stashing

.github/workflows/pr-integration-tests.yml

@@ -94,19 +94,16 @@ jobs:
           cd deployment/docker_compose
           ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
           MULTI_TENANT=true \
-          AUTH_TYPE=cloud \
+          AUTH_TYPE=basic \
           REQUIRE_EMAIL_VERIFICATION=false \
           DISABLE_TELEMETRY=true \
           IMAGE_TAG=test \
-          DEV_MODE=true \
-          docker compose -f docker-compose.multitenant-dev.yml -p danswer-stack up -d
+          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
         id: start_docker_multi_tenant
 
       # In practice, `cloud` Auth type would require OAUTH credentials to be set.
       - name: Run Multi-Tenant Integration Tests
         run: |
-          echo "Waiting for 3 minutes to ensure API server is ready..."
-          sleep 180
           echo "Running integration tests..."
           docker run --rm --network danswer-stack_default \
             --name test-runner \
@@ -122,10 +119,6 @@ jobs:
             -e TEST_WEB_HOSTNAME=test-runner \
             -e AUTH_TYPE=cloud \
             -e MULTI_TENANT=true \
-            -e REQUIRE_EMAIL_VERIFICATION=false \
-            -e DISABLE_TELEMETRY=true \
-            -e IMAGE_TAG=test \
-            -e DEV_MODE=true \
             onyxdotapp/onyx-integration:test \
             /app/tests/integration/multitenant_tests
         continue-on-error: true
@@ -133,17 +126,17 @@ jobs:
 
       - name: Check multi-tenant test results
         run: |
-          if [ ${{ steps.run_multitenant_tests.outcome }} == 'failure' ]; then
-            echo "Multi-tenant integration tests failed. Exiting with error."
+          if [ ${{ steps.run_tests.outcome }} == 'failure' ]; then
+            echo "Integration tests failed. Exiting with error."
             exit 1
           else
-            echo "All multi-tenant integration tests passed successfully."
+            echo "All integration tests passed successfully."
           fi
 
       - name: Stop multi-tenant Docker containers
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.multitenant-dev.yml -p danswer-stack down -v
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
 
       - name: Start Docker containers
         run: |
@@ -223,30 +216,27 @@ jobs:
             echo "All integration tests passed successfully."
           fi
 
-      # ------------------------------------------------------------
-      # Always gather logs BEFORE "down":
-      - name: Dump API server logs
-        if: always()
+      # save before stopping the containers so the logs can be captured
+      - name: Save Docker logs
+        if: success() || failure()
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack logs --no-color api_server > $GITHUB_WORKSPACE/api_server.log || true
-
-      - name: Dump all-container logs (optional)
-        if: always()
-        run: |
-          cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack logs --no-color > $GITHUB_WORKSPACE/docker-compose.log || true
-
-      - name: Upload logs
-        if: always()
-        uses: actions/upload-artifact@v4
-        with:
-          name: docker-all-logs
-          path: ${{ github.workspace }}/docker-compose.log
-      # ------------------------------------------------------------
+          docker compose -f docker-compose.dev.yml -p danswer-stack logs > docker-compose.log
+          mv docker-compose.log ${{ github.workspace }}/docker-compose.log
+
+      - name: Stop Docker containers
+        run: |
+          cd deployment/docker_compose
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+
+      - name: Upload logs
+        if: success() || failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: docker-logs
+          path: ${{ github.workspace }}/docker-compose.log
 
       - name: Stop Docker containers
-        if: always()
         run: |
           cd deployment/docker_compose
           docker compose -f docker-compose.dev.yml -p danswer-stack down -v

backend/alembic/versions/4ee1287bd26a_add_multiple_slack_bot_support.py

@@ -5,6 +5,7 @@ Revises: 47e5bef3a1d7
 Create Date: 2024-11-06 13:15:53.302644
 
 """
+import logging
 from typing import cast
 from alembic import op
 import sqlalchemy as sa
@@ -19,8 +20,13 @@ down_revision = "47e5bef3a1d7"
 branch_labels: None = None
 depends_on: None = None
 
+# Configure logging
+logger = logging.getLogger("alembic.runtime.migration")
+logger.setLevel(logging.INFO)
+
 
 def upgrade() -> None:
+    logger.info(f"{revision}: create_table: slack_bot")
     # Create new slack_bot table
     op.create_table(
         "slack_bot",
@@ -57,6 +63,7 @@ def upgrade() -> None:
     )
 
     # Handle existing Slack bot tokens first
+    logger.info(f"{revision}: Checking for existing Slack bot.")
     bot_token = None
     app_token = None
     first_row_id = None
@@ -64,12 +71,15 @@ def upgrade() -> None:
     try:
         tokens = cast(dict, get_kv_store().load("slack_bot_tokens_config_key"))
     except Exception:
+        logger.warning("No existing Slack bot tokens found.")
         tokens = {}
 
     bot_token = tokens.get("bot_token")
     app_token = tokens.get("app_token")
 
     if bot_token and app_token:
+        logger.info(f"{revision}: Found bot and app tokens.")
+
         session = Session(bind=op.get_bind())
         new_slack_bot = SlackBot(
             name="Slack Bot (Migrated)",
@@ -160,9 +170,10 @@ def upgrade() -> None:
     # Clean up old tokens if they existed
     try:
         if bot_token and app_token:
+            logger.info(f"{revision}: Removing old bot and app tokens.")
             get_kv_store().delete("slack_bot_tokens_config_key")
     except Exception:
-        pass
+        logger.warning("tried to delete tokens in dynamic config but failed")
     # Rename the table
     op.rename_table(
         "slack_bot_config__standard_answer_category",
@@ -179,6 +190,8 @@ def upgrade() -> None:
     # Drop the table with CASCADE to handle dependent objects
     op.execute("DROP TABLE slack_bot_config CASCADE")
 
+    logger.info(f"{revision}: Migration complete.")
+
 
 def downgrade() -> None:
     # Recreate the old slack_bot_config table
@@ -260,7 +273,7 @@ def downgrade() -> None:
             }
             get_kv_store().store("slack_bot_tokens_config_key", tokens)
     except Exception:
-        pass
+        logger.warning("Failed to save tokens back to KV store")
 
     # Drop the new tables in reverse order
     op.drop_table("slack_channel_config")

backend/ee/onyx/server/middleware/tenant_tracking.py

@@ -64,7 +64,6 @@ async def _get_tenant_id_from_request(
 
     try:
         # Look up token data in Redis
-
         token_data = await retrieve_auth_token_data_from_redis(request)
 
         if not token_data:

backend/ee/onyx/server/tenants/provisioning.py

@@ -24,7 +24,6 @@ from ee.onyx.server.tenants.user_mapping import get_tenant_id_for_email
 from ee.onyx.server.tenants.user_mapping import user_owns_a_tenant
 from onyx.auth.users import exceptions
 from onyx.configs.app_configs import CONTROL_PLANE_API_BASE_URL
-from onyx.configs.app_configs import DEV_MODE
 from onyx.configs.constants import MilestoneRecordType
 from onyx.db.engine import get_session_with_tenant
 from onyx.db.engine import get_sqlalchemy_engine
@@ -86,8 +85,7 @@ async def create_tenant(email: str, referral_source: str | None = None) -> str:
         # Provision tenant on data plane
         await provision_tenant(tenant_id, email)
         # Notify control plane
-        if not DEV_MODE:
-            await notify_control_plane(tenant_id, email, referral_source)
+        await notify_control_plane(tenant_id, email, referral_source)
     except Exception as e:
         logger.error(f"Tenant provisioning failed: {e}")
         await rollback_tenant_provisioning(tenant_id)

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 (
@@ -12,39 +12,12 @@ 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 AgentErrorLoggingFormat
-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
-
-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'",
-)
 
 
 def check_sub_answer(
@@ -80,46 +53,14 @@ def check_sub_answer(
 
     graph_config = cast(GraphConfig, config["metadata"]["config"])
     fast_llm = graph_config.tooling.fast_llm
-    agent_error: AgentErrorLoggingFormat | 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,
         )
+    )
 
-    except LLMTimeoutError:
-        agent_error = AgentErrorLoggingFormat(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - check sub answer")
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLoggingFormat(
-            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 - check sub answer")
-
-    if agent_error:
-        answer_quality = True
-        log_result = agent_error.error_result
-
-    else:
-        if response:
-            quality_str: str = cast(str, response.content)
-            answer_quality = binary_string_test(
-                text=quality_str, positive_value=AGENT_POSITIVE_VALUE_STR
-            )
-
-        else:
-            answer_quality = True
-            quality_str = "yes - because LLM error"
-
-        log_result = f"Answer quality: {quality_str}"
+    quality_str: str = merge_message_runs(response, chunk_separator="")[0].content
+    answer_quality = "yes" in quality_str.lower()
 
     return SubQuestionAnswerCheckUpdate(
         answer_quality=answer_quality,
@@ -128,7 +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

@@ -16,20 +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.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 AgentErrorLoggingFormat
-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,
@@ -44,20 +30,11 @@ 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
 
 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.",
-)
-
 
 def generate_sub_answer(
     state: AnswerQuestionState,
@@ -80,8 +57,6 @@ def generate_sub_answer(
 
     if len(context_docs) == 0:
         answer_str = NO_RECOVERED_DOCS
-        cited_documents: list = []
-        log_results = "No documents retrieved"
         write_custom_event(
             "sub_answers",
             AgentAnswerPiece(
@@ -104,67 +79,41 @@ def generate_sub_answer(
 
         response: list[str | list[str | dict[str, Any]]] = []
         dispatch_timings: list[float] = []
-
-        agent_error: AgentErrorLoggingFormat | 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 = AgentErrorLoggingFormat(
-                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 = AgentErrorLoggingFormat(
-                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,
@@ -182,7 +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

@@ -26,18 +26,7 @@ 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.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 AgentErrorLoggingFormat
 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_sections,
 )
@@ -53,16 +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_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.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_W_SUB_QUESTIONS,
+)
 from onyx.prompts.agent_search import (
     INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS,
 )
@@ -72,12 +57,6 @@ 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
 
