nit

.github/workflows/pr-python-connector-tests.yml

@@ -44,9 +44,6 @@ env:
   SHAREPOINT_CLIENT_SECRET: ${{ secrets.SHAREPOINT_CLIENT_SECRET }}
   SHAREPOINT_CLIENT_DIRECTORY_ID: ${{ secrets.SHAREPOINT_CLIENT_DIRECTORY_ID }}
   SHAREPOINT_SITE: ${{ secrets.SHAREPOINT_SITE }}
-  # Gitbook
-  GITBOOK_SPACE_ID: ${{ secrets.GITBOOK_SPACE_ID }}
-  GITBOOK_API_KEY: ${{ secrets.GITBOOK_API_KEY }}
 
 jobs:
   connectors-check:

backend/Dockerfile

@@ -35,9 +35,7 @@ RUN apt-get update && \
         libuuid1=2.38.1-5+deb12u1 \
         libxmlsec1-dev \
         pkg-config \
-        gcc \
-        nano \
-        vim && \
+        gcc && \
     rm -rf /var/lib/apt/lists/* && \
     apt-get clean
 

backend/onyx/agents/agent_search/basic/graph_builder.py

@@ -85,7 +85,7 @@ if __name__ == "__main__":
 
     graph = basic_graph_builder()
     compiled_graph = graph.compile()
-    input = BasicInput(unused=True)
+    input = BasicInput(_unused=True)
     primary_llm, fast_llm = get_default_llms()
     with get_session_context_manager() as db_session:
         config, _ = get_test_config(

backend/onyx/agents/agent_search/basic/states.py

@@ -17,7 +17,7 @@ from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
 class BasicInput(BaseModel):
     # Langgraph needs a nonempty input, but we pass in all static
     # data through a RunnableConfig.
-    unused: bool = True
+    _unused: bool = True
 
 
 ## Graph Output State

backend/onyx/agents/agent_search/core_state.py

@@ -9,6 +9,7 @@ class CoreState(BaseModel):
     This is the core state that is shared across all subgraphs.
     """
 
+    base_question: str = ""
     log_messages: Annotated[list[str], add] = []
 
 
@@ -17,4 +18,4 @@ class SubgraphCoreState(BaseModel):
     This is the core state that is shared across all subgraphs.
     """
 
-    log_messages: Annotated[list[str], add] = []
+    log_messages: Annotated[list[str], add]

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/check_sub_answer.py

@@ -1,8 +1,8 @@
 from datetime import datetime
 from typing import cast
 
-from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
 from langchain_core.runnables.config import RunnableConfig
 
 from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
@@ -12,43 +12,14 @@ from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer
     SubQuestionAnswerCheckUpdate,
 )
 from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    binary_string_test,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_POSITIVE_VALUE_STR,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import AgentLLMErrorType
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
-from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import SUB_ANSWER_CHECK_PROMPT
 from onyx.prompts.agent_search import UNKNOWN_ANSWER
-from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. The sub-answer will be treated as 'relevant'",
-    rate_limit="LLM Rate Limit Error. The sub-answer will be treated as 'relevant'",
-    general_error="General LLM Error. The sub-answer will be treated as 'relevant'",
-)
 
 
-@log_function_time(print_only=True)
 def check_sub_answer(
     state: AnswerQuestionState, config: RunnableConfig
 ) -> SubQuestionAnswerCheckUpdate:
@@ -82,40 +53,14 @@ def check_sub_answer(
 
     graph_config = cast(GraphConfig, config["metadata"]["config"])
     fast_llm = graph_config.tooling.fast_llm
-    agent_error: AgentErrorLog | None = None
-    response: BaseMessage | None = None
-    try:
-        response = fast_llm.invoke(
+    response = list(
+        fast_llm.stream(
             prompt=msg,
-            timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK,
         )
+    )
 
-        quality_str: str = cast(str, response.content)
-        answer_quality = binary_string_test(
-            text=quality_str, positive_value=AGENT_POSITIVE_VALUE_STR
-        )
-        log_result = f"Answer quality: {quality_str}"
-
-    except LLMTimeoutError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        answer_quality = True
-        log_result = agent_error.error_result
-        logger.error("LLM Timeout Error - check sub answer")
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-
-        answer_quality = True
-        log_result = agent_error.error_result
-        logger.error("LLM Rate Limit Error - check sub answer")
+    quality_str: str = merge_message_runs(response, chunk_separator="")[0].content
+    answer_quality = "yes" in quality_str.lower()
 
     return SubQuestionAnswerCheckUpdate(
         answer_quality=answer_quality,
@@ -124,7 +69,7 @@ def check_sub_answer(
                 graph_component="initial  - generate individual sub answer",
                 node_name="check sub answer",
                 node_start_time=node_start_time,
-                result=log_result,
+                result=f"Answer quality: {quality_str}",
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/generate_sub_answer.py

@@ -16,23 +16,6 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     build_sub_question_answer_prompt,
 )
-from onyx.agents.agent_search.shared_graph_utils.calculations import (
-    dedup_sort_inference_section_list,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    LLM_ANSWER_ERROR_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import get_answer_citation_ids
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
@@ -47,23 +30,12 @@ from onyx.chat.models import StreamStopInfo
 from onyx.chat.models import StreamStopReason
 from onyx.chat.models import StreamType
 from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
-from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import NO_RECOVERED_DOCS
 from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
 
 logger = setup_logger()
 
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. A sub-answer could not be constructed and the sub-question will be ignored.",
-    rate_limit="LLM Rate Limit Error. A sub-answer could not be constructed and the sub-question will be ignored.",
-    general_error="General LLM Error. A sub-answer could not be constructed and the sub-question will be ignored.",
-)
 
-
-@log_function_time(print_only=True)
 def generate_sub_answer(
     state: AnswerQuestionState,
     config: RunnableConfig,
@@ -79,17 +51,12 @@ def generate_sub_answer(
     state.verified_reranked_documents
     level, question_num = parse_question_id(state.question_id)
     context_docs = state.context_documents[:AGENT_MAX_ANSWER_CONTEXT_DOCS]
-
-    context_docs = dedup_sort_inference_section_list(context_docs)
-
     persona_contextualized_prompt = get_persona_agent_prompt_expressions(
         graph_config.inputs.search_request.persona
     ).contextualized_prompt
 
     if len(context_docs) == 0:
         answer_str = NO_RECOVERED_DOCS
-        cited_documents: list = []
-        log_results = "No documents retrieved"
         write_custom_event(
             "sub_answers",
             AgentAnswerPiece(
@@ -112,67 +79,41 @@ def generate_sub_answer(
 
         response: list[str | list[str | dict[str, Any]]] = []
         dispatch_timings: list[float] = []
-
-        agent_error: AgentErrorLog | None = None
-
-        try:
-            for message in fast_llm.stream(
-                prompt=msg,
-                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION,
-            ):
-                # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-                content = message.content
-                if not isinstance(content, str):
-                    raise ValueError(
-                        f"Expected content to be a string, but got {type(content)}"
-                    )
-                start_stream_token = datetime.now()
-                write_custom_event(
-                    "sub_answers",
-                    AgentAnswerPiece(
-                        answer_piece=content,
-                        level=level,
-                        level_question_num=question_num,
-                        answer_type="agent_sub_answer",
-                    ),
-                    writer,
+        for message in fast_llm.stream(
+            prompt=msg,
+        ):
+            # TODO: in principle, the answer here COULD contain images, but we don't support that yet
+            content = message.content
+            if not isinstance(content, str):
+                raise ValueError(
+                    f"Expected content to be a string, but got {type(content)}"
                 )
-                end_stream_token = datetime.now()
-                dispatch_timings.append(
-                    (end_stream_token - start_stream_token).microseconds
-                )
-                response.append(content)
-
-        except LLMTimeoutError:
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.TIMEOUT,
-                error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-                error_result=_llm_node_error_strings.timeout,
+            start_stream_token = datetime.now()
+            write_custom_event(
+                "sub_answers",
+                AgentAnswerPiece(
+                    answer_piece=content,
+                    level=level,
+                    level_question_num=question_num,
+                    answer_type="agent_sub_answer",
+                ),
+                writer,
             )
-            logger.error("LLM Timeout Error - generate sub answer")
-        except LLMRateLimitError:
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.RATE_LIMIT,
-                error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-                error_result=_llm_node_error_strings.rate_limit,
+            end_stream_token = datetime.now()
+            dispatch_timings.append(
+                (end_stream_token - start_stream_token).microseconds
             )
-            logger.error("LLM Rate Limit Error - generate sub answer")
+            response.append(content)
 
-        if agent_error:
-            answer_str = LLM_ANSWER_ERROR_MESSAGE
-            cited_documents = []
-            log_results = (
-                agent_error.error_result
-                or "Sub-answer generation failed due to LLM error"
-            )
+        answer_str = merge_message_runs(response, chunk_separator="")[0].content
+        logger.debug(
+            f"Average dispatch time: {sum(dispatch_timings) / len(dispatch_timings)}"
+        )
 
-        else:
-            answer_str = merge_message_runs(response, chunk_separator="")[0].content
-            answer_citation_ids = get_answer_citation_ids(answer_str)
-            cited_documents = [
-                context_docs[id] for id in answer_citation_ids if id < len(context_docs)
-            ]
-            log_results = None
+    answer_citation_ids = get_answer_citation_ids(answer_str)
+    cited_documents = [
+        context_docs[id] for id in answer_citation_ids if id < len(context_docs)
+    ]
 
     stop_event = StreamStopInfo(
         stop_reason=StreamStopReason.FINISHED,
@@ -190,7 +131,7 @@ def generate_sub_answer(
                 graph_component="initial - generate individual sub answer",
                 node_name="generate sub answer",
                 node_start_time=node_start_time,
-                result=log_results or "",
+                result="",
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/states.py

@@ -42,8 +42,10 @@ class SubQuestionRetrievalIngestionUpdate(LoggerUpdate, BaseModel):
 
 
 class SubQuestionAnsweringInput(SubgraphCoreState):
-    question: str
-    question_id: str
+    question: str = ""
+    question_id: str = (
+        ""  # 0_0 is original question, everything else is <level>_<question_num>.
+    )
     # level 0 is original question and first decomposition, level 1 is follow up, etc
     # question_num is a unique number per original question per level.
 

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/nodes/generate_initial_answer.py

@@ -26,31 +26,14 @@ from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     trim_prompt_piece,
 )
-from onyx.agents.agent_search.shared_graph_utils.calculations import (
-    get_answer_generation_documents,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
 from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_section_list,
+    dedup_inference_sections,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     dispatch_main_answer_stop_info,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_deduplicated_structured_subquestion_documents,
-)
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
@@ -59,16 +42,12 @@ from onyx.agents.agent_search.shared_graph_utils.utils import remove_document_ci
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.chat.models import AgentAnswerPiece
 from onyx.chat.models import ExtendedToolResponse
-from onyx.chat.models import StreamingError
 from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
-from onyx.configs.agent_configs import AGENT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER
 from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION,
+from onyx.context.search.models import InferenceSection
+from onyx.prompts.agent_search import (
+    INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS,
 )
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS
 from onyx.prompts.agent_search import (
     INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS,
 )
@@ -77,16 +56,8 @@ from onyx.prompts.agent_search import (
 )
 from onyx.prompts.agent_search import UNKNOWN_ANSWER
 from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
-from onyx.utils.timing import log_function_time
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. The initial answer could not be generated.",
-    rate_limit="LLM Rate Limit Error. The initial answer could not be generated.",
-    general_error="General LLM Error. The initial answer could not be generated.",
-)
 
 
-@log_function_time(print_only=True)
 def generate_initial_answer(
     state: SubQuestionRetrievalState,
     config: RunnableConfig,
@@ -102,19 +73,15 @@ def generate_initial_answer(
     question = graph_config.inputs.search_request.query
     prompt_enrichment_components = get_prompt_enrichment_components(graph_config)
 
-    # get all documents cited in sub-questions
-    structured_subquestion_docs = get_deduplicated_structured_subquestion_documents(
-        state.sub_question_results
-    )
-
+    sub_questions_cited_documents = state.cited_documents
     orig_question_retrieval_documents = state.orig_question_retrieved_documents
 
-    consolidated_context_docs = structured_subquestion_docs.cited_documents
+    consolidated_context_docs: list[InferenceSection] = sub_questions_cited_documents
     counter = 0
     for original_doc_number, original_doc in enumerate(
         orig_question_retrieval_documents
     ):
-        if original_doc_number not in structured_subquestion_docs.cited_documents:
+        if original_doc_number not in sub_questions_cited_documents:
             if (
                 counter <= AGENT_MIN_ORIG_QUESTION_DOCS
                 or len(consolidated_context_docs) < AGENT_MAX_ANSWER_CONTEXT_DOCS
@@ -123,18 +90,15 @@ def generate_initial_answer(
                 counter += 1
 
     # sort docs by their scores - though the scores refer to different questions
-    relevant_docs = dedup_inference_section_list(consolidated_context_docs)
+    relevant_docs = dedup_inference_sections(
+        consolidated_context_docs, consolidated_context_docs
+    )
 
     sub_questions: list[str] = []
-
-    # Create the list of documents to stream out. Start with the
-    # ones that wil be in the context (or, if len == 0, use docs
-    # that were retrieved for the original question)
-    answer_generation_documents = get_answer_generation_documents(
-        relevant_docs=relevant_docs,
-        context_documents=structured_subquestion_docs.context_documents,
-        original_question_docs=orig_question_retrieval_documents,
-        max_docs=AGENT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER,
+    streamed_documents = (
+        relevant_docs
+        if len(relevant_docs) > 0
+        else state.orig_question_retrieved_documents[:15]
     )
 
     # Use the query info from the base document retrieval
@@ -144,13 +108,11 @@ def generate_initial_answer(
         graph_config.tooling.search_tool
     ), "search_tool must be provided for agentic search"
 
-    relevance_list = relevance_from_docs(
-        answer_generation_documents.streaming_documents
-    )
+    relevance_list = relevance_from_docs(relevant_docs)
     for tool_response in yield_search_responses(
         query=question,
-        reranked_sections=answer_generation_documents.streaming_documents,
-        final_context_sections=answer_generation_documents.context_documents,
+        reranked_sections=streamed_documents,
+        final_context_sections=streamed_documents,
         search_query_info=query_info,
         get_section_relevance=lambda: relevance_list,
         search_tool=graph_config.tooling.search_tool,
@@ -166,7 +128,7 @@ def generate_initial_answer(
             writer,
         )
 
-    if len(answer_generation_documents.context_documents) == 0:
+    if len(relevant_docs) == 0:
         write_custom_event(
             "initial_agent_answer",
             AgentAnswerPiece(
@@ -232,7 +194,7 @@ def generate_initial_answer(
 
         model = graph_config.tooling.fast_llm
 
-        doc_context = format_docs(answer_generation_documents.context_documents)
+        doc_context = format_docs(relevant_docs)
         doc_context = trim_prompt_piece(
             config=model.config,
             prompt_piece=doc_context,
@@ -262,82 +224,30 @@ def generate_initial_answer(
 
         streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
         dispatch_timings: list[float] = []
-
-        agent_error: AgentErrorLog | None = None
-
-        try:
-            for message in model.stream(
-                msg,
-                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION,
-            ):
-                # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-                content = message.content
-                if not isinstance(content, str):
-                    raise ValueError(
-                        f"Expected content to be a string, but got {type(content)}"
-                    )
-                start_stream_token = datetime.now()
-
-                write_custom_event(
-                    "initial_agent_answer",
-                    AgentAnswerPiece(
-                        answer_piece=content,
-                        level=0,
-                        level_question_num=0,
-                        answer_type="agent_level_answer",
-                    ),
-                    writer,
+        for message in model.stream(msg):
+            # TODO: in principle, the answer here COULD contain images, but we don't support that yet
+            content = message.content
+            if not isinstance(content, str):
+                raise ValueError(
+                    f"Expected content to be a string, but got {type(content)}"
                 )
-                end_stream_token = datetime.now()
-                dispatch_timings.append(
-                    (end_stream_token - start_stream_token).microseconds
-                )
-                streamed_tokens.append(content)
+            start_stream_token = datetime.now()
 
-        except LLMTimeoutError:
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.TIMEOUT,
-                error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-                error_result=_llm_node_error_strings.timeout,
-            )
-            logger.error("LLM Timeout Error - generate initial answer")
-
-        except LLMRateLimitError:
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.RATE_LIMIT,
-                error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-                error_result=_llm_node_error_strings.rate_limit,
-            )
-            logger.error("LLM Rate Limit Error - generate initial answer")
-
-        if agent_error:
             write_custom_event(
                 "initial_agent_answer",
-                StreamingError(
-                    error=AGENT_LLM_TIMEOUT_MESSAGE,
+                AgentAnswerPiece(
+                    answer_piece=content,
+                    level=0,
+                    level_question_num=0,
+                    answer_type="agent_level_answer",
                 ),
                 writer,
             )
-            return InitialAnswerUpdate(
-                initial_answer=None,
-                answer_error=AgentErrorLog(
-                    error_message=agent_error.error_message or "An LLM error occurred",
-                    error_type=agent_error.error_type,
-                    error_result=agent_error.error_result,
-                ),
-                initial_agent_stats=None,
-                generated_sub_questions=sub_questions,
-                agent_base_end_time=None,
-                agent_base_metrics=None,
-                log_messages=[
-                    get_langgraph_node_log_string(
-                        graph_component="initial - generate initial answer",
-                        node_name="generate initial answer",
-                        node_start_time=node_start_time,
-                        result=agent_error.error_result or "An LLM error occurred",
-                    )
-                ],
+            end_stream_token = datetime.now()
+            dispatch_timings.append(
+                (end_stream_token - start_stream_token).microseconds
             )
+            streamed_tokens.append(content)
 
         logger.debug(
             f"Average dispatch time for initial answer: {sum(dispatch_timings) / len(dispatch_timings)}"

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/nodes/validate_initial_answer.py

@@ -10,10 +10,8 @@ from onyx.agents.agent_search.deep_search.main.states import (
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
-from onyx.utils.timing import log_function_time
 
 
-@log_function_time(print_only=True)
 def validate_initial_answer(
     state: SubQuestionRetrievalState,
 ) -> InitialAnswerQualityUpdate:
@@ -27,7 +25,7 @@ def validate_initial_answer(
         f"--------{node_start_time}--------Checking for base answer validity - for not set True/False manually"
     )
 
-    verdict = True  # not actually required as already streamed out. Refinement will do similar
+    verdict = True
 
     return InitialAnswerQualityUpdate(
         initial_answer_quality_eval=verdict,

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/nodes/decompose_orig_question.py

@@ -23,8 +23,6 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     build_history_prompt,
 )
-from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
@@ -35,30 +33,17 @@ from onyx.chat.models import StreamStopReason
 from onyx.chat.models import StreamType
 from onyx.chat.models import SubQuestionPiece
 from onyx.configs.agent_configs import AGENT_NUM_DOCS_FOR_DECOMPOSITION
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
-    INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH_ASSUMING_REFINEMENT,
+    INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH,
 )
 from onyx.prompts.agent_search import (
-    INITIAL_QUESTION_DECOMPOSITION_PROMPT_ASSUMING_REFINEMENT,
+    INITIAL_QUESTION_DECOMPOSITION_PROMPT,
 )
 from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
 
 logger = setup_logger()
 
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. Sub-questions could not be generated.",
-    rate_limit="LLM Rate Limit Error. Sub-questions could not be generated.",
-    general_error="General LLM Error. Sub-questions could not be generated.",
-)
 
-
-@log_function_time(print_only=True)
 def decompose_orig_question(
     state: SubQuestionRetrievalState,
     config: RunnableConfig,
@@ -100,15 +85,15 @@ def decompose_orig_question(
             ]
         )
 
-        decomposition_prompt = INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH_ASSUMING_REFINEMENT.format(
-            question=question, sample_doc_str=sample_doc_str, history=history
+        decomposition_prompt = (
+            INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH.format(
+                question=question, sample_doc_str=sample_doc_str, history=history
+            )
         )
 
     else:
-        decomposition_prompt = (
-            INITIAL_QUESTION_DECOMPOSITION_PROMPT_ASSUMING_REFINEMENT.format(
-                question=question, history=history
-            )
+        decomposition_prompt = INITIAL_QUESTION_DECOMPOSITION_PROMPT.format(
+            question=question, history=history
         )
 
