From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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524 lines
17 KiB
524 lines
17 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "dfe37963-1af6-44fc-a841-8e462443f5e6", |
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"metadata": {}, |
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"source": [ |
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"## Expert Knowledge Worker\n", |
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"\n", |
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"Features:\n", |
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"- A question answering agent that is an expert knowledge worker\n", |
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"- To be used by employees of Insurellm, an Insurance Tech company\n", |
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"- The agent needs to be accurate and the solution should be low cost.\n", |
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"\n", |
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"This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy.\n", |
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"\n", |
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"Technology:\n", |
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"- RAG: LangChain\n", |
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"- Embedding model: OpenAIEmbeddings or HuggingFace sentence-transformers\n", |
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"- Encoding method: Auto-encoding\n", |
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"- Vector datastore: Chroma or FAISS\n", |
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"- Vector DB visualization: Plotly\n", |
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"- Dimensionality reduction technique: t-SNE\n", |
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"\n", |
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"# Dependencies" |
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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": "802137aa-8a74-45e0-a487-d1974927d7ca", |
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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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"from langchain.document_loaders import DirectoryLoader, TextLoader\n", |
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"from langchain.text_splitter import RecursiveCharacterTextSplitter\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.vectorstores import FAISS\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": "markdown", |
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"id": "7187c181-5b17-4df7-b298-b7cb2b6d09f7", |
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"metadata": {}, |
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"source": [ |
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"# Setup" |
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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": "58c85082-e417-4708-9efe-81a5d55d1424", |
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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\"\n", |
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"db_type = \"Chroma\"\n", |
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"# db_type = \"FAISS\"\n", |
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"embed_type = \"OpenAIEmbeddings\"\n", |
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"# embed_type = \"sentence-transformers\"" |
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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": "ee78efcb-60fe-449e-a944-40bab26261af", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables\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": "markdown", |
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"id": "a2f0866b-5cfb-4ecd-87d1-6da872887dcd", |
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"metadata": {}, |
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"source": [ |
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"# Create Knowledge Base for RAG\n", |
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"\n", |
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"## Load Company Documents\n", |
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"\n", |
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"Uses LangChain to read in a Knowledge Base of documents and to divide up documents into overlaping 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": null, |
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"id": "730711a9-6ffe-4eee-8f48-d6cfb7314905", |
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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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"text_loader_kwargs = {'encoding': 'utf-8'}\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": null, |
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"id": "7310c9c8-03c1-4efc-a104-5e89aec6db1a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"text_splitter = RecursiveCharacterTextSplitter(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": null, |
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"id": "cd06e02f-6d9b-44cc-a43d-e1faa8acc7bb", |
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"metadata": {}, |
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"outputs": [], |
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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": null, |
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"id": "2c54b4b6-06da-463d-bee7-4dd456c2b887", |
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"metadata": {}, |
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"outputs": [], |
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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": "markdown", |
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"id": "77f7d2a6-ccfa-425b-a1c3-5e55b23bd013", |
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"metadata": {}, |
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"source": [ |
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"## Vector Embeddings\n", |
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"\n", |
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"Convert chunks of text into Vectors using OpenAIEmbeddings and store the Vectors in Chroma (or FAISS)." |
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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": "78998399-ac17-4e28-b15f-0b5f51e6ee23", |
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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 = None\n", |
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"# OpenAIEmbeddings is OpenAI's vector embedding models\n", |
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"if embed_type == \"OpenAIEmbeddings\":\n", |
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" embeddings = OpenAIEmbeddings()\n", |
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"\n", |
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"# sentence-transformers is a free Vector embeddings model from HuggingFace\n", |
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"elif embed_type == \"sentence-transformers\":\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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"if embeddings is None:\n", |
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" print(\"ERROR: embeddings not set. Check embed_type is set to a valid model\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "64768521-a775-472c-83c5-0c0d715d44ac", |
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"metadata": {}, |
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"source": [ |
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"## Create Vector Datastore" |
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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": "057868f6-51a6-4087-94d1-380145821550", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Create vectorstore\n", |
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"vectorstore = None\n", |
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"\n", |
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"# Chroma is a popular open source Vector Database based on SQLLite\n", |
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"if db_type == \"Chroma\":\n", |
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" # Delete vector DB if already exists\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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" vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n", |
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" \n", |
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" print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")\n", |
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"\n", |
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" # Get one vector and find how many dimensions it has\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\")\n", |
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" \n", |
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"# FAISS is an in-memory vector DB from Facebook\n", |
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"elif db_type == \"FAISS\":\n", |
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" # Create vectorstore\n", |
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" vectorstore = FAISS.from_documents(chunks, embedding=embeddings)\n", |
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" \n", |
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" total_vectors = vectorstore.index.ntotal\n", |
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" dimensions = vectorstore.index.d\n", |
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" print(f\"There are {total_vectors} vectors with {dimensions:,} dimensions in the vector store\")\n", |
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"\n", |
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"else:\n", |
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" print(\"ERROR: Vector datastore not created. Check db_type is set to a valid database\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "b0d45462-a818-441c-b010-b85b32bcf618", |
