fix: tf/dr flow improvements

backend/onyx/agents/agent_search/dr/constants.py

@@ -27,4 +27,5 @@
 DR_TIME_BUDGET_BY_TYPE = {
     ResearchType.THOUGHTFUL: 3.0,
     ResearchType.DEEP: 12.0,
+    ResearchType.FAST: 0.5,
 }

backend/onyx/agents/agent_search/dr/enums.py

@@ -8,6 +8,7 @@ class ResearchType(str, Enum):
     LEGACY_AGENTIC = "LEGACY_AGENTIC"  # only used for legacy agentic search migrations
     THOUGHTFUL = "THOUGHTFUL"
     DEEP = "DEEP"
+    FAST = "FAST"
 
 
 class ResearchAnswerPurpose(str, Enum):

backend/onyx/agents/agent_search/dr/nodes/dr_a0_clarification.py

@@ -35,6 +35,8 @@
 from onyx.agents.agent_search.shared_graph_utils.utils import run_with_timeout
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.agents.agent_search.utils import create_question_prompt
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
 from onyx.configs.constants import DocumentSource
 from onyx.configs.constants import DocumentSourceDescription
 from onyx.configs.constants import TMP_DRALPHA_PERSONA_NAME
@@ -488,7 +490,7 @@ def clarifier(
                 )
 
                 answer_tokens, _, _ = run_with_timeout(
-                    80,
+                    TF_DR_TIMEOUT_LONG,
                     lambda: stream_llm_answer(
                         llm=graph_config.tooling.primary_llm,
                         prompt=create_question_prompt(
@@ -501,7 +503,7 @@ def clarifier(
                         agent_answer_level=0,
                         agent_answer_question_num=0,
                         agent_answer_type="agent_level_answer",
-                        timeout_override=60,
+                        timeout_override=TF_DR_TIMEOUT_LONG,
                         ind=current_step_nr,
                         context_docs=None,
                         replace_citations=True,
@@ -612,7 +614,7 @@ def clarifier(
 
     clarification = None
 
-    if research_type != ResearchType.THOUGHTFUL:
+    if research_type == ResearchType.DEEP:
         result = _get_existing_clarification_request(graph_config)
         if result is not None:
             clarification, original_question, chat_history_string = result
@@ -645,7 +647,7 @@ def clarifier(
                         assistant_system_prompt, clarification_prompt
                     ),
                     schema=ClarificationGenerationResponse,
-                    timeout_override=25,
+                    timeout_override=TF_DR_TIMEOUT_SHORT,
                     # max_tokens=1500,
                 )
             except Exception as e:
@@ -674,7 +676,7 @@ def clarifier(
                 )
 
                 _, _, _ = run_with_timeout(
-                    80,
+                    TF_DR_TIMEOUT_LONG,
                     lambda: stream_llm_answer(
                         llm=graph_config.tooling.primary_llm,
                         prompt=repeat_prompt,
@@ -683,7 +685,7 @@ def clarifier(
                         agent_answer_level=0,
                         agent_answer_question_num=0,
                         agent_answer_type="agent_level_answer",
-                        timeout_override=60,
+                        timeout_override=TF_DR_TIMEOUT_LONG,
                         answer_piece=StreamingType.MESSAGE_DELTA.value,
                         ind=current_step_nr,
                         # max_tokens=None,

backend/onyx/agents/agent_search/dr/nodes/dr_a1_orchestrator.py

@@ -30,6 +30,8 @@
 from onyx.agents.agent_search.shared_graph_utils.utils import run_with_timeout
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.agents.agent_search.utils import create_question_prompt
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
 from onyx.kg.utils.extraction_utils import get_entity_types_str
 from onyx.kg.utils.extraction_utils import get_relationship_types_str
 from onyx.prompts.dr_prompts import DEFAULLT_DECISION_PROMPT
@@ -170,11 +172,39 @@ def orchestrator(
     reasoning_result = "(No reasoning result provided yet.)"
     tool_calls_string = "(No tool calls provided yet.)"
 
-    if research_type == ResearchType.THOUGHTFUL:
+    if research_type not in ResearchType:
+        raise ValueError(f"Invalid research type: {research_type}")
+
+    if research_type in [ResearchType.THOUGHTFUL, ResearchType.FAST]:
         if iteration_nr == 1:
-            remaining_time_budget = DR_TIME_BUDGET_BY_TYPE[ResearchType.THOUGHTFUL]
+            remaining_time_budget = DR_TIME_BUDGET_BY_TYPE[research_type]
+
+        elif remaining_time_budget <= 0:
+            return OrchestrationUpdate(
+                tools_used=[DRPath.CLOSER.value],
+                current_step_nr=current_step_nr,
+                query_list=[],
+                iteration_nr=iteration_nr,
+                log_messages=[
+                    get_langgraph_node_log_string(
+                        graph_component="main",
+                        node_name="orchestrator",
+                        node_start_time=node_start_time,
+                    )
+                ],
+                plan_of_record=plan_of_record,
+                remaining_time_budget=remaining_time_budget,
+                iteration_instructions=[
+                    IterationInstructions(
+                        iteration_nr=iteration_nr,
+                        plan=None,
+                        reasoning="Time to wrap up.",
+                        purpose="",
+                    )
+                ],
+            )
 