     # Start decomposition
@@ -127,42 +112,32 @@ def decompose_orig_question(
     )
 
     # dispatches custom events for subquestion tokens, adding in subquestion ids.
+    streamed_tokens = dispatch_separated(
+        model.stream(msg),
+        dispatch_subquestion(0, writer),
+        sep_callback=dispatch_subquestion_sep(0, writer),
+    )
 
-    streamed_tokens: list[BaseMessage_Content] = []
+    stop_event = StreamStopInfo(
+        stop_reason=StreamStopReason.FINISHED,
+        stream_type=StreamType.SUB_QUESTIONS,
+        level=0,
+    )
+    write_custom_event("stream_finished", stop_event, writer)
 
-    try:
-        streamed_tokens = dispatch_separated(
-            model.stream(
-                msg,
-                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION,
-            ),
-            dispatch_subquestion(0, writer),
-            sep_callback=dispatch_subquestion_sep(0, writer),
-        )
+    deomposition_response = merge_content(*streamed_tokens)
 
-        decomposition_response = merge_content(*streamed_tokens)
+    # this call should only return strings. Commenting out for efficiency
+    # assert [type(tok) == str for tok in streamed_tokens]
 
-        list_of_subqs = cast(str, decomposition_response).split("\n")
+    # use no-op cast() instead of str() which runs code
+    # list_of_subquestions = clean_and_parse_list_string(cast(str, response))
+    list_of_subqs = cast(str, deomposition_response).split("\n")
 
-        initial_sub_questions = [sq.strip() for sq in list_of_subqs if sq.strip() != ""]
-        log_result = f"decomposed original question into {len(initial_sub_questions)} subquestions"
-
-        stop_event = StreamStopInfo(
-            stop_reason=StreamStopReason.FINISHED,
-            stream_type=StreamType.SUB_QUESTIONS,
-            level=0,
-        )
-        write_custom_event("stream_finished", stop_event, writer)
-
-    except LLMTimeoutError as e:
-        logger.error("LLM Timeout Error - decompose orig question")
-        raise e  # fail loudly on this critical step
-    except LLMRateLimitError as e:
-        logger.error("LLM Rate Limit Error - decompose orig question")
-        raise e
+    decomp_list: list[str] = [sq.strip() for sq in list_of_subqs if sq.strip() != ""]
 
     return InitialQuestionDecompositionUpdate(
-        initial_sub_questions=initial_sub_questions,
+        initial_sub_questions=decomp_list,
         agent_start_time=agent_start_time,
         agent_refined_start_time=None,
         agent_refined_end_time=None,
@@ -176,7 +151,7 @@ def decompose_orig_question(
                 graph_component="initial - generate sub answers",
                 node_name="decompose original question",
                 node_start_time=node_start_time,
-                result=log_result,
+                result=f"decomposed original question into {len(decomp_list)} subquestions",
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/main/graph_builder.py

@@ -26,8 +26,8 @@ from onyx.agents.agent_search.deep_search.main.nodes.decide_refinement_need impo
 from onyx.agents.agent_search.deep_search.main.nodes.extract_entities_terms import (
     extract_entities_terms,
 )
-from onyx.agents.agent_search.deep_search.main.nodes.generate_validate_refined_answer import (
-    generate_validate_refined_answer,
+from onyx.agents.agent_search.deep_search.main.nodes.generate_refined_answer import (
+    generate_refined_answer,
 )
 from onyx.agents.agent_search.deep_search.main.nodes.ingest_refined_sub_answers import (
     ingest_refined_sub_answers,
@@ -126,8 +126,8 @@ def main_graph_builder(test_mode: bool = False) -> StateGraph:
 
     # Node to generate the refined answer
     graph.add_node(
-        node="generate_validate_refined_answer",
-        action=generate_validate_refined_answer,
+        node="generate_refined_answer",
+        action=generate_refined_answer,
     )
 
     # Early node to extract the entities and terms from the initial answer,
@@ -215,11 +215,11 @@ def main_graph_builder(test_mode: bool = False) -> StateGraph:
 
     graph.add_edge(
         start_key="ingest_refined_sub_answers",
-        end_key="generate_validate_refined_answer",
+        end_key="generate_refined_answer",
     )
 
     graph.add_edge(
-        start_key="generate_validate_refined_answer",
+        start_key="generate_refined_answer",
         end_key="compare_answers",
     )
     graph.add_edge(
@@ -252,7 +252,9 @@ if __name__ == "__main__":
             db_session, primary_llm, fast_llm, search_request
         )
 
-        inputs = MainInput(log_messages=[])
+        inputs = MainInput(
+            base_question=graph_config.inputs.search_request.query, log_messages=[]
+        )
 
         for thing in compiled_graph.stream(
             input=inputs,

backend/onyx/agents/agent_search/deep_search/main/nodes/compare_answers.py

@@ -1,7 +1,6 @@
 from datetime import datetime
 from typing import cast
 
-from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
 from langchain_core.runnables import RunnableConfig
 from langgraph.types import StreamWriter
@@ -11,51 +10,16 @@ from onyx.agents.agent_search.deep_search.main.states import (
 )
 from onyx.agents.agent_search.deep_search.main.states import MainState
 from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    binary_string_test,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_POSITIVE_VALUE_STR,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.chat.models import RefinedAnswerImprovement
-from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     INITIAL_REFINED_ANSWER_COMPARISON_PROMPT,
 )
-from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="The LLM timed out, and the answers could not be compared.",
-    rate_limit="The LLM encountered a rate limit, and the answers could not be compared.",
-    general_error="The LLM encountered an error, and the answers could not be compared.",
-)
-
-_ANSWER_QUALITY_NOT_SUFFICIENT_MESSAGE = (
-    "Answer quality is not sufficient, so stay with the initial answer."
-)
 
 
-@log_function_time(print_only=True)
 def compare_answers(
     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
 ) -> InitialRefinedAnswerComparisonUpdate:
@@ -70,75 +34,21 @@ def compare_answers(
     initial_answer = state.initial_answer
     refined_answer = state.refined_answer
 
-    # if answer quality is not sufficient, then stay with the initial answer
-    if not state.refined_answer_quality:
-        write_custom_event(
-            "refined_answer_improvement",
-            RefinedAnswerImprovement(
-                refined_answer_improvement=False,
-            ),
-            writer,
-        )
-
-        return InitialRefinedAnswerComparisonUpdate(
-            refined_answer_improvement_eval=False,
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="main",
-                    node_name="compare answers",
-                    node_start_time=node_start_time,
-                    result=_ANSWER_QUALITY_NOT_SUFFICIENT_MESSAGE,
-                )
-            ],
-        )
-
     compare_answers_prompt = INITIAL_REFINED_ANSWER_COMPARISON_PROMPT.format(
         question=question, initial_answer=initial_answer, refined_answer=refined_answer
     )
 
     msg = [HumanMessage(content=compare_answers_prompt)]
 
-    agent_error: AgentErrorLog | None = None
     # Get the rewritten queries in a defined format
     model = graph_config.tooling.fast_llm
-    resp: BaseMessage | None = None
-    refined_answer_improvement: bool | None = None
+
     # no need to stream this
-    try:
-        resp = model.invoke(
-            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS
-        )
+    resp = model.invoke(msg)
 
-    except LLMTimeoutError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - compare answers")
-        # continue as True in this support step
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - compare answers")
-        # continue as True in this support step
-
-    if agent_error or resp is None:
-        refined_answer_improvement = True
-        if agent_error:
-            log_result = agent_error.error_result
-        else:
-            log_result = "An answer could not be generated."
-
-    else:
-        refined_answer_improvement = binary_string_test(
-            text=cast(str, resp.content),
-            positive_value=AGENT_POSITIVE_VALUE_STR,
-        )
-        log_result = f"Answer comparison: {refined_answer_improvement}"
+    refined_answer_improvement = (
+        isinstance(resp.content, str) and "yes" in resp.content.lower()
+    )
 
     write_custom_event(
         "refined_answer_improvement",
@@ -155,7 +65,7 @@ def compare_answers(
                 graph_component="main",
                 node_name="compare answers",
                 node_start_time=node_start_time,
-                result=log_result,
+                result=f"Answer comparison: {refined_answer_improvement}",
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/main/nodes/create_refined_sub_questions.py

@@ -21,18 +21,6 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     build_history_prompt,
 )
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     format_entity_term_extraction,
@@ -42,31 +30,12 @@ from onyx.agents.agent_search.shared_graph_utils.utils import (
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import StreamingError
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
-    REFINEMENT_QUESTION_DECOMPOSITION_PROMPT_W_INITIAL_SUBQUESTION_ANSWERS,
+    REFINEMENT_QUESTION_DECOMPOSITION_PROMPT,
 )
 from onyx.tools.models import ToolCallKickoff
-from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_ANSWERED_SUBQUESTIONS_DIVIDER = "\n\n---\n\n"
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="The LLM timed out. The sub-questions could not be generated.",
-    rate_limit="The LLM encountered a rate limit. The sub-questions could not be generated.",
-    general_error="The LLM encountered an error. The sub-questions could not be generated.",
-)
 
 
-@log_function_time(print_only=True)
 def create_refined_sub_questions(
     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
 ) -> RefinedQuestionDecompositionUpdate:
@@ -103,10 +72,8 @@ def create_refined_sub_questions(
 
     initial_question_answers = state.sub_question_results
 
-    addressed_subquestions_with_answers = [
-        f"Subquestion: {x.question}\nSubanswer:\n{x.answer}"
-        for x in initial_question_answers
-        if x.verified_high_quality and x.answer
+    addressed_question_list = [
+        x.question for x in initial_question_answers if x.verified_high_quality
     ]
 
     failed_question_list = [
@@ -115,14 +82,12 @@ def create_refined_sub_questions(
 
     msg = [
         HumanMessage(
-            content=REFINEMENT_QUESTION_DECOMPOSITION_PROMPT_W_INITIAL_SUBQUESTION_ANSWERS.format(
+            content=REFINEMENT_QUESTION_DECOMPOSITION_PROMPT.format(
                 question=question,
                 history=history,
                 entity_term_extraction_str=entity_term_extraction_str,
                 base_answer=base_answer,
-                answered_subquestions_with_answers=_ANSWERED_SUBQUESTIONS_DIVIDER.join(
-                    addressed_subquestions_with_answers
-                ),
+                answered_sub_questions="\n - ".join(addressed_question_list),
                 failed_sub_questions="\n - ".join(failed_question_list),
             ),
         )
@@ -131,65 +96,29 @@ def create_refined_sub_questions(
     # Grader
     model = graph_config.tooling.fast_llm
 
-    agent_error: AgentErrorLog | None = None
-    streamed_tokens: list[BaseMessage_Content] = []
-    try:
-        streamed_tokens = dispatch_separated(
-            model.stream(
-                msg,
-                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION,
-            ),
-            dispatch_subquestion(1, writer),
-            sep_callback=dispatch_subquestion_sep(1, writer),
-        )
-    except LLMTimeoutError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - create refined sub questions")
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - create refined sub questions")
-
-    if agent_error:
-        refined_sub_question_dict: dict[int, RefinementSubQuestion] = {}
-        log_result = agent_error.error_result
-        write_custom_event(
-            "refined_sub_question_creation_error",
-            StreamingError(
-                error="Your LLM was not able to create refined sub questions in time and timed out. Please try again.",
-            ),
-            writer,
-        )
+    streamed_tokens = dispatch_separated(
+        model.stream(msg),
+        dispatch_subquestion(1, writer),
+        sep_callback=dispatch_subquestion_sep(1, writer),
+    )
+    response = merge_content(*streamed_tokens)
 
+    if isinstance(response, str):
+        parsed_response = [q for q in response.split("\n") if q.strip() != ""]
     else:
-        response = merge_content(*streamed_tokens)
+        raise ValueError("LLM response is not a string")
 
-        if isinstance(response, str):
-            parsed_response = [q for q in response.split("\n") if q.strip() != ""]
-        else:
-            raise ValueError("LLM response is not a string")
+    refined_sub_question_dict = {}
+    for sub_question_num, sub_question in enumerate(parsed_response):
+        refined_sub_question = RefinementSubQuestion(
+            sub_question=sub_question,
+            sub_question_id=make_question_id(1, sub_question_num + 1),
+            verified=False,
+            answered=False,
+            answer="",
+        )
 
-        refined_sub_question_dict = {}
-        for sub_question_num, sub_question in enumerate(parsed_response):
-            refined_sub_question = RefinementSubQuestion(
-                sub_question=sub_question,
-                sub_question_id=make_question_id(1, sub_question_num + 1),
-                verified=False,
-                answered=False,
-                answer="",
-            )
-
-            refined_sub_question_dict[sub_question_num + 1] = refined_sub_question
-
-        log_result = f"Created {len(refined_sub_question_dict)} refined sub questions"
+        refined_sub_question_dict[sub_question_num + 1] = refined_sub_question
 
     return RefinedQuestionDecompositionUpdate(
         refined_sub_questions=refined_sub_question_dict,
@@ -199,7 +128,7 @@ def create_refined_sub_questions(
                 graph_component="main",
                 node_name="create refined sub questions",
                 node_start_time=node_start_time,
-                result=log_result,
+                result=f"Created {len(refined_sub_question_dict)} refined sub questions",
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/main/nodes/decide_refinement_need.py

@@ -11,10 +11,8 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
-from onyx.utils.timing import log_function_time
 
 
-@log_function_time(print_only=True)
 def decide_refinement_need(
     state: MainState, config: RunnableConfig
 ) -> RequireRefinemenEvalUpdate:
@@ -28,19 +26,6 @@ def decide_refinement_need(
 
     decision = True  # TODO: just for current testing purposes
 
-    if state.answer_error:
-        return RequireRefinemenEvalUpdate(
-            require_refined_answer_eval=False,
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="main",
-                    node_name="decide refinement need",
-                    node_start_time=node_start_time,
-                    result="Timeout Error",
-                )
-            ],
-        )
-
     log_messages = [
         get_langgraph_node_log_string(
             graph_component="main",

backend/onyx/agents/agent_search/deep_search/main/nodes/extract_entities_terms.py

@@ -21,16 +21,11 @@ from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION,
-)
 from onyx.configs.constants import NUM_EXPLORATORY_DOCS
 from onyx.prompts.agent_search import ENTITY_TERM_EXTRACTION_PROMPT
 from onyx.prompts.agent_search import ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE
-from onyx.utils.timing import log_function_time
 
 
-@log_function_time(print_only=True)
 def extract_entities_terms(
     state: MainState, config: RunnableConfig
 ) -> EntityTermExtractionUpdate:
@@ -86,7 +81,6 @@ def extract_entities_terms(
     # Grader
     llm_response = fast_llm.invoke(
         prompt=msg,
-        timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION,
     )
 
     cleaned_response = (

backend/onyx/agents/agent_search/deep_search/main/nodes/generate_refined_answer.py

@@ -11,49 +11,27 @@ from onyx.agents.agent_search.deep_search.main.models import (
     AgentRefinedMetrics,
 )
 from onyx.agents.agent_search.deep_search.main.operations import get_query_info
+from onyx.agents.agent_search.deep_search.main.operations import logger
 from onyx.agents.agent_search.deep_search.main.states import MainState
 from onyx.agents.agent_search.deep_search.main.states import (
     RefinedAnswerUpdate,
 )
 from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    binary_string_test_after_answer_separator,
-)
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     get_prompt_enrichment_components,
 )
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     trim_prompt_piece,
 )
-from onyx.agents.agent_search.shared_graph_utils.calculations import (
-    get_answer_generation_documents,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import AGENT_ANSWER_SEPARATOR
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_POSITIVE_VALUE_STR,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
+from onyx.agents.agent_search.shared_graph_utils.models import InferenceSection
 from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
 from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_section_list,
+    dedup_inference_sections,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     dispatch_main_answer_stop_info,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_deduplicated_structured_subquestion_documents,
-)
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
@@ -65,50 +43,26 @@ from onyx.agents.agent_search.shared_graph_utils.utils import (
 from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
 from onyx.chat.models import AgentAnswerPiece
 from onyx.chat.models import ExtendedToolResponse
-from onyx.chat.models import StreamingError
 from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
-from onyx.configs.agent_configs import AGENT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER
 from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_VALIDATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS,
 )
 from onyx.prompts.agent_search import (
     REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS,
 )
-from onyx.prompts.agent_search import (
-    REFINED_ANSWER_VALIDATION_PROMPT,
-)
 from onyx.prompts.agent_search import (
     SUB_QUESTION_ANSWER_TEMPLATE_REFINED,
 )
 from onyx.prompts.agent_search import UNKNOWN_ANSWER
 from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
-from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="The LLM timed out. The refined answer could not be generated.",
-    rate_limit="The LLM encountered a rate limit. The refined answer could not be generated.",
-    general_error="The LLM encountered an error. The refined answer could not be generated.",
-)
 
 
-@log_function_time(print_only=True)
-def generate_validate_refined_answer(
+def generate_refined_answer(
     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
 ) -> RefinedAnswerUpdate:
     """
-    LangGraph node to generate the refined answer and validate it.
+    LangGraph node to generate the refined answer.
     """
 
     node_start_time = datetime.now()
@@ -122,24 +76,19 @@ def generate_validate_refined_answer(
     )
 
     verified_reranked_documents = state.verified_reranked_documents
-
-    # get all documents cited in sub-questions
-    structured_subquestion_docs = get_deduplicated_structured_subquestion_documents(
-        state.sub_question_results
-    )
-
+    sub_questions_cited_documents = state.cited_documents
     original_question_verified_documents = (
         state.orig_question_verified_reranked_documents
     )
     original_question_retrieved_documents = state.orig_question_retrieved_documents
 
-    consolidated_context_docs = structured_subquestion_docs.cited_documents
+    consolidated_context_docs: list[InferenceSection] = sub_questions_cited_documents
 
     counter = 0
     for original_doc_number, original_doc in enumerate(
         original_question_verified_documents
     ):
-        if original_doc_number not in structured_subquestion_docs.cited_documents:
+        if original_doc_number not in sub_questions_cited_documents:
             if (
                 counter <= AGENT_MIN_ORIG_QUESTION_DOCS
                 or len(consolidated_context_docs)
@@ -150,16 +99,14 @@ def generate_validate_refined_answer(
                 counter += 1
 
     # sort docs by their scores - though the scores refer to different questions
-    relevant_docs = dedup_inference_section_list(consolidated_context_docs)
+    relevant_docs = dedup_inference_sections(
+        consolidated_context_docs, consolidated_context_docs
+    )
 
