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 "cells": [
  {
   "cell_type": "markdown",
   "id": "b4992675",
   "metadata": {},
   "source": [
    "# Personal Knowledge Worker Using RAG\n",
    "\n",
    "Tool for querying personal file storage using RAG. Working with local Llama instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2680f9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip -q install langchain langchain-community sentence-transformers ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b6caab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "from langchain_community.document_loaders import DirectoryLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.document_loaders import TextLoader\n",
    "import glob\n",
    "from langchain_community.llms import Ollama\n",
    "from langchain.embeddings import OllamaEmbeddings\n",
    "from langchain_chroma import Chroma\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "import gradio as gr\n",
    "from langchain_core.callbacks import StdOutCallbackHandler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d42c7523",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using mistral model to run locally\n",
    "\n",
    "MODEL = \"mistral\"\n",
    "db_name = \"vector_db\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3ac38f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_metadata(doc, doc_type):\n",
    "    doc.metadata[\"doc_type\"] = doc_type\n",
    "    return doc\n",
    "\n",
    "\n",
    "# Using reference data as default to avoid repeating the same steps. This can be substituted with any other data later dynamically\n",
    "def load_documents(folders = glob.glob(\"../../knowledge-base/*\")):\n",
    "    text_loader_kwargs = {'encoding': 'utf-8'}\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)}\")\n",
    "\n",
    "    return documents, chunks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a53301",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OllamaEmbeddings(model=MODEL)\n",
    "\n",
    "documents, chunks = load_documents()\n",
    "\n",
    "# Use existing vectorstore if it exists\n",
    "if os.path.exists(db_name):\n",
    "    vectorstore = Chroma(persist_directory=db_name, embedding_function=embeddings)\n",
    "else:\n",
    "    # Create a new vectorstore\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": "2e906f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = Ollama(model=MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f1ca473",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = vectorstore.as_retriever()\n",
    "\n",
    "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
    "\n",
    "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34d5a20a",
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"Please explain what Insurellm is in a couple of sentences\"\n",
    "result = conversation_chain.invoke(question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0ad3702",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(result[\"answer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "652d8420",
   "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\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73bd1d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# And in Gradio:\n",
    "\n",
    "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
   ]
  }
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