Time chat actor responses & chat api request start for perf analysis

src/khoj/routers/api_chat.py

@@ -242,6 +242,7 @@ async def chat(
 ) -> Response:
     user: KhojUser = request.user.object
     q = unquote(q)
+    logger.info("Chat request by {user.username}: {q}")
 
     await is_ready_to_chat(user)
     conversation_commands = [get_conversation_command(query=q, any_references=True)]

src/khoj/routers/helpers.py

@@ -168,6 +168,7 @@ async def aget_relevant_information_sources(query: str, conversation_history: di
         chat_history=chat_history,
     )
 
+    with timer("Chat actor: Infer information sources to refer", logger):
         response = await send_message_to_model_wrapper(relevant_tools_prompt, response_type="json_object")
 
     try:
@@ -212,6 +213,7 @@ async def aget_relevant_output_modes(query: str, conversation_history: dict):
         chat_history=chat_history,
     )
 
+    with timer("Chat actor: Infer output mode for chat response", logger):
         response = await send_message_to_model_wrapper(relevant_mode_prompt)
 
     try:
@@ -245,6 +247,7 @@ async def infer_webpage_urls(q: str, conversation_history: dict, location_data:
         location=location,
     )
 
+    with timer("Chat actor: Infer webpage urls to read", logger):
         response = await send_message_to_model_wrapper(online_queries_prompt, response_type="json_object")
 
     # Validate that the response is a non-empty, JSON-serializable list of URLs
@@ -274,6 +277,7 @@ async def generate_online_subqueries(q: str, conversation_history: dict, locatio
         location=location,
     )
 
+    with timer("Chat actor: Generate online search subqueries", logger):
         response = await send_message_to_model_wrapper(online_queries_prompt, response_type="json_object")
 
     # Validate that the response is a non-empty, JSON-serializable list
@@ -303,6 +307,7 @@ async def extract_relevant_info(q: str, corpus: str) -> Union[str, None]:
         corpus=corpus.strip(),
     )
 
+    with timer("Chat actor: Extract relevant information from data", logger):
         response = await send_message_to_model_wrapper(
             extract_relevant_information, prompts.system_prompt_extract_relevant_information
         )
@@ -346,6 +351,7 @@ async def generate_better_image_prompt(
         online_results=simplified_online_results,
     )
 
+    with timer("Chat actor: Generate contextual image prompt", logger):
         response = await send_message_to_model_wrapper(image_prompt)
 
     return response.strip()