-    # Create the list of documents to stream out. Start with the
-    # ones that wil be in the context (or, if len == 0, use docs
-    # that were retrieved for the original question)
-    answer_generation_documents = get_answer_generation_documents(
-        relevant_docs=relevant_docs,
-        context_documents=structured_subquestion_docs.context_documents,
-        original_question_docs=original_question_retrieved_documents,
-        max_docs=AGENT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER,
+    streaming_docs = (
+        relevant_docs
+        if len(relevant_docs) > 0
+        else original_question_retrieved_documents[:15]
     )
 
     query_info = get_query_info(state.orig_question_sub_query_retrieval_results)
@@ -167,13 +114,11 @@ def generate_validate_refined_answer(
         graph_config.tooling.search_tool
     ), "search_tool must be provided for agentic search"
     # stream refined answer docs, or original question docs if no relevant docs are found
-    relevance_list = relevance_from_docs(
-        answer_generation_documents.streaming_documents
-    )
+    relevance_list = relevance_from_docs(relevant_docs)
     for tool_response in yield_search_responses(
         query=question,
-        reranked_sections=answer_generation_documents.streaming_documents,
-        final_context_sections=answer_generation_documents.context_documents,
+        reranked_sections=streaming_docs,
+        final_context_sections=streaming_docs,
         search_query_info=query_info,
         get_section_relevance=lambda: relevance_list,
         search_tool=graph_config.tooling.search_tool,
@@ -254,7 +199,7 @@ def generate_validate_refined_answer(
     )
 
     model = graph_config.tooling.fast_llm
-    relevant_docs_str = format_docs(answer_generation_documents.context_documents)
+    relevant_docs_str = format_docs(relevant_docs)
     relevant_docs_str = trim_prompt_piece(
         model.config,
         relevant_docs_str,
@@ -286,80 +231,28 @@ def generate_validate_refined_answer(
 
     streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
     dispatch_timings: list[float] = []
-    agent_error: AgentErrorLog | None = None
-
-    try:
-        for message in model.stream(
-            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION
-        ):
-            # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-            content = message.content
-            if not isinstance(content, str):
-                raise ValueError(
-                    f"Expected content to be a string, but got {type(content)}"
-                )
-
-            start_stream_token = datetime.now()
-            write_custom_event(
-                "refined_agent_answer",
-                AgentAnswerPiece(
-                    answer_piece=content,
-                    level=1,
-                    level_question_num=0,
-                    answer_type="agent_level_answer",
-                ),
-                writer,
+    for message in model.stream(msg):
+        # TODO: in principle, the answer here COULD contain images, but we don't support that yet
+        content = message.content
+        if not isinstance(content, str):
+            raise ValueError(
+                f"Expected content to be a string, but got {type(content)}"
             )
-            end_stream_token = datetime.now()
-            dispatch_timings.append(
-                (end_stream_token - start_stream_token).microseconds
-            )
-            streamed_tokens.append(content)
 
-    except LLMTimeoutError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - generate refined answer")
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - generate refined answer")
-
-    if agent_error:
+        start_stream_token = datetime.now()
         write_custom_event(
-            "initial_agent_answer",
-            StreamingError(
-                error=AGENT_LLM_TIMEOUT_MESSAGE,
+            "refined_agent_answer",
+            AgentAnswerPiece(
+                answer_piece=content,
+                level=1,
+                level_question_num=0,
+                answer_type="agent_level_answer",
             ),
             writer,
         )
-
-        return RefinedAnswerUpdate(
-            refined_answer=None,
-            refined_answer_quality=False,  # TODO: replace this with the actual check value
-            refined_agent_stats=None,
-            agent_refined_end_time=None,
-            agent_refined_metrics=AgentRefinedMetrics(
-                refined_doc_boost_factor=0.0,
-                refined_question_boost_factor=0.0,
-                duration_s=None,
-            ),
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="main",
-                    node_name="generate refined answer",
-                    node_start_time=node_start_time,
-                    result=agent_error.error_result or "An LLM error occurred",
-                )
-            ],
-        )
+        end_stream_token = datetime.now()
+        dispatch_timings.append((end_stream_token - start_stream_token).microseconds)
+        streamed_tokens.append(content)
 
     logger.debug(
         f"Average dispatch time for refined answer: {sum(dispatch_timings) / len(dispatch_timings)}"
@@ -368,43 +261,54 @@ def generate_validate_refined_answer(
     response = merge_content(*streamed_tokens)
     answer = cast(str, response)
 
-    # run a validation step for the refined answer only
-
-    msg = [
-        HumanMessage(
-            content=REFINED_ANSWER_VALIDATION_PROMPT.format(
-                question=question,
-                history=prompt_enrichment_components.history,
-                answered_sub_questions=sub_question_answer_str,
-                relevant_docs=relevant_docs_str,
-                proposed_answer=answer,
-                persona_specification=persona_contextualized_prompt,
-            )
-        )
-    ]
-
-    try:
-        validation_response = model.invoke(
-            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_VALIDATION
-        )
-        refined_answer_quality = binary_string_test_after_answer_separator(
-            text=cast(str, validation_response.content),
-            positive_value=AGENT_POSITIVE_VALUE_STR,
-            separator=AGENT_ANSWER_SEPARATOR,
-        )
-    except LLMTimeoutError:
-        refined_answer_quality = True
-        logger.error("LLM Timeout Error - validate refined answer")
-
-    except LLMRateLimitError:
-        refined_answer_quality = True
-        logger.error("LLM Rate Limit Error - validate refined answer")
-
     refined_agent_stats = RefinedAgentStats(
         revision_doc_efficiency=refined_doc_effectiveness,
         revision_question_efficiency=revision_question_efficiency,
     )
 
+    logger.debug(f"\n\n---INITIAL ANSWER ---\n\n Answer:\n Agent: {initial_answer}")
+    logger.debug("-" * 10)
+    logger.debug(f"\n\n---REVISED AGENT ANSWER ---\n\n Answer:\n Agent: {answer}")
+
+    logger.debug("-" * 100)
+
+    if state.initial_agent_stats:
+        initial_doc_boost_factor = state.initial_agent_stats.agent_effectiveness.get(
+            "utilized_chunk_ratio", "--"
+        )
+        initial_support_boost_factor = (
+            state.initial_agent_stats.agent_effectiveness.get("support_ratio", "--")
+        )
+        num_initial_verified_docs = state.initial_agent_stats.original_question.get(
+            "num_verified_documents", "--"
+        )
+        initial_verified_docs_avg_score = (
+            state.initial_agent_stats.original_question.get("verified_avg_score", "--")
+        )
+        initial_sub_questions_verified_docs = (
+            state.initial_agent_stats.sub_questions.get("num_verified_documents", "--")
+        )
+
+        logger.debug("INITIAL AGENT STATS")
+        logger.debug(f"Document Boost Factor: {initial_doc_boost_factor}")
+        logger.debug(f"Support Boost Factor: {initial_support_boost_factor}")
+        logger.debug(f"Originally Verified Docs: {num_initial_verified_docs}")
+        logger.debug(
+            f"Originally Verified Docs Avg Score: {initial_verified_docs_avg_score}"
+        )
+        logger.debug(
+            f"Sub-Questions Verified Docs: {initial_sub_questions_verified_docs}"
+        )
+    if refined_agent_stats:
+        logger.debug("-" * 10)
+        logger.debug("REFINED AGENT STATS")
+        logger.debug(
+            f"Revision Doc Factor: {refined_agent_stats.revision_doc_efficiency}"
+        )
+        logger.debug(
+            f"Revision Question Factor: {refined_agent_stats.revision_question_efficiency}"
+        )
+
     agent_refined_end_time = datetime.now()
     if state.agent_refined_start_time:
         agent_refined_duration = (
@@ -421,7 +325,7 @@ def generate_validate_refined_answer(
 
     return RefinedAnswerUpdate(
         refined_answer=answer,
-        refined_answer_quality=refined_answer_quality,
+        refined_answer_quality=True,  # TODO: replace this with the actual check value
         refined_agent_stats=refined_agent_stats,
         agent_refined_end_time=agent_refined_end_time,
         agent_refined_metrics=agent_refined_metrics,

backend/onyx/agents/agent_search/deep_search/main/states.py

@@ -17,7 +17,6 @@ from onyx.agents.agent_search.orchestration.states import ToolCallUpdate
 from onyx.agents.agent_search.orchestration.states import ToolChoiceInput
 from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
 from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
 from onyx.agents.agent_search.shared_graph_utils.models import (
     EntityRelationshipTermExtraction,
 )
@@ -77,7 +76,6 @@ class InitialAnswerUpdate(LoggerUpdate):
     """
 
     initial_answer: str | None = None
-    answer_error: AgentErrorLog | None = None
     initial_agent_stats: InitialAgentResultStats | None = None
     generated_sub_questions: list[str] = []
     agent_base_end_time: datetime | None = None
@@ -90,7 +88,6 @@ class RefinedAnswerUpdate(RefinedAgentEndStats, LoggerUpdate):
     """
 
     refined_answer: str | None = None
-    answer_error: AgentErrorLog | None = None
     refined_agent_stats: RefinedAgentStats | None = None
     refined_answer_quality: bool = False
 

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/expand_queries.py

@@ -16,44 +16,16 @@ from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states impor
     QueryExpansionUpdate,
 )
 from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_langgraph_node_log_string,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     QUERY_REWRITING_PROMPT,
 )
-from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="Query rewriting failed due to LLM timeout - the original question will be used.",
-    rate_limit="Query rewriting failed due to LLM rate limit - the original question will be used.",
-    general_error="Query rewriting failed due to LLM error - the original question will be used.",
-)
 
 
-@log_function_time(print_only=True)
 def expand_queries(
     state: ExpandedRetrievalInput,
     config: RunnableConfig,
@@ -82,43 +54,13 @@ def expand_queries(
         )
     ]
 
-    agent_error: AgentErrorLog | None = None
-    llm_response_list: list[BaseMessage_Content] = []
-    llm_response = ""
-    rewritten_queries = []
+    llm_response_list = dispatch_separated(
+        llm.stream(prompt=msg), dispatch_subquery(level, question_num, writer)
+    )
 
-    try:
-        llm_response_list = dispatch_separated(
-            llm.stream(
-                prompt=msg,
-                timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION,
-            ),
-            dispatch_subquery(level, question_num, writer),
-        )
-        llm_response = merge_message_runs(llm_response_list, chunk_separator="")[
-            0
-        ].content
-        rewritten_queries = llm_response.split("\n")
-        log_result = f"Number of expanded queries: {len(rewritten_queries)}"
+    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
 
-    except LLMTimeoutError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - expand queries")
-        log_result = agent_error.error_result
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - expand queries")
-        log_result = agent_error.error_result
-    # use subquestion as query if query generation fails
+    rewritten_queries = llm_response.split("\n")
 
     return QueryExpansionUpdate(
         expanded_queries=rewritten_queries,
@@ -127,7 +69,7 @@ def expand_queries(
                 graph_component="shared - expanded retrieval",
                 node_name="expand queries",
                 node_start_time=node_start_time,
-                result=log_result,
+                result=f"Number of expanded queries: {len(rewritten_queries)}",
             )
         ],
     )

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/rerank_documents.py

@@ -26,10 +26,8 @@ from onyx.context.search.postprocessing.postprocessing import rerank_sections
 from onyx.context.search.postprocessing.postprocessing import should_rerank
 from onyx.db.engine import get_session_context_manager
 from onyx.db.search_settings import get_current_search_settings
-from onyx.utils.timing import log_function_time
 
 
-@log_function_time(print_only=True)
 def rerank_documents(
     state: ExpandedRetrievalState, config: RunnableConfig
 ) -> DocRerankingUpdate:

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/retrieve_documents.py

@@ -28,10 +28,8 @@ from onyx.tools.tool_implementations.search.search_tool import (
     SEARCH_RESPONSE_SUMMARY_ID,
 )
 from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
-from onyx.utils.timing import log_function_time
 
 
-@log_function_time(print_only=True)
 def retrieve_documents(
     state: RetrievalInput, config: RunnableConfig
 ) -> DocRetrievalUpdate:

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/verify_documents.py

@@ -1,7 +1,5 @@
-from datetime import datetime
 from typing import cast
 
-from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
 from langchain_core.runnables.config import RunnableConfig
 
@@ -12,38 +10,14 @@ from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states impor
     DocVerificationUpdate,
 )
 from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    binary_string_test,
-)
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     trim_prompt_piece,
 )
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_POSITIVE_VALUE_STR,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.configs.agent_configs import AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.prompts.agent_search import (
     DOCUMENT_VERIFICATION_PROMPT,
 )
-from onyx.utils.logger import setup_logger
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="The LLM timed out. The document could not be verified. The document will be treated as 'relevant'",
-    rate_limit="The LLM encountered a rate limit. The document could not be verified. The document will be treated as 'relevant'",
-    general_error="The LLM encountered an error. The document could not be verified. The document will be treated as 'relevant'",
-)
 
 
-@log_function_time(print_only=True)
 def verify_documents(
     state: DocVerificationInput, config: RunnableConfig
 ) -> DocVerificationUpdate:
@@ -52,14 +26,12 @@ def verify_documents(
 
     Args:
         state (DocVerificationInput): The current state
-        config (RunnableConfig): Configuration containing AgentSearchConfig
+        config (RunnableConfig): Configuration containing ProSearchConfig
 
     Updates:
         verified_documents: list[InferenceSection]
     """
 
-    node_start_time = datetime.now()
-
     question = state.question
     retrieved_document_to_verify = state.retrieved_document_to_verify
     document_content = retrieved_document_to_verify.combined_content
@@ -79,40 +51,12 @@ def verify_documents(
         )
     ]
 
-    response: BaseMessage | None = None
+    response = fast_llm.invoke(msg)
 
-    verified_documents = [
-        retrieved_document_to_verify
-    ]  # default is to treat document as relevant
-
-    try:
-        response = fast_llm.invoke(
-            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION
-        )
-
-        assert isinstance(response.content, str)
-        if not binary_string_test(
-            text=response.content, positive_value=AGENT_POSITIVE_VALUE_STR
-        ):
-            verified_documents = []
-
-    except LLMTimeoutError:
-        # In this case, we decide to continue and don't raise an error, as
-        # little harm in letting some docs through that are less relevant.
-        logger.error("LLM Timeout Error - verify documents")
-
-    except LLMRateLimitError:
-        # In this case, we decide to continue and don't raise an error, as
-        # little harm in letting some docs through that are less relevant.
-        logger.error("LLM Rate Limit Error - verify documents")
+    verified_documents = []
+    if isinstance(response.content, str) and "yes" in response.content.lower():
+        verified_documents.append(retrieved_document_to_verify)
 
     return DocVerificationUpdate(
         verified_documents=verified_documents,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="shared - expanded retrieval",
-                node_name="verify documents",
-                node_start_time=node_start_time,
-            )
-        ],
     )

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/states.py

@@ -21,13 +21,9 @@ from onyx.context.search.models import InferenceSection
 
 
 class ExpandedRetrievalInput(SubgraphCoreState):
-    # exception from 'no default value'for LangGraph input states
-    # Here, sub_question_id default None implies usage for the
-    # original question. This is sometimes needed for nested sub-graphs
-
+    question: str = ""
+    base_search: bool = False
     sub_question_id: str | None = None
-    question: str
-    base_search: bool
 
 
 ## Update/Return States
@@ -38,7 +34,7 @@ class QueryExpansionUpdate(LoggerUpdate, BaseModel):
     log_messages: list[str] = []
 
 
-class DocVerificationUpdate(LoggerUpdate, BaseModel):
+class DocVerificationUpdate(BaseModel):
     verified_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
 
 
@@ -92,4 +88,4 @@ class DocVerificationInput(ExpandedRetrievalInput):
 
 
 class RetrievalInput(ExpandedRetrievalInput):
-    query_to_retrieve: str
+    query_to_retrieve: str = ""

backend/onyx/agents/agent_search/run_graph.py

@@ -12,7 +12,7 @@ from onyx.agents.agent_search.deep_search.main.graph_builder import (
     main_graph_builder as main_graph_builder_a,
 )
 from onyx.agents.agent_search.deep_search.main.states import (
-    MainInput as MainInput,
+    MainInput as MainInput_a,
 )
 from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
@@ -21,7 +21,6 @@ from onyx.chat.models import AnswerPacket
 from onyx.chat.models import AnswerStream
 from onyx.chat.models import ExtendedToolResponse
 from onyx.chat.models import RefinedAnswerImprovement
-from onyx.chat.models import StreamingError
 from onyx.chat.models import StreamStopInfo
 from onyx.chat.models import SubQueryPiece
 from onyx.chat.models import SubQuestionPiece
@@ -34,7 +33,6 @@ from onyx.llm.factory import get_default_llms
 from onyx.tools.tool_runner import ToolCallKickoff
 from onyx.utils.logger import setup_logger
 
-
 logger = setup_logger()
 
 _COMPILED_GRAPH: CompiledStateGraph | None = None
@@ -74,15 +72,13 @@ def _parse_agent_event(
             return cast(AnswerPacket, event["data"])
         elif event["name"] == "refined_answer_improvement":
             return cast(RefinedAnswerImprovement, event["data"])
-        elif event["name"] == "refined_sub_question_creation_error":
-            return cast(StreamingError, event["data"])
     return None
 
 
 def manage_sync_streaming(
     compiled_graph: CompiledStateGraph,
     config: GraphConfig,
-    graph_input: BasicInput | MainInput,
+    graph_input: BasicInput | MainInput_a,
 ) -> Iterable[StreamEvent]:
     message_id = config.persistence.message_id if config.persistence else None
     for event in compiled_graph.stream(
@@ -96,7 +92,7 @@ def manage_sync_streaming(
 def run_graph(
     compiled_graph: CompiledStateGraph,
     config: GraphConfig,
-    input: BasicInput | MainInput,
+    input: BasicInput | MainInput_a,
 ) -> AnswerStream:
     config.behavior.perform_initial_search_decomposition = (
         INITIAL_SEARCH_DECOMPOSITION_ENABLED
@@ -127,7 +123,9 @@ def run_main_graph(
 ) -> AnswerStream:
     compiled_graph = load_compiled_graph()
 
-    input = MainInput(log_messages=[])
+    input = MainInput_a(
+        base_question=config.inputs.search_request.query, log_messages=[]
+    )
 
     # Agent search is not a Tool per se, but this is helpful for the frontend
     yield ToolCallKickoff(
@@ -142,7 +140,7 @@ def run_basic_graph(
 ) -> AnswerStream:
     graph = basic_graph_builder()
     compiled_graph = graph.compile()
-    input = BasicInput(unused=True)
+    input = BasicInput()
     return run_graph(compiled_graph, config, input)
 
 
@@ -174,7 +172,9 @@ if __name__ == "__main__":
             # search_request.persona = get_persona_by_id(1, None, db_session)
             # config.perform_initial_search_path_decision = False
             config.behavior.perform_initial_search_decomposition = True
-            input = MainInput(log_messages=[])
+            input = MainInput_a(
+                base_question=config.inputs.search_request.query, log_messages=[]
+            )
 
             tool_responses: list = []
             for output in run_graph(compiled_graph, config, input):

backend/onyx/agents/agent_search/shared_graph_utils/agent_prompt_ops.py

@@ -7,7 +7,6 @@ from onyx.agents.agent_search.models import GraphConfig
 from onyx.agents.agent_search.shared_graph_utils.models import (
     AgentPromptEnrichmentComponents,
 )
-from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
 from onyx.agents.agent_search.shared_graph_utils.utils import (
     get_persona_agent_prompt_expressions,
 )
@@ -41,7 +40,13 @@ def build_sub_question_answer_prompt(
 
     date_str = build_date_time_string()
 
