feat: 1.12.1初步合并

Merge upstream release cd03e0a (hotfix/1.12.1-fix.0) into main

# Conflicts:
#	api/.env.example
#	api/controllers/service_api/app/annotation.py
#	api/controllers/service_api/app/completion.py
#	api/controllers/service_api/app/conversation.py
#	api/controllers/service_api/app/message.py
#	api/core/file/file_manager.py
#	api/core/rag/datasource/retrieval_service.py
#	api/extensions/ext_celery.py
#	api/libs/gmpy2_pkcs10aep_cipher.py
#	api/uv.lock
#	web/pnpm-lock.yaml
#	web/service/client.ts

.agents/skills/component-refactoring/SKILL.md

@@ -480,4 +480,4 @@ const useButtonState = () => {
 ### Related Skills
 
 - `frontend-testing` - For testing refactored components
-- `web/testing/testing.md` - Testing specification
+- `web/docs/test.md` - Testing specification

.agents/skills/frontend-testing/SKILL.md

@@ -7,7 +7,7 @@ description: Generate Vitest + React Testing Library tests for Dify frontend com
 
 This skill enables Claude to generate high-quality, comprehensive frontend tests for the Dify project following established conventions and best practices.
 
-> **⚠️ Authoritative Source**: This skill is derived from `web/testing/testing.md`. Use Vitest mock/timer APIs (`vi.*`).
+> **⚠️ Authoritative Source**: This skill is derived from `web/docs/test.md`. Use Vitest mock/timer APIs (`vi.*`).
 
 ## When to Apply This Skill
 
@@ -309,7 +309,7 @@ For more detailed information, refer to:
 
 ### Primary Specification (MUST follow)
 
-- **`web/testing/testing.md`** - The canonical testing specification. This skill is derived from this document.
+- **`web/docs/test.md`** - The canonical testing specification. This skill is derived from this document.
 
 ### Reference Examples in Codebase
 

.agents/skills/frontend-testing/references/workflow.md

@@ -4,7 +4,7 @@ This guide defines the workflow for generating tests, especially for complex com
 
 ## Scope Clarification
 
-This guide addresses **multi-file workflow** (how to process multiple test files). For coverage requirements within a single test file, see `web/testing/testing.md` § Coverage Goals.
+This guide addresses **multi-file workflow** (how to process multiple test files). For coverage requirements within a single test file, see `web/docs/test.md` § Coverage Goals.
 
 | Scope | Rule |
 |-------|------|

.github/CODEOWNERS

@@ -9,6 +9,9 @@
 # CODEOWNERS file
 /.github/CODEOWNERS @laipz8200 @crazywoola
 
+# Agents
+/.agents/skills/ @hyoban
+
 # Docs
 /docs/ @crazywoola
 

.github/workflows/api-tests.yml

@@ -72,6 +72,7 @@ jobs:
           OPENDAL_FS_ROOT: /tmp/dify-storage
         run: |
           uv run --project api pytest \
+            -n auto \
             --timeout "${PYTEST_TIMEOUT:-180}" \
             api/tests/integration_tests/workflow \
             api/tests/integration_tests/tools \

.github/workflows/style.yml

@@ -47,13 +47,9 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: uv run --directory api --dev lint-imports
 
-      - name: Run Basedpyright Checks
+      - name: Run Type Checks
         if: steps.changed-files.outputs.any_changed == 'true'
-        run: dev/basedpyright-check
-
-      - name: Run Mypy Type Checks
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: uv --directory api run mypy --exclude-gitignore --exclude 'tests/' --exclude 'migrations/' --check-untyped-defs --disable-error-code=import-untyped .
+        run: make type-check
 
       - name: Dotenv check
         if: steps.changed-files.outputs.any_changed == 'true'

AGENTS.md~upstream_main

@@ -7,7 +7,7 @@ Dify is an open-source platform for developing LLM applications with an intuitiv
 The codebase is split into:
 
 - **Backend API** (`/api`): Python Flask application organized with Domain-Driven Design
-- **Frontend Web** (`/web`): Next.js 15 application using TypeScript and React 19
+- **Frontend Web** (`/web`): Next.js application using TypeScript and React
 - **Docker deployment** (`/docker`): Containerized deployment configurations
 
 ## Backend Workflow
@@ -18,36 +18,7 @@ The codebase is split into:
 
 ## Frontend Workflow
 
-```bash
-cd web
-pnpm lint:fix
-pnpm type-check:tsgo
-pnpm test
-```
-
-### Frontend Linting
-
-ESLint is used for frontend code quality. Available commands:
-
-```bash
-# Lint all files (report only)
-pnpm lint
-
-# Lint and auto-fix issues
-pnpm lint:fix
-
-# Lint specific files or directories
-pnpm lint:fix app/components/base/button/
-pnpm lint:fix app/components/base/button/index.tsx
-
-# Lint quietly (errors only, no warnings)
-pnpm lint:quiet
-
-# Check code complexity
-pnpm lint:complexity
-```
-
-**Important**: Always run `pnpm lint:fix` before committing. The pre-commit hook runs `lint-staged` which only lints staged files.
+- Read `web/AGENTS.md` for details
 
 ## Testing & Quality Practices
 

CONTRIBUTING.md

@@ -77,7 +77,7 @@ How we prioritize:
 
 For setting up the frontend service, please refer to our comprehensive [guide](https://github.com/langgenius/dify/blob/main/web/README.md) in the `web/README.md` file. This document provides detailed instructions to help you set up the frontend environment properly.
 
