bump version and fix related issues

Co-authored-by: Richard Kuo (Danswer) <rkuo@onyx.app>

.github/workflows/docker-build-push-cloud-web-container-on-tag.yml

@@ -67,6 +67,7 @@ jobs:
             NEXT_PUBLIC_SENTRY_DSN=${{ secrets.SENTRY_DSN }}
             NEXT_PUBLIC_GTM_ENABLED=true
             NEXT_PUBLIC_FORGOT_PASSWORD_ENABLED=true
+            NEXT_PUBLIC_INCLUDE_ERROR_POPUP_SUPPORT_LINK=true
             NODE_OPTIONS=--max-old-space-size=8192
           # needed due to weird interactions with the builds for different platforms
           no-cache: true

.github/workflows/docker-build-push-model-server-container-on-tag.yml

@@ -12,7 +12,32 @@ env:
   BUILDKIT_PROGRESS: plain
 
 jobs:
+  # 1) Preliminary job to check if the changed files are relevant
+  check_model_server_changes:
+    runs-on: ubuntu-latest
+    outputs:
+      changed: ${{ steps.check.outputs.changed }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Check if relevant files changed
+        id: check
+        run: |
+          # Default to "false"
+          echo "changed=false" >> $GITHUB_OUTPUT
+
+          # Compare the previous commit (github.event.before) to the current one (github.sha)
+          # If any file in backend/model_server/** or backend/Dockerfile.model_server is changed,
+          # set changed=true
+          if git diff --name-only ${{ github.event.before }} ${{ github.sha }} \
+             | grep -E '^backend/model_server/|^backend/Dockerfile.model_server'; then
+            echo "changed=true" >> $GITHUB_OUTPUT
+          fi
+
   build-amd64:
+    needs: [check_model_server_changes]
+    if: needs.check_model_server_changes.outputs.changed == 'true'
     runs-on:
       [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-amd64"]
     steps:
@@ -52,6 +77,8 @@ jobs:
           provenance: false
 
   build-arm64:
+    needs: [check_model_server_changes]
+    if: needs.check_model_server_changes.outputs.changed == 'true'
     runs-on:
       [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-arm64"]
     steps:
@@ -91,7 +118,8 @@ jobs:
           provenance: false
 
   merge-and-scan:
-    needs: [build-amd64, build-arm64]
+    needs: [build-amd64, build-arm64, check_model_server_changes]
+    if: needs.check_model_server_changes.outputs.changed == 'true'
     runs-on: ubuntu-latest
     steps:
       - name: Login to Docker Hub

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

@@ -94,16 +94,19 @@ jobs:
           cd deployment/docker_compose
           ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
           MULTI_TENANT=true \
-          AUTH_TYPE=basic \
+          AUTH_TYPE=cloud \
           REQUIRE_EMAIL_VERIFICATION=false \
           DISABLE_TELEMETRY=true \
           IMAGE_TAG=test \
-          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
+          DEV_MODE=true \
+          docker compose -f docker-compose.multitenant-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 \
@@ -119,6 +122,10 @@ 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
@@ -126,17 +133,17 @@ jobs:
 
       - name: Check multi-tenant test results
         run: |
-          if [ ${{ steps.run_tests.outcome }} == 'failure' ]; then
-            echo "Integration tests failed. Exiting with error."
+          if [ ${{ steps.run_multitenant_tests.outcome }} == 'failure' ]; then
+            echo "Multi-tenant integration tests failed. Exiting with error."
             exit 1
           else
-            echo "All integration tests passed successfully."
+            echo "All multi-tenant integration tests passed successfully."
           fi
 
       - name: Stop multi-tenant Docker containers
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+          docker compose -f docker-compose.multitenant-dev.yml -p danswer-stack down -v
 
       - name: Start Docker containers
         run: |
@@ -216,27 +223,30 @@ jobs:
             echo "All integration tests passed successfully."
           fi
 
-      # save before stopping the containers so the logs can be captured
-      - name: Save Docker logs
-        if: success() || failure()
+      # ------------------------------------------------------------
+      # Always gather logs BEFORE "down":
+      - name: Dump API server logs
+        if: always()
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack logs > docker-compose.log
-          mv docker-compose.log ${{ github.workspace }}/docker-compose.log
+          docker compose -f docker-compose.dev.yml -p danswer-stack logs --no-color api_server > $GITHUB_WORKSPACE/api_server.log || true
 
-      - name: Stop Docker containers
+      - name: Dump all-container logs (optional)
+        if: always()
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+          docker compose -f docker-compose.dev.yml -p danswer-stack logs --no-color > $GITHUB_WORKSPACE/docker-compose.log || true
 
       - name: Upload logs
-        if: success() || failure()
+        if: always()
         uses: actions/upload-artifact@v4
         with:
-          name: docker-logs
+          name: docker-all-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

.gitignore

@@ -7,4 +7,6 @@
 .vscode/
 *.sw?
 /backend/tests/regression/answer_quality/search_test_config.yaml
-/web/test-results/
+/web/test-results/
+backend/onyx/agent_search/main/test_data.json
+backend/tests/regression/answer_quality/test_data.json

.vscode/env_template.txt

@@ -52,3 +52,9 @@ BING_API_KEY=<REPLACE THIS>
 # Enable the full set of Danswer Enterprise Edition features
 # NOTE: DO NOT ENABLE THIS UNLESS YOU HAVE A PAID ENTERPRISE LICENSE (or if you are using this for local testing/development)
 ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=False
+
+# Agent Search configs  # TODO: Remove give proper namings
+AGENT_RETRIEVAL_STATS=False   # Note: This setting will incur substantial re-ranking effort
+AGENT_RERANKING_STATS=True
+AGENT_MAX_QUERY_RETRIEVAL_RESULTS=20
+AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS=20

README.md

@@ -124,7 +124,7 @@ There are two editions of Onyx:
 To try the Onyx Enterprise Edition:
 
 1. Checkout our [Cloud product](https://cloud.onyx.app/signup).
-2. For self-hosting, contact us at [founders@onyx.app](mailto:founders@onyx.app) or book a call with us on our [Cal](https://cal.com/team/danswer/founders).
+2. For self-hosting, contact us at [founders@onyx.app](mailto:founders@onyx.app) or book a call with us on our [Cal](https://cal.com/team/onyx/founders).
 
 ## 💡 Contributing
 
@@ -133,3 +133,4 @@ Looking to contribute? Please check out the [Contribution Guide](CONTRIBUTING.md
 ## ⭐Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=onyx-dot-app/onyx&type=Date)](https://star-history.com/#onyx-dot-app/onyx&Date)
+

backend/Dockerfile

@@ -101,7 +101,8 @@ COPY ./alembic_tenants /app/alembic_tenants
 COPY ./alembic.ini /app/alembic.ini
 COPY supervisord.conf /usr/etc/supervisord.conf
 
-# Escape hatch
+# Escape hatch scripts
+COPY ./scripts/debugging /app/scripts/debugging
 COPY ./scripts/force_delete_connector_by_id.py /app/scripts/force_delete_connector_by_id.py
 
 # Put logo in assets

backend/alembic/versions/2cdeff6d8c93_set_built_in_to_default.py

@@ -0,0 +1,32 @@
+"""set built in to default
+
+Revision ID: 2cdeff6d8c93
+Revises: f5437cc136c5
+Create Date: 2025-02-11 14:57:51.308775
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "2cdeff6d8c93"
+down_revision = "f5437cc136c5"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Prior to this migration / point in the codebase history,
+    # built in personas were implicitly treated as default personas (with no option to change this)
+    # This migration makes that explicit
+    op.execute(
+        """
+        UPDATE persona
+        SET is_default_persona = TRUE
+        WHERE builtin_persona = TRUE
+    """
+    )
+
+
+def downgrade() -> None:
+    pass

backend/alembic/versions/4ee1287bd26a_add_multiple_slack_bot_support.py

@@ -5,7 +5,6 @@ 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
@@ -20,13 +19,8 @@ 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",
@@ -63,7 +57,6 @@ 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
@@ -71,15 +64,12 @@ 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)",
@@ -170,10 +160,9 @@ 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:
-        logger.warning("tried to delete tokens in dynamic config but failed")
+        pass
     # Rename the table
     op.rename_table(
         "slack_bot_config__standard_answer_category",
@@ -190,8 +179,6 @@ 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
@@ -273,7 +260,7 @@ def downgrade() -> None:
             }
             get_kv_store().store("slack_bot_tokens_config_key", tokens)
     except Exception:
-        logger.warning("Failed to save tokens back to KV store")
+        pass
 
     # Drop the new tables in reverse order
     op.drop_table("slack_channel_config")

backend/alembic/versions/98a5008d8711_agent_tracking.py

@@ -0,0 +1,107 @@
+"""agent_tracking
+
+Revision ID: 98a5008d8711
+Revises: 2f80c6a2550f
+Create Date: 2025-01-29 17:00:00.000001
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+from sqlalchemy.dialects.postgresql import UUID
+
+# revision identifiers, used by Alembic.
+revision = "98a5008d8711"
+down_revision = "2f80c6a2550f"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "agent__search_metrics",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("user_id", postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column("persona_id", sa.Integer(), nullable=True),
+        sa.Column("agent_type", sa.String(), nullable=False),
+        sa.Column("start_time", sa.DateTime(timezone=True), nullable=False),
+        sa.Column("base_duration_s", sa.Float(), nullable=False),
+        sa.Column("full_duration_s", sa.Float(), nullable=False),
+        sa.Column("base_metrics", postgresql.JSONB(), nullable=True),
+        sa.Column("refined_metrics", postgresql.JSONB(), nullable=True),
+        sa.Column("all_metrics", postgresql.JSONB(), nullable=True),
+        sa.ForeignKeyConstraint(
+            ["persona_id"],
+            ["persona.id"],
+        ),
+        sa.ForeignKeyConstraint(["user_id"], ["user.id"], ondelete="CASCADE"),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    # Create sub_question table
+    op.create_table(
+        "agent__sub_question",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column("primary_question_id", sa.Integer, sa.ForeignKey("chat_message.id")),
+        sa.Column(
+            "chat_session_id", UUID(as_uuid=True), sa.ForeignKey("chat_session.id")
+        ),
+        sa.Column("sub_question", sa.Text),
+        sa.Column(
+            "time_created", sa.DateTime(timezone=True), server_default=sa.func.now()
+        ),
+        sa.Column("sub_answer", sa.Text),
+        sa.Column("sub_question_doc_results", postgresql.JSONB(), nullable=True),
+        sa.Column("level", sa.Integer(), nullable=False),
+        sa.Column("level_question_num", sa.Integer(), nullable=False),
+    )
+
+    # Create sub_query table
+    op.create_table(
+        "agent__sub_query",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column(
+            "parent_question_id", sa.Integer, sa.ForeignKey("agent__sub_question.id")
+        ),
+        sa.Column(
+            "chat_session_id", UUID(as_uuid=True), sa.ForeignKey("chat_session.id")
+        ),
+        sa.Column("sub_query", sa.Text),
+        sa.Column(
+            "time_created", sa.DateTime(timezone=True), server_default=sa.func.now()
+        ),
+    )
+
+    # Create sub_query__search_doc association table
+    op.create_table(
+        "agent__sub_query__search_doc",
+        sa.Column(
+            "sub_query_id",
+            sa.Integer,
+            sa.ForeignKey("agent__sub_query.id"),
+            primary_key=True,
+        ),
+        sa.Column(
+            "search_doc_id",
+            sa.Integer,
+            sa.ForeignKey("search_doc.id"),
+            primary_key=True,
+        ),
+    )
+
+    op.add_column(
+        "chat_message",
+        sa.Column(
+            "refined_answer_improvement",
+            sa.Boolean(),
+            nullable=True,
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("chat_message", "refined_answer_improvement")
+    op.drop_table("agent__sub_query__search_doc")
+    op.drop_table("agent__sub_query")
+    op.drop_table("agent__sub_question")
+    op.drop_table("agent__search_metrics")

backend/alembic/versions/eaa3b5593925_add_default_slack_channel_config.py

@@ -0,0 +1,80 @@
+"""add default slack channel config
+
+Revision ID: eaa3b5593925
+Revises: 98a5008d8711
+Create Date: 2025-02-03 18:07:56.552526
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "eaa3b5593925"
+down_revision = "98a5008d8711"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add is_default column
+    op.add_column(
+        "slack_channel_config",
+        sa.Column("is_default", sa.Boolean(), nullable=False, server_default="false"),
+    )
+
+    op.create_index(
+        "ix_slack_channel_config_slack_bot_id_default",
+        "slack_channel_config",
+        ["slack_bot_id", "is_default"],
+        unique=True,
+        postgresql_where=sa.text("is_default IS TRUE"),
+    )
+
+    # Create default channel configs for existing slack bots without one
+    conn = op.get_bind()
+    slack_bots = conn.execute(sa.text("SELECT id FROM slack_bot")).fetchall()
+
+    for slack_bot in slack_bots:
+        slack_bot_id = slack_bot[0]
+        existing_default = conn.execute(
+            sa.text(
+                "SELECT id FROM slack_channel_config WHERE slack_bot_id = :bot_id AND is_default = TRUE"
+            ),
+            {"bot_id": slack_bot_id},
+        ).fetchone()
+
+        if not existing_default:
+            conn.execute(
+                sa.text(
+                    """
+                    INSERT INTO slack_channel_config (
+                        slack_bot_id, persona_id, channel_config, enable_auto_filters, is_default
+                    ) VALUES (
+                        :bot_id, NULL,
+                        '{"channel_name": null, '
+                        '"respond_member_group_list": [], '
+                        '"answer_filters": [], '
+                        '"follow_up_tags": [], '
+                        '"respond_tag_only": true}',
+                        FALSE, TRUE
+                    )
+                """
+                ),
+                {"bot_id": slack_bot_id},
+            )
+
+
+def downgrade() -> None:
+    # Delete default slack channel configs
+    conn = op.get_bind()
+    conn.execute(sa.text("DELETE FROM slack_channel_config WHERE is_default = TRUE"))
+
+    # Remove index
+    op.drop_index(
+        "ix_slack_channel_config_slack_bot_id_default",
+        table_name="slack_channel_config",
+    )
+
+    # Remove is_default column
+    op.drop_column("slack_channel_config", "is_default")

backend/alembic/versions/f39c5794c10a_add_background_errors_table.py

@@ -0,0 +1,40 @@
+"""Add background errors table
+
+Revision ID: f39c5794c10a
+Revises: 2cdeff6d8c93
+Create Date: 2025-02-12 17:11:14.527876
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "f39c5794c10a"
+down_revision = "2cdeff6d8c93"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "background_error",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("message", sa.String(), nullable=False),
+        sa.Column(
+            "time_created",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+        sa.Column("cc_pair_id", sa.Integer(), nullable=True),
+        sa.PrimaryKeyConstraint("id"),
+        sa.ForeignKeyConstraint(
+            ["cc_pair_id"],
+            ["connector_credential_pair.id"],
+            ondelete="CASCADE",
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("background_error")

backend/alembic/versions/f5437cc136c5_delete_non_search_assistants.py

@@ -0,0 +1,53 @@
+"""delete non-search assistants
+
+Revision ID: f5437cc136c5
+Revises: eaa3b5593925
+Create Date: 2025-02-04 16:17:15.677256
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "f5437cc136c5"
+down_revision = "eaa3b5593925"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    pass
+
+
+def downgrade() -> None:
+    # Fix: split the statements into multiple op.execute() calls
+    op.execute(
+        """
+        WITH personas_without_search AS (
+            SELECT p.id
+            FROM persona p
+            LEFT JOIN persona__tool pt ON p.id = pt.persona_id
+            LEFT JOIN tool t ON pt.tool_id = t.id
+            GROUP BY p.id
+            HAVING COUNT(CASE WHEN t.in_code_tool_id = 'run_search' THEN 1 END) = 0
+        )
+        UPDATE slack_channel_config
+        SET persona_id = NULL
+        WHERE is_default = TRUE AND persona_id IN (SELECT id FROM personas_without_search)
+        """
+    )
+
+    op.execute(
+        """
+        WITH personas_without_search AS (
+            SELECT p.id
+            FROM persona p
+            LEFT JOIN persona__tool pt ON p.id = pt.persona_id
+            LEFT JOIN tool t ON pt.tool_id = t.id
+            GROUP BY p.id
+            HAVING COUNT(CASE WHEN t.in_code_tool_id = 'run_search' THEN 1 END) = 0
+        )
+        DELETE FROM slack_channel_config
+        WHERE is_default = FALSE AND persona_id IN (SELECT id FROM personas_without_search)
+        """
+    )

backend/ee/onyx/background/celery/tasks/beat_schedule.py

@@ -1,44 +1,46 @@
 from datetime import timedelta
 from typing import Any
 
+from onyx.background.celery.tasks.beat_schedule import (
+    beat_cloud_tasks as base_beat_system_tasks,
+)
 from onyx.background.celery.tasks.beat_schedule import BEAT_EXPIRES_DEFAULT
 from onyx.background.celery.tasks.beat_schedule import (
-    cloud_tasks_to_schedule as base_cloud_tasks_to_schedule,
+    beat_task_templates as base_beat_task_templates,
 )
+from onyx.background.celery.tasks.beat_schedule import generate_cloud_tasks
 from onyx.background.celery.tasks.beat_schedule import (
-    tasks_to_schedule as base_tasks_to_schedule,
+    get_tasks_to_schedule as base_get_tasks_to_schedule,
 )
-from onyx.configs.constants import ONYX_CLOUD_CELERY_TASK_PREFIX
 from onyx.configs.constants import OnyxCeleryPriority
 from onyx.configs.constants import OnyxCeleryTask
 from shared_configs.configs import MULTI_TENANT
 
-ee_cloud_tasks_to_schedule = [
-    {
-        "name": f"{ONYX_CLOUD_CELERY_TASK_PREFIX}_autogenerate-usage-report",
-        "task": OnyxCeleryTask.CLOUD_BEAT_TASK_GENERATOR,
-        "schedule": timedelta(days=30),
-        "options": {
-            "priority": OnyxCeleryPriority.HIGHEST,
-            "expires": BEAT_EXPIRES_DEFAULT,
+ee_beat_system_tasks: list[dict] = []
+
+ee_beat_task_templates: list[dict] = []
+ee_beat_task_templates.extend(
+    [
+        {
+            "name": "autogenerate-usage-report",
+            "task": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
+            "schedule": timedelta(days=30),
+            "options": {
+                "priority": OnyxCeleryPriority.MEDIUM,
+                "expires": BEAT_EXPIRES_DEFAULT,
+            },
         },
-        "kwargs": {
-            "task_name": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
+        {
+            "name": "check-ttl-management",
+            "task": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
+            "schedule": timedelta(hours=1),
+            "options": {
+                "priority": OnyxCeleryPriority.MEDIUM,
+                "expires": BEAT_EXPIRES_DEFAULT,
+            },
         },
-    },
-    {
-        "name": f"{ONYX_CLOUD_CELERY_TASK_PREFIX}_check-ttl-management",
-        "task": OnyxCeleryTask.CLOUD_BEAT_TASK_GENERATOR,
-        "schedule": timedelta(hours=1),
-        "options": {
-            "priority": OnyxCeleryPriority.HIGHEST,
-            "expires": BEAT_EXPIRES_DEFAULT,
-        },
-        "kwargs": {
-            "task_name": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
-        },
-    },
-]
+    ]
+)
 
 ee_tasks_to_schedule: list[dict] = []
 
@@ -65,9 +67,14 @@ if not MULTI_TENANT:
     ]
 
 
-def get_cloud_tasks_to_schedule() -> list[dict[str, Any]]:
-    return ee_cloud_tasks_to_schedule + base_cloud_tasks_to_schedule
+def get_cloud_tasks_to_schedule(beat_multiplier: float) -> list[dict[str, Any]]:
+    beat_system_tasks = ee_beat_system_tasks + base_beat_system_tasks
+    beat_task_templates = ee_beat_task_templates + base_beat_task_templates
+    cloud_tasks = generate_cloud_tasks(
+        beat_system_tasks, beat_task_templates, beat_multiplier
+    )
+    return cloud_tasks
 
 
 def get_tasks_to_schedule() -> list[dict[str, Any]]:
-    return ee_tasks_to_schedule + base_tasks_to_schedule
+    return ee_tasks_to_schedule + base_get_tasks_to_schedule()

backend/ee/onyx/db/persona.py

@@ -2,8 +2,11 @@ from uuid import UUID
 
 from sqlalchemy.orm import Session
 
+from onyx.configs.constants import NotificationType
 from onyx.db.models import Persona__User
 from onyx.db.models import Persona__UserGroup
+from onyx.db.notification import create_notification
+from onyx.server.features.persona.models import PersonaSharedNotificationData
 
 
 def make_persona_private(
@@ -12,6 +15,9 @@ def make_persona_private(
     group_ids: list[int] | None,
     db_session: Session,
 ) -> None:
+    """NOTE(rkuo): This function batches all updates into a single commit. If we don't
+    dedupe the inputs, the commit will exception."""
+
     db_session.query(Persona__User).filter(
         Persona__User.persona_id == persona_id
     ).delete(synchronize_session="fetch")
@@ -20,11 +26,22 @@ def make_persona_private(
     ).delete(synchronize_session="fetch")
 
     if user_ids:
-        for user_uuid in user_ids:
-            db_session.add(Persona__User(persona_id=persona_id, user_id=user_uuid))
+        user_ids_set = set(user_ids)
+        for user_id in user_ids_set:
+            db_session.add(Persona__User(persona_id=persona_id, user_id=user_id))
+
+            create_notification(
+                user_id=user_id,
+                notif_type=NotificationType.PERSONA_SHARED,
+                db_session=db_session,
+                additional_data=PersonaSharedNotificationData(
+                    persona_id=persona_id,
+                ).model_dump(),
+            )
 
     if group_ids:
-        for group_id in group_ids:
+        group_ids_set = set(group_ids)
+        for group_id in group_ids_set:
             db_session.add(
                 Persona__UserGroup(persona_id=persona_id, user_group_id=group_id)
             )

backend/ee/onyx/db/user_group.py

@@ -218,14 +218,14 @@ def fetch_user_groups_for_user(
     return db_session.scalars(stmt).all()
 
 
-def construct_document_select_by_usergroup(
+def construct_document_id_select_by_usergroup(
     user_group_id: int,
 ) -> Select:
     """This returns a statement that should be executed using
     .yield_per() to minimize overhead. The primary consumers of this function
     are background processing task generators."""
     stmt = (
-        select(Document)
+        select(Document.id)
         .join(
             DocumentByConnectorCredentialPair,
             Document.id == DocumentByConnectorCredentialPair.id,

backend/ee/onyx/external_permissions/confluence/doc_sync.py

@@ -365,7 +365,9 @@ def confluence_doc_sync(
 
     slim_docs = []
     logger.debug("Fetching all slim documents from confluence")
-    for doc_batch in confluence_connector.retrieve_all_slim_documents():
+    for doc_batch in confluence_connector.retrieve_all_slim_documents(
+        callback=callback
+    ):
         logger.debug(f"Got {len(doc_batch)} slim documents from confluence")
         if callback:
             if callback.should_stop():

backend/ee/onyx/external_permissions/confluence/group_sync.py

@@ -1,5 +1,6 @@
 from ee.onyx.db.external_perm import ExternalUserGroup
 from ee.onyx.external_permissions.confluence.constants import ALL_CONF_EMAILS_GROUP_NAME
+from onyx.background.error_logging import emit_background_error
 from onyx.connectors.confluence.onyx_confluence import build_confluence_client
 from onyx.connectors.confluence.onyx_confluence import OnyxConfluence
 from onyx.connectors.confluence.utils import get_user_email_from_username__server
@@ -10,7 +11,7 @@ logger = setup_logger()
 
 
 def _build_group_member_email_map(
-    confluence_client: OnyxConfluence,
+    confluence_client: OnyxConfluence, cc_pair_id: int
 ) -> dict[str, set[str]]:
     group_member_emails: dict[str, set[str]] = {}
     for user_result in confluence_client.paginated_cql_user_retrieval():
@@ -18,8 +19,11 @@ def _build_group_member_email_map(
 
         user = user_result.get("user", {})
         if not user:
-            logger.warning(f"user result missing user field: {user_result}")
+            msg = f"user result missing user field: {user_result}"
+            emit_background_error(msg, cc_pair_id=cc_pair_id)
+            logger.error(msg)
             continue
+
         email = user.get("email")
         if not email:
             # This field is only present in Confluence Server
@@ -32,7 +36,12 @@ def _build_group_member_email_map(
                 )
         if not email:
             # If we still don't have an email, skip this user
-            logger.warning(f"user result missing email field: {user_result}")
+            msg = f"user result missing email field: {user_result}"
+            if user.get("type") == "app":
+                logger.warning(msg)
+            else:
+                emit_background_error(msg, cc_pair_id=cc_pair_id)
+                logger.error(msg)
             continue
 
         all_users_groups: set[str] = set()
@@ -42,11 +51,18 @@ def _build_group_member_email_map(
             group_member_emails.setdefault(group_id, set()).add(email)
             all_users_groups.add(group_id)
 
-        if not group_member_emails:
-            logger.warning(f"No groups found for user with email: {email}")
+        if not all_users_groups:
+            msg = f"No groups found for user with email: {email}"
+            emit_background_error(msg, cc_pair_id=cc_pair_id)
+            logger.error(msg)
         else:
             logger.debug(f"Found groups {all_users_groups} for user with email {email}")
 
+    if not group_member_emails:
+        msg = "No groups found for any users."
+        emit_background_error(msg, cc_pair_id=cc_pair_id)
+        logger.error(msg)
+
     return group_member_emails
 
 
@@ -61,6 +77,7 @@ def confluence_group_sync(
 
     group_member_email_map = _build_group_member_email_map(
         confluence_client=confluence_client,
+        cc_pair_id=cc_pair.id,
     )
     onyx_groups: list[ExternalUserGroup] = []
     all_found_emails = set()

backend/ee/onyx/external_permissions/gmail/doc_sync.py

@@ -15,6 +15,7 @@ logger = setup_logger()
 def _get_slim_doc_generator(
     cc_pair: ConnectorCredentialPair,
     gmail_connector: GmailConnector,
+    callback: IndexingHeartbeatInterface | None = None,
 ) -> GenerateSlimDocumentOutput:
     current_time = datetime.now(timezone.utc)
     start_time = (
@@ -24,7 +25,9 @@ def _get_slim_doc_generator(
     )
 
     return gmail_connector.retrieve_all_slim_documents(
-        start=start_time, end=current_time.timestamp()
+        start=start_time,
+        end=current_time.timestamp(),
+        callback=callback,
     )
 
 
@@ -40,7 +43,9 @@ def gmail_doc_sync(
     gmail_connector = GmailConnector(**cc_pair.connector.connector_specific_config)
     gmail_connector.load_credentials(cc_pair.credential.credential_json)
 
-    slim_doc_generator = _get_slim_doc_generator(cc_pair, gmail_connector)
+    slim_doc_generator = _get_slim_doc_generator(
+        cc_pair, gmail_connector, callback=callback
+    )
 
     document_external_access: list[DocExternalAccess] = []
     for slim_doc_batch in slim_doc_generator:

backend/ee/onyx/external_permissions/google_drive/doc_sync.py

@@ -21,6 +21,7 @@ _PERMISSION_ID_PERMISSION_MAP: dict[str, dict[str, Any]] = {}
 def _get_slim_doc_generator(
     cc_pair: ConnectorCredentialPair,
     google_drive_connector: GoogleDriveConnector,
+    callback: IndexingHeartbeatInterface | None = None,
 ) -> GenerateSlimDocumentOutput:
     current_time = datetime.now(timezone.utc)
     start_time = (
@@ -30,7 +31,9 @@ def _get_slim_doc_generator(
     )
 
     return google_drive_connector.retrieve_all_slim_documents(
-        start=start_time, end=current_time.timestamp()
+        start=start_time,
+        end=current_time.timestamp(),
+        callback=callback,
     )
 
 

backend/ee/onyx/external_permissions/slack/doc_sync.py

@@ -20,19 +20,11 @@ def _get_slack_document_ids_and_channels(
     slack_connector = SlackPollConnector(**cc_pair.connector.connector_specific_config)
     slack_connector.load_credentials(cc_pair.credential.credential_json)
 