-        elif iteration_nr > 1:
+        elif iteration_nr > 1 and remaining_time_budget > 0:
             # for each iteration past the first one, we need to see whether we
             # have enough information to answer the question.
             # if we do, we can stop the iteration and return the answer.
@@ -200,7 +230,7 @@ def orchestrator(
             reasoning_tokens: list[str] = [""]
 
             reasoning_tokens, _, _ = run_with_timeout(
-                80,
+                TF_DR_TIMEOUT_LONG,
                 lambda: stream_llm_answer(
                     llm=graph_config.tooling.primary_llm,
                     prompt=create_question_prompt(
@@ -211,7 +241,7 @@ def orchestrator(
                     agent_answer_level=0,
                     agent_answer_question_num=0,
                     agent_answer_type="agent_level_answer",
-                    timeout_override=60,
+                    timeout_override=TF_DR_TIMEOUT_LONG,
                     answer_piece=StreamingType.REASONING_DELTA.value,
                     ind=current_step_nr,
                     # max_tokens=None,
@@ -297,7 +327,7 @@ def orchestrator(
                         decision_prompt,
                     ),
                     schema=OrchestratorDecisonsNoPlan,
-                    timeout_override=35,
+                    timeout_override=TF_DR_TIMEOUT_SHORT,
                     # max_tokens=2500,
                 )
                 next_step = orchestrator_action.next_step
@@ -320,7 +350,7 @@ def orchestrator(
             reasoning_result = "Time to wrap up."
             next_tool_name = DRPath.CLOSER.value
 
-    else:
+    elif research_type == ResearchType.DEEP:
         if iteration_nr == 1 and not plan_of_record:
             # by default, we start a new iteration, but if there is a feedback request,
             # we start a new iteration 0 again (set a bit later)
@@ -348,7 +378,7 @@ def orchestrator(
                         plan_generation_prompt,
                     ),
                     schema=OrchestrationPlan,
-                    timeout_override=25,
+                    timeout_override=TF_DR_TIMEOUT_SHORT,
                     # max_tokens=3000,
                 )
             except Exception as e:
@@ -368,7 +398,7 @@ def orchestrator(
             )
 
             _, _, _ = run_with_timeout(
-                80,
+                TF_DR_TIMEOUT_LONG,
                 lambda: stream_llm_answer(
                     llm=graph_config.tooling.primary_llm,
                     prompt=repeat_plan_prompt,
@@ -377,7 +407,7 @@ def orchestrator(
                     agent_answer_level=0,
                     agent_answer_question_num=0,
                     agent_answer_type="agent_level_answer",
-                    timeout_override=60,
+                    timeout_override=TF_DR_TIMEOUT_LONG,
                     answer_piece=StreamingType.REASONING_DELTA.value,
                     ind=current_step_nr,
                 ),
@@ -426,7 +456,7 @@ def orchestrator(
                         decision_prompt,
                     ),
                     schema=OrchestratorDecisonsNoPlan,
-                    timeout_override=15,
+                    timeout_override=TF_DR_TIMEOUT_LONG,
                     # max_tokens=1500,
                 )
                 next_step = orchestrator_action.next_step
@@ -460,7 +490,7 @@ def orchestrator(
         )
 
         _, _, _ = run_with_timeout(
-            80,
+            TF_DR_TIMEOUT_LONG,
             lambda: stream_llm_answer(
                 llm=graph_config.tooling.primary_llm,
                 prompt=repeat_reasoning_prompt,
@@ -469,7 +499,7 @@ def orchestrator(
                 agent_answer_level=0,
                 agent_answer_question_num=0,
                 agent_answer_type="agent_level_answer",
-                timeout_override=60,
+                timeout_override=TF_DR_TIMEOUT_LONG,
                 answer_piece=StreamingType.REASONING_DELTA.value,
                 ind=current_step_nr,
                 # max_tokens=None,
@@ -484,6 +514,9 @@ def orchestrator(
 
         current_step_nr += 1
 
+    else:
+        raise NotImplementedError(f"Research type {research_type} is not implemented.")
+
     base_next_step_purpose_prompt = get_dr_prompt_orchestration_templates(
         DRPromptPurpose.NEXT_STEP_PURPOSE,
         ResearchType.DEEP,
@@ -498,48 +531,54 @@ def orchestrator(
     )
 
     purpose_tokens: list[str] = [""]
+    purpose = ""
 
