Local RAG with Llama 3 and LangChain

local_rag.py

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)

For apple silicon i had to pip install langchain langchain_community streamlit ollama bs4 chromadb watchdog.