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import streamlit as st |
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from langchain.document_loaders import TextLoader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.embeddings import HuggingFaceEmbeddings |
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from langchain.vectorstores import FAISS |
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from langchain.chains import RetrievalQA |
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from langchain.llms import HuggingFaceHub |
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@st.cache_resource |
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def load_vector_store(): |
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loader = TextLoader("data/sample.txt") |
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documents = loader.load() |
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splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) |
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chunks = splitter.split_documents(documents) |
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embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") |
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db = FAISS.from_documents(chunks, embedding_model) |
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return db |
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def main(): |
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st.title("📄 Ask Your Document (RAG with LangChain + Hugging Face)") |
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st.write("Upload a document, ask questions, and get answers powered by open-source LLMs!") |
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query = st.text_input("Enter your question:") |
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if query: |
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db = load_vector_store() |
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qa_chain = RetrievalQA.from_chain_type( |
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llm=HuggingFaceHub( |
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repo_id="google/flan-t5-base", |
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model_kwargs={"temperature": 0.5, "max_length": 256} |
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), |
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retriever=db.as_retriever(), |
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return_source_documents=True |
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) |
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result = qa_chain.run(query) |
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st.write("### 📌 Answer") |
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st.write(result) |
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if __name__ == "__main__": |
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main() |
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