-_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.",
-)
-
 
 def generate_initial_answer(
     state: SubQuestionRetrievalState,
@@ -245,82 +224,30 @@ def generate_initial_answer(
 
         streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
         dispatch_timings: list[float] = []
-
-        agent_error: AgentErrorLoggingFormat | 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 = AgentErrorLoggingFormat(
-                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 = AgentErrorLoggingFormat(
-                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,
-                error=AgentErrorLoggingFormat(
-                    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

@@ -25,7 +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

@@ -23,18 +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.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 AgentErrorLoggingFormat
-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,
@@ -45,11 +33,6 @@ 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,
 )
@@ -60,12 +43,6 @@ from onyx.utils.logger import setup_logger
 
 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.",
-)
-
 
 def decompose_orig_question(
     state: SubQuestionRetrievalState,
@@ -135,35 +112,11 @@ def decompose_orig_question(
     )
 
     # dispatches custom events for subquestion tokens, adding in subquestion ids.
-
-    agent_error: AgentErrorLoggingFormat | None = None
-    streamed_tokens: list[BaseMessage_Content] = []
-
-    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),
-        )
-    except LLMTimeoutError as e:
-        agent_error = AgentErrorLoggingFormat(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - decompose orig question")
-        raise e  # fail loudly on this critical step
-    except LLMRateLimitError as e:
-        agent_error = AgentErrorLoggingFormat(
-            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 - decompose orig question")
-        raise e
+    streamed_tokens = dispatch_separated(
+        model.stream(msg),
+        dispatch_subquestion(0, writer),
+        sep_callback=dispatch_subquestion_sep(0, writer),
+    )
 
     stop_event = StreamStopInfo(
         stop_reason=StreamStopReason.FINISHED,
@@ -172,19 +125,19 @@ def decompose_orig_question(
     )
     write_custom_event("stream_finished", stop_event, writer)
 
-    if agent_error:
-        initial_sub_questions: list[str] = []
-        log_result = agent_error.error_result
-    else:
-        deomposition_response = merge_content(*streamed_tokens)
+    deomposition_response = merge_content(*streamed_tokens)
 
-        list_of_subqs = cast(str, deomposition_response).split("\n")
+    # this call should only return strings. Commenting out for efficiency
+    # assert [type(tok) == str for tok in streamed_tokens]
 
-        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"
+    # 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")
+
+    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,
@@ -198,7 +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

@@ -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
@@ -11,37 +10,14 @@ 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.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 AgentErrorLoggingFormat
-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
-
-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.",
-)
 
 
 def compare_answers(
@@ -64,46 +40,15 @@ def compare_answers(
 
     msg = [HumanMessage(content=compare_answers_prompt)]
 
-    agent_error: AgentErrorLoggingFormat | 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 = AgentErrorLoggingFormat(
-            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 = AgentErrorLoggingFormat(
-            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 = (
-            isinstance(resp.content, str) and "yes" in resp.content.lower()
-        )
-        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",
@@ -120,7 +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 AgentErrorLoggingFormat
-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,
@@ -42,25 +30,10 @@ 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,
 )
 from onyx.tools.models import ToolCallKickoff
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-_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.",
-)
 
 
 def create_refined_sub_questions(
@@ -123,65 +96,29 @@ def create_refined_sub_questions(
     # Grader
     model = graph_config.tooling.fast_llm
 
-    agent_error: AgentErrorLoggingFormat | 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 = AgentErrorLoggingFormat(
-            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 = AgentErrorLoggingFormat(
-            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,
@@ -191,7 +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

@@ -26,19 +26,6 @@ def decide_refinement_need(
 
     decision = True  # TODO: just for current testing purposes
 
-    if state.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

@@ -21,9 +21,6 @@ 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
@@ -84,7 +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

@@ -11,6 +11,7 @@ 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,
@@ -22,18 +23,7 @@ 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.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 AgentErrorLoggingFormat
 from onyx.agents.agent_search.shared_graph_utils.models import InferenceSection
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
 from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
 from onyx.agents.agent_search.shared_graph_utils.operators import (
     dedup_inference_sections,
@@ -53,14 +43,8 @@ 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_MIN_ORIG_QUESTION_DOCS
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_OVERRIDE_LLM_REFINED_ANSWER_GENERATION,
-)
-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,
 )
@@ -72,15 +56,6 @@ 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.logger import setup_logger
-
-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.",
-)
 
 
 def generate_refined_answer(
@@ -256,80 +231,28 @@ def generate_refined_answer(
 
     streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
     dispatch_timings: list[float] = []
-    agent_error: AgentErrorLoggingFormat | 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 = AgentErrorLoggingFormat(
-            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 = AgentErrorLoggingFormat(
-            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)}"
@@ -343,6 +266,49 @@ def generate_refined_answer(
         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 = (

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 AgentErrorLoggingFormat
 from onyx.agents.agent_search.shared_graph_utils.models import (
     EntityRelationshipTermExtraction,
 )
@@ -77,7 +76,6 @@ class InitialAnswerUpdate(LoggerUpdate):
     """
 
     initial_answer: str | None = None
-    error: AgentErrorLoggingFormat | 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
-    error: AgentErrorLoggingFormat | 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

@@ -16,40 +16,14 @@ 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 AgentErrorLoggingFormat
-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
-
-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.",
-)
 
 
 def expand_queries(
@@ -80,43 +54,13 @@ def expand_queries(
         )
     ]
 
-    agent_error: AgentErrorLoggingFormat | None = None
-    llm_response_list: list[BaseMessage_Content] = []
+    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),
-        )
-    except LLMTimeoutError:
-        agent_error = AgentErrorLoggingFormat(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - expand queries")
+    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
 
-    except LLMRateLimitError:
-        agent_error = AgentErrorLoggingFormat(
-            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")
-    # use subquestion as query if query generation fails
-    if agent_error:
-        llm_response = ""
-        rewritten_queries = [question]
-        log_result = agent_error.error_result
-    else:
-        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)}"
+    rewritten_queries = llm_response.split("\n")
 
     return QueryExpansionUpdate(
         expanded_queries=rewritten_queries,
@@ -125,7 +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/verify_documents.py

@@ -1,6 +1,5 @@
 from typing import cast
 
-from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
 from langchain_core.runnables.config import RunnableConfig
 
@@ -11,41 +10,12 @@ 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_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 AgentErrorLoggingFormat
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-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
-
-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'",
-)
 
 
 def verify_documents(
@@ -56,7 +26,7 @@ def verify_documents(
 
     Args:
         state (DocVerificationInput): The current state
-        config (RunnableConfig): Configuration containing AgentSearchConfig
+        config (RunnableConfig): Configuration containing ProSearchConfig
 
     Updates:
         verified_documents: list[InferenceSection]
@@ -81,42 +51,11 @@ def verify_documents(
         )
     ]
 
-    agent_error: AgentErrorLoggingFormat | None = None
-    response: BaseMessage | None = None
+    response = fast_llm.invoke(msg)
 
-    try:
-        response = fast_llm.invoke(
-            msg, timeout_override=AGENT_TIMEOUT_OVERRIDE_LLM_DOCUMENT_VERIFICATION
-        )
-
-    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.
-        agent_error = AgentErrorLoggingFormat(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        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.
-        agent_error = AgentErrorLoggingFormat(
-            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 - verify documents")
-
-    if agent_error or response is None:
-        verified_documents = [retrieved_document_to_verify]
-
-    else:
-        verified_documents = []
-        if isinstance(response.content, str) and binary_string_test(
-            text=response.content, positive_value=AGENT_POSITIVE_VALUE_STR
-        ):
-            verified_documents.append(retrieved_document_to_verify)
+    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,

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 Nonoe 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
@@ -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(
@@ -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

@@ -150,17 +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()

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

@@ -1,17 +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"
-
-
-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 AgentErrorLoggingFormat(BaseModel):
-    error_message: str
-    error_type: str
-    error_result: str | None = None
-
-
 class RefinedAgentStats(BaseModel):
     revision_doc_efficiency: float | None
     revision_question_efficiency: float | None
@@ -134,12 +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"
-
-
-BaseMessage_Content = str | list[str | dict[str, Any]]

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

@@ -20,7 +20,6 @@ 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,
 )
@@ -35,9 +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
@@ -50,8 +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,
@@ -71,9 +65,8 @@ 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
@@ -379,24 +372,8 @@ 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
 
 

backend/onyx/configs/agent_configs.py

@@ -13,21 +13,6 @@ 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 = 3  # in seconds
-AGENT_DEFAULT_TIMEOUT_OVERRIDE_LLM_SUBQUESTION_GENERATION = 8  # 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_COMPARE_ANSWERS = 8  # in seconds
-
 #####
 # Agent Configs
 #####
@@ -92,76 +77,4 @@ AGENT_MAX_STATIC_HISTORY_WORD_LENGTH = int(
     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
-
-
 GRAPH_VERSION_NAME: str = "a"

backend/onyx/configs/constants.py

@@ -263,11 +263,6 @@ class PostgresAdvisoryLocks(Enum):
 
 
 class OnyxCeleryQueues:
-    # "celery" is the default queue defined by celery and also the queue
-    # we are running in the primary worker to run system tasks
-    # Tasks running in this queue should be designed specifically to run quickly
-    PRIMARY = "celery"
-
     # Light queue
     VESPA_METADATA_SYNC = "vespa_metadata_sync"
     DOC_PERMISSIONS_UPSERT = "doc_permissions_upsert"

backend/onyx/connectors/linear/connector.py

@@ -91,7 +91,6 @@ class LinearConnector(LoadConnector, PollConnector, OAuthConnector):
             f"&response_type=code"
             f"&scope=read"
             f"&state={state}"
-            f"&prompt=consent"  # prompts user for access; allows choosing workspace
         )
 
     @classmethod

backend/onyx/llm/chat_llm.py

@@ -50,18 +50,6 @@ 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"
@@ -392,7 +380,6 @@ 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
@@ -418,7 +405,7 @@ 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
@@ -437,12 +424,6 @@ 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
@@ -463,7 +444,6 @@ 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()
@@ -471,12 +451,7 @@ 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]
@@ -494,31 +469,19 @@ 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/prompts/agent_search.py

