Created
June 8, 2024 19:32
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Local RAG with Llama 3 and LangChain
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| import streamlit as st | |
| import ollama | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.document_loaders import WebBaseLoader | |
| from langchain_community.vectorstores import Chroma | |
| from langchain_community.embeddings import OllamaEmbeddings | |
| st.title("Chat with Webpage 🌐") | |
| st.caption("This app allows you to chat with a webpage using local Llama-3 and RAG") | |
| webpage_url = st.text_input("Enter Webpage URL", type="default") | |
| if webpage_url: | |
| loader = WebBaseLoader(webpage_url) | |
| docs = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10) | |
| splits = text_splitter.split_documents(docs) | |
| embeddings = OllamaEmbeddings(model="llama3") | |
| vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings) | |
| def ollama_llm(question, context): | |
| formatted_prompt = f"Question: {question}\n\nContext: {context}" | |
| response = ollama.chat(model='llama3', messages=[{'role': 'user', 'content': formatted_prompt}]) | |
| return response['message']['content'] | |
| retriever = vectorstore.as_retriever() | |
| def combine_docs(docs): | |
| return "\n\n".join(doc.page_content for doc in docs) | |
| def rag_chain(question): | |
| retrieved_docs = retriever.invoke(question) | |
| formatted_context = combine_docs(retrieved_docs) | |
| return ollama_llm(question, formatted_context) | |
| st.success(f"Loaded {webpage_url} successfully!") | |
| prompt = st.text_input("Ask any question about the webpage") | |
| if prompt: | |
| result = rag_chain(prompt) | |
| st.write(result) |
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For apple silicon i had to
pip install langchain langchain_community streamlit ollama bs4 chromadb watchdog.