minor update

* alert if revisions are null or query fails

* comment

* mypy

---------

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

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

@@ -8,6 +8,8 @@ on: push
 env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
+  GEN_AI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+  MOCK_LLM_RESPONSE: true
 
 jobs:
   playwright-tests:

.github/workflows/pr-helm-chart-testing.yml

@@ -21,10 +21,10 @@ jobs:
     - name: Set up Helm
       uses: azure/setup-helm@v4.2.0
       with:
-        version: v3.14.4
+        version: v3.17.0
       
     - name: Set up chart-testing
-      uses: helm/chart-testing-action@v2.6.1
+      uses: helm/chart-testing-action@v2.7.0
 
     # even though we specify chart-dirs in ct.yaml, it isn't used by ct for the list-changed command...
     - name: Run chart-testing (list-changed)
@@ -37,22 +37,6 @@ jobs:
           echo "changed=true" >> "$GITHUB_OUTPUT"
         fi
 
-#     rkuo: I don't think we need python?
-#     - name: Set up Python
-#       uses: actions/setup-python@v5
-#       with:
-#         python-version: '3.11'
-#         cache: 'pip'
-#         cache-dependency-path: |
-#           backend/requirements/default.txt
-#           backend/requirements/dev.txt
-#           backend/requirements/model_server.txt
-#     - run: |
-#         python -m pip install --upgrade pip
-#         pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
-#         pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
-#         pip install --retries 5 --timeout 30 -r backend/requirements/model_server.txt
-
     # lint all charts if any changes were detected
     - name: Run chart-testing (lint)
       if: steps.list-changed.outputs.changed == 'true'
@@ -62,7 +46,7 @@ jobs:
 
     - name: Create kind cluster
       if: steps.list-changed.outputs.changed == 'true'
-      uses: helm/kind-action@v1.10.0
+      uses: helm/kind-action@v1.12.0
 
     - name: Run chart-testing (install)
       if: steps.list-changed.outputs.changed == 'true'

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

@@ -39,6 +39,12 @@ env:
   AIRTABLE_TEST_TABLE_ID: ${{ secrets.AIRTABLE_TEST_TABLE_ID }}
   AIRTABLE_TEST_TABLE_NAME: ${{ secrets.AIRTABLE_TEST_TABLE_NAME }}
   AIRTABLE_ACCESS_TOKEN: ${{ secrets.AIRTABLE_ACCESS_TOKEN }}
+  # Sharepoint
+  SHAREPOINT_CLIENT_ID: ${{ secrets.SHAREPOINT_CLIENT_ID }}
+  SHAREPOINT_CLIENT_SECRET: ${{ secrets.SHAREPOINT_CLIENT_SECRET }}
+  SHAREPOINT_CLIENT_DIRECTORY_ID: ${{ secrets.SHAREPOINT_CLIENT_DIRECTORY_ID }}
+  SHAREPOINT_SITE: ${{ secrets.SHAREPOINT_SITE }}
+
 jobs:
   connectors-check:
     # See https://runs-on.com/runners/linux/

.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
 

backend/alembic/versions/2f80c6a2550f_add_chat_session_specific_temperature_.py

@@ -0,0 +1,36 @@
+"""add chat session specific temperature override
+
+Revision ID: 2f80c6a2550f
+Revises: 33ea50e88f24
+Create Date: 2025-01-31 10:30:27.289646
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "2f80c6a2550f"
+down_revision = "33ea50e88f24"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "chat_session", sa.Column("temperature_override", sa.Float(), nullable=True)
+    )
+    op.add_column(
+        "user",
+        sa.Column(
+            "temperature_override_enabled",
+            sa.Boolean(),
+            nullable=False,
+            server_default=sa.false(),
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("chat_session", "temperature_override")
+    op.drop_column("user", "temperature_override_enabled")

