Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import faiss
+import torch
+
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+
+# =====================================
+# 1. LOAD DOCUMENTS
+# =====================================
+def load_documents(path="documents.txt"):
+    with open(path, "r", encoding="utf-8") as f:
+        docs = f.readlines()
+    return [doc.strip() for doc in docs if doc.strip()]
+
+documents = load_documents()
+
+
+# =====================================
+# 2. LOAD EMBEDDING MODEL (HF Open Source)
+# =====================================
+embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+doc_embeddings = embedding_model.encode(documents, convert_to_numpy=True)
+dimension = doc_embeddings.shape[1]
+
+
+# =====================================
+# 3. BUILD FAISS INDEX
+# =====================================
+index = faiss.IndexFlatL2(dimension)
+index.add(doc_embeddings)
+
+
+# =====================================
+# 4. LOAD OPEN-SOURCE LLM (HF)
+# =====================================
+MODEL_NAME = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # change if needed
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer
+)
+
+
+# =====================================
+# 5. RETRIEVAL FUNCTION
+# =====================================
+def retrieve(query, top_k=3):
+    query_embedding = embedding_model.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_embedding, top_k)
+
+    retrieved_docs = [documents[i] for i in indices[0]]
+    return retrieved_docs
+
+
+# =====================================
+# 6. GENERIC LLM CALL
+# =====================================
+def call_llm(prompt):
+    response = generator(
+        prompt,
+        max_new_tokens=300,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9
+    )
+
+    return response[0]["generated_text"]
+
+
+# =====================================
+# 7. RAG PIPELINE
+# =====================================
+def rag_pipeline(query):
+    retrieved_docs = retrieve(query)
+    context = "\n".join(retrieved_docs)
+
+    prompt = f"""
+You are a helpful AI assistant.
+Answer ONLY from the provided context.
+
+Context:
+{context}
+
+Question:
+{query}
+
+Answer:
+"""
+
+    answer = call_llm(prompt)
+
+    return answer
+
+
+# =====================================
+# 8. GRADIO UI
+# =====================================
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 Open Source RAG (HF Only)")
+
+    query = gr.Textbox(label="Ask your question")
+    output = gr.Textbox(label="Answer")
+
+    query.submit(rag_pipeline, query, output)
+
+demo.launch()