50 lines
No EOL
1.8 KiB
Python
50 lines
No EOL
1.8 KiB
Python
import streamlit as st
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import ollama
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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_community.embeddings import OllamaEmbeddings
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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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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="llama3.1")
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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.chat(model='llama3.1', messages=[{'role': 'user', 'content': formatted_prompt}])
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return response['message']['content']
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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) |