backend/alembic/versions/33ea50e88f24_foreign_key_input_prompts.py

@@ -0,0 +1,80 @@
+"""foreign key input prompts
+
+Revision ID: 33ea50e88f24
+Revises: a6df6b88ef81
+Create Date: 2025-01-29 10:54:22.141765
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "33ea50e88f24"
+down_revision = "a6df6b88ef81"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Safely drop constraints if exists
+    op.execute(
+        """
+        ALTER TABLE inputprompt__user
+        DROP CONSTRAINT IF EXISTS inputprompt__user_input_prompt_id_fkey
+        """
+    )
+    op.execute(
+        """
+        ALTER TABLE inputprompt__user
+        DROP CONSTRAINT IF EXISTS inputprompt__user_user_id_fkey
+        """
+    )
+
+    # Recreate with ON DELETE CASCADE
+    op.create_foreign_key(
+        "inputprompt__user_input_prompt_id_fkey",
+        "inputprompt__user",
+        "inputprompt",
+        ["input_prompt_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+
+    op.create_foreign_key(
+        "inputprompt__user_user_id_fkey",
+        "inputprompt__user",
+        "user",
+        ["user_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+
+
+def downgrade() -> None:
+    # Drop the new FKs with ondelete
+    op.drop_constraint(
+        "inputprompt__user_input_prompt_id_fkey",
+        "inputprompt__user",
+        type_="foreignkey",
+    )
+    op.drop_constraint(
+        "inputprompt__user_user_id_fkey",
+        "inputprompt__user",
+        type_="foreignkey",
+    )
+
+    # Recreate them without cascading
+    op.create_foreign_key(
+        "inputprompt__user_input_prompt_id_fkey",
+        "inputprompt__user",
+        "inputprompt",
+        ["input_prompt_id"],
+        ["id"],
+    )
+    op.create_foreign_key(
+        "inputprompt__user_user_id_fkey",
+        "inputprompt__user",
+        "user",
+        ["user_id"],
+        ["id"],
+    )

backend/alembic/versions/4d58345da04a_lowercase_user_emails.py

@@ -0,0 +1,37 @@
+"""lowercase_user_emails
+
+Revision ID: 4d58345da04a
+Revises: f1ca58b2f2ec
+Create Date: 2025-01-29 07:48:46.784041
+
+"""
+from alembic import op
+from sqlalchemy.sql import text
+
+
+# revision identifiers, used by Alembic.
+revision = "4d58345da04a"
+down_revision = "f1ca58b2f2ec"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Get database connection
+    connection = op.get_bind()
+
+    # Update all user emails to lowercase
+    connection.execute(
+        text(
+            """
+            UPDATE "user"
+            SET email = LOWER(email)
+            WHERE email != LOWER(email)
+            """
+        )
+    )
+
+
+def downgrade() -> None:
+    # Cannot restore original case of emails
+    pass

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/a6df6b88ef81_remove_recent_assistants.py

@@ -0,0 +1,29 @@
+"""remove recent assistants
+
+Revision ID: a6df6b88ef81
+Revises: 4d58345da04a
+Create Date: 2025-01-29 10:25:52.790407
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "a6df6b88ef81"
+down_revision = "4d58345da04a"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.drop_column("user", "recent_assistants")
+
+
+def downgrade() -> None:
+    op.add_column(
+        "user",
+        sa.Column(
+            "recent_assistants", postgresql.JSONB(), server_default="[]", nullable=False
+        ),
+    )

backend/alembic/versions/eaa3b5593925_add_default_slack_channel_config.py

@@ -0,0 +1,76 @@
+"""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": []}',
+                        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/ee/onyx/background/celery/apps/primary.py

@@ -32,6 +32,7 @@ def perform_ttl_management_task(
 
 @celery_app.task(
     name="check_ttl_management_task",
+    ignore_result=True,
     soft_time_limit=JOB_TIMEOUT,
 )
 def check_ttl_management_task(*, tenant_id: str | None) -> None:
@@ -56,6 +57,7 @@ def check_ttl_management_task(*, tenant_id: str | None) -> None:
 
 @celery_app.task(
     name="autogenerate_usage_report_task",
+    ignore_result=True,
     soft_time_limit=JOB_TIMEOUT,
 )
 def autogenerate_usage_report_task(*, tenant_id: str | None) -> None:

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

@@ -13,6 +13,7 @@ from onyx.connectors.confluence.onyx_confluence import OnyxConfluence
 from onyx.connectors.confluence.utils import get_user_email_from_username__server
 from onyx.connectors.models import SlimDocument
 from onyx.db.models import ConnectorCredentialPair
+from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -257,6 +258,7 @@ def _fetch_all_page_restrictions(
     slim_docs: list[SlimDocument],
     space_permissions_by_space_key: dict[str, ExternalAccess],
     is_cloud: bool,
+    callback: IndexingHeartbeatInterface | None,
 ) -> list[DocExternalAccess]:
     """
     For all pages, if a page has restrictions, then use those restrictions.
@@ -265,6 +267,12 @@ def _fetch_all_page_restrictions(
     document_restrictions: list[DocExternalAccess] = []
 
     for slim_doc in slim_docs:
+        if callback:
+            if callback.should_stop():
+                raise RuntimeError("confluence_doc_sync: Stop signal detected")
+
+            callback.progress("confluence_doc_sync:fetch_all_page_restrictions", 1)
+
         if slim_doc.perm_sync_data is None:
             raise ValueError(
                 f"No permission sync data found for document {slim_doc.id}"
@@ -334,7 +342,7 @@ def _fetch_all_page_restrictions(
 
 
 def confluence_doc_sync(
-    cc_pair: ConnectorCredentialPair,
+    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -359,6 +367,12 @@ def confluence_doc_sync(
     logger.debug("Fetching all slim documents from confluence")
     for doc_batch in confluence_connector.retrieve_all_slim_documents():
         logger.debug(f"Got {len(doc_batch)} slim documents from confluence")
+        if callback:
+            if callback.should_stop():
+                raise RuntimeError("confluence_doc_sync: Stop signal detected")
+
+            callback.progress("confluence_doc_sync", 1)
+
         slim_docs.extend(doc_batch)
 
     logger.debug("Fetching all page restrictions for space")
@@ -367,4 +381,5 @@ def confluence_doc_sync(
         slim_docs=slim_docs,
         space_permissions_by_space_key=space_permissions_by_space_key,
         is_cloud=is_cloud,
+        callback=callback,
     )

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

@@ -14,6 +14,8 @@ def _build_group_member_email_map(
 ) -> dict[str, set[str]]:
     group_member_emails: dict[str, set[str]] = {}
     for user_result in confluence_client.paginated_cql_user_retrieval():
+        logger.debug(f"Processing groups for user: {user_result}")
+
         user = user_result.get("user", {})
         if not user:
             logger.warning(f"user result missing user field: {user_result}")
@@ -33,10 +35,17 @@ def _build_group_member_email_map(
             logger.warning(f"user result missing email field: {user_result}")
             continue
 
+        all_users_groups: set[str] = set()
         for group in confluence_client.paginated_groups_by_user_retrieval(user):
             # group name uniqueness is enforced by Confluence, so we can use it as a group ID
             group_id = group["name"]
             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}")
+        else:
+            logger.debug(f"Found groups {all_users_groups} for user with email {email}")
 
     return group_member_emails
 

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

@@ -6,6 +6,7 @@ from onyx.access.models import ExternalAccess
 from onyx.connectors.gmail.connector import GmailConnector
 from onyx.connectors.interfaces import GenerateSlimDocumentOutput
 from onyx.db.models import ConnectorCredentialPair
+from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -28,7 +29,7 @@ def _get_slim_doc_generator(
 
 
 def gmail_doc_sync(
-    cc_pair: ConnectorCredentialPair,
+    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -44,6 +45,12 @@ def gmail_doc_sync(
     document_external_access: list[DocExternalAccess] = []
     for slim_doc_batch in slim_doc_generator:
         for slim_doc in slim_doc_batch:
+            if callback:
+                if callback.should_stop():
+                    raise RuntimeError("gmail_doc_sync: Stop signal detected")
+
+                callback.progress("gmail_doc_sync", 1)
+
             if slim_doc.perm_sync_data is None:
                 logger.warning(f"No permissions found for document {slim_doc.id}")
                 continue

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

@@ -10,6 +10,7 @@ from onyx.connectors.google_utils.resources import get_drive_service
 from onyx.connectors.interfaces import GenerateSlimDocumentOutput
 from onyx.connectors.models import SlimDocument
 from onyx.db.models import ConnectorCredentialPair
+from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -42,24 +43,22 @@ def _fetch_permissions_for_permission_ids(
     if not permission_info or not doc_id:
         return []
 