-**Testing**: All React components must have comprehensive test coverage. See [web/testing/testing.md](https://github.com/langgenius/dify/blob/main/web/testing/testing.md) for the canonical frontend testing guidelines and follow every requirement described there.
+**Testing**: All React components must have comprehensive test coverage. See [web/docs/test.md](https://github.com/langgenius/dify/blob/main/web/docs/test.md) for the canonical frontend testing guidelines and follow every requirement described there.
 
 #### Backend
 

Makefile

@@ -68,9 +68,11 @@ lint:
 	@echo "✅ Linting complete"
 
 type-check:
-	@echo "📝 Running type check with basedpyright..."
-	@uv run --directory api --dev basedpyright
-	@echo "✅ Type check complete"
+	@echo "📝 Running type checks (basedpyright + mypy + ty)..."
+	@./dev/basedpyright-check $(PATH_TO_CHECK)
+	@uv --directory api run mypy --exclude-gitignore --exclude 'tests/' --exclude 'migrations/' --check-untyped-defs --disable-error-code=import-untyped .
+	@cd api && uv run ty check
+	@echo "✅ Type checks complete"
 
 test:
 	@echo "🧪 Running backend unit tests..."
@@ -78,7 +80,7 @@ test:
 		echo "Target: $(TARGET_TESTS)"; \
 		uv run --project api --dev pytest $(TARGET_TESTS); \
 	else \
-		uv run --project api --dev dev/pytest/pytest_unit_tests.sh; \
+		PYTEST_XDIST_ARGS="-n auto" uv run --project api --dev dev/pytest/pytest_unit_tests.sh; \
 	fi
 	@echo "✅ Tests complete"
 
@@ -130,7 +132,7 @@ help:
 	@echo "  make format         - Format code with ruff"
 	@echo "  make check          - Check code with ruff"
 	@echo "  make lint           - Format, fix, and lint code (ruff, imports, dotenv)"
-	@echo "  make type-check     - Run type checking with basedpyright"
+	@echo "  make type-check     - Run type checks (basedpyright, mypy, ty)"
 	@echo "  make test           - Run backend unit tests (or TARGET_TESTS=./api/tests/<target_tests>)"
 	@echo ""
 	@echo "Docker Build Targets:"

api/.env.example

@@ -617,6 +617,7 @@ PLUGIN_DAEMON_URL=http://127.0.0.1:5002
 PLUGIN_REMOTE_INSTALL_PORT=5003
 PLUGIN_REMOTE_INSTALL_HOST=localhost
 PLUGIN_MAX_PACKAGE_SIZE=15728640
+PLUGIN_MODEL_SCHEMA_CACHE_TTL=3600
 INNER_API_KEY_FOR_PLUGIN=QaHbTe77CtuXmsfyhR7+vRjI/+XbV1AaFy691iy+kGDv2Jvy0/eAh8Y1
 
 # Marketplace configuration

api/.importlinter

@@ -227,6 +227,9 @@ ignore_imports =
     core.workflow.nodes.knowledge_index.entities -> core.rag.retrieval.retrieval_methods
     core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.retrieval.retrieval_methods
     core.workflow.nodes.knowledge_index.knowledge_index_node -> models.dataset
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> services.summary_index_service
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> tasks.generate_summary_index_task
+    core.workflow.nodes.knowledge_index.knowledge_index_node -> core.rag.index_processor.processor.paragraph_index_processor
     core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node -> core.rag.retrieval.retrieval_methods
     core.workflow.nodes.llm.node -> models.dataset
     core.workflow.nodes.agent.agent_node -> core.tools.utils.message_transformer
@@ -300,6 +303,58 @@ ignore_imports =
     core.workflow.nodes.agent.agent_node -> services
     core.workflow.nodes.tool.tool_node -> services
 