@@ -5,6 +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:

backend/onyx/server/documents/cc_pair.py

@@ -22,8 +22,6 @@ from onyx.background.celery.tasks.pruning.tasks import (
     try_creating_prune_generator_task,
 )
 from onyx.background.celery.versioned_apps.primary import app as primary_app
-from onyx.configs.constants import OnyxCeleryPriority
-from onyx.configs.constants import OnyxCeleryTask
 from onyx.db.connector_credential_pair import add_credential_to_connector
 from onyx.db.connector_credential_pair import (
     get_connector_credential_pair_from_id_for_user,
@@ -230,13 +228,6 @@ def update_cc_pair_status(
 
     db_session.commit()
 
-    # this speeds up the start of indexing by firing the check immediately
-    primary_app.send_task(
-        OnyxCeleryTask.CHECK_FOR_INDEXING,
-        kwargs=dict(tenant_id=tenant_id),
-        priority=OnyxCeleryPriority.HIGH,
-    )
-
     return JSONResponse(
         status_code=HTTPStatus.OK, content={"message": str(HTTPStatus.OK)}
     )
@@ -549,14 +540,7 @@ def associate_credential_to_connector(
     metadata: ConnectorCredentialPairMetadata,
     user: User | None = Depends(current_curator_or_admin_user),
     db_session: Session = Depends(get_session),
-    tenant_id: str = Depends(get_current_tenant_id),
 ) -> StatusResponse[int]:
-    """NOTE(rkuo): internally discussed and the consensus is this endpoint
-    and create_connector_with_mock_credential should be combined.
-
-    The intent of this endpoint is to handle connectors that actually need credentials.
-    """
-
     fetch_ee_implementation_or_noop(
         "onyx.db.user_group", "validate_object_creation_for_user", None
     )(
@@ -579,18 +563,6 @@ def associate_credential_to_connector(
             groups=metadata.groups,
         )
 
-        # trigger indexing immediately
-        primary_app.send_task(
-            OnyxCeleryTask.CHECK_FOR_INDEXING,
-            priority=OnyxCeleryPriority.HIGH,
-            kwargs={"tenant_id": tenant_id},
-        )
-
-        logger.info(
-            f"associate_credential_to_connector - running check_for_indexing: "
-            f"cc_pair={response.data}"
-        )
-
         return response
     except IntegrityError as e:
         logger.error(f"IntegrityError: {e}")

backend/onyx/server/documents/connector.py

@@ -804,14 +804,6 @@ def create_connector_with_mock_credential(
     db_session: Session = Depends(get_session),
     tenant_id: str = Depends(get_current_tenant_id),
 ) -> StatusResponse:
-    """NOTE(rkuo): internally discussed and the consensus is this endpoint
-    and associate_credential_to_connector should be combined.
-
-    The intent of this endpoint is to handle connectors that don't need credentials,
-    AKA web, file, etc ... but there isn't any reason a single endpoint couldn't
-    server this purpose.
-    """
-
     fetch_ee_implementation_or_noop(
         "onyx.db.user_group", "validate_object_creation_for_user", None
     )(
@@ -849,18 +841,6 @@ def create_connector_with_mock_credential(
             groups=connector_data.groups,
         )
 
-        # trigger indexing immediately
-        primary_app.send_task(
-            OnyxCeleryTask.CHECK_FOR_INDEXING,
-            priority=OnyxCeleryPriority.HIGH,
-            kwargs={"tenant_id": tenant_id},
-        )
-
-        logger.info(
-            f"create_connector_with_mock_credential - running check_for_indexing: "
-            f"cc_pair={response.data}"
-        )
-
         create_milestone_and_report(
             user=user,
             distinct_id=user.email if user else tenant_id or "N/A",
@@ -1025,8 +1005,6 @@ def connector_run_once(
         kwargs={"tenant_id": tenant_id},
     )
 
-    logger.info("connector_run_once - running check_for_indexing")
-
     msg = f"Marked {num_triggers} index attempts with indexing triggers."
     return StatusResponse(
         success=True,

backend/onyx/server/documents/standard_oauth.py

@@ -179,10 +179,12 @@ def oauth_callback(
         db_session=db_session,
     )
 
-    # TODO: use a library for url handling
-    sep = "&" if "?" in desired_return_url else "?"
     return CallbackResponse(
-        redirect_url=f"{desired_return_url}{sep}credentialId={credential.id}"
+        redirect_url=(
+            f"{desired_return_url}?credentialId={credential.id}"
+            if "?" not in desired_return_url
+            else f"{desired_return_url}&credentialId={credential.id}"
+        )
     )
 
 

backend/onyx/server/features/document_set/api.py

@@ -6,15 +6,11 @@ from sqlalchemy.orm import Session
 
 from onyx.auth.users import current_curator_or_admin_user
 from onyx.auth.users import current_user
-from onyx.background.celery.versioned_apps.primary import app as primary_app
-from onyx.configs.constants import OnyxCeleryPriority
-from onyx.configs.constants import OnyxCeleryTask
 from onyx.db.document_set import check_document_sets_are_public
 from onyx.db.document_set import fetch_all_document_sets_for_user
 from onyx.db.document_set import insert_document_set
 from onyx.db.document_set import mark_document_set_as_to_be_deleted
 from onyx.db.document_set import update_document_set
-from onyx.db.engine import get_current_tenant_id
 from onyx.db.engine import get_session
 from onyx.db.models import User
 from onyx.server.features.document_set.models import CheckDocSetPublicRequest
@@ -33,7 +29,6 @@ def create_document_set(
     document_set_creation_request: DocumentSetCreationRequest,
     user: User = Depends(current_curator_or_admin_user),
     db_session: Session = Depends(get_session),
-    tenant_id: str = Depends(get_current_tenant_id),
 ) -> int:
     fetch_ee_implementation_or_noop(
         "onyx.db.user_group", "validate_object_creation_for_user", None
@@ -51,13 +46,6 @@ def create_document_set(
         )
     except Exception as e:
         raise HTTPException(status_code=400, detail=str(e))
-
-    primary_app.send_task(
-        OnyxCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
-        kwargs={"tenant_id": tenant_id},
-        priority=OnyxCeleryPriority.HIGH,
-    )
-
     return document_set_db_model.id
 
 
@@ -66,7 +54,6 @@ def patch_document_set(
     document_set_update_request: DocumentSetUpdateRequest,
     user: User = Depends(current_curator_or_admin_user),
     db_session: Session = Depends(get_session),
-    tenant_id: str = Depends(get_current_tenant_id),
 ) -> None:
     fetch_ee_implementation_or_noop(
         "onyx.db.user_group", "validate_object_creation_for_user", None
@@ -85,19 +72,12 @@ def patch_document_set(
     except Exception as e:
         raise HTTPException(status_code=400, detail=str(e))
 
-    primary_app.send_task(
-        OnyxCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
-        kwargs={"tenant_id": tenant_id},
-        priority=OnyxCeleryPriority.HIGH,
-    )
-
 
 @router.delete("/admin/document-set/{document_set_id}")
 def delete_document_set(
     document_set_id: int,
     user: User = Depends(current_curator_or_admin_user),
     db_session: Session = Depends(get_session),
-    tenant_id: str = Depends(get_current_tenant_id),
 ) -> None:
     try:
         mark_document_set_as_to_be_deleted(
@@ -108,12 +88,6 @@ def delete_document_set(
     except Exception as e:
         raise HTTPException(status_code=400, detail=str(e))
 
-    primary_app.send_task(
-        OnyxCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
-        kwargs={"tenant_id": tenant_id},
-        priority=OnyxCeleryPriority.HIGH,
-    )
-
 
 """Endpoints for non-admins"""
 

backend/onyx/server/manage/administrative.py

@@ -197,11 +197,6 @@ def create_deletion_attempt_for_connector_id(
         kwargs={"tenant_id": tenant_id},
     )
 
-    logger.info(
-        f"create_deletion_attempt_for_connector_id - running check_for_connector_deletion: "
-        f"cc_pair={cc_pair.id}"
-    )
-
     if cc_pair.connector.source == DocumentSource.FILE:
         connector = cc_pair.connector
         file_store = get_default_file_store(db_session)

backend/onyx/server/manage/users.py

@@ -34,7 +34,6 @@ from onyx.auth.users import current_curator_or_admin_user
 from onyx.auth.users import current_user
 from onyx.auth.users import optional_user
 from onyx.configs.app_configs import AUTH_TYPE
-from onyx.configs.app_configs import DEV_MODE
 from onyx.configs.app_configs import ENABLE_EMAIL_INVITES
 from onyx.configs.app_configs import SESSION_EXPIRE_TIME_SECONDS
 from onyx.configs.app_configs import VALID_EMAIL_DOMAINS
@@ -287,7 +286,7 @@ def bulk_invite_users(
             detail=f"Invalid email address: {email} - {str(e)}",
         )
 
-    if MULTI_TENANT and not DEV_MODE:
+    if MULTI_TENANT:
         try:
             fetch_ee_implementation_or_noop(
                 "onyx.server.tenants.provisioning", "add_users_to_tenant", None

backend/tests/integration/Dockerfile

@@ -70,8 +70,8 @@ COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf
 # Set up application files
 COPY ./onyx /app/onyx
 COPY ./shared_configs /app/shared_configs
-COPY ./alembic_tenants /app/alembic_tenants
 COPY ./alembic /app/alembic
+COPY ./alembic_tenants /app/alembic_tenants
 COPY ./alembic.ini /app/alembic.ini
 COPY ./pytest.ini /app/pytest.ini
 COPY supervisord.conf /usr/etc/supervisord.conf

backend/tests/integration/common_utils/managers/tenant.py

@@ -24,6 +24,35 @@ def generate_auth_token() -> str:
 
 
 class TenantManager:
+    @staticmethod
+    def create(
+        tenant_id: str | None = None,
+        initial_admin_email: str | None = None,
+        referral_source: str | None = None,
+    ) -> dict[str, str]:
+        body = {
+            "tenant_id": tenant_id,
+            "initial_admin_email": initial_admin_email,
+            "referral_source": referral_source,
+        }
+
+        token = generate_auth_token()
+        headers = {
+            "Authorization": f"Bearer {token}",
+            "X-API-KEY": "",
+            "Content-Type": "application/json",
+        }
+
+        response = requests.post(
+            url=f"{API_SERVER_URL}/tenants/create",
+            json=body,
+            headers=headers,
+        )
+
+        response.raise_for_status()
+
+        return response.json()
+
     @staticmethod
     def get_all_users(
         user_performing_action: DATestUser | None = None,

backend/tests/integration/common_utils/managers/user.py

@@ -92,7 +92,6 @@ class UserManager:
 
