From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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229 lines
6.4 KiB
229 lines
6.4 KiB
{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77", |
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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 json" |
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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 for langchain, plotly and Chroma\n", |
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"\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_chroma import Chroma\n", |
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"import matplotlib.pyplot as plt\n", |
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"from sklearn.manifold import TSNE\n", |
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"import numpy as np \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": 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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"# 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 = \"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": "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 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', '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": null, |
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"id": "b14e6c30-37c6-4eac-845b-5471aa75f587", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"##Load json\n", |
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"with open(\"knowledge-base/auto_shop.json\", 'r') as f: #place auto_shop.json file inside your knowledge-base folder\n", |
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" data = json.load(f)" |
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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": "408bc620-477f-47fd-b9e8-ab9d21843ecd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"#Convert to Langchain\n", |
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"documents = []\n", |
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"for item in data:\n", |
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" content = item[\"content\"]\n", |
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" metadata = item.get(\"metadata\", {})\n", |
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" documents.append(Document(page_content=content, metadata=metadata))" |
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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": "0371d472-cd14-4967-bc09-9b78e233809f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"#Chunk documents\n", |
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"splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50, separators=[\"\\n\\n\", \"\\n\", \",\", \" \", \"\"])\n", |
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"chunks = 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": "91c2404b-b3c9-4c7f-b199-9895e429a3da", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"doc_types = set(chunk.metadata['source'] 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": 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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"embeddings = OpenAIEmbeddings()\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": "ff2e7687-60d4-4920-a1d7-a34b9f70a250", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# # Let's investigate the vectors. Use for debugging if needed\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": "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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"\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 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": "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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"## Now we will bring this up in Gradio using the Chat 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": 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 that in a function\n", |
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"\n", |
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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": "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\").launch(inbrowser=True)" |
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] |
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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": "3.11.12" |
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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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