mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
synced 2026-04-30 07:08:44 -05:00
5d4730e1e3
Modified files: - rag_tutorials/autonomous_rag/autorag.py - rag_tutorials/llama3.1_local_rag/llama3.1_local_rag.py - rag_tutorials/local_hybrid_search_rag/local_main.py
81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
import streamlit as st
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.document_loaders import WebBaseLoader
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from langchain_community.vectorstores import Chroma
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from langchain_ollama import OllamaEmbeddings
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from langchain_ollama import ChatOllama
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st.title("Chat with Webpage 🌐")
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st.caption("This app allows you to chat with a webpage using local llama3 and RAG")
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# Get the webpage URL from the user
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webpage_url = st.text_input("Enter Webpage URL", type="default")
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# Connect to Ollama
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ollama_endpoint = "http://127.0.0.1:11434"
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ollama_model = "llama3.1"
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ollama = ChatOllama(model=ollama_model, base_url=ollama_endpoint)
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if webpage_url:
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# 1. Load the data
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loader = WebBaseLoader(webpage_url)
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docs = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)
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splits = text_splitter.split_documents(docs)
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# 2. Create Ollama embeddings and vector store
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embeddings = OllamaEmbeddings(model=ollama_model, base_url=ollama_endpoint)
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vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
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# 3. Call Ollama Llama3 model
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def ollama_llm(question, context):
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"""Generates a response to a question using the Ollama Llama3 model.
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This function takes a question and its context, formats them into a prompt,
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and invokes the Ollama Llama3 model to generate a response.
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Args:
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question (str): The question to be answered by the model.
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context (str): The context or additional information related to the question.
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Returns:
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str: The response generated by the Ollama Llama3 model, stripped of leading and trailing whitespace."""
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formatted_prompt = f"Question: {question}\n\nContext: {context}"
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response = ollama.invoke([('human', formatted_prompt)])
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return response.content.strip()
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# 4. RAG Setup
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retriever = vectorstore.as_retriever()
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def combine_docs(docs):
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"""Combines the content of multiple document objects into a single string.
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Args:
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docs (list): A list of document objects, each having a 'page_content' attribute.
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Returns:
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str: A string consisting of the combined 'page_content' of all document objects,
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separated by two newline characters."""
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return "\n\n".join(doc.page_content for doc in docs)
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def rag_chain(question):
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"""Processes a question to retrieve and format relevant documents, and generates a response using a language model.
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Args:
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question (str): The question or query that needs to be answered.
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Returns:
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str: The response generated by the language model based on the retrieved and formatted documents."""
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retrieved_docs = retriever.invoke(question)
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formatted_context = combine_docs(retrieved_docs)
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return ollama_llm(question, formatted_context)
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st.success(f"Loaded {webpage_url} successfully!")
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# Ask a question about the webpage
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prompt = st.text_input("Ask any question about the webpage")
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# Chat with the webpage
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if prompt:
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result = rag_chain(prompt)
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st.write(result)
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