-    slim_doc_generator = slack_connector.retrieve_all_slim_documents()
+    slim_doc_generator = slack_connector.retrieve_all_slim_documents(callback=callback)
 
     channel_doc_map: dict[str, list[str]] = {}
     for doc_metadata_batch in slim_doc_generator:
         for doc_metadata in doc_metadata_batch:
-            if callback:
-                if callback.should_stop():
-                    raise RuntimeError(
-                        "_get_slack_document_ids_and_channels: Stop signal detected"
-                    )
-
-                callback.progress("_get_slack_document_ids_and_channels", 1)
-
             if doc_metadata.perm_sync_data is None:
                 continue
             channel_id = doc_metadata.perm_sync_data["channel_id"]
@@ -40,6 +32,14 @@ def _get_slack_document_ids_and_channels(
                 channel_doc_map[channel_id] = []
             channel_doc_map[channel_id].append(doc_metadata.id)
 
+        if callback:
+            if callback.should_stop():
+                raise RuntimeError(
+                    "_get_slack_document_ids_and_channels: Stop signal detected"
+                )
+
+            callback.progress("_get_slack_document_ids_and_channels", 1)
+
     return channel_doc_map
 
 

backend/ee/onyx/onyxbot/slack/handlers/handle_standard_answers.py

@@ -80,7 +80,7 @@ def oneoff_standard_answers(
 def _handle_standard_answers(
     message_info: SlackMessageInfo,
     receiver_ids: list[str] | None,
-    slack_channel_config: SlackChannelConfig | None,
+    slack_channel_config: SlackChannelConfig,
     prompt: Prompt | None,
     logger: OnyxLoggingAdapter,
     client: WebClient,
@@ -94,13 +94,10 @@ def _handle_standard_answers(
     Returns True if standard answers are found to match the user's message and therefore,
     we still need to respond to the users.
     """
-    # if no channel config, then no standard answers are configured
-    if not slack_channel_config:
-        return False
 
     slack_thread_id = message_info.thread_to_respond
     configured_standard_answer_categories = (
-        slack_channel_config.standard_answer_categories if slack_channel_config else []
+        slack_channel_config.standard_answer_categories
     )
     configured_standard_answers = set(
         [

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

@@ -10,6 +10,7 @@ from fastapi import Response
 from ee.onyx.auth.users import decode_anonymous_user_jwt_token
 from ee.onyx.configs.app_configs import ANONYMOUS_USER_COOKIE_NAME
 from onyx.auth.api_key import extract_tenant_from_api_key_header
+from onyx.configs.constants import TENANT_ID_COOKIE_NAME
 from onyx.db.engine import is_valid_schema_name
 from onyx.redis.redis_pool import retrieve_auth_token_data_from_redis
 from shared_configs.configs import MULTI_TENANT
@@ -43,6 +44,7 @@ async def _get_tenant_id_from_request(
     Attempt to extract tenant_id from:
     1) The API key header
     2) The Redis-based token (stored in Cookie: fastapiusersauth)
+    3)  Reset token cookie
     Fallback: POSTGRES_DEFAULT_SCHEMA
     """
     # Check for API key
@@ -62,6 +64,7 @@ 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:
@@ -85,8 +88,18 @@ async def _get_tenant_id_from_request(
         if not is_valid_schema_name(tenant_id):
             raise HTTPException(status_code=400, detail="Invalid tenant ID format")
 
-        return tenant_id
-
     except Exception as e:
         logger.error(f"Unexpected error in _get_tenant_id_from_request: {str(e)}")
         raise HTTPException(status_code=500, detail="Internal server error")
+
+    finally:
+        if tenant_id:
+            return tenant_id
+
+        # As a final step, check for explicit tenant_id cookie
+        tenant_id_cookie = request.cookies.get(TENANT_ID_COOKIE_NAME)
+        if tenant_id_cookie and is_valid_schema_name(tenant_id_cookie):
+            return tenant_id_cookie
+
+        # If we've reached this point, return the default schema
+        return POSTGRES_DEFAULT_SCHEMA

backend/ee/onyx/server/oauth.py

@@ -286,6 +286,7 @@ def prepare_authorization_request(
     oauth_state = (
         base64.urlsafe_b64encode(oauth_uuid.bytes).rstrip(b"=").decode("utf-8")
     )
+    session: str
 
     if connector == DocumentSource.SLACK:
         oauth_url = SlackOAuth.generate_oauth_url(oauth_state)
@@ -554,6 +555,7 @@ def handle_google_drive_oauth_callback(
         )
 
     session_json = session_json_bytes.decode("utf-8")
+    session: GoogleDriveOAuth.OAuthSession
     try:
         session = GoogleDriveOAuth.parse_session(session_json)
 

backend/ee/onyx/server/query_and_chat/chat_backend.py

@@ -179,6 +179,7 @@ def handle_simplified_chat_message(
         chunks_below=0,
         full_doc=chat_message_req.full_doc,
         structured_response_format=chat_message_req.structured_response_format,
+        use_agentic_search=chat_message_req.use_agentic_search,
     )
 
     packets = stream_chat_message_objects(
@@ -301,6 +302,7 @@ def handle_send_message_simple_with_history(
         chunks_below=0,
         full_doc=req.full_doc,
         structured_response_format=req.structured_response_format,
+        use_agentic_search=req.use_agentic_search,
     )
 
     packets = stream_chat_message_objects(

backend/ee/onyx/server/query_and_chat/models.py

@@ -57,6 +57,9 @@ class BasicCreateChatMessageRequest(ChunkContext):
     # https://platform.openai.com/docs/guides/structured-outputs/introduction
     structured_response_format: dict | None = None
 
+    # If True, uses agentic search instead of basic search
+    use_agentic_search: bool = False
+
 
 class BasicCreateChatMessageWithHistoryRequest(ChunkContext):
     # Last element is the new query. All previous elements are historical context
@@ -71,6 +74,8 @@ class BasicCreateChatMessageWithHistoryRequest(ChunkContext):
     # only works if using an OpenAI model. See the following for more details:
     # https://platform.openai.com/docs/guides/structured-outputs/introduction
     structured_response_format: dict | None = None
+    # If True, uses agentic search instead of basic search
+    use_agentic_search: bool = False
 
 
 class SimpleDoc(BaseModel):
@@ -120,9 +125,12 @@ class OneShotQARequest(ChunkContext):
     # will also disable Thread-based Rewording if specified
     query_override: str | None = None
 
-    # If True, skips generative an AI response to the search query
+    # If True, skips generating an AI response to the search query
     skip_gen_ai_answer_generation: bool = False
 
+    # If True, uses agentic search instead of basic search
+    use_agentic_search: bool = False
+
     @model_validator(mode="after")
     def check_persona_fields(self) -> "OneShotQARequest":
         if self.persona_override_config is None and self.persona_id is None:

backend/ee/onyx/server/query_and_chat/query_backend.py

@@ -196,6 +196,8 @@ def get_answer_stream(
         retrieval_details=query_request.retrieval_options,
         rerank_settings=query_request.rerank_settings,
         db_session=db_session,
+        use_agentic_search=query_request.use_agentic_search,
+        skip_gen_ai_answer_generation=query_request.skip_gen_ai_answer_generation,
     )
 
     packets = stream_chat_message_objects(

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

@@ -34,6 +34,7 @@ from onyx.auth.users import get_redis_strategy
 from onyx.auth.users import optional_user
 from onyx.auth.users import User
 from onyx.configs.app_configs import WEB_DOMAIN
+from onyx.configs.constants import FASTAPI_USERS_AUTH_COOKIE_NAME
 from onyx.db.auth import get_user_count
 from onyx.db.engine import get_current_tenant_id
 from onyx.db.engine import get_session
@@ -111,7 +112,7 @@ async def login_as_anonymous_user(
     token = generate_anonymous_user_jwt_token(tenant_id)
 
     response = Response()
-    response.delete_cookie("fastapiusersauth")
+    response.delete_cookie(FASTAPI_USERS_AUTH_COOKIE_NAME)
     response.set_cookie(
         key=ANONYMOUS_USER_COOKIE_NAME,
         value=token,

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

@@ -24,6 +24,7 @@ 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
@@ -85,7 +86,8 @@ 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
-        await notify_control_plane(tenant_id, email, referral_source)
+        if not DEV_MODE:
+            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/model_server/constants.py

@@ -28,3 +28,9 @@ class EmbeddingModelTextType:
     @staticmethod
     def get_type(provider: EmbeddingProvider, text_type: EmbedTextType) -> str:
         return EmbeddingModelTextType.PROVIDER_TEXT_TYPE_MAP[provider][text_type]
+
+
+class GPUStatus:
+    CUDA = "cuda"
+    MAC_MPS = "mps"
+    NONE = "none"

backend/model_server/encoders.py

@@ -12,6 +12,7 @@ import voyageai  # type: ignore
 from cohere import AsyncClient as CohereAsyncClient
 from fastapi import APIRouter
 from fastapi import HTTPException
+from fastapi import Request
 from google.oauth2 import service_account  # type: ignore
 from litellm import aembedding
 from litellm.exceptions import RateLimitError
@@ -320,6 +321,7 @@ async def embed_text(
     prefix: str | None,
     api_url: str | None,
     api_version: str | None,
+    gpu_type: str = "UNKNOWN",
 ) -> list[Embedding]:
     if not all(texts):
         logger.error("Empty strings provided for embedding")
@@ -373,8 +375,11 @@ async def embed_text(
 
         elapsed = time.monotonic() - start
         logger.info(
-            f"Successfully embedded {len(texts)} texts with {total_chars} total characters "
-            f"with provider {provider_type} in {elapsed:.2f}"
+            f"event=embedding_provider "
+            f"texts={len(texts)} "
+            f"chars={total_chars} "
+            f"provider={provider_type} "
+            f"elapsed={elapsed:.2f}"
         )
     elif model_name is not None:
         logger.info(
@@ -403,6 +408,14 @@ async def embed_text(
             f"Successfully embedded {len(texts)} texts with {total_chars} total characters "
             f"with local model {model_name} in {elapsed:.2f}"
         )
+        logger.info(
+            f"event=embedding_model "
+            f"texts={len(texts)} "
+            f"chars={total_chars} "
+            f"model={model_name} "
+            f"gpu={gpu_type} "
+            f"elapsed={elapsed:.2f}"
+        )
     else:
         logger.error("Neither model name nor provider specified for embedding")
         raise ValueError(
@@ -455,8 +468,15 @@ async def litellm_rerank(
 
 
 @router.post("/bi-encoder-embed")
-async def process_embed_request(
+async def route_bi_encoder_embed(
+    request: Request,
     embed_request: EmbedRequest,
+) -> EmbedResponse:
+    return await process_embed_request(embed_request, request.app.state.gpu_type)
+
+
+async def process_embed_request(
+    embed_request: EmbedRequest, gpu_type: str = "UNKNOWN"
 ) -> EmbedResponse:
     if not embed_request.texts:
         raise HTTPException(status_code=400, detail="No texts to be embedded")
@@ -484,6 +504,7 @@ async def process_embed_request(
             api_url=embed_request.api_url,
             api_version=embed_request.api_version,
             prefix=prefix,
+            gpu_type=gpu_type,
         )
         return EmbedResponse(embeddings=embeddings)
     except RateLimitError as e:

backend/model_server/main.py

@@ -16,6 +16,7 @@ from model_server.custom_models import router as custom_models_router
 from model_server.custom_models import warm_up_intent_model
 from model_server.encoders import router as encoders_router
 from model_server.management_endpoints import router as management_router
+from model_server.utils import get_gpu_type
 from onyx import __version__
 from onyx.utils.logger import setup_logger
 from shared_configs.configs import INDEXING_ONLY
@@ -58,12 +59,10 @@ def _move_files_recursively(source: Path, dest: Path, overwrite: bool = False) -
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncGenerator:
-    if torch.cuda.is_available():
-        logger.notice("CUDA GPU is available")
-    elif torch.backends.mps.is_available():
-        logger.notice("Mac MPS is available")
-    else:
-        logger.notice("GPU is not available, using CPU")
+    gpu_type = get_gpu_type()
+    logger.notice(f"Torch GPU Detection: gpu_type={gpu_type}")
+
+    app.state.gpu_type = gpu_type
 
     if TEMP_HF_CACHE_PATH.is_dir():
         logger.notice("Moving contents of temp_huggingface to huggingface cache.")

backend/model_server/management_endpoints.py

@@ -1,7 +1,9 @@
-import torch
 from fastapi import APIRouter
 from fastapi import Response
 
+from model_server.constants import GPUStatus
+from model_server.utils import get_gpu_type
+
 router = APIRouter(prefix="/api")
 
 
@@ -11,10 +13,7 @@ async def healthcheck() -> Response:
 
 
 @router.get("/gpu-status")
-async def gpu_status() -> dict[str, bool | str]:
-    if torch.cuda.is_available():
-        return {"gpu_available": True, "type": "cuda"}
-    elif torch.backends.mps.is_available():
-        return {"gpu_available": True, "type": "mps"}
-    else:
-        return {"gpu_available": False, "type": "none"}
+async def route_gpu_status() -> dict[str, bool | str]:
+    gpu_type = get_gpu_type()
+    gpu_available = gpu_type != GPUStatus.NONE
+    return {"gpu_available": gpu_available, "type": gpu_type}

backend/model_server/utils.py

@@ -8,6 +8,9 @@ from typing import Any
 from typing import cast
 from typing import TypeVar
 
+import torch
+
+from model_server.constants import GPUStatus
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -58,3 +61,12 @@ def simple_log_function_time(
             return cast(F, wrapped_sync_func)
 
     return decorator
+
+
+def get_gpu_type() -> str:
+    if torch.cuda.is_available():
+        return GPUStatus.CUDA
+    if torch.backends.mps.is_available():
+        return GPUStatus.MAC_MPS
+
+    return GPUStatus.NONE

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

@@ -0,0 +1,97 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.basic.states import BasicInput
+from onyx.agents.agent_search.basic.states import BasicOutput
+from onyx.agents.agent_search.basic.states import BasicState
+from onyx.agents.agent_search.orchestration.nodes.basic_use_tool_response import (
+    basic_use_tool_response,
+)
+from onyx.agents.agent_search.orchestration.nodes.llm_tool_choice import llm_tool_choice
+from onyx.agents.agent_search.orchestration.nodes.prepare_tool_input import (
+    prepare_tool_input,
+)
+from onyx.agents.agent_search.orchestration.nodes.tool_call import tool_call
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def basic_graph_builder() -> StateGraph:
+    graph = StateGraph(
+        state_schema=BasicState,
+        input=BasicInput,
+        output=BasicOutput,
+    )
+
+    ### Add nodes ###
+
+    graph.add_node(
+        node="prepare_tool_input",
+        action=prepare_tool_input,
+    )
+
+    graph.add_node(
+        node="llm_tool_choice",
+        action=llm_tool_choice,
+    )
+
+    graph.add_node(
+        node="tool_call",
+        action=tool_call,
+    )
+
+    graph.add_node(
+        node="basic_use_tool_response",
+        action=basic_use_tool_response,
+    )
+
+    ### Add edges ###
+
+    graph.add_edge(start_key=START, end_key="prepare_tool_input")
+
+    graph.add_edge(start_key="prepare_tool_input", end_key="llm_tool_choice")
+
+    graph.add_conditional_edges("llm_tool_choice", should_continue, ["tool_call", END])
+
+    graph.add_edge(
+        start_key="tool_call",
+        end_key="basic_use_tool_response",
+    )
+
+    graph.add_edge(
+        start_key="basic_use_tool_response",
+        end_key=END,
+    )
+
+    return graph
+
+
+def should_continue(state: BasicState) -> str:
+    return (
+        # If there are no tool calls, basic graph already streamed the answer
+        END
+        if state.tool_choice is None
+        else "tool_call"
+    )
+
+
+if __name__ == "__main__":
+    from onyx.db.engine import get_session_context_manager
+    from onyx.context.search.models import SearchRequest
+    from onyx.llm.factory import get_default_llms
+    from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
+
+    graph = basic_graph_builder()
+    compiled_graph = graph.compile()
+    input = BasicInput(unused=True)
+    primary_llm, fast_llm = get_default_llms()
+    with get_session_context_manager() as db_session:
+        config, _ = get_test_config(
+            db_session=db_session,
+            primary_llm=primary_llm,
+            fast_llm=fast_llm,
+            search_request=SearchRequest(query="How does onyx use FastAPI?"),
+        )
+        compiled_graph.invoke(input, config={"metadata": {"config": config}})

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

@@ -0,0 +1,35 @@
+from typing import TypedDict
+
+from langchain_core.messages import AIMessageChunk
+from pydantic import BaseModel
+
+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
+
+# States contain values that change over the course of graph execution,
+# Config is for values that are set at the start and never change.
+# If you are using a value from the config and realize it needs to change,
+# you should add it to the state and use/update the version in the state.
+
+
+## Graph Input State
+class BasicInput(BaseModel):
+    # Langgraph needs a nonempty input, but we pass in all static
+    # data through a RunnableConfig.
+    unused: bool = True
+
+
+## Graph Output State
+class BasicOutput(TypedDict):
+    tool_call_chunk: AIMessageChunk
+
+
+## Graph State
+class BasicState(
+    BasicInput,
+    ToolChoiceInput,
+    ToolCallUpdate,
+    ToolChoiceUpdate,
+):
+    pass

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

@@ -0,0 +1,64 @@
+from collections.abc import Iterator
+from typing import cast
+
+from langchain_core.messages import AIMessageChunk
+from langchain_core.messages import BaseMessage
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.models import LlmDoc
+from onyx.chat.models import OnyxContext
+from onyx.chat.stream_processing.answer_response_handler import AnswerResponseHandler
+from onyx.chat.stream_processing.answer_response_handler import CitationResponseHandler
+from onyx.chat.stream_processing.answer_response_handler import (
+    PassThroughAnswerResponseHandler,
+)
+from onyx.chat.stream_processing.utils import map_document_id_order
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def process_llm_stream(
+    messages: Iterator[BaseMessage],
+    should_stream_answer: bool,
+    writer: StreamWriter,
+    final_search_results: list[LlmDoc] | None = None,
+    displayed_search_results: list[OnyxContext] | list[LlmDoc] | None = None,
+) -> AIMessageChunk:
+    tool_call_chunk = AIMessageChunk(content="")
+
+    if final_search_results and displayed_search_results:
+        answer_handler: AnswerResponseHandler = CitationResponseHandler(
+            context_docs=final_search_results,
+            final_doc_id_to_rank_map=map_document_id_order(final_search_results),
+            display_doc_id_to_rank_map=map_document_id_order(displayed_search_results),
+        )
+    else:
+        answer_handler = PassThroughAnswerResponseHandler()
+
+    full_answer = ""
+    # This stream will be the llm answer if no tool is chosen. When a tool is chosen,
+    # the stream will contain AIMessageChunks with tool call information.
+    for message in messages:
+        answer_piece = message.content
+        if not isinstance(answer_piece, str):
+            # this is only used for logging, so fine to
+            # just add the string representation
+            answer_piece = str(answer_piece)
+        full_answer += answer_piece
+
+        if isinstance(message, AIMessageChunk) and (
+            message.tool_call_chunks or message.tool_calls
+        ):
+            tool_call_chunk += message  # type: ignore
+        elif should_stream_answer:
+            for response_part in answer_handler.handle_response_part(message, []):
+                write_custom_event(
+                    "basic_response",
+                    response_part,
+                    writer,
+                )
+
+    logger.debug(f"Full answer: {full_answer}")
+    return cast(AIMessageChunk, tool_call_chunk)

backend/onyx/agents/agent_search/core_state.py

@@ -0,0 +1,21 @@
+from operator import add
+from typing import Annotated
+
+from pydantic import BaseModel
+
+
+class CoreState(BaseModel):
+    """
+    This is the core state that is shared across all subgraphs.
+    """
+
+    base_question: str = ""
+    log_messages: Annotated[list[str], add] = []
+
+
+class SubgraphCoreState(BaseModel):
+    """
+    This is the core state that is shared across all subgraphs.
+    """
+
+    log_messages: Annotated[list[str], add]

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

@@ -0,0 +1,31 @@
+from collections.abc import Hashable
+from datetime import datetime
+
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def send_to_expanded_retrieval(state: SubQuestionAnsweringInput) -> Send | Hashable:
+    """
+    LangGraph edge to send a sub-question to the expanded retrieval.
+    """
+    edge_start_time = datetime.now()
+
+    return Send(
+        "initial_sub_question_expanded_retrieval",
+        ExpandedRetrievalInput(
+            question=state.question,
+            base_search=False,
+            sub_question_id=state.question_id,
+            log_messages=[f"{edge_start_time} -- Sending to expanded retrieval"],
+        ),
+    )

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

@@ -0,0 +1,137 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.edges import (
+    send_to_expanded_retrieval,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.check_sub_answer import (
+    check_sub_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.format_sub_answer import (
+    format_sub_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.generate_sub_answer import (
+    generate_sub_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.ingest_retrieved_documents import (
+    ingest_retrieved_documents,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.graph_builder import (
+    expanded_retrieval_graph_builder,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def answer_query_graph_builder() -> StateGraph:
+    """
+    LangGraph sub-graph builder for the initial individual sub-answer generation.
+    """
+    graph = StateGraph(
+        state_schema=AnswerQuestionState,
+        input=SubQuestionAnsweringInput,
+        output=AnswerQuestionOutput,
+    )
+
+    ### Add nodes ###
+
+    # The sub-graph that executes the expanded retrieval process for a sub-question
+    expanded_retrieval = expanded_retrieval_graph_builder().compile()
+    graph.add_node(
+        node="initial_sub_question_expanded_retrieval",
+        action=expanded_retrieval,
+    )
+
+    # The node that ingests the retrieved documents and puts them into the proper
+    # state keys.
+    graph.add_node(
+        node="ingest_retrieval",
+        action=ingest_retrieved_documents,
+    )
+
+    # The node that generates the sub-answer
+    graph.add_node(
+        node="generate_sub_answer",
+        action=generate_sub_answer,
+    )
+
+    # The node that checks the sub-answer
+    graph.add_node(
+        node="answer_check",
+        action=check_sub_answer,
+    )
+
+    # The node that formats the sub-answer for the following initial answer generation
+    graph.add_node(
+        node="format_answer",
+        action=format_sub_answer,
+    )
+
+    ### Add edges ###
+
+    graph.add_conditional_edges(
+        source=START,
+        path=send_to_expanded_retrieval,
+        path_map=["initial_sub_question_expanded_retrieval"],
+    )
+    graph.add_edge(
+        start_key="initial_sub_question_expanded_retrieval",
+        end_key="ingest_retrieval",
+    )
+    graph.add_edge(
+        start_key="ingest_retrieval",
+        end_key="generate_sub_answer",
+    )
+    graph.add_edge(
+        start_key="generate_sub_answer",
+        end_key="answer_check",
+    )
+    graph.add_edge(
+        start_key="answer_check",
+        end_key="format_answer",
+    )
+    graph.add_edge(
+        start_key="format_answer",
+        end_key=END,
+    )
+
+    return graph
+
+
+if __name__ == "__main__":
+    from onyx.db.engine import get_session_context_manager
+    from onyx.llm.factory import get_default_llms
+    from onyx.context.search.models import SearchRequest
+
+    graph = answer_query_graph_builder()
+    compiled_graph = graph.compile()
+    primary_llm, fast_llm = get_default_llms()
+    search_request = SearchRequest(
+        query="what can you do with onyx or danswer?",
+    )
+    with get_session_context_manager() as db_session:
+        graph_config, search_tool = get_test_config(
+            db_session, primary_llm, fast_llm, search_request
+        )
+        inputs = SubQuestionAnsweringInput(
+            question="what can you do with onyx?",
+            question_id="0_0",
+            log_messages=[],
+        )
+        for thing in compiled_graph.stream(
+            input=inputs,
+            config={"configurable": {"config": graph_config}},
+        ):
+            logger.debug(thing)

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

@@ -0,0 +1,75 @@
+from datetime import datetime
+from typing import cast
+
+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 (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnswerCheckUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+from onyx.prompts.agent_search import SUB_ANSWER_CHECK_PROMPT
+from onyx.prompts.agent_search import UNKNOWN_ANSWER
+
+
+def check_sub_answer(
+    state: AnswerQuestionState, config: RunnableConfig
+) -> SubQuestionAnswerCheckUpdate:
+    """
+    LangGraph node to check the quality of the sub-answer. The answer
+    is represented as a boolean value.
+    """
+    node_start_time = datetime.now()
+
+    level, question_num = parse_question_id(state.question_id)
+    if state.answer == UNKNOWN_ANSWER:
+        return SubQuestionAnswerCheckUpdate(
+            answer_quality=False,
+            log_messages=[
+                get_langgraph_node_log_string(
+                    graph_component="initial  - generate individual sub answer",
+                    node_name="check sub answer",
+                    node_start_time=node_start_time,
+                    result="unknown answer",
+                )
+            ],
+        )
+    msg = [
+        HumanMessage(
+            content=SUB_ANSWER_CHECK_PROMPT.format(
+                question=state.question,
+                base_answer=state.answer,
+            )
+        )
+    ]
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    fast_llm = graph_config.tooling.fast_llm
+    response = list(
+        fast_llm.stream(
+            prompt=msg,
+        )
+    )
+
+    quality_str: str = merge_message_runs(response, chunk_separator="")[0].content
+    answer_quality = "yes" in quality_str.lower()
+
+    return SubQuestionAnswerCheckUpdate(
+        answer_quality=answer_quality,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="initial  - generate individual sub answer",
+                node_name="check sub answer",
+                node_start_time=node_start_time,
+                result=f"Answer quality: {quality_str}",
+            )
+        ],
+    )

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

@@ -0,0 +1,30 @@
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    SubQuestionAnswerResults,
+)
+
+
+def format_sub_answer(state: AnswerQuestionState) -> AnswerQuestionOutput:
+    """
+    LangGraph node to generate the sub-answer format.
+    """
+    return AnswerQuestionOutput(
+        answer_results=[
+            SubQuestionAnswerResults(
+                question=state.question,
+                question_id=state.question_id,
+                verified_high_quality=state.answer_quality,
+                answer=state.answer,
+                sub_query_retrieval_results=state.expanded_retrieval_results,
+                verified_reranked_documents=state.verified_reranked_documents,
+                context_documents=state.context_documents,
+                cited_documents=state.cited_documents,
+                sub_question_retrieval_stats=state.sub_question_retrieval_stats,
+            )
+        ],
+    )

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

@@ -0,0 +1,137 @@
+from datetime import datetime
+from typing import Any
+from typing import cast
+
+from langchain_core.messages import merge_message_runs
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnswerGenerationUpdate,
+)
+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.utils import get_answer_citation_ids
+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 (
+    get_persona_agent_prompt_expressions,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.models import AgentAnswerPiece
+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.prompts.agent_search import NO_RECOVERED_DOCS
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def generate_sub_answer(
+    state: AnswerQuestionState,
+    config: RunnableConfig,
+    writer: StreamWriter = lambda _: None,
+) -> SubQuestionAnswerGenerationUpdate:
+    """
+    LangGraph node to generate a sub-answer.
+    """
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = state.question
+    state.verified_reranked_documents
+    level, question_num = parse_question_id(state.question_id)
+    context_docs = state.context_documents[:AGENT_MAX_ANSWER_CONTEXT_DOCS]
+    persona_contextualized_prompt = get_persona_agent_prompt_expressions(
+        graph_config.inputs.search_request.persona
+    ).contextualized_prompt
+
+    if len(context_docs) == 0:
+        answer_str = NO_RECOVERED_DOCS
+        write_custom_event(
+            "sub_answers",
+            AgentAnswerPiece(
+                answer_piece=answer_str,
+                level=level,
+                level_question_num=question_num,
+                answer_type="agent_sub_answer",
+            ),
+            writer,
+        )
+    else:
+        fast_llm = graph_config.tooling.fast_llm
+        msg = build_sub_question_answer_prompt(
+            question=question,
+            original_question=graph_config.inputs.search_request.query,
+            docs=context_docs,
+            persona_specification=persona_contextualized_prompt,
+            config=fast_llm.config,
+        )
+
+        response: list[str | list[str | dict[str, Any]]] = []
+        dispatch_timings: list[float] = []
+        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)}"
+                )
+            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,
+            )
+            end_stream_token = datetime.now()
+            dispatch_timings.append(
+                (end_stream_token - start_stream_token).microseconds
+            )
+            response.append(content)
+
+        answer_str = merge_message_runs(response, chunk_separator="")[0].content
+        logger.debug(
+            f"Average dispatch time: {sum(dispatch_timings) / len(dispatch_timings)}"
+        )
+
+    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,
+        stream_type=StreamType.SUB_ANSWER,
+        level=level,
+        level_question_num=question_num,
+    )
+    write_custom_event("stream_finished", stop_event, writer)
+
+    return SubQuestionAnswerGenerationUpdate(
+        answer=answer_str,
+        cited_documents=cited_documents,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="initial - generate individual sub answer",
+                node_name="generate sub answer",
+                node_start_time=node_start_time,
+                result="",
+            )
+        ],
+    )

