for months 25-36, and $64,000 per month for months 37-48, totaling $2,784,000 over the contract term. + +4. **Service Level Agreement:** Insurellm guarantees 99.95% uptime measured monthly, with financial credits: 7% monthly credit for each hour of unplanned downtime exceeding the SLA, maximum 50% monthly credit. + +5. **Confidentiality:** Both parties agree to maintain the highest level of confidentiality regarding AI algorithms, predictive models, member PHI, provider contracts, and strategic initiatives. + +6. **Security and Compliance:** Insurellm commits to maintaining HIPAA compliance, HITRUST certification, SOC 2 Type II, and annual third-party security audits with penetration testing. + +7. **Business Continuity:** Guaranteed 2-hour recovery time objective (RTO) and 15-minute recovery point objective (RPO) with active-active data center configuration. + +--- + +## Renewal + +This agreement includes a mutual 120-day renewal notice period. United Healthcare Alliance receives most-favored-customer pricing guarantees and priority access to all new features. Contract may be extended in 12 or 24-month increments with mutual written agreement and will include joint planning for next-generation healthcare technology initiatives. + +--- + +## Features + +United Healthcare Alliance will receive the complete Healthllm Enterprise suite with custom enhancements: + +1. **Unlimited Member Administration:** No capacity restrictions, supporting United's 250,000+ members with scalability to 1 million+ members as business expands. + +2. **Multi-State Operations:** Complete support for health insurance administration across all 12 states where United operates: + - State-specific plan designs and benefit variations + - 50-state regulatory compliance monitoring + - Multi-state network adequacy reporting + - State-specific form filing and rate approvals + - Local market competitive intelligence + +3. **White-Label Platform:** Complete branding customization: + - Custom domains (members.unitedhealthcarealliance.com, providers.unitedhealthcarealliance.com) + - Branded mobile apps for iOS and Android with full UI/UX customization + - Customized member communications and marketing materials + - Co-branded provider portals and broker platforms + +4. **Advanced AI Claims Adjudication:** Enterprise-grade claims processing: + - Auto-adjudication rate of 85%+ for clean claims + - Real-time fraud detection with 92%+ accuracy + - Predictive payment integrity scoring + - Provider billing pattern analysis + - Upcoding and unbundling detection + - Coordination of benefits (COB) automation + - Subrogation opportunity identification + +5. **Population Health Management:** Comprehensive analytics for value-based care: + - Real-time risk stratification across entire member population + - Chronic disease identification and progression modeling + - Predictive analytics for 30+ conditions + - Hospital readmission prevention protocols + - Care gap analysis for HEDIS and STAR measures + - Social determinants of health (SDOH) integration + - Health equity analytics and disparity identification + +6. **Complete API Access:** Unlimited API calls supporting: + - Real-time integration with United's core systems + - Provider portal and directory synchronization + - Claims clearinghouse EDI transactions (837, 835, 834, 270/271) + - Pharmacy benefit manager (PBM) integration + - Health Information Exchange (HIE) connectivity + - Data warehouse bidirectional sync + - Third-party vendor integrations (lab systems, imaging centers, etc.) + +7. **Advanced Provider Network Management:** + - Credentialing and re-credentialing for 15,000+ providers + - Contract management with version control and approval workflows + - Provider performance scorecards and tiering + - Network adequacy modeling and gap analysis + - Cost and quality analytics by provider + - Center of Excellence (COE) program management + - Value-based payment arrangement tracking + +8. **Comprehensive Medication Management:** + - Multi-tier formulary management (national and state-specific) + - Prior authorization with clinical decision support + - Step therapy and quantity limit enforcement + - Specialty pharmacy program management + - Biosimilar and generic substitution optimization + - Medication adherence prediction and intervention + - Drug spend forecasting and budgeting + - Manufacturer rebate tracking and optimization + +9. **Member Engagement Suite:** + - Omnichannel communication platform (SMS, email, push, voice) + - Personalized member portal with AI-powered chatbot + - Telehealth platform integration (video visits, e-consults) + - Preventive care reminder campaigns + - Wellness program platform with incentives and gamification + - Member satisfaction surveying and feedback analysis + - Health literacy tools and educational content + +10. **Quality and Performance Management:** + - HEDIS measure calculation and reporting + - STAR ratings optimization for Medicare Advantage + - CAHPS survey administration and analysis + - Quality improvement initiative tracking + - Provider pay-for-performance administration + - Shared savings calculation for ACO programs + - Risk adjustment and HCC coding validation + +11. **Advanced Analytics and Business Intelligence:** + - Real-time operational dashboards for all user roles + - Executive dashboards with KPIs and trend analysis + - Predictive modeling for medical cost trends + - Member churn and retention analysis + - Market share and competitive positioning reports + - Financial forecasting and budget variance analysis + - Custom analytics with embedded data science team support + +12. **Regulatory Compliance and Reporting:** + - ACA compliance monitoring (MLR, essential health benefits, preventive services) + - Multi-state regulatory filing management + - CMS reporting for Medicare Advantage products + - State insurance department examinations support + - HIPAA privacy and security compliance with audit trails + - Fraud, waste, and abuse (FWA) detection and reporting + - Annual compliance certification and attestation + +--- + +## Support + +Insurellm commits to comprehensive Enterprise-level support for United Healthcare Alliance: + +1. **Dedicated Success Team:** + - Executive sponsor (CEO-level) with monthly strategic reviews + - Dedicated Vice President of Customer Success with weekly engagement + - Senior Technical Account Manager for system optimization + - Solutions Architect team for ongoing innovation (2 FTE) + - Data Science team for advanced analytics projects (1.5 FTE) + - Quarterly executive business reviews with C-suite from both companies + +2. **24/7/365 Premium Support:** + - Guaranteed 10-minute response for P1 critical issues + - 30-minute response for P2 high-priority issues + - 2-hour response for P3 standard issues + - Dedicated support team of 8+ specialists familiar with United's environment + - Private war room for incident management + - Dedicated Slack and Microsoft Teams channels with engineering access + - Quarterly on-site support visits (minimum 8 per year) + +3. **Comprehensive Implementation:** + - 18-month phased implementation program with 4 major go-live events + - Dedicated program management office (PMO) with weekly steering committee + - Migration of 250,000+ member records from legacy systems + - Integration with 15,000+ provider records and credentialing data + - Training for 300+ United staff across all departments + - Training for 100+ key provider practices and health systems + - Parallel processing with legacy system for 120 days + - Go-live support with 12-week on-site Insurellm team presence (6-10 people) + +4. **Training and Enablement:** + - Comprehensive role-based training programs for all staff + - Executive leadership training on healthcare AI and analytics + - Train-the-trainer certification for United's L&D team + - Annual user conference attendance (10 attendees, all expenses paid) + - Monthly advanced training sessions on new features + - Unlimited online learning platform access with 200+ courses + - Custom training content development for United-specific processes + - Clinical training for care management and utilization review staff + +5. **Custom Development:** + - 3,000 hours annually of custom development included + - Dedicated development team (4 developers, 1 PM, 1 QA) for United-specific features + - Priority feature requests with guaranteed product roadmap inclusion + - Beta access to all new features 120 days before general release + - Joint innovation projects and co-development opportunities + - Input into 5-year product strategy and roadmap + - Potential for United to influence industry direction through partnership + +6. **Integration Services:** + - Enterprise integration with United's complex system landscape: + - HealthEdge iBridge policy administration + - Epic care management system integration + - Salesforce Health Cloud CRM + - Multiple PBMs (CVS Caremark, Express Scripts) + - Availity and Change Healthcare clearinghouses + - Truven Health Analytics + - Provider credentialing systems (CAQH ProView) + - NCQA HEDIS reporting systems + - Financial systems (Oracle ERP, Workday) + - Data lake on Snowflake and AWS + - Dedicated integration team (2 FTE integration engineers) + - Monthly integration health assessments with optimization + - Performance tuning and load testing before peak periods (open enrollment) + +7. **Analytics and Reporting:** + - Custom dashboard development (unlimited) + - Real-time operational dashboards for claims, enrollment, utilization + - Executive dashboards with drill-down capabilities + - Automated reporting suite (daily, weekly, monthly, quarterly, annual) + - Ad-hoc analytics support (unlimited requests with 48-hour SLA) + - Industry benchmarking against top 100 health plans + - Predictive modeling consultation (bi-weekly sessions) + - Embedded data science team for advanced projects + - Access to Insurellm's proprietary healthcare benchmarking database + +8. **Compliance and Regulatory Support:** + - Monthly compliance reviews with detailed documentation + - Annual HIPAA security risk assessment with remediation planning + - Annual HITRUST assessment support + - ACA reporting (1094/1095 forms, MLR calculations, essential health benefits) + - Medicare Advantage CMS reporting support + - Multi-state regulatory filing coordination + - Quality measure calculation (HEDIS, STAR, CAHPS) + - External audit support (state exams, CMS audits, accreditation) + - Fraud, waste, and abuse program support + +9. **Continuous Improvement:** + - Bi-weekly process optimization workshops + - Quarterly efficiency audits with detailed ROI analysis + - Best practice sharing from across Insurellm's customer base + - Innovation lab partnership for emerging technologies: + - AI and machine learning advancements + - Blockchain for claims and credentialing + - Natural language processing for clinical documentation + - Predictive analytics for population health + - Joint whitepapers and case studies for industry publication + - Speaking opportunities at major industry conferences (AHIP, HIMSS) + - Collaboration on thought leadership and regulatory advocacy + +--- + +**Signatures:** + +_________________________________ +**Jennifer Rodriguez** +**Title**: Chief Executive Officer +**Insurellm, Inc.** +**Date**: May 15, 2025 + +_________________________________ +**James Patterson** +**Title**: Chairman & Chief Executive Officer +**United Healthcare Alliance, LLC** +**Date**: May 15, 2025 + +--- + +This agreement establishes United Healthcare Alliance as Insurellm's flagship enterprise health insurance partner, representing the most comprehensive strategic partnership in Insurellm's history. This partnership will drive innovation in healthcare technology and set new standards for AI-powered health insurance operations. This contract supersedes all prior agreements and communications. diff --git a/week5/knowledge-base/contracts/Contract with WellCare Insurance Co. for Healthllm.md b/week5/knowledge-base/contracts/Contract with WellCare Insurance Co. for Healthllm.md new file mode 100644 index 0000000..1f0fc0e --- /dev/null +++ b/week5/knowledge-base/contracts/Contract with WellCare Insurance Co. for Healthllm.md @@ -0,0 +1,166 @@ +# Contract with WellCare Insurance Co. for Healthllm + +**Contract Date:** March 8, 2025 +**Contract Number:** HL-2025-0021 +**Parties:** +- Insurellm, Inc. +- WellCare Insurance Co. + +--- + +## Terms + +1. **Coverage:** Insurellm agrees to provide WellCare Insurance Co. with access to the Healthllm platform, enabling comprehensive health insurance operations including plan design, eligibility verification, claims adjudication, and member engagement. + +2. **Duration:** This agreement is effective for a period of 12 months from the contract date, with automatic renewal unless terminated with 30-day written notice. + +3. **Payment:** WellCare Insurance Co. shall pay a monthly fee of $8,000, due by the 1st of every month for the Essential Tier package. + +4. **Member Coverage**: Essential Tier supports health insurance administration for up to 15,000 covered members. Current enrollment: 11,000 members. + +5. **Confidentiality:** Both parties agree to maintain strict confidentiality of proprietary information, protected health information (PHI), and member data in compliance with HIPAA regulations. + +6. **HIPAA Compliance:** Insurellm operates as a Business Associate and maintains full HIPAA compliance including Business Associate Agreement (BAA). + +7. **Liability:** Insurellm's liability is limited to direct damages not exceeding the total fees paid in the preceding 12 months. + +--- + +## Renewal + +Unless either party provides written notice of termination at least 30 days prior to contract expiration, this agreement automatically renews for successive 12-month terms. Pricing may be adjusted annually with 60 days' advance notice, capped at 10% increase per year. + +--- + +## Features + +WellCare Insurance Co. will receive the following Essential Tier features: + +1. **Intelligent Plan Design:** AI-powered tools for creating competitive health plans: + - Market trend