         # Set cookies in the headers
         test_user.headers["Cookie"] = f"fastapiusersauth={session_cookie}; "
-        test_user.cookies = {"fastapiusersauth": session_cookie}
         return test_user
 
     @staticmethod
@@ -103,7 +102,6 @@ class UserManager:
         response = requests.get(
             url=f"{API_SERVER_URL}/me",
             headers=user_to_verify.headers,
-            cookies=user_to_verify.cookies,
         )
 
         if user_to_verify.is_active is False:

backend/tests/integration/common_utils/reset.py

@@ -242,18 +242,6 @@ def reset_postgres_multitenant() -> None:
         schema_name = schema[0]
         cur.execute(f'DROP SCHEMA "{schema_name}" CASCADE')
 
-    # Drop tables in the public schema
-    cur.execute(
-        """
-        SELECT tablename FROM pg_tables
-        WHERE schemaname = 'public'
-        """
-    )
-    public_tables = cur.fetchall()
-    for table in public_tables:
-        table_name = table[0]
-        cur.execute(f'DROP TABLE IF EXISTS public."{table_name}" CASCADE')
-
     cur.close()
     conn.close()
 

backend/tests/integration/common_utils/test_models.py

@@ -44,7 +44,6 @@ class DATestUser(BaseModel):
     headers: dict
     role: UserRole
     is_active: bool
-    cookies: dict = {}
 
 
 class DATestPersonaLabel(BaseModel):

backend/tests/integration/multitenant_tests/syncing/test_search_permissions.py

@@ -4,6 +4,7 @@ from tests.integration.common_utils.managers.cc_pair import CCPairManager
 from tests.integration.common_utils.managers.chat import ChatSessionManager
 from tests.integration.common_utils.managers.document import DocumentManager
 from tests.integration.common_utils.managers.llm_provider import LLMProviderManager
+from tests.integration.common_utils.managers.tenant import TenantManager
 from tests.integration.common_utils.managers.user import UserManager
 from tests.integration.common_utils.test_models import DATestAPIKey
 from tests.integration.common_utils.test_models import DATestCCPair
@@ -12,28 +13,25 @@ from tests.integration.common_utils.test_models import DATestUser
 
 
 def test_multi_tenant_access_control(reset_multitenant: None) -> None:
-    # Creating an admin user (first user created is automatically an admin and also proviions the tenant
-    admin_user1: DATestUser = UserManager.create(
-        email="admin@onyx-test.com",
-    )
-
-    assert UserManager.is_role(admin_user1, UserRole.ADMIN)
+    # Create Tenant 1 and its Admin User
+    TenantManager.create("tenant_dev1", "test1@test.com", "Data Plane Registration")
+    test_user1: DATestUser = UserManager.create(name="test1", email="test1@test.com")
+    assert UserManager.is_role(test_user1, UserRole.ADMIN)
 
     # Create Tenant 2 and its Admin User
-    admin_user2: DATestUser = UserManager.create(
-        email="admin2@onyx-test.com",
-    )
-    assert UserManager.is_role(admin_user2, UserRole.ADMIN)
+    TenantManager.create("tenant_dev2", "test2@test.com", "Data Plane Registration")
+    test_user2: DATestUser = UserManager.create(name="test2", email="test2@test.com")
+    assert UserManager.is_role(test_user2, UserRole.ADMIN)
 
     # Create connectors for Tenant 1
     cc_pair_1: DATestCCPair = CCPairManager.create_from_scratch(
-        user_performing_action=admin_user1,
+        user_performing_action=test_user1,
     )
     api_key_1: DATestAPIKey = APIKeyManager.create(
-        user_performing_action=admin_user1,
+        user_performing_action=test_user1,
     )
-    api_key_1.headers.update(admin_user1.headers)
-    LLMProviderManager.create(user_performing_action=admin_user1)
+    api_key_1.headers.update(test_user1.headers)
+    LLMProviderManager.create(user_performing_action=test_user1)
 
     # Seed documents for Tenant 1
     cc_pair_1.documents = []
@@ -51,13 +49,13 @@ def test_multi_tenant_access_control(reset_multitenant: None) -> None:
 
     # Create connectors for Tenant 2
     cc_pair_2: DATestCCPair = CCPairManager.create_from_scratch(
-        user_performing_action=admin_user2,
+        user_performing_action=test_user2,
     )
     api_key_2: DATestAPIKey = APIKeyManager.create(
-        user_performing_action=admin_user2,
+        user_performing_action=test_user2,
     )
-    api_key_2.headers.update(admin_user2.headers)
-    LLMProviderManager.create(user_performing_action=admin_user2)
+    api_key_2.headers.update(test_user2.headers)
+    LLMProviderManager.create(user_performing_action=test_user2)
 
     # Seed documents for Tenant 2
     cc_pair_2.documents = []
@@ -78,17 +76,17 @@ def test_multi_tenant_access_control(reset_multitenant: None) -> None:
 
     # Create chat sessions for each user
     chat_session1: DATestChatSession = ChatSessionManager.create(
-        user_performing_action=admin_user1
+        user_performing_action=test_user1
     )
     chat_session2: DATestChatSession = ChatSessionManager.create(
-        user_performing_action=admin_user2
+        user_performing_action=test_user2
     )
 
     # User 1 sends a message and gets a response
     response1 = ChatSessionManager.send_message(
         chat_session_id=chat_session1.id,
         message="What is in Tenant 1's documents?",
-        user_performing_action=admin_user1,
+        user_performing_action=test_user1,
     )
     # Assert that the search tool was used
     assert response1.tool_name == "run_search"
@@ -102,16 +100,14 @@ def test_multi_tenant_access_control(reset_multitenant: None) -> None:
     ), "Tenant 2 document IDs should not be in the response"
 
     # Assert that the contents are correct
-    assert any(
-        doc["content"] == "Tenant 1 Document Content"
-        for doc in response1.tool_result or []
-    ), "Tenant 1 Document Content not found in any document"
+    for doc in response1.tool_result or []:
+        assert doc["content"] == "Tenant 1 Document Content"
 
     # User 2 sends a message and gets a response
     response2 = ChatSessionManager.send_message(
         chat_session_id=chat_session2.id,
         message="What is in Tenant 2's documents?",
-        user_performing_action=admin_user2,
+        user_performing_action=test_user2,
     )
     # Assert that the search tool was used
     assert response2.tool_name == "run_search"
@@ -123,18 +119,15 @@ def test_multi_tenant_access_control(reset_multitenant: None) -> None:
     assert not response_doc_ids.intersection(
         tenant1_doc_ids
     ), "Tenant 1 document IDs should not be in the response"
-
     # Assert that the contents are correct
-    assert any(
-        doc["content"] == "Tenant 2 Document Content"
-        for doc in response2.tool_result or []
-    ), "Tenant 2 Document Content not found in any document"
+    for doc in response2.tool_result or []:
+        assert doc["content"] == "Tenant 2 Document Content"
 
     # User 1 tries to access Tenant 2's documents
     response_cross = ChatSessionManager.send_message(
         chat_session_id=chat_session1.id,
         message="What is in Tenant 2's documents?",
-        user_performing_action=admin_user1,
+        user_performing_action=test_user1,
     )
     # Assert that the search tool was used
     assert response_cross.tool_name == "run_search"
@@ -147,7 +140,7 @@ def test_multi_tenant_access_control(reset_multitenant: None) -> None:
     response_cross2 = ChatSessionManager.send_message(
         chat_session_id=chat_session2.id,
         message="What is in Tenant 1's documents?",
-        user_performing_action=admin_user2,
+        user_performing_action=test_user2,
     )
     # Assert that the search tool was used
     assert response_cross2.tool_name == "run_search"

backend/tests/integration/multitenant_tests/tenants/test_tenant_creation.py

@@ -4,12 +4,14 @@ from onyx.db.models import UserRole
 from tests.integration.common_utils.managers.cc_pair import CCPairManager
 from tests.integration.common_utils.managers.connector import ConnectorManager
 from tests.integration.common_utils.managers.credential import CredentialManager
+from tests.integration.common_utils.managers.tenant import TenantManager
 from tests.integration.common_utils.managers.user import UserManager
 from tests.integration.common_utils.test_models import DATestUser
 
 
 # Test flow from creating tenant to registering as a user
 def test_tenant_creation(reset_multitenant: None) -> None:
+    TenantManager.create("tenant_dev", "test@test.com", "Data Plane Registration")
     test_user: DATestUser = UserManager.create(name="test", email="test@test.com")
 
     assert UserManager.is_role(test_user, UserRole.ADMIN)

backend/tests/integration/tests/index_attempt/test_index_attempt_pagination.py

@@ -1,23 +1,23 @@
-import time
 from datetime import datetime
 
 from onyx.db.models import IndexingStatus
 from tests.integration.common_utils.managers.cc_pair import CCPairManager
 from tests.integration.common_utils.managers.index_attempt import IndexAttemptManager
 from tests.integration.common_utils.managers.user import UserManager
+from tests.integration.common_utils.test_models import DATestIndexAttempt
 from tests.integration.common_utils.test_models import DATestUser
 
 
 def _verify_index_attempt_pagination(
     cc_pair_id: int,
-    index_attempt_ids: list[int],
+    index_attempts: list[DATestIndexAttempt],
     page_size: int = 5,
     user_performing_action: DATestUser | None = None,
 ) -> None:
     retrieved_attempts: list[int] = []
     last_time_started = None  # Track the last time_started seen
 
-    for i in range(0, len(index_attempt_ids), page_size):
+    for i in range(0, len(index_attempts), page_size):
         paginated_result = IndexAttemptManager.get_index_attempt_page(
             cc_pair_id=cc_pair_id,
             page=(i // page_size),
@@ -26,9 +26,9 @@ def _verify_index_attempt_pagination(
         )
 
         # Verify that the total items is equal to the length of the index attempts list
-        assert paginated_result.total_items == len(index_attempt_ids)
+        assert paginated_result.total_items == len(index_attempts)
         # Verify that the number of items in the page is equal to the page size
-        assert len(paginated_result.items) == min(page_size, len(index_attempt_ids) - i)
+        assert len(paginated_result.items) == min(page_size, len(index_attempts) - i)
 
