Update src/qa.py

src/qa.py

@@ -264,18 +264,41 @@ def retrieve_chunks(query: str, index, chunks: list, top_k: int = 7,
 # ==========================================================
 # 8️⃣ Answer Generation
 # ==========================================================
-def generate_answer(query: str, retrieved_chunks: list, reasoning_mode: bool = False):
+# ==========================================================
+# 8️⃣ Answer Generation (Lazy GPT-4o Initialization + Language-Aware)
+# ==========================================================
+def generate_answer(query: str, retrieved_chunks: list, reasoning_mode: bool = False, doc_lang: str = "en"):
+    """
+    Generates an answer using GPT-4o (SAP Gen AI Hub proxy).
+    Now supports Hindi or English response formatting automatically.
+    """
     if not retrieved_chunks:
         return "Sorry, I couldn’t find relevant information in the document."
 
+    # Try lazy initialization
     try:
         chat_llm_local = get_chat_llm()
     except Exception:
         return "⚠️ GPT-4o not initialized. Check credentials or rebuild the Space."
 
+    # Build context
     context = "\n".join(f"[Chunk {i+1}] {chunk.strip()}" for i, chunk in enumerate(retrieved_chunks))
-    prompt = (REASONING_PROMPT if reasoning_mode else STRICT_PROMPT).format(context=context, query=query)
 
+    # 🌐 Language-specific prompt logic
+    if doc_lang == "hi":
+        # Hindi-language response
+        prompt = (
+            f"आप एक दस्तावेज़ सहायक हैं जो दिए गए अंशों के आधार पर सटीक उत्तर देता है। "
+            f"कृपया नीचे दिए गए संदर्भ का उपयोग करते हुए प्रश्न का उत्तर हिंदी में दें। "
+            f"यदि उत्तर स्पष्ट रूप से दस्तावेज़ में नहीं है, तो कहें — "
+            f"'मुझे इस दस्तावेज़ के आधार पर उत्तर ज्ञात नहीं है।'\n\n"
+            f"संदर्भ:\n{context}\n\nप्रश्न: {query}\nउत्तर:"
+        )
+    else:
+        # Default English prompts
+        prompt = (REASONING_PROMPT if reasoning_mode else STRICT_PROMPT).format(context=context, query=query)
+
+    # System role
     messages = [
         {"role": "system", "content":
             "You are an expert enterprise documentation assistant. "
@@ -287,6 +310,7 @@ def generate_answer(query: str, retrieved_chunks: list, reasoning_mode: bool = F
         {"role": "user", "content": prompt},
     ]
 
+    # Generate answer
     try:
         response = chat_llm_local.invoke(messages)
         return response.content.strip()
@@ -295,6 +319,7 @@ def generate_answer(query: str, retrieved_chunks: list, reasoning_mode: bool = F
         return "⚠️ Error: Could not generate an answer."
 
 
+
 # ==========================================================
 # 9️⃣ Generic Text Generation Helper
 # ==========================================================