added first changes

app.py

@@ -1,6 +1,47 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
 
+# =========================
+# Load and Prepare Gita Text
+# =========================
+
+with open("gita.txt", "r", encoding="utf-8") as f:
+    raw_text = f.read()
+
+def chunk_text(text, chunk_size=500, overlap=50):
+    chunks = []
+    start = 0
+    while start < len(text):
+        end = start + chunk_size
+        chunks.append(text[start:end])
+        start += chunk_size - overlap
+    return chunks
+
+documents = chunk_text(raw_text)
+
+# Embedding model (small + free)
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+doc_embeddings = embedder.encode(documents)
+dimension = doc_embeddings.shape[1]
+
+index = faiss.IndexFlatL2(dimension)
+index.add(np.array(doc_embeddings))
+
+
+def retrieve(query, top_k=4):
+    query_embedding = embedder.encode([query])
+    distances, indices = index.search(np.array(query_embedding), top_k)
+    results = [documents[i] for i in indices[0]]
+    return "\n\n".join(results)
+
+
+# =========================
+# RAG Chat Function
+# =========================
 
 def respond(
     message,
@@ -16,10 +57,20 @@ def respond(
     """
     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
 
-    messages = [{"role": "system", "content": system_message}]
+    # Retrieve relevant Gita chunks
+    context = retrieve(message)
 
-    messages.extend(history)
+    augmented_system_message = (
+        system_message
+        + "\n\nYou are RAGVeda, an expert in Indian philosophy."
+        + "\nAnswer ONLY using the Bhagavad Gita context below."
+        + "\nIf answer not found, say you do not know."
+        + "\n\nContext:\n"
+        + context
+    )
 
+    messages = [{"role": "system", "content": augmented_system_message}]
+    messages.extend(history)
     messages.append({"role": "user", "content": message})
 
     response = ""
@@ -47,7 +98,10 @@ chatbot = gr.ChatInterface(
     respond,
     type="messages",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(
+            value="You are RAGVeda, a calm and wise assistant rooted in the Bhagavad Gita.",
+            label="System message",
+        ),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(