+[importlinter:contract:model-runtime-no-internal-imports]
+name = Model Runtime Internal Imports
+type = forbidden
+source_modules =
+    core.model_runtime
+forbidden_modules =
+    configs
+    controllers
+    extensions
+    models
+    services
+    tasks
+    core.agent
+    core.app
+    core.base
+    core.callback_handler
+    core.datasource
+    core.db
+    core.entities
+    core.errors
+    core.extension
+    core.external_data_tool
+    core.file
+    core.helper
+    core.hosting_configuration
+    core.indexing_runner
+    core.llm_generator
+    core.logging
+    core.mcp
+    core.memory
+    core.model_manager
+    core.moderation
+    core.ops
+    core.plugin
+    core.prompt
+    core.provider_manager
+    core.rag
+    core.repositories
+    core.schemas
+    core.tools
+    core.trigger
+    core.variables
+    core.workflow
+ignore_imports =
+    core.model_runtime.model_providers.__base.ai_model -> configs
+    core.model_runtime.model_providers.__base.ai_model -> extensions.ext_redis
+    core.model_runtime.model_providers.__base.large_language_model -> configs
+    core.model_runtime.model_providers.__base.text_embedding_model -> core.entities.embedding_type
+    core.model_runtime.model_providers.model_provider_factory -> configs
+    core.model_runtime.model_providers.model_provider_factory -> extensions.ext_redis
+    core.model_runtime.model_providers.model_provider_factory -> models.provider_ids
+
 [importlinter:contract:rsc]
 name = RSC
 type = layers

api/.ruff.toml

@@ -53,6 +53,7 @@ select = [
     "S301", # suspicious-pickle-usage, disallow use of `pickle` and its wrappers.
     "S302", # suspicious-marshal-usage, disallow use of `marshal` module
     "S311", # suspicious-non-cryptographic-random-usage,
+    "TID",   # flake8-tidy-imports
 
 ]
 
@@ -88,6 +89,7 @@ ignore = [
     "SIM113",  # enumerate-for-loop
     "SIM117",  # multiple-with-statements
     "SIM210",  # if-expr-with-true-false
+    "TID252",  # allow relative imports from parent modules
 ]
 
 [lint.per-file-ignores]
@@ -109,10 +111,20 @@ ignore = [
     "S110", # allow ignoring exceptions in tests code (currently)
 
 ]
+"controllers/console/explore/trial.py" = ["TID251"]
+"controllers/console/human_input_form.py" = ["TID251"]
+"controllers/web/human_input_form.py" = ["TID251"]
 
 [lint.pyflakes]
 allowed-unused-imports = [
-    "_pytest.monkeypatch",
     "tests.integration_tests",
     "tests.unit_tests",
 ]
+
+[lint.flake8-tidy-imports]
+
+[lint.flake8-tidy-imports.banned-api."flask_restx.reqparse"]
+msg = "Use Pydantic payload/query models instead of reqparse."
+
+[lint.flake8-tidy-imports.banned-api."flask_restx.reqparse.RequestParser"]
+msg = "Use Pydantic payload/query models instead of reqparse."

api/app.py

@@ -1,4 +1,12 @@
+from __future__ import annotations
+
 import sys
+from typing import TYPE_CHECKING, cast
+
+if TYPE_CHECKING:
+    from celery import Celery
+
+    celery: Celery
 
 
 def is_db_command() -> bool:
@@ -23,7 +31,7 @@ else:
     from app_factory import create_app
 
     app = create_app()
-    celery = app.extensions["celery"]
+    celery = cast("Celery", app.extensions["celery"])
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=5001)

api/app_factory.py

@@ -149,7 +149,7 @@ def initialize_extensions(app: DifyApp):
             logger.info("Loaded %s (%s ms)", short_name, round((end_time - start_time) * 1000, 2))
 
 
-def create_migrations_app():
+def create_migrations_app() -> DifyApp:
     app = create_flask_app_with_configs()
     from extensions import ext_database, ext_migrate
 

