k

Changes addressing YS questions from 01/22/25

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

@@ -8,8 +8,6 @@ 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: v3.17.0
+        version: v3.14.4
       
     - name: Set up chart-testing
-      uses: helm/chart-testing-action@v2.7.0
+      uses: helm/chart-testing-action@v2.6.1
 
     # 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)
@@ -37,6 +37,22 @@ 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'
@@ -46,7 +62,7 @@ jobs:
 
     - name: Create kind cluster
       if: steps.list-changed.outputs.changed == 'true'
-      uses: helm/kind-action@v1.12.0
+      uses: helm/kind-action@v1.10.0
 
     - name: Run chart-testing (install)
       if: steps.list-changed.outputs.changed == 'true'

.github/workflows/pr-linear-check.yml

@@ -9,9 +9,9 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check PR body for Linear link or override
-        env:
-          PR_BODY: ${{ github.event.pull_request.body }}
         run: |
+          PR_BODY="${{ github.event.pull_request.body }}"
+
           # Looking for "https://linear.app" in the body
           if echo "$PR_BODY" | grep -qE "https://linear\.app"; then
             echo "Found a Linear link. Check passed."

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

@@ -39,12 +39,6 @@ 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/

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/onyx/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/danswer/founders).
 
 ## 💡 Contributing
 

Untitled-12

@@ -0,0 +1 @@
+ 

backend/Dockerfile

@@ -9,10 +9,8 @@ founders@onyx.app for more information. Please visit https://github.com/onyx-dot
 
 # Default ONYX_VERSION, typically overriden during builds by GitHub Actions.
 ARG ONYX_VERSION=0.8-dev
-# DO_NOT_TRACK is used to disable telemetry for Unstructured
 ENV ONYX_VERSION=${ONYX_VERSION} \
-    DANSWER_RUNNING_IN_DOCKER="true" \
-    DO_NOT_TRACK="true"
+    DANSWER_RUNNING_IN_DOCKER="true"
 
 
 RUN echo "ONYX_VERSION: ${ONYX_VERSION}"

backend/alembic/versions/1adf5ea20d2b_agent_doc_result_col.py

@@ -0,0 +1,29 @@
+"""agent_doc_result_col
+
+Revision ID: 1adf5ea20d2b
+Revises: e9cf2bd7baed
+Create Date: 2025-01-05 13:14:58.344316
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "1adf5ea20d2b"
+down_revision = "e9cf2bd7baed"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add the new column with JSONB type
+    op.add_column(
+        "sub_question",
+        sa.Column("sub_question_doc_results", postgresql.JSONB(), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    # Drop the column
+    op.drop_column("sub_question", "sub_question_doc_results")

backend/alembic/versions/2f80c6a2550f_add_chat_session_specific_temperature_.py

@@ -1,36 +0,0 @@
-"""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

@@ -1,80 +0,0 @@
-"""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

@@ -1,37 +0,0 @@
-"""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/925b58bd75b6_agent_metric_col_rename__s.py

@@ -0,0 +1,35 @@
+"""agent_metric_col_rename__s
+
+Revision ID: 925b58bd75b6
+Revises: 9787be927e58
+Create Date: 2025-01-06 11:20:26.752441
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "925b58bd75b6"
+down_revision = "9787be927e58"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Rename columns using PostgreSQL syntax
+    op.alter_column(
+        "agent__search_metrics", "base_duration_s", new_column_name="base_duration__s"
+    )
+    op.alter_column(
+        "agent__search_metrics", "full_duration_s", new_column_name="full_duration__s"
+    )
+
+
+def downgrade() -> None:
+    # Revert the column renames
+    op.alter_column(
+        "agent__search_metrics", "base_duration__s", new_column_name="base_duration_s"
+    )
+    op.alter_column(
+        "agent__search_metrics", "full_duration__s", new_column_name="full_duration_s"
+    )

backend/alembic/versions/9787be927e58_agent_metric_table_renames__agent__.py

@@ -0,0 +1,25 @@
+"""agent_metric_table_renames__agent__
+
+Revision ID: 9787be927e58
+Revises: bceb76d618ec
+Create Date: 2025-01-06 11:01:44.210160
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "9787be927e58"
+down_revision = "bceb76d618ec"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Rename table from agent_search_metrics to agent__search_metrics
+    op.rename_table("agent_search_metrics", "agent__search_metrics")
+
+
+def downgrade() -> None:
+    # Rename table back from agent__search_metrics to agent_search_metrics
+    op.rename_table("agent__search_metrics", "agent_search_metrics")

backend/alembic/versions/98a5008d8711_agent_tracking.py

@@ -1,25 +1,24 @@
 """agent_tracking
 
 Revision ID: 98a5008d8711
-Revises: 2f80c6a2550f
-Create Date: 2025-01-29 17:00:00.000001
+Revises: f1ca58b2f2ec
+Create Date: 2025-01-04 14:41:52.732238
 
 """
 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"
+down_revision = "f1ca58b2f2ec"
 branch_labels = None
 depends_on = None
 
 
 def upgrade() -> None:
     op.create_table(
-        "agent__search_metrics",
+        "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),
@@ -38,70 +37,6 @@ def upgrade() -> None:
         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")
+    op.drop_table("agent_search_metrics")

backend/alembic/versions/a6df6b88ef81_remove_recent_assistants.py

@@ -1,29 +0,0 @@
-"""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/bceb76d618ec_agent_table_renames__agent__.py

@@ -0,0 +1,84 @@
+"""agent_table_renames__agent__
+
+Revision ID: bceb76d618ec
+Revises: c0132518a25b
+Create Date: 2025-01-06 10:50:48.109285
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "bceb76d618ec"
+down_revision = "c0132518a25b"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.drop_constraint(
+        "sub_query__search_doc_sub_query_id_fkey",
+        "sub_query__search_doc",
+        type_="foreignkey",
+    )
+    op.drop_constraint(
+        "sub_query__search_doc_search_doc_id_fkey",
+        "sub_query__search_doc",
+        type_="foreignkey",
+    )
+    # Rename tables
+    op.rename_table("sub_query", "agent__sub_query")
+    op.rename_table("sub_question", "agent__sub_question")
+    op.rename_table("sub_query__search_doc", "agent__sub_query__search_doc")
+
+    # Update both foreign key constraints for agent__sub_query__search_doc
+
+    # Create new foreign keys with updated names
+    op.create_foreign_key(
+        "agent__sub_query__search_doc_sub_query_id_fkey",
+        "agent__sub_query__search_doc",
+        "agent__sub_query",
+        ["sub_query_id"],
+        ["id"],
+    )
+    op.create_foreign_key(
+        "agent__sub_query__search_doc_search_doc_id_fkey",
+        "agent__sub_query__search_doc",
+        "search_doc",  # This table name doesn't change
+        ["search_doc_id"],
+        ["id"],
+    )
+
+
+def downgrade() -> None:
+    # Update foreign key constraints for sub_query__search_doc
+    op.drop_constraint(
+        "agent__sub_query__search_doc_sub_query_id_fkey",
+        "agent__sub_query__search_doc",
+        type_="foreignkey",
+    )
+    op.drop_constraint(
+        "agent__sub_query__search_doc_search_doc_id_fkey",
+        "agent__sub_query__search_doc",
+        type_="foreignkey",
+    )
+
+    # Rename tables back
+    op.rename_table("agent__sub_query__search_doc", "sub_query__search_doc")
+    op.rename_table("agent__sub_question", "sub_question")
+    op.rename_table("agent__sub_query", "sub_query")
+
+    op.create_foreign_key(
+        "sub_query__search_doc_sub_query_id_fkey",
+        "sub_query__search_doc",
+        "sub_query",
+        ["sub_query_id"],
+        ["id"],
+    )
+    op.create_foreign_key(
+        "sub_query__search_doc_search_doc_id_fkey",
+        "sub_query__search_doc",
+        "search_doc",  # This table name doesn't change
+        ["search_doc_id"],
+        ["id"],
+    )