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

@@ -0,0 +1,25 @@
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionRetrievalIngestionUpdate,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalOutput,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+
+
+def ingest_retrieved_documents(
+    state: ExpandedRetrievalOutput,
+) -> SubQuestionRetrievalIngestionUpdate:
+    """
+    LangGraph node to ingest the retrieved documents to format it for the sub-answer.
+    """
+    sub_question_retrieval_stats = state.expanded_retrieval_result.retrieval_stats
+    if sub_question_retrieval_stats is None:
+        sub_question_retrieval_stats = [AgentChunkRetrievalStats()]
+
+    return SubQuestionRetrievalIngestionUpdate(
+        expanded_retrieval_results=state.expanded_retrieval_result.expanded_query_results,
+        verified_reranked_documents=state.expanded_retrieval_result.verified_reranked_documents,
+        context_documents=state.expanded_retrieval_result.context_documents,
+        sub_question_retrieval_stats=sub_question_retrieval_stats,
+    )

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

@@ -0,0 +1,75 @@
+from operator import add
+from typing import Annotated
+
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.core_state import SubgraphCoreState
+from onyx.agents.agent_search.deep_search.main.states import LoggerUpdate
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    SubQuestionAnswerResults,
+)
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_inference_sections,
+)
+from onyx.context.search.models import InferenceSection
+
+
+## Update States
+class SubQuestionAnswerCheckUpdate(LoggerUpdate, BaseModel):
+    answer_quality: bool = False
+    log_messages: list[str] = []
+
+
+class SubQuestionAnswerGenerationUpdate(LoggerUpdate, BaseModel):
+    answer: str = ""
+    log_messages: list[str] = []
+    cited_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
+    # answer_stat: AnswerStats
+
+
+class SubQuestionRetrievalIngestionUpdate(LoggerUpdate, BaseModel):
+    expanded_retrieval_results: list[QueryRetrievalResult] = []
+    verified_reranked_documents: Annotated[
+        list[InferenceSection], dedup_inference_sections
+    ] = []
+    context_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
+    sub_question_retrieval_stats: AgentChunkRetrievalStats = AgentChunkRetrievalStats()
+
+
+## Graph Input State
+
+
+class SubQuestionAnsweringInput(SubgraphCoreState):
+    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.
+
+
+## Graph State
+
+
+class AnswerQuestionState(
+    SubQuestionAnsweringInput,
+    SubQuestionAnswerGenerationUpdate,
+    SubQuestionAnswerCheckUpdate,
+    SubQuestionRetrievalIngestionUpdate,
+):
+    pass
+
+
+## Graph Output State
+
+
+class AnswerQuestionOutput(LoggerUpdate, BaseModel):
+    """
+    This is a list of results even though each call of this subgraph only returns one result.
+    This is because if we parallelize the answer query subgraph, there will be multiple
+      results in a list so the add operator is used to add them together.
+    """
+
+    answer_results: Annotated[list[SubQuestionAnswerResults], add] = []

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

@@ -0,0 +1,50 @@
+from collections.abc import Hashable
+from datetime import datetime
+
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
+    SubQuestionRetrievalState,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
+
+
+def parallelize_initial_sub_question_answering(
+    state: SubQuestionRetrievalState,
+) -> list[Send | Hashable]:
+    """
+    LangGraph edge to parallelize the initial sub-question answering. If there are no sub-questions,
+    we send empty answers to the initial answer generation, and that answer would be generated
+    solely based on the documents retrieved for the original question.
+    """
+    edge_start_time = datetime.now()
+    if len(state.initial_sub_questions) > 0:
+        return [
+            Send(
+                "answer_query_subgraph",
+                SubQuestionAnsweringInput(
+                    question=question,
+                    question_id=make_question_id(0, question_num + 1),
+                    log_messages=[
+                        f"{edge_start_time} -- Main Edge - Parallelize Initial Sub-question Answering"
+                    ],
+                ),
+            )
+            for question_num, question in enumerate(state.initial_sub_questions)
+        ]
+
+    else:
+        return [
+            Send(
+                "ingest_answers",
+                AnswerQuestionOutput(
+                    answer_results=[],
+                ),
+            )
+        ]

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

@@ -0,0 +1,96 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.nodes.generate_initial_answer import (
+    generate_initial_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.nodes.validate_initial_answer import (
+    validate_initial_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
+    SubQuestionRetrievalInput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
+    SubQuestionRetrievalState,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.graph_builder import (
+    generate_sub_answers_graph_builder,
+)
+from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.graph_builder import (
+    retrieve_orig_question_docs_graph_builder,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def generate_initial_answer_graph_builder(test_mode: bool = False) -> StateGraph:
+    """
+    LangGraph graph builder for the initial answer generation.
+    """
+    graph = StateGraph(
+        state_schema=SubQuestionRetrievalState,
+        input=SubQuestionRetrievalInput,
+    )
+
+    # The sub-graph that generates the initial sub-answers
+    generate_sub_answers = generate_sub_answers_graph_builder().compile()
+    graph.add_node(
+        node="generate_sub_answers_subgraph",
+        action=generate_sub_answers,
+    )
+
+    # The sub-graph that retrieves the original question documents. This is run
+    # in parallel with the sub-answer generation process
+    retrieve_orig_question_docs = retrieve_orig_question_docs_graph_builder().compile()
+    graph.add_node(
+        node="retrieve_orig_question_docs_subgraph_wrapper",
+        action=retrieve_orig_question_docs,
+    )
+
+    # Node that generates the initial answer using the results of the previous
+    # two sub-graphs
+    graph.add_node(
+        node="generate_initial_answer",
+        action=generate_initial_answer,
+    )
+
+    # Node that validates the initial answer
+    graph.add_node(
+        node="validate_initial_answer",
+        action=validate_initial_answer,
+    )
+
+    ### Add edges ###
+
+    graph.add_edge(
+        start_key=START,
+        end_key="retrieve_orig_question_docs_subgraph_wrapper",
+    )
+
+    graph.add_edge(
+        start_key=START,
+        end_key="generate_sub_answers_subgraph",
+    )
+
+    # Wait for both, the original question docs and the sub-answers to be generated before proceeding
+    graph.add_edge(
+        start_key=[
+            "retrieve_orig_question_docs_subgraph_wrapper",
+            "generate_sub_answers_subgraph",
+        ],
+        end_key="generate_initial_answer",
+    )
+
+    graph.add_edge(
+        start_key="generate_initial_answer",
+        end_key="validate_initial_answer",
+    )
+
+    graph.add_edge(
+        start_key="validate_initial_answer",
+        end_key=END,
+    )
+
+    return graph

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

@@ -0,0 +1,313 @@
+from datetime import datetime
+from typing import Any
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_content
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
+    SubQuestionRetrievalState,
+)
+from onyx.agents.agent_search.deep_search.main.models import AgentBaseMetrics
+from onyx.agents.agent_search.deep_search.main.operations import (
+    calculate_initial_agent_stats,
+)
+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 (
+    InitialAnswerUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    get_prompt_enrichment_components,
+)
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    trim_prompt_piece,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_inference_sections,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    dispatch_main_answer_stop_info,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import relevance_from_docs
+from onyx.agents.agent_search.shared_graph_utils.utils import remove_document_citations
+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.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
+from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
+from onyx.context.search.models import InferenceSection
+from onyx.prompts.agent_search import (
+    INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS,
+)
+from onyx.prompts.agent_search import (
+    INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS,
+)
+from onyx.prompts.agent_search import (
+    SUB_QUESTION_ANSWER_TEMPLATE,
+)
+from onyx.prompts.agent_search import UNKNOWN_ANSWER
+from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
+
+
+def generate_initial_answer(
+    state: SubQuestionRetrievalState,
+    config: RunnableConfig,
+    writer: StreamWriter = lambda _: None,
+) -> InitialAnswerUpdate:
+    """
+    LangGraph node to generate the initial answer, using the initial sub-questions/sub-answers and the
+    documents retrieved for the original question.
+    """
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = graph_config.inputs.search_request.query
+    prompt_enrichment_components = get_prompt_enrichment_components(graph_config)
+
+    sub_questions_cited_documents = state.cited_documents
+    orig_question_retrieval_documents = state.orig_question_retrieved_documents
+
+    consolidated_context_docs: list[InferenceSection] = sub_questions_cited_documents
+    counter = 0
+    for original_doc_number, original_doc in enumerate(
+        orig_question_retrieval_documents
+    ):
+        if original_doc_number not in sub_questions_cited_documents:
+            if (
+                counter <= AGENT_MIN_ORIG_QUESTION_DOCS
+                or len(consolidated_context_docs) < AGENT_MAX_ANSWER_CONTEXT_DOCS
+            ):
+                consolidated_context_docs.append(original_doc)
+                counter += 1
+
+    # sort docs by their scores - though the scores refer to different questions
+    relevant_docs = dedup_inference_sections(
+        consolidated_context_docs, consolidated_context_docs
+    )
+
+    sub_questions: list[str] = []
+    streamed_documents = (
+        relevant_docs
+        if len(relevant_docs) > 0
+        else state.orig_question_retrieved_documents[:15]
+    )
+
+    # Use the query info from the base document retrieval
+    query_info = get_query_info(state.orig_question_sub_query_retrieval_results)
+
+    assert (
+        graph_config.tooling.search_tool
+    ), "search_tool must be provided for agentic search"
+
+    relevance_list = relevance_from_docs(relevant_docs)
+    for tool_response in yield_search_responses(
+        query=question,
+        reranked_sections=streamed_documents,
+        final_context_sections=streamed_documents,
+        search_query_info=query_info,
+        get_section_relevance=lambda: relevance_list,
+        search_tool=graph_config.tooling.search_tool,
+    ):
+        write_custom_event(
+            "tool_response",
+            ExtendedToolResponse(
+                id=tool_response.id,
+                response=tool_response.response,
+                level=0,
+                level_question_num=0,  # 0, 0 is the base question
+            ),
+            writer,
+        )
+
+    if len(relevant_docs) == 0:
+        write_custom_event(
+            "initial_agent_answer",
+            AgentAnswerPiece(
+                answer_piece=UNKNOWN_ANSWER,
+                level=0,
+                level_question_num=0,
+                answer_type="agent_level_answer",
+            ),
+            writer,
+        )
+        dispatch_main_answer_stop_info(0, writer)
+
+        answer = UNKNOWN_ANSWER
+        initial_agent_stats = InitialAgentResultStats(
+            sub_questions={},
+            original_question={},
+            agent_effectiveness={},
+        )
+
+    else:
+        sub_question_answer_results = state.sub_question_results
+
+        # Collect the sub-questions and sub-answers and construct an appropriate
+        # prompt string.
+        # Consider replacing by a function.
+        answered_sub_questions: list[str] = []
+        all_sub_questions: list[str] = []  # Separate list for tracking all questions
+
+        for idx, sub_question_answer_result in enumerate(
+            sub_question_answer_results, start=1
+        ):
+            all_sub_questions.append(sub_question_answer_result.question)
+
+            is_valid_answer = (
+                sub_question_answer_result.verified_high_quality
+                and sub_question_answer_result.answer
+                and sub_question_answer_result.answer != UNKNOWN_ANSWER
+            )
+
+            if is_valid_answer:
+                answered_sub_questions.append(
+                    SUB_QUESTION_ANSWER_TEMPLATE.format(
+                        sub_question=sub_question_answer_result.question,
+                        sub_answer=sub_question_answer_result.answer,
+                        sub_question_num=idx,
+                    )
+                )
+
+        sub_question_answer_str = (
+            "\n\n------\n\n".join(answered_sub_questions)
+            if answered_sub_questions
+            else ""
+        )
+
+        # Use the appropriate prompt based on whether there are sub-questions.
+        base_prompt = (
+            INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS
+            if answered_sub_questions
+            else INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS
+        )
+
+        sub_questions = all_sub_questions  # Replace the original assignment
+
+        model = graph_config.tooling.fast_llm
+
+        doc_context = format_docs(relevant_docs)
+        doc_context = trim_prompt_piece(
+            config=model.config,
+            prompt_piece=doc_context,
+            reserved_str=(
+                base_prompt
+                + sub_question_answer_str
+                + prompt_enrichment_components.persona_prompts.contextualized_prompt
+                + prompt_enrichment_components.history
+                + prompt_enrichment_components.date_str
+            ),
+        )
+
+        msg = [
+            HumanMessage(
+                content=base_prompt.format(
+                    question=question,
+                    answered_sub_questions=remove_document_citations(
+                        sub_question_answer_str
+                    ),
+                    relevant_docs=doc_context,
+                    persona_specification=prompt_enrichment_components.persona_prompts.contextualized_prompt,
+                    history=prompt_enrichment_components.history,
+                    date_prompt=prompt_enrichment_components.date_str,
+                )
+            )
+        ]
+
+        streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
+        dispatch_timings: list[float] = []
+        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)}"
+                )
+            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,
+            )
+            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)}"
+        )
+
+        dispatch_main_answer_stop_info(0, writer)
+        response = merge_content(*streamed_tokens)
+        answer = cast(str, response)
+
+        initial_agent_stats = calculate_initial_agent_stats(
+            state.sub_question_results, state.orig_question_retrieval_stats
+        )
+
+        logger.debug(
+            f"\n\nYYYYY--Sub-Questions:\n\n{sub_question_answer_str}\n\nStats:\n\n"
+        )
+
+        if initial_agent_stats:
+            logger.debug(initial_agent_stats.original_question)
+            logger.debug(initial_agent_stats.sub_questions)
+            logger.debug(initial_agent_stats.agent_effectiveness)
+
+    agent_base_end_time = datetime.now()
+
+    if agent_base_end_time and state.agent_start_time:
+        duration_s = (agent_base_end_time - state.agent_start_time).total_seconds()
+    else:
+        duration_s = None
+
+    agent_base_metrics = AgentBaseMetrics(
+        num_verified_documents_total=len(relevant_docs),
+        num_verified_documents_core=state.orig_question_retrieval_stats.verified_count,
+        verified_avg_score_core=state.orig_question_retrieval_stats.verified_avg_scores,
+        num_verified_documents_base=initial_agent_stats.sub_questions.get(
+            "num_verified_documents"
+        ),
+        verified_avg_score_base=initial_agent_stats.sub_questions.get(
+            "verified_avg_score"
+        ),
+        base_doc_boost_factor=initial_agent_stats.agent_effectiveness.get(
+            "utilized_chunk_ratio"
+        ),
+        support_boost_factor=initial_agent_stats.agent_effectiveness.get(
+            "support_ratio"
+        ),
+        duration_s=duration_s,
+    )
+
+    return InitialAnswerUpdate(
+        initial_answer=answer,
+        initial_agent_stats=initial_agent_stats,
+        generated_sub_questions=sub_questions,
+        agent_base_end_time=agent_base_end_time,
+        agent_base_metrics=agent_base_metrics,
+        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="",
+            )
+        ],
+    )

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

@@ -0,0 +1,40 @@
+from datetime import datetime
+
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
+    SubQuestionRetrievalState,
+)
+from onyx.agents.agent_search.deep_search.main.operations import logger
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialAnswerQualityUpdate,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+
+
+def validate_initial_answer(
+    state: SubQuestionRetrievalState,
+) -> InitialAnswerQualityUpdate:
+    """
+    Check whether the initial answer sufficiently addresses the original user question.
+    """
+
+    node_start_time = datetime.now()
+
+    logger.debug(
+        f"--------{node_start_time}--------Checking for base answer validity - for not set True/False manually"
+    )
+
+    verdict = True
+
+    return InitialAnswerQualityUpdate(
+        initial_answer_quality_eval=verdict,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="initial - generate initial answer",
+                node_name="validate initial answer",
+                node_start_time=node_start_time,
+                result="",
+            )
+        ],
+    )

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

@@ -0,0 +1,51 @@
+from operator import add
+from typing import Annotated
+from typing import TypedDict
+
+from onyx.agents.agent_search.core_state import CoreState
+from onyx.agents.agent_search.deep_search.main.states import (
+    ExploratorySearchUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialAnswerQualityUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialAnswerUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialQuestionDecompositionUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    OrigQuestionRetrievalUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    SubQuestionResultsUpdate,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.models import (
+    QuestionRetrievalResult,
+)
+from onyx.context.search.models import InferenceSection
+
+
+### States ###
+class SubQuestionRetrievalInput(CoreState):
+    exploratory_search_results: list[InferenceSection]
+
+
+## Graph State
+class SubQuestionRetrievalState(
+    # This includes the core state
+    SubQuestionRetrievalInput,
+    InitialQuestionDecompositionUpdate,
+    InitialAnswerUpdate,
+    SubQuestionResultsUpdate,
+    OrigQuestionRetrievalUpdate,
+    InitialAnswerQualityUpdate,
+    ExploratorySearchUpdate,
+):
+    base_raw_search_result: Annotated[list[QuestionRetrievalResult], add]
+
+
+## Graph Output State
+class SubQuestionRetrievalOutput(TypedDict):
+    log_messages: list[str]

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/edges.py

@@ -0,0 +1,48 @@
+from collections.abc import Hashable
+from datetime import datetime
+
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
+    SubQuestionRetrievalState,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
+
+
+def parallelize_initial_sub_question_answering(
+    state: SubQuestionRetrievalState,
+) -> list[Send | Hashable]:
+    """
+    LangGraph edge to parallelize the initial sub-question answering.
+    """
+    edge_start_time = datetime.now()
+    if len(state.initial_sub_questions) > 0:
+        return [
+            Send(
+                "answer_sub_question_subgraphs",
+                SubQuestionAnsweringInput(
+                    question=question,
+                    question_id=make_question_id(0, question_num + 1),
+                    log_messages=[
+                        f"{edge_start_time} -- Main Edge - Parallelize Initial Sub-question Answering"
+                    ],
+                ),
+            )
+            for question_num, question in enumerate(state.initial_sub_questions)
+        ]
+
+    else:
+        return [
+            Send(
+                "ingest_answers",
+                AnswerQuestionOutput(
+                    answer_results=[],
+                ),
+            )
+        ]

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/graph_builder.py

@@ -0,0 +1,81 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.graph_builder import (
+    answer_query_graph_builder,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.edges import (
+    parallelize_initial_sub_question_answering,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.nodes.decompose_orig_question import (
+    decompose_orig_question,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.nodes.format_initial_sub_answers import (
+    format_initial_sub_answers,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.states import (
+    SubQuestionAnsweringState,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+test_mode = False
+
+
+def generate_sub_answers_graph_builder() -> StateGraph:
+    """
+    LangGraph graph builder for the initial sub-answer generation process.
+    It generates the initial sub-questions and produces the answers.
+    """
+
+    graph = StateGraph(
+        state_schema=SubQuestionAnsweringState,
+        input=SubQuestionAnsweringInput,
+    )
+
+    # Decompose the original question into sub-questions
+    graph.add_node(
+        node="decompose_orig_question",
+        action=decompose_orig_question,
+    )
+
+    # The sub-graph that executes the initial sub-question answering for
+    # each of the sub-questions.
+    answer_sub_question_subgraphs = answer_query_graph_builder().compile()
+    graph.add_node(
+        node="answer_sub_question_subgraphs",
+        action=answer_sub_question_subgraphs,
+    )
+
+    # Node that collects and formats the initial sub-question answers
+    graph.add_node(
+        node="format_initial_sub_question_answers",
+        action=format_initial_sub_answers,
+    )
+
+    graph.add_edge(
+        start_key=START,
+        end_key="decompose_orig_question",
+    )
+
+    graph.add_conditional_edges(
+        source="decompose_orig_question",
+        path=parallelize_initial_sub_question_answering,
+        path_map=["answer_sub_question_subgraphs"],
+    )
+    graph.add_edge(
+        start_key=["answer_sub_question_subgraphs"],
+        end_key="format_initial_sub_question_answers",
+    )
+
+    graph.add_edge(
+        start_key="format_initial_sub_question_answers",
+        end_key=END,
+    )
+
+    return graph

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

@@ -0,0 +1,157 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_content
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
+    SubQuestionRetrievalState,
+)
+from onyx.agents.agent_search.deep_search.main.models import (
+    AgentRefinedMetrics,
+)
+from onyx.agents.agent_search.deep_search.main.operations import dispatch_subquestion
+from onyx.agents.agent_search.deep_search.main.operations import (
+    dispatch_subquestion_sep,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialQuestionDecompositionUpdate,
+)
+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.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 write_custom_event
+from onyx.chat.models import StreamStopInfo
+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.prompts.agent_search import (
+    INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH,
+)
+from onyx.prompts.agent_search import (
+    INITIAL_QUESTION_DECOMPOSITION_PROMPT,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def decompose_orig_question(
+    state: SubQuestionRetrievalState,
+    config: RunnableConfig,
+    writer: StreamWriter = lambda _: None,
+) -> InitialQuestionDecompositionUpdate:
+    """
+    LangGraph node to decompose the original question into sub-questions.
+    """
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = graph_config.inputs.search_request.query
+    perform_initial_search_decomposition = (
+        graph_config.behavior.perform_initial_search_decomposition
+    )
+    # Get the rewritten queries in a defined format
+    model = graph_config.tooling.fast_llm
+
+    history = build_history_prompt(graph_config, question)
+
+    # Use the initial search results to inform the decomposition
+    agent_start_time = datetime.now()
+
+    # Initial search to inform decomposition. Just get top 3 fits
+
+    if perform_initial_search_decomposition:
+        # Due to unfortunate state representation in LangGraph, we need here to double check that the retrieval has
+        # happened prior to this point, allowing silent failure here since it is not critical for decomposition in
+        # all queries.
+        if not state.exploratory_search_results:
+            logger.error("Initial search for decomposition failed")
+
+        sample_doc_str = "\n\n".join(
+            [
+                doc.combined_content
+                for doc in state.exploratory_search_results[
+                    :AGENT_NUM_DOCS_FOR_DECOMPOSITION
+                ]
+            ]
+        )
+
+        decomposition_prompt = (
+            INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH.format(
+                question=question, sample_doc_str=sample_doc_str, history=history
+            )
+        )
+
+    else:
+        decomposition_prompt = INITIAL_QUESTION_DECOMPOSITION_PROMPT.format(
+            question=question, history=history
+        )
+
+    # Start decomposition
+
+    msg = [HumanMessage(content=decomposition_prompt)]
+
+    # Send the initial question as a subquestion with number 0
+    write_custom_event(
+        "decomp_qs",
+        SubQuestionPiece(
+            sub_question=question,
+            level=0,
+            level_question_num=0,
+        ),
+        writer,
+    )
+
+    # dispatches custom events for subquestion tokens, adding in subquestion ids.
+    streamed_tokens = dispatch_separated(
+        model.stream(msg),
+        dispatch_subquestion(0, writer),
+        sep_callback=dispatch_subquestion_sep(0, writer),
+    )
+
+    stop_event = StreamStopInfo(
+        stop_reason=StreamStopReason.FINISHED,
+        stream_type=StreamType.SUB_QUESTIONS,
+        level=0,
+    )
+    write_custom_event("stream_finished", stop_event, writer)
+
+    deomposition_response = merge_content(*streamed_tokens)
+
+    # this call should only return strings. Commenting out for efficiency
+    # assert [type(tok) == str for tok in streamed_tokens]
+
+    # 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=decomp_list,
+        agent_start_time=agent_start_time,
+        agent_refined_start_time=None,
+        agent_refined_end_time=None,
+        agent_refined_metrics=AgentRefinedMetrics(
+            refined_doc_boost_factor=None,
+            refined_question_boost_factor=None,
+            duration_s=None,
+        ),
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="initial - generate sub answers",
+                node_name="decompose original question",
+                node_start_time=node_start_time,
+                result=f"decomposed original question into {len(decomp_list)} subquestions",
+            )
+        ],
+    )

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

@@ -0,0 +1,50 @@
+from datetime import datetime
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    SubQuestionResultsUpdate,
+)
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_inference_sections,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+
+
+def format_initial_sub_answers(
+    state: AnswerQuestionOutput,
+) -> SubQuestionResultsUpdate:
+    """
+    LangGraph node to format the answers to the initial sub-questions, including
+    deduping verified documents and context documents.
+    """
+    node_start_time = datetime.now()
+
+    documents = []
+    context_documents = []
+    cited_documents = []
+    answer_results = state.answer_results
+    for answer_result in answer_results:
+        documents.extend(answer_result.verified_reranked_documents)
+        context_documents.extend(answer_result.context_documents)
+        cited_documents.extend(answer_result.cited_documents)
+
+    return SubQuestionResultsUpdate(
+        # Deduping is done by the documents operator for the main graph
+        # so we might not need to dedup here
+        verified_reranked_documents=dedup_inference_sections(documents, []),
+        context_documents=dedup_inference_sections(context_documents, []),
+        cited_documents=dedup_inference_sections(cited_documents, []),
+        sub_question_results=answer_results,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="initial - generate sub answers",
+                node_name="format initial sub answers",
+                node_start_time=node_start_time,
+                result="",
+            )
+        ],
+    )

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

@@ -0,0 +1,34 @@
+from typing import TypedDict
+
+from onyx.agents.agent_search.core_state import CoreState
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialAnswerUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialQuestionDecompositionUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    SubQuestionResultsUpdate,
+)
+from onyx.context.search.models import InferenceSection
+
+
+### States ###
+class SubQuestionAnsweringInput(CoreState):
+    exploratory_search_results: list[InferenceSection]
+
+
+## Graph State
+class SubQuestionAnsweringState(
+    # This includes the core state
+    SubQuestionAnsweringInput,
+    InitialQuestionDecompositionUpdate,
+    InitialAnswerUpdate,
+    SubQuestionResultsUpdate,
+):
+    pass
+
+
+## Graph Output State
+class SubQuestionAnsweringOutput(TypedDict):
+    log_messages: list[str]

backend/onyx/agents/agent_search/deep_search/initial/retrieve_orig_question_docs/graph_builder.py

@@ -0,0 +1,81 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.nodes.format_orig_question_search_input import (
+    format_orig_question_search_input,
+)
+from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.nodes.format_orig_question_search_output import (
+    format_orig_question_search_output,
+)
+from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.states import (
+    BaseRawSearchInput,
+)
+from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.states import (
+    BaseRawSearchOutput,
+)
+from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.states import (
+    BaseRawSearchState,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.graph_builder import (
+    expanded_retrieval_graph_builder,
+)
+
+
+def retrieve_orig_question_docs_graph_builder() -> StateGraph:
+    """
+    LangGraph graph builder for the retrieval of documents
+    that are relevant to the original question. This is
+    largely a wrapper around the expanded retrieval process to
+    ensure parallelism with the sub-question answer process.
+    """
+    graph = StateGraph(
+        state_schema=BaseRawSearchState,
+        input=BaseRawSearchInput,
+        output=BaseRawSearchOutput,
+    )
+
+    ### Add nodes ###
+
+    # Format the original question search output
+    graph.add_node(
+        node="format_orig_question_search_output",
+        action=format_orig_question_search_output,
+    )
+
+    # The sub-graph that executes the expanded retrieval process
+    expanded_retrieval = expanded_retrieval_graph_builder().compile()
+    graph.add_node(
+        node="retrieve_orig_question_docs_subgraph",
+        action=expanded_retrieval,
+    )
+
+    # Format the original question search input
+    graph.add_node(
+        node="format_orig_question_search_input",
+        action=format_orig_question_search_input,
+    )
+
+    ### Add edges ###
+
+    graph.add_edge(start_key=START, end_key="format_orig_question_search_input")
+
+    graph.add_edge(
+        start_key="format_orig_question_search_input",
+        end_key="retrieve_orig_question_docs_subgraph",
+    )
+    graph.add_edge(
+        start_key="retrieve_orig_question_docs_subgraph",
+        end_key="format_orig_question_search_output",
+    )
+
+    graph.add_edge(
+        start_key="format_orig_question_search_output",
+        end_key=END,
+    )
+
+    return graph
+
+
+if __name__ == "__main__":
+    pass

backend/onyx/agents/agent_search/deep_search/initial/retrieve_orig_question_docs/nodes/format_orig_question_search_input.py