api/commands.py

@@ -1450,54 +1450,58 @@ def clear_orphaned_file_records(force: bool):
         all_ids_in_tables = []
         for ids_table in ids_tables:
             query = ""
-            if ids_table["type"] == "uuid":
-                click.echo(
-                    click.style(
-                        f"- Listing file ids in column {ids_table['column']} in table {ids_table['table']}", fg="white"
+            match ids_table["type"]:
+                case "uuid":
+                    click.echo(
+                        click.style(
+                            f"- Listing file ids in column {ids_table['column']} in table {ids_table['table']}",
+                            fg="white",
+                        )
                     )
-                )
-                query = (
-                    f"SELECT {ids_table['column']} FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
-                )
-                with db.engine.begin() as conn:
-                    rs = conn.execute(sa.text(query))
-                for i in rs:
-                    all_ids_in_tables.append({"table": ids_table["table"], "id": str(i[0])})
-            elif ids_table["type"] == "text":
-                click.echo(
-                    click.style(
-                        f"- Listing file-id-like strings in column {ids_table['column']} in table {ids_table['table']}",
-                        fg="white",
+                    c = ids_table["column"]
+                    query = f"SELECT {c} FROM {ids_table['table']} WHERE {c} IS NOT NULL"
+                    with db.engine.begin() as conn:
+                        rs = conn.execute(sa.text(query))
+                    for i in rs:
+                        all_ids_in_tables.append({"table": ids_table["table"], "id": str(i[0])})
+                case "text":
+                    t = ids_table["table"]
+                    click.echo(
+                        click.style(
+                            f"- Listing file-id-like strings in column {ids_table['column']} in table {t}",
+                            fg="white",
+                        )
                     )
-                )
-                query = (
-                    f"SELECT regexp_matches({ids_table['column']}, '{guid_regexp}', 'g') AS extracted_id "
-                    f"FROM {ids_table['table']}"
-                )
-                with db.engine.begin() as conn:
-                    rs = conn.execute(sa.text(query))
-                for i in rs:
-                    for j in i[0]:
-                        all_ids_in_tables.append({"table": ids_table["table"], "id": j})
-            elif ids_table["type"] == "json":
-                click.echo(
-                    click.style(
-                        (
-                            f"- Listing file-id-like JSON string in column {ids_table['column']} "
-                            f"in table {ids_table['table']}"
-                        ),
-                        fg="white",
+                    query = (
+                        f"SELECT regexp_matches({ids_table['column']}, '{guid_regexp}', 'g') AS extracted_id "
+                        f"FROM {ids_table['table']}"
                     )
-                )
-                query = (
-                    f"SELECT regexp_matches({ids_table['column']}::text, '{guid_regexp}', 'g') AS extracted_id "
-                    f"FROM {ids_table['table']}"
-                )
-                with db.engine.begin() as conn:
-                    rs = conn.execute(sa.text(query))
-                for i in rs:
-                    for j in i[0]:
-                        all_ids_in_tables.append({"table": ids_table["table"], "id": j})
+                    with db.engine.begin() as conn:
+                        rs = conn.execute(sa.text(query))
+                    for i in rs:
+                        for j in i[0]:
+                            all_ids_in_tables.append({"table": ids_table["table"], "id": j})
+                case "json":
+                    click.echo(
+                        click.style(
+                            (
+                                f"- Listing file-id-like JSON string in column {ids_table['column']} "
+                                f"in table {ids_table['table']}"
+                            ),
+                            fg="white",
+                        )
+                    )
+                    query = (
+                        f"SELECT regexp_matches({ids_table['column']}::text, '{guid_regexp}', 'g') AS extracted_id "
+                        f"FROM {ids_table['table']}"
+                    )
+                    with db.engine.begin() as conn:
+                        rs = conn.execute(sa.text(query))
+                    for i in rs:
+                        for j in i[0]:
+                            all_ids_in_tables.append({"table": ids_table["table"], "id": j})
+                case _:
+                    pass
         click.echo(click.style(f"Found {len(all_ids_in_tables)} file ids in tables.", fg="white"))
 
     except Exception as e:
@@ -1737,59 +1741,18 @@ def file_usage(
                 if src_filter != src:
                     continue
 
-        if ids_table["type"] == "uuid":
-            # Direct UUID match
-            query = (
-                f"SELECT {ids_table['pk_column']}, {ids_table['column']} "
-                f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
-            )
-            with db.engine.begin() as conn:
-                rs = conn.execute(sa.text(query))
-                for row in rs:
-                    record_id = str(row[0])
-                    ref_file_id = str(row[1])
-                    if ref_file_id not in file_key_map:
-                        continue
-                    storage_key = file_key_map[ref_file_id]
-
-                    # Apply filters
-                    if file_id and ref_file_id != file_id:
-                        continue
-                    if key and not storage_key.endswith(key):
-                        continue
-
-                    # Only collect items within the requested page range
-                    if offset <= total_count < offset + limit:
-                        paginated_usages.append(
-                            {
-                                "src": f"{ids_table['table']}.{ids_table['column']}",
-                                "record_id": record_id,
-                                "file_id": ref_file_id,
-                                "key": storage_key,
-                            }
-                        )
-                    total_count += 1
-
-        elif ids_table["type"] in ("text", "json"):
-            # Extract UUIDs from text/json content
-            column_cast = f"{ids_table['column']}::text" if ids_table["type"] == "json" else ids_table["column"]
-            query = (
-                f"SELECT {ids_table['pk_column']}, {column_cast} "
-                f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
-            )
-            with db.engine.begin() as conn:
-                rs = conn.execute(sa.text(query))
-                for row in rs:
-                    record_id = str(row[0])
-                    content = str(row[1])
-
-                    # Find all UUIDs in the content
-                    import re
-
-                    uuid_pattern = re.compile(guid_regexp, re.IGNORECASE)
-                    matches = uuid_pattern.findall(content)
-
-                    for ref_file_id in matches:
+        match ids_table["type"]:
+            case "uuid":
+                # Direct UUID match
+                query = (
+                    f"SELECT {ids_table['pk_column']}, {ids_table['column']} "
+                    f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
+                )
+                with db.engine.begin() as conn:
+                    rs = conn.execute(sa.text(query))
+                    for row in rs:
+                        record_id = str(row[0])
+                        ref_file_id = str(row[1])
                         if ref_file_id not in file_key_map:
                             continue
                         storage_key = file_key_map[ref_file_id]
@@ -1812,6 +1775,50 @@ def file_usage(
                             )
                         total_count += 1
 