backend/alembic/versions/c0132518a25b_agent_table_changes_rename_level.py

@@ -0,0 +1,40 @@
+"""agent_table_changes_rename_level
+
+Revision ID: c0132518a25b
+Revises: 1adf5ea20d2b
+Create Date: 2025-01-05 16:38:37.660152
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "c0132518a25b"
+down_revision = "1adf5ea20d2b"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add level and level_question_nr columns with NOT NULL constraint
+    op.add_column(
+        "sub_question",
+        sa.Column("level", sa.Integer(), nullable=False, server_default="0"),
+    )
+    op.add_column(
+        "sub_question",
+        sa.Column(
+            "level_question_nr", sa.Integer(), nullable=False, server_default="0"
+        ),
+    )
+
+    # Remove the server_default after the columns are created
+    op.alter_column("sub_question", "level", server_default=None)
+    op.alter_column("sub_question", "level_question_nr", server_default=None)
+
+
+def downgrade() -> None:
+    # Remove the columns
+    op.drop_column("sub_question", "level_question_nr")
+    op.drop_column("sub_question", "level")

backend/alembic/versions/e9cf2bd7baed_create_pro_search_persistence_tables.py

@@ -0,0 +1,68 @@
+"""create pro search persistence tables
+
+Revision ID: e9cf2bd7baed
+Revises: 98a5008d8711
+Create Date: 2025-01-02 17:55:56.544246
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects.postgresql import UUID
+
+
+# revision identifiers, used by Alembic.
+revision = "e9cf2bd7baed"
+down_revision = "98a5008d8711"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Create sub_question table
+    op.create_table(
+        "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),
+    )
+
+    # Create sub_query table
+    op.create_table(
+        "sub_query",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column("parent_question_id", sa.Integer, sa.ForeignKey("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(
+        "sub_query__search_doc",
+        sa.Column(
+            "sub_query_id", sa.Integer, sa.ForeignKey("sub_query.id"), primary_key=True
+        ),
+        sa.Column(
+            "search_doc_id",
+            sa.Integer,
+            sa.ForeignKey("search_doc.id"),
+            primary_key=True,
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("sub_query__search_doc")
+    op.drop_table("sub_query")
+    op.drop_table("sub_question")

backend/alembic/versions/eaa3b5593925_add_default_slack_channel_config.py

@@ -1,76 +0,0 @@
-"""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

@@ -32,7 +32,6 @@ 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:
@@ -57,7 +56,6 @@ 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/background/celery/tasks/beat_schedule.py

@@ -1,72 +1,30 @@
 from datetime import timedelta
 from typing import Any
 
-from onyx.background.celery.tasks.beat_schedule import BEAT_EXPIRES_DEFAULT
 from onyx.background.celery.tasks.beat_schedule import (
     cloud_tasks_to_schedule as base_cloud_tasks_to_schedule,
 )
 from onyx.background.celery.tasks.beat_schedule import (
     tasks_to_schedule as base_tasks_to_schedule,
 )
-from onyx.configs.constants import ONYX_CLOUD_CELERY_TASK_PREFIX
-from onyx.configs.constants import OnyxCeleryPriority
 from onyx.configs.constants import OnyxCeleryTask
-from shared_configs.configs import MULTI_TENANT
 
-ee_cloud_tasks_to_schedule = [
+ee_tasks_to_schedule = [
     {
-        "name": f"{ONYX_CLOUD_CELERY_TASK_PREFIX}_autogenerate-usage-report",
-        "task": OnyxCeleryTask.CLOUD_BEAT_TASK_GENERATOR,
-        "schedule": timedelta(days=30),
-        "options": {
-            "priority": OnyxCeleryPriority.HIGHEST,
-            "expires": BEAT_EXPIRES_DEFAULT,
-        },
-        "kwargs": {
-            "task_name": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
-        },
+        "name": "autogenerate-usage-report",
+        "task": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
+        "schedule": timedelta(days=30),  # TODO: change this to config flag
     },
     {
-        "name": f"{ONYX_CLOUD_CELERY_TASK_PREFIX}_check-ttl-management",
-        "task": OnyxCeleryTask.CLOUD_BEAT_TASK_GENERATOR,
+        "name": "check-ttl-management",
+        "task": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
         "schedule": timedelta(hours=1),
-        "options": {
-            "priority": OnyxCeleryPriority.HIGHEST,
-            "expires": BEAT_EXPIRES_DEFAULT,
-        },
-        "kwargs": {
-            "task_name": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
-        },
     },
 ]
 
-ee_tasks_to_schedule: list[dict] = []
-
-if not MULTI_TENANT:
-    ee_tasks_to_schedule = [
-        {
-            "name": "autogenerate-usage-report",
-            "task": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
-            "schedule": timedelta(days=30),  # TODO: change this to config flag
-            "options": {
-                "priority": OnyxCeleryPriority.MEDIUM,
-                "expires": BEAT_EXPIRES_DEFAULT,
-            },
-        },
-        {
-            "name": "check-ttl-management",
-            "task": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
-            "schedule": timedelta(hours=1),
-            "options": {
-                "priority": OnyxCeleryPriority.MEDIUM,
-                "expires": BEAT_EXPIRES_DEFAULT,
-            },
-        },
-    ]
-
 
 def get_cloud_tasks_to_schedule() -> list[dict[str, Any]]:
-    return ee_cloud_tasks_to_schedule + base_cloud_tasks_to_schedule
+    return base_cloud_tasks_to_schedule
 
 
 def get_tasks_to_schedule() -> list[dict[str, Any]]:

backend/ee/onyx/configs/app_configs.py

@@ -4,20 +4,6 @@ import os
 # Applicable for OIDC Auth
 OPENID_CONFIG_URL = os.environ.get("OPENID_CONFIG_URL", "")
 
-# Applicable for OIDC Auth, allows you to override the scopes that
-# are requested from the OIDC provider. Currently used when passing
-# over access tokens to tool calls and the tool needs more scopes
-OIDC_SCOPE_OVERRIDE: list[str] | None = None
-_OIDC_SCOPE_OVERRIDE = os.environ.get("OIDC_SCOPE_OVERRIDE")
-
-if _OIDC_SCOPE_OVERRIDE:
-    try:
-        OIDC_SCOPE_OVERRIDE = [
-            scope.strip() for scope in _OIDC_SCOPE_OVERRIDE.split(",")
-        ]
-    except Exception:
-        pass
-
 # Applicable for SAML Auth
 SAML_CONF_DIR = os.environ.get("SAML_CONF_DIR") or "/app/ee/onyx/configs/saml_config"
 

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

@@ -13,7 +13,6 @@ 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()
@@ -258,7 +257,6 @@ 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.
@@ -267,12 +265,6 @@ 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}"
@@ -342,7 +334,7 @@ def _fetch_all_page_restrictions(
 
 
 def confluence_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -367,12 +359,6 @@ 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")
@@ -381,5 +367,4 @@ 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

@@ -14,8 +14,6 @@ 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}")
@@ -35,17 +33,10 @@ 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

@@ -6,7 +6,6 @@ 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()
@@ -29,7 +28,7 @@ def _get_slim_doc_generator(
 
 
 def gmail_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -45,12 +44,6 @@ 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

@@ -10,7 +10,6 @@ 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()
@@ -43,22 +42,24 @@ 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",
@@ -68,6 +69,7 @@ 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
@@ -129,7 +131,7 @@ def _get_permissions_from_slim_doc(
 
 
 def gdrive_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -147,12 +149,6 @@ 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