analysis and competitive benchmarking + - Member demographic analysis + - Healthcare utilization pattern analysis + - Benefit structure optimization + - Deductible and copay modeling + - Network configuration recommendations + +2. **Real-Time Eligibility Verification:** Instant coverage verification: + - Provider network integration for real-time checking + - Member status validation + - Coverage level confirmation + - Dependent eligibility verification + - Prior authorization status + - Reduces claim denials due to eligibility issues + +3. **AI-Driven Claims Adjudication:** Automated claims processing: + - Machine learning-based claims review + - Fraud detection and prevention + - Payment accuracy verification + - Auto-adjudication for clean claims + - Denial management and appeals tracking + - Learning from historical claim patterns + +4. **Provider Network Management:** Comprehensive network administration: + - Provider contract management + - Credentialing and re-credentialing workflows + - Network adequacy monitoring + - Provider performance metrics + - Contract negotiation analytics + - Cost savings opportunity identification + +5. **Member Engagement Platform:** Mobile-first member portal: + - Digital ID cards + - Claims status tracking + - Provider directory search + - Coverage information access + - Telehealth service integration + - Push notifications for preventive care + +6. **Basic Medication Management:** + - Formulary management tools + - Prior authorization workflows + - Generic substitution recommendations + - Drug interaction checking + - Medication adherence tracking + +7. **Regulatory Compliance Engine:** + - ACA (Affordable Care Act) compliance monitoring + - State mandate tracking + - HIPAA privacy and security compliance + - Automated regulatory reporting + - Form filing and approval tracking + +8. **Standard Analytics:** Dashboards tracking: + - Claims volume and processing time + - Medical loss ratio (MLR) + - Member satisfaction scores + - Provider network utilization + - Cost per member per month (PMPM) + +--- + +## Support + +Insurellm commits to providing comprehensive support to WellCare Insurance Co.: + +1. **Onboarding:** 4-week implementation program including: + - System configuration for WellCare's plan designs + - Training for up to 15 staff members (30 hours total) + - Integration with existing systems (policy admin, provider network) + - Data migration for current 11,000 members + - Provider network data import and validation + +2. **Technical Support:** + - Email and phone support Monday-Friday 7 AM - 7 PM EST + - Response time: 8 hours for critical issues, 24 hours for standard + - Online ticketing system with status tracking + - Knowledge base and FAQ library + - Video tutorials and user guides + +3. **Platform Updates:** + - Quarterly feature releases with new capabilities + - Monthly security patches and bug fixes + - Regulatory compliance updates as laws change + - Release notes and upgrade documentation + - Advance notice of updates (minimum 21 days) + +4. **Account Management:** + - Named customer success manager + - Quarterly business review meetings + - Usage analytics and optimization recommendations + - Industry trends and best practices sharing + - Assistance with tier upgrade evaluation + +5. **Compliance Support:** + - Annual HIPAA compliance review + - Regulatory update notifications + - State insurance department filing assistance + - ACA reporting support (1095 forms) + - Audit preparation assistance + +6. **Integration Support:** Technical assistance connecting Healthllm with: + - Existing policy administration systems + - Provider credentialing systems + - Pharmacy benefit managers (PBMs) + - Clearinghouses for EDI transactions + - Member portal and mobile apps + +--- + +**Signatures:** + +_________________________________ +**Sarah Chen** +**Title**: Vice President of Sales +**Insurellm, Inc.** +**Date**: March 8, 2025 + +_________________________________ +**Dr. Raymond Foster** +**Title**: Chief Medical Officer & COO +**WellCare Insurance Co.** +**Date**: March 8, 2025 + +--- + +This agreement enables WellCare Insurance Co. to modernize their health insurance operations with AI-powered technology while ensuring regulatory compliance and improved member outcomes. diff --git a/week5/knowledge-base/employees/Alex Chen.md b/week5/knowledge-base/employees/Alex Chen.md index f25b9d4..bd915df 100644 --- a/week5/knowledge-base/employees/Alex Chen.md +++ b/week5/knowledge-base/employees/Alex Chen.md @@ -3,9 +3,10 @@ # Alex Chen ## Summary -- **Date of Birth:** March 15, 1990 -- **Job Title:** Backend Software Engineer -- **Location:** San Francisco, California +- **Date of Birth:** March 15, 1990 +- **Job Title:** Backend Software Engineer +- **Location:** San Francisco, California +- **Current Salary:** $115,000 ## Insurellm Career Progression - **April 2020:** Joined Insurellm as a Junior Backend Developer. Focused on building APIs to enhance customer data security. diff --git a/week5/knowledge-base/employees/Alex Harper.md b/week5/knowledge-base/employees/Alex Harper.md index 7e78f2a..4ced055 100644 --- a/week5/knowledge-base/employees/Alex Harper.md +++ b/week5/knowledge-base/employees/Alex Harper.md @@ -3,9 +3,10 @@ # Alex Harper ## Summary -- **Date of Birth**: March 15, 1993 -- **Job Title**: Sales Development Representative (SDR) -- **Location**: Denver, Colorado +- **Date of Birth**: March 15, 1993 +- **Job Title**: Sales Development Representative (SDR) +- **Location**: Denver, Colorado +- **Current Salary**: $75,000 ## Insurellm Career Progression - **July 2021**: Joined Insurellm as a Sales Development Representative, focusing on lead generation and nurturing B2B relationships. diff --git a/week5/knowledge-base/employees/Alex Thomson.md b/week5/knowledge-base/employees/Alex Thomson.md index 0731963..acbed67 100644 --- a/week5/knowledge-base/employees/Alex Thomson.md +++ b/week5/knowledge-base/employees/Alex Thomson.md @@ -3,9 +3,10 @@ # Alex Thomson ## Summary -- **Date of Birth:** March 15, 1995 -- **Job Title:** Sales Development Representative (SDR) -- **Location:** Austin, Texas +- **Date of Birth:** March 15, 1995 +- **Job Title:** Sales Development Representative (SDR) +- **Location:** Austin, Texas +- **Current Salary:** $65,000 ## Insurellm Career Progression - **November 2022** - Joined Insurellm as a Sales Development Representative. Alex Thomson quickly adapted to the team, demonstrating exceptional communication and rapport-building skills. diff --git a/week5/knowledge-base/employees/Amanda Foster.md b/week5/knowledge-base/employees/Amanda Foster.md new file mode 100644 index 0000000..c1440f9 --- /dev/null +++ b/week5/knowledge-base/employees/Amanda Foster.md @@ -0,0 +1,67 @@ +# HR Record + +# Amanda Foster + +## Summary +- **Date of Birth:** August 14, 1982 +- **Job Title:** HR Business Partner +- **Location:** San Francisco, California +- **Current Salary:** $115,000 + +## Insurellm Career Progression +- **September 2016 - Present:** HR Business Partner + - Partners with engineering and product leadership teams + - Supports 85 employees across multiple departments + - Leads talent acquisition strategy for technical roles + - Drives performance management and organizational development initiatives + +- **March 2013 - August 2016:** Senior HR Generalist at TechVentures Inc. + - Managed full-cycle HR operations for 150-person startup + - Led benefits administration and employee relations + +- **June 2009 - February 2013:** HR Coordinator at Enterprise Corp. + - Supported recruiting and onboarding processes + - Assisted with HR compliance and policy implementation + +## Annual Performance History +- **2023:** Rating: 4.5/5 + *Strong performance. Successfully led diversity hiring initiative resulting in 40% increase in underrepresented candidates. Effective partner to leadership.* + +- **2022:** Rating: 4.3/5 + *Exceeded expectations. Implemented new performance review system well-received by employees. Strong employee relations skills.* + +- **2021:** Rating: 4.0/5 + *Good performance. Managed challenging employee situations effectively. Supported smooth remote-to-hybrid transition.* + +- **2020:** Rating: 3.7/5 + *Solid performance during unprecedented pandemic challenges. Helped navigate layoffs and policy changes with empathy.* + +- **2019:** Rating: 4.2/5 + *Strong year. Improved hiring velocity for engineering roles by 35%. Built strong relationships with department heads.* + +- **2018:** Rating: 4.1/5 + *Good performance. Successfully scaled HR support as company grew rapidly.* + +- **2017:** Rating: 3.9/5 + *Solid first full year. Learning Insurellm culture and building credibility with leadership.* + +- **2016:** Rating: 3.8/5 + *Good onboarding. Quick to understand business needs and HR priorities.* + +## Compensation History +- **2023:** Base Salary: $115,000 + Bonus: $12,000 +- **2022:** Base Salary: $110,000 + Bonus: $10,000 +- **2021:** Base Salary: $105,000 + Bonus: $8,000 +- **2020:** Base Salary: $100,000 + Bonus: $5,000 +- **2019:** Base Salary: $96,000 + Bonus: $8,000 +- **2018:** Base Salary: $92,000 + Bonus: $7,000 +- **2017:** Base Salary: $88,000 + Bonus: $6,000 +- **2016:** Base Salary: $85,000 + +## Other HR Notes +- **Education:** MS in Human Resources Management from Cornell University, BA in Psychology from UCLA +- **Certifications:** SHRM-SCP, Certified Diversity Professional +- **Recognition:** HR Excellence Award 2023 for diversity initiatives +- **Leadership:** Chairs Insurellm's Diversity & Inclusion committee +- **Skills:** Expert in talent acquisition, performance management, employee relations, and organizational development. Strong coach and advisor to leadership. +- **Feedback:** Trusted HR partner with excellent judgment and discretion. Strategic thinker who balances employee advocacy with business needs. Strong emotional intelligence. diff --git a/week5/knowledge-base/employees/Avery Lancaster.md b/week5/knowledge-base/employees/Avery Lancaster.md index 470624d..0e1dd41 100644 --- a/week5/knowledge-base/employees/Avery Lancaster.md +++ b/week5/knowledge-base/employees/Avery Lancaster.md @@ -1,9 +1,10 @@ # Avery Lancaster ## Summary -- **Date of Birth**: March 15, 1985 -- **Job Title**: Co-Founder & Chief Executive Officer (CEO) -- **Location**: San Francisco, California +- **Date of Birth**: March 15, 1985 +- **Job Title**: Co-Founder & Chief Executive Officer (CEO) +- **Location**: San Francisco, California +- **Current Salary**: $225,000 ## Insurellm Career Progression - **2015 - Present**: Co-Founder & CEO diff --git a/week5/knowledge-base/employees/Brandon Walker.md b/week5/knowledge-base/employees/Brandon Walker.md new file mode 100644 index 0000000..709d8bb --- /dev/null +++ b/week5/knowledge-base/employees/Brandon Walker.md @@ -0,0 +1,47 @@ +# HR Record + +# Brandon Walker + +## Summary +- **Date of Birth:** December 5, 1993 +- **Job Title:** Technical Support Specialist +- **Location:** Remote (Based in Phoenix, Arizona) +- **Current Salary:** $62,000 + +## Insurellm Career Progression +- **October 2021 - Present:** Technical Support Specialist + - Provides tier 2/3 technical support for all Insurellm products + - Troubleshoots complex technical issues for enterprise clients + - Creates knowledge base articles and internal documentation + - Maintains 4.8/5 customer satisfaction rating + +- **May 2019 - September 2021:** Support Specialist at TechHelp Inc. + - Handled tier 1 technical support for SaaS products + - Resolved customer issues via phone, email, and chat + +- **January 2018 - April 2019:** IT Help Desk Technician at Regional Bank + - Provided internal IT support for bank employees + - Managed ticket queue and resolved hardware/software issues + +## Annual Performance History +- **2023:** Rating: 3.3/5 + *Meets basic expectations. Good technical knowledge but customer satisfaction scores declined slightly. Needs to improve empathy in customer interactions.* + +- **2022:** Rating: 2.9/5 + *Below expectations. Struggled with increased ticket volume and complex escalations. Response times often exceeded SLA targets. Enrolled in customer service training.* + +- **2021:** Rating: 3.6/5 + *Adequate performance for a new hire. Good technical skills but needs to develop better troubleshooting methodology.* + +## Compensation History +- **2023:** Base Salary: $62,000 + Bonus: $1,500 +- **2022:** Base Salary: $60,000 + Bonus: $0 (no bonus due to performance) +- **2021:** Base Salary: $58,000 (started mid-year) + +## Other HR Notes +- **Education:** Associate Degree in Information Technology from Phoenix Community College +- **Certifications:** CompTIA A+, working toward Network+ certification +- **Performance Improvement Plan:** Currently on 90-day PIP (started August 2023) focusing on response time improvements and customer communication skills +- **Skills:** Strong in system troubleshooting, SQL basics, and API debugging. Needs improvement in soft skills and time management. +- **Development:** Working with manager on structured troubleshooting approach and empathetic customer communication +- **Feedback:** Technically competent but struggles under pressure. Tends to focus on technical details rather than customer experience. Improving but needs consistent focus. diff --git a/week5/knowledge-base/employees/Carlos Rodriguez.md b/week5/knowledge-base/employees/Carlos Rodriguez.md new file mode 100644 index 0000000..0580c70 --- /dev/null +++ b/week5/knowledge-base/employees/Carlos Rodriguez.md @@ -0,0 +1,55 @@ +# HR Record + +# Carlos Rodriguez + +## Summary +- **Date of Birth:** October 12, 1989 +- **Job Title:** Solutions Engineer +- **Location:** Remote (Based in Miami, Florida) +- **Current Salary:** $125,000 + +## Insurellm Career Progression +- **June 2019 - Present:** Solutions Engineer + - Provides technical expertise during enterprise sales cycles + - Conducts product demonstrations and proof-of-concept implementations + - Works closely with sales team to close complex deals + - Supports post-sale technical implementation and integration + +- **March 2017 - May 2019:** Senior Support Engineer at CloudTech Solutions + - Led technical support team for enterprise SaaS platform + - Handled escalated technical issues and customer implementations + +- **January 2015 - February 2017:** Application Developer at FinTech Systems + - Developed financial software applications + - Worked with Java, Python, and SQL technologies + +## Annual Performance History +- **2023:** Rating: 4.9/5 + *Outstanding performance. Instrumental in closing $3M in new business. Excellent technical presentations and client relationships.