-    docs_str = format_docs(docs)
+    # TODO: This should include document metadata and title
+    docs_format_list = [
+        f"Document Number: [D{doc_num + 1}]\nContent: {doc.combined_content}\n\n"
+        for doc_num, doc in enumerate(docs)
+    ]
+
+    docs_str = "\n\n".join(docs_format_list)
 
     docs_str = trim_prompt_piece(
         config,
@@ -145,38 +150,3 @@ def get_prompt_enrichment_components(
         history=history,
         date_str=date_str,
     )
-
-
-def binary_string_test(text: str, positive_value: str = "yes") -> bool:
-    """
-    Tests if a string contains a positive value (case-insensitive).
-
-    Args:
-        text: The string to test
-        positive_value: The value to look for (defaults to "yes")
-
-    Returns:
-        True if the positive value is found in the text
-    """
-    return positive_value.lower() in text.lower()
-
-
-def binary_string_test_after_answer_separator(
-    text: str, positive_value: str = "yes", separator: str = "Answer:"
-) -> bool:
-    """
-    Tests if a string contains a positive value (case-insensitive).
-
-    Args:
-        text: The string to test
-        positive_value: The value to look for (defaults to "yes")
-
-    Returns:
-        True if the positive value is found in the text
-    """
-
-    if separator not in text:
-        return False
-    relevant_text = text.split(f"{separator}")[-1]
-
-    return binary_string_test(relevant_text, positive_value)

backend/onyx/agents/agent_search/shared_graph_utils/calculations.py

@@ -1,11 +1,7 @@
 import numpy as np
 
-from onyx.agents.agent_search.shared_graph_utils.models import AnswerGenerationDocuments
 from onyx.agents.agent_search.shared_graph_utils.models import RetrievalFitScoreMetrics
 from onyx.agents.agent_search.shared_graph_utils.models import RetrievalFitStats
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_section_list,
-)
 from onyx.chat.models import SectionRelevancePiece
 from onyx.context.search.models import InferenceSection
 from onyx.utils.logger import setup_logger
@@ -100,106 +96,3 @@ def get_fit_scores(
     )
 
     return fit_eval
-
-
-def get_answer_generation_documents(
-    relevant_docs: list[InferenceSection],
-    context_documents: list[InferenceSection],
-    original_question_docs: list[InferenceSection],
-    max_docs: int,
-) -> AnswerGenerationDocuments:
-    """
-    Create a deduplicated list of documents to stream, prioritizing relevant docs.
-
-    Args:
-        relevant_docs: Primary documents to include
-        context_documents: Additional context documents to append
-        original_question_docs: Original question documents to append
-        max_docs: Maximum number of documents to return
-
-    Returns:
-        List of deduplicated documents, limited to max_docs
-    """
-    # get relevant_doc ids
-    relevant_doc_ids = [doc.center_chunk.document_id for doc in relevant_docs]
-
-    # Start with relevant docs or fallback to original question docs
-    streaming_documents = relevant_docs.copy()
-
-    # Use a set for O(1) lookups of document IDs
-    seen_doc_ids = {doc.center_chunk.document_id for doc in streaming_documents}
-
-    # Combine additional documents to check in one iteration
-    additional_docs = context_documents + original_question_docs
-    for doc_idx, doc in enumerate(additional_docs):
-        doc_id = doc.center_chunk.document_id
-        if doc_id not in seen_doc_ids:
-            streaming_documents.append(doc)
-            seen_doc_ids.add(doc_id)
-
-    streaming_documents = dedup_inference_section_list(streaming_documents)
-
-    relevant_streaming_docs = [
-        doc
-        for doc in streaming_documents
-        if doc.center_chunk.document_id in relevant_doc_ids
-    ]
-    relevant_streaming_docs = dedup_sort_inference_section_list(relevant_streaming_docs)
-
-    additional_streaming_docs = [
-        doc
-        for doc in streaming_documents
-        if doc.center_chunk.document_id not in relevant_doc_ids
-    ]
-    additional_streaming_docs = dedup_sort_inference_section_list(
-        additional_streaming_docs
-    )
-
-    for doc in additional_streaming_docs:
-        if doc.center_chunk.score:
-            doc.center_chunk.score += -2.0
-        else:
-            doc.center_chunk.score = -2.0
-
-    sorted_streaming_documents = relevant_streaming_docs + additional_streaming_docs
-
-    return AnswerGenerationDocuments(
-        streaming_documents=sorted_streaming_documents[:max_docs],
-        context_documents=relevant_streaming_docs[:max_docs],
-    )
-
-
-def dedup_sort_inference_section_list(
-    sections: list[InferenceSection],
-) -> list[InferenceSection]:
-    """Deduplicates InferenceSections by document_id and sorts by score.
-
-    Args:
-        sections: List of InferenceSections to deduplicate and sort
-
-    Returns:
-        Deduplicated list of InferenceSections sorted by score in descending order
-    """
-    # dedupe/merge with existing framework
-    sections = dedup_inference_section_list(sections)
-
-    # Use dict to deduplicate by document_id, keeping highest scored version
-    unique_sections: dict[str, InferenceSection] = {}
-    for section in sections:
-        doc_id = section.center_chunk.document_id
-        if doc_id not in unique_sections:
-            unique_sections[doc_id] = section
-            continue
-
-        # Keep version with higher score
-        existing_score = unique_sections[doc_id].center_chunk.score or 0
-        new_score = section.center_chunk.score or 0
-        if new_score > existing_score:
-            unique_sections[doc_id] = section
-
-    # Sort by score in descending order, handling None scores
-    sorted_sections = sorted(
-        unique_sections.values(), key=lambda x: x.center_chunk.score or 0, reverse=True
-    )
-
-    return sorted_sections

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

@@ -1,19 +0,0 @@
-from enum import Enum
-
-AGENT_LLM_TIMEOUT_MESSAGE = "The agent timed out. Please try again."
-AGENT_LLM_ERROR_MESSAGE = "The agent encountered an error. Please try again."
-AGENT_LLM_RATELIMIT_MESSAGE = (
-    "The agent encountered a rate limit error. Please try again."
-)
-LLM_ANSWER_ERROR_MESSAGE = "The question was not answered due to an LLM error."
-
-AGENT_POSITIVE_VALUE_STR = "yes"
-AGENT_NEGATIVE_VALUE_STR = "no"
-
-AGENT_ANSWER_SEPARATOR = "Answer:"
-
-
-class AgentLLMErrorType(str, Enum):
-    TIMEOUT = "timeout"
-    RATE_LIMIT = "rate_limit"
-    GENERAL_ERROR = "general_error"

backend/onyx/agents/agent_search/shared_graph_utils/models.py

@@ -1,5 +1,3 @@
-from typing import Any
-
 from pydantic import BaseModel
 
 from onyx.agents.agent_search.deep_search.main.models import (
@@ -58,12 +56,6 @@ class InitialAgentResultStats(BaseModel):
     agent_effectiveness: dict[str, float | int | None]
 
 
-class AgentErrorLog(BaseModel):
-    error_message: str
-    error_type: str
-    error_result: str
-
-
 class RefinedAgentStats(BaseModel):
     revision_doc_efficiency: float | None
     revision_question_efficiency: float | None
@@ -118,11 +110,6 @@ class SubQuestionAnswerResults(BaseModel):
     sub_question_retrieval_stats: AgentChunkRetrievalStats
 
 
-class StructuredSubquestionDocuments(BaseModel):
-    cited_documents: list[InferenceSection]
-    context_documents: list[InferenceSection]
-
-
 class CombinedAgentMetrics(BaseModel):
     timings: AgentTimings
     base_metrics: AgentBaseMetrics | None
@@ -139,17 +126,3 @@ class AgentPromptEnrichmentComponents(BaseModel):
     persona_prompts: PersonaPromptExpressions
     history: str
     date_str: str
-
-
-class LLMNodeErrorStrings(BaseModel):
-    timeout: str = "LLM Timeout Error"
-    rate_limit: str = "LLM Rate Limit Error"
-    general_error: str = "General LLM Error"
-
-
-class AnswerGenerationDocuments(BaseModel):
-    streaming_documents: list[InferenceSection]
-    context_documents: list[InferenceSection]
-
-
-BaseMessage_Content = str | list[str | dict[str, Any]]

backend/onyx/agents/agent_search/shared_graph_utils/operators.py

@@ -12,13 +12,6 @@ def dedup_inference_sections(
     return deduped
 
 
-def dedup_inference_section_list(
-    list: list[InferenceSection],
-) -> list[InferenceSection]:
-    deduped = _merge_sections(list)
-    return deduped
-
-
 def dedup_question_answer_results(
     question_answer_results_1: list[SubQuestionAnswerResults],
     question_answer_results_2: list[SubQuestionAnswerResults],

backend/onyx/agents/agent_search/shared_graph_utils/utils.py

@@ -20,18 +20,10 @@ from onyx.agents.agent_search.models import GraphInputs
 from onyx.agents.agent_search.models import GraphPersistence
 from onyx.agents.agent_search.models import GraphSearchConfig
 from onyx.agents.agent_search.models import GraphTooling
-from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
 from onyx.agents.agent_search.shared_graph_utils.models import (
     EntityRelationshipTermExtraction,
 )
 from onyx.agents.agent_search.shared_graph_utils.models import PersonaPromptExpressions
-from onyx.agents.agent_search.shared_graph_utils.models import (
-    StructuredSubquestionDocuments,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import SubQuestionAnswerResults
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_section_list,
-)
 from onyx.chat.models import AnswerPacket
 from onyx.chat.models import AnswerStyleConfig
 from onyx.chat.models import CitationConfig
@@ -42,9 +34,6 @@ from onyx.chat.models import StreamStopInfo
 from onyx.chat.models import StreamStopReason
 from onyx.chat.models import StreamType
 from onyx.chat.prompt_builder.answer_prompt_builder import AnswerPromptBuilder
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION,
-)
 from onyx.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
 from onyx.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
 from onyx.configs.constants import DEFAULT_PERSONA_ID
@@ -57,8 +46,6 @@ from onyx.context.search.models import SearchRequest
 from onyx.db.engine import get_session_context_manager
 from onyx.db.persona import get_persona_by_id
 from onyx.db.persona import Persona
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
 from onyx.llm.interfaces import LLM
 from onyx.prompts.agent_search import (
     ASSISTANT_SYSTEM_PROMPT_DEFAULT,
@@ -79,9 +66,8 @@ from onyx.tools.tool_implementations.search.search_tool import (
 from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
 from onyx.tools.tool_implementations.search.search_tool import SearchTool
 from onyx.tools.utils import explicit_tool_calling_supported
-from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+BaseMessage_Content = str | list[str | dict[str, Any]]
 
 
 # Post-processing
@@ -394,24 +380,8 @@ def summarize_history(
         )
     )
 
-    try:
-        history_response = llm.invoke(
-            history_context_prompt,
-            timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION,
-        )
-    except LLMTimeoutError:
-        logger.error("LLM Timeout Error - summarize history")
-        return (
-            history  # this is what is done at this point anyway, so we default to this
-        )
-    except LLMRateLimitError:
-        logger.error("LLM Rate Limit Error - summarize history")
-        return (
-            history  # this is what is done at this point anyway, so we default to this
-        )
-
+    history_response = llm.invoke(history_context_prompt)
     assert isinstance(history_response.content, str)
-
     return history_response.content
 
 
@@ -477,27 +447,3 @@ def remove_document_citations(text: str) -> str:
     #   \d+  - one or more digits
     #   \]   - literal ] character
     return re.sub(r"\[(?:D|Q)?\d+\]", "", text)
-
-
-def get_deduplicated_structured_subquestion_documents(
-    sub_question_results: list[SubQuestionAnswerResults],
-) -> StructuredSubquestionDocuments:
-    """
-    Extract and deduplicate all cited documents from sub-question results.
-
-    Args:
-        sub_question_results: List of sub-question results containing cited documents
-
-    Returns:
-        Deduplicated list of cited documents
-    """
-    cited_docs = [
-        doc for result in sub_question_results for doc in result.cited_documents
-    ]
-    context_docs = [
-        doc for result in sub_question_results for doc in result.context_documents
-    ]
-    return StructuredSubquestionDocuments(
-        cited_documents=dedup_inference_section_list(cited_docs),
-        context_documents=dedup_inference_section_list(context_docs),
-    )

backend/onyx/auth/users.py

@@ -94,7 +94,6 @@ from onyx.db.models import User
 from onyx.db.users import get_user_by_email
 from onyx.redis.redis_pool import get_async_redis_connection
 from onyx.redis.redis_pool import get_redis_client
-from onyx.server.utils import BasicAuthenticationError
 from onyx.utils.logger import setup_logger
 from onyx.utils.telemetry import create_milestone_and_report
 from onyx.utils.telemetry import optional_telemetry
@@ -108,6 +107,11 @@ from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 logger = setup_logger()
 
 
+class BasicAuthenticationError(HTTPException):
+    def __init__(self, detail: str):
+        super().__init__(status_code=status.HTTP_403_FORBIDDEN, detail=detail)
+
+
 def is_user_admin(user: User | None) -> bool:
     if AUTH_TYPE == AuthType.DISABLED:
         return True

backend/onyx/background/celery/tasks/monitoring/tasks.py

@@ -190,9 +190,9 @@ def _build_connector_start_latency_metric(
         desired_start_time = cc_pair.connector.time_created
     else:
         if not cc_pair.connector.refresh_freq:
-            task_logger.debug(
-                "Connector has no refresh_freq and this is a non-initial index attempt. "
-                "Assuming user manually triggered indexing, so we'll skip start latency metric."
+            task_logger.error(
+                "Found non-initial index attempt for connector "
+                "without refresh_freq. This should never happen."
             )
             return None
 

backend/onyx/background/celery/tasks/vespa/tasks.py

@@ -78,10 +78,6 @@ logger = setup_logger()
 def check_for_vespa_sync_task(self: Task, *, tenant_id: str | None) -> bool | None:
     """Runs periodically to check if any document needs syncing.
     Generates sets of tasks for Celery if syncing is needed."""
-
-    # Useful for debugging timing issues with reacquisitions. TODO: remove once more generalized logging is in place
-    task_logger.info("check_for_vespa_sync_task started")
-
     time_start = time.monotonic()
 
     r = get_redis_client(tenant_id=tenant_id)
@@ -496,21 +492,13 @@ def monitor_document_set_taskset(
             task_logger.info(
                 f"Successfully synced document set: document_set={document_set_id}"
             )
-
-        try:
-            update_sync_record_status(
-                db_session=db_session,
-                entity_id=document_set_id,
-                sync_type=SyncType.DOCUMENT_SET,
-                sync_status=SyncStatus.SUCCESS,
-                num_docs_synced=initial_count,
-            )
-        except Exception:
-            task_logger.exception(
-                "update_sync_record_status exceptioned. "
-                f"document_set_id={document_set_id} "
-                "Resetting document set regardless."
-            )
+        update_sync_record_status(
+            db_session=db_session,
+            entity_id=document_set_id,
+            sync_type=SyncType.DOCUMENT_SET,
+            sync_status=SyncStatus.SUCCESS,
+            num_docs_synced=initial_count,
+        )
 
     rds.reset()
 

backend/onyx/configs/agent_configs.py

@@ -8,44 +8,14 @@ AGENT_DEFAULT_RERANKING_HITS = 10
 AGENT_DEFAULT_SUB_QUESTION_MAX_CONTEXT_HITS = 8
 AGENT_DEFAULT_NUM_DOCS_FOR_INITIAL_DECOMPOSITION = 3
 AGENT_DEFAULT_NUM_DOCS_FOR_REFINED_DECOMPOSITION = 5
-
-AGENT_DEFAULT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER = 25
-AGENT_DEFAULT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER = 35
-
-
 AGENT_DEFAULT_EXPLORATORY_SEARCH_RESULTS = 5
 AGENT_DEFAULT_MIN_ORIG_QUESTION_DOCS = 3
 AGENT_DEFAULT_MAX_ANSWER_CONTEXT_DOCS = 10
 AGENT_DEFAULT_MAX_STATIC_HISTORY_WORD_LENGTH = 2000
 
-INITIAL_SEARCH_DECOMPOSITION_ENABLED = True
-ALLOW_REFINEMENT = True
-
-AGENT_DEFAULT_RETRIEVAL_HITS = 15
-AGENT_DEFAULT_RERANKING_HITS = 10
-AGENT_DEFAULT_SUB_QUESTION_MAX_CONTEXT_HITS = 8
-AGENT_DEFAULT_NUM_DOCS_FOR_INITIAL_DECOMPOSITION = 3
-AGENT_DEFAULT_NUM_DOCS_FOR_REFINED_DECOMPOSITION = 5
-AGENT_DEFAULT_EXPLORATORY_SEARCH_RESULTS = 5
-AGENT_DEFAULT_MIN_ORIG_QUESTION_DOCS = 3
-AGENT_DEFAULT_MAX_ANSWER_CONTEXT_DOCS = 10
-AGENT_DEFAULT_MAX_STATIC_HISTORY_WORD_LENGTH = 2000
-
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION = 30  # in seconds
-
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION = 10  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION = 25  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION = 4  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION = 1  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION = 3  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION = 12  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK = 8  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION = 25  # in seconds
-
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION = 6  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION = 25  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_VALIDATION = 8  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS = 8  # in seconds
+#####
+# Agent Configs
+#####
 
 
 AGENT_RETRIEVAL_STATS = (
@@ -107,151 +77,4 @@ AGENT_MAX_STATIC_HISTORY_WORD_LENGTH = int(
     or AGENT_DEFAULT_MAX_STATIC_HISTORY_WORD_LENGTH
 )  # 2000
 
-AGENT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER = int(
-    os.environ.get("AGENT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER")
-    or AGENT_DEFAULT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER
-)  # 25
-
-AGENT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER = int(
-    os.environ.get("AGENT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER")
-    or AGENT_DEFAULT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER
-)  # 35
-
-
-AGENT_RETRIEVAL_STATS = (
-    not os.environ.get("AGENT_RETRIEVAL_STATS") == "False"
-) or True  # default True
-
-
-AGENT_MAX_QUERY_RETRIEVAL_RESULTS = int(
-    os.environ.get("AGENT_MAX_QUERY_RETRIEVAL_RESULTS") or AGENT_DEFAULT_RETRIEVAL_HITS
-)  # 15
-
-AGENT_MAX_QUERY_RETRIEVAL_RESULTS = int(
-    os.environ.get("AGENT_MAX_QUERY_RETRIEVAL_RESULTS") or AGENT_DEFAULT_RETRIEVAL_HITS
-)  # 15
-
-# Reranking agent configs
-# Reranking stats - no influence on flow outside of stats collection
-AGENT_RERANKING_STATS = (
-    not os.environ.get("AGENT_RERANKING_STATS") == "True"
-) or False  # default False
-
-AGENT_MAX_QUERY_RETRIEVAL_RESULTS = int(
-    os.environ.get("AGENT_MAX_QUERY_RETRIEVAL_RESULTS") or AGENT_DEFAULT_RETRIEVAL_HITS
-)  # 15
-
-AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS = int(
-    os.environ.get("AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS")
-    or AGENT_DEFAULT_RERANKING_HITS
-)  # 10
-
-AGENT_NUM_DOCS_FOR_DECOMPOSITION = int(
-    os.environ.get("AGENT_NUM_DOCS_FOR_DECOMPOSITION")
-    or AGENT_DEFAULT_NUM_DOCS_FOR_INITIAL_DECOMPOSITION
-)  # 3
-
-AGENT_NUM_DOCS_FOR_REFINED_DECOMPOSITION = int(
-    os.environ.get("AGENT_NUM_DOCS_FOR_REFINED_DECOMPOSITION")
-    or AGENT_DEFAULT_NUM_DOCS_FOR_REFINED_DECOMPOSITION
-)  # 5
-
-AGENT_EXPLORATORY_SEARCH_RESULTS = int(
-    os.environ.get("AGENT_EXPLORATORY_SEARCH_RESULTS")
-    or AGENT_DEFAULT_EXPLORATORY_SEARCH_RESULTS
-)  # 5
-
-AGENT_MIN_ORIG_QUESTION_DOCS = int(
-    os.environ.get("AGENT_MIN_ORIG_QUESTION_DOCS")
-    or AGENT_DEFAULT_MIN_ORIG_QUESTION_DOCS
-)  # 3
-
-AGENT_MAX_ANSWER_CONTEXT_DOCS = int(
-    os.environ.get("AGENT_MAX_ANSWER_CONTEXT_DOCS")
-    or AGENT_DEFAULT_SUB_QUESTION_MAX_CONTEXT_HITS
-)  # 8
-
-
-AGENT_MAX_STATIC_HISTORY_WORD_LENGTH = int(
-    os.environ.get("AGENT_MAX_STATIC_HISTORY_WORD_LENGTH")
-    or AGENT_DEFAULT_MAX_STATIC_HISTORY_WORD_LENGTH
-)  # 2000
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_ENTITY_TERM_EXTRACTION
-)  # 25
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION
-)  # 3
-
-AGENT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_GENERAL_GENERATION
-)  # 30
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION
-)  # 8
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_GENERATION
-)  # 12
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_INITIAL_ANSWER_GENERATION
-)  # 25
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION
-)  # 25
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBANSWER_CHECK
-)  # 8
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_SUBQUESTION_GENERATION
-)  # 6
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_QUERY_REWRITING_GENERATION
-)  # 1
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_HISTORY_SUMMARY_GENERATION
-)  # 4
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_COMPARE_ANSWERS
-)  # 8
-
-
-AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_VALIDATION = int(
-    os.environ.get("AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_VALIDATION")
-    or AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_VALIDATION
-)  # 8
-
 GRAPH_VERSION_NAME: str = "a"