@@ -7,7 +7,6 @@ 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
 
 
@@ -15,7 +14,7 @@ logger = setup_logger()
 
 
 def _get_slack_document_ids_and_channels(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> dict[str, list[str]]:
     slack_connector = SlackPollConnector(**cc_pair.connector.connector_specific_config)
     slack_connector.load_credentials(cc_pair.credential.credential_json)
@@ -25,14 +24,6 @@ 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"]
@@ -123,7 +114,7 @@ def _fetch_channel_permissions(
 
 
 def slack_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -136,7 +127,7 @@ 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, callback=callback
+        cc_pair=cc_pair,
     )
     workspace_permissions = _fetch_workspace_permissions(
         user_id_to_email_map=user_id_to_email_map,

backend/ee/onyx/external_permissions/sync_params.py

@@ -15,13 +15,11 @@ 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/main.py

@@ -1,9 +1,7 @@
 from fastapi import FastAPI
 from httpx_oauth.clients.google import GoogleOAuth2
-from httpx_oauth.clients.openid import BASE_SCOPES
 from httpx_oauth.clients.openid import OpenID
 
-from ee.onyx.configs.app_configs import OIDC_SCOPE_OVERRIDE
 from ee.onyx.configs.app_configs import OPENID_CONFIG_URL
 from ee.onyx.server.analytics.api import router as analytics_router
 from ee.onyx.server.auth_check import check_ee_router_auth
@@ -90,13 +88,7 @@ def get_application() -> FastAPI:
         include_auth_router_with_prefix(
             application,
             create_onyx_oauth_router(
-                OpenID(
-                    OAUTH_CLIENT_ID,
-                    OAUTH_CLIENT_SECRET,
-                    OPENID_CONFIG_URL,
-                    # BASE_SCOPES is the same as not setting this
-                    base_scopes=OIDC_SCOPE_OVERRIDE or BASE_SCOPES,
-                ),
+                OpenID(OAUTH_CLIENT_ID, OAUTH_CLIENT_SECRET, OPENID_CONFIG_URL),
                 auth_backend,
                 USER_AUTH_SECRET,
                 associate_by_email=True,

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,
+    slack_channel_config: SlackChannelConfig | None,
     prompt: Prompt | None,
     logger: OnyxLoggingAdapter,
     client: WebClient,
@@ -94,10 +94,13 @@ 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
+        slack_channel_config.standard_answer_categories if slack_channel_config else []
     )
     configured_standard_answers = set(
         [

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

@@ -10,7 +10,6 @@ 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
@@ -44,7 +43,6 @@ 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
@@ -92,12 +90,3 @@ 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

@@ -286,7 +286,6 @@ 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)
@@ -555,7 +554,6 @@ 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/models.py

@@ -125,10 +125,10 @@ class OneShotQARequest(ChunkContext):
     # will also disable Thread-based Rewording if specified
     query_override: str | None = None
 
-    # If True, skips generating an AI response to the search query
+    # If True, skips generative an AI response to the search query
     skip_gen_ai_answer_generation: bool = False
 
-    # If True, uses agentic search instead of basic search
+    # If True, uses pro search instead of basic search
     use_agentic_search: bool = False
 
     @model_validator(mode="after")

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

@@ -34,7 +34,6 @@ 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
@@ -112,7 +111,6 @@ 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

@@ -58,7 +58,6 @@ 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

@@ -2,17 +2,14 @@ from langgraph.graph import END
 from langgraph.graph import START
 from langgraph.graph import StateGraph
 
+from onyx.agents.agent_search.basic.nodes.basic_use_tool_response import (
+    basic_use_tool_response,
+)
+from onyx.agents.agent_search.basic.nodes.llm_tool_choice import llm_tool_choice
+from onyx.agents.agent_search.basic.nodes.tool_call import tool_call
 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()
@@ -27,11 +24,6 @@ def basic_graph_builder() -> StateGraph:
 
     ### Add nodes ###
 
-    graph.add_node(
-        node="prepare_tool_input",
-        action=prepare_tool_input,
-    )
-
     graph.add_node(
         node="llm_tool_choice",
         action=llm_tool_choice,
@@ -49,9 +41,7 @@ def basic_graph_builder() -> StateGraph:
 
     ### 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_edge(start_key=START, end_key="llm_tool_choice")
 
     graph.add_conditional_edges("llm_tool_choice", should_continue, ["tool_call", END])
 
@@ -72,26 +62,10 @@ 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
+        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}})
+    pass

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

@@ -0,0 +1,63 @@
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+
+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 AgentSearchConfig
+from onyx.chat.models import LlmDoc
+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,
+)
+
+
+def basic_use_tool_response(state: BasicState, config: RunnableConfig) -> BasicOutput:
+    agent_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    structured_response_format = agent_config.structured_response_format
+    llm = agent_config.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.prompt_builder
+    tool_call_summary = state["tool_call_summary"]
+    tool_call_responses = state["tool_call_responses"]
+    state["tool_call_final_result"]
+    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.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 = yield_item.response.contexts
+            for doc in search_contexts:
+                if doc.document_id not in initial_search_results:
+                    initial_search_results.append(doc)
+
+            initial_search_results = cast(list[LlmDoc], initial_search_results)
+
+    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
+    process_llm_stream(
+        stream,
+        True,
+        final_search_results=final_search_results,
+        displayed_search_results=initial_search_results,
+    )
+
+    return BasicOutput()

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

@@ -3,14 +3,12 @@ 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.states import BasicState
+from onyx.agents.agent_search.basic.states import ToolChoice
+from onyx.agents.agent_search.basic.states import ToolChoiceUpdate
 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.agents.agent_search.models import AgentSearchConfig
 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,
@@ -25,29 +23,22 @@ logger = setup_logger()
 # 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:
+def llm_tool_choice(state: BasicState, config: RunnableConfig) -> 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
+    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
+    agent_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    using_tool_calling_llm = agent_config.using_tool_calling_llm
+    prompt_builder = agent_config.prompt_builder
+    llm = agent_config.primary_llm
+    skip_gen_ai_answer_generation = agent_config.skip_gen_ai_answer_generation
 
-    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
+    structured_response_format = agent_config.structured_response_format
+    tools = agent_config.tools or []
+    force_use_tool = agent_config.force_use_tool
 
     tool, tool_args = None, None
     if force_use_tool.force_use and force_use_tool.args is not None:
@@ -68,7 +59,7 @@ def llm_tool_choice(
         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.
+    # If we have a tool and tool args, we are redy 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(
@@ -87,32 +78,21 @@ def llm_tool_choice(
             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,
+        prompt=prompt_builder.build(),
         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,
-    )
+    tool_message = process_llm_stream(stream, should_stream_answer)
 
     # 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,
         )
@@ -142,7 +122,7 @@ def llm_tool_choice(
             f"Tool call attempted with tool {selected_tool}, request {selected_tool_call_request}"
         )
 
-    logger.debug(f"Selected tool: {selected_tool.name}")
+    logger.info(f"Selected tool: {selected_tool.name}")
     logger.debug(f"Selected tool call request: {selected_tool_call_request}")
 
     return ToolChoiceUpdate(

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

@@ -0,0 +1,69 @@
+from typing import cast
+
+from langchain_core.callbacks.manager import dispatch_custom_event
+from langchain_core.messages import AIMessageChunk
+from langchain_core.messages.tool import ToolCall
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.basic.states import BasicState
+from onyx.agents.agent_search.basic.states import ToolCallUpdate
+from onyx.agents.agent_search.models import AgentSearchConfig
+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()
+
+
+def emit_packet(packet: AnswerPacket) -> None:
+    dispatch_custom_event("basic_response", packet)
+
+
+# TODO: handle is_cancelled
+def tool_call(state: BasicState, config: RunnableConfig) -> ToolCallUpdate:
+    """Calls the tool specified in the state and updates the state with the result"""
+    # TODO: implement
+
+    cast(AgentSearchConfig, config["metadata"]["config"])
+    # Unnecessary now, node should only be called if there is a tool call
+    # if not self.tool_call_chunk or not self.tool_call_chunk.tool_calls:
+    #     return
+
+    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()
+
+    # TODO: custom events for yields
+    emit_packet(tool_kickoff)
+
+    tool_responses = []
+    for response in tool_runner.tool_responses():
+        tool_responses.append(response)
+        emit_packet(response)
+
+    tool_final_result = tool_runner.tool_final_result()
+    emit_packet(tool_final_result)
+
+    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()
+        ),
+    )
+
+    return ToolCallUpdate(
+        tool_call_summary=tool_call_summary,
+        tool_call_kickoff=tool_kickoff,
+        tool_call_responses=tool_responses,
+        tool_call_final_result=tool_final_result,
+    )