* + +- **2022:** Rating: 4.6/5 + *Exceeded expectations. Strong technical demos that significantly influenced win rates. Great collaboration with sales team.* + +- **2021:** Rating: 4.4/5 + *Solid performance with good sales support. Needed to improve documentation of technical solutions.* + +- **2020:** Rating: 3.9/5 + *Good performance during challenging remote transition. Some gaps in product knowledge addressed through training.* + +- **2019:** Rating: 4.1/5 + *Strong start as Solutions Engineer. Quick learner with excellent technical communication skills.* + +## Compensation History +- **2023:** Base Salary: $125,000 + Bonus: $22,000 +- **2022:** Base Salary: $118,000 + Bonus: $18,000 +- **2021:** Base Salary: $110,000 + Bonus: $14,000 +- **2020:** Base Salary: $102,000 + Bonus: $10,000 +- **2019:** Base Salary: $95,000 + Bonus: $8,000 + +## Other HR Notes +- **Education:** MS in Computer Science from University of Florida +- **Certifications:** AWS Solutions Architect Professional, Salesforce Certified Technical Architect +- **Languages:** Fluent in English and Spanish, frequently supports Latin American clients +- **Recognition:** Solutions Engineer of the Year 2023 +- **Skills:** Expert in API integrations, cloud architecture, and technical sales. Strong presenter with ability to explain complex concepts to non-technical audiences. +- **Feedback:** Top performer who combines deep technical knowledge with excellent sales acumen. Natural leader who mentors junior SEs. diff --git a/week5/knowledge-base/employees/Daniel Park.md b/week5/knowledge-base/employees/Daniel Park.md new file mode 100644 index 0000000..58fc1f2 --- /dev/null +++ b/week5/knowledge-base/employees/Daniel Park.md @@ -0,0 +1,51 @@ +# HR Record + +# Daniel Park + +## Summary +- **Date of Birth:** October 3, 1991 +- **Job Title:** QA Engineer +- **Location:** Austin, Texas +- **Current Salary:** $88,000 + +## Insurellm Career Progression +- **September 2020 - Present:** QA Engineer + - Develops and executes test plans for all Insurellm products + - Builds automated test suites using Selenium and Cypress + - Manages bug tracking and quality metrics + - Collaborates with development teams on quality standards + +- **May 2018 - August 2020:** QA Analyst at SoftwareTest Inc. + - Performed manual and automated testing for web applications + - Created test documentation and test cases + +- **July 2016 - April 2018:** Junior QA Tester at AppQuality Corp. + - Conducted manual testing and regression testing + - Learned QA fundamentals and testing methodologies + +## Annual Performance History +- **2023:** Rating: 4.2/5 + *Strong performance. Expanded test automation coverage to 65%. Found critical bugs preventing major production issues.* + +- **2022:** Rating: 3.9/5 + *Good performance. Improved automated testing but missed some edge cases in major release. Learning from experience.* + +- **2021:** Rating: 4.0/5 + *Solid year. Successfully implemented new test framework. Good collaboration with engineering teams.* + +- **2020:** Rating: 3.7/5 + *Adequate performance in first year. Building knowledge of products and establishing testing processes.* + +## Compensation History +- **2023:** Base Salary: $88,000 + Bonus: $7,000 +- **2022:** Base Salary: $82,000 + Bonus: $5,000 +- **2021:** Base Salary: $76,000 + Bonus: $6,000 +- **2020:** Base Salary: $72,000 + Bonus: $3,000 + +## Other HR Notes +- **Education:** BS in Computer Science from Texas State University +- **Certifications:** ISTQB Certified Tester, Selenium WebDriver Certified +- **Skills:** Expert in test automation (Selenium, Cypress, Jest), API testing, performance testing, CI/CD integration +- **Professional Development:** Completed Advanced Test Automation workshop (2023). Learning performance testing tools. +- **Recognition:** Quality Champion Award 2023 for preventing critical production bug +- **Feedback:** Detail-oriented QA professional with strong technical skills. Proactive in identifying quality risks. Good advocate for quality throughout development process. diff --git a/week5/knowledge-base/employees/David Kim.md b/week5/knowledge-base/employees/David Kim.md new file mode 100644 index 0000000..8fbc1e3 --- /dev/null +++ b/week5/knowledge-base/employees/David Kim.md @@ -0,0 +1,47 @@ +# HR Record + +# David Kim + +## Summary +- **Date of Birth:** September 22, 1992 +- **Job Title:** DevOps Engineer +- **Location:** New York, New York +- **Current Salary:** $118,000 + +## Insurellm Career Progression +- **August 2021 - Present:** DevOps Engineer + - Manages AWS infrastructure for all Insurellm products + - Implemented CI/CD pipelines reducing deployment time by 60% + - Led migration to Kubernetes, improving system scalability and reliability + +- **March 2019 - July 2021:** Junior DevOps Engineer + - Assisted with infrastructure monitoring and incident response + - Automated manual deployment processes using Jenkins and Terraform + - Supported senior engineers in cloud infrastructure management + +- **June 2017 - February 2019:** Systems Administrator at CloudFirst Solutions + - Maintained Linux servers and network infrastructure + - Provided technical support for cloud-based services + +## Annual Performance History +- **2023:** Rating: 4.5/5 + *Strong performance with excellent infrastructure improvements. Successfully led Kubernetes migration with minimal downtime.* + +- **2022:** Rating: 3.5/5 + *Adequate performance but struggled with on-call responsibilities. Improved response times after additional training.* + +- **2021:** Rating: 4.0/5 + *Solid first year as DevOps Engineer. Quick learner who adapted well to increased responsibilities.* + +## Compensation History +- **2023:** Base Salary: $118,000 + Bonus: $12,000 +- **2022:** Base Salary: $108,000 + Bonus: $8,000 +- **2021:** Base Salary: $100,000 + Bonus: $5,000 +- **2020:** Base Salary: $85,000 + Bonus: $4,000 +- **2019:** Base Salary: $75,000 + +## Other HR Notes +- **Certifications:** AWS Certified Solutions Architect (2022), Certified Kubernetes Administrator (2023) +- **Training:** Completed advanced Docker and container orchestration courses. Currently pursuing AWS DevOps Professional certification. +- **On-Call Rotation:** Participates in weekly on-call rotation for production systems. +- **Feedback:** Technically strong with excellent problem-solving abilities. Working on improving communication with non-technical stakeholders. diff --git a/week5/knowledge-base/employees/Emily Carter.md b/week5/knowledge-base/employees/Emily Carter.md index 179d14b..cb6808a 100644 --- a/week5/knowledge-base/employees/Emily Carter.md +++ b/week5/knowledge-base/employees/Emily Carter.md @@ -3,9 +3,10 @@ # Emily Carter ## Summary -- **Date of Birth:** August 12, 1990 -- **Job Title:** Account Executive -- **Location:** Austin, Texas +- **Date of Birth:** August 12, 1990 +- **Job Title:** Account Executive +- **Location:** Austin, Texas +- **Current Salary:** $70,000 ## Insurellm Career Progression - **2021-Present:** Account Executive diff --git a/week5/knowledge-base/employees/Emily Tran.md b/week5/knowledge-base/employees/Emily Tran.md index 83cc5a2..c89c13c 100644 --- a/week5/knowledge-base/employees/Emily Tran.md +++ b/week5/knowledge-base/employees/Emily Tran.md @@ -3,9 +3,10 @@ # Emily Tran ## Summary -- **Date of Birth:** March 18, 1991 -- **Job Title:** Digital Marketing Specialist -- **Location:** San Francisco, CA +- **Date of Birth:** March 18, 1991 +- **Job Title:** Digital Marketing Specialist +- **Location:** San Francisco, CA +- **Current Salary:** $75,000 --- diff --git a/week5/knowledge-base/employees/James Wilson.md b/week5/knowledge-base/employees/James Wilson.md new file mode 100644 index 0000000..235a8bd --- /dev/null +++ b/week5/knowledge-base/employees/James Wilson.md @@ -0,0 +1,70 @@ +# HR Record + +# James Wilson + +## Summary +- **Date of Birth:** April 5, 1978 +- **Job Title:** Chief Technology Officer (CTO) +- **Location:** San Francisco, California +- **Current Salary:** $285,000 + +## Insurellm Career Progression +- **January 2017 - Present:** Chief Technology Officer + - Reports directly to CEO, member of executive leadership team + - Oversees all technology strategy and engineering operations + - Manages 85-person engineering, product, and data organization + - Led company through major platform modernization and cloud migration + - Drove adoption of AI/ML capabilities across product suite + +- **March 2012 - December 2016:** VP of Engineering at TechScale Inc. + - Built and scaled engineering organization from 15 to 60 engineers + - Led product development for B2B SaaS platform + - Drove technical strategy and architecture decisions + +- **June 2008 - February 2012:** Senior Engineering Manager at CloudSoft Corp. + - Managed multiple engineering teams + - Delivered major platform features and infrastructure improvements + +- **August 2003 - May 2008:** Software Architect at Enterprise Systems Inc. + - Designed large-scale distributed systems + - Led technical architecture for critical business applications + +## Annual Performance History +- **2023:** Rating: 4.9/5 + *Outstanding performance. Successfully led AI transformation initiative. Exceptional strategic leadership and team building. Key driver of company growth.* + +- **2022:** Rating: 4.7/5 + *Exceeded expectations. Strong execution on platform modernization. Improved engineering productivity and velocity significantly.* + +- **2021:** Rating: 4.6/5 + *Excellent performance navigating remote transition. Built strong engineering culture and maintained high delivery standards.* + +- **2020:** Rating: 4.4/5 + *Strong performance during challenging year. Successfully pivoted technology strategy for remote-first world.* + +- **2019:** Rating: 4.8/5 + *Exceptional leadership. Scaled engineering organization effectively while maintaining quality and culture.* + +- **2018:** Rating: 4.5/5 + *Excellent year with successful cloud migration. Strong technical vision and execution.* + +- **2017:** Rating: 4.3/5 + *Strong start as CTO. Quickly assessed technical landscape and began strategic improvements.* + +## Compensation History +- **2023:** Base Salary: $285,000 + Bonus: $125,000 + Equity refresh +- **2022:** Base Salary: $270,000 + Bonus: $110,000 + Equity refresh +- **2021:** Base Salary: $255,000 + Bonus: $100,000 +- **2020:** Base Salary: $240,000 + Bonus: $85,000 +- **2019:** Base Salary: $225,000 + Bonus: $95,000 +- **2018:** Base Salary: $210,000 + Bonus: $80,000 +- **2017:** Base Salary: $195,000 + Bonus: $70,000 + Significant equity grant + +## Other HR Notes +- **Education:** MS in Computer Science from MIT, BS in Computer Engineering from Stanford University +- **Board Participation:** Serves on technical advisory board for two InsurTech startups +- **Industry Recognition:** Named to InsureTech 100 list (2021, 2022, 2023), speaker at major technology conferences +- **Patents:** 5 patents in distributed systems and insurance technology +- **Leadership:** Known for building high-performing teams and fostering innovation culture. Strong mentor to technical leaders. +- **Strategic Impact:** Transformed Insurellm's technology from legacy systems to modern cloud-native architecture. Key architect of AI strategy. +- **Feedback:** Visionary technology leader with exceptional strategic thinking and execution capabilities. Trusted partner to CEO and board. Critical to company's technical success and future growth. diff --git a/week5/knowledge-base/employees/Jennifer Adams.md b/week5/knowledge-base/employees/Jennifer Adams.md new file mode 100644 index 0000000..a91c0cc --- /dev/null +++ b/week5/knowledge-base/employees/Jennifer Adams.md @@ -0,0 +1,41 @@ +# HR Record + +# Jennifer Adams + +## Summary +- **Date of Birth:** July 7, 1997 +- **Job Title:** Sales Development Representative +- **Location:** Remote (Based in Denver, Colorado) +- **Current Salary:** $58,000 + +## Insurellm Career Progression +- **March 2023 - Present:** Sales Development Representative + - Generates qualified leads for enterprise sales team + - Conducts outbound prospecting via phone, email, and LinkedIn + - Researches target accounts in insurance industry + - Books discovery meetings for account executives + +- **June 2022 - February 2023:** SDR Intern + - Completed 9-month internship program + - Learned sales processes and CRM tools + - Shadowed senior SDRs and participated in training + +- **August 2019 - May 2022:** Customer Service Representative at RetailCo + - Handled customer inquiries and resolved issues + - Developed communication and problem-solving skills + +## Annual Performance History +- **2023:** Rating: 3.0/5 + *Meets basic expectations. Achieved 78% of lead generation quota. Shows effort but struggles with conversion rates. Needs improvement in discovery call quality.