@@ -0,0 +1,28 @@
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.core_state import CoreState
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def format_orig_question_search_input(
+    state: CoreState, config: RunnableConfig
+) -> ExpandedRetrievalInput:
+    """
+    LangGraph node to format the search input for the original question.
+    """
+    logger.debug("generate_raw_search_data")
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    return ExpandedRetrievalInput(
+        question=graph_config.inputs.search_request.query,
+        base_search=True,
+        sub_question_id=None,  # This graph is always and only used for the original question
+        log_messages=[],
+    )

backend/onyx/agents/agent_search/deep_search/initial/retrieve_orig_question_docs/nodes/format_orig_question_search_output.py

@@ -0,0 +1,30 @@
+from onyx.agents.agent_search.deep_search.main.states import OrigQuestionRetrievalUpdate
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalOutput,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def format_orig_question_search_output(
+    state: ExpandedRetrievalOutput,
+) -> OrigQuestionRetrievalUpdate:
+    """
+    LangGraph node to format the search result for the original question into the
+    proper format.
+    """
+    sub_question_retrieval_stats = state.expanded_retrieval_result.retrieval_stats
+    if sub_question_retrieval_stats is None:
+        sub_question_retrieval_stats = AgentChunkRetrievalStats()
+    else:
+        sub_question_retrieval_stats = sub_question_retrieval_stats
+
+    return OrigQuestionRetrievalUpdate(
+        orig_question_verified_reranked_documents=state.expanded_retrieval_result.verified_reranked_documents,
+        orig_question_sub_query_retrieval_results=state.expanded_retrieval_result.expanded_query_results,
+        orig_question_retrieved_documents=state.retrieved_documents,
+        orig_question_retrieval_stats=sub_question_retrieval_stats,
+        log_messages=[],
+    )

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

@@ -0,0 +1,29 @@
+from onyx.agents.agent_search.deep_search.main.states import (
+    OrigQuestionRetrievalUpdate,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+
+
+## Graph Input State
+class BaseRawSearchInput(ExpandedRetrievalInput):
+    pass
+
+
+## Graph Output State
+class BaseRawSearchOutput(OrigQuestionRetrievalUpdate):
+    """
+    This is a list of results even though each call of this subgraph only returns one result.
+    This is because if we parallelize the answer query subgraph, there will be multiple
+      results in a list so the add operator is used to add them together.
+    """
+
+    # base_expanded_retrieval_result: QuestionRetrievalResult = QuestionRetrievalResult()
+
+
+## Graph State
+class BaseRawSearchState(
+    BaseRawSearchInput, BaseRawSearchOutput, OrigQuestionRetrievalUpdate
+):
+    pass

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

@@ -0,0 +1,113 @@
+from collections.abc import Hashable
+from datetime import datetime
+from typing import cast
+from typing import Literal
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.main.states import MainState
+from onyx.agents.agent_search.deep_search.main.states import (
+    RequireRefinemenEvalUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def route_initial_tool_choice(
+    state: MainState, config: RunnableConfig
+) -> Literal["tool_call", "start_agent_search", "logging_node"]:
+    """
+    LangGraph edge to route to agent search.
+    """
+    agent_config = cast(GraphConfig, config["metadata"]["config"])
+    if state.tool_choice is not None:
+        if (
+            agent_config.behavior.use_agentic_search
+            and agent_config.tooling.search_tool is not None
+            and state.tool_choice.tool.name == agent_config.tooling.search_tool.name
+        ):
+            return "start_agent_search"
+        else:
+            return "tool_call"
+    else:
+        return "logging_node"
+
+
+def parallelize_initial_sub_question_answering(
+    state: MainState,
+) -> list[Send | Hashable]:
+    edge_start_time = datetime.now()
+    if len(state.initial_sub_questions) > 0:
+        return [
+            Send(
+                "answer_query_subgraph",
+                SubQuestionAnsweringInput(
+                    question=question,
+                    question_id=make_question_id(0, question_num + 1),
+                    log_messages=[
+                        f"{edge_start_time} -- Main Edge - Parallelize Initial Sub-question Answering"
+                    ],
+                ),
+            )
+            for question_num, question in enumerate(state.initial_sub_questions)
+        ]
+
+    else:
+        return [
+            Send(
+                "ingest_answers",
+                AnswerQuestionOutput(
+                    answer_results=[],
+                ),
+            )
+        ]
+
+
+# Define the function that determines whether to continue or not
+def continue_to_refined_answer_or_end(
+    state: RequireRefinemenEvalUpdate,
+) -> Literal["create_refined_sub_questions", "logging_node"]:
+    if state.require_refined_answer_eval:
+        return "create_refined_sub_questions"
+    else:
+        return "logging_node"
+
+
+def parallelize_refined_sub_question_answering(
+    state: MainState,
+) -> list[Send | Hashable]:
+    edge_start_time = datetime.now()
+    if len(state.refined_sub_questions) > 0:
+        return [
+            Send(
+                "answer_refined_question_subgraphs",
+                SubQuestionAnsweringInput(
+                    question=question_data.sub_question,
+                    question_id=make_question_id(1, question_num),
+                    log_messages=[
+                        f"{edge_start_time} -- Main Edge - Parallelize Refined Sub-question Answering"
+                    ],
+                ),
+            )
+            for question_num, question_data in state.refined_sub_questions.items()
+        ]
+
+    else:
+        return [
+            Send(
+                "ingest_refined_sub_answers",
+                AnswerQuestionOutput(
+                    answer_results=[],
+                ),
+            )
+        ]

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

@@ -0,0 +1,265 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.graph_builder import (
+    generate_initial_answer_graph_builder,
+)
+from onyx.agents.agent_search.deep_search.main.edges import (
+    continue_to_refined_answer_or_end,
+)
+from onyx.agents.agent_search.deep_search.main.edges import (
+    parallelize_refined_sub_question_answering,
+)
+from onyx.agents.agent_search.deep_search.main.edges import (
+    route_initial_tool_choice,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.compare_answers import (
+    compare_answers,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.create_refined_sub_questions import (
+    create_refined_sub_questions,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.decide_refinement_need import (
+    decide_refinement_need,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.extract_entities_terms import (
+    extract_entities_terms,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.generate_refined_answer import (
+    generate_refined_answer,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.ingest_refined_sub_answers import (
+    ingest_refined_sub_answers,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.persist_agent_results import (
+    persist_agent_results,
+)
+from onyx.agents.agent_search.deep_search.main.nodes.start_agent_search import (
+    start_agent_search,
+)
+from onyx.agents.agent_search.deep_search.main.states import MainInput
+from onyx.agents.agent_search.deep_search.main.states import MainState
+from onyx.agents.agent_search.deep_search.refinement.consolidate_sub_answers.graph_builder import (
+    answer_refined_query_graph_builder,
+)
+from onyx.agents.agent_search.orchestration.nodes.basic_use_tool_response import (
+    basic_use_tool_response,
+)
+from onyx.agents.agent_search.orchestration.nodes.llm_tool_choice import llm_tool_choice
+from onyx.agents.agent_search.orchestration.nodes.prepare_tool_input import (
+    prepare_tool_input,
+)
+from onyx.agents.agent_search.orchestration.nodes.tool_call import tool_call
+from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+test_mode = False
+
+
+def main_graph_builder(test_mode: bool = False) -> StateGraph:
+    """
+    LangGraph graph builder for the main agent search process.
+    """
+    graph = StateGraph(
+        state_schema=MainState,
+        input=MainInput,
+    )
+
+    # Prepare the tool input
+    graph.add_node(
+        node="prepare_tool_input",
+        action=prepare_tool_input,
+    )
+
+    # Choose the initial tool
+    graph.add_node(
+        node="initial_tool_choice",
+        action=llm_tool_choice,
+    )
+
+    # Call the tool, if required
+    graph.add_node(
+        node="tool_call",
+        action=tool_call,
+    )
+
+    # Use the tool response
+    graph.add_node(
+        node="basic_use_tool_response",
+        action=basic_use_tool_response,
+    )
+
+    # Start the agent search process
+    graph.add_node(
+        node="start_agent_search",
+        action=start_agent_search,
+    )
+
+    # The sub-graph for the initial answer generation
+    generate_initial_answer_subgraph = generate_initial_answer_graph_builder().compile()
+    graph.add_node(
+        node="generate_initial_answer_subgraph",
+        action=generate_initial_answer_subgraph,
+    )
+
+    # Create the refined sub-questions
+    graph.add_node(
+        node="create_refined_sub_questions",
+        action=create_refined_sub_questions,
+    )
+
+    # Subgraph for the refined sub-answer generation
+    answer_refined_question = answer_refined_query_graph_builder().compile()
+    graph.add_node(
+        node="answer_refined_question_subgraphs",
+        action=answer_refined_question,
+    )
+
+    # Ingest the refined sub-answers
+    graph.add_node(
+        node="ingest_refined_sub_answers",
+        action=ingest_refined_sub_answers,
+    )
+
+    # Node to generate the refined answer
+    graph.add_node(
+        node="generate_refined_answer",
+        action=generate_refined_answer,
+    )
+
+    # Early node to extract the entities and terms from the initial answer,
+    # This information is used to inform the creation the refined sub-questions
+    graph.add_node(
+        node="extract_entity_term",
+        action=extract_entities_terms,
+    )
+
+    # Decide if the answer needs to be refined (currently always true)
+    graph.add_node(
+        node="decide_refinement_need",
+        action=decide_refinement_need,
+    )
+
+    # Compare the initial and refined answers, and determine whether
+    # the refined answer is sufficiently better
+    graph.add_node(
+        node="compare_answers",
+        action=compare_answers,
+    )
+
+    # Log the results. This will log the stats as well as the answers, sub-questions, and sub-answers
+    graph.add_node(
+        node="logging_node",
+        action=persist_agent_results,
+    )
+
+    ### Add edges ###
+
+    graph.add_edge(start_key=START, end_key="prepare_tool_input")
+
+    graph.add_edge(
+        start_key="prepare_tool_input",
+        end_key="initial_tool_choice",
+    )
+
+    graph.add_conditional_edges(
+        "initial_tool_choice",
+        route_initial_tool_choice,
+        ["tool_call", "start_agent_search", "logging_node"],
+    )
+
+    graph.add_edge(
+        start_key="tool_call",
+        end_key="basic_use_tool_response",
+    )
+    graph.add_edge(
+        start_key="basic_use_tool_response",
+        end_key="logging_node",
+    )
+
+    graph.add_edge(
+        start_key="start_agent_search",
+        end_key="generate_initial_answer_subgraph",
+    )
+
+    graph.add_edge(
+        start_key="start_agent_search",
+        end_key="extract_entity_term",
+    )
+
+    # Wait for the initial answer generation and the entity/term extraction to be complete
+    # before deciding if a refinement is needed.
+    graph.add_edge(
+        start_key=["generate_initial_answer_subgraph", "extract_entity_term"],
+        end_key="decide_refinement_need",
+    )
+
+    graph.add_conditional_edges(
+        source="decide_refinement_need",
+        path=continue_to_refined_answer_or_end,
+        path_map=["create_refined_sub_questions", "logging_node"],
+    )
+
+    graph.add_conditional_edges(
+        source="create_refined_sub_questions",
+        path=parallelize_refined_sub_question_answering,
+        path_map=["answer_refined_question_subgraphs"],
+    )
+    graph.add_edge(
+        start_key="answer_refined_question_subgraphs",
+        end_key="ingest_refined_sub_answers",
+    )
+
+    graph.add_edge(
+        start_key="ingest_refined_sub_answers",
+        end_key="generate_refined_answer",
+    )
+
+    graph.add_edge(
+        start_key="generate_refined_answer",
+        end_key="compare_answers",
+    )
+    graph.add_edge(
+        start_key="compare_answers",
+        end_key="logging_node",
+    )
+
+    graph.add_edge(
+        start_key="logging_node",
+        end_key=END,
+    )
+
+    return graph
+
+
+if __name__ == "__main__":
+    pass
+
+    from onyx.db.engine import get_session_context_manager
+    from onyx.llm.factory import get_default_llms
+    from onyx.context.search.models import SearchRequest
+
+    graph = main_graph_builder()
+    compiled_graph = graph.compile()
+    primary_llm, fast_llm = get_default_llms()
+
+    with get_session_context_manager() as db_session:
+        search_request = SearchRequest(query="Who created Excel?")
+        graph_config = get_test_config(
+            db_session, primary_llm, fast_llm, search_request
+        )
+
+        inputs = MainInput(
+            base_question=graph_config.inputs.search_request.query, log_messages=[]
+        )
+
+        for thing in compiled_graph.stream(
+            input=inputs,
+            config={"configurable": {"config": graph_config}},
+            stream_mode="custom",
+            subgraphs=True,
+        ):
+            logger.debug(thing)

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

@@ -0,0 +1,36 @@
+from pydantic import BaseModel
+
+
+class RefinementSubQuestion(BaseModel):
+    sub_question: str
+    sub_question_id: str
+    verified: bool
+    answered: bool
+    answer: str
+
+
+class AgentTimings(BaseModel):
+    base_duration_s: float | None
+    refined_duration_s: float | None
+    full_duration_s: float | None
+
+
+class AgentBaseMetrics(BaseModel):
+    num_verified_documents_total: int | None
+    num_verified_documents_core: int | None
+    verified_avg_score_core: float | None
+    num_verified_documents_base: int | float | None
+    verified_avg_score_base: float | None = None
+    base_doc_boost_factor: float | None = None
+    support_boost_factor: float | None = None
+    duration_s: float | None = None
+
+
+class AgentRefinedMetrics(BaseModel):
+    refined_doc_boost_factor: float | None = None
+    refined_question_boost_factor: float | None = None
+    duration_s: float | None = None
+
+
+class AgentAdditionalMetrics(BaseModel):
+    pass

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

@@ -0,0 +1,71 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.main.states import (
+    InitialRefinedAnswerComparisonUpdate,
+)
+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.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.prompts.agent_search import (
+    INITIAL_REFINED_ANSWER_COMPARISON_PROMPT,
+)
+
+
+def compare_answers(
+    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+) -> InitialRefinedAnswerComparisonUpdate:
+    """
+    LangGraph node to compare the initial answer and the refined answer and determine if the
+    refined answer is sufficiently better than the initial answer.
+    """
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = graph_config.inputs.search_request.query
+    initial_answer = state.initial_answer
+    refined_answer = state.refined_answer
+
+    compare_answers_prompt = INITIAL_REFINED_ANSWER_COMPARISON_PROMPT.format(
+        question=question, initial_answer=initial_answer, refined_answer=refined_answer
+    )
+
+    msg = [HumanMessage(content=compare_answers_prompt)]
+
+    # Get the rewritten queries in a defined format
+    model = graph_config.tooling.fast_llm
+
+    # no need to stream this
+    resp = model.invoke(msg)
+
+    refined_answer_improvement = (
+        isinstance(resp.content, str) and "yes" in resp.content.lower()
+    )
+
+    write_custom_event(
+        "refined_answer_improvement",
+        RefinedAnswerImprovement(
+            refined_answer_improvement=refined_answer_improvement,
+        ),
+        writer,
+    )
+
+    return InitialRefinedAnswerComparisonUpdate(
+        refined_answer_improvement_eval=refined_answer_improvement,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="main",
+                node_name="compare answers",
+                node_start_time=node_start_time,
+                result=f"Answer comparison: {refined_answer_improvement}",
+            )
+        ],
+    )

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

@@ -0,0 +1,134 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_content
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.main.models import (
+    RefinementSubQuestion,
+)
+from onyx.agents.agent_search.deep_search.main.operations import dispatch_subquestion
+from onyx.agents.agent_search.deep_search.main.operations import (
+    dispatch_subquestion_sep,
+)
+from onyx.agents.agent_search.deep_search.main.states import MainState
+from onyx.agents.agent_search.deep_search.main.states import (
+    RefinedQuestionDecompositionUpdate,
+)
+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.utils import dispatch_separated
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    format_entity_term_extraction,
+)
+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 make_question_id
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.prompts.agent_search import (
+    REFINEMENT_QUESTION_DECOMPOSITION_PROMPT,
+)
+from onyx.tools.models import ToolCallKickoff
+
+
+def create_refined_sub_questions(
+    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+) -> RefinedQuestionDecompositionUpdate:
+    """
+    LangGraph node to create refined sub-questions based on the initial answer, the history,
+    the entity term extraction results found earlier, and the sub-questions that were answered and failed.
+    """
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    write_custom_event(
+        "start_refined_answer_creation",
+        ToolCallKickoff(
+            tool_name="agent_search_1",
+            tool_args={
+                "query": graph_config.inputs.search_request.query,
+                "answer": state.initial_answer,
+            },
+        ),
+        writer,
+    )
+
+    node_start_time = datetime.now()
+
+    agent_refined_start_time = datetime.now()
+
+    question = graph_config.inputs.search_request.query
+    base_answer = state.initial_answer
+    history = build_history_prompt(graph_config, question)
+    # get the entity term extraction dict and properly format it
+    entity_retlation_term_extractions = state.entity_relation_term_extractions
+
+    entity_term_extraction_str = format_entity_term_extraction(
+        entity_retlation_term_extractions
+    )
+
+    initial_question_answers = state.sub_question_results
+
+    addressed_question_list = [
+        x.question for x in initial_question_answers if x.verified_high_quality
+    ]
+
+    failed_question_list = [
+        x.question for x in initial_question_answers if not x.verified_high_quality
+    ]
+
+    msg = [
+        HumanMessage(
+            content=REFINEMENT_QUESTION_DECOMPOSITION_PROMPT.format(
+                question=question,
+                history=history,
+                entity_term_extraction_str=entity_term_extraction_str,
+                base_answer=base_answer,
+                answered_sub_questions="\n - ".join(addressed_question_list),
+                failed_sub_questions="\n - ".join(failed_question_list),
+            ),
+        )
+    ]
+
+    # Grader
+    model = graph_config.tooling.fast_llm
+
+    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:
+        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[sub_question_num + 1] = refined_sub_question
+
+    return RefinedQuestionDecompositionUpdate(
+        refined_sub_questions=refined_sub_question_dict,
+        agent_refined_start_time=agent_refined_start_time,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="main",
+                node_name="create refined sub questions",
+                node_start_time=node_start_time,
+                result=f"Created {len(refined_sub_question_dict)} refined sub questions",
+            )
+        ],
+    )

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

@@ -0,0 +1,47 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.runnables import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.main.states import MainState
+from onyx.agents.agent_search.deep_search.main.states import (
+    RequireRefinemenEvalUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+
+
+def decide_refinement_need(
+    state: MainState, config: RunnableConfig
+) -> RequireRefinemenEvalUpdate:
+    """
+    LangGraph node to decide if refinement is needed based on the initial answer and the question.
+    At present, we always refine.
+    """
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+
+    decision = True  # TODO: just for current testing purposes
+
+    log_messages = [
+        get_langgraph_node_log_string(
+            graph_component="main",
+            node_name="decide refinement need",
+            node_start_time=node_start_time,
+            result=f"Refinement decision: {decision}",
+        )
+    ]
+
+    if graph_config.behavior.allow_refinement:
+        return RequireRefinemenEvalUpdate(
+            require_refined_answer_eval=decision,
+            log_messages=log_messages,
+        )
+    else:
+        return RequireRefinemenEvalUpdate(
+            require_refined_answer_eval=False,
+            log_messages=log_messages,
+        )

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

@@ -0,0 +1,116 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.main.operations import logger
+from onyx.agents.agent_search.deep_search.main.states import (
+    EntityTermExtractionUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import MainState
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    trim_prompt_piece,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import EntityExtractionResult
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    EntityRelationshipTermExtraction,
+)
+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.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
+
+
+def extract_entities_terms(
+    state: MainState, config: RunnableConfig
+) -> EntityTermExtractionUpdate:
+    """
+    LangGraph node to extract entities, relationships, and terms from the initial search results.
+    This data is used to inform particularly the sub-questions that are created for the refined answer.
+    """
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    if not graph_config.behavior.allow_refinement:
+        return EntityTermExtractionUpdate(
+            entity_relation_term_extractions=EntityRelationshipTermExtraction(
+                entities=[],
+                relationships=[],
+                terms=[],
+            ),
+            log_messages=[
+                get_langgraph_node_log_string(
+                    graph_component="main",
+                    node_name="extract entities terms",
+                    node_start_time=node_start_time,
+                    result="Refinement is not allowed",
+                )
+            ],
+        )
+
+    # first four lines duplicates from generate_initial_answer
+    question = graph_config.inputs.search_request.query
+    initial_search_docs = state.exploratory_search_results[:NUM_EXPLORATORY_DOCS]
+
+    # start with the entity/term/extraction
+    doc_context = format_docs(initial_search_docs)
+
+    # Calculation here is only approximate
+    doc_context = trim_prompt_piece(
+        graph_config.tooling.fast_llm.config,
+        doc_context,
+        ENTITY_TERM_EXTRACTION_PROMPT
+        + question
+        + ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE,
+    )
+
+    msg = [
+        HumanMessage(
+            content=ENTITY_TERM_EXTRACTION_PROMPT.format(
+                question=question, context=doc_context
+            )
+            + ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE,
+        )
+    ]
+    fast_llm = graph_config.tooling.fast_llm
+    # Grader
+    llm_response = fast_llm.invoke(
+        prompt=msg,
+    )
+
+    cleaned_response = (
+        str(llm_response.content).replace("```json\n", "").replace("\n```", "")
+    )
+    first_bracket = cleaned_response.find("{")
+    last_bracket = cleaned_response.rfind("}")
+    cleaned_response = cleaned_response[first_bracket : last_bracket + 1]
+
+    try:
+        entity_extraction_result = EntityExtractionResult.model_validate_json(
+            cleaned_response
+        )
+    except ValueError:
+        logger.error("Failed to parse LLM response as JSON in Entity-Term Extraction")
+        entity_extraction_result = EntityExtractionResult(
+            retrieved_entities_relationships=EntityRelationshipTermExtraction(
+                entities=[],
+                relationships=[],
+                terms=[],
+            ),
+        )
+
+    return EntityTermExtractionUpdate(
+        entity_relation_term_extractions=entity_extraction_result.retrieved_entities_relationships,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="main",
+                node_name="extract entities terms",
+                node_start_time=node_start_time,
+            )
+        ],
+    )

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

@@ -0,0 +1,339 @@
+from datetime import datetime
+from typing import Any
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_content
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.main.models import (
+    AgentRefinedMetrics,
+)
+from onyx.agents.agent_search.deep_search.main.operations import get_query_info
+from onyx.agents.agent_search.deep_search.main.operations import logger
+from onyx.agents.agent_search.deep_search.main.states import MainState
+from onyx.agents.agent_search.deep_search.main.states import (
+    RefinedAnswerUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    get_prompt_enrichment_components,
+)
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    trim_prompt_piece,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import InferenceSection
+from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_inference_sections,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    dispatch_main_answer_stop_info,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+from onyx.agents.agent_search.shared_graph_utils.utils import relevance_from_docs
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    remove_document_citations,
+)
+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.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
+from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
+from onyx.prompts.agent_search import (
+    REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS,
+)
+from onyx.prompts.agent_search import (
+    REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS,
+)
+from onyx.prompts.agent_search import (
+    SUB_QUESTION_ANSWER_TEMPLATE_REFINED,
+)
+from onyx.prompts.agent_search import UNKNOWN_ANSWER
+from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
+
+
+def generate_refined_answer(
+    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+) -> RefinedAnswerUpdate:
+    """
+    LangGraph node to generate the refined answer.
+    """
+
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = graph_config.inputs.search_request.query
+    prompt_enrichment_components = get_prompt_enrichment_components(graph_config)
+
+    persona_contextualized_prompt = (
+        prompt_enrichment_components.persona_prompts.contextualized_prompt
+    )
+
+    verified_reranked_documents = state.verified_reranked_documents
+    sub_questions_cited_documents = state.cited_documents
+    original_question_verified_documents = (
+        state.orig_question_verified_reranked_documents
+    )
+    original_question_retrieved_documents = state.orig_question_retrieved_documents
+
+    consolidated_context_docs: list[InferenceSection] = sub_questions_cited_documents
+
+    counter = 0
+    for original_doc_number, original_doc in enumerate(
+        original_question_verified_documents
+    ):
+        if original_doc_number not in sub_questions_cited_documents:
+            if (
+                counter <= AGENT_MIN_ORIG_QUESTION_DOCS
+                or len(consolidated_context_docs)
+                < 1.5
+                * AGENT_MAX_ANSWER_CONTEXT_DOCS  # allow for larger context in refinement
+            ):
+                consolidated_context_docs.append(original_doc)
+                counter += 1
+
+    # sort docs by their scores - though the scores refer to different questions
+    relevant_docs = dedup_inference_sections(
+        consolidated_context_docs, consolidated_context_docs
+    )
+
+    streaming_docs = (
+        relevant_docs
+        if len(relevant_docs) > 0
+        else original_question_retrieved_documents[:15]
+    )
+
+    query_info = get_query_info(state.orig_question_sub_query_retrieval_results)
+    assert (
+        graph_config.tooling.search_tool
+    ), "search_tool must be provided for agentic search"
+    # stream refined answer docs, or original question docs if no relevant docs are found
+    relevance_list = relevance_from_docs(relevant_docs)
+    for tool_response in yield_search_responses(
+        query=question,
+        reranked_sections=streaming_docs,
+        final_context_sections=streaming_docs,
+        search_query_info=query_info,
+        get_section_relevance=lambda: relevance_list,
+        search_tool=graph_config.tooling.search_tool,
+    ):
+        write_custom_event(
+            "tool_response",
+            ExtendedToolResponse(
+                id=tool_response.id,
+                response=tool_response.response,
+                level=1,
+                level_question_num=0,  # 0, 0 is the base question
+            ),
+            writer,
+        )
+
+    if len(verified_reranked_documents) > 0:
+        refined_doc_effectiveness = len(relevant_docs) / len(
+            verified_reranked_documents
+        )
+    else:
+        refined_doc_effectiveness = 10.0
+
+    sub_question_answer_results = state.sub_question_results
+
+    answered_sub_question_answer_list: list[str] = []
+    sub_questions: list[str] = []
+    initial_answered_sub_questions: set[str] = set()
+    refined_answered_sub_questions: set[str] = set()
+
+    for i, result in enumerate(sub_question_answer_results, 1):
+        question_level, _ = parse_question_id(result.question_id)
+        sub_questions.append(result.question)
+
+        if (
+            result.verified_high_quality
+            and result.answer
+            and result.answer != UNKNOWN_ANSWER
+        ):
+            sub_question_type = "initial" if question_level == 0 else "refined"
+            question_set = (
+                initial_answered_sub_questions
+                if question_level == 0
+                else refined_answered_sub_questions
+            )
+            question_set.add(result.question)
+
+            answered_sub_question_answer_list.append(
+                SUB_QUESTION_ANSWER_TEMPLATE_REFINED.format(
+                    sub_question=result.question,
+                    sub_answer=result.answer,
+                    sub_question_num=i,
+                    sub_question_type=sub_question_type,
+                )
+            )
+
+    # Calculate efficiency
+    total_answered_questions = (
+        initial_answered_sub_questions | refined_answered_sub_questions
+    )
+    revision_question_efficiency = (
+        len(total_answered_questions) / len(initial_answered_sub_questions)
+        if initial_answered_sub_questions
+        else 10.0
+        if refined_answered_sub_questions
+        else 1.0
+    )
+
+    sub_question_answer_str = "\n\n------\n\n".join(
+        set(answered_sub_question_answer_list)
+    )
+    initial_answer = state.initial_answer or ""
+
+    # Choose appropriate prompt template
+    base_prompt = (
+        REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS
+        if answered_sub_question_answer_list
+        else REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS
+    )
+
+    model = graph_config.tooling.fast_llm
+    relevant_docs_str = format_docs(relevant_docs)
+    relevant_docs_str = trim_prompt_piece(
+        model.config,
+        relevant_docs_str,
+        base_prompt
+        + question
+        + sub_question_answer_str
+        + initial_answer
+        + persona_contextualized_prompt
+        + prompt_enrichment_components.history,
+    )
+
+    msg = [
+        HumanMessage(
+            content=base_prompt.format(
+                question=question,
+                history=prompt_enrichment_components.history,
+                answered_sub_questions=remove_document_citations(
+                    sub_question_answer_str
+                ),
+                relevant_docs=relevant_docs_str,
+                initial_answer=remove_document_citations(initial_answer)
+                if initial_answer
+                else None,
+                persona_specification=persona_contextualized_prompt,
+                date_prompt=prompt_enrichment_components.date_str,
+            )
+        )
+    ]
+
+    streamed_tokens: list[str | list[str | dict[str, Any]]] = [""]
+    dispatch_timings: list[float] = []
+    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)}"
+            )
+
+        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,
+        )
+        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)}"
+    )
+    dispatch_main_answer_stop_info(1, writer)
+    response = merge_content(*streamed_tokens)
+    answer = cast(str, response)
+
+    refined_agent_stats = RefinedAgentStats(
+        revision_doc_efficiency=refined_doc_effectiveness,
+        revision_question_efficiency=revision_question_efficiency,
+    )
+
+    logger.debug(f"\n\n---INITIAL ANSWER ---\n\n Answer:\n Agent: {initial_answer}")
+    logger.debug("-" * 10)
+    logger.debug(f"\n\n---REVISED AGENT ANSWER ---\n\n Answer:\n Agent: {answer}")
+
+    logger.debug("-" * 100)
+
+    if state.initial_agent_stats:
+        initial_doc_boost_factor = state.initial_agent_stats.agent_effectiveness.get(
+            "utilized_chunk_ratio", "--"
+        )
+        initial_support_boost_factor = (
+            state.initial_agent_stats.agent_effectiveness.get("support_ratio", "--")
+        )
+        num_initial_verified_docs = state.initial_agent_stats.original_question.get(
+            "num_verified_documents", "--"
+        )
+        initial_verified_docs_avg_score = (
+            state.initial_agent_stats.original_question.get("verified_avg_score", "--")
+        )
+        initial_sub_questions_verified_docs = (
+            state.initial_agent_stats.sub_questions.get("num_verified_documents", "--")
+        )
+
+        logger.debug("INITIAL AGENT STATS")
+        logger.debug(f"Document Boost Factor: {initial_doc_boost_factor}")
+        logger.debug(f"Support Boost Factor: {initial_support_boost_factor}")
+        logger.debug(f"Originally Verified Docs: {num_initial_verified_docs}")
+        logger.debug(
+            f"Originally Verified Docs Avg Score: {initial_verified_docs_avg_score}"
+        )
+        logger.debug(
+            f"Sub-Questions Verified Docs: {initial_sub_questions_verified_docs}"
+        )
+    if refined_agent_stats:
+        logger.debug("-" * 10)
+        logger.debug("REFINED AGENT STATS")
+        logger.debug(
+            f"Revision Doc Factor: {refined_agent_stats.revision_doc_efficiency}"
+        )
+        logger.debug(
+            f"Revision Question Factor: {refined_agent_stats.revision_question_efficiency}"
+        )
+
+    agent_refined_end_time = datetime.now()
+    if state.agent_refined_start_time:
+        agent_refined_duration = (
+            agent_refined_end_time - state.agent_refined_start_time
+        ).total_seconds()
+    else:
+        agent_refined_duration = None
+
+    agent_refined_metrics = AgentRefinedMetrics(
+        refined_doc_boost_factor=refined_agent_stats.revision_doc_efficiency,
+        refined_question_boost_factor=refined_agent_stats.revision_question_efficiency,
+        duration_s=agent_refined_duration,
+    )
+
+    return RefinedAnswerUpdate(
+        refined_answer=answer,
+        refined_answer_quality=True,  # TODO: replace this with the actual check value
+        refined_agent_stats=refined_agent_stats,
+        agent_refined_end_time=agent_refined_end_time,
+        agent_refined_metrics=agent_refined_metrics,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="main",
+                node_name="generate refined answer",
+                node_start_time=node_start_time,
+            )
+        ],
+    )