-    # Check cache first for all permission IDs
     permissions = [
         _PERMISSION_ID_PERMISSION_MAP[pid]
         for pid in permission_ids
         if pid in _PERMISSION_ID_PERMISSION_MAP
     ]
 
-    # If we found all permissions in cache, return them
     if len(permissions) == len(permission_ids):
         return permissions
 
     owner_email = permission_info.get("owner_email")
+
     drive_service = get_drive_service(
         creds=google_drive_connector.creds,
         user_email=(owner_email or google_drive_connector.primary_admin_email),
     )
 
-    # Otherwise, fetch all permissions and update cache
     fetched_permissions = execute_paginated_retrieval(
         retrieval_function=drive_service.permissions().list,
         list_key="permissions",
@@ -69,7 +68,6 @@ def _fetch_permissions_for_permission_ids(
     )
 
     permissions_for_doc_id = []
-    # Update cache and return all permissions
     for permission in fetched_permissions:
         permissions_for_doc_id.append(permission)
         _PERMISSION_ID_PERMISSION_MAP[permission["id"]] = permission
@@ -131,7 +129,7 @@ def _get_permissions_from_slim_doc(
 
 
 def gdrive_doc_sync(
-    cc_pair: ConnectorCredentialPair,
+    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -149,6 +147,12 @@ def gdrive_doc_sync(
     document_external_accesses = []
     for slim_doc_batch in slim_doc_generator:
         for slim_doc in slim_doc_batch:
+            if callback:
+                if callback.should_stop():
+                    raise RuntimeError("gdrive_doc_sync: Stop signal detected")
+
+                callback.progress("gdrive_doc_sync", 1)
+
             ext_access = _get_permissions_from_slim_doc(
                 google_drive_connector=google_drive_connector,
                 slim_doc=slim_doc,

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

@@ -7,6 +7,7 @@ from onyx.connectors.slack.connector import get_channels
 from onyx.connectors.slack.connector import make_paginated_slack_api_call_w_retries
 from onyx.connectors.slack.connector import SlackPollConnector
 from onyx.db.models import ConnectorCredentialPair
+from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 
@@ -14,7 +15,7 @@ logger = setup_logger()
 
 
 def _get_slack_document_ids_and_channels(
-    cc_pair: ConnectorCredentialPair,
+    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
 ) -> dict[str, list[str]]:
     slack_connector = SlackPollConnector(**cc_pair.connector.connector_specific_config)
     slack_connector.load_credentials(cc_pair.credential.credential_json)
@@ -24,6 +25,14 @@ def _get_slack_document_ids_and_channels(
     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"]
@@ -114,7 +123,7 @@ def _fetch_channel_permissions(
 
 
 def slack_doc_sync(
-    cc_pair: ConnectorCredentialPair,
+    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -127,7 +136,7 @@ def slack_doc_sync(
     )
     user_id_to_email_map = fetch_user_id_to_email_map(slack_client)
     channel_doc_map = _get_slack_document_ids_and_channels(
-        cc_pair=cc_pair,
+        cc_pair=cc_pair, callback=callback
     )
     workspace_permissions = _fetch_workspace_permissions(
         user_id_to_email_map=user_id_to_email_map,

backend/ee/onyx/external_permissions/sync_params.py

@@ -15,11 +15,13 @@ from ee.onyx.external_permissions.slack.doc_sync import slack_doc_sync
 from onyx.access.models import DocExternalAccess
 from onyx.configs.constants import DocumentSource
 from onyx.db.models import ConnectorCredentialPair
+from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 
 # Defining the input/output types for the sync functions
 DocSyncFuncType = Callable[
     [
         ConnectorCredentialPair,
+        IndexingHeartbeatInterface | None,
     ],
     list[DocExternalAccess],
 ]

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
@@ -90,3 +92,12 @@ async def _get_tenant_id_from_request(
     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:
+        # 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
@@ -111,6 +112,7 @@ async def login_as_anonymous_user(
     token = generate_anonymous_user_jwt_token(tenant_id)
 
     response = Response()
+    response.delete_cookie(FASTAPI_USERS_AUTH_COOKIE_NAME)
     response.set_cookie(
         key=ANONYMOUS_USER_COOKIE_NAME,
         value=token,