* + +## Compensation History +- **2023:** Base Salary: $58,000 + Commission: $4,200 +- **2022:** Intern Stipend: $22/hour + +## Other HR Notes +- **Education:** BA in Business Administration from University of Denver (graduated 2022) +- **Skills:** Learning Salesforce, LinkedIn Sales Navigator, and sales prospecting techniques +- **Development Plan:** Enrolled in sales skills training program. Manager providing weekly coaching on objection handling and qualification techniques. +- **Challenges:** Struggles with phone confidence and needs to improve research quality before outreach. Working on building pipeline consistency. +- **Feedback:** Enthusiastic and coachable. Shows potential but needs more experience and skill development. Sometimes gives up too easily when facing rejection. Improving gradually with support. +- **Goals:** Aiming to hit 100% of quota in Q4 2024 and qualify for SDR of the Quarter recognition diff --git a/week5/knowledge-base/employees/Jessica Liu.md b/week5/knowledge-base/employees/Jessica Liu.md new file mode 100644 index 0000000..4cb1db0 --- /dev/null +++ b/week5/knowledge-base/employees/Jessica Liu.md @@ -0,0 +1,50 @@ +# HR Record + +# Jessica Liu + +## Summary +- **Date of Birth:** April 30, 1996 +- **Job Title:** Frontend Developer +- **Location:** Remote (Based in Seattle, Washington) +- **Current Salary:** $92,000 + +## Insurellm Career Progression +- **July 2022 - Present:** Frontend Developer + - Develops user interfaces for Rellm reinsurance platform using React + - Implements responsive designs and ensures cross-browser compatibility + - Collaborates with UX designers and backend engineers + +- **January 2020 - June 2022:** Junior Frontend Developer + - Built UI components for internal tools and customer-facing applications + - Fixed bugs and improved performance of existing web applications + - Participated in code reviews and learned best practices + +- **June 2018 - December 2019:** Web Developer Intern at StartupLabs + - Created landing pages and marketing websites + - Learned HTML, CSS, JavaScript, and React fundamentals + +## Annual Performance History +- **2023:** Rating: 4.1/5 + *Good performance with steady contribution to team goals. Delivered features on time with good code quality.* + +- **2022:** Rating: 3.7/5 + *Solid transition to Frontend Developer role. Showed improvement in technical skills but needed more guidance on complex features.* + +- **2021:** Rating: 3.4/5 + *Adequate performance with some challenges. Struggled with React state management initially but improved after mentoring.* + +- **2020:** Rating: 3.8/5 + *Good start as Junior Developer. Eager to learn and receptive to feedback.* + +## Compensation History +- **2023:** Base Salary: $92,000 + Bonus: $6,000 +- **2022:** Base Salary: $85,000 + Bonus: $4,000 +- **2021:** Base Salary: $72,000 + Bonus: $2,000 +- **2020:** Base Salary: $68,000 + +## Other HR Notes +- **Education:** BS in Computer Science from University of Manchester +- **Skills:** Proficient in React, TypeScript, HTML/CSS, Jest for testing. Learning Next.js and GraphQL. +- **Professional Development:** Completed Advanced React Patterns course (2023). Actively contributes to open-source projects. +- **Work Style:** Prefers remote work. Strong written communicator. Participates actively in team standups and planning sessions. +- **Feedback:** Reliable developer with good attention to UI details. Improving technical decision-making skills. Would benefit from more ownership of larger features. diff --git a/week5/knowledge-base/employees/Jordan Blake.md b/week5/knowledge-base/employees/Jordan Blake.md index 69b53a4..6818039 100644 --- a/week5/knowledge-base/employees/Jordan Blake.md +++ b/week5/knowledge-base/employees/Jordan Blake.md @@ -3,9 +3,10 @@ # Jordan Blake ## Summary -- **Date of Birth:** March 15, 1993 -- **Job Title:** Sales Development Representative (SDR) -- **Location:** Austin, Texas +- **Date of Birth:** March 15, 1993 +- **Job Title:** Sales Development Representative (SDR) +- **Location:** Austin, Texas +- **Current Salary:** $65,000 ## Insurellm Career Progression - **2021-06:** Joined Insurellm as an Entry-Level SDR diff --git a/week5/knowledge-base/employees/Jordan K. Bishop.md b/week5/knowledge-base/employees/Jordan K. Bishop.md index 4ec0633..6fce57f 100644 --- a/week5/knowledge-base/employees/Jordan K. Bishop.md +++ b/week5/knowledge-base/employees/Jordan K. Bishop.md @@ -6,6 +6,7 @@ - **Date of Birth:** March 15, 1990 - **Job Title:** Frontend Software Engineer - **Location:** Austin, Texas +- **Current Salary:** $92,000 ## Insurellm Career Progression - **June 2018:** Hired as a Frontend Software Engineer. diff --git a/week5/knowledge-base/employees/Kevin Zhang.md b/week5/knowledge-base/employees/Kevin Zhang.md new file mode 100644 index 0000000..2a9d160 --- /dev/null +++ b/week5/knowledge-base/employees/Kevin Zhang.md @@ -0,0 +1,51 @@ +# HR Record + +# Kevin Zhang + +## Summary +- **Date of Birth:** March 27, 1990 +- **Job Title:** Mobile Developer +- **Location:** San Francisco, California +- **Current Salary:** $128,000 + +## Insurellm Career Progression +- **May 2020 - Present:** Mobile Developer + - Leads iOS development for Marketllm consumer mobile app + - Built Android version achieving 4.6 star rating on Play Store + - Implemented push notification system increasing user engagement by 42% + - Manages mobile release cycles and app store submissions + +- **February 2018 - April 2020:** iOS Developer at AppWorks Studio + - Developed consumer mobile applications for various clients + - Specialized in Swift and iOS frameworks + +- **July 2015 - January 2018:** Junior Mobile Developer at StartupTech + - Built mobile features for social networking app + - Learned iOS and Android development fundamentals + +## Annual Performance History +- **2023:** Rating: 4.6/5 + *Excellent performance. Successfully launched Android app on time and under budget. Strong technical leadership.* + +- **2022:** Rating: 4.4/5 + *Exceeded expectations with major iOS app redesign. Improved app performance and user retention significantly.* + +- **2021:** Rating: 4.2/5 + *Strong performance. Delivered all planned features and maintained high app quality standards.* + +- **2020:** Rating: 4.0/5 + *Good first year at Insurellm. Quick to learn insurance domain and contribute meaningfully to mobile strategy.* + +## Compensation History +- **2023:** Base Salary: $128,000 + Bonus: $16,000 +- **2022:** Base Salary: $120,000 + Bonus: $14,000 +- **2021:** Base Salary: $112,000 + Bonus: $12,000 +- **2020:** Base Salary: $105,000 + Bonus: $8,000 + +## Other HR Notes +- **Education:** BS in Computer Science from UC Berkeley +- **Skills:** Expert in Swift, Kotlin, React Native, mobile UI/UX patterns, App Store optimization +- **Recognition:** Mobile Innovation Award 2023 for Android app launch +- **Open Source:** Active contributor to mobile development open-source projects +- **Speaking:** Presented at Mobile DevCon 2023 on cross-platform development strategies +- **Feedback:** Highly skilled mobile developer with excellent product sense. Takes ownership of mobile platform and drives continuous improvement. Strong mentor to other developers. diff --git a/week5/knowledge-base/employees/Lisa Anderson.md b/week5/knowledge-base/employees/Lisa Anderson.md new file mode 100644 index 0000000..5f2a40f --- /dev/null +++ b/week5/knowledge-base/employees/Lisa Anderson.md @@ -0,0 +1,55 @@ +# HR Record + +# Lisa Anderson + +## Summary +- **Date of Birth:** November 18, 1985 +- **Job Title:** Marketing Manager +- **Location:** Austin, Texas +- **Current Salary:** $105,000 + +## Insurellm Career Progression +- **April 2019 - Present:** Marketing Manager + - Leads demand generation and content marketing teams + - Manages $2M annual marketing budget + - Oversees digital campaigns, events, and brand initiatives + - Increased qualified leads by 65% since 2021 + +- **January 2016 - March 2019:** Senior Marketing Specialist at GrowthTech + - Managed B2B marketing campaigns for SaaS products + - Specialized in content marketing and marketing automation + +- **June 2012 - December 2015:** Marketing Coordinator at Enterprise Solutions + - Supported marketing team with campaign execution + - Managed social media and email marketing programs + +## Annual Performance History +- **2023:** Rating: 4.7/5 + *Outstanding performance. Led successful rebranding initiative and achieved record lead generation numbers. Excellent team leadership.* + +- **2022:** Rating: 4.4/5 + *Exceeded expectations. Strong campaign performance and improved marketing ROI by 38%.* + +- **2021:** Rating: 4.1/5 + *Solid performance with good lead quality improvements. Successfully navigated shift to digital events.* + +- **2020:** Rating: 3.8/5 + *Good performance during challenging pandemic year. Adapted marketing strategy effectively to all-digital approach.* + +- **2019:** Rating: 4.0/5 + *Strong start as Marketing Manager. Quick to understand insurance tech market and develop effective strategies.* + +## Compensation History +- **2023:** Base Salary: $105,000 + Bonus: $18,000 +- **2022:** Base Salary: $98,000 + Bonus: $15,000 +- **2021:** Base Salary: $92,000 + Bonus: $12,000 +- **2020:** Base Salary: $88,000 + Bonus: $8,000 +- **2019:** Base Salary: $85,000 + Bonus: $10,000 + +## Other HR Notes +- **Education:** MBA in Marketing from University of Texas at Austin, BA in Communications +- **Certifications:** Google Analytics Certified, HubSpot Marketing Automation Expert +- **Recognition:** Marketing Excellence Award 2023, led award-winning rebranding campaign +- **Team Leadership:** Manages team of 5 marketing specialists. Known for developing talent and creating high-performing culture. +- **Skills:** Expert in demand generation, content marketing, marketing analytics, marketing automation (HubSpot, Marketo), and brand strategy +- **Feedback:** Strategic marketing leader with strong analytical skills and creative vision. Excellent cross-functional collaborator who drives measurable business results. diff --git a/week5/knowledge-base/employees/Marcus Johnson.md b/week5/knowledge-base/employees/Marcus Johnson.md new file mode 100644 index 0000000..bd691f6 --- /dev/null +++ b/week5/knowledge-base/employees/Marcus Johnson.md @@ -0,0 +1,52 @@ +# HR Record + +# Marcus Johnson + +## Summary +- **Date of Birth:** February 17, 1988 +- **Job Title:** Customer Success Manager +- **Location:** New York, New York +- **Current Salary:** $98,000 + +## Insurellm Career Progression +- **April 2020 - Present:** Customer Success Manager + - Manages portfolio of 25 enterprise clients generating $5M in annual revenue + - Achieved 95% client retention rate over past three years + - Leads quarterly business reviews and strategic planning sessions with clients + +- **September 2018 - March 2020:** Customer Success Associate + - Supported senior CSMs with client onboarding and training + - Managed day-to-day communication with mid-market clients + - Created customer success playbooks and best practices documentation + +- **January 2016 - August 2018:** Account Manager at SaaS Solutions Inc. + - Managed B2B client relationships in software sector + - Focused on upselling and customer retention + +## Annual Performance History +- **2023:** Rating: 4.7/5 + *Exceptional performance with highest client satisfaction scores in the team. Successfully expanded three key accounts.* + +- **2022:** Rating: 4.3/5 + *Strong performance with good retention numbers. Faced challenges with one difficult client but resolved successfully.* + +- **2021:** Rating: 3.8/5 + *Solid year but missed expansion revenue targets in Q2 and Q3. Improved significantly in Q4.* + +- **2020:** Rating: 4.0/5 + *Good transition to CSM role. Quickly built rapport with enterprise clients and demonstrated strategic thinking.* + +## Compensation History +- **2023:** Base Salary: $98,000 + Bonus: $15,000 +- **2022:** Base Salary: $92,000 + Bonus: $12,000 +- **2021:** Base Salary: $85,000 + Bonus: $8,000 +- **2020:** Base Salary: $78,000 + Bonus: $10,000 +- **2019:** Base Salary: $68,000 + Bonus: $5,000 +- **2018:** Base Salary: $65,000 + +## Other HR Notes +- **Education:** BA in Business Administration from NYU Stern School of Business +- **Certifications:** Certified Customer Success Manager (CCSM), Salesforce Administrator +- **Recognition:** Customer Champion Award 2023 for highest NPS scores +- **Mentorship:** Actively mentors new Customer Success Associates +- **Feedback:** Excellent relationship builder with deep product knowledge. Strong strategic advisor to clients. Known for going above and beyond to ensure client success. diff --git a/week5/knowledge-base/employees/Maxine Thompson.md b/week5/knowledge-base/employees/Maxine Thompson.md index 30259dc..20a7365 100644 --- a/week5/knowledge-base/employees/Maxine Thompson.md +++ b/week5/knowledge-base/employees/Maxine Thompson.md @@ -3,9 +3,10 @@ # Maxine Thompson ## Summary -- **Date of Birth:** January 15, 1991 -- **Job Title:** Data Engineer -- **Location:** Austin, Texas +- **Date of Birth:** January 15, 1991 +- **Job Title:** Data Engineer +- **Location:** Austin, Texas +- **Current Salary:** $120,000 ## Insurellm Career Progression - **January 2017 - October 2018**: **Junior Data Engineer** @@ -48,6 +49,6 @@ ## Other HR Notes - Maxine participated in various company-sponsored trainings related to big data technologies and cloud infrastructure. -- She was recognized for her contributions with the “Insurellm Innovator Award” in 2022. +- She was recognized for her contributions with the prestigious Insurellm IIOTY Innovator Award in 2023. - Maxine is currently involved in the women-in-tech initiative and participates in mentorship programs to guide junior employees. - Future development areas include improving her stakeholder communication skills to ensure smoother project transitions and collaboration. \ No newline at end of file diff --git a/week5/knowledge-base/employees/Maya Thompson.md b/week5/knowledge-base/employees/Maya Thompson.md new file mode 100644 index 0000000..1af3e64 --- /dev/null +++ b/week5/knowledge-base/employees/Maya Thompson.md @@ -0,0 +1,46 @@ +# HR Record + +# Maya Thompson + +## Summary +- **Date of Birth:** June 14, 1993 +- **Job Title:** Data Engineer +- **Location:** New York, New York +- **Current Salary:** $110,000 + +## Insurellm Career Progression +- **March 2021 - Present:** Data Engineer + - Builds and maintains data pipelines for all Insurellm products + - Manages data warehouse infrastructure on Snowflake + - Optimized ETL processes reducing processing time by 45% + - Collaborates with data science and analytics teams + +- **July 2019 - February 2021:** Junior Data Engineer at DataFlow Systems + - Developed ETL pipelines for enterprise data integration + - Worked with Airflow, Python, and SQL + +- **June 2017 - June 2019:** Data Analyst at Analytics Corp. + - Analyzed business data and created reports + - Transitioned from analysis to engineering role + +## Annual Performance History +- **2023:** Rating: 4.3/5 + *Strong performance. Delivered critical data infrastructure improvements. Good collaboration with stakeholders.