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

@@ -1,35 +1,55 @@
 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
+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
 
 # 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
+
+
+class BasicInput(TypedDict):
+    should_stream_answer: bool
 
 
 ## Graph Output State
+
+
 class BasicOutput(TypedDict):
-    tool_call_chunk: AIMessageChunk
+    pass
+
+
+## Update States
+class ToolCallUpdate(TypedDict):
+    tool_call_summary: ToolCallSummary
+    tool_call_kickoff: ToolCallKickoff
+    tool_call_responses: list[ToolResponse]
+    tool_call_final_result: ToolCallFinalResult
+
+
+class ToolChoice(TypedDict):
+    tool: Tool
+    tool_args: dict
+    id: str | None
+
+
+class ToolChoiceUpdate(TypedDict):
+    tool_choice: ToolChoice | None
 
 
 ## Graph State
+
+
 class BasicState(
     BasicInput,
-    ToolChoiceInput,
     ToolCallUpdate,
     ToolChoiceUpdate,
+    BasicOutput,
 ):
     pass

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

@@ -1,13 +1,11 @@
 from collections.abc import Iterator
 from typing import cast
 
+from langchain_core.callbacks.manager import dispatch_custom_event
 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 (
@@ -18,15 +16,23 @@ from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 
+# TODO: handle citations here; below is what was previously passed in
+# see basic_use_tool_response.py for where these variables come from
+# answer_handler = 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),
+# )
+
 
 def process_llm_stream(
-    messages: Iterator[BaseMessage],
+    stream: Iterator[BaseMessage],
     should_stream_answer: bool,
-    writer: StreamWriter,
     final_search_results: list[LlmDoc] | None = None,
-    displayed_search_results: list[OnyxContext] | list[LlmDoc] | None = None,
+    displayed_search_results: list[LlmDoc] | None = None,
 ) -> AIMessageChunk:
     tool_call_chunk = AIMessageChunk(content="")
+    # for response in response_handler_manager.handle_llm_response(stream):
 
     if final_search_results and displayed_search_results:
         answer_handler: AnswerResponseHandler = CitationResponseHandler(
@@ -37,28 +43,25 @@ def process_llm_stream(
     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
+    for response in stream:
+        answer_piece = response.content
         if not isinstance(answer_piece, str):
-            # this is only used for logging, so fine to
-            # just add the string representation
+            # TODO: handle non-string content
+            logger.warning(f"Received non-string content: {type(answer_piece)}")
             answer_piece = str(answer_piece)
-        full_answer += answer_piece
 
-        if isinstance(message, AIMessageChunk) and (
-            message.tool_call_chunks or message.tool_calls
+        if isinstance(response, AIMessageChunk) and (
+            response.tool_call_chunks or response.tool_calls
         ):
-            tool_call_chunk += message  # type: ignore
+            tool_call_chunk += response  # type: ignore
         elif should_stream_answer:
-            for response_part in answer_handler.handle_response_part(message, []):
-                write_custom_event(
+            # TODO: handle emitting of CitationInfo
+            for response_part in answer_handler.handle_response_part(response, []):
+                dispatch_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/db_operations.py

@@ -0,0 +1,66 @@
+from uuid import UUID
+
+from sqlalchemy.orm import Session
+
+from onyx.db.models import AgentSubQuery
+from onyx.db.models import AgentSubQuestion
+
+
+def create_sub_question(
+    db_session: Session,
+    chat_session_id: UUID,
+    primary_message_id: int,
+    sub_question: str,
+    sub_answer: str,
+) -> AgentSubQuestion:
+    """Create a new sub-question record in the database."""
+    sub_q = AgentSubQuestion(
+        chat_session_id=chat_session_id,
+        primary_question_id=primary_message_id,
+        sub_question=sub_question,
+        sub_answer=sub_answer,
+    )
+    db_session.add(sub_q)
+    db_session.flush()
+    return sub_q
+
+
+def create_sub_query(
+    db_session: Session,
+    chat_session_id: UUID,
+    parent_question_id: int,
+    sub_query: str,
+) -> AgentSubQuery:
+    """Create a new sub-query record in the database."""
+    sub_q = AgentSubQuery(
+        chat_session_id=chat_session_id,
+        parent_question_id=parent_question_id,
+        sub_query=sub_query,
+    )
+    db_session.add(sub_q)
+    db_session.flush()
+    return sub_q
+
+
+def get_sub_questions_for_message(
+    db_session: Session,
+    primary_message_id: int,
+) -> list[AgentSubQuestion]:
+    """Get all sub-questions for a given primary message."""
+    return (
+        db_session.query(AgentSubQuestion)
+        .filter(AgentSubQuestion.primary_question_id == primary_message_id)
+        .all()
+    )
+
+
+def get_sub_queries_for_question(
+    db_session: Session,
+    sub_question_id: int,
+) -> list[AgentSubQuery]:
+    """Get all sub-queries for a given sub-question."""
+    return (
+        db_session.query(AgentSubQuery)
+        .filter(AgentSubQuery.parent_question_id == sub_question_id)
+        .all()
+    )

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

@@ -1,31 +0,0 @@
-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/nodes/check_sub_answer.py

@@ -1,75 +0,0 @@
-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

@@ -1,30 +0,0 @@
-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

@@ -1,137 +0,0 @@
-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

@@ -1,25 +0,0 @@
-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_initial_answer/edges.py

@@ -1,50 +0,0 @@
-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

@@ -1,96 +0,0 @@
-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

@@ -1,313 +0,0 @@
-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

@@ -1,40 +0,0 @@
-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

@@ -1,51 +0,0 @@
-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

@@ -1,48 +0,0 @@
-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

@@ -1,81 +0,0 @@
-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

@@ -1,153 +0,0 @@
-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

@@ -1,50 +0,0 @@
-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

@@ -1,34 +0,0 @@
-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

@@ -1,81 +0,0 @@
-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_output.py

@@ -1,30 +0,0 @@
-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

@@ -1,29 +0,0 @@
-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

@@ -1,113 +0,0 @@
-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

@@ -1,265 +0,0 @@
-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/nodes/compare_answers.py

@@ -1,71 +0,0 @@
-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

@@ -1,131 +0,0 @@
-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

@@ -1,47 +0,0 @@
-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

@@ -1,116 +0,0 @@
-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

@@ -1,339 +0,0 @@
-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

@@ -1,42 +0,0 @@
-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

@@ -1,129 +0,0 @@
-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

@@ -1,52 +0,0 @@
-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/states.py

@@ -1,172 +0,0 @@
-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/shared/expanded_retrieval/graph_builder.py

@@ -1,161 +0,0 @@
-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

@@ -1,13 +0,0 @@
-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

@@ -1,75 +0,0 @@
-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_results.py

@@ -1,91 +0,0 @@
-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

@@ -1,44 +0,0 @@
-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

@@ -1,105 +0,0 @@
-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_a/answer_initial_sub_question/edges.py