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

@@ -0,0 +1,42 @@
+from datetime import datetime
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    SubQuestionResultsUpdate,
+)
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_inference_sections,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+
+
+def ingest_refined_sub_answers(
+    state: AnswerQuestionOutput,
+) -> SubQuestionResultsUpdate:
+    """
+    LangGraph node to ingest and format the refined sub-answers and retrieved documents.
+    """
+    node_start_time = datetime.now()
+
+    documents = []
+    answer_results = state.answer_results
+    for answer_result in answer_results:
+        documents.extend(answer_result.verified_reranked_documents)
+
+    return SubQuestionResultsUpdate(
+        # Deduping is done by the documents operator for the main graph
+        # so we might not need to dedup here
+        verified_reranked_documents=dedup_inference_sections(documents, []),
+        sub_question_results=answer_results,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="main",
+                node_name="ingest refined answers",
+                node_start_time=node_start_time,
+            )
+        ],
+    )

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

@@ -0,0 +1,129 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.runnables import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.main.models import (
+    AgentAdditionalMetrics,
+)
+from onyx.agents.agent_search.deep_search.main.models import AgentTimings
+from onyx.agents.agent_search.deep_search.main.operations import logger
+from onyx.agents.agent_search.deep_search.main.states import MainOutput
+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.models import CombinedAgentMetrics
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.db.chat import log_agent_metrics
+from onyx.db.chat import log_agent_sub_question_results
+
+
+def persist_agent_results(state: MainState, config: RunnableConfig) -> MainOutput:
+    """
+    LangGraph node to persist the agent results, including agent logging data.
+    """
+    node_start_time = datetime.now()
+
+    agent_start_time = state.agent_start_time
+    agent_base_end_time = state.agent_base_end_time
+    agent_refined_start_time = state.agent_refined_start_time
+    agent_refined_end_time = state.agent_refined_end_time
+    agent_end_time = agent_refined_end_time or agent_base_end_time
+
+    agent_base_duration = None
+    if agent_base_end_time and agent_start_time:
+        agent_base_duration = (agent_base_end_time - agent_start_time).total_seconds()
+
+    agent_refined_duration = None
+    if agent_refined_start_time and agent_refined_end_time:
+        agent_refined_duration = (
+            agent_refined_end_time - agent_refined_start_time
+        ).total_seconds()
+
+    agent_full_duration = None
+    if agent_end_time and agent_start_time:
+        agent_full_duration = (agent_end_time - agent_start_time).total_seconds()
+
+    agent_type = "refined" if agent_refined_duration else "base"
+
+    agent_base_metrics = state.agent_base_metrics
+    agent_refined_metrics = state.agent_refined_metrics
+
+    combined_agent_metrics = CombinedAgentMetrics(
+        timings=AgentTimings(
+            base_duration_s=agent_base_duration,
+            refined_duration_s=agent_refined_duration,
+            full_duration_s=agent_full_duration,
+        ),
+        base_metrics=agent_base_metrics,
+        refined_metrics=agent_refined_metrics,
+        additional_metrics=AgentAdditionalMetrics(),
+    )
+
+    persona_id = None
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    if graph_config.inputs.search_request.persona:
+        persona_id = graph_config.inputs.search_request.persona.id
+
+    user_id = None
+    assert (
+        graph_config.tooling.search_tool
+    ), "search_tool must be provided for agentic search"
+    user = graph_config.tooling.search_tool.user
+    if user:
+        user_id = user.id
+
+    # log the agent metrics
+    if graph_config.persistence:
+        if agent_base_duration is not None:
+            log_agent_metrics(
+                db_session=graph_config.persistence.db_session,
+                user_id=user_id,
+                persona_id=persona_id,
+                agent_type=agent_type,
+                start_time=agent_start_time,
+                agent_metrics=combined_agent_metrics,
+            )
+
+        # Persist the sub-answer in the database
+        db_session = graph_config.persistence.db_session
+        chat_session_id = graph_config.persistence.chat_session_id
+        primary_message_id = graph_config.persistence.message_id
+        sub_question_answer_results = state.sub_question_results
+
+        log_agent_sub_question_results(
+            db_session=db_session,
+            chat_session_id=chat_session_id,
+            primary_message_id=primary_message_id,
+            sub_question_answer_results=sub_question_answer_results,
+        )
+
+    main_output = MainOutput(
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="main",
+                node_name="persist agent results",
+                node_start_time=node_start_time,
+            )
+        ],
+    )
+
+    for log_message in state.log_messages:
+        logger.debug(log_message)
+
+    if state.agent_base_metrics:
+        logger.debug(f"Initial loop: {state.agent_base_metrics.duration_s}")
+    if state.agent_refined_metrics:
+        logger.debug(f"Refined loop: {state.agent_refined_metrics.duration_s}")
+    if (
+        state.agent_base_metrics
+        and state.agent_refined_metrics
+        and state.agent_base_metrics.duration_s
+        and state.agent_refined_metrics.duration_s
+    ):
+        logger.debug(
+            f"Total time: {float(state.agent_base_metrics.duration_s) + float(state.agent_refined_metrics.duration_s)}"
+        )
+
+    return main_output

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

@@ -0,0 +1,52 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.runnables import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.main.states import (
+    ExploratorySearchUpdate,
+)
+from onyx.agents.agent_search.deep_search.main.states import MainState
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    build_history_prompt,
+)
+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 retrieve_search_docs
+from onyx.configs.agent_configs import AGENT_EXPLORATORY_SEARCH_RESULTS
+from onyx.context.search.models import InferenceSection
+
+
+def start_agent_search(
+    state: MainState, config: RunnableConfig
+) -> ExploratorySearchUpdate:
+    """
+    LangGraph node to start the agentic search process.
+    """
+    node_start_time = datetime.now()
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = graph_config.inputs.search_request.query
+
+    history = build_history_prompt(graph_config, question)
+
+    # Initial search to inform decomposition. Just get top 3 fits
+    search_tool = graph_config.tooling.search_tool
+    assert search_tool, "search_tool must be provided for agentic search"
+    retrieved_docs: list[InferenceSection] = retrieve_search_docs(search_tool, question)
+
+    exploratory_search_results = retrieved_docs[:AGENT_EXPLORATORY_SEARCH_RESULTS]
+
+    return ExploratorySearchUpdate(
+        exploratory_search_results=exploratory_search_results,
+        previous_history_summary=history,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="main",
+                node_name="start agent search",
+                node_start_time=node_start_time,
+            )
+        ],
+    )

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

@@ -0,0 +1,151 @@
+from collections.abc import Callable
+
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    SubQuestionAnswerResults,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.models import StreamStopInfo
+from onyx.chat.models import StreamStopReason
+from onyx.chat.models import StreamType
+from onyx.chat.models import SubQuestionPiece
+from onyx.context.search.models import IndexFilters
+from onyx.tools.models import SearchQueryInfo
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def dispatch_subquestion(
+    level: int, writer: StreamWriter
+) -> Callable[[str, int], None]:
+    def _helper(sub_question_part: str, sep_num: int) -> None:
+        write_custom_event(
+            "decomp_qs",
+            SubQuestionPiece(
+                sub_question=sub_question_part,
+                level=level,
+                level_question_num=sep_num,
+            ),
+            writer,
+        )
+
+    return _helper
+
+
+def dispatch_subquestion_sep(level: int, writer: StreamWriter) -> Callable[[int], None]:
+    def _helper(sep_num: int) -> None:
+        write_custom_event(
+            "stream_finished",
+            StreamStopInfo(
+                stop_reason=StreamStopReason.FINISHED,
+                stream_type=StreamType.SUB_QUESTIONS,
+                level=level,
+                level_question_num=sep_num,
+            ),
+            writer,
+        )
+
+    return _helper
+
+
+def calculate_initial_agent_stats(
+    decomp_answer_results: list[SubQuestionAnswerResults],
+    original_question_stats: AgentChunkRetrievalStats,
+) -> InitialAgentResultStats:
+    initial_agent_result_stats: InitialAgentResultStats = InitialAgentResultStats(
+        sub_questions={},
+        original_question={},
+        agent_effectiveness={},
+    )
+
+    orig_verified = original_question_stats.verified_count
+    orig_support_score = original_question_stats.verified_avg_scores
+
+    verified_document_chunk_ids = []
+    support_scores = 0.0
+
+    for decomp_answer_result in decomp_answer_results:
+        verified_document_chunk_ids += (
+            decomp_answer_result.sub_question_retrieval_stats.verified_doc_chunk_ids
+        )
+        if (
+            decomp_answer_result.sub_question_retrieval_stats.verified_avg_scores
+            is not None
+        ):
+            support_scores += (
+                decomp_answer_result.sub_question_retrieval_stats.verified_avg_scores
+            )
+
+    verified_document_chunk_ids = list(set(verified_document_chunk_ids))
+
+    # Calculate sub-question stats
+    if (
+        verified_document_chunk_ids
+        and len(verified_document_chunk_ids) > 0
+        and support_scores is not None
+    ):
+        sub_question_stats: dict[str, float | int | None] = {
+            "num_verified_documents": len(verified_document_chunk_ids),
+            "verified_avg_score": float(support_scores / len(decomp_answer_results)),
+        }
+    else:
+        sub_question_stats = {"num_verified_documents": 0, "verified_avg_score": None}
+
+    initial_agent_result_stats.sub_questions.update(sub_question_stats)
+
+    # Get original question stats
+    initial_agent_result_stats.original_question.update(
+        {
+            "num_verified_documents": original_question_stats.verified_count,
+            "verified_avg_score": original_question_stats.verified_avg_scores,
+        }
+    )
+
+    # Calculate chunk utilization ratio
+    sub_verified = initial_agent_result_stats.sub_questions["num_verified_documents"]
+
+    chunk_ratio: float | None = None
+    if sub_verified is not None and orig_verified is not None and orig_verified > 0:
+        chunk_ratio = (float(sub_verified) / orig_verified) if sub_verified > 0 else 0.0
+    elif sub_verified is not None and sub_verified > 0:
+        chunk_ratio = 10.0
+
+    initial_agent_result_stats.agent_effectiveness["utilized_chunk_ratio"] = chunk_ratio
+
+    if (
+        orig_support_score is None
+        or orig_support_score == 0.0
+        and initial_agent_result_stats.sub_questions["verified_avg_score"] is None
+    ):
+        initial_agent_result_stats.agent_effectiveness["support_ratio"] = None
+    elif orig_support_score is None or orig_support_score == 0.0:
+        initial_agent_result_stats.agent_effectiveness["support_ratio"] = 10
+    elif initial_agent_result_stats.sub_questions["verified_avg_score"] is None:
+        initial_agent_result_stats.agent_effectiveness["support_ratio"] = 0
+    else:
+        initial_agent_result_stats.agent_effectiveness["support_ratio"] = (
+            initial_agent_result_stats.sub_questions["verified_avg_score"]
+            / orig_support_score
+        )
+
+    return initial_agent_result_stats
+
+
+def get_query_info(results: list[QueryRetrievalResult]) -> SearchQueryInfo:
+    # Use the query info from the base document retrieval
+    # this is used for some fields that are the same across the searches done
+    query_info = None
+    for result in results:
+        if result.query_info is not None:
+            query_info = result.query_info
+            break
+    return query_info or SearchQueryInfo(
+        predicted_search=None,
+        final_filters=IndexFilters(access_control_list=None),
+        recency_bias_multiplier=1.0,
+    )

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

@@ -0,0 +1,172 @@
+from datetime import datetime
+from operator import add
+from typing import Annotated
+from typing import TypedDict
+
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.core_state import CoreState
+from onyx.agents.agent_search.deep_search.main.models import AgentBaseMetrics
+from onyx.agents.agent_search.deep_search.main.models import (
+    AgentRefinedMetrics,
+)
+from onyx.agents.agent_search.deep_search.main.models import (
+    RefinementSubQuestion,
+)
+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 (
+    EntityRelationshipTermExtraction,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
+from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    SubQuestionAnswerResults,
+)
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_inference_sections,
+)
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_question_answer_results,
+)
+from onyx.context.search.models import InferenceSection
+
+
+### States ###
+class LoggerUpdate(BaseModel):
+    log_messages: Annotated[list[str], add] = []
+
+
+class RefinedAgentStartStats(BaseModel):
+    agent_refined_start_time: datetime | None = None
+
+
+class RefinedAgentEndStats(BaseModel):
+    agent_refined_end_time: datetime | None = None
+    agent_refined_metrics: AgentRefinedMetrics = AgentRefinedMetrics()
+
+
+class InitialQuestionDecompositionUpdate(
+    RefinedAgentStartStats, RefinedAgentEndStats, LoggerUpdate
+):
+    agent_start_time: datetime | None = None
+    previous_history: str | None = None
+    initial_sub_questions: list[str] = []
+
+
+class ExploratorySearchUpdate(LoggerUpdate):
+    exploratory_search_results: list[InferenceSection] = []
+    previous_history_summary: str | None = None
+
+
+class InitialRefinedAnswerComparisonUpdate(LoggerUpdate):
+    """
+    Evaluation of whether the refined answer is better than the initial answer
+    """
+
+    refined_answer_improvement_eval: bool = False
+
+
+class InitialAnswerUpdate(LoggerUpdate):
+    """
+    Initial answer information
+    """
+
+    initial_answer: str | None = None
+    initial_agent_stats: InitialAgentResultStats | None = None
+    generated_sub_questions: list[str] = []
+    agent_base_end_time: datetime | None = None
+    agent_base_metrics: AgentBaseMetrics | None = None
+
+
+class RefinedAnswerUpdate(RefinedAgentEndStats, LoggerUpdate):
+    """
+    Refined answer information
+    """
+
+    refined_answer: str | None = None
+    refined_agent_stats: RefinedAgentStats | None = None
+    refined_answer_quality: bool = False
+
+
+class InitialAnswerQualityUpdate(LoggerUpdate):
+    """
+    Initial answer quality evaluation
+    """
+
+    initial_answer_quality_eval: bool = False
+
+
+class RequireRefinemenEvalUpdate(LoggerUpdate):
+    require_refined_answer_eval: bool = True
+
+
+class SubQuestionResultsUpdate(LoggerUpdate):
+    verified_reranked_documents: Annotated[
+        list[InferenceSection], dedup_inference_sections
+    ] = []
+    context_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
+    cited_documents: Annotated[
+        list[InferenceSection], dedup_inference_sections
+    ] = []  # cited docs from sub-answers are used for answer context
+    sub_question_results: Annotated[
+        list[SubQuestionAnswerResults], dedup_question_answer_results
+    ] = []
+
+
+class OrigQuestionRetrievalUpdate(LoggerUpdate):
+    orig_question_retrieved_documents: Annotated[
+        list[InferenceSection], dedup_inference_sections
+    ]
+    orig_question_verified_reranked_documents: Annotated[
+        list[InferenceSection], dedup_inference_sections
+    ]
+    orig_question_sub_query_retrieval_results: list[QueryRetrievalResult] = []
+    orig_question_retrieval_stats: AgentChunkRetrievalStats = AgentChunkRetrievalStats()
+
+
+class EntityTermExtractionUpdate(LoggerUpdate):
+    entity_relation_term_extractions: EntityRelationshipTermExtraction = (
+        EntityRelationshipTermExtraction()
+    )
+
+
+class RefinedQuestionDecompositionUpdate(RefinedAgentStartStats, LoggerUpdate):
+    refined_sub_questions: dict[int, RefinementSubQuestion] = {}
+
+
+## Graph Input State
+class MainInput(CoreState):
+    pass
+
+
+## Graph State
+class MainState(
+    # This includes the core state
+    MainInput,
+    ToolChoiceInput,
+    ToolCallUpdate,
+    ToolChoiceUpdate,
+    InitialQuestionDecompositionUpdate,
+    InitialAnswerUpdate,
+    SubQuestionResultsUpdate,
+    OrigQuestionRetrievalUpdate,
+    EntityTermExtractionUpdate,
+    InitialAnswerQualityUpdate,
+    RequireRefinemenEvalUpdate,
+    RefinedQuestionDecompositionUpdate,
+    RefinedAnswerUpdate,
+    RefinedAgentStartStats,
+    RefinedAgentEndStats,
+    InitialRefinedAnswerComparisonUpdate,
+    ExploratorySearchUpdate,
+):
+    pass
+
+
+## Graph Output State - presently not used
+class MainOutput(TypedDict):
+    log_messages: list[str]

backend/onyx/agents/agent_search/deep_search/refinement/consolidate_sub_answers/edges.py

@@ -0,0 +1,33 @@
+from collections.abc import Hashable
+from datetime import datetime
+
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def send_to_expanded_refined_retrieval(
+    state: SubQuestionAnsweringInput,
+) -> Send | Hashable:
+    """
+    LangGraph edge to sends a refined sub-question extended retrieval.
+    """
+    logger.debug("sending to expanded retrieval for follow up question via edge")
+    datetime.now()
+    return Send(
+        "refined_sub_question_expanded_retrieval",
+        ExpandedRetrievalInput(
+            question=state.question,
+            sub_question_id=state.question_id,
+            base_search=False,
+            log_messages=[f"{datetime.now()} -- Sending to expanded retrieval"],
+        ),
+    )

backend/onyx/agents/agent_search/deep_search/refinement/consolidate_sub_answers/graph_builder.py

@@ -0,0 +1,132 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.check_sub_answer import (
+    check_sub_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.format_sub_answer import (
+    format_sub_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.generate_sub_answer import (
+    generate_sub_answer,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.ingest_retrieved_documents import (
+    ingest_retrieved_documents,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+    SubQuestionAnsweringInput,
+)
+from onyx.agents.agent_search.deep_search.refinement.consolidate_sub_answers.edges import (
+    send_to_expanded_refined_retrieval,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.graph_builder import (
+    expanded_retrieval_graph_builder,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def answer_refined_query_graph_builder() -> StateGraph:
+    """
+    LangGraph graph builder for the refined sub-answer generation process.
+    """
+    graph = StateGraph(
+        state_schema=AnswerQuestionState,
+        input=SubQuestionAnsweringInput,
+        output=AnswerQuestionOutput,
+    )
+
+    ### Add nodes ###
+
+    # Subgraph for the expanded retrieval process
+    expanded_retrieval = expanded_retrieval_graph_builder().compile()
+    graph.add_node(
+        node="refined_sub_question_expanded_retrieval",
+        action=expanded_retrieval,
+    )
+
+    # Ingest the retrieved documents
+    graph.add_node(
+        node="ingest_refined_retrieval",
+        action=ingest_retrieved_documents,
+    )
+
+    # Generate the refined sub-answer
+    graph.add_node(
+        node="generate_refined_sub_answer",
+        action=generate_sub_answer,
+    )
+
+    # Check if the refined sub-answer is correct
+    graph.add_node(
+        node="refined_sub_answer_check",
+        action=check_sub_answer,
+    )
+
+    # Format the refined sub-answer
+    graph.add_node(
+        node="format_refined_sub_answer",
+        action=format_sub_answer,
+    )
+
+    ### Add edges ###
+
+    graph.add_conditional_edges(
+        source=START,
+        path=send_to_expanded_refined_retrieval,
+        path_map=["refined_sub_question_expanded_retrieval"],
+    )
+    graph.add_edge(
+        start_key="refined_sub_question_expanded_retrieval",
+        end_key="ingest_refined_retrieval",
+    )
+    graph.add_edge(
+        start_key="ingest_refined_retrieval",
+        end_key="generate_refined_sub_answer",
+    )
+    graph.add_edge(
+        start_key="generate_refined_sub_answer",
+        end_key="refined_sub_answer_check",
+    )
+    graph.add_edge(
+        start_key="refined_sub_answer_check",
+        end_key="format_refined_sub_answer",
+    )
+    graph.add_edge(
+        start_key="format_refined_sub_answer",
+        end_key=END,
+    )
+
+    return graph
+
+
+if __name__ == "__main__":
+    from onyx.db.engine import get_session_context_manager
+    from onyx.llm.factory import get_default_llms
+    from onyx.context.search.models import SearchRequest
+
+    graph = answer_refined_query_graph_builder()
+    compiled_graph = graph.compile()
+    primary_llm, fast_llm = get_default_llms()
+    search_request = SearchRequest(
+        query="what can you do with onyx or danswer?",
+    )
+    with get_session_context_manager() as db_session:
+        inputs = SubQuestionAnsweringInput(
+            question="what can you do with onyx?",
+            question_id="0_0",
+            log_messages=[],
+        )
+        for thing in compiled_graph.stream(
+            input=inputs,
+            stream_mode="custom",
+        ):
+            logger.debug(thing)

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

@@ -0,0 +1,42 @@
+from collections.abc import Hashable
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalState,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    RetrievalInput,
+)
+from onyx.agents.agent_search.models import GraphConfig
+
+
+def parallel_retrieval_edge(
+    state: ExpandedRetrievalState, config: RunnableConfig
+) -> list[Send | Hashable]:
+    """
+    LangGraph edge to parallelize the retrieval process for each of the
+    generated sub-queries and the original question.
+    """
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = (
+        state.question if state.question else graph_config.inputs.search_request.query
+    )
+
+    query_expansions = state.expanded_queries + [question]
+
+    return [
+        Send(
+            "retrieve_documents",
+            RetrievalInput(
+                query_to_retrieve=query,
+                question=question,
+                base_search=False,
+                sub_question_id=state.sub_question_id,
+                log_messages=[],
+            ),
+        )
+        for query in query_expansions
+    ]

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

@@ -0,0 +1,161 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.edges import (
+    parallel_retrieval_edge,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.expand_queries import (
+    expand_queries,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.format_queries import (
+    format_queries,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.format_results import (
+    format_results,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.kickoff_verification import (
+    kickoff_verification,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.rerank_documents import (
+    rerank_documents,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.retrieve_documents import (
+    retrieve_documents,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.verify_documents import (
+    verify_documents,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalOutput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalState,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def expanded_retrieval_graph_builder() -> StateGraph:
+    """
+    LangGraph graph builder for the expanded retrieval process.
+    """
+    graph = StateGraph(
+        state_schema=ExpandedRetrievalState,
+        input=ExpandedRetrievalInput,
+        output=ExpandedRetrievalOutput,
+    )
+
+    ### Add nodes ###
+
+    # Convert the question into multiple sub-queries
+    graph.add_node(
+        node="expand_queries",
+        action=expand_queries,
+    )
+
+    # Format the sub-queries into a list of strings
+    graph.add_node(
+        node="format_queries",
+        action=format_queries,
+    )
+
+    # Retrieve the documents for each sub-query
+    graph.add_node(
+        node="retrieve_documents",
+        action=retrieve_documents,
+    )
+
+    # Start verification process that the documents are relevant to the question (not the query)
+    graph.add_node(
+        node="kickoff_verification",
+        action=kickoff_verification,
+    )
+
+    # Verify that a given document is relevant to the question (not the query)
+    graph.add_node(
+        node="verify_documents",
+        action=verify_documents,
+    )
+
+    # Rerank the documents that have been verified
+    graph.add_node(
+        node="rerank_documents",
+        action=rerank_documents,
+    )
+
+    # Format the results into a list of strings
+    graph.add_node(
+        node="format_results",
+        action=format_results,
+    )
+
+    ### Add edges ###
+    graph.add_edge(
+        start_key=START,
+        end_key="expand_queries",
+    )
+    graph.add_edge(
+        start_key="expand_queries",
+        end_key="format_queries",
+    )
+
+    graph.add_conditional_edges(
+        source="format_queries",
+        path=parallel_retrieval_edge,
+        path_map=["retrieve_documents"],
+    )
+    graph.add_edge(
+        start_key="retrieve_documents",
+        end_key="kickoff_verification",
+    )
+    graph.add_edge(
+        start_key="verify_documents",
+        end_key="rerank_documents",
+    )
+    graph.add_edge(
+        start_key="rerank_documents",
+        end_key="format_results",
+    )
+    graph.add_edge(
+        start_key="format_results",
+        end_key=END,
+    )
+
+    return graph
+
+
+if __name__ == "__main__":
+    from onyx.db.engine import get_session_context_manager
+    from onyx.llm.factory import get_default_llms
+    from onyx.context.search.models import SearchRequest
+
+    graph = expanded_retrieval_graph_builder()
+    compiled_graph = graph.compile()
+    primary_llm, fast_llm = get_default_llms()
+    search_request = SearchRequest(
+        query="what can you do with onyx or danswer?",
+    )
+
+    with get_session_context_manager() as db_session:
+        graph_config, search_tool = get_test_config(
+            db_session, primary_llm, fast_llm, search_request
+        )
+        inputs = ExpandedRetrievalInput(
+            question="what can you do with onyx?",
+            base_search=False,
+            sub_question_id=None,
+            log_messages=[],
+        )
+        for thing in compiled_graph.stream(
+            input=inputs,
+            config={"configurable": {"config": graph_config}},
+            stream_mode="custom",
+            subgraphs=True,
+        ):
+            logger.debug(thing)