* + +- **2022:** Rating: 4.5/5 + *Excellent performance. Led successful migration to Snowflake. Strong technical expertise and project management.* + +- **2021:** Rating: 4.0/5 + *Good first year at Insurellm. Quickly ramped up on data infrastructure and delivered reliable pipelines.* + +## Compensation History +- **2023:** Base Salary: $110,000 + Bonus: $12,000 +- **2022:** Base Salary: $102,000 + Bonus: $14,000 +- **2021:** Base Salary: $95,000 + Bonus: $8,000 + +## Other HR Notes +- **Education:** MS in Data Science from Columbia University, BS in Mathematics from University of Michigan +- **Skills:** Expert in Python, SQL, Airflow, dbt, Snowflake, AWS data services +- **Certifications:** AWS Certified Data Analytics, Snowflake SnowPro Core +- **Professional Development:** Regularly attends data engineering conferences and contributes to tech blog +- **Feedback:** Strong technical contributor with excellent data architecture skills. Reliable and detail-oriented. Good communicator who translates technical concepts for non-technical stakeholders. diff --git a/week5/knowledge-base/employees/Michael O'Brien.md b/week5/knowledge-base/employees/Michael O'Brien.md new file mode 100644 index 0000000..0d24e85 --- /dev/null +++ b/week5/knowledge-base/employees/Michael O'Brien.md @@ -0,0 +1,63 @@ +# HR Record + +# Michael O'Brien + +## Summary +- **Date of Birth:** May 20, 1984 +- **Job Title:** Account Executive +- **Location:** Chicago, Illinois +- **Current Salary:** $95,000 + +## Insurellm Career Progression +- **January 2017 - Present:** Account Executive + - Manages enterprise sales for Midwest territory + - Focuses on reinsurance and large insurance carrier accounts + - Generated $8.2M in new business over past 3 years + - Maintains book of business worth $12M ARR + +- **June 2014 - December 2016:** Senior Sales Representative at InsureTech Partners + - Sold insurance software solutions to mid-market companies + - Consistently exceeded quota by 30-50% + +- **August 2010 - May 2014:** Sales Representative at Business Solutions Inc. + - B2B software sales for various industries + - Developed foundational sales skills and territory management + +## Annual Performance History +- **2023:** Rating: 3.4/5 + *Meets expectations but below peak performance. Hit 92% of quota. Faced headwinds in reinsurance market. Need to diversify pipeline.* + +- **2022:** Rating: 3.8/5 + *Good performance with solid quota attainment at 105%. Some key deals slipped to next quarter affecting timing.* + +- **2021:** Rating: 4.5/5 + *Excellent year. Exceeded quota at 142%. Closed two major enterprise deals. Strong relationship building.* + +- **2020:** Rating: 3.6/5 + *Challenging year with pandemic impact. Hit 88% of quota but showed resilience and adapted to virtual selling.* + +- **2019:** Rating: 4.2/5 + *Strong performance at 125% quota attainment. Expanded territory successfully.* + +- **2018:** Rating: 4.0/5 + *Solid year with 110% quota achievement. Building strong pipeline in new territory.* + +- **2017:** Rating: 3.5/5 + *Good onboarding year. Learning curve with insurance industry specifics but showed promise.* + +## Compensation History +- **2023:** Base Salary: $95,000 + Commission: $48,000 +- **2022:** Base Salary: $92,000 + Commission: $62,000 +- **2021:** Base Salary: $88,000 + Commission: $88,000 +- **2020:** Base Salary: $85,000 + Commission: $42,000 +- **2019:** Base Salary: $82,000 + Commission: $68,000 +- **2018:** Base Salary: $78,000 + Commission: $58,000 +- **2017:** Base Salary: $75,000 + Commission: $35,000 + +## Other HR Notes +- **Education:** BA in Business from University of Illinois +- **Awards:** Sales Excellence Award 2021, President's Club member 2019, 2021 +- **Pipeline Management:** Currently working to rebuild pipeline after softer 2023. Manager providing additional support and marketing resources. +- **Skills:** Strong at enterprise relationship building and complex deal negotiation. Experienced with long sales cycles. +- **Development Focus:** Working on account-based selling strategies and leveraging social selling techniques +- **Feedback:** Seasoned sales professional with deep industry knowledge. Performs best with large strategic deals. Needs to maintain consistent prospecting discipline. diff --git a/week5/knowledge-base/employees/Michelle Rivera.md b/week5/knowledge-base/employees/Michelle Rivera.md new file mode 100644 index 0000000..001b51c --- /dev/null +++ b/week5/knowledge-base/employees/Michelle Rivera.md @@ -0,0 +1,55 @@ +# HR Record + +# Michelle Rivera + +## Summary +- **Date of Birth:** December 16, 1988 +- **Job Title:** Product Designer +- **Location:** New York, New York +- **Current Salary:** $118,000 + +## Insurellm Career Progression +- **February 2019 - Present:** Product Designer + - Leads design for Rellm reinsurance platform + - Creates user experiences for complex B2B workflows + - Collaborates with product managers and engineers + - Conducts user research with enterprise clients + +- **August 2016 - January 2019:** Senior UX/UI Designer at DesignStudio NYC + - Designed digital products for various clients + - Specialized in enterprise software and complex workflows + +- **May 2013 - July 2016:** UX Designer at CreativeAgency + - Created user interfaces for consumer and business applications + - Developed skills in user research and interaction design + +## Annual Performance History +- **2023:** Rating: 4.8/5 + *Outstanding performance. Led major Rellm redesign that improved user efficiency by 40%. Exceptional user research and design execution.* + +- **2022:** Rating: 4.5/5 + *Exceeded expectations. Strong design leadership and excellent collaboration with engineering team.* + +- **2021:** Rating: 4.3/5 + *Strong performance. Successfully balanced multiple design projects and maintained high quality standards.* + +- **2020:** Rating: 4.1/5 + *Good performance adapting to remote design collaboration. Maintained design quality during challenging year.* + +- **2019:** Rating: 4.0/5 + *Solid first year at Insurellm. Quick to learn complex insurance domain and establish design process.* + +## Compensation History +- **2023:** Base Salary: $118,000 + Bonus: $16,000 +- **2022:** Base Salary: $112,000 + Bonus: $14,000 +- **2021:** Base Salary: $105,000 + Bonus: $12,000 +- **2020:** Base Salary: $98,000 + Bonus: $10,000 +- **2019:** Base Salary: $92,000 + Bonus: $8,000 + +## Other HR Notes +- **Education:** MFA in Interaction Design from School of Visual Arts, BFA in Graphic Design from Parsons +- **Skills:** Expert in Figma, Sketch, prototyping, user research methodologies, information architecture, design systems +- **Recognition:** Design Excellence Award 2023, featured in UX Design Magazine for Rellm case study +- **Speaking:** Presented at UX Week 2023 on "Designing for Complex Enterprise Workflows" +- **Mentorship:** Mentors junior designers and leads design critique sessions +- **Feedback:** Exceptional designer with rare ability to simplify complex problems. Strong strategic thinker who balances user needs with business goals. Trusted design leader. diff --git a/week5/knowledge-base/employees/Nina Patel.md b/week5/knowledge-base/employees/Nina Patel.md new file mode 100644 index 0000000..1283c8e --- /dev/null +++ b/week5/knowledge-base/employees/Nina Patel.md @@ -0,0 +1,54 @@ +# HR Record + +# Nina Patel + +## Summary +- **Date of Birth:** July 25, 1991 +- **Job Title:** Business Intelligence Analyst +- **Location:** Chicago, Illinois +- **Current Salary:** $82,000 + +## Insurellm Career Progression +- **February 2021 - Present:** Business Intelligence Analyst + - Builds dashboards and reports using Tableau and Looker for executive team + - Analyzes business metrics across all product lines + - Partners with sales and marketing teams on data-driven insights + +- **August 2019 - January 2021:** Junior BI Analyst + - Created SQL queries and basic reports for business stakeholders + - Supported senior analysts with data extraction and validation + - Maintained existing dashboards and fixed data quality issues + +- **May 2017 - July 2019:** Data Analyst at RetailMetrics Inc. + - Analyzed retail sales data and customer behavior patterns + - Built Excel-based reporting tools for operations team + +## Annual Performance History +- **2023:** Rating: 3.5/5 + *Meets expectations but has room for growth. Delivered all required reports but showed limited proactivity in identifying new insights.* + +- **2022:** Rating: 3.2/5 + *Below expectations. Struggled with complex SQL queries and missed several deadlines. Enrolled in advanced analytics training.* + +- **2021:** Rating: 3.8/5 + *Solid performance in first year as BI Analyst. Demonstrated good grasp of business metrics and stakeholder communication.* + +- **2020:** Rating: 4.0/5 + *Strong performance as Junior Analyst. Showed initiative and eagerness to learn new tools.* + +- **2019:** Rating: 3.9/5 + *Good onboarding year. Quick to adapt to Insurellm's data systems.* + +## Compensation History +- **2023:** Base Salary: $82,000 + Bonus: $4,000 +- **2022:** Base Salary: $78,000 + Bonus: $2,000 +- **2021:** Base Salary: $75,000 + Bonus: $5,000 +- **2020:** Base Salary: $65,000 + Bonus: $3,000 +- **2019:** Base Salary: $60,000 + +## Other HR Notes +- **Education:** BS in Statistics from University of Illinois at Chicago +- **Skills:** Proficient in SQL, Tableau, Looker, Excel. Learning Python for advanced analytics. +- **Development Areas:** Needs to improve proactive problem-solving and complex analytical thinking. Working with mentor to develop these skills. +- **Training:** Completed Advanced SQL course (2022), enrolled in Data Science fundamentals program (2023) +- **Feedback:** Reliable team member with strong attention to detail. Works well on defined projects but needs guidance on ambiguous problems. diff --git a/week5/knowledge-base/employees/Oliver Spencer.md b/week5/knowledge-base/employees/Oliver Spencer.md index 9404430..06c8b20 100644 --- a/week5/knowledge-base/employees/Oliver Spencer.md +++ b/week5/knowledge-base/employees/Oliver Spencer.md @@ -3,9 +3,10 @@ # Oliver Spencer ## Summary -- **Date of Birth**: May 14, 1990 -- **Job Title**: Backend Software Engineer -- **Location**: Austin, Texas +- **Date of Birth**: May 14, 1990 +- **Job Title**: Backend Software Engineer +- **Location**: Austin, Texas +- **Current Salary**: $125,000 ## Insurellm Career Progression - **March 2018**: Joined Insurellm as a Backend Developer I, focusing on API development for customer management systems. diff --git a/week5/knowledge-base/employees/Priya Sharma.md b/week5/knowledge-base/employees/Priya Sharma.md new file mode 100644 index 0000000..ce98ae1 --- /dev/null +++ b/week5/knowledge-base/employees/Priya Sharma.md @@ -0,0 +1,60 @@ +# HR Record + +# Priya Sharma + +## Summary +- **Date of Birth:** January 8, 1986 +- **Job Title:** Senior Data Scientist +- **Location:** San Francisco, California +- **Current Salary:** $145,000 + +## Insurellm Career Progression +- **March 2018 - Present:** Senior Data Scientist + - Leads machine learning initiatives for risk prediction models + - Built recommendation engine for Marketllm increasing conversion by 28% + - Mentors team of 3 junior data scientists + - Published 2 research papers on insurance ML applications + +- **June 2015 - February 2018:** Data Scientist at FinML Analytics + - Developed predictive models for financial services clients + - Specialized in customer churn prediction and fraud detection + +- **August 2012 - May 2015:** Research Scientist at UC Berkeley AI Lab + - Conducted research in machine learning and natural language processing + - Published 5 peer-reviewed papers + +## Annual Performance History +- **2023:** Rating: 4.9/5 + *Exceptional performance. Led breakthrough AI-driven underwriting project. Excellent technical leadership and mentorship.* + +- **2022:** Rating: 4.7/5 + *Outstanding work on risk prediction models. Strong collaboration with product team and technical innovation.* + +- **2021:** Rating: 4.5/5 + *Exceeded expectations with successful launch of recommendation engine. Effective mentor to junior team members.* + +- **2020:** Rating: 4.3/5 + *Strong performance despite remote transition challenges. Maintained high-quality model development.* + +- **2019:** Rating: 4.6/5 + *Excellent contributions to fraud detection systems. Recognized for thought leadership in ML applications.* + +- **2018:** Rating: 4.2/5 + *Solid first year. Quick to understand insurance domain and apply ML expertise effectively.* + +## Compensation History +- **2023:** Base Salary: $145,000 + Bonus: $25,000 +- **2022:** Base Salary: $138,000 + Bonus: $22,000 +- **2021:** Base Salary: $130,000 + Bonus: $20,000 +- **2020:** Base Salary: $122,000 + Bonus: $18,000 +- **2019:** Base Salary: $115,000 + Bonus: $15,000 +- **2018:** Base Salary: $108,000 + Bonus: $12,000 + +## Other HR Notes +- **Education:** PhD in Computer Science (Machine Learning) from Stanford University +- **Publications:** 7+ peer-reviewed papers in ML conferences and journals +- **Speaking:** Regular speaker at ML and InsurTech conferences. Represented Insurellm at 4 major conferences in 2023. +- **Patents:** Co-inventor on 2 pending patents for insurance ML applications +- **Recognition:** Data Science Excellence Award 2023, featured in InsureTech Innovation Magazine +- **Skills:** Expert in Python, TensorFlow, PyTorch, scikit-learn, SQL, and cloud ML platforms +- **Feedback:** World-class technical talent with strong business acumen. Natural leader who elevates entire team. Key retention priority. diff --git a/week5/knowledge-base/employees/Rachel Martinez.md b/week5/knowledge-base/employees/Rachel Martinez.md new file mode 100644 index 0000000..88b0686 --- /dev/null +++ b/week5/knowledge-base/employees/Rachel Martinez.md @@ -0,0 +1,53 @@ +# HR Record + +# Rachel Martinez + +## Summary +- **Date of Birth:** June 8, 1987 +- **Job Title:** Product Manager +- **Location:** San Francisco, California +- **Current Salary:** $135,000 + +## Insurellm Career Progression +- **March 2019 - Present:** Product Manager + - Leads product strategy for Carllm, the auto insurance portal + - Successfully launched three major feature releases that increased user engagement by 45% + - Manages cross-functional teams including engineering, design, and sales + +- **January 2017 - February 2019:** Associate Product Manager + - Supported product development for Marketllm marketplace + - Conducted user research and competitive analysis + - Collaborated with engineering teams on feature prioritization + +- **June 2015 - December 2016:** Business Analyst at TechInsure Corp + - Analyzed market trends and customer behavior in insurance tech sector + - Created business requirements documents for new product initiatives + +## Annual Performance History +- **2023:** Rating: 4.6/5 + *Exceptional year with successful launch of AI-powered claims prediction feature. Strong leadership and stakeholder management.