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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import os\n", |
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"import glob\n", |
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"from dotenv import load_dotenv\n", |
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"import gradio as gr\n", |
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"# import gemini\n", |
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"import google.generativeai" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 18, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports for langchain\n", |
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"\n", |
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"from langchain.document_loaders import DirectoryLoader, TextLoader\n", |
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"from langchain.text_splitter import CharacterTextSplitter\n", |
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"from langchain.schema import Document\n", |
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"# from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", |
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"from langchain_chroma import Chroma\n", |
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"from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI\n", |
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"import numpy as np\n", |
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"from sklearn.manifold import TSNE\n", |
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"import plotly.graph_objects as go\n", |
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"from langchain.memory import ConversationBufferMemory\n", |
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"from langchain.chains import ConversationalRetrievalChain" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# price is a factor for our company, so we're going to use a low cost model\n", |
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"\n", |
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"MODEL = \"gemini-1.5-flash\"\n", |
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"db_name = \"vector_db\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"\n", |
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"load_dotenv()\n", |
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"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 6, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"google.generativeai.configure()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 7, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Read in documents using LangChain's loaders\n", |
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"# Take everything in all the sub-folders of our knowledgebase\n", |
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"\n", |
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"folders = glob.glob(\"knowledge-base/*\")\n", |
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"\n", |
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"# With thanks to CG and Jon R, students on the course, for this fix needed for some users \n", |
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"text_loader_kwargs = {'encoding': 'utf-8'}\n", |
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"# If that doesn't work, some Windows users might need to uncomment the next line instead\n", |
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"# text_loader_kwargs={'autodetect_encoding': True}\n", |
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"\n", |
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"documents = []\n", |
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"for folder in folders:\n", |
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" doc_type = os.path.basename(folder)\n", |
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" loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n", |
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" folder_docs = loader.load()\n", |
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" for doc in folder_docs:\n", |
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" doc.metadata[\"doc_type\"] = doc_type\n", |
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" documents.append(doc)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 8, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"Created a chunk of size 1088, which is longer than the specified 1000\n" |
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] |
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} |
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], |
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"source": [ |
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", |
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"chunks = text_splitter.split_documents(documents)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 9, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"123" |
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] |
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}, |
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"execution_count": 9, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"len(chunks)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 10, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Document types found: company, contracts, employees, products\n" |
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] |
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} |
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], |
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"source": [ |
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"doc_types = set(chunk.metadata['doc_type'] for chunk in chunks)\n", |
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"print(f\"Document types found: {', '.join(doc_types)}\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 11, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Vectorstore created with 123 documents\n" |
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] |
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} |
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], |
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"source": [ |
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"embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n", |
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"\n", |
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"# Check if a Chroma Datastore already exists - if so, delete the collection to start from scratch\n", |
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"\n", |
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"if os.path.exists(db_name):\n", |
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" Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n", |
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"\n", |
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"# Create our Chroma vectorstore!\n", |
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"\n", |
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"vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", |
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"print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 12, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"The vectors have 768 dimensions\n" |
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] |
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} |
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], |
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"source": [ |
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"# Get one vector and find how many dimensions it has\n", |
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"\n", |
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"collection = vectorstore._collection\n", |
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"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n", |
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"dimensions = len(sample_embedding)\n", |
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"print(f\"The vectors have {dimensions:,} dimensions\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 13, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Prework\n", |
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"\n", |
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"result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n", |
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"vectors = np.array(result['embeddings'])\n", |
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"documents = result['documents']\n", |
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"doc_types = [metadata['doc_type'] for metadata in result['metadatas']]\n", |
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"colors = [['blue', 'green', 'red', 'orange'][['products', 'employees', 'contracts', 'company'].index(t)] for t in doc_types]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# We humans find it easier to visalize things in 2D!\n", |