@@ -0,0 +1,29 @@
+from collections.abc import Hashable
+from datetime import datetime
+
+from langgraph.types import Send
+
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    AnswerQuestionInput,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def send_to_expanded_retrieval(state: AnswerQuestionInput) -> Send | Hashable:
+    logger.debug("sending to expanded retrieval via edge")
+    now_start = 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"{now_start} -- Sending to expanded retrieval"],
+        ),
+    )

backend/onyx/agents/agent_search/deep_search_a/answer_initial_sub_question/graph_builder.py

@@ -2,31 +2,31 @@ 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 (
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.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_a.answer_initial_sub_question.nodes.answer_check import (
+    answer_check,
 )
-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_a.answer_initial_sub_question.nodes.answer_generation import (
+    answer_generation,
 )
-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_a.answer_initial_sub_question.nodes.format_answer import (
+    format_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_a.answer_initial_sub_question.nodes.ingest_retrieval import (
+    ingest_retrieval,
 )
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    AnswerQuestionInput,
+)
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
     AnswerQuestionOutput,
 )
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.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 (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.graph_builder import (
     expanded_retrieval_graph_builder,
 )
 from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
@@ -36,47 +36,34 @@ 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,
+        input=AnswerQuestionInput,
         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,
+        action=answer_check,
+    )
+    graph.add_node(
+        node="answer_generation",
+        action=answer_generation,
     )
-
-    # The node that formats the sub-answer for the following initial answer generation
     graph.add_node(
         node="format_answer",
-        action=format_sub_answer,
+        action=format_answer,
+    )
+    graph.add_node(
+        node="ingest_retrieval",
+        action=ingest_retrieval,
     )
 
     ### Add edges ###
@@ -92,10 +79,10 @@ def answer_query_graph_builder() -> StateGraph:
     )
     graph.add_edge(
         start_key="ingest_retrieval",
-        end_key="generate_sub_answer",
+        end_key="answer_generation",
     )
     graph.add_edge(
-        start_key="generate_sub_answer",
+        start_key="answer_generation",
         end_key="answer_check",
     )
     graph.add_edge(
@@ -122,16 +109,18 @@ if __name__ == "__main__":
         query="what can you do with onyx or danswer?",
     )
     with get_session_context_manager() as db_session:
-        graph_config, search_tool = get_test_config(
+        agent_search_config, search_tool = get_test_config(
             db_session, primary_llm, fast_llm, search_request
         )
-        inputs = SubQuestionAnsweringInput(
+        inputs = AnswerQuestionInput(
             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}},
+            config={"configurable": {"config": agent_search_config}},
+            # debug=True,
+            # subgraphs=True,
         ):
             logger.debug(thing)

backend/onyx/agents/agent_search/deep_search_a/answer_initial_sub_question/models.py

@@ -0,0 +1,8 @@
+from pydantic import BaseModel
+
+
+### Models ###
+
+
+class AnswerRetrievalStats(BaseModel):
+    answer_retrieval_stats: dict[str, float | int]

backend/onyx/agents/agent_search/deep_search_a/answer_initial_sub_question/nodes/answer_check.py

@@ -0,0 +1,59 @@
+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_a.answer_initial_sub_question.states import (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    QACheckUpdate,
+)
+from onyx.agents.agent_search.models import AgentSearchConfig
+from onyx.agents.agent_search.shared_graph_utils.prompts import SUB_CHECK_NO
+from onyx.agents.agent_search.shared_graph_utils.prompts import SUB_CHECK_PROMPT
+from onyx.agents.agent_search.shared_graph_utils.prompts import UNKNOWN_ANSWER
+from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+
+
+def answer_check(state: AnswerQuestionState, config: RunnableConfig) -> QACheckUpdate:
+    now_start = datetime.now()
+
+    level, question_num = parse_question_id(state.question_id)
+    if state.answer == UNKNOWN_ANSWER:
+        now_end = datetime.now()
+        return QACheckUpdate(
+            answer_quality=SUB_CHECK_NO,
+            log_messages=[
+                f"{now_end} -- Answer check SQ-{level}-{question_num} - unknown answer,  Time taken: {now_end - now_start}"
+            ],
+        )
+    msg = [
+        HumanMessage(
+            content=SUB_CHECK_PROMPT.format(
+                question=state.question,
+                base_answer=state.answer,
+            )
+        )
+    ]
+
+    agent_searchch_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    fast_llm = agent_searchch_config.fast_llm
+    response = list(
+        fast_llm.stream(
+            prompt=msg,
+        )
+    )
+
+    quality_str = merge_message_runs(response, chunk_separator="")[0].content
+
+    now_end = datetime.now()
+    return QACheckUpdate(
+        answer_quality=quality_str,
+        log_messages=[
+            f"""{now_end} -- Answer check SQ-{level}-{question_num} - Answer quality: {quality_str},
+ Time taken: {now_end - now_start}"""
+        ],
+    )

backend/onyx/agents/agent_search/deep_search_a/answer_initial_sub_question/nodes/answer_generation.py

@@ -0,0 +1,116 @@
+from datetime import datetime
+from typing import Any
+from typing import cast
+
+from langchain_core.callbacks.manager import dispatch_custom_event
+from langchain_core.messages import merge_message_runs
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    QAGenerationUpdate,
+)
+from onyx.agents.agent_search.models import AgentSearchConfig
+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.prompts import (
+    ASSISTANT_SYSTEM_PROMPT_DEFAULT,
+)
+from onyx.agents.agent_search.shared_graph_utils.prompts import (
+    ASSISTANT_SYSTEM_PROMPT_PERSONA,
+)
+from onyx.agents.agent_search.shared_graph_utils.prompts import NO_RECOVERED_DOCS
+from onyx.agents.agent_search.shared_graph_utils.utils import get_persona_prompt
+from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
+from onyx.chat.models import AgentAnswerPiece
+from onyx.chat.models import StreamStopInfo
+from onyx.chat.models import StreamStopReason
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def answer_generation(
+    state: AnswerQuestionState, config: RunnableConfig
+) -> QAGenerationUpdate:
+    now_start = datetime.now()
+    logger.debug(f"--------{now_start}--------START ANSWER GENERATION---")
+
+    agent_search_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    question = state.question
+    docs = state.documents
+    level, question_nr = parse_question_id(state.question_id)
+    context_docs = state.context_documents
+    persona_prompt = get_persona_prompt(agent_search_config.search_request.persona)
+
+    if len(context_docs) == 0:
+        answer_str = NO_RECOVERED_DOCS
+        dispatch_custom_event(
+            "sub_answers",
+            AgentAnswerPiece(
+                answer_piece=answer_str,
+                level=level,
+                level_question_nr=question_nr,
+                answer_type="agent_sub_answer",
+            ),
+        )
+    else:
+        if len(persona_prompt) > 0:
+            persona_specification = ASSISTANT_SYSTEM_PROMPT_DEFAULT
+        else:
+            persona_specification = ASSISTANT_SYSTEM_PROMPT_PERSONA.format(
+                persona_prompt=persona_prompt
+            )
+
+        logger.debug(f"Number of verified retrieval docs: {len(docs)}")
+
+        fast_llm = agent_search_config.fast_llm
+        msg = build_sub_question_answer_prompt(
+            question=question,
+            original_question=agent_search_config.search_request.query,
+            docs=docs,
+            persona_specification=persona_specification,
+            config=fast_llm.config,
+        )
+
+        response: list[str | list[str | dict[str, Any]]] = []
+        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)}"
+                )
+            dispatch_custom_event(
+                "sub_answers",
+                AgentAnswerPiece(
+                    answer_piece=content,
+                    level=level,
+                    level_question_nr=question_nr,
+                    answer_type="agent_sub_answer",
+                ),
+            )
+            response.append(content)
+
+        answer_str = merge_message_runs(response, chunk_separator="")[0].content
+
+    stop_event = StreamStopInfo(
+        stop_reason=StreamStopReason.FINISHED,
+        stream_type="sub_answer",
+        level=level,
+        level_question_nr=question_nr,
+    )
+    dispatch_custom_event("stream_finished", stop_event)
+
+    now_end = datetime.now()
+    return QAGenerationUpdate(
+        answer=answer_str,
+        log_messages=[
+            f"{now_end} -- Answer generation SQ-{level} - Q{question_nr} - Time taken: {now_end - now_start}"
+        ],
+    )

backend/onyx/agents/agent_search/deep_search_a/answer_initial_sub_question/nodes/format_answer.py

