From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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359 lines
11 KiB
359 lines
11 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "c25c6e94-f3de-4367-b2bf-269ba7160977", |
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"metadata": {}, |
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"source": [ |
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"## An Expert Knowledge Worker Question-Answering Agent using RAG" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "15169580-cf11-4dee-8ec7-3a4ef59b19ee", |
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"metadata": {}, |
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"source": [ |
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"Aims\n", |
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"- Reads README.md files and loads data using TextLoader\n", |
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"- Splits into chunks using CharacterTextSplitter\n", |
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"- Converts chunks into vector embeddings and creates a datastore\n", |
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"- 2D and 3D visualisations\n", |
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"- Langchain to set up a conversation retrieval chain" |
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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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"id": "051cf881-357d-406b-8eae-1610651e40f1", |
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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" |
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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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"id": "ccfd403a-5bdb-4a8c-b3fd-d47ae79e43f7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports for langchain, plotly and Chroma\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.embeddings import HuggingFaceEmbeddings\n", |
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"from langchain_chroma import Chroma\n", |
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"from langchain.memory import ConversationBufferMemory\n", |
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"from langchain.chains import ConversationalRetrievalChain\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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"import plotly.express as px\n", |
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"import matplotlib.pyplot as plt" |
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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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"id": "2d853868-d2f6-43e1-b27c-b8e91d06b724", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"MODEL = \"gpt-4o-mini\"\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": null, |
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"id": "f152fc3b-0bf4-4d51-948f-95da1ebc030a", |
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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(override=True)\n", |
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')" |
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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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"id": "24e621ac-df06-4af6-a60d-a9ed7adb884a", |
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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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"\n", |
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"folder = \"my-knowledge-base/\"\n", |
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"text_loader_kwargs={'autodetect_encoding': True}\n", |
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"\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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"\n", |
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"for doc in folder_docs:\n", |
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" filename_md = os.path.basename(doc.metadata[\"source\"]) \n", |
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" filename, _ = os.path.splitext(filename_md) \n", |
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" doc.metadata[\"filename\"] = filename\n", |
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"\n", |
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"documents = folder_docs \n", |
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"\n", |
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"text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n", |
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"chunks = text_splitter.split_documents(documents)\n", |
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"\n", |
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"print(f\"Total number of chunks: {len(chunks)}\")\n", |
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"print(f\"Files found: {set(doc.metadata['filename'] for doc in 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": null, |
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"id": "f02f08ee-5ade-4f79-a500-045a8f1a532f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", |
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"\n", |
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"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n", |
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"\n", |
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"# Delete if already exists\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 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": null, |
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"id": "7f665f4d-ccb1-43fb-b901-040117925732", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's investigate the vectors\n", |
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"\n", |
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"collection = vectorstore._collection\n", |
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"count = collection.count()\n", |
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"\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\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")" |
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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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"id": "6208a971-e8b7-48bc-be7a-6dcb82967fd2", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# pre work\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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"metadatas = result['metadatas']\n", |
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"filenames = [metadata['filename'] for metadata in metadatas]" |
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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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"id": "eb27bc8a-453b-4b19-84b4-dc495bb0e544", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import random\n", |
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"def random_color():\n", |
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" return f\"rgb({random.randint(0,255)},{random.randint(0,255)},{random.randint(0,255)})\"" |
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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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"id": "78db67e5-ef10-4581-b8ac-3e0281ceba45", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def show_embeddings_2d(result):\n", |
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" vectors = np.array(result['embeddings']) \n", |
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" documents = result['documents']\n", |
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" metadatas = result['metadatas']\n", |
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" filenames = [metadata['filename'] for metadata in metadatas]\n", |
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" filenames_unique = sorted(set(filenames))\n", |
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"\n", |
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" # color assignment\n", |
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" color_map = {name: random_color() for name in filenames_unique}\n", |
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" colors = [color_map[name] for name in filenames]\n", |
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"\n", |
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" tsne = TSNE(n_components=2, random_state=42,perplexity=4)\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(filenames, 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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"id": "2c250166-cb5b-4a75-8981-fae2d6dfe509", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"show_embeddings_2d(result)" |
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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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"id": "3b290e38-0800-4453-b664-7a7622ff5ed2", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def show_embeddings_3d(result):\n", |
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" vectors = np.array(result['embeddings']) \n", |
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" documents = result['documents']\n", |
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" metadatas = result['metadatas']\n", |
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" filenames = [metadata['filename'] for metadata in metadatas]\n", |
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" filenames_unique = sorted(set(filenames))\n", |
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"\n", |
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" # color assignment\n", |
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" color_map = {name: random_color() for name in filenames_unique}\n", |
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" colors = [color_map[name] for name in filenames]\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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" 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(filenames, 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": "code", |
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"execution_count": null, |
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"id": "45d1d034-2503-4176-b1e4-f248e31c4770", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"show_embeddings_3d(result)" |
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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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"id": "e79946a1-f93a-4b3a-8d19-deef40dec223", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# create a new Chat with OpenAI\n", |
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"llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\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(search_kwargs={\"k\": 50})\n", |
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"\n", |
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"# putting it together: set up the conversation chain with the GPT 3.5 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": null, |
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"id": "59f90c85-c113-4482-8574-8a728ef25459", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def chat(question, history):\n", |
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" result = conversation_chain.invoke({\"question\": question})\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": null, |
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"id": "0520a8ff-01a4-4fa6-9dc8-57da87272edc", |
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"metadata": {}, |
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"outputs": [], |
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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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"id": "b4949b17-cd9c-4bff-bd5b-0f80df72e7dc", |
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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": "Python 3 (ipykernel)", |
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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": 5 |
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}
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