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

@@ -0,0 +1,13 @@
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
+from onyx.context.search.models import InferenceSection
+
+
+class QuestionRetrievalResult(BaseModel):
+    expanded_query_results: list[QueryRetrievalResult] = []
+    retrieved_documents: list[InferenceSection] = []
+    verified_reranked_documents: list[InferenceSection] = []
+    context_documents: list[InferenceSection] = []
+    retrieval_stats: AgentChunkRetrievalStats = AgentChunkRetrievalStats()

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

@@ -0,0 +1,75 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.messages import merge_message_runs
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.operations import (
+    dispatch_subquery,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    QueryExpansionUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+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.prompts.agent_search import (
+    QUERY_REWRITING_PROMPT,
+)
+
+
+def expand_queries(
+    state: ExpandedRetrievalInput,
+    config: RunnableConfig,
+    writer: StreamWriter = lambda _: None,
+) -> QueryExpansionUpdate:
+    """
+    LangGraph node to expand a question into multiple search queries.
+    """
+    # Sometimes we want to expand the original question, sometimes we want to expand a sub-question.
+    # When we are running this node on the original question, no question is explictly passed in.
+    # Instead, we use the original question from the search request.
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    node_start_time = datetime.now()
+    question = state.question
+
+    llm = graph_config.tooling.fast_llm
+    sub_question_id = state.sub_question_id
+    if sub_question_id is None:
+        level, question_num = 0, 0
+    else:
+        level, question_num = parse_question_id(sub_question_id)
+
+    msg = [
+        HumanMessage(
+            content=QUERY_REWRITING_PROMPT.format(question=question),
+        )
+    ]
+
+    llm_response_list = dispatch_separated(
+        llm.stream(prompt=msg), dispatch_subquery(level, question_num, writer)
+    )
+
+    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
+
+    rewritten_queries = llm_response.split("\n")
+
+    return QueryExpansionUpdate(
+        expanded_queries=rewritten_queries,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="shared - expanded retrieval",
+                node_name="expand queries",
+                node_start_time=node_start_time,
+                result=f"Number of expanded queries: {len(rewritten_queries)}",
+            )
+        ],
+    )

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

@@ -0,0 +1,19 @@
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalState,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    QueryExpansionUpdate,
+)
+
+
+def format_queries(
+    state: ExpandedRetrievalState, config: RunnableConfig
+) -> QueryExpansionUpdate:
+    """
+    LangGraph node to format the expanded queries into a list of strings.
+    """
+    return QueryExpansionUpdate(
+        expanded_queries=state.expanded_queries,
+    )

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

@@ -0,0 +1,91 @@
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.deep_search.main.operations import get_query_info
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.models import (
+    QuestionRetrievalResult,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.operations import (
+    calculate_sub_question_retrieval_stats,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalState,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+from onyx.agents.agent_search.shared_graph_utils.utils import relevance_from_docs
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.models import ExtendedToolResponse
+from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
+
+
+def format_results(
+    state: ExpandedRetrievalState,
+    config: RunnableConfig,
+    writer: StreamWriter = lambda _: None,
+) -> ExpandedRetrievalUpdate:
+    """
+    LangGraph node that constructs the proper expanded retrieval format.
+    """
+    level, question_num = parse_question_id(state.sub_question_id or "0_0")
+    query_info = get_query_info(state.query_retrieval_results)
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+
+    # Main question docs will be sent later after aggregation and deduping with sub-question docs
+    reranked_documents = state.reranked_documents
+
+    if not (level == 0 and question_num == 0):
+        if len(reranked_documents) == 0:
+            # The sub-question is used as the last query. If no verified documents are found, stream
+            # the top 3 for that one. We may want to revisit this.
+            reranked_documents = state.query_retrieval_results[-1].retrieved_documents[
+                :3
+            ]
+
+        assert (
+            graph_config.tooling.search_tool
+        ), "search_tool must be provided for agentic search"
+
+        relevance_list = relevance_from_docs(reranked_documents)
+        for tool_response in yield_search_responses(
+            query=state.question,
+            reranked_sections=state.retrieved_documents,
+            final_context_sections=reranked_documents,
+            search_query_info=query_info,
+            get_section_relevance=lambda: relevance_list,
+            search_tool=graph_config.tooling.search_tool,
+        ):
+            write_custom_event(
+                "tool_response",
+                ExtendedToolResponse(
+                    id=tool_response.id,
+                    response=tool_response.response,
+                    level=level,
+                    level_question_num=question_num,
+                ),
+                writer,
+            )
+    sub_question_retrieval_stats = calculate_sub_question_retrieval_stats(
+        verified_documents=state.verified_documents,
+        expanded_retrieval_results=state.query_retrieval_results,
+    )
+
+    if sub_question_retrieval_stats is None:
+        sub_question_retrieval_stats = AgentChunkRetrievalStats()
+
+    return ExpandedRetrievalUpdate(
+        expanded_retrieval_result=QuestionRetrievalResult(
+            expanded_query_results=state.query_retrieval_results,
+            retrieved_documents=state.retrieved_documents,
+            verified_reranked_documents=reranked_documents,
+            context_documents=state.reranked_documents,
+            retrieval_stats=sub_question_retrieval_stats,
+        ),
+    )

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

@@ -0,0 +1,44 @@
+from typing import Literal
+
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import Command
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    DocVerificationInput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalState,
+)
+
+
+def kickoff_verification(
+    state: ExpandedRetrievalState,
+    config: RunnableConfig,
+) -> Command[Literal["verify_documents"]]:
+    """
+    LangGraph node (Command node!) that kicks off the verification process for the retrieved documents.
+    Note that this is a Command node and does the routing as well. (At present, no state updates
+    are done here, so this could be replaced with an edge. But we may choose to make state
+    updates later.)
+    """
+    retrieved_documents = state.retrieved_documents
+    verification_question = state.question
+
+    sub_question_id = state.sub_question_id
+    return Command(
+        update={},
+        goto=[
+            Send(
+                node="verify_documents",
+                arg=DocVerificationInput(
+                    retrieved_document_to_verify=document,
+                    question=verification_question,
+                    base_search=False,
+                    sub_question_id=sub_question_id,
+                    log_messages=[],
+                ),
+            )
+            for document in retrieved_documents
+        ],
+    )

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

@@ -0,0 +1,97 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.operations import (
+    logger,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    DocRerankingUpdate,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    ExpandedRetrievalState,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.calculations import get_fit_scores
+from onyx.agents.agent_search.shared_graph_utils.models import RetrievalFitStats
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.configs.agent_configs import AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS
+from onyx.configs.agent_configs import AGENT_RERANKING_STATS
+from onyx.context.search.models import InferenceSection
+from onyx.context.search.models import RerankingDetails
+from onyx.context.search.postprocessing.postprocessing import rerank_sections
+from onyx.context.search.postprocessing.postprocessing import should_rerank
+from onyx.db.engine import get_session_context_manager
+from onyx.db.search_settings import get_current_search_settings
+
+
+def rerank_documents(
+    state: ExpandedRetrievalState, config: RunnableConfig
+) -> DocRerankingUpdate:
+    """
+    LangGraph node to rerank the retrieved and verified documents. A part of the
+    pre-existing pipeline is used here.
+    """
+    node_start_time = datetime.now()
+    verified_documents = state.verified_documents
+
+    # Rerank post retrieval and verification. First, create a search query
+    # then create the list of reranked sections
+    # If no question defined/question is None in the state, use the original
+    # question from the search request as query
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    question = (
+        state.question if state.question else graph_config.inputs.search_request.query
+    )
+    assert (
+        graph_config.tooling.search_tool
+    ), "search_tool must be provided for agentic search"
+
+    # Note that these are passed in values from the API and are overrides which are typically None
+    rerank_settings = graph_config.inputs.search_request.rerank_settings
+
+    if rerank_settings is None:
+        with get_session_context_manager() as db_session:
+            search_settings = get_current_search_settings(db_session)
+            if not search_settings.disable_rerank_for_streaming:
+                rerank_settings = RerankingDetails.from_db_model(search_settings)
+
+    if should_rerank(rerank_settings) and len(verified_documents) > 0:
+        if len(verified_documents) > 1:
+            reranked_documents = rerank_sections(
+                query_str=question,
+                # if runnable, then rerank_settings is not None
+                rerank_settings=cast(RerankingDetails, rerank_settings),
+                sections_to_rerank=verified_documents,
+            )
+        else:
+            logger.warning(
+                f"{len(verified_documents)} verified document(s) found, skipping reranking"
+            )
+            reranked_documents = verified_documents
+    else:
+        logger.warning("No reranking settings found, using unranked documents")
+        reranked_documents = verified_documents
+
+    if AGENT_RERANKING_STATS:
+        fit_scores = get_fit_scores(verified_documents, reranked_documents)
+    else:
+        fit_scores = RetrievalFitStats(fit_score_lift=0, rerank_effect=0, fit_scores={})
+
+    return DocRerankingUpdate(
+        reranked_documents=[
+            doc for doc in reranked_documents if type(doc) == InferenceSection
+        ][:AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS],
+        sub_question_retrieval_stats=fit_scores,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="shared - expanded retrieval",
+                node_name="rerank documents",
+                node_start_time=node_start_time,
+            )
+        ],
+    )

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

@@ -0,0 +1,113 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.operations import (
+    logger,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    DocRetrievalUpdate,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    RetrievalInput,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.calculations import get_fit_scores
+from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.configs.agent_configs import AGENT_MAX_QUERY_RETRIEVAL_RESULTS
+from onyx.configs.agent_configs import AGENT_RETRIEVAL_STATS
+from onyx.context.search.models import InferenceSection
+from onyx.db.engine import get_session_context_manager
+from onyx.tools.models import SearchQueryInfo
+from onyx.tools.tool_implementations.search.search_tool import (
+    SEARCH_RESPONSE_SUMMARY_ID,
+)
+from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
+
+
+def retrieve_documents(
+    state: RetrievalInput, config: RunnableConfig
+) -> DocRetrievalUpdate:
+    """
+    LangGraph node to retrieve documents from the search tool.
+    """
+    node_start_time = datetime.now()
+    query_to_retrieve = state.query_to_retrieve
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    search_tool = graph_config.tooling.search_tool
+
+    retrieved_docs: list[InferenceSection] = []
+    if not query_to_retrieve.strip():
+        logger.warning("Empty query, skipping retrieval")
+
+        return DocRetrievalUpdate(
+            query_retrieval_results=[],
+            retrieved_documents=[],
+            log_messages=[
+                get_langgraph_node_log_string(
+                    graph_component="shared - expanded retrieval",
+                    node_name="retrieve documents",
+                    node_start_time=node_start_time,
+                    result="Empty query, skipping retrieval",
+                )
+            ],
+        )
+
+    query_info = None
+    if search_tool is None:
+        raise ValueError("search_tool must be provided for agentic search")
+
+    callback_container: list[list[InferenceSection]] = []
+
+    # new db session to avoid concurrency issues
+    with get_session_context_manager() as db_session:
+        for tool_response in search_tool.run(
+            query=query_to_retrieve,
+            force_no_rerank=True,
+            alternate_db_session=db_session,
+            retrieved_sections_callback=callback_container.append,
+        ):
+            # get retrieved docs to send to the rest of the graph
+            if tool_response.id == SEARCH_RESPONSE_SUMMARY_ID:
+                response = cast(SearchResponseSummary, tool_response.response)
+                retrieved_docs = response.top_sections
+                query_info = SearchQueryInfo(
+                    predicted_search=response.predicted_search,
+                    final_filters=response.final_filters,
+                    recency_bias_multiplier=response.recency_bias_multiplier,
+                )
+                break
+
+    retrieved_docs = retrieved_docs[:AGENT_MAX_QUERY_RETRIEVAL_RESULTS]
+
+    if AGENT_RETRIEVAL_STATS:
+        pre_rerank_docs = callback_container[0]
+        fit_scores = get_fit_scores(
+            pre_rerank_docs,
+            retrieved_docs,
+        )
+    else:
+        fit_scores = None
+
+    expanded_retrieval_result = QueryRetrievalResult(
+        query=query_to_retrieve,
+        retrieved_documents=retrieved_docs,
+        stats=fit_scores,
+        query_info=query_info,
+    )
+
+    return DocRetrievalUpdate(
+        query_retrieval_results=[expanded_retrieval_result],
+        retrieved_documents=retrieved_docs,
+        log_messages=[
+            get_langgraph_node_log_string(
+                graph_component="shared - expanded retrieval",
+                node_name="retrieve documents",
+                node_start_time=node_start_time,
+            )
+        ],
+    )

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

@@ -0,0 +1,62 @@
+from typing import cast
+
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    DocVerificationInput,
+)
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+    DocVerificationUpdate,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+    trim_prompt_piece,
+)
+from onyx.prompts.agent_search import (
+    DOCUMENT_VERIFICATION_PROMPT,
+)
+
+
+def verify_documents(
+    state: DocVerificationInput, config: RunnableConfig
+) -> DocVerificationUpdate:
+    """
+    LangGraph node to check whether the document is relevant for the original user question
+
+    Args:
+        state (DocVerificationInput): The current state
+        config (RunnableConfig): Configuration containing ProSearchConfig
+
+    Updates:
+        verified_documents: list[InferenceSection]
+    """
+
+    question = state.question
+    retrieved_document_to_verify = state.retrieved_document_to_verify
+    document_content = retrieved_document_to_verify.combined_content
+
+    graph_config = cast(GraphConfig, config["metadata"]["config"])
+    fast_llm = graph_config.tooling.fast_llm
+
+    document_content = trim_prompt_piece(
+        fast_llm.config, document_content, DOCUMENT_VERIFICATION_PROMPT + question
+    )
+
+    msg = [
+        HumanMessage(
+            content=DOCUMENT_VERIFICATION_PROMPT.format(
+                question=question, document_content=document_content
+            )
+        )
+    ]
+
+    response = fast_llm.invoke(msg)
+
+    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/operations.py

@@ -0,0 +1,93 @@
+from collections import defaultdict
+from collections.abc import Callable
+
+import numpy as np
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.models import SubQueryPiece
+from onyx.context.search.models import InferenceSection
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def dispatch_subquery(
+    level: int, question_num: int, writer: StreamWriter
+) -> Callable[[str, int], None]:
+    def helper(token: str, num: int) -> None:
+        write_custom_event(
+            "subqueries",
+            SubQueryPiece(
+                sub_query=token,
+                level=level,
+                level_question_num=question_num,
+                query_id=num,
+            ),
+            writer,
+        )
+
+    return helper
+
+
+def calculate_sub_question_retrieval_stats(
+    verified_documents: list[InferenceSection],
+    expanded_retrieval_results: list[QueryRetrievalResult],
+) -> AgentChunkRetrievalStats:
+    chunk_scores: dict[str, dict[str, list[int | float]]] = defaultdict(
+        lambda: defaultdict(list)
+    )
+
+    for expanded_retrieval_result in expanded_retrieval_results:
+        for doc in expanded_retrieval_result.retrieved_documents:
+            doc_chunk_id = f"{doc.center_chunk.document_id}_{doc.center_chunk.chunk_id}"
+            if doc.center_chunk.score is not None:
+                chunk_scores[doc_chunk_id]["score"].append(doc.center_chunk.score)
+
+    verified_doc_chunk_ids = [
+        f"{verified_document.center_chunk.document_id}_{verified_document.center_chunk.chunk_id}"
+        for verified_document in verified_documents
+    ]
+    dismissed_doc_chunk_ids = []
+
+    raw_chunk_stats_counts: dict[str, int] = defaultdict(int)
+    raw_chunk_stats_scores: dict[str, float] = defaultdict(float)
+    for doc_chunk_id, chunk_data in chunk_scores.items():
+        valid_chunk_scores = [
+            score for score in chunk_data["score"] if score is not None
+        ]
+        key = "verified" if doc_chunk_id in verified_doc_chunk_ids else "rejected"
+        raw_chunk_stats_counts[f"{key}_count"] += 1
+
+        raw_chunk_stats_scores[f"{key}_scores"] += float(np.mean(valid_chunk_scores))
+
+        if key == "rejected":
+            dismissed_doc_chunk_ids.append(doc_chunk_id)
+
+    if raw_chunk_stats_counts["verified_count"] == 0:
+        verified_avg_scores = 0.0
+    else:
+        verified_avg_scores = raw_chunk_stats_scores["verified_scores"] / float(
+            raw_chunk_stats_counts["verified_count"]
+        )
+
+    rejected_scores = raw_chunk_stats_scores.get("rejected_scores")
+    if rejected_scores is not None:
+        rejected_avg_scores = rejected_scores / float(
+            raw_chunk_stats_counts["rejected_count"]
+        )
+    else:
+        rejected_avg_scores = None
+
+    chunk_stats = AgentChunkRetrievalStats(
+        verified_count=raw_chunk_stats_counts["verified_count"],
+        verified_avg_scores=verified_avg_scores,
+        rejected_count=raw_chunk_stats_counts["rejected_count"],
+        rejected_avg_scores=rejected_avg_scores,
+        verified_doc_chunk_ids=verified_doc_chunk_ids,
+        dismissed_doc_chunk_ids=dismissed_doc_chunk_ids,
+    )
+
+    return chunk_stats

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

@@ -0,0 +1,91 @@
+from operator import add
+from typing import Annotated
+
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.core_state import SubgraphCoreState
+from onyx.agents.agent_search.deep_search.main.states import LoggerUpdate
+from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.models import (
+    QuestionRetrievalResult,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
+from onyx.agents.agent_search.shared_graph_utils.models import RetrievalFitStats
+from onyx.agents.agent_search.shared_graph_utils.operators import (
+    dedup_inference_sections,
+)
+from onyx.context.search.models import InferenceSection
+
+### States ###
+
+## Graph Input State
+
+
+class ExpandedRetrievalInput(SubgraphCoreState):
+    question: str = ""
+    base_search: bool = False
+    sub_question_id: str | None = None
+
+
+## Update/Return States
+
+
+class QueryExpansionUpdate(LoggerUpdate, BaseModel):
+    expanded_queries: list[str] = []
+    log_messages: list[str] = []
+
+
+class DocVerificationUpdate(BaseModel):
+    verified_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
+
+
+class DocRetrievalUpdate(LoggerUpdate, BaseModel):
+    query_retrieval_results: Annotated[list[QueryRetrievalResult], add] = []
+    retrieved_documents: Annotated[
+        list[InferenceSection], dedup_inference_sections
+    ] = []
+
+
+class DocRerankingUpdate(LoggerUpdate, BaseModel):
+    reranked_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
+    sub_question_retrieval_stats: RetrievalFitStats | None = None
+
+
+class ExpandedRetrievalUpdate(LoggerUpdate, BaseModel):
+    expanded_retrieval_result: QuestionRetrievalResult
+
+
+## Graph Output State
+
+
+class ExpandedRetrievalOutput(LoggerUpdate, BaseModel):
+    expanded_retrieval_result: QuestionRetrievalResult = QuestionRetrievalResult()
+    base_expanded_retrieval_result: QuestionRetrievalResult = QuestionRetrievalResult()
+    retrieved_documents: Annotated[
+        list[InferenceSection], dedup_inference_sections
+    ] = []
+
+
+## Graph State
+
+
+class ExpandedRetrievalState(
+    # This includes the core state
+    ExpandedRetrievalInput,
+    QueryExpansionUpdate,
+    DocRetrievalUpdate,
+    DocVerificationUpdate,
+    DocRerankingUpdate,
+    ExpandedRetrievalOutput,
+):
+    pass
+
+
+## Conditional Input States
+
+
+class DocVerificationInput(ExpandedRetrievalInput):
+    retrieved_document_to_verify: InferenceSection
+
+
+class RetrievalInput(ExpandedRetrievalInput):
+    query_to_retrieve: str = ""

backend/onyx/agents/agent_search/models.py

@@ -0,0 +1,90 @@
+from uuid import UUID
+
+from pydantic import BaseModel
+from pydantic import model_validator
+from sqlalchemy.orm import Session
+
+from onyx.chat.prompt_builder.answer_prompt_builder import AnswerPromptBuilder
+from onyx.context.search.models import SearchRequest
+from onyx.file_store.utils import InMemoryChatFile
+from onyx.llm.interfaces import LLM
+from onyx.tools.force import ForceUseTool
+from onyx.tools.tool import Tool
+from onyx.tools.tool_implementations.search.search_tool import SearchTool
+
+
+class GraphInputs(BaseModel):
+    """Input data required for the graph execution"""
+
+    search_request: SearchRequest
+    prompt_builder: AnswerPromptBuilder
+    files: list[InMemoryChatFile] | None = None
+    structured_response_format: dict | None = None
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
+class GraphTooling(BaseModel):
+    """Tools and LLMs available to the graph"""
+
+    primary_llm: LLM
+    fast_llm: LLM
+    search_tool: SearchTool | None = None
+    tools: list[Tool]
+    # Whether to force use of a tool, or to
+    # force tool args IF the tool is used
+    force_use_tool: ForceUseTool
+    using_tool_calling_llm: bool = False
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
+class GraphPersistence(BaseModel):
+    """Configuration for data persistence"""
+
+    chat_session_id: UUID
+    # The message ID of the to-be-created first agent message
+    # in response to the user message that triggered the Pro Search
+    message_id: int
+
+    # The database session the user and initial agent
+    # message were flushed to; only needed for agentic search
+    db_session: Session
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
+class GraphSearchConfig(BaseModel):
+    """Configuration controlling search behavior"""
+
+    use_agentic_search: bool = False
+    # Whether to perform initial search to inform decomposition
+    perform_initial_search_decomposition: bool = True
+
+    # Whether to allow creation of refinement questions (and entity extraction, etc.)
+    allow_refinement: bool = True
+    skip_gen_ai_answer_generation: bool = False
+
+
+class GraphConfig(BaseModel):
+    """
+    Main container for data needed for Langgraph execution
+    """
+
+    inputs: GraphInputs
+    tooling: GraphTooling
+    behavior: GraphSearchConfig
+    # Only needed for agentic search
+    persistence: GraphPersistence
+
+    @model_validator(mode="after")
+    def validate_search_tool(self) -> "GraphConfig":
+        if self.behavior.use_agentic_search and self.tooling.search_tool is None:
+            raise ValueError("search_tool must be provided for agentic search")
+        return self
+
+    class Config:
+        arbitrary_types_allowed = True

backend/onyx/agents/agent_search/orchestration/nodes/basic_use_tool_response.py

@@ -0,0 +1,77 @@
+from typing import cast
+
+from langchain_core.messages import AIMessageChunk
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.basic.states import BasicOutput
+from onyx.agents.agent_search.basic.states import BasicState
+from onyx.agents.agent_search.basic.utils import process_llm_stream
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.chat.models import LlmDoc
+from onyx.chat.models import OnyxContexts
+from onyx.tools.tool_implementations.search.search_tool import (
+    SEARCH_DOC_CONTENT_ID,
+)
+from onyx.tools.tool_implementations.search_like_tool_utils import (
+    FINAL_CONTEXT_DOCUMENTS_ID,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def basic_use_tool_response(
+    state: BasicState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+) -> BasicOutput:
+    agent_config = cast(GraphConfig, config["metadata"]["config"])
+    structured_response_format = agent_config.inputs.structured_response_format
+    llm = agent_config.tooling.primary_llm
+    tool_choice = state.tool_choice
+    if tool_choice is None:
+        raise ValueError("Tool choice is None")
+    tool = tool_choice.tool
+    prompt_builder = agent_config.inputs.prompt_builder
+    if state.tool_call_output is None:
+        raise ValueError("Tool call output is None")
+    tool_call_output = state.tool_call_output
+    tool_call_summary = tool_call_output.tool_call_summary
+    tool_call_responses = tool_call_output.tool_call_responses
+
+    new_prompt_builder = tool.build_next_prompt(
+        prompt_builder=prompt_builder,
+        tool_call_summary=tool_call_summary,
+        tool_responses=tool_call_responses,
+        using_tool_calling_llm=agent_config.tooling.using_tool_calling_llm,
+    )
+
+    final_search_results = []
+    initial_search_results = []
+    for yield_item in tool_call_responses:
+        if yield_item.id == FINAL_CONTEXT_DOCUMENTS_ID:
+            final_search_results = cast(list[LlmDoc], yield_item.response)
+        elif yield_item.id == SEARCH_DOC_CONTENT_ID:
+            search_contexts = cast(OnyxContexts, yield_item.response).contexts
+            for doc in search_contexts:
+                if doc.document_id not in initial_search_results:
+                    initial_search_results.append(doc)
+
+    new_tool_call_chunk = AIMessageChunk(content="")
+    if not agent_config.behavior.skip_gen_ai_answer_generation:
+        stream = llm.stream(
+            prompt=new_prompt_builder.build(),
+            structured_response_format=structured_response_format,
+        )
+
+        # For now, we don't do multiple tool calls, so we ignore the tool_message
+        new_tool_call_chunk = process_llm_stream(
+            stream,
+            True,
+            writer,
+            final_search_results=final_search_results,
+            # when the search tool is called with specific doc ids, initial search
+            # results are not output. But, we still want i.e. citations to be processed.
+            displayed_search_results=initial_search_results or final_search_results,
+        )
+
+    return BasicOutput(tool_call_chunk=new_tool_call_chunk)