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

@@ -58,6 +58,7 @@ class UserGroup(BaseModel):
                     credential=CredentialSnapshot.from_credential_db_model(
                         cc_pair_relationship.cc_pair.credential
                     ),
+                    access_type=cc_pair_relationship.cc_pair.access_type,
                 )
                 for cc_pair_relationship in user_group_model.cc_pair_relationships
                 if cc_pair_relationship.is_current

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 +1,153 @@
+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.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)
+    )
+
+    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 +1,131 @@
+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.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)
+    )
+    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 +1,132 @@
+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 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 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 +1,105 @@
+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 SearchRequest
+from onyx.context.search.pipeline import retrieval_preprocessing
+from onyx.context.search.postprocessing.postprocessing import rerank_sections
+from onyx.db.engine import get_session_context_manager
+
+
+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
+
+    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"
+    with get_session_context_manager() as db_session:
+        # we ignore some of the user specified fields since this search is
+        # internal to agentic search, but we still want to pass through
+        # persona (for stuff like document sets) and rerank settings
+        # (to not make an unnecessary db call).
+        search_request = SearchRequest(
+            query=question,
+            persona=graph_config.inputs.search_request.persona,
+            rerank_settings=graph_config.inputs.search_request.rerank_settings,
+        )
+        _search_query = retrieval_preprocessing(
+            search_request=search_request,
+            user=graph_config.tooling.search_tool.user,  # bit of a hack
+            llm=graph_config.tooling.fast_llm,
+            db_session=db_session,
+        )
+
+    # skip section filtering
+
+    if (
+        _search_query.rerank_settings
+        and _search_query.rerank_settings.rerank_model_name
+        and _search_query.rerank_settings.num_rerank > 0
+        and len(verified_documents) > 0
+    ):
+        if len(verified_documents) > 1:
+            reranked_documents = rerank_sections(
+                _search_query,
+                verified_documents,
+            )
+        else:
+            num = "No" if len(verified_documents) == 0 else "One"
+            logger.warning(f"{num} 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()
+    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 +1,433 @@
+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: 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)
+            num += 1
+            _dispatch_nonempty(
+                "".join(sub_question_parts[1:]).strip(), dispatch_event, num
+            )
+        else:
+            _dispatch_nonempty(content, dispatch_event, num)
+        streamed_tokens.append(content)
+
+    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

@@ -10,6 +10,7 @@ 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
 
 
@@ -65,9 +66,13 @@ def send_forgot_password_email(
     user_email: str,
     token: str,
     mail_from: str = EMAIL_FROM,
+    tenant_id: str | None = None,
 ) -> None:
     subject = "Onyx Forgot Password"
     link = f"{WEB_DOMAIN}/auth/reset-password?token={token}"
+    if tenant_id:
+        link += f"&{TENANT_ID_COOKIE_NAME}={tenant_id}"
+        # Keep search param same name as cookie for simplicity
     body = f"Click the following link to reset your password: {link}"
     send_email(user_email, subject, body, mail_from)
 

backend/onyx/auth/schemas.py

@@ -42,6 +42,10 @@ class UserCreate(schemas.BaseUserCreate):
     tenant_id: str | None = None
 
 
+class UserUpdateWithRole(schemas.BaseUserUpdate):
+    role: UserRole
+
+
 class UserUpdate(schemas.BaseUserUpdate):
     """
     Role updates are not allowed through the user update endpoint for security reasons

