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