* + +- **2022:** Rating: 4.2/5 + *Met all product milestones but faced challenges with team alignment during Q3. Improved communication strategies by year end.* + +- **2021:** Rating: 4.8/5 + *Outstanding performance. Led product redesign that reduced customer churn by 20%. Received Product Excellence Award.* + +- **2020:** Rating: 3.8/5 + *Solid performance but struggled with remote team coordination during pandemic transition. Adapted well by Q4.* + +- **2019:** Rating: 4.0/5 + *Strong start as Product Manager. Successfully transitioned from APM role with minimal friction.* + +## Compensation History +- **2023:** Base Salary: $135,000 + Bonus: $20,000 +- **2022:** Base Salary: $128,000 + Bonus: $15,000 +- **2021:** Base Salary: $120,000 + Bonus: $18,000 +- **2020:** Base Salary: $110,000 + Bonus: $8,000 +- **2019:** Base Salary: $105,000 + Bonus: $10,000 + +## Other HR Notes +- **Professional Development:** Completed Product Leadership Certification from Pragmatic Institute (2021). Regular attendee at ProductCon and InsurTech conferences. +- **Mentorship:** Currently mentors two Associate Product Managers and actively participates in the Women in Tech initiative at Insurellm. +- **Skills:** Expert in Agile/Scrum methodologies, JIRA, Figma, SQL, and product analytics tools. +- **Feedback:** Known for her strategic thinking and ability to balance customer needs with business objectives. Strong communicator who bridges technical and business stakeholders effectively. diff --git a/week5/knowledge-base/employees/Robert Chen.md b/week5/knowledge-base/employees/Robert Chen.md new file mode 100644 index 0000000..892412e --- /dev/null +++ b/week5/knowledge-base/employees/Robert Chen.md @@ -0,0 +1,67 @@ +# HR Record + +# Robert Chen + +## Summary +- **Date of Birth:** February 28, 1983 +- **Job Title:** Senior Full Stack Engineer +- **Location:** San Francisco, California +- **Current Salary:** $152,000 + +## Insurellm Career Progression +- **January 2016 - Present:** Senior Full Stack Engineer + - Technical lead for Homellm home insurance portal + - Architects full-stack solutions using React, Node.js, and PostgreSQL + - Mentors team of 6 engineers + - Led 3 major platform releases + +- **June 2012 - December 2015:** Full Stack Developer at WebSolutions Inc. + - Built web applications for enterprise clients + - Worked with various JavaScript frameworks and backend technologies + +- **August 2008 - May 2012:** Software Engineer at TechStartup + - Developed features for SaaS platform + - Gained experience across full technology stack + +## Annual Performance History +- **2023:** Rating: 4.8/5 + *Exceptional performance. Led critical platform modernization project. Outstanding technical leadership and mentorship.* + +- **2022:** Rating: 4.6/5 + *Exceeded expectations. Delivered major features ahead of schedule with high quality. Strong team collaboration.* + +- **2021:** Rating: 4.4/5 + *Strong performance with successful migration to microservices architecture. Effective technical decision-making.* + +- **2020:** Rating: 4.2/5 + *Good performance maintaining productivity during remote transition. Supported team well through change.* + +- **2019:** Rating: 4.5/5 + *Excellent year with successful Homellm rewrite. Strong architectural contributions.* + +- **2018:** Rating: 4.3/5 + *Solid performance with consistent high-quality delivery. Growing into technical leadership role.* + +- **2017:** Rating: 4.1/5 + *Good performance. Expanding expertise across full stack and taking on more complex features.* + +- **2016:** Rating: 4.0/5 + *Strong start at Insurellm. Quick to learn domain and contribute effectively.* + +## Compensation History +- **2023:** Base Salary: $152,000 + Bonus: $28,000 +- **2022:** Base Salary: $145,000 + Bonus: $25,000 +- **2021:** Base Salary: $138,000 + Bonus: $22,000 +- **2020:** Base Salary: $132,000 + Bonus: $20,000 +- **2019:** Base Salary: $125,000 + Bonus: $18,000 +- **2018:** Base Salary: $118,000 + Bonus: $15,000 +- **2017:** Base Salary: $112,000 + Bonus: $12,000 +- **2016:** Base Salary: $105,000 + Bonus: $10,000 + +## Other HR Notes +- **Education:** MS in Computer Science from Stanford University, BS in Computer Engineering from MIT +- **Technical Expertise:** Expert in React, Node.js, TypeScript, PostgreSQL, AWS, microservices architecture +- **Recognition:** Engineering Excellence Award 2023, Technical Leadership Award 2021 +- **Mentorship:** Mentored 12 engineers during tenure. Known for developing junior talent into strong contributors. +- **Patents:** Co-inventor on 1 patent for insurance processing optimization +- **Feedback:** World-class engineer with exceptional technical and leadership skills. Trusted advisor to engineering leadership. Critical to company's technical success. diff --git a/week5/knowledge-base/employees/Samantha Greene.md b/week5/knowledge-base/employees/Samantha Greene.md index b67f212..1242df1 100644 --- a/week5/knowledge-base/employees/Samantha Greene.md +++ b/week5/knowledge-base/employees/Samantha Greene.md @@ -4,6 +4,7 @@ - **Date of Birth:** October 14, 1990 - **Job Title:** HR Generalist - **Location:** Denver, Colorado +- **Current Salary:** $70,000 ## Insurellm Career Progression - **2020** - Joined Insurellm as a HR Coordinator diff --git a/week5/knowledge-base/employees/Samuel Trenton.md b/week5/knowledge-base/employees/Samuel Trenton.md index 8452765..49a729b 100644 --- a/week5/knowledge-base/employees/Samuel Trenton.md +++ b/week5/knowledge-base/employees/Samuel Trenton.md @@ -3,9 +3,10 @@ # Samuel Trenton ## Summary -- **Date of Birth:** April 12, 1989 -- **Job Title:** Senior Data Scientist -- **Location:** Austin, Texas +- **Date of Birth:** April 12, 1989 +- **Job Title:** Senior Data Scientist +- **Location:** Austin, Texas +- **Current Salary:** $115,000 ## Insurellm Career Progression - **January 2020 - Present:** Senior Data Scientist diff --git a/week5/knowledge-base/employees/Sarah Williams.md b/week5/knowledge-base/employees/Sarah Williams.md new file mode 100644 index 0000000..a7ff209 --- /dev/null +++ b/week5/knowledge-base/employees/Sarah Williams.md @@ -0,0 +1,50 @@ +# HR Record + +# Sarah Williams + +## Summary +- **Date of Birth:** November 3, 1994 +- **Job Title:** UX Designer +- **Location:** Remote (Based in Portland, Oregon) +- **Current Salary:** $95,000 + +## Insurellm Career Progression +- **January 2022 - Present:** UX Designer + - Leads design for Homellm home insurance portal + - Conducted comprehensive user research resulting in 35% improvement in user satisfaction scores + - Collaborates with product and engineering teams on feature design + +- **June 2020 - December 2021:** Junior UX Designer + - Supported senior designers on Marketllm marketplace redesign + - Created wireframes, prototypes, and user flow diagrams + - Participated in user testing and feedback sessions + +- **August 2018 - May 2020:** UI/UX Designer at StartupHub Inc. + - Designed mobile and web interfaces for various startup clients + - Worked closely with developers to implement design systems + +## Annual Performance History +- **2023:** Rating: 4.4/5 + *Strong design contributions with excellent user research skills. Led successful Homellm redesign project.* + +- **2022:** Rating: 4.0/5 + *Good first year as UX Designer. Showed initiative and creativity, though sometimes needed guidance on complex projects.* + +- **2021:** Rating: 3.6/5 + *Adequate performance but struggled with time management on multiple concurrent projects. Improved by year end.* + +- **2020:** Rating: 4.2/5 + *Solid performance as Junior Designer. Quick to learn Insurellm's design system and processes.* + +## Compensation History +- **2023:** Base Salary: $95,000 + Bonus: $8,000 +- **2022:** Base Salary: $88,000 + Bonus: $6,000 +- **2021:** Base Salary: $78,000 + Bonus: $3,000 +- **2020:** Base Salary: $70,000 + +## Other HR Notes +- **Education:** BFA in Graphic Design from Rhode Island School of Design +- **Skills:** Expert in Figma, Sketch, Adobe Creative Suite. Proficient in HTML/CSS for design prototyping. +- **Professional Development:** Completed UX Research Certification from Nielsen Norman Group (2023). Regularly attends design conferences. +- **Portfolio:** Maintains active design portfolio showcasing Insurellm work (approved for external sharing). +- **Feedback:** Creative thinker with strong empathy for users. Works well remotely and maintains excellent communication with distributed teams. diff --git a/week5/knowledge-base/employees/Tyler Brooks.md b/week5/knowledge-base/employees/Tyler Brooks.md new file mode 100644 index 0000000..0f6f0ab --- /dev/null +++ b/week5/knowledge-base/employees/Tyler Brooks.md @@ -0,0 +1,41 @@ +# HR Record + +# Tyler Brooks + +## Summary +- **Date of Birth:** September 9, 1998 +- **Job Title:** Junior Backend Developer +- **Location:** Austin, Texas +- **Current Salary:** $75,000 + +## Insurellm Career Progression +- **August 2022 - Present:** Junior Backend Developer + - Develops backend services for Carllm auto insurance portal + - Works with Python, Django, and PostgreSQL + - Participates in code reviews and learns from senior engineers + - Fixed 45+ bugs and implemented 12 minor features + +- **June 2021 - July 2022:** Software Engineering Intern + - Completed 14-month internship program at Insurellm + - Worked on various backend tasks and learned company tech stack + - Contributed to internal tools development + +## Annual Performance History +- **2023:** Rating: 3.2/5 + *Meets basic expectations. Shows potential but needs to improve code quality and take more initiative. Sometimes requires excessive guidance on tasks.* + +- **2022:** Rating: 3.5/5 + *Adequate performance for a junior developer. Good learning attitude but needs to develop stronger problem-solving skills.* + +## Compensation History +- **2023:** Base Salary: $75,000 + Bonus: $2,000 +- **2022:** Base Salary: $72,000 + Bonus: $1,500 +- **2021:** Intern Stipend: $30/hour + +## Other HR Notes +- **Education:** BS in Computer Science from University of Texas at Austin (graduated 2021) +- **Skills:** Proficient in Python and SQL. Learning system design and API development best practices. +- **Development Areas:** Needs to improve debugging skills, code documentation, and independent problem-solving. Working with mentor on technical growth. +- **Training:** Enrolled in advanced Python course and backend architecture fundamentals program +- **Feedback:** Eager to learn but sometimes lacks confidence. Needs encouragement to ask questions earlier rather than struggling alone. Shows promise with continued mentorship and experience. +- **Career Goals:** Aspires to become a mid-level backend engineer within 2 years diff --git a/week5/knowledge-base/products/Bizllm.md b/week5/knowledge-base/products/Bizllm.md new file mode 100644 index 0000000..4867ebf --- /dev/null +++ b/week5/knowledge-base/products/Bizllm.md @@ -0,0 +1,59 @@ +# Product Summary + +# Bizllm + +## Summary + +Bizllm is Insurellm's enterprise-grade commercial insurance platform designed to revolutionize how insurers serve business customers. From small businesses to large corporations, Bizllm provides comprehensive tools for underwriting, policy administration, and risk management across multiple commercial lines including general liability, professional liability, property, workers' compensation, and cyber insurance. By leveraging AI and industry-specific data analytics, Bizllm enables commercial insurers to assess complex risks accurately, price policies competitively, and deliver exceptional service to business clients. + +## Features + +### 1. Multi-Line Underwriting Engine +Bizllm's sophisticated underwriting platform handles diverse commercial insurance products within a single unified system. AI-powered risk assessment evaluates business operations, industry-specific exposures, and financial stability to generate accurate quotes across multiple coverage types. + +### 2. Business Intelligence Integration +The platform automatically gathers and analyzes data from business registries, financial databases, industry reports, and third-party risk intelligence sources to provide comprehensive underwriting insights without manual research. + +### 3. Cyber Risk Assessment +Specialized cyber insurance capabilities include automated evaluation of digital security postures, breach history analysis, and IT infrastructure assessment. The platform identifies vulnerabilities and recommends risk mitigation measures to reduce exposure. + +### 4. Workers' Compensation Optimization +Integrated workers' comp tools analyze payroll data, industry classifications, claims history, and safety programs to accurately price coverage and identify opportunities for premium credits through loss control initiatives. + +### 5. Commercial Property Evaluation +Advanced property risk modeling incorporates location data, building characteristics, catastrophe modeling, and environmental hazards to assess property risks accurately. Integration with aerial imagery and IoT sensors provides real-time