backend/onyx/auth/users.py

@@ -57,7 +57,7 @@ from onyx.auth.invited_users import get_invited_users
 from onyx.auth.schemas import AuthBackend
 from onyx.auth.schemas import UserCreate
 from onyx.auth.schemas import UserRole
-from onyx.auth.schemas import UserUpdate
+from onyx.auth.schemas import UserUpdateWithRole
 from onyx.configs.app_configs import AUTH_BACKEND
 from onyx.configs.app_configs import AUTH_COOKIE_EXPIRE_TIME_SECONDS
 from onyx.configs.app_configs import AUTH_TYPE
@@ -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
@@ -216,9 +217,26 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
     reset_password_token_secret = USER_AUTH_SECRET
     verification_token_secret = USER_AUTH_SECRET
     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,
@@ -246,10 +264,10 @@ 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)
             verify_email_domain(user_create.email)
             if MULTI_TENANT:
@@ -268,16 +286,16 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                     user_create.role = UserRole.ADMIN
                 else:
                     user_create.role = UserRole.BASIC
-
             try:
                 user = await super().create(user_create, safe=safe, request=request)  # type: ignore
             except exceptions.UserAlreadyExists:
                 user = await self.get_by_email(user_create.email)
                 # Handle case where user has used product outside of web and is now creating an account through web
                 if not user.role.is_web_login() and user_create.role.is_web_login():
-                    user_update = UserUpdate(
+                    user_update = UserUpdateWithRole(
                         password=user_create.password,
                         is_verified=user_create.is_verified,
+                        role=user_create.role,
                     )
                     user = await self.update(user_update, user)
                 else:
@@ -285,7 +303,6 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
 
             finally:
                 CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
-
         return user
 
     async def validate_password(self, password: str, _: schemas.UC | models.UP) -> None:
@@ -372,6 +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)
@@ -504,9 +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
@@ -580,6 +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,
 )
 
 
@@ -1047,6 +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")

backend/onyx/background/celery/apps/app_base.py

@@ -24,6 +24,7 @@ from onyx.configs.constants import ONYX_CLOUD_CELERY_TASK_PREFIX
 from onyx.configs.constants import OnyxRedisLocks
 from onyx.db.engine import get_sqlalchemy_engine
 from onyx.document_index.vespa.shared_utils.utils import wait_for_vespa_with_timeout
+from onyx.httpx.httpx_pool import HttpxPool
 from onyx.redis.redis_connector import RedisConnector
 from onyx.redis.redis_connector_credential_pair import RedisConnectorCredentialPair
 from onyx.redis.redis_connector_delete import RedisConnectorDelete
@@ -197,7 +198,8 @@ def on_celeryd_init(sender: str, conf: Any = None, **kwargs: Any) -> None:
 
 def wait_for_redis(sender: Any, **kwargs: Any) -> None:
     """Waits for redis to become ready subject to a hardcoded timeout.
-    Will raise WorkerShutdown to kill the celery worker if the timeout is reached."""
+    Will raise WorkerShutdown to kill the celery worker if the timeout
+    is reached."""
 
     r = get_redis_client(tenant_id=None)
 
@@ -316,6 +318,8 @@ def on_worker_ready(sender: Any, **kwargs: Any) -> None:
 
 
 def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
+    HttpxPool.close_all()
+
     if not celery_is_worker_primary(sender):
         return
 

backend/onyx/background/celery/apps/beat.py

@@ -1,6 +1,5 @@
 from datetime import timedelta
 from typing import Any
-from typing import cast
 
 from celery import Celery
 from celery import signals
@@ -8,7 +7,6 @@ from celery.beat import PersistentScheduler  # type: ignore
 from celery.signals import beat_init
 
 import onyx.background.celery.apps.app_base as app_base
-from onyx.configs.constants import ONYX_CLOUD_CELERY_TASK_PREFIX
 from onyx.configs.constants import POSTGRES_CELERY_BEAT_APP_NAME
 from onyx.db.engine import get_all_tenant_ids
 from onyx.db.engine import SqlEngine
@@ -132,21 +130,25 @@ class DynamicTenantScheduler(PersistentScheduler):
         # get current schedule and extract current tenants
         current_schedule = self.schedule.items()
 
-        current_tenants = set()
-        for task_name, _ in current_schedule:
-            task_name = cast(str, task_name)
-            if task_name.startswith(ONYX_CLOUD_CELERY_TASK_PREFIX):
-                continue
+        # there are no more per tenant beat tasks, so comment this out
+        # NOTE: we may not actualy need this scheduler any more and should
+        # test reverting to a regular beat schedule implementation
 