backend/onyx/agents/agent_search/orchestration/nodes/llm_tool_choice.py

@@ -0,0 +1,154 @@
+from typing import cast
+from uuid import uuid4
+
+from langchain_core.messages import ToolCall
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.basic.utils import process_llm_stream
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.orchestration.states import ToolChoice
+from onyx.agents.agent_search.orchestration.states import ToolChoiceState
+from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
+from onyx.chat.prompt_builder.answer_prompt_builder import AnswerPromptBuilder
+from onyx.chat.tool_handling.tool_response_handler import get_tool_by_name
+from onyx.chat.tool_handling.tool_response_handler import (
+    get_tool_call_for_non_tool_calling_llm_impl,
+)
+from onyx.tools.tool import Tool
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+# TODO: break this out into an implementation function
+# and a function that handles extracting the necessary fields
+# from the state and config
+# TODO: fan-out to multiple tool call nodes? Make this configurable?
+def llm_tool_choice(
+    state: ToolChoiceState,
+    config: RunnableConfig,
+    writer: StreamWriter = lambda _: None,
+) -> ToolChoiceUpdate:
+    """
+    This node is responsible for calling the LLM to choose a tool. If no tool is chosen,
+    The node MAY emit an answer, depending on whether state["should_stream_answer"] is set.
+    """
+    should_stream_answer = state.should_stream_answer
+
+    agent_config = cast(GraphConfig, config["metadata"]["config"])
+    using_tool_calling_llm = agent_config.tooling.using_tool_calling_llm
+    prompt_builder = state.prompt_snapshot or agent_config.inputs.prompt_builder
+
+    llm = agent_config.tooling.primary_llm
+    skip_gen_ai_answer_generation = agent_config.behavior.skip_gen_ai_answer_generation
+
+    structured_response_format = agent_config.inputs.structured_response_format
+    tools = [
+        tool for tool in (agent_config.tooling.tools or []) if tool.name in state.tools
+    ]
+    force_use_tool = agent_config.tooling.force_use_tool
+
+    tool, tool_args = None, None
+    if force_use_tool.force_use and force_use_tool.args is not None:
+        tool_name, tool_args = (
+            force_use_tool.tool_name,
+            force_use_tool.args,
+        )
+        tool = get_tool_by_name(tools, tool_name)
+
+    # special pre-logic for non-tool calling LLM case
+    elif not using_tool_calling_llm and tools:
+        chosen_tool_and_args = get_tool_call_for_non_tool_calling_llm_impl(
+            force_use_tool=force_use_tool,
+            tools=tools,
+            prompt_builder=prompt_builder,
+            llm=llm,
+        )
+        if chosen_tool_and_args:
+            tool, tool_args = chosen_tool_and_args
+
+    # If we have a tool and tool args, we are ready to request a tool call.
+    # This only happens if the tool call was forced or we are using a non-tool calling LLM.
+    if tool and tool_args:
+        return ToolChoiceUpdate(
+            tool_choice=ToolChoice(
+                tool=tool,
+                tool_args=tool_args,
+                id=str(uuid4()),
+            ),
+        )
+
+    # if we're skipping gen ai answer generation, we should only
+    # continue if we're forcing a tool call (which will be emitted by
+    # the tool calling llm in the stream() below)
+    if skip_gen_ai_answer_generation and not force_use_tool.force_use:
+        return ToolChoiceUpdate(
+            tool_choice=None,
+        )
+
+    built_prompt = (
+        prompt_builder.build()
+        if isinstance(prompt_builder, AnswerPromptBuilder)
+        else prompt_builder.built_prompt
+    )
+    # At this point, we are either using a tool calling LLM or we are skipping the tool call.
+    # DEBUG: good breakpoint
+    stream = llm.stream(
+        # For tool calling LLMs, we want to insert the task prompt as part of this flow, this is because the LLM
+        # may choose to not call any tools and just generate the answer, in which case the task prompt is needed.
+        prompt=built_prompt,
+        tools=[tool.tool_definition() for tool in tools] or None,
+        tool_choice=("required" if tools and force_use_tool.force_use else None),
+        structured_response_format=structured_response_format,
+    )
+
+    tool_message = process_llm_stream(
+        stream,
+        should_stream_answer
+        and not agent_config.behavior.skip_gen_ai_answer_generation,
+        writer,
+    )
+
+    # If no tool calls are emitted by the LLM, we should not choose a tool
+    if len(tool_message.tool_calls) == 0:
+        logger.debug("No tool calls emitted by LLM")
+        return ToolChoiceUpdate(
+            tool_choice=None,
+        )
+
+    # TODO: here we could handle parallel tool calls. Right now
+    # we just pick the first one that matches.
+    selected_tool: Tool | None = None
+    selected_tool_call_request: ToolCall | None = None
+    for tool_call_request in tool_message.tool_calls:
+        known_tools_by_name = [
+            tool for tool in tools if tool.name == tool_call_request["name"]
+        ]
+
+        if known_tools_by_name:
+            selected_tool = known_tools_by_name[0]
+            selected_tool_call_request = tool_call_request
+            break
+
+        logger.error(
+            "Tool call requested with unknown name field. \n"
+            f"tools: {tools}"
+            f"tool_call_request: {tool_call_request}"
+        )
+
+    if not selected_tool or not selected_tool_call_request:
+        raise ValueError(
+            f"Tool call attempted with tool {selected_tool}, request {selected_tool_call_request}"
+        )
+
+    logger.debug(f"Selected tool: {selected_tool.name}")
+    logger.debug(f"Selected tool call request: {selected_tool_call_request}")
+
+    return ToolChoiceUpdate(
+        tool_choice=ToolChoice(
+            tool=selected_tool,
+            tool_args=selected_tool_call_request["args"],
+            id=selected_tool_call_request["id"],
+        ),
+    )

backend/onyx/agents/agent_search/orchestration/nodes/prepare_tool_input.py

@@ -0,0 +1,17 @@
+from typing import Any
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.orchestration.states import ToolChoiceInput
+
+
+def prepare_tool_input(state: Any, config: RunnableConfig) -> ToolChoiceInput:
+    agent_config = cast(GraphConfig, config["metadata"]["config"])
+    return ToolChoiceInput(
+        # NOTE: this node is used at the top level of the agent, so we always stream
+        should_stream_answer=True,
+        prompt_snapshot=None,  # uses default prompt builder
+        tools=[tool.name for tool in (agent_config.tooling.tools or [])],
+    )

backend/onyx/agents/agent_search/orchestration/nodes/tool_call.py

@@ -0,0 +1,79 @@
+from typing import cast
+
+from langchain_core.messages import AIMessageChunk
+from langchain_core.messages.tool import ToolCall
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.orchestration.states import ToolCallOutput
+from onyx.agents.agent_search.orchestration.states import ToolCallUpdate
+from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.models import AnswerPacket
+from onyx.tools.message import build_tool_message
+from onyx.tools.message import ToolCallSummary
+from onyx.tools.tool_runner import ToolRunner
+from onyx.utils.logger import setup_logger
+
+
+logger = setup_logger()
+
+
+class ToolCallException(Exception):
+    """Exception raised for errors during tool calls."""
+
+
+def emit_packet(packet: AnswerPacket, writer: StreamWriter) -> None:
+    write_custom_event("basic_response", packet, writer)
+
+
+def tool_call(
+    state: ToolChoiceUpdate,
+    config: RunnableConfig,
+    writer: StreamWriter = lambda _: None,
+) -> ToolCallUpdate:
+    """Calls the tool specified in the state and updates the state with the result"""
+
+    cast(GraphConfig, config["metadata"]["config"])
+
+    tool_choice = state.tool_choice
+    if tool_choice is None:
+        raise ValueError("Cannot invoke tool call node without a tool choice")
+
+    tool = tool_choice.tool
+    tool_args = tool_choice.tool_args
+    tool_id = tool_choice.id
+    tool_runner = ToolRunner(tool, tool_args)
+    tool_kickoff = tool_runner.kickoff()
+
+    emit_packet(tool_kickoff, writer)
+
+    try:
+        tool_responses = []
+        for response in tool_runner.tool_responses():
+            tool_responses.append(response)
+            emit_packet(response, writer)
+
+        tool_final_result = tool_runner.tool_final_result()
+        emit_packet(tool_final_result, writer)
+    except Exception as e:
+        raise ToolCallException(
+            f"Error during tool call for {tool.display_name}: {e}"
+        ) from e
+
+    tool_call = ToolCall(name=tool.name, args=tool_args, id=tool_id)
+    tool_call_summary = ToolCallSummary(
+        tool_call_request=AIMessageChunk(content="", tool_calls=[tool_call]),
+        tool_call_result=build_tool_message(
+            tool_call, tool_runner.tool_message_content()
+        ),
+    )
+
+    tool_call_output = ToolCallOutput(
+        tool_call_summary=tool_call_summary,
+        tool_call_kickoff=tool_kickoff,
+        tool_call_responses=tool_responses,
+        tool_call_final_result=tool_final_result,
+    )
+    return ToolCallUpdate(tool_call_output=tool_call_output)

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

@@ -0,0 +1,48 @@
+from pydantic import BaseModel
+
+from onyx.chat.prompt_builder.answer_prompt_builder import PromptSnapshot
+from onyx.tools.message import ToolCallSummary
+from onyx.tools.models import ToolCallFinalResult
+from onyx.tools.models import ToolCallKickoff
+from onyx.tools.models import ToolResponse
+from onyx.tools.tool import Tool
+
+
+# TODO: adapt the tool choice/tool call to allow for parallel tool calls by
+# creating a subgraph that can be invoked in parallel via Send/Command APIs
+class ToolChoiceInput(BaseModel):
+    should_stream_answer: bool = True
+    # default to the prompt builder from the config, but
+    # allow overrides for arbitrary tool calls
+    prompt_snapshot: PromptSnapshot | None = None
+
+    # names of tools to use for tool calling. Filters the tools available in the config
+    tools: list[str] = []
+
+
+class ToolCallOutput(BaseModel):
+    tool_call_summary: ToolCallSummary
+    tool_call_kickoff: ToolCallKickoff
+    tool_call_responses: list[ToolResponse]
+    tool_call_final_result: ToolCallFinalResult
+
+
+class ToolCallUpdate(BaseModel):
+    tool_call_output: ToolCallOutput | None = None
+
+
+class ToolChoice(BaseModel):
+    tool: Tool
+    tool_args: dict
+    id: str | None
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
+class ToolChoiceUpdate(BaseModel):
+    tool_choice: ToolChoice | None = None
+
+
+class ToolChoiceState(ToolChoiceUpdate, ToolChoiceInput):
+    pass

backend/onyx/agents/agent_search/run_graph.py

@@ -0,0 +1,213 @@
+from collections.abc import Iterable
+from datetime import datetime
+from typing import cast
+
+from langchain_core.runnables.schema import CustomStreamEvent
+from langchain_core.runnables.schema import StreamEvent
+from langgraph.graph.state import CompiledStateGraph
+
+from onyx.agents.agent_search.basic.graph_builder import basic_graph_builder
+from onyx.agents.agent_search.basic.states import BasicInput
+from onyx.agents.agent_search.deep_search.main.graph_builder import (
+    main_graph_builder as main_graph_builder_a,
+)
+from onyx.agents.agent_search.deep_search.main.states import (
+    MainInput as MainInput_a,
+)
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
+from onyx.chat.models import AgentAnswerPiece
+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 StreamStopInfo
+from onyx.chat.models import SubQueryPiece
+from onyx.chat.models import SubQuestionPiece
+from onyx.chat.models import ToolResponse
+from onyx.configs.agent_configs import ALLOW_REFINEMENT
+from onyx.configs.agent_configs import INITIAL_SEARCH_DECOMPOSITION_ENABLED
+from onyx.context.search.models import SearchRequest
+from onyx.db.engine import get_session_context_manager
+from onyx.llm.factory import get_default_llms
+from onyx.tools.tool_runner import ToolCallKickoff
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+_COMPILED_GRAPH: CompiledStateGraph | None = None
+
+
+def _parse_agent_event(
+    event: StreamEvent,
+) -> AnswerPacket | None:
+    """
+    Parse the event into a typed object.
+    Return None if we are not interested in the event.
+    """
+
+    event_type = event["event"]
+
+    # We always just yield the event data, but this piece is useful for two development reasons:
+    # 1. It's a list of the names of every place we dispatch a custom event
+    # 2. We maintain the intended types yielded by each event
+    if event_type == "on_custom_event":
+        if event["name"] == "decomp_qs":
+            return cast(SubQuestionPiece, event["data"])
+        elif event["name"] == "subqueries":
+            return cast(SubQueryPiece, event["data"])
+        elif event["name"] == "sub_answers":
+            return cast(AgentAnswerPiece, event["data"])
+        elif event["name"] == "stream_finished":
+            return cast(StreamStopInfo, event["data"])
+        elif event["name"] == "initial_agent_answer":
+            return cast(AgentAnswerPiece, event["data"])
+        elif event["name"] == "refined_agent_answer":
+            return cast(AgentAnswerPiece, event["data"])
+        elif event["name"] == "start_refined_answer_creation":
+            return cast(ToolCallKickoff, event["data"])
+        elif event["name"] == "tool_response":
+            return cast(ToolResponse, event["data"])
+        elif event["name"] == "basic_response":
+            return cast(AnswerPacket, event["data"])
+        elif event["name"] == "refined_answer_improvement":
+            return cast(RefinedAnswerImprovement, event["data"])
+    return None
+
+
+def manage_sync_streaming(
+    compiled_graph: CompiledStateGraph,
+    config: GraphConfig,
+    graph_input: BasicInput | MainInput_a,
+) -> Iterable[StreamEvent]:
+    message_id = config.persistence.message_id if config.persistence else None
+    for event in compiled_graph.stream(
+        stream_mode="custom",
+        input=graph_input,
+        config={"metadata": {"config": config, "thread_id": str(message_id)}},
+    ):
+        yield cast(CustomStreamEvent, event)
+
+
+def run_graph(
+    compiled_graph: CompiledStateGraph,
+    config: GraphConfig,
+    input: BasicInput | MainInput_a,
+) -> AnswerStream:
+    config.behavior.perform_initial_search_decomposition = (
+        INITIAL_SEARCH_DECOMPOSITION_ENABLED
+    )
+    config.behavior.allow_refinement = ALLOW_REFINEMENT
+
+    for event in manage_sync_streaming(
+        compiled_graph=compiled_graph, config=config, graph_input=input
+    ):
+        if not (parsed_object := _parse_agent_event(event)):
+            continue
+
+        yield parsed_object
+
+
+# It doesn't actually take very long to load the graph, but we'd rather
+# not compile it again on every request.
+def load_compiled_graph() -> CompiledStateGraph:
+    global _COMPILED_GRAPH
+    if _COMPILED_GRAPH is None:
+        graph = main_graph_builder_a()
+        _COMPILED_GRAPH = graph.compile()
+    return _COMPILED_GRAPH
+
+
+def run_main_graph(
+    config: GraphConfig,
+) -> AnswerStream:
+    compiled_graph = load_compiled_graph()
+
+    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(
+        tool_name="agent_search_0",
+        tool_args={"query": config.inputs.search_request.query},
+    )
+    yield from run_graph(compiled_graph, config, input)
+
+
+def run_basic_graph(
+    config: GraphConfig,
+) -> AnswerStream:
+    graph = basic_graph_builder()
+    compiled_graph = graph.compile()
+    input = BasicInput(unused=True)
+    return run_graph(compiled_graph, config, input)
+
+
+if __name__ == "__main__":
+    for _ in range(1):
+        query_start_time = datetime.now()
+        logger.debug(f"Start at {query_start_time}")
+        graph = main_graph_builder_a()
+        compiled_graph = graph.compile()
+        query_end_time = datetime.now()
+        logger.debug(f"Graph compiled in {query_end_time - query_start_time} seconds")
+        primary_llm, fast_llm = get_default_llms()
+        search_request = SearchRequest(
+            # query="what can you do with gitlab?",
+            # query="What are the guiding principles behind the development of cockroachDB",
+            # query="What are the temperatures in Munich, Hawaii, and New York?",
+            # query="When was Washington born?",
+            # query="What is Onyx?",
+            # query="What is the difference between astronomy and astrology?",
+            query="Do a search to tell me what is the difference between astronomy and astrology?",
+        )
+
+        with get_session_context_manager() as db_session:
+            config = get_test_config(db_session, primary_llm, fast_llm, search_request)
+            assert (
+                config.persistence is not None
+            ), "set a chat session id to run this test"
+
+            # search_request.persona = get_persona_by_id(1, None, db_session)
+            # config.perform_initial_search_path_decision = False
+            config.behavior.perform_initial_search_decomposition = True
+            input = MainInput_a(
+                base_question=config.inputs.search_request.query, log_messages=[]
+            )
+
+            tool_responses: list = []
+            for output in run_graph(compiled_graph, config, input):
+                if isinstance(output, ToolCallKickoff):
+                    pass
+                elif isinstance(output, ExtendedToolResponse):
+                    tool_responses.append(output.response)
+                    logger.info(
+                        f"   ---- ET {output.level} - {output.level_question_num} |  "
+                    )
+                elif isinstance(output, SubQueryPiece):
+                    logger.info(
+                        f"Sq {output.level} - {output.level_question_num} - {output.sub_query} | "
+                    )
+                elif isinstance(output, SubQuestionPiece):
+                    logger.info(
+                        f"SQ {output.level} - {output.level_question_num} - {output.sub_question} | "
+                    )
+                elif (
+                    isinstance(output, AgentAnswerPiece)
+                    and output.answer_type == "agent_sub_answer"
+                ):
+                    logger.info(
+                        f"   ---- SA {output.level} - {output.level_question_num} {output.answer_piece} | "
+                    )
+                elif (
+                    isinstance(output, AgentAnswerPiece)
+                    and output.answer_type == "agent_level_answer"
+                ):
+                    logger.info(
+                        f"   ---------- FA {output.level} - {output.level_question_num}  {output.answer_piece} | "
+                    )
+                elif isinstance(output, RefinedAnswerImprovement):
+                    logger.info(
+                        f"   ---------- RE {output.refined_answer_improvement} | "
+                    )

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

@@ -0,0 +1,152 @@
+from langchain.schema import AIMessage
+from langchain.schema import HumanMessage
+from langchain.schema import SystemMessage
+from langchain_core.messages.tool import ToolMessage
+
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    AgentPromptEnrichmentComponents,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_persona_agent_prompt_expressions,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import remove_document_citations
+from onyx.agents.agent_search.shared_graph_utils.utils import summarize_history
+from onyx.configs.agent_configs import AGENT_MAX_STATIC_HISTORY_WORD_LENGTH
+from onyx.configs.constants import MessageType
+from onyx.context.search.models import InferenceSection
+from onyx.llm.interfaces import LLMConfig
+from onyx.llm.utils import get_max_input_tokens
+from onyx.natural_language_processing.utils import get_tokenizer
+from onyx.natural_language_processing.utils import tokenizer_trim_content
+from onyx.prompts.agent_search import HISTORY_FRAMING_PROMPT
+from onyx.prompts.agent_search import SUB_QUESTION_RAG_PROMPT
+from onyx.prompts.prompt_utils import build_date_time_string
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def build_sub_question_answer_prompt(
+    question: str,
+    original_question: str,
+    docs: list[InferenceSection],
+    persona_specification: str,
+    config: LLMConfig,
+) -> list[SystemMessage | HumanMessage | AIMessage | ToolMessage]:
+    system_message = SystemMessage(
+        content=persona_specification,
+    )
+
+    date_str = build_date_time_string()
+
+    # TODO: This should include document metadata and title
+    docs_format_list = [
+        f"Document Number: [D{doc_num + 1}]\nContent: {doc.combined_content}\n\n"
+        for doc_num, doc in enumerate(docs)
+    ]
+
+    docs_str = "\n\n".join(docs_format_list)
+
+    docs_str = trim_prompt_piece(
+        config,
+        docs_str,
+        SUB_QUESTION_RAG_PROMPT + question + original_question + date_str,
+    )
+    human_message = HumanMessage(
+        content=SUB_QUESTION_RAG_PROMPT.format(
+            question=question,
+            original_question=original_question,
+            context=docs_str,
+            date_prompt=date_str,
+        )
+    )
+
+    return [system_message, human_message]
+
+
+def trim_prompt_piece(config: LLMConfig, prompt_piece: str, reserved_str: str) -> str:
+    # TODO: save the max input tokens in LLMConfig
+    max_tokens = get_max_input_tokens(
+        model_provider=config.model_provider,
+        model_name=config.model_name,
+    )
+
+    # no need to trim if a conservative estimate of one token
+    # per character is already less than the max tokens
+    if len(prompt_piece) + len(reserved_str) < max_tokens:
+        return prompt_piece
+
+    llm_tokenizer = get_tokenizer(
+        provider_type=config.model_provider,
+        model_name=config.model_name,
+    )
+
+    # slightly conservative trimming
+    return tokenizer_trim_content(
+        content=prompt_piece,
+        desired_length=max_tokens - len(llm_tokenizer.encode(reserved_str)),
+        tokenizer=llm_tokenizer,
+    )
+
+
+def build_history_prompt(config: GraphConfig, question: str) -> str:
+    prompt_builder = config.inputs.prompt_builder
+    persona_base = get_persona_agent_prompt_expressions(
+        config.inputs.search_request.persona
+    ).base_prompt
+
+    if prompt_builder is None:
+        return ""
+
+    if prompt_builder.single_message_history is not None:
+        history = prompt_builder.single_message_history
+    else:
+        history_components = []
+        previous_message_type = None
+        for message in prompt_builder.raw_message_history:
+            if message.message_type == MessageType.USER:
+                history_components.append(f"User: {message.message}\n")
+                previous_message_type = MessageType.USER
+            elif message.message_type == MessageType.ASSISTANT:
+                # Previously there could be multiple assistant messages in a row
+                # Now this is handled at the message history construction
+                assert previous_message_type is not MessageType.ASSISTANT
+                history_components.append(f"You/Agent: {message.message}\n")
+                previous_message_type = MessageType.ASSISTANT
+            else:
+                # Other message types are not included here, currently there should be no other message types
+                logger.error(
+                    f"Unhandled message type: {message.message_type} with message: {message.message}"
+                )
+                continue
+
+        history = "\n".join(history_components)
+        history = remove_document_citations(history)
+        if len(history.split()) > AGENT_MAX_STATIC_HISTORY_WORD_LENGTH:
+            history = summarize_history(
+                history=history,
+                question=question,
+                persona_specification=persona_base,
+                llm=config.tooling.fast_llm,
+            )
+
+    return HISTORY_FRAMING_PROMPT.format(history=history) if history else ""
+
+
+def get_prompt_enrichment_components(
+    config: GraphConfig,
+) -> AgentPromptEnrichmentComponents:
+    persona_prompts = get_persona_agent_prompt_expressions(
+        config.inputs.search_request.persona
+    )
+
+    history = build_history_prompt(config, config.inputs.search_request.query)
+
+    date_str = build_date_time_string()
+
+    return AgentPromptEnrichmentComponents(
+        persona_prompts=persona_prompts,
+        history=history,
+        date_str=date_str,
+    )

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

@@ -0,0 +1,98 @@
+import numpy as np
+
+from onyx.agents.agent_search.shared_graph_utils.models import RetrievalFitScoreMetrics
+from onyx.agents.agent_search.shared_graph_utils.models import RetrievalFitStats
+from onyx.chat.models import SectionRelevancePiece
+from onyx.context.search.models import InferenceSection
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def unique_chunk_id(doc: InferenceSection) -> str:
+    return f"{doc.center_chunk.document_id}_{doc.center_chunk.chunk_id}"
+
+
+def calculate_rank_shift(list1: list, list2: list, top_n: int = 20) -> float:
+    shift = 0
+    for rank_first, doc_id in enumerate(list1[:top_n], 1):
+        try:
+            rank_second = list2.index(doc_id) + 1
+        except ValueError:
+            rank_second = len(list2)  # Document not found in second list
+
+        shift += np.abs(rank_first - rank_second) / np.log(1 + rank_first * rank_second)
+
+    return shift / top_n
+
+
+def get_fit_scores(
+    pre_reranked_results: list[InferenceSection],
+    post_reranked_results: list[InferenceSection] | list[SectionRelevancePiece],
+) -> RetrievalFitStats | None:
+    """
+    Calculate retrieval metrics for search purposes
+    """
+
+    if len(pre_reranked_results) == 0 or len(post_reranked_results) == 0:
+        return None
+
+    ranked_sections = {
+        "initial": pre_reranked_results,
+        "reranked": post_reranked_results,
+    }
+
+    fit_eval: RetrievalFitStats = RetrievalFitStats(
+        fit_score_lift=0,
+        rerank_effect=0,
+        fit_scores={
+            "initial": RetrievalFitScoreMetrics(scores={}, chunk_ids=[]),
+            "reranked": RetrievalFitScoreMetrics(scores={}, chunk_ids=[]),
+        },
+    )
+
+    for rank_type, docs in ranked_sections.items():
+        logger.debug(f"rank_type: {rank_type}")
+
+        for i in [1, 5, 10]:
+            fit_eval.fit_scores[rank_type].scores[str(i)] = (
+                sum(
+                    [
+                        float(doc.center_chunk.score)
+                        for doc in docs[:i]
+                        if type(doc) == InferenceSection
+                        and doc.center_chunk.score is not None
+                    ]
+                )
+                / i
+            )
+
+        fit_eval.fit_scores[rank_type].scores["fit_score"] = (
+            1
+            / 3
+            * (
+                fit_eval.fit_scores[rank_type].scores["1"]
+                + fit_eval.fit_scores[rank_type].scores["5"]
+                + fit_eval.fit_scores[rank_type].scores["10"]
+            )
+        )
+
+        fit_eval.fit_scores[rank_type].scores["fit_score"] = fit_eval.fit_scores[
+            rank_type
+        ].scores["1"]
+
+        fit_eval.fit_scores[rank_type].chunk_ids = [
+            unique_chunk_id(doc) for doc in docs if type(doc) == InferenceSection
+        ]
+
+    fit_eval.fit_score_lift = (
+        fit_eval.fit_scores["reranked"].scores["fit_score"]
+        / fit_eval.fit_scores["initial"].scores["fit_score"]
+    )
+
+    fit_eval.rerank_effect = calculate_rank_shift(
+        fit_eval.fit_scores["initial"].chunk_ids,
+        fit_eval.fit_scores["reranked"].chunk_ids,
+    )
+
+    return fit_eval

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

@@ -0,0 +1,128 @@
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.deep_search.main.models import (
+    AgentAdditionalMetrics,
+)
+from onyx.agents.agent_search.deep_search.main.models import AgentBaseMetrics
+from onyx.agents.agent_search.deep_search.main.models import (
+    AgentRefinedMetrics,
+)
+from onyx.agents.agent_search.deep_search.main.models import AgentTimings
+from onyx.context.search.models import InferenceSection
+from onyx.tools.models import SearchQueryInfo
+
+
+# Pydantic models for structured outputs
+# class RewrittenQueries(BaseModel):
+#     rewritten_queries: list[str]
+
+
+# class BinaryDecision(BaseModel):
+#     decision: Literal["yes", "no"]
+
+
+# class BinaryDecisionWithReasoning(BaseModel):
+#     reasoning: str
+#     decision: Literal["yes", "no"]
+
+
+class RetrievalFitScoreMetrics(BaseModel):
+    scores: dict[str, float]
+    chunk_ids: list[str]
+
+
+class RetrievalFitStats(BaseModel):
+    fit_score_lift: float
+    rerank_effect: float
+    fit_scores: dict[str, RetrievalFitScoreMetrics]
+
+
+# class AgentChunkScores(BaseModel):
+#     scores: dict[str, dict[str, list[int | float]]]
+
+
+class AgentChunkRetrievalStats(BaseModel):
+    verified_count: int | None = None
+    verified_avg_scores: float | None = None
+    rejected_count: int | None = None
+    rejected_avg_scores: float | None = None
+    verified_doc_chunk_ids: list[str] = []
+    dismissed_doc_chunk_ids: list[str] = []
+
+
+class InitialAgentResultStats(BaseModel):
+    sub_questions: dict[str, float | int | None]
+    original_question: dict[str, float | int | None]
+    agent_effectiveness: dict[str, float | int | None]
+
+
+class RefinedAgentStats(BaseModel):
+    revision_doc_efficiency: float | None
+    revision_question_efficiency: float | None
+
+
+class Term(BaseModel):
+    term_name: str = ""
+    term_type: str = ""
+    term_similar_to: list[str] = []
+
+
+### Models ###
+
+
+class Entity(BaseModel):
+    entity_name: str = ""
+    entity_type: str = ""
+
+
+class Relationship(BaseModel):
+    relationship_name: str = ""
+    relationship_type: str = ""
+    relationship_entities: list[str] = []
+
+
+class EntityRelationshipTermExtraction(BaseModel):
+    entities: list[Entity] = []
+    relationships: list[Relationship] = []
+    terms: list[Term] = []
+
+
+class EntityExtractionResult(BaseModel):
+    retrieved_entities_relationships: EntityRelationshipTermExtraction
+
+
+class QueryRetrievalResult(BaseModel):
+    query: str
+    retrieved_documents: list[InferenceSection]
+    stats: RetrievalFitStats | None
+    query_info: SearchQueryInfo | None
+
+
+class SubQuestionAnswerResults(BaseModel):
+    question: str
+    question_id: str
+    answer: str
+    verified_high_quality: bool
+    sub_query_retrieval_results: list[QueryRetrievalResult]
+    verified_reranked_documents: list[InferenceSection]
+    context_documents: list[InferenceSection]
+    cited_documents: list[InferenceSection]
+    sub_question_retrieval_stats: AgentChunkRetrievalStats
+
+
+class CombinedAgentMetrics(BaseModel):
+    timings: AgentTimings
+    base_metrics: AgentBaseMetrics | None
+    refined_metrics: AgentRefinedMetrics
+    additional_metrics: AgentAdditionalMetrics
+
+
+class PersonaPromptExpressions(BaseModel):
+    contextualized_prompt: str
+    base_prompt: str | None
+
+
+class AgentPromptEnrichmentComponents(BaseModel):
+    persona_prompts: PersonaPromptExpressions
+    history: str
+    date_str: str