property condition monitoring. + +### 6. Professional Liability Specialization +Industry-specific professional liability underwriting for sectors including healthcare, legal, accounting, technology, and consulting. The platform evaluates professional credentials, claims history, and industry-specific risk factors. + +### 7. Portfolio Management Dashboard +Comprehensive analytics provide insurers with portfolio-level insights including line-of-business performance, loss ratios, renewal rates, and geographic concentrations. Predictive analytics identify profitable growth opportunities and emerging risks. + +### 8. Agent and Broker Portal +Dedicated portals enable distribution partners to quote, bind, and service commercial policies efficiently. Real-time commission tracking, marketing materials, and training resources support productive agency relationships. + +### 9. Claims Management Integration +Seamless connection with claims systems enables fast incident reporting, reserve establishment, and loss tracking. AI-powered tools identify subrogation opportunities and predict claim severity for better reserves. + +## Pricing + +Bizllm's pricing structure reflects the complexity and value of commercial insurance operations: + +- **Business Tier:** $6,000/month for regional commercial insurers writing select lines of business, including core underwriting and policy administration. +- **Professional Tier:** $12,000/month for multi-line carriers, adding cyber risk tools, advanced analytics, and expanded integration capabilities. +- **Enterprise Tier:** Custom pricing for national and specialty commercial insurers requiring full customization, API access, dedicated environments, and white-label options. + +All tiers include implementation services, staff training, and continuous platform updates. + +## Roadmap + +Bizllm's forward-looking development roadmap includes: + +- **Q2 2025:** Launch of Bizllm version 1.0 with core multi-line underwriting, quoting, and policy administration. +- **Q4 2025:** Introduction of enhanced cyber insurance capabilities including active threat monitoring and breach response coordination. +- **Q2 2026:** Release of embedded IoT integration for real-time risk monitoring in property and workers' comp lines. +- **Q4 2026:** Launch of parametric insurance module enabling rapid payout triggers for weather events and business interruption. +- **Q2 2027:** Introduction of climate risk modeling tools incorporating forward-looking environmental data and transition risk analysis. +- **Q4 2027:** Expansion into international markets with country-specific commercial insurance products and regulatory compliance. + +Bizllm empowers commercial insurers to compete effectively in complex markets while delivering the digital experiences today's business customers demand. Transform your commercial insurance operations with Bizllm! diff --git a/week5/knowledge-base/products/Claimllm.md b/week5/knowledge-base/products/Claimllm.md new file mode 100644 index 0000000..e47d16d --- /dev/null +++ b/week5/knowledge-base/products/Claimllm.md @@ -0,0 +1,62 @@ +# Product Summary + +# Claimllm + +## Summary + +Claimllm is Insurellm's revolutionary claims processing platform that transforms the claims experience for insurers, adjusters, and policyholders. Powered by advanced AI, machine learning, and computer vision, Claimllm automates claims handling across all insurance lines—from first notice of loss through final settlement. By dramatically reducing processing time, improving accuracy, and enhancing fraud detection, Claimllm enables insurers to deliver exceptional claims service while significantly reducing operational costs. The platform seamlessly integrates with existing policy administration and core systems to create a unified insurance ecosystem. + +## Features + +### 1. Intelligent FNOL Processing +Claimllm's AI-powered first notice of loss intake captures claim details through multiple channels including mobile apps, web portals, phone integrations, and chatbots. Natural language processing extracts key information automatically, creating structured claim files without manual data entry. + +### 2. Automated Triage and Routing +Machine learning algorithms instantly assess claim severity, complexity, and fraud potential to route claims to appropriate handlers. Simple claims enter straight-through processing while complex cases receive immediate adjuster assignment with pre-populated investigation guidance. + +### 3. Computer Vision Damage Assessment +Advanced image recognition technology analyzes photos and videos of damaged property or vehicles to estimate repair costs accurately. The system identifies damage types, measures extent, and generates preliminary estimates in minutes rather than days. + +### 4. Predictive Fraud Detection +Sophisticated fraud analytics evaluate claims against historical patterns, network analysis, and anomaly detection to flag suspicious claims for investigation. The system learns continuously from confirmed fraud cases to improve detection accuracy. + +### 5. Smart Document Processing +Optical character recognition and natural language processing automatically extract information from medical records, police reports, repair estimates, and supporting documentation. The system validates information against policy terms and flags discrepancies for review. + +### 6. Dynamic Reserve Setting +AI-powered predictive modeling analyzes claim characteristics, historical outcomes, and current case details to recommend accurate reserves. Automated reserve adjustments occur as new information becomes available, ensuring financial accuracy. + +### 7. Vendor Management Platform +Integrated tools coordinate with repair shops, medical providers, lawyers, and other service providers. Automated assignment based on location, capacity, and performance metrics ensures efficient vendor utilization and quality outcomes. + +### 8. Payment Automation +Straight-through payment processing for approved claims includes direct deposit, digital payments, and traditional check issuance. Multi-party payment splitting handles complex scenarios involving mortgagees, lessors, and medical providers. + +### 9. Claimant Communication Hub +Omnichannel communication tools keep claimants informed via their preferred method—text, email, app notifications, or phone. Automated status updates reduce incoming inquiries while improving customer satisfaction. + +### 10. Analytics and Reporting +Comprehensive dashboards track key metrics including cycle time, loss ratios, settlement patterns, and customer satisfaction. Predictive analytics identify process bottlenecks and opportunities for improvement. + +## Pricing + +Claimllm offers flexible pricing models to accommodate insurers of all sizes: + +- **Core Tier:** $4,500/month for smaller insurers processing up to 5,000 claims annually, including essential automation features and standard integrations. +- **Advanced Tier:** $9,500/month for mid-sized insurers handling up to 25,000 claims annually, adding computer vision, predictive analytics, and expanded vendor management. +- **Enterprise Tier:** Custom pricing for high-volume insurers requiring unlimited claim capacity, full API access, dedicated infrastructure, advanced fraud analytics, and white-label capabilities. + +All tiers include comprehensive implementation, claims staff training, and continuous platform enhancements. + +## Roadmap + +Claimllm's ambitious development roadmap includes: + +- **Q1 2025:** Launch of Claimllm version 1.0 with core claims processing automation, FNOL capture, and basic fraud detection. +- **Q3 2025:** Introduction of advanced computer vision capabilities for property and auto damage assessment with repair cost estimation. +- **Q1 2026:** Release of predictive litigation analytics identifying claims likely to involve attorneys and recommending settlement strategies. +- **Q3 2026:** Launch of blockchain-based subrogation network enabling automated inter-carrier claim recovery and settlement. +- **Q1 2027:** Introduction of real-time settlement negotiation AI that optimizes settlement amounts based on claim details and historical outcomes. +- **Q3 2027:** Expansion into catastrophe claims management with disaster response coordination and large-loss handling capabilities. + +Claimllm represents the future of insurance claims—faster, smarter, and more customer-centric. Transform your claims operation and deliver the service your policyholders deserve! diff --git a/week5/knowledge-base/products/Healthllm.md b/week5/knowledge-base/products/Healthllm.md new file mode 100644 index 0000000..719ceee --- /dev/null +++ b/week5/knowledge-base/products/Healthllm.md @@ -0,0 +1,56 @@ +# Product Summary + +# Healthllm + +## Summary + +Healthllm is Insurellm's comprehensive health insurance platform that empowers insurance providers to deliver modern, personalized health coverage. By combining advanced AI technology with healthcare data analytics, Healthllm streamlines every aspect of health insurance operations—from plan design and enrollment to claims processing and member engagement. Built for the complexities of the healthcare industry, Healthllm helps insurers reduce costs, improve member outcomes, and navigate the evolving regulatory landscape with confidence. + +## Features + +### 1. Intelligent Plan Design +Healthllm's AI-powered tools help insurers create competitive health plans by analyzing market trends, member demographics, and healthcare utilization patterns. The platform suggests optimal benefit structures, deductibles, and network configurations to maximize both member value and profitability. + +### 2. Real-Time Eligibility Verification +Integrated eligibility checking ensures instant verification of coverage status, reducing claim denials and improving provider satisfaction. The system connects with healthcare provider networks for seamless real-time validation. + +### 3. AI-Driven Claims Adjudication +Automated claims processing uses machine learning to review claims for accuracy, identify potential fraud, and expedite legitimate payments. The system learns from historical data to continuously improve processing accuracy and efficiency. + +### 4. Predictive Healthcare Analytics +Advanced analytics identify high-risk members who would benefit from preventive care interventions, enabling insurers to implement proactive wellness programs that reduce long-term costs and improve member health outcomes. + +### 5. Provider Network Management +Comprehensive tools for managing provider relationships, contract negotiations, and network adequacy. The platform tracks provider performance metrics and identifies opportunities for cost savings through strategic partnerships. + +### 6. Member Engagement Platform +A mobile-first member portal provides easy access to coverage information, digital ID cards, claims status, provider directories, and telehealth services. Push notifications keep members informed about preventive care opportunities and wellness programs. + +### 7. Medication Management +Integration with pharmacy benefit managers enables formulary management, prior authorization automation, and medication adherence tracking. AI-powered tools identify cost-saving generic alternatives and flag potential drug interactions. + +### 8. Regulatory Compliance Engine +Built-in compliance monitoring ensures adherence to ACA requirements, state mandates, and HIPAA regulations. Automated reporting simplifies regulatory filings and reduces compliance risk. + +## Pricing + +Healthllm offers tiered pricing to serve health insurers across the spectrum: + +- **Essential Tier:** $8,000/month for regional health plans, providing core platform capabilities and standard integrations. +- **Professional Tier:** $15,000/month for larger insurers, including advanced analytics, predictive modeling, and expanded integration options. +- **Enterprise Tier:** Custom pricing for national carriers and health systems requiring extensive customization, multi-state support, dedicated infrastructure, and premium support services. + +All plans include implementation support, staff training, and ongoing platform enhancements. + +## Roadmap + +Healthllm's strategic development roadmap includes: + +- **Q1 2025:** Launch of Healthllm version 1.0 featuring core claims processing, eligibility verification, and member portal. +- **Q3 2025:** Introduction of predictive analytics module for population health management and risk stratification. +- **Q1 2026:** Release of advanced AI claims adjudication with automated fraud detection and payment optimization. +- **Q3 2026:** Launch of social determinants of health (SDOH) integration, enabling holistic member care and targeted interventions. +- **Q1 2027:** Introduction of value-based care management tools supporting ACO and bundled payment models. +- **Q3 2027:** Global expansion with international healthcare system integrations and multi-language support. + +Healthllm represents Insurellm's commitment to transforming health insurance through technology that improves outcomes for insurers, providers, and members alike. Join us in building the future of health insurance! diff --git a/week5/knowledge-base/products/Lifellm.md b/week5/knowledge-base/products/Lifellm.md new file mode 100644 index 0000000..331151e --- /dev/null +++ b/week5/knowledge-base/products/Lifellm.md @@ -0,0 +1,52 @@ +# Product Summary + +# Lifellm + +## Summary + +Lifellm is an advanced life insurance platform developed by Insurellm that revolutionizes how insurance providers offer life coverage to their customers. Leveraging cutting-edge AI and predictive analytics, Lifellm enables insurers to provide personalized life insurance policies with streamlined underwriting, accurate risk assessment, and exceptional customer experiences. Designed for both B2B and B2C markets, Lifellm helps insurance companies modernize their life insurance operations while reducing costs and improving customer satisfaction. + +## Features + +### 1. AI-Powered Underwriting +Lifellm's sophisticated AI engine analyzes health records, lifestyle data, and demographic