From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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195 lines
5.0 KiB
195 lines
5.0 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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"### 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." |
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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": "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" |
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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\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" |
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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": "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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"\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": null, |
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"id": "252f17e9-3529-4e81-996c-cfa9f08e75a8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"len(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": "7e8decb0-d9b0-4d51-8402-7a6174d22159", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"documents[24]" |
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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 = 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": 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": "d2562754-9052-4aae-92c1-37236435ea06", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"chunks[6]" |
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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": "code", |
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"execution_count": null, |
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"id": "128c73f7-f149-4904-a554-8140941fce0c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"for chunk in chunks:\n", |
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" if 'CEO' in chunk.page_content:\n", |
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" print(chunk)\n", |
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" print(\"_________\")" |
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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": "3.11.11" |
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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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