-            if "_" in task_name:
-                # example: "check-for-condition-tenant_12345678-abcd-efgh-ijkl-12345678"
-                # -> "12345678-abcd-efgh-ijkl-12345678"
-                current_tenants.add(task_name.split("_")[-1])
-        logger.info(f"Found {len(current_tenants)} existing items in schedule")
+        # current_tenants = set()
+        # for task_name, _ in current_schedule:
+        #     task_name = cast(str, task_name)
+        #     if task_name.startswith(ONYX_CLOUD_CELERY_TASK_PREFIX):
+        #         continue
 
-        for tenant_id in tenant_ids:
-            if tenant_id not in current_tenants:
-                logger.info(f"Processing new tenant: {tenant_id}")
+        #     if "_" in task_name:
+        #         # example: "check-for-condition-tenant_12345678-abcd-efgh-ijkl-12345678"
+        #         # -> "12345678-abcd-efgh-ijkl-12345678"
+        #         current_tenants.add(task_name.split("_")[-1])
+        # logger.info(f"Found {len(current_tenants)} existing items in schedule")
+
+        # for tenant_id in tenant_ids:
+        #     if tenant_id not in current_tenants:
+        #         logger.info(f"Processing new tenant: {tenant_id}")
 
         new_schedule = self._generate_schedule(tenant_ids)
 

backend/onyx/background/celery/apps/light.py

@@ -10,6 +10,10 @@ from celery.signals import worker_ready
 from celery.signals import worker_shutdown
 
 import onyx.background.celery.apps.app_base as app_base
+from onyx.background.celery.celery_utils import httpx_init_vespa_pool
+from onyx.configs.app_configs import MANAGED_VESPA
+from onyx.configs.app_configs import VESPA_CLOUD_CERT_PATH
+from onyx.configs.app_configs import VESPA_CLOUD_KEY_PATH
 from onyx.configs.constants import POSTGRES_CELERY_WORKER_LIGHT_APP_NAME
 from onyx.db.engine import SqlEngine
 from onyx.utils.logger import setup_logger
@@ -54,12 +58,23 @@ def on_celeryd_init(sender: str, conf: Any = None, **kwargs: Any) -> None:
 
 @worker_init.connect
 def on_worker_init(sender: Worker, **kwargs: Any) -> None:
+    EXTRA_CONCURRENCY = 8  # small extra fudge factor for connection limits
+
     logger.info("worker_init signal received.")
 
     logger.info(f"Concurrency: {sender.concurrency}")  # type: ignore
 
     SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_LIGHT_APP_NAME)
-    SqlEngine.init_engine(pool_size=sender.concurrency, max_overflow=8)  # type: ignore
+    SqlEngine.init_engine(pool_size=sender.concurrency, max_overflow=EXTRA_CONCURRENCY)  # type: ignore
+
+    if MANAGED_VESPA:
+        httpx_init_vespa_pool(
+            sender.concurrency + EXTRA_CONCURRENCY,  # type: ignore
+            ssl_cert=VESPA_CLOUD_CERT_PATH,
+            ssl_key=VESPA_CLOUD_KEY_PATH,
+        )
+    else:
+        httpx_init_vespa_pool(sender.concurrency + EXTRA_CONCURRENCY)  # type: ignore
 
     app_base.wait_for_redis(sender, **kwargs)
     app_base.wait_for_db(sender, **kwargs)

backend/onyx/background/celery/apps/primary.py

@@ -21,13 +21,16 @@ from onyx.background.celery.tasks.indexing.utils import (
     get_unfenced_index_attempt_ids,
 )
 from onyx.configs.constants import CELERY_PRIMARY_WORKER_LOCK_TIMEOUT
+from onyx.configs.constants import OnyxRedisConstants
 from onyx.configs.constants import OnyxRedisLocks
 from onyx.configs.constants import POSTGRES_CELERY_WORKER_PRIMARY_APP_NAME
 from onyx.db.engine import get_session_with_default_tenant
 from onyx.db.engine import SqlEngine
 from onyx.db.index_attempt import get_index_attempt
 from onyx.db.index_attempt import mark_attempt_canceled
-from onyx.redis.redis_connector_credential_pair import RedisConnectorCredentialPair
+from onyx.redis.redis_connector_credential_pair import (
+    RedisGlobalConnectorCredentialPair,
+)
 from onyx.redis.redis_connector_delete import RedisConnectorDelete
 from onyx.redis.redis_connector_doc_perm_sync import RedisConnectorPermissionSync
 from onyx.redis.redis_connector_ext_group_sync import RedisConnectorExternalGroupSync
@@ -141,23 +144,16 @@ def on_worker_init(sender: Worker, **kwargs: Any) -> None:
     r.delete(OnyxRedisLocks.CHECK_VESPA_SYNC_BEAT_LOCK)
     r.delete(OnyxRedisLocks.MONITOR_VESPA_SYNC_BEAT_LOCK)
 