backend/onyx/configs/constants.py

@@ -125,7 +125,6 @@ class DocumentSource(str, Enum):
     GMAIL = "gmail"
     REQUESTTRACKER = "requesttracker"
     GITHUB = "github"
-    GITBOOK = "gitbook"
     GITLAB = "gitlab"
     GURU = "guru"
     BOOKSTACK = "bookstack"

backend/onyx/connectors/factory.py

@@ -20,7 +20,6 @@ from onyx.connectors.egnyte.connector import EgnyteConnector
 from onyx.connectors.file.connector import LocalFileConnector
 from onyx.connectors.fireflies.connector import FirefliesConnector
 from onyx.connectors.freshdesk.connector import FreshdeskConnector
-from onyx.connectors.gitbook.connector import GitbookConnector
 from onyx.connectors.github.connector import GithubConnector
 from onyx.connectors.gitlab.connector import GitlabConnector
 from onyx.connectors.gmail.connector import GmailConnector
@@ -72,7 +71,6 @@ def identify_connector_class(
         DocumentSource.GITHUB: GithubConnector,
         DocumentSource.GMAIL: GmailConnector,
         DocumentSource.GITLAB: GitlabConnector,
-        DocumentSource.GITBOOK: GitbookConnector,
         DocumentSource.GOOGLE_DRIVE: GoogleDriveConnector,
         DocumentSource.BOOKSTACK: BookstackConnector,
         DocumentSource.CONFLUENCE: ConfluenceConnector,

backend/onyx/connectors/gitbook/connector.py

@@ -1,279 +0,0 @@
-from datetime import datetime
-from datetime import timezone
-from typing import Any
-from urllib.parse import urljoin
-
-import requests
-
-from onyx.configs.app_configs import INDEX_BATCH_SIZE
-from onyx.configs.constants import DocumentSource
-from onyx.connectors.interfaces import GenerateDocumentsOutput
-from onyx.connectors.interfaces import LoadConnector
-from onyx.connectors.interfaces import PollConnector
-from onyx.connectors.interfaces import SecondsSinceUnixEpoch
-from onyx.connectors.models import ConnectorMissingCredentialError
-from onyx.connectors.models import Document
-from onyx.connectors.models import Section
-from onyx.utils.logger import setup_logger
-
-
-logger = setup_logger()
-
-GITBOOK_API_BASE = "https://api.gitbook.com/v1/"
-
-
-class GitbookApiClient:
-    def __init__(self, access_token: str) -> None:
-        self.access_token = access_token
-
-    def get(self, endpoint: str, params: dict[str, Any] | None = None) -> Any:
-        headers = {
-            "Authorization": f"Bearer {self.access_token}",
-            "Content-Type": "application/json",
-        }
-
-        url = urljoin(GITBOOK_API_BASE, endpoint.lstrip("/"))
-        response = requests.get(url, headers=headers, params=params)
-        response.raise_for_status()
-        return response.json()
-
-    def get_page_content(self, space_id: str, page_id: str) -> dict[str, Any]:
-        return self.get(f"/spaces/{space_id}/content/page/{page_id}")
-
-
-def _extract_text_from_document(document: dict[str, Any]) -> str:
-    """Extract text content from GitBook document structure by parsing the document nodes
-    into markdown format."""
-
-    def parse_leaf(leaf: dict[str, Any]) -> str:
-        text = leaf.get("text", "")
-        leaf.get("marks", [])
-        return text
-
-    def parse_text_node(node: dict[str, Any]) -> str:
-        text = ""
-        for leaf in node.get("leaves", []):
-            text += parse_leaf(leaf)
-        return text
-
-    def parse_block_node(node: dict[str, Any]) -> str:
-        block_type = node.get("type", "")
-        result = ""
-
-        if block_type == "heading-1":
-            text = "".join(parse_text_node(n) for n in node.get("nodes", []))
-            result = f"# {text}\n\n"
-
-        elif block_type == "heading-2":
-            text = "".join(parse_text_node(n) for n in node.get("nodes", []))
-            result = f"## {text}\n\n"
-
-        elif block_type == "heading-3":
-            text = "".join(parse_text_node(n) for n in node.get("nodes", []))
-            result = f"### {text}\n\n"
-
-        elif block_type == "heading-4":
-            text = "".join(parse_text_node(n) for n in node.get("nodes", []))
-            result = f"#### {text}\n\n"
-
-        elif block_type == "heading-5":
-            text = "".join(parse_text_node(n) for n in node.get("nodes", []))
-            result = f"##### {text}\n\n"
-
-        elif block_type == "heading-6":
-            text = "".join(parse_text_node(n) for n in node.get("nodes", []))
-            result = f"###### {text}\n\n"
-
-        elif block_type == "list-unordered":
-            for list_item in node.get("nodes", []):
-                paragraph = list_item.get("nodes", [])[0]
-                text = "".join(parse_text_node(n) for n in paragraph.get("nodes", []))
-                result += f"* {text}\n"
-            result += "\n"
-
-        elif block_type == "paragraph":
-            text = "".join(parse_text_node(n) for n in node.get("nodes", []))
-            result = f"{text}\n\n"
-
-        elif block_type == "list-tasks":
-            for task_item in node.get("nodes", []):
-                checked = task_item.get("data", {}).get("checked", False)
-                paragraph = task_item.get("nodes", [])[0]
-                text = "".join(parse_text_node(n) for n in paragraph.get("nodes", []))
-                checkbox = "[x]" if checked else "[ ]"
-                result += f"- {checkbox} {text}\n"
-            result += "\n"
-
-        elif block_type == "code":
-            for code_line in node.get("nodes", []):
-                if code_line.get("type") == "code-line":
-                    text = "".join(
-                        parse_text_node(n) for n in code_line.get("nodes", [])
-                    )
-                    result += f"{text}\n"
-            result += "\n"
-
-        elif block_type == "blockquote":
-            for quote_node in node.get("nodes", []):
-                if quote_node.get("type") == "paragraph":
-                    text = "".join(
-                        parse_text_node(n) for n in quote_node.get("nodes", [])
-                    )
-                    result += f"> {text}\n"
-            result += "\n"
-
-        elif block_type == "table":
-            records = node.get("data", {}).get("records", {})
-            definition = node.get("data", {}).get("definition", {})
-            view = node.get("data", {}).get("view", {})
-
-            columns = view.get("columns", [])
-
-            header_cells = []
-            for col_id in columns:
-                col_def = definition.get(col_id, {})
-                header_cells.append(col_def.get("title", ""))
-
-            result = "| " + " | ".join(header_cells) + " |\n"
-            result += "|" + "---|" * len(header_cells) + "\n"
-
-            sorted_records = sorted(
-                records.items(), key=lambda x: x[1].get("orderIndex", "")
-            )
-
-            for record_id, record_data in sorted_records:
-                values = record_data.get("values", {})
-                row_cells = []
-                for col_id in columns:
-                    fragment_id = values.get(col_id, "")
-                    fragment_text = ""
-                    for fragment in node.get("fragments", []):
-                        if fragment.get("fragment") == fragment_id:
-                            for frag_node in fragment.get("nodes", []):
-                                if frag_node.get("type") == "paragraph":
-                                    fragment_text = "".join(
-                                        parse_text_node(n)
-                                        for n in frag_node.get("nodes", [])
-                                    )
-                                    break
-                    row_cells.append(fragment_text)
-                result += "| " + " | ".join(row_cells) + " |\n"
-
-            result += "\n"
-        return result
-
-    if not document or "document" not in document:
-        return ""
-
-    markdown = ""
-    nodes = document["document"].get("nodes", [])
-
-    for node in nodes:
-        markdown += parse_block_node(node)
-
-    return markdown
-
-
-def _convert_page_to_document(
-    client: GitbookApiClient, space_id: str, page: dict[str, Any]
-) -> Document:
-    page_id = page["id"]
-    page_content = client.get_page_content(space_id, page_id)
-
-    return Document(
-        id=f"gitbook-{space_id}-{page_id}",
-        sections=[
-            Section(
-                link=page.get("urls", {}).get("app", ""),
-                text=_extract_text_from_document(page_content),
-            )
-        ],
-        source=DocumentSource.GITBOOK,
-        semantic_identifier=page.get("title", ""),
-        doc_updated_at=datetime.fromisoformat(page["updatedAt"]).replace(
-            tzinfo=timezone.utc
-        ),
-        metadata={
-            "path": page.get("path", ""),
-            "type": page.get("type", ""),
-            "kind": page.get("kind", ""),
-        },
-    )
-
-
-class GitbookConnector(LoadConnector, PollConnector):
-    def __init__(
-        self,
-        space_id: str,
-        batch_size: int = INDEX_BATCH_SIZE,
-    ) -> None:
-        self.space_id = space_id
-        self.batch_size = batch_size
-        self.access_token: str | None = None
-        self.client: GitbookApiClient | None = None
-
-    def load_credentials(self, credentials: dict[str, Any]) -> None:
-        access_token = credentials.get("gitbook_api_key")
-        if not access_token:
-            raise ConnectorMissingCredentialError("GitBook access token")
-        self.access_token = access_token
-        self.client = GitbookApiClient(access_token)
-
-    def _fetch_all_pages(
-        self,
-        start: datetime | None = None,
-        end: datetime | None = None,
-    ) -> GenerateDocumentsOutput:
-        if not self.client:
-            raise ConnectorMissingCredentialError("GitBook")
-
-        try:
-            content = self.client.get(f"/spaces/{self.space_id}/content")
-            pages = content.get("pages", [])
-
-            current_batch: list[Document] = []
-            for page in pages:
-                updated_at = datetime.fromisoformat(page["updatedAt"])
-
-                if start and updated_at < start:
-                    if current_batch:
-                        yield current_batch
-                    return
-                if end and updated_at > end:
-                    continue
-
-                current_batch.append(
-                    _convert_page_to_document(self.client, self.space_id, page)
-                )
-
-                if len(current_batch) >= self.batch_size:
-                    yield current_batch
-                    current_batch = []
-
-            if current_batch:
-                yield current_batch
-
-        except requests.RequestException as e:
-            logger.error(f"Error fetching GitBook content: {str(e)}")
-            raise
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        return self._fetch_all_pages()
-
-    def poll_source(
-        self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
-    ) -> GenerateDocumentsOutput:
-        start_datetime = datetime.fromtimestamp(start, tz=timezone.utc)
-        end_datetime = datetime.fromtimestamp(end, tz=timezone.utc)
-        return self._fetch_all_pages(start_datetime, end_datetime)
-
-
-if __name__ == "__main__":
-    import os
-
-    connector = GitbookConnector(
-        space_id=os.environ["GITBOOK_SPACE_ID"],
-    )
-    connector.load_credentials({"gitbook_api_key": os.environ["GITBOOK_API_KEY"]})
-    document_batches = connector.load_from_state()
-    print(next(document_batches))

backend/onyx/connectors/google_drive/connector.py

@@ -302,7 +302,7 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
             if e.status_code == 401:
                 # fail gracefully, let the other impersonations continue
                 # one user without access shouldn't block the entire connector
-                logger.warning(
+                logger.exception(
                     f"User '{user_email}' does not have access to the drive APIs."
                 )
                 return

backend/onyx/db/chat.py

@@ -350,14 +350,13 @@ def delete_chat_session(
     user_id: UUID | None,
     chat_session_id: UUID,
     db_session: Session,
-    include_deleted: bool = False,
     hard_delete: bool = HARD_DELETE_CHATS,
 ) -> None:
     chat_session = get_chat_session_by_id(
         chat_session_id=chat_session_id, user_id=user_id, db_session=db_session
     )
 
-    if chat_session.deleted and not include_deleted:
+    if chat_session.deleted:
         raise ValueError("Cannot delete an already deleted chat session")
 
     if hard_delete:
@@ -381,15 +380,7 @@ def delete_chat_sessions_older_than(days_old: int, db_session: Session) -> None:
     ).fetchall()
 
     for user_id, session_id in old_sessions:
-        try:
-            delete_chat_session(
-                user_id, session_id, db_session, include_deleted=True, hard_delete=True
-            )
-        except Exception:
-            logger.exception(
-                "delete_chat_session exceptioned. "
-                f"user_id={user_id} session_id={session_id}"
-            )
+        delete_chat_session(user_id, session_id, db_session, hard_delete=True)
 
 
 def get_chat_message(
@@ -902,18 +893,14 @@ def translate_db_sub_questions_to_server_objects(
                 question=sub_question.sub_question,
                 answer=sub_question.sub_answer,
                 sub_queries=sub_queries,
-                context_docs=get_retrieval_docs_from_search_docs(
-                    verified_docs, sort_by_score=False
-                ),
+                context_docs=get_retrieval_docs_from_search_docs(verified_docs),
             )
         )
     return sub_questions
 
 
 def get_retrieval_docs_from_search_docs(
-    search_docs: list[SearchDoc],
-    remove_doc_content: bool = False,
-    sort_by_score: bool = True,
+    search_docs: list[SearchDoc], remove_doc_content: bool = False
 ) -> RetrievalDocs:
     top_documents = [
         translate_db_search_doc_to_server_search_doc(
@@ -921,8 +908,7 @@ def get_retrieval_docs_from_search_docs(
         )
         for db_doc in search_docs
     ]
-    if sort_by_score:
-        top_documents = sorted(top_documents, key=lambda doc: doc.score, reverse=True)  # type: ignore
+    top_documents = sorted(top_documents, key=lambda doc: doc.score, reverse=True)  # type: ignore
     return RetrievalDocs(top_documents=top_documents)
 
 
@@ -1032,7 +1018,7 @@ def log_agent_sub_question_results(
         sub_question = sub_question_answer_result.question
         sub_answer = sub_question_answer_result.answer
         sub_document_results = _create_citation_format_list(
-            sub_question_answer_result.context_documents
+            sub_question_answer_result.verified_reranked_documents
         )
 
         sub_question_object = AgentSubQuestion(

backend/onyx/llm/chat_llm.py

@@ -52,18 +52,6 @@ litellm.telemetry = False
 _LLM_PROMPT_LONG_TERM_LOG_CATEGORY = "llm_prompt"
 
 
-class LLMTimeoutError(Exception):
-    """
-    Exception raised when an LLM call times out.
-    """
-
-
-class LLMRateLimitError(Exception):
-    """
-    Exception raised when an LLM call is rate limited.
-    """
-
-
 def _base_msg_to_role(msg: BaseMessage) -> str:
     if isinstance(msg, HumanMessage) or isinstance(msg, HumanMessageChunk):
         return "user"
@@ -401,7 +389,6 @@ class DefaultMultiLLM(LLM):
         tool_choice: ToolChoiceOptions | None,
         stream: bool,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> litellm.ModelResponse | litellm.CustomStreamWrapper:
         # litellm doesn't accept LangChain BaseMessage objects, so we need to convert them
         # to a dict representation
@@ -432,7 +419,7 @@ class DefaultMultiLLM(LLM):
                 stream=stream,
                 # model params
                 temperature=0,
-                timeout=timeout_override or self._timeout,
+                timeout=self._timeout,
                 # For now, we don't support parallel tool calls
                 # NOTE: we can't pass this in if tools are not specified
                 # or else OpenAI throws an error
@@ -451,12 +438,6 @@ class DefaultMultiLLM(LLM):
         except Exception as e:
             self._record_error(processed_prompt, e)
             # for break pointing
-            if isinstance(e, litellm.Timeout):
-                raise LLMTimeoutError(e)
-
-            elif isinstance(e, litellm.RateLimitError):
-                raise LLMRateLimitError(e)
-
             raise e
 
     @property
@@ -477,7 +458,6 @@ class DefaultMultiLLM(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> BaseMessage:
         if LOG_DANSWER_MODEL_INTERACTIONS:
             self.log_model_configs()
@@ -485,12 +465,7 @@ class DefaultMultiLLM(LLM):
         response = cast(
             litellm.ModelResponse,
             self._completion(
-                prompt=prompt,
-                tools=tools,
-                tool_choice=tool_choice,
-                stream=False,
-                structured_response_format=structured_response_format,
-                timeout_override=timeout_override,
+                prompt, tools, tool_choice, False, structured_response_format
             ),
         )
         choice = response.choices[0]
@@ -508,31 +483,19 @@ class DefaultMultiLLM(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         if LOG_DANSWER_MODEL_INTERACTIONS:
             self.log_model_configs()
 
         if DISABLE_LITELLM_STREAMING:
-            yield self.invoke(
-                prompt,
-                tools,
-                tool_choice,
-                structured_response_format,
-                timeout_override,
-            )
+            yield self.invoke(prompt, tools, tool_choice, structured_response_format)
             return
 
         output = None
         response = cast(
             litellm.CustomStreamWrapper,
             self._completion(
-                prompt=prompt,
-                tools=tools,
-                tool_choice=tool_choice,
-                stream=True,
-                structured_response_format=structured_response_format,
-                timeout_override=timeout_override,
+                prompt, tools, tool_choice, True, structured_response_format
             ),
         )
         try:

backend/onyx/llm/custom_llm.py

@@ -81,7 +81,6 @@ class CustomModelServer(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> BaseMessage:
         return self._execute(prompt)
 
@@ -91,6 +90,5 @@ class CustomModelServer(LLM):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         yield self._execute(prompt)

backend/onyx/llm/interfaces.py

@@ -90,13 +90,12 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> BaseMessage:
         self._precall(prompt)
         # TODO add a postcall to log model outputs independent of concrete class
         # implementation
         return self._invoke_implementation(
-            prompt, tools, tool_choice, structured_response_format, timeout_override
+            prompt, tools, tool_choice, structured_response_format
         )
 
     @abc.abstractmethod
@@ -106,7 +105,6 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> BaseMessage:
         raise NotImplementedError
 
@@ -116,13 +114,12 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         self._precall(prompt)
         # TODO add a postcall to log model outputs independent of concrete class
         # implementation
         messages = self._stream_implementation(
-            prompt, tools, tool_choice, structured_response_format, timeout_override
+            prompt, tools, tool_choice, structured_response_format
         )
 
         tokens = []
@@ -141,6 +138,5 @@ class LLM(abc.ABC):
         tools: list[dict] | None = None,
         tool_choice: ToolChoiceOptions | None = None,
         structured_response_format: dict | None = None,
-        timeout_override: int | None = None,
     ) -> Iterator[BaseMessage]:
         raise NotImplementedError

backend/onyx/main.py

@@ -238,20 +238,14 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
         await close_auth_limiter()
 
 
-def log_http_error(request: Request, exc: Exception) -> JSONResponse:
+def log_http_error(_: Request, exc: Exception) -> JSONResponse:
     status_code = getattr(exc, "status_code", 500)
 
     if isinstance(exc, BasicAuthenticationError):
-        # For BasicAuthenticationError, just log a brief message without stack trace
-        # (almost always spammy)
-        logger.debug(f"Authentication failed: {str(exc)}")
-
-    elif status_code == 404 and request.url.path == "/metrics":
-        # Log 404 errors for the /metrics endpoint with debug level
-        logger.debug(f"404 error for /metrics endpoint: {str(exc)}")
+        # For BasicAuthenticationError, just log a brief message without stack trace (almost always spam)
+        logger.warning(f"Authentication failed: {str(exc)}")
 
     elif status_code >= 400:
-        print("FORMATTING ERROR")
         error_msg = f"{str(exc)}\n"
         error_msg += "".join(traceback.format_tb(exc.__traceback__))
         logger.error(error_msg)

backend/onyx/prompts/agent_search.py

@@ -1,7 +1,3 @@
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_ANSWER_SEPARATOR,
-)
-
 # Standards
 SEPARATOR_LINE = "-------"
 SEPARATOR_LINE_LONG = "---------------"
@@ -9,6 +5,8 @@ UNKNOWN_ANSWER = "I do not have enough information to answer this question."
 NO_RECOVERED_DOCS = "No relevant information recovered"
 YES = "yes"
 NO = "no"
+
+
 # Framing/Support/Template Prompts
 HISTORY_FRAMING_PROMPT = f"""
 For more context, here is the history of the conversation so far that preceded this question:
@@ -18,43 +16,6 @@ For more context, here is the history of the conversation so far that preceded t
 """.strip()
 
 
-COMMON_RAG_RULES = f"""
-IMPORTANT RULES:
- - If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer. \
-You may give some additional facts you learned, but do not try to invent an answer.
-
- - If the information is empty or irrelevant, just say "{UNKNOWN_ANSWER}".
-
- - If the information is relevant but not fully conclusive, provide an answer to the extent you can but also specify that \
-the information is not conclusive and why.
-
-- When constructing/considering categories, focus less on the question and more on the context actually provided! \
-Example: if the question is about the products of company A, and the content provided lists a number of products, \
-do automatically NOT ASSUME that those belong to company A!  So you cannot list those as products of company A, despite \
-the fact that the question is about company A's products. What you should say instead is maybe something like \
-"Here are a number of products, but I cannot say whether some or all of them belong to company A: \
-<proceed with listing the products>". It is ABSOLUTELY ESSENTIAL that the answer constructed reflects \
-actual knowledge. For that matter, also consider the title of the document and other information that may be \
-provided. If that does not make it clear that - in the example above - the products belong to company A, \
-then do not list them as products of company A, just maybe as "A list products that may not necessarily \
-belong to company A". THIS IS IMPORTANT!
-
-- Related, if the context provides a list of items with associated data or other information that seems \
-to align with the categories in the question, but does not specify whether the items or the information is \
-specific to the exact requested category, then present the information with a disclaimer. Use a title such as \
-"I am not sure whether these items (or the information provided) is specific to [relevant category] or whether \
-these are all [specific group], but I found this information may be helpful:" \
-followed by the list of items and associated data/or information discovered.
-
- - Do not group together items amongst one headline where not all items belong to the category of the headline! \
-(Example: "Products used by Company A" where some products listed are not built by Company A, but other companies,
-or it is not clear that the products are built by Company A). Only state what you know for sure!
-
- - Do NOT perform any calculations in the answer! Just report on facts.
-
- - If appropriate, organizing your answer in bullet points is often useful.
-""".strip()
-
 ASSISTANT_SYSTEM_PROMPT_DEFAULT = "You are an assistant for question-answering tasks."
 