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"# Reduce the dimensionality of the vectors to 2D using t-SNE\n", |
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"# (t-distributed stochastic neighbor embedding)\n", |
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"\n", |
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"tsne = TSNE(n_components=2, random_state=42)\n", |
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"reduced_vectors = tsne.fit_transform(vectors)\n", |
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"\n", |
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"# Create the 2D scatter plot\n", |
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"fig = go.Figure(data=[go.Scatter(\n", |
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" x=reduced_vectors[:, 0],\n", |
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" y=reduced_vectors[:, 1],\n", |
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" mode='markers',\n", |
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" marker=dict(size=5, color=colors, opacity=0.8),\n", |
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" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", |
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" hoverinfo='text'\n", |
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")])\n", |
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"\n", |
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"fig.update_layout(\n", |
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" title='2D Chroma Vector Store Visualization',\n", |
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" scene=dict(xaxis_title='x',yaxis_title='y'),\n", |
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" width=800,\n", |
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" height=600,\n", |
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" margin=dict(r=20, b=10, l=10, t=40)\n", |
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")\n", |
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"\n", |
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"fig.show()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's try 3D!\n", |
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"\n", |
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"tsne = TSNE(n_components=3, random_state=42)\n", |
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"reduced_vectors = tsne.fit_transform(vectors)\n", |
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"\n", |
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"# Create the 3D scatter plot\n", |
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"fig = go.Figure(data=[go.Scatter3d(\n", |
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" x=reduced_vectors[:, 0],\n", |
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" y=reduced_vectors[:, 1],\n", |
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" z=reduced_vectors[:, 2],\n", |
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" mode='markers',\n", |
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" marker=dict(size=5, color=colors, opacity=0.8),\n", |
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" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n", |
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" hoverinfo='text'\n", |
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")])\n", |
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"\n", |
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"fig.update_layout(\n", |
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" title='3D Chroma Vector Store Visualization',\n", |
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" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n", |
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" width=900,\n", |
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" height=700,\n", |
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" margin=dict(r=20, b=10, l=10, t=40)\n", |
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")\n", |
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"\n", |
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"fig.show()" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"RAG pipeline using langchain" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 19, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"C:\\Users\\GANESH\\AppData\\Local\\Temp\\ipykernel_524\\4130109764.py:5: LangChainDeprecationWarning:\n", |
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"\n", |
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"Please see the migration guide at: https://python.langchain.com/docs/versions/migrating_memory/\n", |
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"\n" |
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] |
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} |
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], |
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"source": [ |
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"# create a new Chat with ChatGoogleGenerativeAI\n", |
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"llm = ChatGoogleGenerativeAI(model=MODEL, temperature=0.7)\n", |
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"\n", |
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"# set up the conversation memory for the chat\n", |
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"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", |
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"\n", |
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"# the retriever is an abstraction over the VectorStore that will be used during RAG\n", |
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"retriever = vectorstore.as_retriever()\n", |
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"\n", |
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"# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", |
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"conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 20, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"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" |
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] |
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} |
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], |
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"source": [ |
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"query = \"Can you describe Insurellm in a few sentences\"\n", |
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"result = conversation_chain.invoke({\"question\":query})\n", |
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"print(result[\"answer\"])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 21, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# set up a new conversation memory for the chat\n", |
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"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", |
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"\n", |
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"# putting it together: set up the conversation chain with the GPT 4o-mini LLM, the vector store and memory\n", |
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"conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"Gradio User Interface" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 22, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def chat(message, history):\n", |
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" result = conversation_chain.invoke({\"question\": message})\n", |
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" return result[\"answer\"]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 23, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"* Running on local URL: http://127.0.0.1:7860\n", |
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"\n", |
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"To create a public link, set `share=True` in `launch()`.\n" |
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] |
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}, |
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{ |
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"data": { |
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"text/html": [ |
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"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
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], |
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"text/plain": [ |
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"<IPython.core.display.HTML object>" |
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] |
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}, |
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"metadata": {}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "llms", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.11" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 2 |
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} |
Loading…
Reference in new issue