Merge pull request #78 from dinorrusso/community-contributions-branch
Added week5 day5 RAG example using Ollama all local
This commit is contained in:
Ed Donner
GitHub
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week5/community-contributions/day 5 - ollama_rag_1.ipynb
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224
week5/community-contributions/day 5 - ollama_rag_1.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Document loading, retrieval methods and text splitting\n",
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"# !pip install -qU langchain langchain_community\n",
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"\n",
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"# # Local vector store via Chroma\n",
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"# !pip install -qU langchain_chroma\n",
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"\n",
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"# # Local inference and embeddings via Ollama\n",
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"# !pip install -qU langchain_ollama\n",
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"\n",
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"# # Web Loader\n",
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"# !pip install -qU beautifulsoup4\n",
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"\n",
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"# # Pull the model first\n",
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"# !ollama pull nomic-embed-text\n",
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"\n",
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"# !pip install -qU pypdf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Imports\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_community.document_loaders import PyPDFLoader, DirectoryLoader\n",
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"from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
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"from langchain_chroma import Chroma\n",
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"from langchain_ollama import OllamaEmbeddings\n",
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"from langchain_ollama import ChatOllama\n",
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_core.runnables import RunnablePassthrough"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 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(\"Manuals/*\")\n",
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"\n",
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"def add_metadata(doc, doc_type):\n",
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" doc.metadata[\"doc_type\"] = doc_type\n",
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" return doc\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=\"**/*.pdf\", loader_cls=PyPDFLoader)\n",
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" folder_docs = loader.load()\n",
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" documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n",
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"\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, 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\"Document types found: {set(doc.metadata['doc_type'] 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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"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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"# Chroma is a popular open source Vector Database based on SQLLite\n",
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"DB_NAME = \"vector_db\"\n",
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"\n",
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"embeddings = OllamaEmbeddings(model=\"nomic-embed-text\")\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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"metadata": {},
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"outputs": [],
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"source": [
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"#run a quick test - should return a list of documents = 4\n",
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"question = \"What kind of grill is the Spirt II?\"\n",
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"docs = vectorstore.similarity_search(question)\n",
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"len(docs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"docs[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# create a new Chat with Ollama\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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"MODEL = \"llama3.2:latest\"\n",
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"llm = ChatOllama(temperature=0.7, model=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 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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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's try a simple question\n",
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"\n",
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"query = \"How do I change the water bottle ?\"\n",
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"result = conversation_chain.invoke({\"question\": query})\n",
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"print(result[\"answer\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"# set up a new conversation memory for the chat\n",
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"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
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"\n",
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"# putting it together: set up the conversation chain with the 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": 16,
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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": "markdown",
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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 -\n",
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"\n",
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"A quick and easy way to prototype a chat with an LLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 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": "venv",
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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": "3.12.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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