dad10e82cb
Fixes The class `OllamaEmbeddings` was deprecated in LangChain 0.3.1 and will be removed in 1.0.0. An updated version of the class exists in the :class:`~langchain-ollama package and should be used instead.
54 lines
1.9 KiB
Python
54 lines
1.9 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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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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return "\n\n".join(doc.page_content for doc in docs)
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def rag_chain(question):
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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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