 ASSISTANT_SYSTEM_PROMPT_PERSONA = f"""
@@ -168,44 +129,20 @@ History summary:
 # Sub-question
 # Intentionally left a copy in case we want to modify this one differently
 INITIAL_QUESTION_DECOMPOSITION_PROMPT = f"""
-Please create a list of no more than 3 sub-questions whose answers would help to inform the answer \
-to the initial question.
-
-The purpose for these sub-questions could be:
-  1) decomposition to isolate individual entities (i.e., 'compare sales of company A and company B' -> \
+Decompose the initial user question into no more than 3 appropriate sub-questions that help to answer the \
+original question. The purpose for this decomposition may be to:
+  1) isolate individual entities (i.e., 'compare sales of company A and company B' -> \
 ['what are sales for company A', 'what are sales for company B'])
-
-  2) clarification and/or disambiguation of ambiguous terms (i.e., 'what is our success with company A' -> \
+  2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' -> \
 ['what are our sales with company A','what is our market share with company A', \
 'is company A a reference customer for us', etc.])
+  3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you \
+are generally familiar with the entity, then you can decompose the question into sub-questions that are more \
+specific to components (i.e., 'what do we do to improve scalability of product X', 'what do we to to improve \
+scalability of product X', 'what do we do to improve stability of product X', ...])
+  4) research an area that could really help to answer the question.
 
-  3) if a term or a metric is essentially clear, but it could relate to various aspects of an entity and you \
-are generally familiar with the entity, then you can create sub-questions that are more \
-specific (i.e.,  'what do we do to improve product X' -> 'what do we do to improve scalability of product X', \
-'what do we do to improve performance of product X', 'what do we do to improve stability of product X', ...)
-
-  4) research individual questions and areas that should really help to ultimately answer the question.
-
-Important:
-
- - Each sub-question should lend itself to be answered by a RAG system. Correspondingly, phrase the question \
-in a way that is amenable to that. An example set of sub-questions based on an initial question could look like this:
-'what can I do to improve the performance of workflow X' -> \
-'what are the settings affecting performance for workflow X', 'are there complaints and bugs related to \
-workflow X performance', 'what are performance benchmarks for workflow X', ...
-
- - Consequently, again, don't just decompose, but make sure that the sub-questions have the proper form. I.e., no \
- 'I', etc.
-
- - Do not(!) create sub-questions that are clarifying question to the person who asked the question, \
-like making suggestions or asking the user for more information! This is not useful for the actual \
-question-answering process! You need to take the information from the user as it is given to you! \
-For example, should the question be of the type 'why does product X perform poorly for customer A', DO NOT create a \
-sub-question of the type 'what are the settings that customer A uses for product X?'! A valid sub-question \
-could rather be 'which settings for product X have been shown to lead to poor performance for customers?'
-
-
-And here is the initial question to create sub-questions for, so that you have the full context:
+Here is the initial question to decompose:
 {SEPARATOR_LINE}
 {{question}}
 {SEPARATOR_LINE}
@@ -213,79 +150,7 @@ And here is the initial question to create sub-questions for, so that you have t
 {{history}}
 
 Do NOT include any text in your answer outside of the list of sub-questions!
-Please formulate your answer as a newline-separated list of questions like so (and please ONLY ANSWER WITH THIS LIST! Do not \
-add any explanations or other text!):
-
- <sub-question>
- <sub-question>
- <sub-question>
- ...
-
-Answer:
-""".strip()
-
-# INITIAL PHASE - AWARE OF REFINEMENT
-# Sub-question
-# Suggest augmenting question generation as well, that a future refinement phase could use
-# to generate new questions
-# Intentionally left a copy in case we want to modify this one differently
-INITIAL_QUESTION_DECOMPOSITION_PROMPT_ASSUMING_REFINEMENT = f"""
-Please create a list of no more than 3 sub-questions whose answers would help to inform the answer \
-to the initial question.
-
-The purpose for these sub-questions could be:
-  1) decomposition to isolate individual entities (i.e., 'compare sales of company A and company B' -> \
-['what are sales for company A', 'what are sales for company B'])
-
-  2) clarification and/or disambiguation of ambiguous terms (i.e., 'what is our success with company A' -> \
-['what are our sales with company A','what is our market share with company A', \
-'is company A a reference customer for us', etc.])
-
-  3) if a term or a metric is essentially clear, but it could relate to various aspects of an entity and you \
-are generally familiar with the entity, then you can create sub-questions that are more \
-specific (i.e.,  'what do we do to improve product X' -> 'what do we do to improve scalability of product X', \
-'what do we do to improve performance of product X', 'what do we do to improve stability of product X', ...)
-
-  4) research individual questions and areas that should really help to ultimately answer the question.
-
-  5) if meaningful, find relevant facts that may inform another set of sub-questions generate after the set you \
-create now are answered. Example: 'which products have we implemented at company A, and is this different to \
-its competitors?'  could potentially create sub-questions 'what products have we implemented at company A', \
-and 'who are the competitors of company A'. The additional round of sub-question generation which sees the \
-answers for this initial round of sub-question creation could then use the answer to the second sub-question \
-(which could be 'company B and C are competitors of company A') to then ask 'which products have we implemented \
-at company B', 'which products have we implemented at company C'...
-
-Important:
-
- - Each sub-question should lend itself to be answered by a RAG system. Correspondingly, phrase the question \
-in a way that is amenable to that. An example set of sub-questions based on an initial question could look like this:
-'what can I do to improve the performance of workflow X' -> \
-'what are the settings affecting performance for workflow X', 'are there complaints and bugs related to \
-workflow X performance', 'what are performance benchmarks for workflow X', ...
-
- - Consequently, again, don't just decompose, but make sure that the sub-questions have the proper form. I.e., no \
- 'I', etc.
-
- - Do not(!) create sub-questions that are clarifying question to the person who asked the question, \
-like making suggestions or asking the user for more information! This is not useful for the actual \
-question-answering process! You need to take the information from the user as it is given to you! \
-For example, should the question be of the type 'why does product X perform poorly for customer A', DO NOT create a \
-sub-question of the type 'what are the settings that customer A uses for product X?'! A valid sub-question \
-could rather be 'which settings for product X have been shown to lead to poor performance for customers?'
-
-
-And here is the initial question to create sub-questions for:
-{SEPARATOR_LINE}
-{{question}}
-{SEPARATOR_LINE}
-
-{{history}}
-
-Do NOT include any text in your answer outside of the list of sub-questions!
-Please formulate your answer as a newline-separated list of questions like so (and please ONLY ANSWER WITH THIS LIST! Do not \
-add any explanations or other text!):
-
+Please formulate your answer as a newline-separated list of questions like so:
  <sub-question>
  <sub-question>
  <sub-question>
@@ -297,47 +162,23 @@ Answer:
 
 # TODO: combine shared pieces with INITIAL_QUESTION_DECOMPOSITION_PROMPT
 INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH = f"""
-Please create a list of no more than 3 sub-questions whose answers would help to inform the answer \
-to the initial question.
-
-The purpose for these sub-questions could be:
-  1) decomposition to isolate individual entities (i.e., 'compare sales of company A and company B' -> \
+Decompose the initial user question into no more than 3 appropriate sub-questions that help to answer the \
+original question. The purpose for this decomposition may be to:
+  1) isolate individual entities (i.e., 'compare sales of company A and company B' -> \
 ['what are sales for company A', 'what are sales for company B'])
-
-  2) clarification and/or disambiguation of ambiguous terms (i.e., 'what is our success with company A' -> \
+  2) clarify or disambiguate ambiguous terms (i.e., 'what is our success with company A' -> \
 ['what are our sales with company A','what is our market share with company A', \
 'is company A a reference customer for us', etc.])
-
-  3) if a term or a metric is essentially clear, but it could relate to various aspects of an entity and you \
-are generally familiar with the entity, then you can create sub-questions that are more \
-specific (i.e.,  'what do we do to improve product X' -> 'what do we do to improve scalability of product X', \
-'what do we do to improve performance of product X', 'what do we do to improve stability of product X', ...)
-
-  4) research individual questions and areas that should really help to ultimately answer the question.
-
-Important:
-
- - Each sub-question should lend itself to be answered by a RAG system. Correspondingly, phrase the question \
-in a way that is amenable to that. An example set of sub-questions based on an initial question could look like this:
-'what can I do to improve the performance of workflow X' -> \
-'what are the settings affecting performance for workflow X', 'are there complaints and bugs related to \
-workflow X performance', 'what are performance benchmarks for workflow X', ...
-
- - Consequently, again, don't just decompose, but make sure that the sub-questions have the proper form. I.e., no \
- 'I', etc.
-
- - Do not(!) create sub-questions that are clarifying question to the person who asked the question, \
-like making suggestions or asking the user for more information! This is not useful for the actual \
-question-answering process! You need to take the information from the user as it is given to you! \
-For example, should the question be of the type 'why does product X perform poorly for customer A', DO NOT create a \
-sub-question of the type 'what are the settings that customer A uses for product X?'! A valid sub-question \
-could rather be 'which settings for product X have been shown to lead to poor performance for customers?'
-
+  3) if a term or a metric is essentially clear, but it could relate to various components of an entity and you \
+are generally familiar with the entity, then you can decompose the question into sub-questions that are more \
+specific to components (i.e., 'what do we do to improve scalability of product X', 'what do we to to improve \
+scalability of product X', 'what do we do to improve stability of product X', ...])
+  4) research an area that could really help to answer the question.
 
 To give you some context, you will see below also some documents that may relate to the question. Please only \
 use this information to learn what the question is approximately asking about, but do not focus on the details \
 to construct the sub-questions! Also, some of the entities, relationships and terms that are in the dataset may \
-not be in these few documents, so DO NOT focus too much on the documents when constructing the sub-questions! \
+not be in these few documents, so DO NOT focussed too much on the documents when constructing the sub-questions! \
 Decomposition and disambiguations are most important!
 
 Here are the sample docs to give you some context:
@@ -345,7 +186,7 @@ Here are the sample docs to give you some context:
 {{sample_doc_str}}
 {SEPARATOR_LINE}
 
-And here is the initial question to create sub-questions for, so that you have the full context:
+And here is the initial question to decompose:
 {SEPARATOR_LINE}
 {{question}}
 {SEPARATOR_LINE}
@@ -353,9 +194,7 @@ And here is the initial question to create sub-questions for, so that you have t
 {{history}}
 
 Do NOT include any text in your answer outside of the list of sub-questions!\
-Please formulate your answer as a newline-separated list of questions like so (and please ONLY ANSWER WITH THIS LIST! Do not \
-add any explanations or other text!):
-
+Please formulate your answer as a newline-separated list of questions like so:
  <sub-question>
  <sub-question>
  <sub-question>
@@ -364,84 +203,6 @@ add any explanations or other text!):
 Answer:
 """.strip()
 
-INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH_ASSUMING_REFINEMENT = f"""
-Please create a list of no more than 3 sub-questions whose answers would help to inform the answer \
-to the initial question.
-
-The purpose for these sub-questions could be:
-  1) decomposition to isolate individual entities (i.e., 'compare sales of company A and company B' -> \
-['what are sales for company A', 'what are sales for company B'])
-
-  2) clarification and/or disambiguation of ambiguous terms (i.e., 'what is our success with company A' -> \
-['what are our sales with company A','what is our market share with company A', \
-'is company A a reference customer for us', etc.])
-
-  3) if a term or a metric is essentially clear, but it could relate to various aspects of an entity and you \
-are generally familiar with the entity, then you can create sub-questions that are more \
-specific (i.e.,  'what do we do to improve product X' -> 'what do we do to improve scalability of product X', \
-'what do we do to improve performance of product X', 'what do we do to improve stability of product X', ...)
-
-  4) research individual questions and areas that should really help to ultimately answer the question.
-
-  5) if applicable and useful, consider using sub-questions to gather relevant information that can inform a \
-subsequent set of sub-questions. The answers to your initial sub-questions will be available when generating \
-the next set.
-For example, if you start with the question, "Which products have we implemented at Company A, and how does \
-this compare to its competitors?" you might first create sub-questions like "What products have we implemented \
-at Company A?" and "Who are the competitors of Company A?"
-The answer to the second sub-question, such as "Company B and C are competitors of Company A," can then be used \
-to generate more specific sub-questions in the next round, like "Which products have we implemented at Company B?" \
-and "Which products have we implemented at Company C?"
-
-You'll be the judge!
-
-Important:
-
- - Each sub-question should lend itself to be answered by a RAG system. Correspondingly, phrase the question \
-in a way that is amenable to that. An example set of sub-questions based on an initial question could look like this:
-'what can I do to improve the performance of workflow X' -> \
-'what are the settings affecting performance for workflow X', 'are there complaints and bugs related to \
-workflow X performance', 'what are performance benchmarks for workflow X', ...
-
- - Consequently, again, don't just decompose, but make sure that the sub-questions have the proper form. I.e., no \
- 'I', etc.
-
- - Do not(!) create sub-questions that are clarifying question to the person who asked the question, \
-like making suggestions or asking the user for more information! This is not useful for the actual \
-question-answering process! You need to take the information from the user as it is given to you! \
-For example, should the question be of the type 'why does product X perform poorly for customer A', DO NOT create a \
-sub-question of the type 'what are the settings that customer A uses for product X?'! A valid sub-question \
-could rather be 'which settings for product X have been shown to lead to poor performance for customers?'
-
-To give you some context, you will see below also some documents that may relate to the question. Please only \
-use this information to learn what the question is approximately asking about, but do not focus on the details \
-to construct the sub-questions! Also, some of the entities, relationships and terms that are in the dataset may \
-not be in these few documents, so DO NOT focus too much on the documents when constructing the sub-questions! \
-Decomposition and disambiguations are most important!
-
-Here are the sample docs to give you some context:
-{SEPARATOR_LINE}
-{{sample_doc_str}}
-{SEPARATOR_LINE}
-
-And here is the initial question to create sub-questions for, so that you have the full context:
-{SEPARATOR_LINE}
-{{question}}
-{SEPARATOR_LINE}
-
-{{history}}
-
-Do NOT include any text in your answer outside of the list of sub-questions!\
-Please formulate your answer as a newline-separated list of questions like so (and please ONLY ANSWER WITH THIS LIST! Do not \
-add any explanations or other text!):
-
- <sub-question>
- <sub-question>
- <sub-question>
- ...
-
-Answer:
-""".strip()
 
 # Retrieval
 QUERY_REWRITING_PROMPT = f"""
@@ -496,35 +257,23 @@ Answer:
 """.strip()
 
 
-# Sub-Question Answer Generation
+# Sub-Question Anser Generation
 SUB_QUESTION_RAG_PROMPT = f"""
 Use the context provided below - and only the provided context - to answer the given question. \
 (Note that the answer is in service of answering a broader question, given below as 'motivation').
 
-Make sure that you keep all relevant information, specifically as it concerns the ultimate goal. \
+Again, only use the provided context and do not use your internal knowledge! If you cannot answer the \
+question based on the context, say "{UNKNOWN_ANSWER}". It is a matter of life and death that you do NOT \
+use your internal knowledge, just the provided information!
+
+Make sure that you keep all relevant information, specifically as it concerns to the ultimate goal. \
 (But keep other details as well.)
 
-{COMMON_RAG_RULES}
-
- - Make sure that you only state what you actually can positively learn from the provided context! Particularly \
-don't make assumptions!  Example: if i) a question you should answer is asking for products of companies that \
-are competitors of company A, and ii) the context mentions products of companies A, B, C, D, E, etc., do NOT assume \
-that B, C, D, E, etc. are competitors of A! All you know is that these are products of a number of companies, and you \
-would have to rely on another question - that you do not have access to - to learn which companies are competitors of A.
-Correspondingly, you should not say that these are the products of competitors of A, but rather something like \
-"Here are some products of various companies".
-
-It is critical that you provide inline citations in the format [D1], [D2], [D3], etc! Please use format [D1][D2] and NOT \
-[D1, D2] format if you cite two or more documents together! \
+It is critical that you provide inline citations in the format [D1], [D2], [D3], etc! \
 It is important that the citation is close to the information it supports. \
 Proper citations are very important to the user!
 
-Here is the document context for you to consider:
-{SEPARATOR_LINE}
-{{context}}
-{SEPARATOR_LINE}
-
-For your general information, here is the ultimate motivation for the question you need to answer:
+For your general information, here is the ultimate motivation:
 {SEPARATOR_LINE}
 {{original_question}}
 {SEPARATOR_LINE}
@@ -534,8 +283,12 @@ And here is the actual question I want you to answer based on the context above
 {{question}}
 {SEPARATOR_LINE}
 
-Please keep your answer brief and concise, and focus on facts and data. (Again, only state what you see in the documents \
-for sure and communicate if/in what way this may or may not relate to the question you need to answer!)
+Here is the context:
+{SEPARATOR_LINE}
+{{context}}
+{SEPARATOR_LINE}
+
+Please keep your answer brief and concise, and focus on facts and data.
 
 Answer:
 """.strip()
@@ -568,18 +321,22 @@ Use the information provided below - and only the provided information - to answ
 
 The information provided below consists of:
   1) a number of answered sub-questions - these are very important to help you organize your thoughts and your answer
-  2) a number of documents that are deemed relevant for the question.
+  2) a number of documents that deemed relevant for the question.
 
 {{history}}
 
-It is critical that you provide proper inline citations to documents in the format [D1], [D2], [D3], etc.! \
+It is critical that you provide prover inline citations to documents in the format [D1], [D2], [D3], etc.! \
 It is important that the citation is close to the information it supports. If you have multiple citations that support \
 a fact, please cite for example as [D1][D3], or [D2][D4], etc. \
 Feel free to also cite sub-questions in addition to documents, but make sure that you have documents cited with the \
 sub-question citation. If you want to cite both a document and a sub-question, please use [D1][Q3], or [D2][D7][Q4], etc. \
 Again, please NEVER cite sub-questions without a document citation! Proper citations are very important for the user!
 
-{COMMON_RAG_RULES}
+IMPORTANT RULES:
+ - If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer. \
+You may give some additional facts you learned, but do not try to invent an answer.
+ - If the information is empty or irrelevant, just say "{UNKNOWN_ANSWER}".
+ - If the information is relevant but not fully conclusive, specify that the information is not conclusive and say why.
 