information to accelerate the underwriting process. What traditionally took weeks can now be completed in hours or even minutes, enabling insurers to issue policies faster while maintaining accuracy. + +### 2. Predictive Risk Modeling +Advanced machine learning algorithms assess mortality risk by analyzing vast datasets including medical histories, genetic markers, and lifestyle factors. This enables insurers to price policies more accurately and identify opportunities for coverage expansion. + +### 3. Digital Health Integration +Lifellm seamlessly integrates with wearables and health tracking apps, allowing insurers to offer dynamic policies that reward healthy behaviors. This creates win-win scenarios where customers maintain better health while enjoying premium discounts. + +### 4. Automated Policy Management +The platform provides a comprehensive suite of tools for policy administration, including beneficiary management, premium calculations, coverage adjustments, and policy renewals. Automation reduces administrative burden and minimizes errors. + +### 5. Smart Document Processing +Lifellm utilizes natural language processing and optical character recognition to automatically extract and validate information from medical records, applications, and supporting documentation, significantly reducing manual data entry. + +### 6. Customer Portal +A user-friendly portal enables policyholders to manage their coverage, update beneficiaries, access policy documents, and track wellness incentives 24/7. Mobile app integration ensures customers can manage their life insurance anywhere. + +### 7. Compliance Automation +Built-in regulatory compliance tools ensure that policies meet state and federal requirements, with automatic updates as regulations change. This reduces compliance risk for insurers operating across multiple jurisdictions. + +## Pricing + +Lifellm's flexible pricing structure accommodates insurance providers of all sizes: + +- **Starter Tier:** $3,500/month for small insurers, including core underwriting features and basic integrations. +- **Growth Tier:** $7,500/month for mid-sized companies, adding predictive analytics, digital health integration, and advanced reporting. +- **Enterprise Tier:** Custom pricing for large insurance providers requiring full customization, white-label options, dedicated support, and enterprise-grade security. + +All tiers include comprehensive onboarding, training programs, and regular platform updates. + +## Roadmap + +The development roadmap for Lifellm includes these strategic milestones: + +- **Q2 2025:** Launch of Lifellm version 1.0 with core AI underwriting and policy management capabilities. +- **Q4 2025:** Introduction of digital health platform integrations with major wearable device manufacturers and health app providers. +- **Q2 2026:** Release of Lifellm version 2.0 featuring enhanced predictive modeling, genetic risk assessment capabilities, and blockchain-based policy verification. +- **Q4 2026:** Launch of embedded life insurance API allowing partners to offer life coverage within their own platforms and applications. +- **Q2 2027:** Expansion into international markets with localized regulatory compliance features and multi-currency support. + +With Lifellm, Insurellm is transforming life insurance into a modern, customer-centric experience that benefits both insurers and policyholders. Experience the future of life insurance technology today! diff --git a/week5/pro_implementation/answer.py b/week5/pro_implementation/answer.py new file mode 100644 index 0000000..1153c3f --- /dev/null +++ b/week5/pro_implementation/answer.py @@ -0,0 +1,145 @@ +from openai import OpenAI +from dotenv import load_dotenv +from chromadb import PersistentClient +from litellm import completion +from pydantic import BaseModel, Field +from pathlib import Path +from tenacity import retry, wait_exponential + + +load_dotenv(override=True) + +# MODEL = "openai/gpt-4.1-nano" +MODEL = "groq/openai/gpt-oss-120b" +DB_NAME = str(Path(__file__).parent.parent / "preprocessed_db") +KNOWLEDGE_BASE_PATH = Path(__file__).parent.parent / "knowledge-base" +SUMMARIES_PATH = Path(__file__).parent.parent / "summaries" + +collection_name = "docs" +embedding_model = "text-embedding-3-large" +wait = wait_exponential(multiplier=1, min=10, max=240) + +openai = OpenAI() + +chroma = PersistentClient(path=DB_NAME) +collection = chroma.get_or_create_collection(collection_name) + +RETRIEVAL_K = 20 +FINAL_K = 10 + +SYSTEM_PROMPT = """ +You are a knowledgeable, friendly assistant representing the company Insurellm. +You are chatting with a user about Insurellm. +Your answer will be evaluated for accuracy, relevance and completeness, so make sure it only answers the question and fully answers it. +If you don't know the answer, say so. +For context, here are specific extracts from the Knowledge Base that might be directly relevant to the user's question: +{context} + +With this context, please answer the user's question. Be accurate, relevant and complete. +""" + + +class Result(BaseModel): + page_content: str + metadata: dict + + +class RankOrder(BaseModel): + order: list[int] = Field( + description="The order of relevance of chunks, from most relevant to least relevant, by chunk id number" + ) + + +@retry(wait=wait) +def rerank(question, chunks): + system_prompt = """ +You are a document re-ranker. +You are provided with a question and a list of relevant chunks of text from a query of a knowledge base. +The chunks are provided in the order they were retrieved; this should be approximately ordered by relevance, but you may be able to improve on that. +You must rank order the provided chunks by relevance to the question, with the most relevant chunk first. +Reply only with the list of ranked chunk ids, nothing else. Include all the chunk ids you are provided with, reranked. +""" + user_prompt = f"The user has asked the following question:\n\n{question}\n\nOrder all the chunks of text by relevance to the question, from most relevant to least relevant. Include all the chunk ids you are provided with, reranked.\n\n" + user_prompt += "Here are the chunks:\n\n" + for index, chunk in enumerate(chunks): + user_prompt += f"# CHUNK ID: {index + 1}:\n\n{chunk.page_content}\n\n" + user_prompt += "Reply only with the list of ranked chunk ids, nothing else." + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + response = completion(model=MODEL, messages=messages, response_format=RankOrder) + reply = response.choices[0].message.content + order = RankOrder.model_validate_json(reply).order + return [chunks[i - 1] for i in order] + + +def make_rag_messages(question, history, chunks): + context = "\n\n".join( + f"Extract from {chunk.metadata['source']}:\n{chunk.page_content}" for chunk in chunks + ) + system_prompt = SYSTEM_PROMPT.format(context=context) + return ( + [{"role": "system", "content": system_prompt}] + + history + + [{"role": "user", "content": question}] + ) + + +@retry(wait=wait) +def rewrite_query(question, history=[]): + """Rewrite the user's question to be a more specific question that is more likely to surface relevant content in the Knowledge Base.""" + message = f""" +You are in a conversation with a user, answering questions about the company Insurellm. +You are about to look up information in a Knowledge Base to answer the user's question. + +This is the history of your conversation so far with the user: +{history} + +And this is the user's current question: +{question} + +Respond only with a short, refined question that you will use to search the Knowledge Base. +It should be a VERY short specific question most likely to surface content. Focus on the question details. +IMPORTANT: Respond ONLY with the precise knowledgebase query, nothing else. +""" + response = completion(model=MODEL, messages=[{"role": "system", "content": message}]) + return response.choices[0].message.content + + +def merge_chunks(chunks, reranked): + merged = chunks[:] + existing = [chunk.page_content for chunk in chunks] + for chunk in reranked: + if chunk.page_content not in existing: + merged.append(chunk) + return merged + + +def fetch_context_unranked(question): + query = openai.embeddings.create(model=embedding_model, input=[question]).data[0].embedding + results = collection.query(query_embeddings=[query], n_results=RETRIEVAL_K) + chunks = [] + for result in zip(results["documents"][0], results["metadatas"][0]): + chunks.append(Result(page_content=result[0], metadata=result[1])) + return chunks + + +def fetch_context(original_question): + rewritten_question = rewrite_query(original_question) + chunks1 = fetch_context_unranked(original_question) + chunks2 = fetch_context_unranked(rewritten_question) + chunks = merge_chunks(chunks1, chunks2) + reranked = rerank(original_question, chunks) + return reranked[:FINAL_K] + + +@retry(wait=wait) +def answer_question(question: str, history: list[dict] = []) -> tuple[str, list]: + """ + Answer a question using RAG and return the answer and the retrieved context + """ + chunks = fetch_context(question) + messages = make_rag_messages(question, history, chunks) + response = completion(model=MODEL, messages=messages) + return response.choices[0].message.content, chunks diff --git a/week5/pro_implementation/ingest.py b/week5/pro_implementation/ingest.py new file mode 100644 index 0000000..e2bbfa0 --- /dev/null +++ b/week5/pro_implementation/ingest.py @@ -0,0 +1,146 @@ +from pathlib import Path +from openai import OpenAI +from dotenv import load_dotenv +from pydantic import BaseModel, Field +from chromadb import PersistentClient +from tqdm import tqdm +from litellm import completion +from multiprocessing import Pool +from tenacity import retry, wait_exponential + + +load_dotenv(override=True) + +MODEL = "openai/gpt-4.1-nano" + +DB_NAME = str(Path(__file__).parent.parent / "preprocessed_db") +collection_name = "docs" +embedding_model = "text-embedding-3-large" +KNOWLEDGE_BASE_PATH = Path(__file__).parent.parent / "knowledge-base" +AVERAGE_CHUNK_SIZE = 100 +wait = wait_exponential(multiplier=1, min=10, max=240) + + +WORKERS = 3 + +openai = OpenAI() + + +class Result(BaseModel): + page_content: str + metadata: dict + + +class Chunk(BaseModel): + headline: str = Field( + description="A brief heading for this chunk, typically a few words, that is most likely to be surfaced in a query", + ) + summary: str = Field( + description="A few sentences summarizing the content of this chunk to answer common questions" + ) + original_text: str = Field( + description="The original text of this chunk from the provided document, exactly as is, not changed in any way" + ) + + def as_result(self, document): + metadata = {"source": document["source"], "type": document["type"]} + return Result( + page_content=self.headline + "\n\n" + self.summary + "\n\n" + self.original_text, + metadata=metadata, + ) + + +class Chunks(BaseModel): + chunks: list[Chunk] + + +def fetch_documents(): + """A homemade version of the LangChain DirectoryLoader""" + + documents = [] + + for folder in KNOWLEDGE_BASE_PATH.iterdir(): + doc_type = folder.name + for file in folder.rglob("*.md"): + with open(file, "r", encoding="utf-8") as f: + documents.append({"type": doc_type, "source": file.as_posix(), "text": f.read()}) + + print(f"Loaded {len(documents)} documents") + return documents + + +def make_prompt(document): + how_many = (len(document["text"]) // AVERAGE_CHUNK_SIZE) + 1 + return f""" +You take a document and you split the document into overlapping chunks for a KnowledgeBase. + +The document is from the shared drive of a company called Insurellm. +The document is of type: {document["type"]} +The document has been retrieved from: {document["source"]} + +A chatbot will use these chunks to answer questions about the company. +You should divide up the document as you see fit, being sure that the entire document is returned across the chunks - don't leave anything out. +This document should probably be split into at least {how_many} chunks, but you can have more or less as appropriate, ensuring that there are individual chunks to answer specific questions. +There should be overlap between the chunks as appropriate; typically about 25% overlap or about 50 words, so you have the same text in multiple chunks for best retrieval results. + +For each chunk, you should provide a headline, a summary, and the original text of the chunk. +Together your chunks should represent the entire document with overlap. + +Here is the document: + +{document["text"]} + +Respond with the chunks. +""" + + +def make_messages(document): + return [ + {"role": "user", "content": make_prompt(document)}, + ] + + +@retry(wait=wait) +def process_document(document): + messages = make_messages(document) + response = completion(model=MODEL, messages=messages, response_format=Chunks) + reply = response.choices[0].message.content + doc_as_chunks = Chunks.model_validate_json(reply).chunks + return [chunk.as_result(document) for chunk in doc_as_chunks] + + +def create_chunks(documents): + """ + Create chunks using a number of workers in parallel. + If you get a rate limit error, set the WORKERS to 1. + """ + chunks = [] + with Pool(processes=WORKERS) as pool: + for result in tqdm(pool.imap_unordered(process_document, documents), total=len(documents)): + chunks.extend(result) + return chunks + + +def create_embeddings(chunks): + chroma = PersistentClient(path=DB_NAME) + if collection_name in [c.name for c in chroma.list_collections()]: + chroma.delete_collection(collection_name) + + texts = [chunk.page_content for chunk in chunks] + emb = openai.embeddings.create(model=embedding_model, input=texts).data + vectors = [e.embedding for e in emb] + + collection = chroma.get_or_create_collection(collection_name) + + ids = [str(i) for i in range(len(chunks))] + metas = [chunk.metadata for chunk in chunks] + + collection.add(ids=ids, embeddings=vectors, documents=texts, metadatas=metas) + print(f"Vectorstore created with {collection.count()} documents") + + +if __name__ == "__main__": + documents = fetch_documents() + chunks = create_chunks(documents) + create_embeddings(chunks) + print("Ingestion complete")