Do not retrieve relevant notes when having a general chat with Khoj

- This improves latency of @general chat by avoiding unnecessary
  compute
- It also avoids passing references in API response when they haven't
  been used to generate the chat response. So interfaces don't have to
  add logic to not render them unnecessarily

src/khoj/processor/conversation/gpt.py

@@ -153,9 +153,7 @@ def converse(references, user_query, conversation_log={}, model="gpt-3.5-turbo",
     compiled_references = "\n\n".join({f"# {item}" for item in references})
 
     # Get Conversation Primer appropriate to Conversation Type
-    conversation_type = "general" if user_query.startswith("@general") or compiled_references.strip() == "" else "notes"
-    logger.debug(f"Conversation Type: {conversation_type}")
-    if conversation_type == "general":
+    if compiled_references == []:
         conversation_primer = prompts.general_conversation.format(current_date=current_date, query=user_query)
     else:
         conversation_primer = prompts.notes_conversation.format(

src/khoj/routers/api.py

@@ -228,6 +228,9 @@ def chat(q: Optional[str] = None):
     model = state.processor_config.conversation.model
     chat_model = state.processor_config.conversation.chat_model
     user_message_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    conversation_type = "general" if q.startswith("@general") else "notes"
+    compiled_references = []
+    inferred_queries = []
 
     # Load Conversation History
     chat_session = state.processor_config.conversation.chat_session
@@ -240,16 +243,21 @@ def chat(q: Optional[str] = None):
         else:
             return {"status": "ok", "response": []}
 
-    # Infer search queries from user message
-    with timer("Extracting search queries took", logger):
-        inferred_queries = extract_questions(q, model=model, api_key=api_key, conversation_log=meta_log)
+    if conversation_type == "notes":
+        # Infer search queries from user message
+        with timer("Extracting search queries took", logger):
+            inferred_queries = extract_questions(q, model=model, api_key=api_key, conversation_log=meta_log)
 
-    # Collate search results as context for GPT
-    with timer("Searching knowledge base took", logger):
-        result_list = []
-        for query in inferred_queries:
-            result_list.extend(search(query, n=5, r=True, score_threshold=-5.0, dedupe=False))
-        compiled_references = [item.additional["compiled"] for item in result_list]
+        # Collate search results as context for GPT
+        with timer("Searching knowledge base took", logger):
+            result_list = []
+            for query in inferred_queries:
+                result_list.extend(search(query, n=5, r=True, score_threshold=-5.0, dedupe=False))
+            compiled_references = [item.additional["compiled"] for item in result_list]
+
+    # Switch to general conversation type if no relevant notes found for the given query
+    conversation_type = "notes" if compiled_references else "general"
+    logger.debug(f"Conversation Type: {conversation_type}")
 
     try:
         with timer("Generating chat response took", logger):