+            case "text" | "json":
+                # Extract UUIDs from text/json content
+                column_cast = f"{ids_table['column']}::text" if ids_table["type"] == "json" else ids_table["column"]
+                query = (
+                    f"SELECT {ids_table['pk_column']}, {column_cast} "
+                    f"FROM {ids_table['table']} WHERE {ids_table['column']} IS NOT NULL"
+                )
+                with db.engine.begin() as conn:
+                    rs = conn.execute(sa.text(query))
+                    for row in rs:
+                        record_id = str(row[0])
+                        content = str(row[1])
+
+                        # Find all UUIDs in the content
+                        import re
+
+                        uuid_pattern = re.compile(guid_regexp, re.IGNORECASE)
+                        matches = uuid_pattern.findall(content)
+
+                        for ref_file_id in matches:
+                            if ref_file_id not in file_key_map:
+                                continue
+                            storage_key = file_key_map[ref_file_id]
+
+                            # Apply filters
+                            if file_id and ref_file_id != file_id:
+                                continue
+                            if key and not storage_key.endswith(key):
+                                continue
+
+                            # Only collect items within the requested page range
+                            if offset <= total_count < offset + limit:
+                                paginated_usages.append(
+                                    {
+                                        "src": f"{ids_table['table']}.{ids_table['column']}",
+                                        "record_id": record_id,
+                                        "file_id": ref_file_id,
+                                        "key": storage_key,
+                                    }
+                                )
+                            total_count += 1
+            case _:
+                pass
+
     # Output results
     if output_json:
         result = {

api/configs/feature/__init__.py

@@ -243,6 +243,11 @@ class PluginConfig(BaseSettings):
         default=15728640 * 12,
     )
 
+    PLUGIN_MODEL_SCHEMA_CACHE_TTL: PositiveInt = Field(
+        description="TTL in seconds for caching plugin model schemas in Redis",
+        default=60 * 60,
+    )
+
 
 class MarketplaceConfig(BaseSettings):
     """

api/contexts/__init__.py

@@ -6,7 +6,6 @@ from contexts.wrapper import RecyclableContextVar
 
 if TYPE_CHECKING:
     from core.datasource.__base.datasource_provider import DatasourcePluginProviderController
-    from core.model_runtime.entities.model_entities import AIModelEntity
     from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
     from core.tools.plugin_tool.provider import PluginToolProviderController
     from core.trigger.provider import PluginTriggerProviderController
@@ -29,12 +28,6 @@ plugin_model_providers_lock: RecyclableContextVar[Lock] = RecyclableContextVar(
     ContextVar("plugin_model_providers_lock")
 )
 
-plugin_model_schema_lock: RecyclableContextVar[Lock] = RecyclableContextVar(ContextVar("plugin_model_schema_lock"))
-
-plugin_model_schemas: RecyclableContextVar[dict[str, "AIModelEntity"]] = RecyclableContextVar(
-    ContextVar("plugin_model_schemas")
-)
-
 datasource_plugin_providers: RecyclableContextVar[dict[str, "DatasourcePluginProviderController"]] = (
     RecyclableContextVar(ContextVar("datasource_plugin_providers"))
 )

api/controllers/console/admin.py

@@ -243,15 +243,13 @@ class InsertExploreBannerApi(Resource):
     def post(self):
         payload = InsertExploreBannerPayload.model_validate(console_ns.payload)
 
-        content = {
-            "category": payload.category,
-            "title": payload.title,
-            "description": payload.description,
-            "img-src": payload.img_src,
-        }
-
         banner = ExporleBanner(
-            content=content,
+            content={
+                "category": payload.category,
+                "title": payload.title,
+                "description": payload.description,
+                "img-src": payload.img_src,
+            },
             link=payload.link,
             sort=payload.sort,
             language=payload.language,

api/controllers/console/app/annotation.py

@@ -1,10 +1,11 @@
 from typing import Any, Literal
 
 from flask import abort, make_response, request
-from flask_restx import Resource, fields, marshal, marshal_with
-from pydantic import BaseModel, Field, field_validator
+from flask_restx import Resource
+from pydantic import BaseModel, Field, TypeAdapter, field_validator
 
