week5/day4.ipynb

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    "## 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",
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   "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(override=True)\n",
    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')"
   ]
  },
  {
   "cell_type": "code",
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   "id": "730711a9-6ffe-4eee-8f48-d6cfb7314905",
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   "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",
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   "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)"
   ]
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   "cell_type": "code",
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   "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)}\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013",
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   "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}<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": {},
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   "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": "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": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#900;\">PLEASE READ ME! Ignoring the Deprecation Warning</h2>\n",
    "            <span style=\"color:#900;\">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.<br/><br/>\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",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "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 4o-mini 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": "e6eb99fb-33ec-4025-ab92-b634ede03647",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3536590-85c7-4155-bd87-ae78a1467670",
   "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\"]"
   ]
  },
  {
   "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": []
  }
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