{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import glob\n", "from dotenv import load_dotenv\n", "import gradio as gr\n", "# import gemini\n", "import google.generativeai" ] }, { "cell_type": "code", "execution_count": 18, "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_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI\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": 4, "metadata": {}, "outputs": [], "source": [ "# price is a factor for our company, so we're going to use a low cost model\n", "\n", "MODEL = \"gemini-1.5-flash\"\n", "db_name = \"vector_db\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Load environment variables in a file called .env\n", "\n", "load_dotenv()\n", "os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "google.generativeai.configure()" ] }, { "cell_type": "code", "execution_count": 7, "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": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "123" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(chunks)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Document types found: company, contracts, employees, products\n" ] } ], "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": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vectorstore created with 123 documents\n" ] } ], "source": [ "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n", "\n", "# 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()\n", "\n", "# 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": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The vectors have 768 dimensions\n" ] } ], "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": 13, "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, "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, "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", "metadata": {}, "source": [ "RAG pipeline using langchain" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\GANESH\\AppData\\Local\\Temp\\ipykernel_524\\4130109764.py:5: LangChainDeprecationWarning:\n", "\n", "Please see the migration guide at: https://python.langchain.com/docs/versions/migrating_memory/\n", "\n" ] } ], "source": [ "# create a new Chat with ChatGoogleGenerativeAI\n", "llm = ChatGoogleGenerativeAI(model=MODEL, temperature=0.7)\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": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Insurellm is an insurance technology company with 200 employees and over 300 clients worldwide. They offer four software products, including Homellm, a portal for home insurance companies that integrates with existing platforms and offers a customer portal for policy management. Their pricing model is based on provider size and customization needs.\n" ] } ], "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": 21, "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", "metadata": {}, "source": [ "Gradio User Interface" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def chat(message, history):\n", " result = conversation_chain.invoke({\"question\": message})\n", " return result[\"answer\"]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7860\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "llms", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 2 }