@@ -0,0 +1,28 @@
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    AnswerQuestionOutput,
+)
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    AnswerQuestionState,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import (
+    QuestionAnswerResults,
+)
+
+
+def format_answer(state: AnswerQuestionState) -> AnswerQuestionOutput:
+    return AnswerQuestionOutput(
+        answer_results=[
+            QuestionAnswerResults(
+                question=state.question,
+                question_id=state.question_id,
+                quality=state.answer_quality
+                if hasattr(state, "answer_quality")
+                else "No",
+                answer=state.answer,
+                expanded_retrieval_results=state.expanded_retrieval_results,
+                documents=state.documents,
+                context_documents=state.context_documents,
+                sub_question_retrieval_stats=state.sub_question_retrieval_stats,
+            )
+        ],
+    )

backend/onyx/agents/agent_search/deep_search_a/answer_initial_sub_question/nodes/ingest_retrieval.py

@@ -0,0 +1,22 @@
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    RetrievalIngestionUpdate,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    ExpandedRetrievalOutput,
+)
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkStats
+
+
+def ingest_retrieval(state: ExpandedRetrievalOutput) -> RetrievalIngestionUpdate:
+    sub_question_retrieval_stats = (
+        state.expanded_retrieval_result.sub_question_retrieval_stats
+    )
+    if sub_question_retrieval_stats is None:
+        sub_question_retrieval_stats = [AgentChunkStats()]
+
+    return RetrievalIngestionUpdate(
+        expanded_retrieval_results=state.expanded_retrieval_result.expanded_queries_results,
+        documents=state.expanded_retrieval_result.all_documents,
+        context_documents=state.expanded_retrieval_result.context_documents,
+        sub_question_retrieval_stats=sub_question_retrieval_stats,
+    )

backend/onyx/agents/agent_search/deep_search_a/answer_initial_sub_question/states.py

@@ -4,11 +4,10 @@ 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 AgentChunkStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryResult
 from onyx.agents.agent_search.shared_graph_utils.models import (
-    SubQuestionAnswerResults,
+    QuestionAnswerResults,
 )
 from onyx.agents.agent_search.shared_graph_utils.operators import (
     dedup_inference_sections,
@@ -17,31 +16,28 @@ from onyx.context.search.models import InferenceSection
 
 
 ## Update States
-class SubQuestionAnswerCheckUpdate(LoggerUpdate, BaseModel):
-    answer_quality: bool = False
+class QACheckUpdate(BaseModel):
+    answer_quality: str = ""
     log_messages: list[str] = []
 
 
-class SubQuestionAnswerGenerationUpdate(LoggerUpdate, BaseModel):
+class QAGenerationUpdate(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
-    ] = []
+class RetrievalIngestionUpdate(BaseModel):
+    expanded_retrieval_results: list[QueryResult] = []
+    documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
     context_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
-    sub_question_retrieval_stats: AgentChunkRetrievalStats = AgentChunkRetrievalStats()
+    sub_question_retrieval_stats: AgentChunkStats = AgentChunkStats()
 
 
 ## Graph Input State
 
 
-class SubQuestionAnsweringInput(SubgraphCoreState):
+class AnswerQuestionInput(SubgraphCoreState):
     question: str = ""
     question_id: str = (
         ""  # 0_0 is original question, everything else is <level>_<question_num>.
@@ -54,10 +50,10 @@ class SubQuestionAnsweringInput(SubgraphCoreState):
 
 
 class AnswerQuestionState(
-    SubQuestionAnsweringInput,
-    SubQuestionAnswerGenerationUpdate,
-    SubQuestionAnswerCheckUpdate,
-    SubQuestionRetrievalIngestionUpdate,
+    AnswerQuestionInput,
+    QAGenerationUpdate,
+    QACheckUpdate,
+    RetrievalIngestionUpdate,
 ):
     pass
 
@@ -65,11 +61,11 @@ class AnswerQuestionState(
 ## Graph Output State
 
 
-class AnswerQuestionOutput(LoggerUpdate, BaseModel):
+class AnswerQuestionOutput(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] = []
+    answer_results: Annotated[list[QuestionAnswerResults], add] = []

backend/onyx/agents/agent_search/deep_search_a/answer_refinement_sub_question/edges.py

@@ -3,10 +3,10 @@ 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_a.answer_initial_sub_question.states import (
+    AnswerQuestionInput,
 )
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     ExpandedRetrievalInput,
 )
 from onyx.utils.logger import setup_logger
@@ -14,12 +14,7 @@ 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.
-    """
+def send_to_expanded_refined_retrieval(state: AnswerQuestionInput) -> Send | Hashable:
     logger.debug("sending to expanded retrieval for follow up question via edge")
     datetime.now()
     return Send(

backend/onyx/agents/agent_search/deep_search_a/answer_refinement_sub_question/graph_builder.py

@@ -2,31 +2,31 @@ 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_a.answer_initial_sub_question.nodes.answer_check import (
+    answer_check,
 )
-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_a.answer_initial_sub_question.nodes.answer_generation import (
+    answer_generation,
 )
-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_a.answer_initial_sub_question.nodes.format_answer import (
+    format_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_a.answer_initial_sub_question.nodes.ingest_retrieval import (
+    ingest_retrieval,
 )
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
+    AnswerQuestionInput,
+)
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.states import (
     AnswerQuestionOutput,
 )
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
+from onyx.agents.agent_search.deep_search_a.answer_initial_sub_question.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 (
+from onyx.agents.agent_search.deep_search_a.answer_refinement_sub_question.edges import (
     send_to_expanded_refined_retrieval,
 )
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.graph_builder import (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.graph_builder import (
     expanded_retrieval_graph_builder,
 )
 from onyx.utils.logger import setup_logger
@@ -35,46 +35,34 @@ 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,
+        input=AnswerQuestionInput,
         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,
+        action=answer_check,
+    )
+    graph.add_node(
+        node="refined_sub_answer_generation",
+        action=answer_generation,
     )
-
-    # Format the refined sub-answer
     graph.add_node(
         node="format_refined_sub_answer",
-        action=format_sub_answer,
+        action=format_answer,
+    )
+    graph.add_node(
+        node="ingest_refined_retrieval",
+        action=ingest_retrieval,
     )
 
     ### Add edges ###
@@ -90,10 +78,10 @@ def answer_refined_query_graph_builder() -> StateGraph:
     )
     graph.add_edge(
         start_key="ingest_refined_retrieval",
-        end_key="generate_refined_sub_answer",
+        end_key="refined_sub_answer_generation",
     )
     graph.add_edge(
-        start_key="generate_refined_sub_answer",
+        start_key="refined_sub_answer_generation",
         end_key="refined_sub_answer_check",
     )
     graph.add_edge(
@@ -120,13 +108,16 @@ if __name__ == "__main__":
         query="what can you do with onyx or danswer?",
     )
     with get_session_context_manager() as db_session:
-        inputs = SubQuestionAnsweringInput(
+        inputs = AnswerQuestionInput(
             question="what can you do with onyx?",
             question_id="0_0",
             log_messages=[],
         )
         for thing in compiled_graph.stream(
             input=inputs,
-            stream_mode="custom",
+            # debug=True,
+            # subgraphs=True,
         ):
             logger.debug(thing)
+        # output = compiled_graph.invoke(inputs)
+        #  logger.debug(output)

backend/onyx/agents/agent_search/deep_search_a/answer_refinement_sub_question/models.py