 from controllers.common.errors import NoFileUploadedError, TooManyFilesError
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.wraps import (
     account_initialization_required,
@@ -16,9 +17,11 @@ from controllers.console.wraps import (
 )
 from extensions.ext_redis import redis_client
 from fields.annotation_fields import (
-    annotation_fields,
-    annotation_hit_history_fields,
-    build_annotation_model,
+    Annotation,
+    AnnotationExportList,
+    AnnotationHitHistory,
+    AnnotationHitHistoryList,
+    AnnotationList,
 )
 from libs.helper import uuid_value
 from libs.login import login_required
@@ -89,6 +92,14 @@ reg(CreateAnnotationPayload)
 reg(UpdateAnnotationPayload)
 reg(AnnotationReplyStatusQuery)
 reg(AnnotationFilePayload)
+register_schema_models(
+    console_ns,
+    Annotation,
+    AnnotationList,
+    AnnotationExportList,
+    AnnotationHitHistory,
+    AnnotationHitHistoryList,
+)
 
 
 @console_ns.route("/apps/<uuid:app_id>/annotation-reply/<string:action>")
@@ -107,10 +118,11 @@ class AnnotationReplyActionApi(Resource):
     def post(self, app_id, action: Literal["enable", "disable"]):
         app_id = str(app_id)
         args = AnnotationReplyPayload.model_validate(console_ns.payload)
-        if action == "enable":
-            result = AppAnnotationService.enable_app_annotation(args.model_dump(), app_id)
-        elif action == "disable":
-            result = AppAnnotationService.disable_app_annotation(app_id)
+        match action:
+            case "enable":
+                result = AppAnnotationService.enable_app_annotation(args.model_dump(), app_id)
+            case "disable":
+                result = AppAnnotationService.disable_app_annotation(app_id)
         return result, 200
 
 
@@ -201,33 +213,33 @@ class AnnotationApi(Resource):
 
         app_id = str(app_id)
         annotation_list, total = AppAnnotationService.get_annotation_list_by_app_id(app_id, page, limit, keyword)
-        response = {
-            "data": marshal(annotation_list, annotation_fields),
-            "has_more": len(annotation_list) == limit,
-            "limit": limit,
-            "total": total,
-            "page": page,
-        }
-        return response, 200
+        annotation_models = TypeAdapter(list[Annotation]).validate_python(annotation_list, from_attributes=True)
+        response = AnnotationList(
+            data=annotation_models,
+            has_more=len(annotation_list) == limit,
+            limit=limit,
+            total=total,
+            page=page,
+        )
+        return response.model_dump(mode="json"), 200
 
     @console_ns.doc("create_annotation")
     @console_ns.doc(description="Create a new annotation for an app")
     @console_ns.doc(params={"app_id": "Application ID"})
     @console_ns.expect(console_ns.models[CreateAnnotationPayload.__name__])
-    @console_ns.response(201, "Annotation created successfully", build_annotation_model(console_ns))
+    @console_ns.response(201, "Annotation created successfully", console_ns.models[Annotation.__name__])
     @console_ns.response(403, "Insufficient permissions")
     @setup_required
     @login_required
     @account_initialization_required
     @cloud_edition_billing_resource_check("annotation")
-    @marshal_with(annotation_fields)
     @edit_permission_required
     def post(self, app_id):
         app_id = str(app_id)
         args = CreateAnnotationPayload.model_validate(console_ns.payload)
         data = args.model_dump(exclude_none=True)
         annotation = AppAnnotationService.up_insert_app_annotation_from_message(data, app_id)
-        return annotation
+        return Annotation.model_validate(annotation, from_attributes=True).model_dump(mode="json")
 
     @setup_required
     @login_required
@@ -264,7 +276,7 @@ class AnnotationExportApi(Resource):
     @console_ns.response(
         200,
         "Annotations exported successfully",
-        console_ns.model("AnnotationList", {"data": fields.List(fields.Nested(build_annotation_model(console_ns)))}),
+        console_ns.models[AnnotationExportList.__name__],
     )
     @console_ns.response(403, "Insufficient permissions")
     @setup_required
@@ -274,7 +286,8 @@ class AnnotationExportApi(Resource):
     def get(self, app_id):
         app_id = str(app_id)
         annotation_list = AppAnnotationService.export_annotation_list_by_app_id(app_id)
-        response_data = {"data": marshal(annotation_list, annotation_fields)}
+        annotation_models = TypeAdapter(list[Annotation]).validate_python(annotation_list, from_attributes=True)
+        response_data = AnnotationExportList(data=annotation_models).model_dump(mode="json")
 
