{ "cells": [ { "cell_type": "markdown", "id": "dfe37963-1af6-44fc-a841-8e462443f5e6", "metadata": {}, "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": 1, "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": 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\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": 3, "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": 4, "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": 5, "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": 6, "id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Created a chunk of size 1088, which is longer than the specified 1000\n" ] } ], "source": [ "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", "chunks = text_splitter.split_documents(documents)" ] }, { "cell_type": "code", "execution_count": 7, "id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "123" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "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}
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": "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": [] } ], "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": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }