Skip summarize while it's broken, and snip some other parts of the workflow while under construction

src/khoj/processor/tools/online_search.py

@@ -45,6 +45,8 @@ OLOSTEP_QUERY_PARAMS = {
     "expandMarkdown": "True",
     "expandHtml": "False",
 }
+
+# TODO: Should this be 0 to let advanced model decide which web pages to read?
 MAX_WEBPAGES_TO_READ = 1
 
 

src/khoj/routers/api_chat.py

@@ -743,11 +743,6 @@ async def chat(
                 uploaded_image_url=uploaded_image_url,
                 agent=agent,
             )
-            conversation_commands_str = ", ".join([cmd.value for cmd in conversation_commands])
-            async for result in send_event(
-                ChatEvent.STATUS, f"**Chose Data Sources to Search:** {conversation_commands_str}"
-            ):
-                yield result
 
             mode = await aget_relevant_output_modes(q, meta_log, is_automated_task, uploaded_image_url, agent)
             async for result in send_event(ChatEvent.STATUS, f"**Decided Response Mode:** {mode.value}"):
@@ -876,11 +871,15 @@ async def chat(
                 defiltered_query = result[2]
 
         if not is_none_or_empty(compiled_references):
-            headings = "\n- " + "\n- ".join(set([c.get("compiled", c).split("\n")[0] for c in compiled_references]))
-            # Strip only leading # from headings
-            headings = headings.replace("#", "")
-            async for result in send_event(ChatEvent.STATUS, f"**Found Relevant Notes**: {headings}"):
-                yield result
+            try:
+                headings = "\n- " + "\n- ".join(set([c.get("compiled", c).split("\n")[0] for c in compiled_references]))
+                # Strip only leading # from headings
+                headings = headings.replace("#", "")
+                async for result in send_event(ChatEvent.STATUS, f"**Found Relevant Notes**: {headings}"):
+                    yield result
+            except Exception as e:
+                # TODO Get correct type for compiled across research notes extraction
+                logger.error(f"Error extracting references: {e}", exc_info=True)
 
         if conversation_commands == [ConversationCommand.Notes] and not await EntryAdapters.auser_has_entries(user):
             async for result in send_llm_response(f"{no_entries_found.format()}"):

src/khoj/routers/research.py

@@ -4,7 +4,7 @@ from typing import Any, Callable, Dict, List, Optional
 
 from fastapi import Request
 
-from khoj.database.adapters import EntryAdapters
+from khoj.database.adapters import ConversationAdapters, EntryAdapters
 from khoj.database.models import Agent, KhojUser
 from khoj.processor.conversation import prompts
 from khoj.processor.conversation.utils import remove_json_codeblock
@@ -76,6 +76,7 @@ async def apick_next_tool(
         prompts.personality_context.format(personality=agent.personality) if agent and agent.personality else ""
     )
 
+    # TODO Add current date/time to the query
     function_planning_prompt = prompts.plan_function_execution.format(
         query=query,
         tools=tool_options_str,
@@ -84,11 +85,14 @@ async def apick_next_tool(
         previous_iterations=previous_iterations_history,
     )
 
+    chat_model_option = await ConversationAdapters.aget_advanced_conversation_config()
+
     with timer("Chat actor: Infer information sources to refer", logger):
         response = await send_message_to_model_wrapper(
             function_planning_prompt,
             response_type="json_object",
             subscribed=subscribed,
+            chat_model_option=chat_model_option,
         )
 
     try:
@@ -98,6 +102,8 @@ async def apick_next_tool(
         suggested_data_source = response.get("data_source", None)
         suggested_query = response.get("query", None)
 
+        logger.info(f"Response for determining relevant tools: {response}")
+
         return InformationCollectionIteration(
             data_source=suggested_data_source,
             query=suggested_query,

src/khoj/utils/helpers.py

@@ -349,7 +349,6 @@ function_calling_description_for_llm = {
     ConversationCommand.Notes: "Use this if you think the user's personal knowledge base contains relevant context.",
     ConversationCommand.Online: "Use this if you think the there's important information on the internet related to the query.",
     ConversationCommand.Webpage: "Use this if the user has provided a webpage URL or you are share of a webpage URL that will help you directly answer this query",
-    ConversationCommand.Summarize: "Use this if you want to retrieve an answer that depends on reading an entire corpus.",
 }
 
 mode_descriptions_for_llm = {