         # Create response with secure headers for CSV export
         response = make_response(response_data, 200)
@@ -289,7 +302,7 @@ class AnnotationUpdateDeleteApi(Resource):
     @console_ns.doc("update_delete_annotation")
     @console_ns.doc(description="Update or delete an annotation")
     @console_ns.doc(params={"app_id": "Application ID", "annotation_id": "Annotation ID"})
-    @console_ns.response(200, "Annotation updated successfully", build_annotation_model(console_ns))
+    @console_ns.response(200, "Annotation updated successfully", console_ns.models[Annotation.__name__])
     @console_ns.response(204, "Annotation deleted successfully")
     @console_ns.response(403, "Insufficient permissions")
     @console_ns.expect(console_ns.models[UpdateAnnotationPayload.__name__])
@@ -298,7 +311,6 @@ class AnnotationUpdateDeleteApi(Resource):
     @account_initialization_required
     @cloud_edition_billing_resource_check("annotation")
     @edit_permission_required
-    @marshal_with(annotation_fields)
     def post(self, app_id, annotation_id):
         app_id = str(app_id)
         annotation_id = str(annotation_id)
@@ -306,7 +318,7 @@ class AnnotationUpdateDeleteApi(Resource):
         annotation = AppAnnotationService.update_app_annotation_directly(
             args.model_dump(exclude_none=True), app_id, annotation_id
         )
-        return annotation
+        return Annotation.model_validate(annotation, from_attributes=True).model_dump(mode="json")
 
     @setup_required
     @login_required
@@ -414,14 +426,7 @@ class AnnotationHitHistoryListApi(Resource):
     @console_ns.response(
         200,
         "Hit histories retrieved successfully",
-        console_ns.model(
-            "AnnotationHitHistoryList",
-            {
-                "data": fields.List(
-                    fields.Nested(console_ns.model("AnnotationHitHistoryItem", annotation_hit_history_fields))
-                )
-            },
-        ),
+        console_ns.models[AnnotationHitHistoryList.__name__],
     )
     @console_ns.response(403, "Insufficient permissions")
     @setup_required
@@ -436,11 +441,14 @@ class AnnotationHitHistoryListApi(Resource):
         annotation_hit_history_list, total = AppAnnotationService.get_annotation_hit_histories(
             app_id, annotation_id, page, limit
         )
-        response = {
-            "data": marshal(annotation_hit_history_list, annotation_hit_history_fields),
-            "has_more": len(annotation_hit_history_list) == limit,
-            "limit": limit,
-            "total": total,
-            "page": page,
-        }
-        return response
+        history_models = TypeAdapter(list[AnnotationHitHistory]).validate_python(
+            annotation_hit_history_list, from_attributes=True
+        )
+        response = AnnotationHitHistoryList(
+            data=history_models,
+            has_more=len(annotation_hit_history_list) == limit,
+            limit=limit,
+            total=total,
+            page=page,
+        )
+        return response.model_dump(mode="json")

api/controllers/console/app/audio.py

@@ -6,6 +6,7 @@ from pydantic import BaseModel, Field
 from werkzeug.exceptions import InternalServerError
 
 import services
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.app.error import (
     AppUnavailableError,
@@ -33,7 +34,6 @@ from services.errors.audio import (
 )
 
 logger = logging.getLogger(__name__)
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
 class TextToSpeechPayload(BaseModel):
@@ -47,13 +47,11 @@ class TextToSpeechVoiceQuery(BaseModel):
     language: str = Field(..., description="Language code")
 
 
-console_ns.schema_model(
-    TextToSpeechPayload.__name__, TextToSpeechPayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0)
-)
-console_ns.schema_model(
-    TextToSpeechVoiceQuery.__name__,
-    TextToSpeechVoiceQuery.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
-)
+class AudioTranscriptResponse(BaseModel):
+    text: str = Field(description="Transcribed text from audio")
+
+
+register_schema_models(console_ns, AudioTranscriptResponse, TextToSpeechPayload, TextToSpeechVoiceQuery)
 
 
 @console_ns.route("/apps/<uuid:app_id>/audio-to-text")
@@ -64,7 +62,7 @@ class ChatMessageAudioApi(Resource):
     @console_ns.response(
         200,
         "Audio transcription successful",
-        console_ns.model("AudioTranscriptResponse", {"text": fields.String(description="Transcribed text from audio")}),
+        console_ns.models[AudioTranscriptResponse.__name__],
     )
     @console_ns.response(400, "Bad request - No audio uploaded or unsupported type")
     @console_ns.response(413, "Audio file too large")

api/controllers/console/app/conversation.py

@@ -508,16 +508,19 @@ class ChatConversationApi(Resource):
                 case "created_at" | "-created_at" | _:
                     query = query.where(Conversation.created_at <= end_datetime_utc)
 
