{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dfe37963-1af6-44fc-a841-8e462443f5e6",
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   "source": [
    "## Expert Knowledge Worker\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",
    "\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": {},
   "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()\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 Jon R, a student on the course, for this fix needed for some users \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",
    "More details in the resources."
   ]
  },
  {
   "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",
    "# 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}<br>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}<br>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": "98a10481",
   "metadata": {},
   "source": [
    "## If you followed the advice to preload your vectorstore (separated out the ingestion (pipeline) from the main file (this file))\n",
    "\n",
    "As was discussed in day 3 (day3.ipynb) of this section, when you implement this in a project, you'll likely separate out the loading, chunking,\n",
    "embedding, and storing of the vector database. \n",
    "\n",
    "At this point, all of the code after the # Read in documents using LangChain's loaders (above) can be replaced with the next block of code.\n",
    "Essentially, you are simply loading up the vectorstore with what you stored before. Note that whenever there are \n",
    "changes to your data, you'll need to re-run the ingestion (pipeline) script to revectorize the database. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74bae594",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the above with the following:\n",
    "\n",
    "# Read Vectorstore\n",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "# Load the existing vector database that you created from the ingest/pipeline script\n",
    "if os.path.exists(db_name):\n",
    "  vectorstore = Chroma(persist_directory=db_name, embedding_function=embeddings)\n",
    "  print(f\"Vectorstore loaded with {vectorstore._collection.count()} documents\")\n",
    "else:\n",
    "  print(\"Vectorstore does not exist. Please run the ingestor script first.\")\n"
   ]
  },
  {
   "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": "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).launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5435b2b9-935c-48cd-aaf3-73a837ecde49",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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