fixed bugs

app/services/llm.py

@@ -32,11 +32,10 @@ class LLMService:
         except Exception as e:
             logger.error(f"LLM error ({provider}/{model}): {e}")
             # Fallback to a basic model if available
-            if model != "accounts/fireworks/models/llama-v3p1-70b-instruct" and settings.fireworks_api_key:
-                logger.info("Falling back to Fireworks AI")
+            if model != "accounts/fireworks/models/kimi-k2-instruct-0905" and settings.fireworks_api_key:
                 return await self._call_fireworks(
                     messages,
-                    "accounts/fireworks/models/llama-v3p1-70b-instruct",
+                    "accounts/fireworks/models/kimi-k2-instruct-0905",
                     max_tokens,
                     temperature,
                 )
@@ -137,7 +136,7 @@ class LLMService:
         max_tokens: int,
         temperature: float,
     ) -> Dict[str, Any]:
-        import google.generativeai as genai
+        import google.genai as genai
 
         genai.configure(api_key=settings.google_api_key)
         gemini_model = genai.GenerativeModel(model)

app/services/rag.py

@@ -103,7 +103,7 @@ class RAGEngine:
         messages.append({"role": "user", "content": query})
 
         # Step 5: Generate response
-        model = chatbot_config.get("model", "accounts/fireworks/models/llama-v3p1-70b-instruct")
+        model = chatbot_config.get("model", "accounts/fireworks/models/kimi-k2-instruct-0905")
         try:
             result = await self.llm_svc.generate(
                 messages=messages,

app/services/vector_store.py

@@ -29,7 +29,6 @@ class VectorStoreService:
         self.client = get_qdrant_client()
 
     def create_collection(self, collection_name: str) -> bool:
-        """Create a new collection for a chatbot"""
         try:
             self.client.create_collection(
                 collection_name=collection_name,
@@ -38,6 +37,12 @@ class VectorStoreService:
                     distance=Distance.COSINE,
                 ),
             )
+            # Create payload index for filtering/deleting by document_id
+            self.client.create_payload_index(
+                collection_name=collection_name,
+                field_name="document_id",
+                field_schema="keyword",
+            )
             logger.info(f"Created collection: {collection_name}")
             return True
         except Exception as e:
@@ -102,12 +107,12 @@ class VectorStoreService:
     ) -> List[Dict[str, Any]]:
         """Search for similar vectors"""
         try:
-            results = self.client.search(
+            results = self.client.query_points(
                 collection_name=collection_name,
-                query_vector=query_vector,
+                query=query_vector,
                 limit=limit,
                 score_threshold=score_threshold,
-            )
+            ).points
             return [
                 {
                     "id": str(r.id),