 Again, you should be sure that the answer is supported by the information provided!
 
@@ -604,9 +361,7 @@ And here is the question I want you to answer based on the information above:
 {{question}}
 {SEPARATOR_LINE}
 
-Please keep your answer brief and concise, and focus on facts and data. (Again, only state what you see in the documents for \
-sure and communicate if/in what way this may or may not relate to the question you need to answer! Use the answered \
-sub-questions as well, but be cautious and reconsider the docments again for validation.)
+Please keep your answer brief and concise, and focus on facts and data.
 
 Answer:
 """.strip()
@@ -621,7 +376,11 @@ The information provided below consists of a number of documents that were deeme
 
 {{history}}
 
-{COMMON_RAG_RULES}
+IMPORTANT RULES:
+ - If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer. \
+You may give some additional facts you learned, but do not try to invent an answer.
+ - If the information is irrelevant, just say "{UNKNOWN_ANSWER}".
+ - If the information is relevant but not fully conclusive, specify that the information is not conclusive and say why.
 
 Again, you should be sure that the answer is supported by the information provided!
 
@@ -640,8 +399,7 @@ And here is the question I want you to answer based on the context above:
 {{question}}
 {SEPARATOR_LINE}
 
-Please keep your answer brief and concise, and focus on facts and data. (Again, only state what you see in the documents \
-for sure and communicate if/in what way this may or may not relate to the question you need to answer!)
+Please keep your answer brief and concise, and focus on facts and data.
 
 Answer:
 """.strip()
@@ -681,12 +439,6 @@ independently without the original question available
 - For each sub-question, please also provide a search term that can be used to retrieve relevant documents from a document store.
 - Consider specifically the sub-questions that were suggested but not answered. This is a sign that they are not answerable \
 with the available context, and you should not ask similar questions.
- - Do not(!) create sub-questions that are clarifying question to the person who asked the question, \
-like making suggestions or asking the user for more information! This is not useful for the actual \
-question-answering process! You need to take the information from the user as it is given to you! \
-For example, should the question be of the type 'why does product X perform poorly for customer A', DO NOT create a \
-sub-question of the type 'what are the settings that customer A uses for product X?'! A valid sub-question \
-could rather be 'which settings for product X have been shown to lead to poor performance for customers?'
 
 Here is the initial question:
 {SEPARATOR_LINE}
@@ -722,111 +474,7 @@ objects/relationships/terms you can ask about! Do not ask about entities, terms
 Again, please find questions that are NOT overlapping too much with the already answered sub-questions or those that \
 already were suggested and failed. In other words - what can we try in addition to what has been tried so far?
 
-Generate the list of questions separated by one new line like this (and please ONLY ANSWER WITH THIS LIST! Do not \
-add any explanations or other text!):
-
-<sub-question 1>
-<sub-question 2>
-<sub-question 3>
-...""".strip()
-
-REFINEMENT_QUESTION_DECOMPOSITION_PROMPT_W_INITIAL_SUBQUESTION_ANSWERS = f"""
-An initial user question needs to be answered. An initial answer has been provided but it wasn't quite good enough. \
-Also, some sub-questions had been answered and this information has been used to provide the initial answer. \
-Some other subquestions may have been suggested based on little knowledge, but they were not directly answerable. \
-Also, some entities, relationships and terms are given to you so that you have an idea of how the available data looks like.
-
-Your role is to generate 2-4 new sub-questions that would help to answer the initial question, considering:
-
-1) The initial question
-2) The initial answer that was found to be unsatisfactory
-3) The sub-questions that were answered AND their answers
-4) The sub-questions that were suggested but not answered (and that you should not repeat!)
-5) The entities, relationships and terms that were extracted from the context
-
-The individual questions should be answerable by a good RAG system. So a good idea would be to use the sub-questions to \
-resolve ambiguities and/or to separate the question for different entities that may be involved in the original question, \
-but in a way that does not duplicate questions that were already tried.
-
-Additional Guidelines:
-
-- The new sub-questions should be specific to the question and provide richer context for the question, resolve ambiguities, \
-or address shortcoming of the initial answer
-
-- Each new sub-question - when answered - should be relevant for the answer to the original question
-
-- The new sub-questions should be free from comparisons, ambiguities,judgements, aggregations, or any other complications that \
-may require extra context
-
-- The new sub-questions MUST have the full context of the original question so that it can be executed by a RAG system \
-independently without the original question available
-    Example:
-    - initial question: "What is the capital of France?"
-    - bad sub-question: "What is the name of the river there?"
-    - good sub-question: "What is the name of the river that flows through Paris?"
-
-    - For each new sub-question, please also provide a search term that can be used to retrieve relevant documents \
-from a document store.
-
-- Consider specifically the sub-questions that were suggested but not answered. This is a sign that they are not answerable \
-with the available context, and you should not ask similar questions.
-
-- Pay attention to the answers of previous sub-question to make your sub-questions more specific! \
-Often the initial sub-questions were set up to give you critical information that you should use to generate new sub-questions.\
-For example, if the answer to a an earlier sub-question is \
-'Company B and C are competitors of Company A', you should not ask now a new sub-question involving the term 'competitors', \
-as you already have the information to create a more precise question - you should instead explicitly reference \
-'Company B' and 'Company C' in your new sub-questions, as these are the competitors based on the previously answered question.
-
-- Be precise(!) and don't make inferences you cannot be sure about! For example, in the previous example \
-where Company B and Company C were identified as competitors of Company A, and then you also get information on \
-companies D and E, do not make the inference that these are also competitors of Company A! Stick to the information you have!
-(Also, don't assume that companies B and C arethe only competitors of A, unless stated!)
-
-- Do not(!) create sub-questions that are clarifying question *to the person who asked the question*, \
-like making suggestions or asking the user for more information! This is not useful for the actual \
-question-answering process! You need to take the information from the user as it is given to you! \
-For example, should the question be of the type 'why does product X perform poorly for customer A', DO NOT create a \
-sub-question of the type 'what are the settings that customer A uses for product X?'! A valid sub-question \
-could rather be 'which settings for product X have been shown to lead to poor performance for customers?'
-
-Here is the initial question:
-{SEPARATOR_LINE}
-{{question}}
-{SEPARATOR_LINE}
-{{history}}
-
-Here is the initial sub-optimal answer:
-{SEPARATOR_LINE}
-{{base_answer}}
-{SEPARATOR_LINE}
-
-Here are the sub-questions that were answered:
-{SEPARATOR_LINE}
-{{answered_subquestions_with_answers}}
-{SEPARATOR_LINE}
-
-Here are the sub-questions that were suggested but not answered:
-{SEPARATOR_LINE}
-{{failed_sub_questions}}
-{SEPARATOR_LINE}
-
-And here are the entities, relationships and terms extracted from the context:
-{SEPARATOR_LINE}
-{{entity_term_extraction_str}}
-{SEPARATOR_LINE}
-
-Please generate the list of good, fully contextualized sub-questions that would help to address the main question. \
-Specifically pay attention also to the entities, relationships and terms extracted, as these indicate what type of \
-objects/relationships/terms you can ask about! Do not ask about entities, terms or relationships that are not mentioned \
-in the 'entities, relationships and terms' section.
-
-Again, please find questions that are NOT overlapping too much with the already answered sub-questions or those that \
-already were suggested and failed. In other words - what can we try in addition to what has been tried so far?
-
-Generate the list of questions separated by one new line like this (and please ONLY ANSWER WITH THIS LIST! Do not \
-add any explanations or other text!):
-
+Generate the list of questions separated by one new line like this:
 <sub-question 1>
 <sub-question 2>
 <sub-question 3>
@@ -841,7 +489,7 @@ Your task is to improve on a given answer to a question, as the initial answer w
 Use the information provided below - and only the provided information - to write your new and improved answer.
 
 The information provided below consists of:
-  1) an initial answer that was given but likely found to be lacking in some way.
+  1) an initial answer that was given but found to be lacking in some way.
   2) a number of answered sub-questions - these are very important(!) and definitely should help you to answer the main \
 question. Note that the sub-questions have a type, 'initial' and 'refined'. The 'initial' ones were available for the \
 creation of the initial answer, but the 'refined' were not, they are new. So please use the 'refined' sub-questions in \
@@ -851,7 +499,6 @@ particular to update/extend/correct/enrich the initial answer and to add more de
 the relevant document for a fact!
 
 It is critical that you provide proper inline citations to documents in the format [D1], [D2], [D3], etc! \
-Please use format [D1][D2] and NOT [D1, D2] format if you cite two or more documents together! \
 It is important that the citation is close to the information it supports. \
 DO NOT just list all of the citations at the very end. \
 Feel free to also cite sub-questions in addition to documents, \
@@ -862,7 +509,14 @@ Proper citations are very important for the user!
 
 {{history}}
 
-{COMMON_RAG_RULES}
+IMPORTANT RULES:
+ - If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer. \
+You may give some additional facts you learned, but do not try to invent an answer.
+ - If the information is empty or irrelevant, just say "{UNKNOWN_ANSWER}".
+ - If the information is relevant but not fully conclusive, provide an answer to the extent you can but also specify that \
+the information is not conclusive and why.
+ - Ignore any existing citations within the answered sub-questions, like [D1]... and [Q2]! The citations you will need to \
+use will need to refer to the documents (and sub-questions) that you are explicitly presented with below!
 
 Again, you should be sure that the answer is supported by the information provided!
 
@@ -891,9 +545,7 @@ Lastly, here is the main question I want you to answer based on the information
 {{question}}
 {SEPARATOR_LINE}
 
-Please keep your answer brief and concise, and focus on facts and data. (Again, only state what you see in the documents for \
-sure and communicate if/in what way this may or may not relate to the question you need to answer! Use the answered \
-sub-questions as well, but be cautious and reconsider the docments again for validation.)
+Please keep your answer brief and concise, and focus on facts and data.
 
 Answer:
 """.strip()
@@ -909,13 +561,18 @@ The information provided below consists of:
   2) a number of documents that were also deemed relevant for the question.
 
 It is critical that you provide proper inline citations to documents in the format [D1], [D2], [D3], etc! \
-Please use format [D1][D2] and NOT [D1, D2] format if you cite two or more documents together! \
 It is important that the citation is close to the information it supports. \
 DO NOT just list all of the citations at the very end of your response. Citations are very important for the user!
 
 {{history}}
 
-{COMMON_RAG_RULES}
+IMPORTANT RULES:
+ - If you cannot reliably answer the question solely using the provided information, say that you cannot reliably answer. \
+You may give some additional facts you learned, but do not try to invent an answer.
+ - If the information is empty or irrelevant, just say "{UNKNOWN_ANSWER}".
+ - If the information is relevant but not fully conclusive, provide an answer to the extent you can but also specify that \
+the information is not conclusive and why.
+
 Again, you should be sure that the answer is supported by the information provided!
 
 Try to keep your answer concise. But also highlight uncertainties you may have should there be substantial ones, \
@@ -940,103 +597,11 @@ Lastly, here is the question I want you to answer based on the information above
 {{question}}
 {SEPARATOR_LINE}
 
-Please keep your answer brief and concise, and focus on facts and data. (Again, only state what you see in the documents for \
-sure and communicate if/in what way this may or may not relate to the question you need to answer!)
+Please keep your answer brief and concise, and focus on facts and data.
 
 Answer:
 """.strip()
 
-REFINED_ANSWER_VALIDATION_PROMPT = f"""
-{{persona_specification}}
-
-Your task is to verify whether a given answer is truthful and accurate, and supported by the facts that you \
-will be provided with.
-
-The information provided below consists of:
-
-  1) a question that needed to be answered
-
-  2) a proposed answer to the question, whose accuracy you should assess
-
-  3) potentially, a brief summary of the history of the conversation thus far, as it may give more context \
-to the question. Note that the statements in the history are NOT considered as facts, ONLY but serve to to \
-give context to the question.
-
-  4) a number of answered sub-questions - you can take the answers as facts for these purposes.
-
-  5) a number of relevant documents that should support the answer and that you should use as fact, \
-i.e., if a statement in the document backs up a statement in the answer, then that statement in the answer \
-should be considered as true.
-
-
-IMPORTANT RULES AND CONSIDERATIONS:
-
- - Please consider the statements made in the proposed answer and assess whether they are truthful and accurate, based \
-on the provided sub-answered and the documents. (Again, the history is NOT considered as facts!)
-
- - Look in particular for:
-    * material statements that are not supported by the sub-answered or the documents
-    * assignments and groupings that are not supported, like company A is competitor of company B, but this is not \
-explicitly supported by documents or sub-answers, guesses or interpretations unless explicitly asked for
-
- - look also at the citations in the proposed answer and assess whether they are appropriate given the statements \
-made in the proposed answer that cites the document.
-
- - Are items grouped together amongst one headline where not all items belong to the category of the headline? \
-(Example: "Products used by Company A" where some products listed are not used by Company A)
-
- - Does the proposed answer address the question in full?
-
- - Is the answer specific to the question? Example: if the question asks for the prices for products by Company A, \
-but the answer lists the prices for products by Company A and Company B, or products it cannot be sure are by \
-Company A, then this is not quite specific enough to the question and the answer should be rejected.
-
-- Similarly, if the question asks for properties of a certain class but the proposed answer lists or includes entities \
-that are not of that class without very explicitly saying so, then the answer should be considered inaccurate.
-
- - If there are any calculations in the proposed answer that are not supported by the documents, they need to be tested. \
-If any calculation is wrong, the proposed answer should be considered as not trustworthy.
-
-
-Here is the information:
-{SEPARATOR_LINE_LONG}
-
-QUESTION:
-{SEPARATOR_LINE}
-{{question}}
-{SEPARATOR_LINE}
-
-PROPOSED ANSWER:
-{SEPARATOR_LINE}
-{{proposed_answer}}
-{SEPARATOR_LINE}
-
-Here is the additional contextual information:
-{SEPARATOR_LINE_LONG}
-
-{{history}}
-
-Sub-questions and their answers (to be considered as facts):
-{SEPARATOR_LINE}
-{{answered_sub_questions}}
-{SEPARATOR_LINE}
-
-And here are the relevant documents that support the sub-question answers, and that are relevant for the actual question:
-{SEPARATOR_LINE}
-{{relevant_docs}}
-{SEPARATOR_LINE}
-
-
-Please think through this step by step. Format your response just as a string in the following format:
-
-Analysis: <think through your reasoning as outlined in the 'IMPORTANT RULES AND CONSIDERATIONS' section above, \
-but keep it short. Come to a conclusion whether the proposed answer can be trusted>
-Comments: <state your condensed comments you would give to a user reading the proposed answer, regarding the accuracy and \
-specificity.>
-{AGENT_ANSWER_SEPARATOR} <answer here only with yes or no, whether the proposed answer can be trusted. Base this on your \
-analysis, but only say 'yes' (trustworthy) or 'no' (not trustworthy)>
-""".strip()
-
 
 INITIAL_REFINED_ANSWER_COMPARISON_PROMPT = f"""
 For the given question, please compare the initial answer and the refined answer and determine if the refined answer is \

backend/onyx/server/settings/store.py

@@ -1,7 +1,6 @@
 from onyx.configs.constants import KV_SETTINGS_KEY
 from onyx.configs.constants import OnyxRedisLocks
 from onyx.key_value_store.factory import get_kv_store
-from onyx.key_value_store.interface import KvKeyNotFoundError
 from onyx.redis.redis_pool import get_redis_client
 from onyx.server.settings.models import Settings
 from onyx.utils.logger import setup_logger
@@ -18,9 +17,6 @@ def load_settings() -> Settings:
         settings = (
             Settings.model_validate(stored_settings) if stored_settings else Settings()
         )
-    except KvKeyNotFoundError:
-        logger.error(f"No settings found in KV store for key: {KV_SETTINGS_KEY}")
-        settings = Settings()
     except Exception as e:
         logger.error(f"Error loading settings from KV store: {str(e)}")
         settings = Settings()

backend/requirements/default.txt

@@ -34,8 +34,8 @@ langchain-core==0.3.24
 langchain-openai==0.2.9
 langchain-text-splitters==0.3.2
 langchainhub==0.1.21
-langgraph==0.2.72
-langgraph-checkpoint==2.0.13
+langgraph==0.2.59
+langgraph-checkpoint==2.0.5
 langgraph-sdk==0.1.44
 litellm==1.60.2
 lxml==5.3.0

backend/tests/daily/connectors/gitbook/test_gitbook_connector.py

@@ -1,81 +0,0 @@
-import os
-import time
-
-import pytest
-
-from onyx.configs.constants import DocumentSource
-from onyx.connectors.gitbook.connector import GitbookConnector
-
-
-@pytest.fixture
-def gitbook_connector() -> GitbookConnector:
-    connector = GitbookConnector(
-        space_id=os.environ["GITBOOK_SPACE_ID"],
-    )
-    connector.load_credentials(
-        {
-            "gitbook_api_key": os.environ["GITBOOK_API_KEY"],
-        }
-    )
-    return connector
-
-
-def test_gitbook_connector_basic(gitbook_connector: GitbookConnector) -> None:
-    doc_batch_generator = gitbook_connector.load_from_state()
-
-    # Get first batch of documents
-    doc_batch = next(doc_batch_generator)
-    assert len(doc_batch) > 0
-
-    # Verify first document structure
-    doc = doc_batch[0]
-
-    # Basic document properties
-    assert doc.id.startswith("gitbook-")
-    assert doc.semantic_identifier == "Acme Corp Internal Handbook"
-    assert doc.source == DocumentSource.GITBOOK
-
-    # Metadata checks
-    assert "path" in doc.metadata
-    assert "type" in doc.metadata
-    assert "kind" in doc.metadata
-
-    # Section checks
-    assert len(doc.sections) == 1
-    section = doc.sections[0]
-
-    # Content specific checks
-    content = section.text
-
-    # Check for specific content elements
-    assert "* Fruit Shopping List:" in content
-    assert "> test quote it doesn't mean anything" in content
-
-    # Check headings
-    assert "# Heading 1" in content
-    assert "## Heading 2" in content
-    assert "### Heading 3" in content
-
-    # Check task list
-    assert "- [ ] Uncompleted Task" in content
-    assert "- [x] Completed Task" in content
-
-    # Check table content
-    assert "| ethereum | 10 | 3000 |" in content
-    assert "| bitcoin | 2 | 98000 |" in content
-
-    # Check paragraph content
-    assert "New York City comprises 5 boroughs" in content
-    assert "Empire State Building" in content
-
-    # Check code block (just verify presence of some unique code elements)
-    assert "function fizzBuzz(n)" in content
-    assert 'res.push("FizzBuzz")' in content
-
-    assert section.link  # Should have a URL
-
-    # Time-based polling test
-    current_time = time.time()
-    poll_docs = gitbook_connector.poll_source(0, current_time)
-    poll_batch = next(poll_docs)
-    assert len(poll_batch) > 0

backend/tests/regression/answer_quality/agent_test.py

@@ -1,39 +1,18 @@
 import csv
+import datetime
 import json
 import os
-from collections import defaultdict
-from datetime import datetime
-from datetime import timedelta
-from typing import Any
 
 import yaml
 
 from onyx.agents.agent_search.deep_search.main.graph_builder import (
     main_graph_builder,
 )
-from onyx.agents.agent_search.deep_search.main.graph_builder import (
-    main_graph_builder as main_graph_builder_a,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    MainInput as MainInput_a,
-)
-from onyx.agents.agent_search.run_graph import run_basic_graph
-from onyx.agents.agent_search.run_graph import run_main_graph
+from onyx.agents.agent_search.deep_search.main.states import MainInput
 from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
-from onyx.chat.models import AgentAnswerPiece
-from onyx.chat.models import OnyxAnswerPiece
-from onyx.chat.models import RefinedAnswerImprovement
-from onyx.chat.models import StreamStopInfo
-from onyx.chat.models import StreamType
-from onyx.chat.models import SubQuestionPiece
 from onyx.context.search.models import SearchRequest
 from onyx.db.engine import get_session_context_manager
 from onyx.llm.factory import get_default_llms
-from onyx.tools.force import ForceUseTool
-from onyx.tools.tool_implementations.search.search_tool import SearchTool
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
 
 
 cwd = os.getcwd()
@@ -56,183 +35,95 @@ input_file_object = open(
 )
 output_file = f"{OUTPUT_DIR}/agent_test_output.csv"
 