@@ -0,0 +1,19 @@
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkStats
+from onyx.context.search.models import InferenceSection
+
+### Models ###
+
+
+class AnswerRetrievalStats(BaseModel):
+    answer_retrieval_stats: dict[str, float | int]
+
+
+class QuestionAnswerResults(BaseModel):
+    question: str
+    answer: str
+    quality: str
+    # expanded_retrieval_results: list[QueryResult]
+    documents: list[InferenceSection]
+    sub_question_retrieval_stats: AgentChunkStats

backend/onyx/agents/agent_search/deep_search_a/base_raw_search/graph_builder.py

@@ -0,0 +1,76 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search_a.base_raw_search.nodes.format_raw_search_results import (
+    format_raw_search_results,
+)
+from onyx.agents.agent_search.deep_search_a.base_raw_search.nodes.generate_raw_search_data import (
+    generate_raw_search_data,
+)
+from onyx.agents.agent_search.deep_search_a.base_raw_search.states import (
+    BaseRawSearchInput,
+)
+from onyx.agents.agent_search.deep_search_a.base_raw_search.states import (
+    BaseRawSearchOutput,
+)
+from onyx.agents.agent_search.deep_search_a.base_raw_search.states import (
+    BaseRawSearchState,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.graph_builder import (
+    expanded_retrieval_graph_builder,
+)
+
+
+def base_raw_search_graph_builder() -> StateGraph:
+    graph = StateGraph(
+        state_schema=BaseRawSearchState,
+        input=BaseRawSearchInput,
+        output=BaseRawSearchOutput,
+    )
+
+    ### Add nodes ###
+
+    graph.add_node(
+        node="generate_raw_search_data",
+        action=generate_raw_search_data,
+    )
+
+    expanded_retrieval = expanded_retrieval_graph_builder().compile()
+    graph.add_node(
+        node="expanded_retrieval_base_search",
+        action=expanded_retrieval,
+    )
+    graph.add_node(
+        node="format_raw_search_results",
+        action=format_raw_search_results,
+    )
+
+    ### Add edges ###
+
+    graph.add_edge(start_key=START, end_key="generate_raw_search_data")
+
+    graph.add_edge(
+        start_key="generate_raw_search_data",
+        end_key="expanded_retrieval_base_search",
+    )
+    graph.add_edge(
+        start_key="expanded_retrieval_base_search",
+        end_key="format_raw_search_results",
+    )
+
+    # graph.add_edge(
+    #     start_key="expanded_retrieval_base_search",
+    #     end_key=END,
+    # )
+
+    graph.add_edge(
+        start_key="format_raw_search_results",
+        end_key=END,
+    )
+
+    return graph
+
+
+if __name__ == "__main__":
+    pass

backend/onyx/agents/agent_search/deep_search_a/base_raw_search/models.py

@@ -0,0 +1,20 @@
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryResult
+from onyx.context.search.models import InferenceSection
+
+### Models ###
+
+
+class AnswerRetrievalStats(BaseModel):
+    answer_retrieval_stats: dict[str, float | int]
+
+
+class QuestionAnswerResults(BaseModel):
+    question: str
+    answer: str
+    quality: str
+    expanded_retrieval_results: list[QueryResult]
+    documents: list[InferenceSection]
+    sub_question_retrieval_stats: list[AgentChunkStats]

backend/onyx/agents/agent_search/deep_search_a/base_raw_search/nodes/format_raw_search_results.py

@@ -0,0 +1,18 @@
+from onyx.agents.agent_search.deep_search_a.base_raw_search.states import (
+    BaseRawSearchOutput,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    ExpandedRetrievalOutput,
+)
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def format_raw_search_results(state: ExpandedRetrievalOutput) -> BaseRawSearchOutput:
+    logger.debug("format_raw_search_results")
+    return BaseRawSearchOutput(
+        base_expanded_retrieval_result=state.expanded_retrieval_result,
+        # base_retrieval_results=[state.expanded_retrieval_result],
+        # base_search_documents=[],
+    )

backend/onyx/agents/agent_search/deep_search_a/base_raw_search/nodes/generate_raw_search_data.py

@@ -3,25 +3,22 @@ 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 (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     ExpandedRetrievalInput,
 )
-from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.models import AgentSearchConfig
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 
 
-def format_orig_question_search_input(
+def generate_raw_search_data(
     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"])
+    agent_a_config = cast(AgentSearchConfig, config["metadata"]["config"])
     return ExpandedRetrievalInput(
-        question=graph_config.inputs.search_request.query,
+        question=agent_a_config.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_a/base_raw_search/states.py

@@ -0,0 +1,43 @@
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.models import (
+    ExpandedRetrievalResult,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+
+
+## Update States
+
+
+## Graph Input State
+
+
+class BaseRawSearchInput(ExpandedRetrievalInput):
+    pass
+
+
+## Graph Output State
+
+
+class BaseRawSearchOutput(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.
+    """
+
+    # base_search_documents: Annotated[list[InferenceSection], dedup_inference_sections]
+    # base_retrieval_results: Annotated[list[ExpandedRetrievalResult], add]
+    base_expanded_retrieval_result: ExpandedRetrievalResult = ExpandedRetrievalResult()
+
+
+## Graph State
+
+
+class BaseRawSearchState(
+    BaseRawSearchInput,
+    BaseRawSearchOutput,
+):
+    pass

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/edges.py

@@ -4,32 +4,27 @@ 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 (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     ExpandedRetrievalState,
 )
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     RetrievalInput,
 )
-from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.models import AgentSearchConfig
 
 
 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
+    agent_a_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    question = state.question if state.question else agent_a_config.search_request.query
+
+    query_expansions = (
+        state.expanded_queries if state.expanded_queries else [] + [question]
     )
-
-    query_expansions = state.expanded_queries + [question]
-
     return [
         Send(
-            "retrieve_documents",
+            "doc_retrieval",
             RetrievalInput(
                 query_to_retrieve=query,
                 question=question,

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/graph_builder.py

@@ -0,0 +1,147 @@
+from langgraph.graph import END
+from langgraph.graph import START
+from langgraph.graph import StateGraph
+
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.edges import (
+    parallel_retrieval_edge,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.nodes.doc_reranking import (
+    doc_reranking,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.nodes.doc_retrieval import (
+    doc_retrieval,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.nodes.doc_verification import (
+    doc_verification,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.nodes.dummy import (
+    dummy,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.nodes.expand_queries import (
+    expand_queries,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.nodes.format_results import (
+    format_results,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.nodes.verification_kickoff import (
+    verification_kickoff,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    ExpandedRetrievalInput,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    ExpandedRetrievalOutput,
+)
+from onyx.agents.agent_search.deep_search_a.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:
+    graph = StateGraph(
+        state_schema=ExpandedRetrievalState,
+        input=ExpandedRetrievalInput,
+        output=ExpandedRetrievalOutput,
+    )
+
+    ### Add nodes ###
+
+    graph.add_node(
+        node="expand_queries",
+        action=expand_queries,
+    )
+
+    graph.add_node(
+        node="dummy",
+        action=dummy,
+    )
+
+    graph.add_node(
+        node="doc_retrieval",
+        action=doc_retrieval,
+    )
+    graph.add_node(
+        node="verification_kickoff",
+        action=verification_kickoff,
+    )
+    graph.add_node(
+        node="doc_verification",
+        action=doc_verification,
+    )
+    graph.add_node(
+        node="doc_reranking",
+        action=doc_reranking,
+    )
+    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="dummy",
+    )
+
+    graph.add_conditional_edges(
+        source="dummy",
+        path=parallel_retrieval_edge,
+        path_map=["doc_retrieval"],
+    )
+    graph.add_edge(
+        start_key="doc_retrieval",
+        end_key="verification_kickoff",
+    )
+    graph.add_edge(
+        start_key="doc_verification",
+        end_key="doc_reranking",
+    )
+    graph.add_edge(
+        start_key="doc_reranking",
+        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:
+        agent_a_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": agent_a_config}},
+            # debug=True,
+            subgraphs=True,
+        ):
+            logger.debug(thing)