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"metadata": {}, |
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"source": [ |
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"# Visualizing the Vector Store\n", |
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"\n", |
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"Humans are not very good at visualizing things with more than 3 dimensions so to visualize a vector datastore with thousands of dimesions. We need to use techniques like projecting down to reduce the dimensions to only 2 or 3 dimensions in a way that does the best possible job at separating things out to stay faithful to the multi-dimensional representation.\n", |
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"\n", |
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"For example, things that are far apart in these multiple dimensions will still be far apart even when projected down to 2 dimensions.\n", |
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"\n", |
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"[t-distributed stochastic neighbor embedding (t-SNE)](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions.\n", |
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"\n", |
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"## Configure Visualization" |
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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": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b", |
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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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"if db_type == \"Chroma\":\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]\n", |
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"\n", |
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"elif db_type == \"FAISS\":\n", |
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" vectors = []\n", |
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" documents = []\n", |
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" doc_types = []\n", |
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" colors = []\n", |
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" color_map = {'products':'blue', 'employees':'green', 'contracts':'red', 'company':'orange'}\n", |
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" \n", |
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" for i in range(total_vectors):\n", |
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" vectors.append(vectorstore.index.reconstruct(i))\n", |
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" doc_id = vectorstore.index_to_docstore_id[i]\n", |
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" document = vectorstore.docstore.search(doc_id)\n", |
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" documents.append(document.page_content)\n", |
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" doc_type = document.metadata['doc_type']\n", |
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" doc_types.append(doc_type)\n", |
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" colors.append(color_map[doc_type])\n", |
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" \n", |
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" vectors = np.array(vectors)\n", |
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"\n", |
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"else:\n", |
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" print(\"ERROR: Vector datastore not created. Check db_type is set to a valid database\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "bb279701-0086-44aa-a2da-14341aecf529", |
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"metadata": {}, |
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"source": [ |
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"## Reduce the dimensionality to 2D" |
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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": "427149d5-e5d8-4abd-bb6f-7ef0333cca21", |
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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=f'2D {db_type} 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": "markdown", |
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"id": "e2b724f3-e3ad-4d42-bfa4-a89386d6414e", |
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"metadata": {}, |
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"source": [ |
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"## Reduce the dimensionality to 3D" |
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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": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# 3D representation isn't as easy to navigate\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=f'3D {db_type} 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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"id": "9468860b-86a2-41df-af01-b2400cc985be", |
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"metadata": {}, |
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"source": [ |
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"# Expert Knowledge Worker\n", |
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"\n", |
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"Use LangChain to bring it all together by creating a conversation chain with RAG and memory.\n", |
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"\n", |
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"Key abstractions in LangChain:\n", |
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"- LLM: represents abstraction around a model\n", |
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"- Retriever: interface onto somthing like a vector store used for RAG retrieval\n", |
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"- Memory: represents a history of a conversation with a chatbot in memory\n", |
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"\n", |
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"Because LangChain abstracts the reprentation of the LLM, retriever and memory the code is the same for any model and knowledge base.\n", |
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"\n", |
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"Note: ok to ignore _Deprecation Warning_ for now; LangChain are not expected to remove ConversationBufferMemory any time soon.\n", |
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"\n", |
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"## Setup" |
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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": "129c7d1e-0094-4479-9459-f9360b95f244", |
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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()\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": null, |
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"id": "968e7bf2-e862-4679-a11f-6c1efb6ec8ca", |
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"metadata": {}, |
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"outputs": [], |
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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": "markdown", |
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"id": "990a2917-562c-461a-8ce9-a8ad8ad1646d", |
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"metadata": {}, |
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"source": [ |
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"## Clear Memory\n", |
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"\n", |
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"Clear the memory from the testing and restart conversation chain for UI." |
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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": "e6eb99fb-33ec-4025-ab92-b634ede03647", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# clear the memory and restart conversation chain for UI\n", |
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"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\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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"id": "bbbcb659-13ce-47ab-8a5e-01b930494964", |
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"metadata": {}, |
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"source": [ |
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"## Functions" |
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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": "c3536590-85c7-4155-bd87-ae78a1467670", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Wrapping in a function - note that history isn't used, as the memory is in the conversation_chain\n", |
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"\n", |
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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": "markdown", |
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"id": "b655d3da-277b-45a9-8113-747314ec0889", |
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"metadata": {}, |
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"source": [ |
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"## UI" |
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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": "b252d8c1-61a8-406d-b57a-8f708a62b014", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# And in Gradio:\n", |
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"\n", |
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"view = gr.ChatInterface(chat, type=\"messages\", examples=[\"what is insurellm?\",\"what did avery do before?\", \"does insurellm offer any products in the auto industry space?\"], title=\"Insurellm Expert Knowledge Worker\").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": "5435b2b9-935c-48cd-aaf3-73a837ecde49", |
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"metadata": {}, |
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"outputs": [], |
|
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} |
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], |
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"display_name": "Python 3 (ipykernel)", |
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}, |
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