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| import gradio as gr | |
| from transformers import pipeline | |
| from langchain_community.vectorstores import FAISS # Updated import | |
| from langchain_community.embeddings import HuggingFaceEmbeddings # Updated import | |
| # Load models | |
| generator = pipeline("text-generation", model="gpt2") | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") | |
| vector_db = FAISS.load_local("faiss_index", embeddings) | |
| def rag_response(question): | |
| # Retrieve relevant context | |
| docs = vector_db.similarity_search(question, k=2) | |
| context = "\n".join([d.page_content for d in docs]) | |
| # Generate answer | |
| prompt = f"""Answer the question using this context: | |
| {context} | |
| Question: {question} | |
| Answer:""" | |
| result = generator( | |
| prompt, | |
| max_length=200, | |
| num_return_sequences=1, | |
| temperature=0.3 | |
| ) | |
| return result[0]['generated_text'] | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=rag_response, | |
| inputs="text", | |
| outputs="text", | |
| title="RAG Demo", | |
| examples=["What is photosynthesis?", "Who discovered penicillin?"] | |
| ) | |
| iface.launch() |