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

@@ -0,0 +1,31 @@
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    SubQuestionAnswerResults,
+)
+from onyx.chat.prune_and_merge import _merge_sections
+from onyx.context.search.models import InferenceSection
+
+
+def dedup_inference_sections(
+    list1: list[InferenceSection], list2: list[InferenceSection]
+) -> list[InferenceSection]:
+    deduped = _merge_sections(list1 + list2)
+    return deduped
+
+
+def dedup_question_answer_results(
+    question_answer_results_1: list[SubQuestionAnswerResults],
+    question_answer_results_2: list[SubQuestionAnswerResults],
+) -> list[SubQuestionAnswerResults]:
+    deduped_question_answer_results: list[
+        SubQuestionAnswerResults
+    ] = question_answer_results_1
+    utilized_question_ids: set[str] = set(
+        [x.question_id for x in question_answer_results_1]
+    )
+
+    for question_answer_result in question_answer_results_2:
+        if question_answer_result.question_id not in utilized_question_ids:
+            deduped_question_answer_results.append(question_answer_result)
+            utilized_question_ids.add(question_answer_result.question_id)
+
+    return deduped_question_answer_results

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

@@ -0,0 +1,441 @@
+import os
+import re
+from collections.abc import Callable
+from collections.abc import Iterator
+from collections.abc import Sequence
+from datetime import datetime
+from typing import Any
+from typing import cast
+from typing import Literal
+from typing import TypedDict
+from uuid import UUID
+
+from langchain_core.messages import BaseMessage
+from langchain_core.messages import HumanMessage
+from langgraph.types import StreamWriter
+from sqlalchemy.orm import Session
+
+from onyx.agents.agent_search.models import GraphConfig
+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 (
+    EntityRelationshipTermExtraction,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import PersonaPromptExpressions
+from onyx.chat.models import AnswerPacket
+from onyx.chat.models import AnswerStyleConfig
+from onyx.chat.models import CitationConfig
+from onyx.chat.models import DocumentPruningConfig
+from onyx.chat.models import PromptConfig
+from onyx.chat.models import SectionRelevancePiece
+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.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
+from onyx.configs.constants import DISPATCH_SEP_CHAR
+from onyx.configs.constants import FORMAT_DOCS_SEPARATOR
+from onyx.context.search.enums import LLMEvaluationType
+from onyx.context.search.models import InferenceSection
+from onyx.context.search.models import RetrievalDetails
+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.interfaces import LLM
+from onyx.prompts.agent_search import (
+    ASSISTANT_SYSTEM_PROMPT_DEFAULT,
+)
+from onyx.prompts.agent_search import (
+    ASSISTANT_SYSTEM_PROMPT_PERSONA,
+)
+from onyx.prompts.agent_search import (
+    HISTORY_CONTEXT_SUMMARY_PROMPT,
+)
+from onyx.prompts.prompt_utils import handle_onyx_date_awareness
+from onyx.tools.force import ForceUseTool
+from onyx.tools.tool_constructor import SearchToolConfig
+from onyx.tools.tool_implementations.search.search_tool import (
+    SEARCH_RESPONSE_SUMMARY_ID,
+)
+from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
+from onyx.tools.tool_implementations.search.search_tool import SearchTool
+from onyx.tools.utils import explicit_tool_calling_supported
+
+BaseMessage_Content = str | list[str | dict[str, Any]]
+
+
+# Post-processing
+def format_docs(docs: Sequence[InferenceSection]) -> str:
+    formatted_doc_list = []
+
+    for doc_num, doc in enumerate(docs):
+        title: str | None = doc.center_chunk.title
+        metadata: dict[str, str | list[str]] | None = (
+            doc.center_chunk.metadata if doc.center_chunk.metadata else None
+        )
+
+        doc_str = f"**Document: D{doc_num + 1}**"
+        if title:
+            doc_str += f"\nTitle: {title}"
+        if metadata:
+            metadata_str = ""
+            for key, value in metadata.items():
+                if isinstance(value, str):
+                    metadata_str += f" - {key}: {value}"
+                elif isinstance(value, list):
+                    metadata_str += f" - {key}: {', '.join(value)}"
+            doc_str += f"\nMetadata: {metadata_str}"
+        doc_str += f"\nContent:\n{doc.combined_content}"
+
+        formatted_doc_list.append(doc_str)
+
+    return FORMAT_DOCS_SEPARATOR.join(formatted_doc_list)
+
+
+def format_entity_term_extraction(
+    entity_term_extraction_dict: EntityRelationshipTermExtraction,
+) -> str:
+    entities = entity_term_extraction_dict.entities
+    terms = entity_term_extraction_dict.terms
+    relationships = entity_term_extraction_dict.relationships
+
+    entity_strs = ["\nEntities:\n"]
+    for entity in entities:
+        entity_str = f"{entity.entity_name} ({entity.entity_type})"
+        entity_strs.append(entity_str)
+
+    entity_str = "\n - ".join(entity_strs)
+
+    relationship_strs = ["\n\nRelationships:\n"]
+    for relationship in relationships:
+        relationship_name = relationship.relationship_name
+        relationship_type = relationship.relationship_type
+        relationship_entities = relationship.relationship_entities
+        relationship_str = (
+            f"""{relationship_name} ({relationship_type}): {relationship_entities}"""
+        )
+        relationship_strs.append(relationship_str)
+
+    relationship_str = "\n - ".join(relationship_strs)
+
+    term_strs = ["\n\nTerms:\n"]
+    for term in terms:
+        term_str = f"{term.term_name} ({term.term_type}): similar to {', '.join(term.term_similar_to)}"
+        term_strs.append(term_str)
+
+    term_str = "\n - ".join(term_strs)
+
+    return "\n".join(entity_strs + relationship_strs + term_strs)
+
+
+def get_test_config(
+    db_session: Session,
+    primary_llm: LLM,
+    fast_llm: LLM,
+    search_request: SearchRequest,
+    use_agentic_search: bool = True,
+) -> GraphConfig:
+    persona = get_persona_by_id(DEFAULT_PERSONA_ID, None, db_session)
+    document_pruning_config = DocumentPruningConfig(
+        max_chunks=int(
+            persona.num_chunks
+            if persona.num_chunks is not None
+            else MAX_CHUNKS_FED_TO_CHAT
+        ),
+        max_window_percentage=CHAT_TARGET_CHUNK_PERCENTAGE,
+    )
+
+    answer_style_config = AnswerStyleConfig(
+        citation_config=CitationConfig(
+            # The docs retrieved by this flow are already relevance-filtered
+            all_docs_useful=True
+        ),
+        document_pruning_config=document_pruning_config,
+        structured_response_format=None,
+    )
+
+    search_tool_config = SearchToolConfig(
+        answer_style_config=answer_style_config,
+        document_pruning_config=document_pruning_config,
+        retrieval_options=RetrievalDetails(),  # may want to set dedupe_docs=True
+        rerank_settings=None,  # Can use this to change reranking model
+        selected_sections=None,
+        latest_query_files=None,
+        bypass_acl=False,
+    )
+
+    prompt_config = PromptConfig.from_model(persona.prompts[0])
+
+    search_tool = SearchTool(
+        db_session=db_session,
+        user=None,
+        persona=persona,
+        retrieval_options=search_tool_config.retrieval_options,
+        prompt_config=prompt_config,
+        llm=primary_llm,
+        fast_llm=fast_llm,
+        pruning_config=search_tool_config.document_pruning_config,
+        answer_style_config=search_tool_config.answer_style_config,
+        selected_sections=search_tool_config.selected_sections,
+        chunks_above=search_tool_config.chunks_above,
+        chunks_below=search_tool_config.chunks_below,
+        full_doc=search_tool_config.full_doc,
+        evaluation_type=(
+            LLMEvaluationType.BASIC
+            if persona.llm_relevance_filter
+            else LLMEvaluationType.SKIP
+        ),
+        rerank_settings=search_tool_config.rerank_settings,
+        bypass_acl=search_tool_config.bypass_acl,
+    )
+
+    graph_inputs = GraphInputs(
+        search_request=search_request,
+        prompt_builder=AnswerPromptBuilder(
+            user_message=HumanMessage(content=search_request.query),
+            message_history=[],
+            llm_config=primary_llm.config,
+            raw_user_query=search_request.query,
+            raw_user_uploaded_files=[],
+        ),
+        structured_response_format=answer_style_config.structured_response_format,
+    )
+
+    using_tool_calling_llm = explicit_tool_calling_supported(
+        primary_llm.config.model_provider, primary_llm.config.model_name
+    )
+    graph_tooling = GraphTooling(
+        primary_llm=primary_llm,
+        fast_llm=fast_llm,
+        search_tool=search_tool,
+        tools=[search_tool],
+        force_use_tool=ForceUseTool(force_use=False, tool_name=""),
+        using_tool_calling_llm=using_tool_calling_llm,
+    )
+
+    chat_session_id = os.environ.get("ONYX_AS_CHAT_SESSION_ID")
+    assert (
+        chat_session_id is not None
+    ), "ONYX_AS_CHAT_SESSION_ID must be set for backend tests"
+    graph_persistence = GraphPersistence(
+        db_session=db_session,
+        chat_session_id=UUID(chat_session_id),
+        message_id=1,
+    )
+
+    search_behavior_config = GraphSearchConfig(
+        use_agentic_search=use_agentic_search,
+        skip_gen_ai_answer_generation=False,
+        allow_refinement=True,
+    )
+    graph_config = GraphConfig(
+        inputs=graph_inputs,
+        tooling=graph_tooling,
+        persistence=graph_persistence,
+        behavior=search_behavior_config,
+    )
+
+    return graph_config
+
+
+def get_persona_agent_prompt_expressions(
+    persona: Persona | None,
+) -> PersonaPromptExpressions:
+    if persona is None or len(persona.prompts) == 0:
+        # TODO base_prompt should be None, but no time to properly fix
+        return PersonaPromptExpressions(
+            contextualized_prompt=ASSISTANT_SYSTEM_PROMPT_DEFAULT, base_prompt=""
+        )
+
+    # Only a 1:1 mapping between personas and prompts currently
+    prompt = persona.prompts[0]
+    prompt_config = PromptConfig.from_model(prompt)
+    datetime_aware_system_prompt = handle_onyx_date_awareness(
+        prompt_str=prompt_config.system_prompt,
+        prompt_config=prompt_config,
+        add_additional_info_if_no_tag=prompt.datetime_aware,
+    )
+
+    return PersonaPromptExpressions(
+        contextualized_prompt=ASSISTANT_SYSTEM_PROMPT_PERSONA.format(
+            persona_prompt=datetime_aware_system_prompt
+        ),
+        base_prompt=datetime_aware_system_prompt,
+    )
+
+
+def make_question_id(level: int, question_num: int) -> str:
+    return f"{level}_{question_num}"
+
+
+def parse_question_id(question_id: str) -> tuple[int, int]:
+    level, question_num = question_id.split("_")
+    return int(level), int(question_num)
+
+
+def _dispatch_nonempty(
+    content: str, dispatch_event: Callable[[str, int], None], sep_num: int
+) -> None:
+    """
+    Dispatch a content string if it is not empty using the given callback.
+    This function is used in the context of dispatching some arbitrary number
+    of similar objects which are separated by a separator during the LLM stream.
+    The callback expects a sep_num denoting which object is being dispatched; these
+    numbers go from 1 to however many strings the LLM decides to stream.
+    """
+    if content != "":
+        dispatch_event(content, sep_num)
+
+
+def dispatch_separated(
+    tokens: Iterator[BaseMessage],
+    dispatch_event: Callable[[str, int], None],
+    sep_callback: Callable[[int], None] | None = None,
+    sep: str = DISPATCH_SEP_CHAR,
+) -> list[BaseMessage_Content]:
+    num = 1
+    streamed_tokens: list[BaseMessage_Content] = []
+    for token in tokens:
+        content = cast(str, token.content)
+        if sep in content:
+            sub_question_parts = content.split(sep)
+            _dispatch_nonempty(sub_question_parts[0], dispatch_event, num)
+
+            if sep_callback:
+                sep_callback(num)
+
+            num += 1
+            _dispatch_nonempty(
+                "".join(sub_question_parts[1:]).strip(), dispatch_event, num
+            )
+        else:
+            _dispatch_nonempty(content, dispatch_event, num)
+        streamed_tokens.append(content)
+
+    if sep_callback:
+        sep_callback(num)
+
+    return streamed_tokens
+
+
+def dispatch_main_answer_stop_info(level: int, writer: StreamWriter) -> None:
+    stop_event = StreamStopInfo(
+        stop_reason=StreamStopReason.FINISHED,
+        stream_type=StreamType.MAIN_ANSWER,
+        level=level,
+    )
+    write_custom_event("stream_finished", stop_event, writer)
+
+
+def retrieve_search_docs(
+    search_tool: SearchTool, question: str
+) -> list[InferenceSection]:
+    retrieved_docs: list[InferenceSection] = []
+
+    # new db session to avoid concurrency issues
+    with get_session_context_manager() as db_session:
+        for tool_response in search_tool.run(
+            query=question,
+            force_no_rerank=True,
+            alternate_db_session=db_session,
+        ):
+            # get retrieved docs to send to the rest of the graph
+            if tool_response.id == SEARCH_RESPONSE_SUMMARY_ID:
+                response = cast(SearchResponseSummary, tool_response.response)
+                retrieved_docs = response.top_sections
+                break
+
+    return retrieved_docs
+
+
+def get_answer_citation_ids(answer_str: str) -> list[int]:
+    """
+    Extract citation numbers of format [D<number>] from the answer string.
+    """
+    citation_ids = re.findall(r"\[D(\d+)\]", answer_str)
+    return list(set([(int(id) - 1) for id in citation_ids]))
+
+
+def summarize_history(
+    history: str, question: str, persona_specification: str | None, llm: LLM
+) -> str:
+    history_context_prompt = remove_document_citations(
+        HISTORY_CONTEXT_SUMMARY_PROMPT.format(
+            persona_specification=persona_specification,
+            question=question,
+            history=history,
+        )
+    )
+
+    history_response = llm.invoke(history_context_prompt)
+    assert isinstance(history_response.content, str)
+    return history_response.content
+
+
+# taken from langchain_core.runnables.schema
+# we don't use the one from their library because
+# it includes ids they generate
+class CustomStreamEvent(TypedDict):
+    # Overwrite the event field to be more specific.
+    event: Literal["on_custom_event"]  # type: ignore[misc]
+    """The event type."""
+    name: str
+    """User defined name for the event."""
+    data: Any
+    """The data associated with the event. Free form and can be anything."""
+
+
+def write_custom_event(
+    name: str, event: AnswerPacket, stream_writer: StreamWriter
+) -> None:
+    stream_writer(CustomStreamEvent(event="on_custom_event", name=name, data=event))
+
+
+def relevance_from_docs(
+    relevant_docs: list[InferenceSection],
+) -> list[SectionRelevancePiece]:
+    return [
+        SectionRelevancePiece(
+            relevant=True,
+            content=doc.center_chunk.content,
+            document_id=doc.center_chunk.document_id,
+            chunk_id=doc.center_chunk.chunk_id,
+        )
+        for doc in relevant_docs
+    ]
+
+
+def get_langgraph_node_log_string(
+    graph_component: str,
+    node_name: str,
+    node_start_time: datetime,
+    result: str | None = None,
+) -> str:
+    duration = datetime.now() - node_start_time
+    results_str = "" if result is None else f" -- Result: {result}"
+    return f"{node_start_time} -- {graph_component} - {node_name} -- Time taken: {duration}{results_str}"
+
+
+def remove_document_citations(text: str) -> str:
+    """
+    Removes citation expressions of format '[[D1]]()' from text.
+    The number after D can vary.
+
+    Args:
+        text: Input text containing citations
+
+    Returns:
+        Text with citations removed
+    """
+    # Pattern explanation:
+    # \[(?:D|Q)?\d+\]  matches:
+    #   \[   - literal [ character
+    #   (?:D|Q)?  - optional D or Q character
+    #   \d+  - one or more digits
+    #   \]   - literal ] character
+    return re.sub(r"\[(?:D|Q)?\d+\]", "", text)

backend/onyx/auth/email_utils.py

@@ -1,7 +1,7 @@
 import smtplib
+from datetime import datetime
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
-from textwrap import dedent
 
 from onyx.configs.app_configs import EMAIL_CONFIGURED
 from onyx.configs.app_configs import EMAIL_FROM
@@ -10,25 +10,153 @@ from onyx.configs.app_configs import SMTP_PORT
 from onyx.configs.app_configs import SMTP_SERVER
 from onyx.configs.app_configs import SMTP_USER
 from onyx.configs.app_configs import WEB_DOMAIN
+from onyx.configs.constants import TENANT_ID_COOKIE_NAME
 from onyx.db.models import User
 
+HTML_EMAIL_TEMPLATE = """\
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width" />
+  <title>{title}</title>
+  <style>
+    body, table, td, a {{
+      font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+      text-size-adjust: 100%;
+      margin: 0;
+      padding: 0;
+      -webkit-font-smoothing: antialiased;
+      -webkit-text-size-adjust: none;
+    }}
+    body {{
+      background-color: #f7f7f7;
+      color: #333;
+    }}
+    .body-content {{
+      color: #333;
+    }}
+    .email-container {{
+      width: 100%;
+      max-width: 600px;
+      margin: 0 auto;
+      background-color: #ffffff;
+      border-radius: 6px;
+      overflow: hidden;
+      border: 1px solid #eaeaea;
+    }}
+    .header {{
+      background-color: #000000;
+      padding: 20px;
+      text-align: center;
+    }}
+    .header img {{
+      max-width: 140px;
+    }}
+    .body-content {{
+      padding: 20px 30px;
+    }}
+    .title {{
+      font-size: 20px;
+      font-weight: bold;
+      margin: 0 0 10px;
+    }}
+    .message {{
+      font-size: 16px;
+      line-height: 1.5;
+      margin: 0 0 20px;
+    }}
+    .cta-button {{
+      display: inline-block;
+      padding: 12px 20px;
+      background-color: #000000;
+      color: #ffffff !important;
+      text-decoration: none;
+      border-radius: 4px;
+      font-weight: 500;
+    }}
+    .footer {{
+      font-size: 13px;
+      color: #6A7280;
+      text-align: center;
+      padding: 20px;
+    }}
+    .footer a {{
+      color: #6b7280;
+      text-decoration: underline;
+    }}
+  </style>
+</head>
+<body>
+  <table role="presentation" class="email-container" cellpadding="0" cellspacing="0">
+    <tr>
+      <td class="header">
+        <img
+          style="background-color: #ffffff; border-radius: 8px;"
+          src="https://www.onyx.app/logos/customer/onyx.png"
+          alt="Onyx Logo"
+        >
+      </td>
+    </tr>
+    <tr>
+      <td class="body-content">
+        <h1 class="title">{heading}</h1>
+        <div class="message">
+          {message}
+        </div>
+        {cta_block}
+      </td>
+    </tr>
+    <tr>
+      <td class="footer">
+        © {year} Onyx. All rights reserved.
+        <br>
+        Have questions? Join our Slack community <a href="https://join.slack.com/t/onyx-dot-app/shared_invite/zt-2twesxdr6-5iQitKZQpgq~hYIZ~dv3KA">here</a>.
+      </td>
+    </tr>
+  </table>
+</body>
+</html>
+"""
+
+
+def build_html_email(
+    heading: str, message: str, cta_text: str | None = None, cta_link: str | None = None
+) -> str:
+    if cta_text and cta_link:
+        cta_block = f'<a class="cta-button" href="{cta_link}">{cta_text}</a>'
+    else:
+        cta_block = ""
+    return HTML_EMAIL_TEMPLATE.format(
+        title=heading,
+        heading=heading,
+        message=message,
+        cta_block=cta_block,
+        year=datetime.now().year,
+    )
+
 
 def send_email(
     user_email: str,
     subject: str,
-    body: str,
+    html_body: str,
+    text_body: str,
     mail_from: str = EMAIL_FROM,
 ) -> None:
     if not EMAIL_CONFIGURED:
         raise ValueError("Email is not configured.")
 
-    msg = MIMEMultipart()
+    msg = MIMEMultipart("alternative")
     msg["Subject"] = subject
     msg["To"] = user_email
     if mail_from:
         msg["From"] = mail_from
 
-    msg.attach(MIMEText(body))
+    part_text = MIMEText(text_body, "plain")
+    part_html = MIMEText(html_body, "html")
+
+    msg.attach(part_text)
+    msg.attach(part_html)
 
     try:
         with smtplib.SMTP(SMTP_SERVER, SMTP_PORT) as s:
@@ -39,37 +167,61 @@ def send_email(
         raise e
 
 
+def send_subscription_cancellation_email(user_email: str) -> None:
+    # Example usage of the reusable HTML
+    subject = "Your Onyx Subscription Has Been Canceled"
+    heading = "Subscription Canceled"
+    message = (
+        "<p>We’re sorry to see you go.</p>"
+        "<p>Your subscription has been canceled and will end on your next billing date.</p>"
+        "<p>If you change your mind, you can always come back!</p>"
+    )
+    cta_text = "Renew Subscription"
+    cta_link = "https://www.onyx.app/pricing"
+    html_content = build_html_email(heading, message, cta_text, cta_link)
+    text_content = (
+        "We're sorry to see you go.\n"
+        "Your subscription has been canceled and will end on your next billing date.\n"
+        "If you change your mind, visit https://www.onyx.app/pricing"
+    )
+    send_email(user_email, subject, html_content, text_content)
+
+
 def send_user_email_invite(user_email: str, current_user: User) -> None:
     subject = "Invitation to Join Onyx Organization"
-    body = dedent(
-        f"""\
-        Hello,
-
-        You have been invited to join an organization on Onyx.
-
-        To join the organization, please visit the following link:
-
-        {WEB_DOMAIN}/auth/signup?email={user_email}
-
-        You'll be asked to set a password or login with Google to complete your registration.
-
-        Best regards,
-        The Onyx Team
-    """
+    heading = "You've Been Invited!"
+    message = (
+        f"<p>You have been invited by {current_user.email} to join an organization on Onyx.</p>"
+        "<p>To join the organization, please click the button below to set a password "
+        "or login with Google and complete your registration.</p>"
     )
-
-    send_email(user_email, subject, body, current_user.email)
+    cta_text = "Join Organization"
+    cta_link = f"{WEB_DOMAIN}/auth/signup?email={user_email}"
+    html_content = build_html_email(heading, message, cta_text, cta_link)
+    text_content = (
+        f"You have been invited by {current_user.email} to join an organization on Onyx.\n"
+        "To join the organization, please visit the following link:\n"
+        f"{WEB_DOMAIN}/auth/signup?email={user_email}\n"
+        "You'll be asked to set a password or login with Google to complete your registration."
+    )
+    send_email(user_email, subject, html_content, text_content)
 
 
 def send_forgot_password_email(
     user_email: str,
     token: str,
     mail_from: str = EMAIL_FROM,
+    tenant_id: str | None = None,
 ) -> None:
+    # Builds a forgot password email with or without fancy HTML
     subject = "Onyx Forgot Password"
     link = f"{WEB_DOMAIN}/auth/reset-password?token={token}"
-    body = f"Click the following link to reset your password: {link}"
-    send_email(user_email, subject, body, mail_from)
+    if tenant_id:
+        link += f"&{TENANT_ID_COOKIE_NAME}={tenant_id}"
+    message = f"<p>Click the following link to reset your password:</p><p>{link}</p>"
+    html_content = build_html_email("Reset Your Password", message)
+    text_content = f"Click the following link to reset your password: {link}"
+    send_email(user_email, subject, html_content, text_content, mail_from)
 
 
 def send_user_verification_email(
@@ -77,7 +229,12 @@ def send_user_verification_email(
     token: str,
     mail_from: str = EMAIL_FROM,
 ) -> None:
+    # Builds a verification email
     subject = "Onyx Email Verification"
     link = f"{WEB_DOMAIN}/auth/verify-email?token={token}"
-    body = f"Click the following link to verify your email address: {link}"
-    send_email(user_email, subject, body, mail_from)
+    message = (
+        f"<p>Click the following link to verify your email address:</p><p>{link}</p>"
+    )
+    html_content = build_html_email("Verify Your Email", message)
+    text_content = f"Click the following link to verify your email address: {link}"
+    send_email(user_email, subject, html_content, text_content, mail_from)

backend/onyx/auth/users.py

@@ -73,6 +73,7 @@ from onyx.configs.app_configs import WEB_DOMAIN
 from onyx.configs.constants import AuthType
 from onyx.configs.constants import DANSWER_API_KEY_DUMMY_EMAIL_DOMAIN
 from onyx.configs.constants import DANSWER_API_KEY_PREFIX
+from onyx.configs.constants import FASTAPI_USERS_AUTH_COOKIE_NAME
 from onyx.configs.constants import MilestoneRecordType
 from onyx.configs.constants import OnyxRedisLocks
 from onyx.configs.constants import PASSWORD_SPECIAL_CHARS
@@ -218,6 +219,24 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
     verification_token_lifetime_seconds = AUTH_COOKIE_EXPIRE_TIME_SECONDS
     user_db: SQLAlchemyUserDatabase[User, uuid.UUID]
 
+    async def get_by_email(self, user_email: str) -> User:
+        tenant_id = fetch_ee_implementation_or_noop(
+            "onyx.server.tenants.user_mapping", "get_tenant_id_for_email", None
+        )(user_email)
+        async with get_async_session_with_tenant(tenant_id) as db_session:
+            if MULTI_TENANT:
+                tenant_user_db = SQLAlchemyUserAdminDB[User, uuid.UUID](
+                    db_session, User, OAuthAccount
+                )
+                user = await tenant_user_db.get_by_email(user_email)
+            else:
+                user = await self.user_db.get_by_email(user_email)
+
+        if not user:
+            raise exceptions.UserNotExists()
+
+        return user
+
     async def create(
         self,
         user_create: schemas.UC | UserCreate,
@@ -245,6 +264,8 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
             referral_source=referral_source,
             request=request,
         )
+        user: User
+
         async with get_async_session_with_tenant(tenant_id) as db_session:
             token = CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
             verify_email_is_invited(user_create.email)
@@ -368,6 +389,8 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 "refresh_token": refresh_token,
             }
 
+            user: User
+
             try:
                 # Attempt to get user by OAuth account
                 user = await self.get_by_oauth_account(oauth_name, account_id)
@@ -500,9 +523,15 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
             )
             raise HTTPException(
                 status.HTTP_500_INTERNAL_SERVER_ERROR,
-                "Your admin has not enbaled this feature.",
+                "Your admin has not enabled this feature.",
             )
-        send_forgot_password_email(user.email, token)
+        tenant_id = await fetch_ee_implementation_or_noop(
+            "onyx.server.tenants.provisioning",
+            "get_or_provision_tenant",
+            async_return_default_schema,
+        )(email=user.email)
+
+        send_forgot_password_email(user.email, token, tenant_id=tenant_id)
 
     async def on_after_request_verify(
         self, user: User, token: str, request: Optional[Request] = None
@@ -576,6 +605,7 @@ async def get_user_manager(
 cookie_transport = CookieTransport(
     cookie_max_age=SESSION_EXPIRE_TIME_SECONDS,
     cookie_secure=WEB_DOMAIN.startswith("https"),
+    cookie_name=FASTAPI_USERS_AUTH_COOKIE_NAME,
 )
 
 
@@ -1043,6 +1073,8 @@ async def api_key_dep(
     if AUTH_TYPE == AuthType.DISABLED:
         return None
 
+    user: User | None = None
+
     hashed_api_key = get_hashed_api_key_from_request(request)
     if not hashed_api_key:
         raise HTTPException(status_code=401, detail="Missing API key")