         # Verify time ordering within the page (descending order)
         for attempt in paginated_result.items:
@@ -42,7 +42,7 @@ def _verify_index_attempt_pagination(
         retrieved_attempts.extend([attempt.id for attempt in paginated_result.items])
 
     # Create a set of all the expected index attempt IDs
-    all_expected_attempts = set(index_attempt_ids)
+    all_expected_attempts = set(attempt.id for attempt in index_attempts)
     # Create a set of all the retrieved index attempt IDs
     all_retrieved_attempts = set(retrieved_attempts)
 
@@ -51,9 +51,6 @@ def _verify_index_attempt_pagination(
 
 
 def test_index_attempt_pagination(reset: None) -> None:
-    MAX_WAIT = 60
-    all_attempt_ids: list[int] = []
-
     # Create an admin user to perform actions
     user_performing_action: DATestUser = UserManager.create(
         name="admin_performing_action",
@@ -65,49 +62,20 @@ def test_index_attempt_pagination(reset: None) -> None:
         user_performing_action=user_performing_action,
     )
 
-    # Creating a CC pair will create an index attempt as well. wait for it.
-    start = time.monotonic()
-    while True:
-        paginated_result = IndexAttemptManager.get_index_attempt_page(
-            cc_pair_id=cc_pair.id,
-            page=0,
-            page_size=5,
-            user_performing_action=user_performing_action,
-        )
-
-        if paginated_result.total_items == 1:
-            all_attempt_ids.append(paginated_result.items[0].id)
-            print("Initial index attempt from cc_pair creation detected. Continuing...")
-            break
-
-        elapsed = time.monotonic() - start
-        if elapsed > MAX_WAIT:
-            raise TimeoutError(
-                f"Initial index attempt: Not detected within {MAX_WAIT} seconds."
-            )
-
-        print(
-            f"Waiting for initial index attempt: elapsed={elapsed:.2f} timeout={MAX_WAIT}"
-        )
-        time.sleep(1)
-
-    # Create 299 successful index attempts (for 300 total)
+    # Create 300 successful index attempts
     base_time = datetime.now()
-    generated_attempts = IndexAttemptManager.create_test_index_attempts(
-        num_attempts=299,
+    all_attempts = IndexAttemptManager.create_test_index_attempts(
+        num_attempts=300,
         cc_pair_id=cc_pair.id,
         status=IndexingStatus.SUCCESS,
         base_time=base_time,
     )
 
-    for attempt in generated_attempts:
-        all_attempt_ids.append(attempt.id)
-
     # Verify basic pagination with different page sizes
     print("Verifying basic pagination with page size 5")
     _verify_index_attempt_pagination(
         cc_pair_id=cc_pair.id,
-        index_attempt_ids=all_attempt_ids,
+        index_attempts=all_attempts,
         page_size=5,
         user_performing_action=user_performing_action,
     )
@@ -116,7 +84,7 @@ def test_index_attempt_pagination(reset: None) -> None:
     print("Verifying pagination with page size 100")
     _verify_index_attempt_pagination(
         cc_pair_id=cc_pair.id,
-        index_attempt_ids=all_attempt_ids,
+        index_attempts=all_attempts,
         page_size=100,
         user_performing_action=user_performing_action,
     )