-csv_output_data: list[list[str]] = []
 
 test_data = json.load(input_file_object)
 example_data = test_data["examples"]
 example_ids = test_data["example_ids"]
 
-failed_example_ids: list[int] = []
-
 with get_session_context_manager() as db_session:
-    output_data: dict[str, Any] = {}
-
-    primary_llm, fast_llm = get_default_llms()
+    output_data = []
 
     for example in example_data:
-        query_start_time: datetime = datetime.now()
-        example_id: int = int(example.get("id"))
-        example_question: str = example.get("question")
-        if not example_question or not example_id:
-            continue
+        example_id = example["id"]
         if len(example_ids) > 0 and example_id not in example_ids:
             continue
 
-        logger.info(f"{query_start_time} -- Processing example {example_id}")
+        example_question = example["question"]
+        target_sub_questions = example.get("target_sub_questions", [])
+        num_target_sub_questions = len(target_sub_questions)
+        search_request = SearchRequest(query=example_question)
 
-        try:
-            example_question = example["question"]
-            target_sub_questions = example.get("target_sub_questions", [])
-            num_target_sub_questions = len(target_sub_questions)
-            search_request = SearchRequest(query=example_question)
+        config, search_tool = get_test_config(
+            db_session=db_session,
+            primary_llm=primary_llm,
+            fast_llm=fast_llm,
+            search_request=search_request,
+        )
 
-            initial_answer_duration: timedelta | None = None
-            refined_answer_duration: timedelta | None = None
-            base_answer_duration: timedelta | None = None
+        inputs = MainInput()
 
-            logger.debug("\n\nTEST QUERY START\n\n")
+        start_time = datetime.datetime.now()
 
-            graph = main_graph_builder_a()
-            compiled_graph = graph.compile()
-            query_end_time = datetime.now()
+        question_result = compiled_graph.invoke(
+            input=inputs, config={"metadata": {"config": config}}
+        )
+        end_time = datetime.datetime.now()
 
-            search_request = SearchRequest(
-                # query="what can you do with gitlab?",
-                # query="What are the guiding principles behind the development of cockroachDB",
-                # query="What are the temperatures in Munich, Hawaii, and New York?",
-                # query="When was Washington born?",
-                # query="What is Onyx?",
-                # query="What is the difference between astronomy and astrology?",
-                query=example_question,
+        duration = end_time - start_time
+        if num_target_sub_questions > 0:
+            chunk_expansion_ratio = (
+                question_result["initial_agent_stats"]
+                .get("agent_effectiveness", {})
+                .get("utilized_chunk_ratio", None)
             )
+            support_effectiveness_ratio = (
+                question_result["initial_agent_stats"]
+                .get("agent_effectiveness", {})
+                .get("support_ratio", None)
+            )
+        else:
+            chunk_expansion_ratio = None
+            support_effectiveness_ratio = None
 
-            answer_tokens: dict[str, list[str]] = defaultdict(list)
+        generated_sub_questions = question_result.get("generated_sub_questions", [])
+        num_generated_sub_questions = len(generated_sub_questions)
+        base_answer = question_result["initial_base_answer"].split("==")[-1]
+        agent_answer = question_result["initial_answer"].split("==")[-1]
 
-            with get_session_context_manager() as db_session:
-                config = get_test_config(
-                    db_session, primary_llm, fast_llm, search_request
-                )
-                assert (
-                    config.persistence is not None
-                ), "set a chat session id to run this test"
+        output_point = {
+            "example_id": example_id,
+            "question": example_question,
+            "duration": duration,
+            "target_sub_questions": target_sub_questions,
+            "generated_sub_questions": generated_sub_questions,
+            "num_target_sub_questions": num_target_sub_questions,
+            "num_generated_sub_questions": num_generated_sub_questions,
+            "chunk_expansion_ratio": chunk_expansion_ratio,
+            "support_effectiveness_ratio": support_effectiveness_ratio,
+            "base_answer": base_answer,
+            "agent_answer": agent_answer,
+        }
 
-                # search_request.persona = get_persona_by_id(1, None, db_session)
-                # config.perform_initial_search_path_decision = False
-                config.behavior.perform_initial_search_decomposition = True
-                input = MainInput_a()
-
-                # Base Flow
-                base_flow_start_time: datetime = datetime.now()
-                for output in run_basic_graph(config):
-                    if isinstance(output, OnyxAnswerPiece):
-                        answer_tokens["base_answer"].append(output.answer_piece or "")
-
-                output_data["base_answer"] = "".join(answer_tokens["base_answer"])
-                output_data["base_answer_duration"] = (
-                    datetime.now() - base_flow_start_time
-                )
-
-                # Agent Flow
-                agent_flow_start_time: datetime = datetime.now()
-                config = get_test_config(
-                    db_session,
-                    primary_llm,
-                    fast_llm,
-                    search_request,
-                    use_agentic_search=True,
-                )
-
-                config.tooling.force_use_tool = ForceUseTool(
-                    force_use=True, tool_name=SearchTool._NAME
-                )
-
-                tool_responses: list = []
-
-                sub_question_dict_tokens: dict[int, dict[int, str]] = defaultdict(
-                    lambda: defaultdict(str)
-                )
-
-                for output in run_main_graph(config):
-                    if isinstance(output, AgentAnswerPiece):
-                        if output.level == 0 and output.level_question_num == 0:
-                            answer_tokens["initial"].append(output.answer_piece)
-                        elif output.level == 1 and output.level_question_num == 0:
-                            answer_tokens["refined"].append(output.answer_piece)
-                    elif isinstance(output, SubQuestionPiece):
-                        if (
-                            output.level is not None
-                            and output.level_question_num is not None
-                        ):
-                            sub_question_dict_tokens[output.level][
-                                output.level_question_num
-                            ] += output.sub_question
-                    elif isinstance(output, StreamStopInfo):
-                        if (
-                            output.stream_type == StreamType.MAIN_ANSWER
-                            and output.level == 0
-                        ):
-                            initial_answer_duration = (
-                                datetime.now() - agent_flow_start_time
-                            )
-                    elif isinstance(output, RefinedAnswerImprovement):
-                        output_data["refined_answer_improves_on_initial_answer"] = str(
-                            output.refined_answer_improvement
-                        )
-
-                refined_answer_duration = datetime.now() - agent_flow_start_time
-
-                output_data["example_id"] = example_id
-                output_data["question"] = example_question
-                output_data["initial_answer"] = "".join(answer_tokens["initial"])
-                output_data["refined_answer"] = "".join(answer_tokens["refined"])
-                output_data["initial_answer_duration"] = initial_answer_duration or ""
-                output_data["refined_answer_duration"] = refined_answer_duration
-
-                output_data["initial_sub_questions"] = "\n---\n".join(
-                    [x for x in sub_question_dict_tokens[0].values()]
-                )
-                output_data["refined_sub_questions"] = "\n---\n".join(
-                    [x for x in sub_question_dict_tokens[1].values()]
-                )
-
-                csv_output_data.append(
-                    [
-                        str(example_id),
-                        example_question,
-                        output_data["base_answer"],
-                        output_data["base_answer_duration"],
-                        output_data["initial_sub_questions"],
-                        output_data["initial_answer"],
-                        output_data["initial_answer_duration"],
-                        output_data["refined_sub_questions"],
-                        output_data["refined_answer"],
-                        output_data["refined_answer_duration"],
-                        output_data["refined_answer_improves_on_initial_answer"],
-                    ]
-                )
-        except Exception as e:
-            logger.error(f"Error processing example {example_id}: {e}")
-            failed_example_ids.append(example_id)
-            continue
+        output_data.append(output_point)
 
 
 with open(output_file, "w", newline="") as csvfile:
-    writer = csv.writer(csvfile, delimiter="\t")
-    writer.writerow(
-        [
-            "example_id",
-            "question",
-            "base_answer",
-            "base_answer_duration",
-            "initial_sub_questions",
-            "initial_answer",
-            "initial_answer_duration",
-            "refined_sub_questions",
-            "refined_answer",
-            "refined_answer_duration",
-            "refined_answer_improves_on_initial_answer",
-        ]
-    )
-    writer.writerows(csv_output_data)
+    fieldnames = [
+        "example_id",
+        "question",
+        "duration",
+        "target_sub_questions",
+        "generated_sub_questions",
+        "num_target_sub_questions",
+        "num_generated_sub_questions",
+        "chunk_expansion_ratio",
+        "support_effectiveness_ratio",
+        "base_answer",
+        "agent_answer",
+    ]
+
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter="\t")
+    writer.writeheader()
+    writer.writerows(output_data)
 
 print("DONE")

web/src/app/chat/ChatPage.tsx

@@ -1122,7 +1122,7 @@ export function ChatPage({
         "Continue Generating (pick up exactly where you left off)",
     });
   };
-  const [uncaughtError, setUncaughtError] = useState<string | null>(null);
+  const [gener, setFinishedStreaming] = useState(false);
 
   const onSubmit = async ({
     messageIdToResend,
@@ -1445,6 +1445,7 @@ export function ChatPage({
               Object.hasOwn(packet, "level_question_num")
             ) {
               if ((packet as StreamStopInfo).stream_type == "main_answer") {
+                setFinishedStreaming(true);
                 updateChatState("streaming", frozenSessionId);
               }
               if (
@@ -1562,23 +1563,8 @@ export function ChatPage({
                 }
               );
             } else if (Object.hasOwn(packet, "error")) {
-              if (
-                sub_questions.length > 0 &&
-                sub_questions
-                  .filter((q) => q.level === 0)
-                  .every((q) => q.is_stopped === true)
-              ) {
-                setUncaughtError((packet as StreamingError).error);
-                updateChatState("input");
-                setAgenticGenerating(false);
-                setAlternativeGeneratingAssistant(null);
-                setSubmittedMessage("");
-                return;
-                // throw new Error((packet as StreamingError).error);
-              } else {
-                error = (packet as StreamingError).error;
-                stackTrace = (packet as StreamingError).stack_trace;
-              }
+              error = (packet as StreamingError).error;
+              stackTrace = (packet as StreamingError).stack_trace;
             } else if (Object.hasOwn(packet, "message_id")) {
               finalMessage = packet as BackendMessage;
             } else if (Object.hasOwn(packet, "stop_reason")) {
@@ -2068,7 +2054,6 @@ export function ChatPage({
         }
 
         const data = await response.json();
-
         router.push(data.redirect_url);
       } catch (error) {
         console.error("Error seeding chat from Slack:", error);
@@ -2664,7 +2649,6 @@ export function ChatPage({
                                     {message.sub_questions &&
                                     message.sub_questions.length > 0 ? (
                                       <AgenticMessage
-                                        error={uncaughtError}
                                         isStreamingQuestions={
                                           message.isStreamingQuestions ?? false
                                         }

web/src/app/chat/message/AgenticMessage.tsx

@@ -81,7 +81,6 @@ export const AgenticMessage = ({
   agenticDocs,
   secondLevelSubquestions,
   toggleDocDisplay,
-  error,
 }: {
   isStreamingQuestions: boolean;
   isGenerating: boolean;
@@ -114,7 +113,6 @@ export const AgenticMessage = ({
   regenerate?: (modelOverRide: LlmOverride) => Promise<void>;
   setPresentingDocument?: (document: OnyxDocument) => void;
   toggleDocDisplay?: (agentic: boolean) => void;
-  error?: string | null;
 }) => {
   const [noShowingMessage, setNoShowingMessage] = useState(isComplete);
 
@@ -493,28 +491,11 @@ export const AgenticMessage = ({
                           ) : (
                             content
                           )}
-                          {error && (
-                            <p className="mt-2 text-red-700 text-sm my-auto">
-                              {error}
-                            </p>
-                          )}
                         </div>
                       </div>
                     </>
-                  ) : isComplete ? (
-                    error && (
-                      <p className="mt-2 mx-4 text-red-700 text-sm my-auto">
-                        {error}
-                      </p>
-                    )
-                  ) : (
-                    <>
-                      {error && (
-                        <p className="mt-2 mx-4 text-red-700 text-sm my-auto">
-                          {error}
-                        </p>
-                      )}
-                    </>
+                  ) : isComplete ? null : (
+                    <></>
                   )}
                   {handleFeedback &&
                     (isActive ? (

web/src/app/chat/message/MemoizedTextComponents.tsx

@@ -95,7 +95,7 @@ export const MemoizedAnchor = memo(
     }
     return (
       <MemoizedLink updatePresentingDocument={updatePresentingDocument}>
-        {children}
+        {children}l
       </MemoizedLink>
     );
   }

web/src/app/chat/message/Messages.tsx

@@ -186,7 +186,6 @@ export const AIMessage = ({
   setPresentingDocument,
   index,
   documentSidebarVisible,
-  removePadding,
 }: {
   index?: number;
   shared?: boolean;
@@ -215,7 +214,6 @@ export const AIMessage = ({
   overriddenModel?: string;
   regenerate?: (modelOverRide: LlmOverride) => Promise<void>;
   setPresentingDocument: (document: OnyxDocument) => void;
-  removePadding?: boolean;
 }) => {
   const toolCallGenerating = toolCall && !toolCall.tool_result;
 
@@ -404,9 +402,7 @@ export const AIMessage = ({
     <div
       id={isComplete ? "onyx-ai-message" : undefined}
       ref={trackedElementRef}
-      className={`py-5  ml-4 lg:px-5 relative flex
-        
-        ${removePadding && "!pl-24 -mt-12"}`}
+      className={`py-5 text-text ml-4 lg:px-5 relative flex `}
     >
       <div
         className={`mx-auto ${
@@ -415,13 +411,11 @@ export const AIMessage = ({
       >
         <div className={`lg:mr-12 ${!shared && "mobile:ml-0 md:ml-8"}`}>
           <div className="flex items-start">
-            {!removePadding && (
-              <AssistantIcon
-                className="mobile:hidden"
-                size={24}
-                assistant={alternativeAssistant || currentPersona}
-              />
-            )}
+            <AssistantIcon
+              className="mobile:hidden"
+              size={24}
+              assistant={alternativeAssistant || currentPersona}
+            />
 
             <div className="w-full">
               <div className="max-w-message-max break-words">
@@ -602,8 +596,7 @@ export const AIMessage = ({
                     )}
                   </div>
 
-                  {!removePadding &&
-                    handleFeedback &&
+                  {handleFeedback &&
                     (isActive ? (
                       <div
                         className={`

web/src/components/credentials/actions/CreateCredential.tsx

@@ -194,7 +194,7 @@ export default function CreateCredential({
               for information on setting up this connector.
             </p>
           )}
-          <CardSection className="w-full items-start  dark:bg-neutral-900 mt-4 flex flex-col gap-y-6">
+          <CardSection className="w-full items-start bg-blue-200 !border-0 mt-4 flex flex-col gap-y-6">
             <TextFormField
               name="name"
               placeholder="(Optional) credential name.."
@@ -215,7 +215,7 @@ export default function CreateCredential({
               />
             ))}
             {!swapConnector && (
-              <div className="mt-4 flex w-full flex-col sm:flex-row justify-between items-end">
+              <div className="mt-4 flex flex-col sm:flex-row justify-between items-end">
                 <div className="w-full sm:w-3/4 mb-4 sm:mb-0">
                   {isPaidEnterpriseFeaturesEnabled && (
                     <div className="flex flex-col items-start">

web/src/components/icons/icons.tsx

@@ -76,8 +76,7 @@ import r2Icon from "../../../public/r2.png";
 import salesforceIcon from "../../../public/Salesforce.png";
 import freshdeskIcon from "../../../public/Freshdesk.png";
 import firefliesIcon from "../../../public/Fireflies.png";
-import gitbookDarkIcon from "../../../public/GitBookDark.png";
-import gitbookLightIcon from "../../../public/GitBookLight.png";
+
 import sharepointIcon from "../../../public/Sharepoint.png";
 import teamsIcon from "../../../public/Teams.png";
 import mediawikiIcon from "../../../public/MediaWiki.svg";
@@ -2890,20 +2889,6 @@ export const AirtableIcon = ({
   return <LogoIcon size={size} className={className} src={airtableIcon} />;
 };
 
-export const GitbookIcon = ({
-  size = 16,
-  className = defaultTailwindCSS,
-}: IconProps) => (
-  <div className="flex items-center justify-center">
-    <div className="dark:hidden">
-      <LogoIcon size={size} className={className} src={gitbookDarkIcon} />
-    </div>
-    <div className="hidden dark:block">
-      <LogoIcon size={size} className={className} src={gitbookLightIcon} />
-    </div>
-  </div>
-);
-
 export const PinnedIcon = ({
   size = 16,
   className = defaultTailwindCSS,

web/src/components/logo/Logo.tsx

@@ -2,6 +2,7 @@
 
 import { useContext } from "react";
 import { SettingsContext } from "../settings/SettingsProvider";
+import { useTheme } from "next-themes";
 import { OnyxIcon, OnyxLogoTypeIcon } from "../icons/icons";
 
 export function Logo({

web/src/lib/connectors/connectors.tsx

@@ -227,24 +227,6 @@ export const connectorConfigs: Record<
     ],
     advanced_values: [],
   },
-  gitbook: {
-    description: "Configure GitBook connector",
-    values: [
-      {
-        type: "text",
-        query: "Enter the space ID:",
-        label: "Space ID",
-        name: "space_id",
-        optional: false,
-        description:
-          "The ID of the GitBook space to index. This can be found in the URL " +
-          "of a page in the space. For example, if your URL looks like " +
-          "`https://app.gitbook.com/o/ccLx08XZ5wZ54LwdP9QU/s/8JkzVx8QCIGRrmxhGHU8/`, " +
-          "then your space ID is `8JkzVx8QCIGRrmxhGHU8`.",
-      },
-    ],
-    advanced_values: [],
-  },
   google_drive: {
     description: "Configure Google Drive connector",
     values: [

web/src/lib/connectors/credentials.ts

@@ -30,10 +30,6 @@ export interface GithubCredentialJson {
   github_access_token: string;
 }
 
-export interface GitbookCredentialJson {
-  gitbook_api_key: string;
-}
-
 export interface GitlabCredentialJson {
   gitlab_url: string;
   gitlab_access_token: string;
@@ -348,9 +344,6 @@ export const credentialTemplates: Record<ValidSources, any> = {
   // NOTE: These are Special Cases
   google_drive: { google_tokens: "" } as GoogleDriveCredentialJson,
   gmail: { google_tokens: "" } as GmailCredentialJson,
-  gitbook: {
-    gitbook_api_key: "",
-  } as GitbookCredentialJson,
 };
 
 export const credentialDisplayNames: Record<string, string> = {
@@ -481,10 +474,6 @@ export const credentialDisplayNames: Record<string, string> = {
 
   // Fireflies
   fireflies_api_key: "Fireflies API Key",
-
-  // GitBook
-  gitbook_space_id: "GitBook Space ID",
-  gitbook_api_key: "GitBook API Key",
 };
 
 export function getDisplayNameForCredentialKey(key: string): string {

web/src/lib/sources.ts

@@ -43,7 +43,6 @@ import {
   AirtableIcon,
   GlobeIcon2,
   FileIcon2,
-  GitbookIcon,
 } from "@/components/icons/icons";
 import { ValidSources } from "./types";
 import {
@@ -322,12 +321,6 @@ export const SOURCE_METADATA_MAP: SourceMap = {
     category: SourceCategory.Other,
     docs: "https://docs.onyx.app/connectors/airtable",
   },
-  gitbook: {
-    icon: GitbookIcon,
-    displayName: "GitBook",
-    category: SourceCategory.Wiki,
-    docs: "https://docs.onyx.app/connectors/gitbook",
-  },
   // currently used for the Internet Search tool docs, which is why
   // a globe is used
   not_applicable: {

web/src/lib/types.ts

@@ -350,7 +350,6 @@ export enum ValidSources {
   Fireflies = "fireflies",
   Egnyte = "egnyte",
   Airtable = "airtable",
-  Gitbook = "gitbook",
 }
 
 export const validAutoSyncSources = [