-    try:
+    if research_type in [ResearchType.THOUGHTFUL, ResearchType.DEEP]:
 
-        write_custom_event(
-            current_step_nr,
-            ReasoningStart(),
-            writer,
-        )
+        try:
 
-        purpose_tokens, _, _ = run_with_timeout(
-            80,
-            lambda: stream_llm_answer(
-                llm=graph_config.tooling.primary_llm,
-                prompt=create_question_prompt(
-                    decision_system_prompt,
-                    orchestration_next_step_purpose_prompt,
+            write_custom_event(
+                current_step_nr,
+                ReasoningStart(),
+                writer,
+            )
+
+            purpose_tokens, _, _ = run_with_timeout(
+                TF_DR_TIMEOUT_LONG,
+                lambda: stream_llm_answer(
+                    llm=graph_config.tooling.primary_llm,
+                    prompt=create_question_prompt(
+                        decision_system_prompt,
+                        orchestration_next_step_purpose_prompt,
+                    ),
+                    event_name="basic_response",
+                    writer=writer,
+                    agent_answer_level=0,
+                    agent_answer_question_num=0,
+                    agent_answer_type="agent_level_answer",
+                    timeout_override=TF_DR_TIMEOUT_LONG,
+                    answer_piece=StreamingType.REASONING_DELTA.value,
+                    ind=current_step_nr,
+                    # max_tokens=None,
                 ),
-                event_name="basic_response",
-                writer=writer,
-                agent_answer_level=0,
-                agent_answer_question_num=0,
-                agent_answer_type="agent_level_answer",
-                timeout_override=60,
-                answer_piece=StreamingType.REASONING_DELTA.value,
-                ind=current_step_nr,
-                # max_tokens=None,
-            ),
-        )
+            )
 
-        write_custom_event(
-            current_step_nr,
-            SectionEnd(),
-            writer,
-        )
+            write_custom_event(
+                current_step_nr,
+                SectionEnd(),
+                writer,
+            )
 
-        current_step_nr += 1
+            current_step_nr += 1
+
+        except Exception as e:
+            logger.error("Error in orchestration next step purpose.")
+            raise e
 
-    except Exception as e:
-        logger.error(f"Error in orchestration next step purpose: {e}")
-        raise e
+        purpose = cast(str, merge_content(*purpose_tokens))
 
-    purpose = cast(str, merge_content(*purpose_tokens))
+    elif research_type == ResearchType.FAST:
+        purpose = f"Answering the question using the {next_tool_name}"
 
     if not next_tool_name:
         raise ValueError("The next step has not been defined. This should not happen.")

backend/onyx/agents/agent_search/dr/nodes/dr_a2_closer.py

@@ -33,6 +33,7 @@
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.agents.agent_search.utils import create_question_prompt
 from onyx.chat.chat_utils import llm_doc_from_inference_section
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
 from onyx.context.search.models import InferenceSection
 from onyx.db.chat import create_search_doc_from_inference_section
 from onyx.db.chat import update_db_session_with_messages
@@ -276,7 +277,7 @@ def closer(
                 test_info_complete_prompt + (assistant_task_prompt or ""),
             ),
             schema=TestInfoCompleteResponse,
-            timeout_override=40,
+            timeout_override=TF_DR_TIMEOUT_LONG,
             # max_tokens=1000,
         )
 
@@ -311,10 +312,12 @@ def closer(
         writer,
     )
 
-    if research_type == ResearchType.THOUGHTFUL:
+    if research_type in [ResearchType.THOUGHTFUL, ResearchType.FAST]:
         final_answer_base_prompt = FINAL_ANSWER_PROMPT_WITHOUT_SUB_ANSWERS
-    else:
+    elif research_type == ResearchType.DEEP:
         final_answer_base_prompt = FINAL_ANSWER_PROMPT_W_SUB_ANSWERS
+    else:
+        raise ValueError(f"Invalid research type: {research_type}")
 
     estimated_final_answer_prompt_tokens = check_number_of_tokens(
         final_answer_base_prompt.build(
@@ -353,7 +356,7 @@ def closer(
 
     try:
         streamed_output, _, citation_infos = run_with_timeout(
-            240,
+            int(3 * TF_DR_TIMEOUT_LONG),
             lambda: stream_llm_answer(
                 llm=graph_config.tooling.primary_llm,
                 prompt=create_question_prompt(
@@ -365,7 +368,7 @@ def closer(
                 agent_answer_level=0,
                 agent_answer_question_num=0,
                 agent_answer_type="agent_level_answer",
-                timeout_override=60,
+                timeout_override=int(2 * TF_DR_TIMEOUT_LONG),
                 answer_piece=StreamingType.MESSAGE_DELTA.value,
                 ind=current_step_nr,
                 context_docs=all_context_llmdocs,