deployment/docker_compose/docker-compose.multitenant-dev.yml

@@ -1,423 +0,0 @@
-services:
-  api_server:
-    image: onyxdotapp/onyx-backend:${IMAGE_TAG:-latest}
-    build:
-      context: ../../backend
-      dockerfile: Dockerfile
-    command: >
-      /bin/sh -c "
-      alembic -n schema_private upgrade head &&
-      echo \"Starting Onyx Api Server\" &&
-      uvicorn onyx.main:app --host 0.0.0.0 --port 8080"
-    depends_on:
-      - relational_db
-      - index
-      - cache
-      - inference_model_server
-    restart: always
-    ports:
-      - "8080:8080"
-    environment:
-      - ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true
-      - MULTI_TENANT=true
-      - LOG_LEVEL=DEBUG
-      - AUTH_TYPE=cloud
-      - REQUIRE_EMAIL_VERIFICATION=false
-      - DISABLE_TELEMETRY=true
-      - IMAGE_TAG=test
-      - DEV_MODE=true
-      # Auth Settings
-      - SESSION_EXPIRE_TIME_SECONDS=${SESSION_EXPIRE_TIME_SECONDS:-}
-      - ENCRYPTION_KEY_SECRET=${ENCRYPTION_KEY_SECRET:-}
-      - VALID_EMAIL_DOMAINS=${VALID_EMAIL_DOMAINS:-}
-      - GOOGLE_OAUTH_CLIENT_ID=${GOOGLE_OAUTH_CLIENT_ID:-}
-      - GOOGLE_OAUTH_CLIENT_SECRET=${GOOGLE_OAUTH_CLIENT_SECRET:-}
-      - SMTP_SERVER=${SMTP_SERVER:-}
-      - SMTP_PORT=${SMTP_PORT:-587}
-      - SMTP_USER=${SMTP_USER:-}
-      - SMTP_PASS=${SMTP_PASS:-}
-      - ENABLE_EMAIL_INVITES=${ENABLE_EMAIL_INVITES:-}
-      - EMAIL_FROM=${EMAIL_FROM:-}
-      - OAUTH_CLIENT_ID=${OAUTH_CLIENT_ID:-}
-      - OAUTH_CLIENT_SECRET=${OAUTH_CLIENT_SECRET:-}
-      - OPENID_CONFIG_URL=${OPENID_CONFIG_URL:-}
-      - TRACK_EXTERNAL_IDP_EXPIRY=${TRACK_EXTERNAL_IDP_EXPIRY:-}
-      - CORS_ALLOWED_ORIGIN=${CORS_ALLOWED_ORIGIN:-}
-      # Gen AI Settings
-      - GEN_AI_MAX_TOKENS=${GEN_AI_MAX_TOKENS:-}
-      - QA_TIMEOUT=${QA_TIMEOUT:-}
-      - MAX_CHUNKS_FED_TO_CHAT=${MAX_CHUNKS_FED_TO_CHAT:-}
-      - DISABLE_LLM_CHOOSE_SEARCH=${DISABLE_LLM_CHOOSE_SEARCH:-}
-      - DISABLE_LLM_QUERY_REPHRASE=${DISABLE_LLM_QUERY_REPHRASE:-}
-      - DISABLE_GENERATIVE_AI=${DISABLE_GENERATIVE_AI:-}
-      - DISABLE_LITELLM_STREAMING=${DISABLE_LITELLM_STREAMING:-}
-      - LITELLM_EXTRA_HEADERS=${LITELLM_EXTRA_HEADERS:-}
-      - BING_API_KEY=${BING_API_KEY:-}
-      - DISABLE_LLM_DOC_RELEVANCE=${DISABLE_LLM_DOC_RELEVANCE:-}
-      - GEN_AI_API_KEY=${GEN_AI_API_KEY:-}
-      - TOKEN_BUDGET_GLOBALLY_ENABLED=${TOKEN_BUDGET_GLOBALLY_ENABLED:-}
-      # Query Options
-      - DOC_TIME_DECAY=${DOC_TIME_DECAY:-}
-      - HYBRID_ALPHA=${HYBRID_ALPHA:-}
-      - EDIT_KEYWORD_QUERY=${EDIT_KEYWORD_QUERY:-}
-      - MULTILINGUAL_QUERY_EXPANSION=${MULTILINGUAL_QUERY_EXPANSION:-}
-      - LANGUAGE_HINT=${LANGUAGE_HINT:-}
-      - LANGUAGE_CHAT_NAMING_HINT=${LANGUAGE_CHAT_NAMING_HINT:-}
-      - QA_PROMPT_OVERRIDE=${QA_PROMPT_OVERRIDE:-}
-      # Other services
-      - POSTGRES_HOST=relational_db
-      - POSTGRES_DEFAULT_SCHEMA=${POSTGRES_DEFAULT_SCHEMA:-}
-      - VESPA_HOST=index
-      - REDIS_HOST=cache
-      - WEB_DOMAIN=${WEB_DOMAIN:-}
-      # Don't change the NLP model configs unless you know what you're doing
-      - EMBEDDING_BATCH_SIZE=${EMBEDDING_BATCH_SIZE:-}
-      - DOCUMENT_ENCODER_MODEL=${DOCUMENT_ENCODER_MODEL:-}
-      - DOC_EMBEDDING_DIM=${DOC_EMBEDDING_DIM:-}
-      - NORMALIZE_EMBEDDINGS=${NORMALIZE_EMBEDDINGS:-}
-      - ASYM_QUERY_PREFIX=${ASYM_QUERY_PREFIX:-}
-      - DISABLE_RERANK_FOR_STREAMING=${DISABLE_RERANK_FOR_STREAMING:-}
-      - MODEL_SERVER_HOST=${MODEL_SERVER_HOST:-inference_model_server}
-      - MODEL_SERVER_PORT=${MODEL_SERVER_PORT:-}
-      - LOG_ALL_MODEL_INTERACTIONS=${LOG_ALL_MODEL_INTERACTIONS:-}
-      - LOG_DANSWER_MODEL_INTERACTIONS=${LOG_DANSWER_MODEL_INTERACTIONS:-}
-      - LOG_INDIVIDUAL_MODEL_TOKENS=${LOG_INDIVIDUAL_MODEL_TOKENS:-}
-      - LOG_VESPA_TIMING_INFORMATION=${LOG_VESPA_TIMING_INFORMATION:-}
-      - LOG_ENDPOINT_LATENCY=${LOG_ENDPOINT_LATENCY:-}
-      - LOG_POSTGRES_LATENCY=${LOG_POSTGRES_LATENCY:-}
-      - LOG_POSTGRES_CONN_COUNTS=${LOG_POSTGRES_CONN_COUNTS:-}
-      - CELERY_BROKER_POOL_LIMIT=${CELERY_BROKER_POOL_LIMIT:-}
-      - LITELLM_CUSTOM_ERROR_MESSAGE_MAPPINGS=${LITELLM_CUSTOM_ERROR_MESSAGE_MAPPINGS:-}
-      # Egnyte OAuth Configs
-      - EGNYTE_CLIENT_ID=${EGNYTE_CLIENT_ID:-}
-      - EGNYTE_CLIENT_SECRET=${EGNYTE_CLIENT_SECRET:-}
-      - EGNYTE_LOCALHOST_OVERRIDE=${EGNYTE_LOCALHOST_OVERRIDE:-}
-      # Linear OAuth Configs
-      - LINEAR_CLIENT_ID=${LINEAR_CLIENT_ID:-}
-      - LINEAR_CLIENT_SECRET=${LINEAR_CLIENT_SECRET:-}
-      # Analytics Configs
-      - SENTRY_DSN=${SENTRY_DSN:-}
-      # Chat Configs
-      - HARD_DELETE_CHATS=${HARD_DELETE_CHATS:-}
-      # Enables the use of bedrock models or IAM Auth
-      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID:-}
-      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY:-}
-      - AWS_REGION_NAME=${AWS_REGION_NAME:-}
-      - API_KEY_HASH_ROUNDS=${API_KEY_HASH_ROUNDS:-}
-      # Seeding configuration
-      - USE_IAM_AUTH=${USE_IAM_AUTH:-}
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    logging:
-      driver: json-file
-      options:
-        max-size: "50m"
-        max-file: "6"
-
-  background:
-    image: onyxdotapp/onyx-backend:${IMAGE_TAG:-latest}
-    build:
-      context: ../../backend
-      dockerfile: Dockerfile
-    command: >
-      /bin/sh -c "
-      if [ -f /etc/ssl/certs/custom-ca.crt ]; then
-        update-ca-certificates;
-      fi &&
-      /usr/bin/supervisord -c /etc/supervisor/conf.d/supervisord.conf"
-    depends_on:
-      - relational_db
-      - index
-      - cache
-      - inference_model_server
-      - indexing_model_server
-    restart: always
-    environment:
-      - ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true
-      - MULTI_TENANT=true
-      - LOG_LEVEL=DEBUG
-      - AUTH_TYPE=cloud
-      - REQUIRE_EMAIL_VERIFICATION=false
-      - DISABLE_TELEMETRY=true
-      - IMAGE_TAG=test
-      - ENCRYPTION_KEY_SECRET=${ENCRYPTION_KEY_SECRET:-}
-      - JWT_PUBLIC_KEY_URL=${JWT_PUBLIC_KEY_URL:-}
-      # Gen AI Settings (Needed by OnyxBot)
-      - GEN_AI_MAX_TOKENS=${GEN_AI_MAX_TOKENS:-}
-      - QA_TIMEOUT=${QA_TIMEOUT:-}
-      - MAX_CHUNKS_FED_TO_CHAT=${MAX_CHUNKS_FED_TO_CHAT:-}
-      - DISABLE_LLM_CHOOSE_SEARCH=${DISABLE_LLM_CHOOSE_SEARCH:-}
-      - DISABLE_LLM_QUERY_REPHRASE=${DISABLE_LLM_QUERY_REPHRASE:-}
-      - DISABLE_GENERATIVE_AI=${DISABLE_GENERATIVE_AI:-}
-      - GENERATIVE_MODEL_ACCESS_CHECK_FREQ=${GENERATIVE_MODEL_ACCESS_CHECK_FREQ:-}
-      - DISABLE_LITELLM_STREAMING=${DISABLE_LITELLM_STREAMING:-}
-      - LITELLM_EXTRA_HEADERS=${LITELLM_EXTRA_HEADERS:-}
-      - GEN_AI_API_KEY=${GEN_AI_API_KEY:-}
-      - BING_API_KEY=${BING_API_KEY:-}
-      # Query Options
-      - DOC_TIME_DECAY=${DOC_TIME_DECAY:-}
-      - HYBRID_ALPHA=${HYBRID_ALPHA:-}
-      - EDIT_KEYWORD_QUERY=${EDIT_KEYWORD_QUERY:-}
-      - MULTILINGUAL_QUERY_EXPANSION=${MULTILINGUAL_QUERY_EXPANSION:-}
-      - LANGUAGE_HINT=${LANGUAGE_HINT:-}
-      - LANGUAGE_CHAT_NAMING_HINT=${LANGUAGE_CHAT_NAMING_HINT:-}
-      - QA_PROMPT_OVERRIDE=${QA_PROMPT_OVERRIDE:-}
-      # Other Services
-      - POSTGRES_HOST=relational_db
-      - POSTGRES_USER=${POSTGRES_USER:-}
-      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-}
-      - POSTGRES_DB=${POSTGRES_DB:-}
-      - POSTGRES_DEFAULT_SCHEMA=${POSTGRES_DEFAULT_SCHEMA:-}
-      - VESPA_HOST=index
-      - REDIS_HOST=cache
-      - WEB_DOMAIN=${WEB_DOMAIN:-}
-      # Don't change the NLP model configs unless you know what you're doing
-      - DOCUMENT_ENCODER_MODEL=${DOCUMENT_ENCODER_MODEL:-}
-      - DOC_EMBEDDING_DIM=${DOC_EMBEDDING_DIM:-}
-      - NORMALIZE_EMBEDDINGS=${NORMALIZE_EMBEDDINGS:-}
-      - ASYM_QUERY_PREFIX=${ASYM_QUERY_PREFIX:-}
-      - ASYM_PASSAGE_PREFIX=${ASYM_PASSAGE_PREFIX:-}
-      - MODEL_SERVER_HOST=${MODEL_SERVER_HOST:-inference_model_server}
-      - MODEL_SERVER_PORT=${MODEL_SERVER_PORT:-}
-      - INDEXING_MODEL_SERVER_HOST=${INDEXING_MODEL_SERVER_HOST:-indexing_model_server}
-      # Indexing Configs
-      - VESPA_SEARCHER_THREADS=${VESPA_SEARCHER_THREADS:-}
-      - NUM_INDEXING_WORKERS=${NUM_INDEXING_WORKERS:-}
-      - ENABLED_CONNECTOR_TYPES=${ENABLED_CONNECTOR_TYPES:-}
-      - DISABLE_INDEX_UPDATE_ON_SWAP=${DISABLE_INDEX_UPDATE_ON_SWAP:-}
-      - DASK_JOB_CLIENT_ENABLED=${DASK_JOB_CLIENT_ENABLED:-}
-      - CONTINUE_ON_CONNECTOR_FAILURE=${CONTINUE_ON_CONNECTOR_FAILURE:-}
-      - EXPERIMENTAL_CHECKPOINTING_ENABLED=${EXPERIMENTAL_CHECKPOINTING_ENABLED:-}
-      - CONFLUENCE_CONNECTOR_LABELS_TO_SKIP=${CONFLUENCE_CONNECTOR_LABELS_TO_SKIP:-}
-      - JIRA_CONNECTOR_LABELS_TO_SKIP=${JIRA_CONNECTOR_LABELS_TO_SKIP:-}
-      - WEB_CONNECTOR_VALIDATE_URLS=${WEB_CONNECTOR_VALIDATE_URLS:-}
-      - JIRA_API_VERSION=${JIRA_API_VERSION:-}
-      - GONG_CONNECTOR_START_TIME=${GONG_CONNECTOR_START_TIME:-}
-      - NOTION_CONNECTOR_ENABLE_RECURSIVE_PAGE_LOOKUP=${NOTION_CONNECTOR_ENABLE_RECURSIVE_PAGE_LOOKUP:-}
-      - GITHUB_CONNECTOR_BASE_URL=${GITHUB_CONNECTOR_BASE_URL:-}
-      - MAX_DOCUMENT_CHARS=${MAX_DOCUMENT_CHARS:-}
-      - MAX_FILE_SIZE_BYTES=${MAX_FILE_SIZE_BYTES:-}