-    r.delete(RedisConnectorCredentialPair.get_taskset_key())
-    r.delete(RedisConnectorCredentialPair.get_fence_key())
+    r.delete(OnyxRedisConstants.ACTIVE_FENCES)
 
+    RedisGlobalConnectorCredentialPair.reset_all(r)
     RedisDocumentSet.reset_all(r)
-
     RedisUserGroup.reset_all(r)
-
     RedisConnectorDelete.reset_all(r)
-
     RedisConnectorPrune.reset_all(r)
-
     RedisConnectorIndex.reset_all(r)
-
     RedisConnectorStop.reset_all(r)
-
     RedisConnectorPermissionSync.reset_all(r)
-
     RedisConnectorExternalGroupSync.reset_all(r)
 
     # mark orphaned index attempts as failed

backend/onyx/background/celery/celery_redis.py

@@ -91,6 +91,28 @@ def celery_find_task(task_id: str, queue: str, r: Redis) -> int:
     return False
 
 
+def celery_get_queued_task_ids(queue: str, r: Redis) -> set[str]:
+    """This is a redis specific way to build a list of tasks in a queue.
+
+    This helps us read the queue once and then efficiently look for missing tasks
+    in the queue.
+    """
+
+    task_set: set[str] = set()
+
+    for priority in range(len(OnyxCeleryPriority)):
+        queue_name = f"{queue}{CELERY_SEPARATOR}{priority}" if priority > 0 else queue
+
+        tasks = cast(list[bytes], r.lrange(queue_name, 0, -1))
+        for task in tasks:
+            task_dict: dict[str, Any] = json.loads(task.decode("utf-8"))
+            task_id = task_dict.get("headers", {}).get("id")
+            if task_id:
+                task_set.add(task_id)
+
+    return task_set
+
+
 def celery_inspect_get_workers(name_filter: str | None, app: Celery) -> list[str]:
     """Returns a list of current workers containing name_filter, or all workers if
     name_filter is None.

backend/onyx/background/celery/celery_utils.py

@@ -1,10 +1,13 @@
 from datetime import datetime
 from datetime import timezone
 from typing import Any
+from typing import cast
 
+import httpx
 from sqlalchemy.orm import Session
 
 from onyx.configs.app_configs import MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE
+from onyx.configs.app_configs import VESPA_REQUEST_TIMEOUT
 from onyx.connectors.cross_connector_utils.rate_limit_wrapper import (
     rate_limit_builder,
 )
@@ -17,6 +20,7 @@ from onyx.db.connector_credential_pair import get_connector_credential_pair
 from onyx.db.enums import ConnectorCredentialPairStatus
 from onyx.db.enums import TaskStatus
 from onyx.db.models import TaskQueueState
+from onyx.httpx.httpx_pool import HttpxPool
 from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.redis.redis_connector import RedisConnector
 from onyx.server.documents.models import DeletionAttemptSnapshot
@@ -154,3 +158,25 @@ def celery_is_worker_primary(worker: Any) -> bool:
         return True
 
     return False
+
+
+def httpx_init_vespa_pool(
+    max_keepalive_connections: int,
+    timeout: int = VESPA_REQUEST_TIMEOUT,
+    ssl_cert: str | None = None,
+    ssl_key: str | None = None,
+) -> None:
+    httpx_cert = None
+    httpx_verify = False
+    if ssl_cert and ssl_key:
+        httpx_cert = cast(tuple[str, str], (ssl_cert, ssl_key))
+        httpx_verify = True
+
+    HttpxPool.init_client(
+        name="vespa",
+        cert=httpx_cert,
+        verify=httpx_verify,
+        timeout=timeout,
+        http2=False,
+        limits=httpx.Limits(max_keepalive_connections=max_keepalive_connections),
+    )