Upload app_hf_space.py

app_hf_space.py

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+# app_hf_space.py
+import streamlit as st
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores.faiss import FAISS
+from langchain.chains import RetrievalQA
+from langchain_huggingface.llms import HuggingFacePipeline
+
+# --- Configuration ---
+# Use Hugging Face inference API via the `pipeline` from langchain-huggingface
+EMBED_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2"
+LLM_MODEL_ID = "google/flan-t5-small"
+INDEX_DIR = "faiss_index"
+
+# 1. Initialize embeddings via HF Inference API
+embeddings = HuggingFaceEmbeddings(
+    model_name=EMBED_MODEL_ID,
+    cache_dir=".hf_cache"
+)
+
+# 2. Load FAISS index
+store = FAISS.load_local(
+    INDEX_DIR,
+    embeddings
+)
+
+# 3. Initialize HF LLM via pipeline (inference API)
+llm = HuggingFacePipeline.from_model_id(
+    model_id=LLM_MODEL_ID,
+    task="text2text-generation"
+)
+
+# 4. Build RetrievalQA chain
+aqa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=store.as_retriever()
+)
+
+# 5. Streamlit UI
+st.title("🦜🔗 RAG App via HF Spaces")
+query = st.text_input("Ask a question about your docs:")
+
+if query:
+    with st.spinner("Generating answer via HF Space..."):
+        answer = aqa_chain.run(query)
+    st.markdown(f"**Answer:** {answer}")
+
+# NOTE: Deploy this to Hugging Face Spaces for fully-managed hosting.
+# Just push this file to your repo on HF and enable Streamlit space.