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/models.py

@@ -0,0 +1,12 @@
+from pydantic import BaseModel
+
+from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkStats
+from onyx.agents.agent_search.shared_graph_utils.models import QueryResult
+from onyx.context.search.models import InferenceSection
+
+
+class ExpandedRetrievalResult(BaseModel):
+    expanded_queries_results: list[QueryResult] = []
+    all_documents: list[InferenceSection] = []
+    context_documents: list[InferenceSection] = []
+    sub_question_retrieval_stats: AgentChunkStats = AgentChunkStats()

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/nodes/doc_reranking.py

@@ -0,0 +1,74 @@
+from datetime import datetime
+from typing import cast
+
+from langchain_core.runnables.config import RunnableConfig
+
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.operations import logger
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    DocRerankingUpdate,
+)
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
+    ExpandedRetrievalState,
+)
+from onyx.agents.agent_search.models import AgentSearchConfig
+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.configs.dev_configs import AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS
+from onyx.configs.dev_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 doc_reranking(
+    state: ExpandedRetrievalState, config: RunnableConfig
+) -> DocRerankingUpdate:
+    now_start = datetime.now()
+    verified_documents = state.verified_documents
+
+    # Rerank post retrieval and verification. First, create a search query
+    # then create the list of reranked sections
+
+    agent_a_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    question = state.question if state.question else agent_a_config.search_request.query
+    with get_session_context_manager() as db_session:
+        _search_query = retrieval_preprocessing(
+            search_request=SearchRequest(query=question),
+            user=agent_a_config.search_tool.user,  # bit of a hack
+            llm=agent_a_config.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
+    ):
+        reranked_documents = rerank_sections(
+            _search_query,
+            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={})
+
+    # TODO: stream deduped docs here, or decide to use search tool ranking/verification
+    now_end = datetime.now()
+    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=[
+            f"{now_end} -- Expanded Retrieval - Reranking - Time taken: {now_end - now_start}"
+        ],
+    )

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/nodes/doc_retrieval.py

@@ -3,23 +3,18 @@ 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 (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.operations import logger
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     DocRetrievalUpdate,
 )
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     RetrievalInput,
 )
-from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.models import AgentSearchConfig
 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.agents.agent_search.shared_graph_utils.models import QueryResult
+from onyx.configs.dev_configs import AGENT_MAX_QUERY_RETRIEVAL_RESULTS
+from onyx.configs.dev_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
@@ -29,47 +24,42 @@ from onyx.tools.tool_implementations.search.search_tool import (
 from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
 
 
-def retrieve_documents(
-    state: RetrievalInput, config: RunnableConfig
-) -> DocRetrievalUpdate:
+def doc_retrieval(state: RetrievalInput, config: RunnableConfig) -> DocRetrievalUpdate:
     """
-    LangGraph node to retrieve documents from the search tool.
+    Retrieve documents
+
+    Args:
+        state (RetrievalInput): Primary state + the query to retrieve
+        config (RunnableConfig): Configuration containing ProSearchConfig
+
+    Updates:
+        expanded_retrieval_results: list[ExpandedRetrievalResult]
+        retrieved_documents: list[InferenceSection]
     """
-    node_start_time = datetime.now()
+    now_start = datetime.now()
     query_to_retrieve = state.query_to_retrieve
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    search_tool = graph_config.tooling.search_tool
+    agent_a_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    search_tool = agent_a_config.search_tool
 
     retrieved_docs: list[InferenceSection] = []
     if not query_to_retrieve.strip():
         logger.warning("Empty query, skipping retrieval")
-
+        now_end = datetime.now()
         return DocRetrievalUpdate(
-            query_retrieval_results=[],
+            expanded_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",
-                )
+                f"{now_end} -- Expanded Retrieval - Retrieval - Empty Query - Time taken: {now_end - now_start}"
             ],
         )
 
     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:
@@ -83,9 +73,13 @@ def retrieve_documents(
                 break
 
     retrieved_docs = retrieved_docs[:AGENT_MAX_QUERY_RETRIEVAL_RESULTS]
+    pre_rerank_docs = retrieved_docs
+    if search_tool.search_pipeline is not None:
+        pre_rerank_docs = (
+            search_tool.search_pipeline._retrieved_sections or retrieved_docs
+        )
 
     if AGENT_RETRIEVAL_STATS:
-        pre_rerank_docs = callback_container[0]
         fit_scores = get_fit_scores(
             pre_rerank_docs,
             retrieved_docs,
@@ -93,21 +87,17 @@ def retrieve_documents(
     else:
         fit_scores = None
 
-    expanded_retrieval_result = QueryRetrievalResult(
+    expanded_retrieval_result = QueryResult(
         query=query_to_retrieve,
-        retrieved_documents=retrieved_docs,
+        search_results=retrieved_docs,
         stats=fit_scores,
         query_info=query_info,
     )
-
+    now_end = datetime.now()
     return DocRetrievalUpdate(
-        query_retrieval_results=[expanded_retrieval_result],
+        expanded_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,
-            )
+            f"{now_end} -- Expanded Retrieval - Retrieval - Time taken: {now_end - now_start}"
         ],
     )

backend/onyx/agents/agent_search/deep_search_a/expanded_retrieval/nodes/doc_verification.py

@@ -3,26 +3,24 @@ 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 (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     DocVerificationInput,
 )
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
+from onyx.agents.agent_search.deep_search_a.expanded_retrieval.states import (
     DocVerificationUpdate,
 )
-from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.models import AgentSearchConfig
 from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
     trim_prompt_piece,
 )
-from onyx.prompts.agent_search import (
-    DOCUMENT_VERIFICATION_PROMPT,
-)
+from onyx.agents.agent_search.shared_graph_utils.prompts import VERIFIER_PROMPT
 
 
-def verify_documents(
+def doc_verification(
     state: DocVerificationInput, config: RunnableConfig
 ) -> DocVerificationUpdate:
     """
-    LangGraph node to check whether the document is relevant for the original user question
+    Check whether the document is relevant for the original user question
 
     Args:
         state (DocVerificationInput): The current state
@@ -33,19 +31,19 @@ def verify_documents(
     """
 
     question = state.question
-    retrieved_document_to_verify = state.retrieved_document_to_verify
-    document_content = retrieved_document_to_verify.combined_content
+    doc_to_verify = state.doc_to_verify
+    document_content = doc_to_verify.combined_content
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    fast_llm = graph_config.tooling.fast_llm
+    agent_a_config = cast(AgentSearchConfig, config["metadata"]["config"])
+    fast_llm = agent_a_config.fast_llm
 
     document_content = trim_prompt_piece(
-        fast_llm.config, document_content, DOCUMENT_VERIFICATION_PROMPT + question
+        fast_llm.config, document_content, VERIFIER_PROMPT + question
     )
 
     msg = [
         HumanMessage(
-            content=DOCUMENT_VERIFICATION_PROMPT.format(
+            content=VERIFIER_PROMPT.format(
                 question=question, document_content=document_content
             )
         )
@@ -55,7 +53,7 @@ def verify_documents(
 
     verified_documents = []
     if isinstance(response.content, str) and "yes" in response.content.lower():
-        verified_documents.append(retrieved_document_to_verify)
+        verified_documents.append(doc_to_verify)
 
     return DocVerificationUpdate(
         verified_documents=verified_documents,