-      # Egnyte OAuth Configs
-      - EGNYTE_CLIENT_ID=${EGNYTE_CLIENT_ID:-}
-      - EGNYTE_CLIENT_SECRET=${EGNYTE_CLIENT_SECRET:-}
-      - EGNYTE_LOCALHOST_OVERRIDE=${EGNYTE_LOCALHOST_OVERRIDE:-}
-      # Lienar OAuth Configs
-      - LINEAR_CLIENT_ID=${LINEAR_CLIENT_ID:-}
-      - LINEAR_CLIENT_SECRET=${LINEAR_CLIENT_SECRET:-}
-      # Celery Configs (defaults are set in the supervisord.conf file.
-      # prefer doing that to have one source of defaults)
-      - CELERY_WORKER_INDEXING_CONCURRENCY=${CELERY_WORKER_INDEXING_CONCURRENCY:-}
-      - CELERY_WORKER_LIGHT_CONCURRENCY=${CELERY_WORKER_LIGHT_CONCURRENCY:-}
-      - CELERY_WORKER_LIGHT_PREFETCH_MULTIPLIER=${CELERY_WORKER_LIGHT_PREFETCH_MULTIPLIER:-}
-
-      # Onyx SlackBot Configs
-      - DANSWER_BOT_DISABLE_DOCS_ONLY_ANSWER=${DANSWER_BOT_DISABLE_DOCS_ONLY_ANSWER:-}
-      - DANSWER_BOT_FEEDBACK_VISIBILITY=${DANSWER_BOT_FEEDBACK_VISIBILITY:-}
-      - DANSWER_BOT_DISPLAY_ERROR_MSGS=${DANSWER_BOT_DISPLAY_ERROR_MSGS:-}
-      - DANSWER_BOT_RESPOND_EVERY_CHANNEL=${DANSWER_BOT_RESPOND_EVERY_CHANNEL:-}
-      - DANSWER_BOT_DISABLE_COT=${DANSWER_BOT_DISABLE_COT:-} # Currently unused
-      - NOTIFY_SLACKBOT_NO_ANSWER=${NOTIFY_SLACKBOT_NO_ANSWER:-}
-      - DANSWER_BOT_MAX_QPM=${DANSWER_BOT_MAX_QPM:-}
-      - DANSWER_BOT_MAX_WAIT_TIME=${DANSWER_BOT_MAX_WAIT_TIME:-}
-      # Logging
-      # Leave this on pretty please? Nothing sensitive is collected!
-      # https://docs.onyx.app/more/telemetry
-      - DISABLE_TELEMETRY=${DISABLE_TELEMETRY:-}
-      - LOG_LEVEL=${LOG_LEVEL:-info} # Set to debug to get more fine-grained logs
-      - LOG_ALL_MODEL_INTERACTIONS=${LOG_ALL_MODEL_INTERACTIONS:-} # LiteLLM Verbose Logging
-      # Log all of Onyx prompts and interactions with the LLM
-      - LOG_DANSWER_MODEL_INTERACTIONS=${LOG_DANSWER_MODEL_INTERACTIONS:-}
-      - LOG_INDIVIDUAL_MODEL_TOKENS=${LOG_INDIVIDUAL_MODEL_TOKENS:-}
-      - LOG_VESPA_TIMING_INFORMATION=${LOG_VESPA_TIMING_INFORMATION:-}
-
-      # Analytics Configs
-      - SENTRY_DSN=${SENTRY_DSN:-}
-
-      # Enterprise Edition stuff
-      - ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=${ENABLE_PAID_ENTERPRISE_EDITION_FEATURES:-false}
-      - USE_IAM_AUTH=${USE_IAM_AUTH:-}
-      - AWS_REGION_NAME=${AWS_REGION_NAME:-}
-      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID-}
-      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY-}
-    # Uncomment the line below to use if IAM_AUTH is true and you are using iam auth for postgres
-    # volumes:
-    #   - ./bundle.pem:/app/bundle.pem:ro
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    logging:
-      driver: json-file
-      options:
-        max-size: "50m"
-        max-file: "6"
-    # Uncomment the following lines if you need to include a custom CA certificate
-    # This section enables the use of a custom CA certificate
-    # If present, the custom CA certificate is mounted as a volume
-    # The container checks for its existence and updates the system's CA certificates
-    # This allows for secure communication with services using custom SSL certificates
-    # Optional volume mount for CA certificate
-    # volumes:
-    #   # Maps to the CA_CERT_PATH environment variable in the Dockerfile
-    #   - ${CA_CERT_PATH:-./custom-ca.crt}:/etc/ssl/certs/custom-ca.crt:ro
-
-  web_server:
-    image: onyxdotapp/onyx-web-server:${IMAGE_TAG:-latest}
-    build:
-      context: ../../web
-      dockerfile: Dockerfile
-      args:
-        - NEXT_PUBLIC_DISABLE_STREAMING=${NEXT_PUBLIC_DISABLE_STREAMING:-false}
-        - NEXT_PUBLIC_NEW_CHAT_DIRECTS_TO_SAME_PERSONA=${NEXT_PUBLIC_NEW_CHAT_DIRECTS_TO_SAME_PERSONA:-false}
-        - NEXT_PUBLIC_POSITIVE_PREDEFINED_FEEDBACK_OPTIONS=${NEXT_PUBLIC_POSITIVE_PREDEFINED_FEEDBACK_OPTIONS:-}
-        - NEXT_PUBLIC_NEGATIVE_PREDEFINED_FEEDBACK_OPTIONS=${NEXT_PUBLIC_NEGATIVE_PREDEFINED_FEEDBACK_OPTIONS:-}
-        - NEXT_PUBLIC_DISABLE_LOGOUT=${NEXT_PUBLIC_DISABLE_LOGOUT:-}
-        - NEXT_PUBLIC_DEFAULT_SIDEBAR_OPEN=${NEXT_PUBLIC_DEFAULT_SIDEBAR_OPEN:-}
-        - NEXT_PUBLIC_FORGOT_PASSWORD_ENABLED=${NEXT_PUBLIC_FORGOT_PASSWORD_ENABLED:-}
-        # Enterprise Edition only
-        - NEXT_PUBLIC_THEME=${NEXT_PUBLIC_THEME:-}
-        # DO NOT TURN ON unless you have EXPLICIT PERMISSION from Onyx.
-        - NEXT_PUBLIC_DO_NOT_USE_TOGGLE_OFF_DANSWER_POWERED=${NEXT_PUBLIC_DO_NOT_USE_TOGGLE_OFF_DANSWER_POWERED:-false}
-    depends_on:
-      - api_server
-    restart: always
-    environment:
-      - INTERNAL_URL=http://api_server:8080
-      - WEB_DOMAIN=${WEB_DOMAIN:-}
-      - THEME_IS_DARK=${THEME_IS_DARK:-}
-      - DISABLE_LLM_DOC_RELEVANCE=${DISABLE_LLM_DOC_RELEVANCE:-}
-
-      # Enterprise Edition only
-      - ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=${ENABLE_PAID_ENTERPRISE_EDITION_FEATURES:-false}
-      - NEXT_PUBLIC_CUSTOM_REFRESH_URL=${NEXT_PUBLIC_CUSTOM_REFRESH_URL:-}
-
-  inference_model_server:
-    image: onyxdotapp/onyx-model-server:${IMAGE_TAG:-latest}
-    build:
-      context: ../../backend
-      dockerfile: Dockerfile.model_server
-    command: >
-      /bin/sh -c "if [ \"${DISABLE_MODEL_SERVER:-false}\" = \"True\" ]; then
-        echo 'Skipping service...';
-        exit 0;
-      else
-        exec uvicorn model_server.main:app --host 0.0.0.0 --port 9000;
-      fi"
-    restart: on-failure
-    environment:
-      - MIN_THREADS_ML_MODELS=${MIN_THREADS_ML_MODELS:-}
-      # Set to debug to get more fine-grained logs
-      - LOG_LEVEL=${LOG_LEVEL:-info}
-
-      # Analytics Configs
-      - SENTRY_DSN=${SENTRY_DSN:-}
-    volumes:
-      # Not necessary, this is just to reduce download time during startup
-      - model_cache_huggingface:/root/.cache/huggingface/
-    logging:
-      driver: json-file
-      options:
-        max-size: "50m"
-        max-file: "6"
-
-  indexing_model_server:
-    image: onyxdotapp/onyx-model-server:${IMAGE_TAG:-latest}
-    build:
-      context: ../../backend
-      dockerfile: Dockerfile.model_server
-    command: >
-      /bin/sh -c "if [ \"${DISABLE_MODEL_SERVER:-false}\" = \"True\" ]; then
-        echo 'Skipping service...';
-        exit 0;
-      else
-        exec uvicorn model_server.main:app --host 0.0.0.0 --port 9000;
-      fi"
-    restart: on-failure
-    environment:
-      - INDEX_BATCH_SIZE=${INDEX_BATCH_SIZE:-}
-      - MIN_THREADS_ML_MODELS=${MIN_THREADS_ML_MODELS:-}
-      - INDEXING_ONLY=True
-      # Set to debug to get more fine-grained logs
-      - LOG_LEVEL=${LOG_LEVEL:-info}
-      - CLIENT_EMBEDDING_TIMEOUT=${CLIENT_EMBEDDING_TIMEOUT:-}
-
-      # Analytics Configs
-      - SENTRY_DSN=${SENTRY_DSN:-}
-    volumes:
-      # Not necessary, this is just to reduce download time during startup
-      - indexing_huggingface_model_cache:/root/.cache/huggingface/
-    logging:
-      driver: json-file
-      options:
-        max-size: "50m"
-        max-file: "6"
-
-  relational_db:
-    image: postgres:15.2-alpine
-    command: -c 'max_connections=250'
-    restart: always
-    environment:
-      - POSTGRES_USER=${POSTGRES_USER:-postgres}
-      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-password}
-    ports:
-      - "5432:5432"
-    volumes:
-      - db_volume:/var/lib/postgresql/data
-
-  # This container name cannot have an underscore in it due to Vespa expectations of the URL
-  index:
-    image: vespaengine/vespa:8.277.17
-    restart: always
-    ports:
-      - "19071:19071"
-      - "8081:8081"
-    volumes:
-      - vespa_volume:/opt/vespa/var
-    logging:
-      driver: json-file
-      options:
-        max-size: "50m"
-        max-file: "6"
-
-  nginx:
-    image: nginx:1.23.4-alpine
-    restart: always
-    # nginx will immediately crash with `nginx: [emerg] host not found in upstream`
-    # if api_server / web_server are not up
-    depends_on:
-      - api_server
-      - web_server
-    environment:
-      - DOMAIN=localhost
-    ports:
-      - "80:80"
-      - "3000:80" # allow for localhost:3000 usage, since that is the norm
-    volumes:
-      - ../data/nginx:/etc/nginx/conf.d
-    logging:
-      driver: json-file
-      options:
-        max-size: "50m"
-        max-file: "6"
-    # The specified script waits for the api_server to start up.
-    # Without this we've seen issues where nginx shows no error logs but
-    # does not recieve any traffic
-    # NOTE: we have to use dos2unix to remove Carriage Return chars from the file
-    # in order to make this work on both Unix-like systems and windows
-    command: >
-      /bin/sh -c "dos2unix /etc/nginx/conf.d/run-nginx.sh 
-      && /etc/nginx/conf.d/run-nginx.sh app.conf.template.dev"
-
-  cache:
-    image: redis:7.4-alpine
-    restart: always
-    ports:
-      - "6379:6379"
-    # docker silently mounts /data even without an explicit volume mount, which enables
-    # persistence. explicitly setting save and appendonly forces ephemeral behavior.
-    command: redis-server --save "" --appendonly no
-
-volumes:
-  db_volume:
-  vespa_volume: # Created by the container itself
-
-  model_cache_huggingface:
-  indexing_huggingface_model_cache:

web/src/app/admin/connectors/[connector]/AddConnectorPage.tsx

@@ -18,11 +18,7 @@ import AdvancedFormPage from "./pages/Advanced";
 import DynamicConnectionForm from "./pages/DynamicConnectorCreationForm";
 import CreateCredential from "@/components/credentials/actions/CreateCredential";
 import ModifyCredential from "@/components/credentials/actions/ModifyCredential";
-import {
-  ConfigurableSources,
-  oauthSupportedSources,
-  ValidSources,
-} from "@/lib/types";
+import { ConfigurableSources, oauthSupportedSources } from "@/lib/types";
 import {
   Credential,
   credentialTemplates,
@@ -448,7 +444,7 @@ export default function AddConnector({
               <CardSection>
                 <Title className="mb-2 text-lg">Select a credential</Title>
 
-                {connector == ValidSources.Gmail ? (
+                {connector == "gmail" ? (
                   <GmailMain />
                 ) : (
                   <>

web/src/app/chat/ChatPage.tsx

@@ -949,15 +949,15 @@ export function ChatPage({
       // Check if all messages are currently rendered
       if (currentVisibleRange.end < messageHistory.length) {
         // Update visible range to include the last messages
-        updateCurrentVisibleRange({
-          start: Math.max(
-            0,
-            messageHistory.length -
-              (currentVisibleRange.end - currentVisibleRange.start)
-          ),
-          end: messageHistory.length,
-          mostVisibleMessageId: currentVisibleRange.mostVisibleMessageId,
-        });
+        // updateCurrentVisibleRange({
+        //   start: Math.max(
+        //     0,
+        //     messageHistory.length -
+        //       (currentVisibleRange.end - currentVisibleRange.start)
+        //   ),
+        //   end: messageHistory.length,
+        //   mostVisibleMessageId: currentVisibleRange.mostVisibleMessageId,
+        // });
 
         // Wait for the state update and re-render before scrolling
         setTimeout(() => {
@@ -1121,7 +1121,6 @@ export function ChatPage({
         "Continue Generating (pick up exactly where you left off)",
     });
   };
-  const [uncaughtError, setUncaughtError] = useState<string | null>(null);
 
   const onSubmit = async ({
     messageIdToResend,
@@ -1550,23 +1549,8 @@ 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")) {
@@ -1885,6 +1869,7 @@ export function ChatPage({
     newRange: VisibleRange,
     forceUpdate?: boolean
   ) => {
+    console.log("updateCurrentVisibleRange", newRange);
     if (
       scrollInitialized.current &&
       visibleRange.get(loadedIdSessionRef.current) == undefined &&
@@ -1923,26 +1908,54 @@ export function ChatPage({
       scrollInitialized.current = true;
     }
   };
+  const setVisibleRangeForCurrentSessionId = (newRange: VisibleRange) => {
+    console.log("setVisibleRangeForCurrentSessionId", newRange);
+    setVisibleRange((prevState) => {
+      const newState = new Map(prevState);
+      newState.set(currentSessionId(), newRange);
+      return newState;
+    });
+  };
 
-  const updateVisibleRangeBasedOnScroll = () => {
-    if (!scrollInitialized.current) return;
+  function updateVisibleRangeBasedOnScroll() {
     const scrollableDiv = scrollableDivRef.current;
     if (!scrollableDiv) return;
 
-    const viewportHeight = scrollableDiv.clientHeight;
-    let mostVisibleMessageIndex = -1;
+    const distanceFromBottom =
+      scrollableDiv.scrollHeight -
+      scrollableDiv.scrollTop -
+      scrollableDiv.clientHeight;
 
+    const isNearBottom = distanceFromBottom < 200;
+
+    // If user is near bottom, we treat the last message as "most visible"
+    if (isNearBottom) {
+      const startIndex = Math.max(0, messageHistory.length - BUFFER_COUNT);
+      const endIndex = messageHistory.length;
+      setVisibleRangeForCurrentSessionId({
+        start: startIndex,
+        end: endIndex,
+        mostVisibleMessageId:
+          messageHistory.length > 0
+            ? messageHistory[messageHistory.length - 1].messageId
+            : null,
+      });
+      return;
+    }
+
+    // otherwise do the bounding rect logic:
+    let mostVisibleMessageIndex = -1;
+    const viewportHeight = scrollableDiv.clientHeight;
     messageHistory.forEach((message, index) => {
-      const messageElement = document.getElementById(
-        `message-${message.messageId}`
-      );
-      if (messageElement) {
-        const rect = messageElement.getBoundingClientRect();
-        const isVisible = rect.bottom <= viewportHeight && rect.bottom > 0;
+      const elem = document.getElementById(`message-${message.messageId}`);
+      if (elem) {
+        const rect = elem.getBoundingClientRect();
+        const isVisible = rect.top < viewportHeight && rect.bottom >= 0;
         if (isVisible && index > mostVisibleMessageIndex) {
           mostVisibleMessageIndex = index;
         }
       }
+      // clientScrollToBottom;
     });
 
     if (mostVisibleMessageIndex !== -1) {
@@ -1951,34 +1964,50 @@ export function ChatPage({
         messageHistory.length,
         mostVisibleMessageIndex + BUFFER_COUNT + 1
       );
-
-      updateCurrentVisibleRange({
+      setVisibleRangeForCurrentSessionId({
         start: startIndex,
         end: endIndex,
         mostVisibleMessageId: messageHistory[mostVisibleMessageIndex].messageId,
       });
     }
-  };
+  }
 
   useEffect(() => {
     initializeVisibleRange();
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [router, messageHistory]);
 
-  useLayoutEffect(() => {
-    const scrollableDiv = scrollableDivRef.current;
+  useEffect(() => {
+    console.log("useEffect has been called");
+    const scrollEl = scrollableDivRef.current;
+    if (!scrollEl) {
+      console.log("no scrollEl");
+      return;
+    }
 
     const handleScroll = () => {
-      updateVisibleRangeBasedOnScroll();
+      requestAnimationFrame(() => {
+        updateVisibleRangeBasedOnScroll();
+      });
     };
 
-    scrollableDiv?.addEventListener("scroll", handleScroll);
+    const attachScrollListener = () => {
+      if (scrollEl) {
+        scrollEl.addEventListener("scroll", handleScroll);
+      } else {
+        console.log("scrollEl not available, retrying in 100ms");
+        setTimeout(attachScrollListener, 100);
+      }
+    };
+
+    attachScrollListener();
 
     return () => {
-      scrollableDiv?.removeEventListener("scroll", handleScroll);
+      if (scrollEl) {
+        scrollEl.removeEventListener("scroll", handleScroll);
+      }
     };
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [messageHistory]);
+  }, [scrollableDivRef, messageHistory, currentSessionId()]);
 
   const imageFileInMessageHistory = useMemo(() => {
     return messageHistory
@@ -2055,7 +2084,6 @@ export function ChatPage({
         }
 
         const data = await response.json();
-
         router.push(data.redirect_url);
       } catch (error) {
         console.error("Error seeding chat from Slack:", error);
@@ -2510,7 +2538,7 @@ export function ChatPage({
                               ? messageHistory
                               : messageHistory.slice(
                                   currentVisibleRange.start,
-                                  currentVisibleRange.end
+                                  currentVisibleRange.end + 2
                                 )
                             ).map((message, fauxIndex) => {
                               const i =
@@ -2650,7 +2678,6 @@ export function ChatPage({
                                     {message.sub_questions &&
                                     message.sub_questions.length > 0 ? (
                                       <AgenticMessage
-                                        error={uncaughtError}
                                         docSidebarToggled={
                                           documentSidebarToggled &&
                                           (selectedMessageForDocDisplay ==

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

@@ -80,7 +80,6 @@ export const AgenticMessage = ({
   agenticDocs,
   secondLevelSubquestions,
   toggleDocDisplay,
-  error,
 }: {
   docSidebarToggled?: boolean;
   isImprovement?: boolean | null;
@@ -111,7 +110,6 @@ export const AgenticMessage = ({
   regenerate?: (modelOverRide: LlmOverride) => Promise<void>;
   setPresentingDocument?: (document: OnyxDocument) => void;
   toggleDocDisplay?: (agentic: boolean) => void;
-  error?: string | null;
 }) => {
   const [noShowingMessage, setNoShowingMessage] = useState(isComplete);
 
@@ -485,28 +483,11 @@ 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/Messages.tsx

@@ -185,7 +185,6 @@ export const AIMessage = ({
   setPresentingDocument,
   index,
   toggledDocumentSidebar,
-  removePadding,
 }: {
   index?: number;
   shared?: boolean;
@@ -214,7 +213,6 @@ export const AIMessage = ({
   overriddenModel?: string;
   regenerate?: (modelOverRide: LlmOverride) => Promise<void>;
   setPresentingDocument?: (document: OnyxDocument) => void;
-  removePadding?: boolean;
 }) => {
   const toolCallGenerating = toolCall && !toolCall.tool_result;
 
@@ -400,9 +398,7 @@ 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 ml-4 lg:px-5 relative flex `}
     >
       <div
         className={`mx-auto ${
@@ -411,13 +407,11 @@ export const AIMessage = ({
       >
         <div className={`lg:mr-12 ${!shared && "mobile:ml-0 md:ml-8"}`}>
           <div className="flex">
-            {!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">
@@ -594,8 +588,7 @@ export const AIMessage = ({
                     )}
                   </div>
 
-                  {!removePadding &&
-                    handleFeedback &&
+                  {handleFeedback &&
                     (isActive ? (
                       <div
                         className={`

web/src/lib/connectors/connectors.tsx

@@ -772,7 +772,7 @@ For example, specifying .*-support.* as a "channel" will cause the connector to
     advanced_values: [],
   },
   linear: {
-    description: "Configure Linear connector",
+    description: "Configure Dropbox connector",
     values: [],
     advanced_values: [],
   },

web/src/lib/connectors/credentials.ts

@@ -114,7 +114,7 @@ export interface LoopioCredentialJson {
 }
 
 export interface LinearCredentialJson {
-  linear_access_token: string;
+  linear_api_key: string;
 }
 
 export interface HubSpotCredentialJson {
@@ -250,7 +250,7 @@ export const credentialTemplates: Record<ValidSources, any> = {
     gong_access_key_secret: "",
   } as GongCredentialJson,
   zulip: { zuliprc_content: "" } as ZulipCredentialJson,
-  linear: { linear_access_token: "" } as LinearCredentialJson,
+  linear: { linear_api_key: "" } as LinearCredentialJson,
   hubspot: { hubspot_access_token: "" } as HubSpotCredentialJson,
   document360: {
     portal_id: "",
@@ -404,7 +404,7 @@ export const credentialDisplayNames: Record<string, string> = {
   loopio_client_token: "Loopio Client Token",
 
   // Linear
-  linear_access_token: "Linear Access Token",
+  linear_api_key: "Linear API Key",
 
   // HubSpot
   hubspot_access_token: "HubSpot Access Token",