-        if args.annotation_status == "annotated":
-            query = query.options(joinedload(Conversation.message_annotations)).join(  # type: ignore
-                MessageAnnotation, MessageAnnotation.conversation_id == Conversation.id
-            )
-        elif args.annotation_status == "not_annotated":
-            query = (
-                query.outerjoin(MessageAnnotation, MessageAnnotation.conversation_id == Conversation.id)
-                .group_by(Conversation.id)
-                .having(func.count(MessageAnnotation.id) == 0)
-            )
+        match args.annotation_status:
+            case "annotated":
+                query = query.options(joinedload(Conversation.message_annotations)).join(  # type: ignore
+                    MessageAnnotation, MessageAnnotation.conversation_id == Conversation.id
+                )
+            case "not_annotated":
+                query = (
+                    query.outerjoin(MessageAnnotation, MessageAnnotation.conversation_id == Conversation.id)
+                    .group_by(Conversation.id)
+                    .having(func.count(MessageAnnotation.id) == 0)
+                )
+            case "all":
+                pass
 
         if app_model.mode == AppMode.ADVANCED_CHAT:
             query = query.where(Conversation.invoke_from != InvokeFrom.DEBUGGER)

api/controllers/console/app/generator.py

@@ -1,5 +1,4 @@
 from collections.abc import Sequence
-from typing import Any
 
 from flask_restx import Resource
 from pydantic import BaseModel, Field
@@ -12,10 +11,12 @@ from controllers.console.app.error import (
     ProviderQuotaExceededError,
 )
 from controllers.console.wraps import account_initialization_required, setup_required
+from core.app.app_config.entities import ModelConfig
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.helper.code_executor.code_node_provider import CodeNodeProvider
 from core.helper.code_executor.javascript.javascript_code_provider import JavascriptCodeProvider
 from core.helper.code_executor.python3.python3_code_provider import Python3CodeProvider
+from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
 from core.llm_generator.llm_generator import LLMGenerator
 from core.model_runtime.errors.invoke import InvokeError
 from extensions.ext_database import db
@@ -26,28 +27,13 @@ from services.workflow_service import WorkflowService
 DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
-class RuleGeneratePayload(BaseModel):
-    instruction: str = Field(..., description="Rule generation instruction")
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
-    no_variable: bool = Field(default=False, description="Whether to exclude variables")
-
-
-class RuleCodeGeneratePayload(RuleGeneratePayload):
-    code_language: str = Field(default="javascript", description="Programming language for code generation")
-
-
-class RuleStructuredOutputPayload(BaseModel):
-    instruction: str = Field(..., description="Structured output generation instruction")
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
-
-
 class InstructionGeneratePayload(BaseModel):
     flow_id: str = Field(..., description="Workflow/Flow ID")
     node_id: str = Field(default="", description="Node ID for workflow context")
     current: str = Field(default="", description="Current instruction text")
     language: str = Field(default="javascript", description="Programming language (javascript/python)")
     instruction: str = Field(..., description="Instruction for generation")
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
+    model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
     ideal_output: str = Field(default="", description="Expected ideal output")
 
 
@@ -64,6 +50,7 @@ reg(RuleCodeGeneratePayload)
 reg(RuleStructuredOutputPayload)
 reg(InstructionGeneratePayload)
 reg(InstructionTemplatePayload)
+reg(ModelConfig)
 
 
 @console_ns.route("/rule-generate")
@@ -82,12 +69,7 @@ class RuleGenerateApi(Resource):
         _, current_tenant_id = current_account_with_tenant()
 
         try:
-            rules = LLMGenerator.generate_rule_config(
-                tenant_id=current_tenant_id,
-                instruction=args.instruction,
-                model_config=args.model_config_data,
-                no_variable=args.no_variable,
-            )
+            rules = LLMGenerator.generate_rule_config(tenant_id=current_tenant_id, args=args)
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
@@ -118,9 +100,7 @@ class RuleCodeGenerateApi(Resource):
         try:
             code_result = LLMGenerator.generate_code(
                 tenant_id=current_tenant_id,
-                instruction=args.instruction,
-                model_config=args.model_config_data,
-                code_language=args.code_language,
+                args=args,
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -152,8 +132,7 @@ class RuleStructuredOutputGenerateApi(Resource):
         try:
             structured_output = LLMGenerator.generate_structured_output(
                 tenant_id=current_tenant_id,
-                instruction=args.instruction,
-                model_config=args.model_config_data,
+                args=args,
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -204,23 +183,29 @@ class InstructionGenerateApi(Resource):
                     case "llm":
                         return LLMGenerator.generate_rule_config(
                             current_tenant_id,
-                            instruction=args.instruction,
-                            model_config=args.model_config_data,
-                            no_variable=True,
+                            args=RuleGeneratePayload(
+                                instruction=args.instruction,
+                                model_config=args.model_config_data,
+                                no_variable=True,
+                            ),
                         )
                     case "agent":
                         return LLMGenerator.generate_rule_config(
                             current_tenant_id,
-                            instruction=args.instruction,
-                            model_config=args.model_config_data,
-                            no_variable=True,
+                            args=RuleGeneratePayload(
+                                instruction=args.instruction,
+                                model_config=args.model_config_data,
+                                no_variable=True,
+                            ),
                         )
                     case "code":
                         return LLMGenerator.generate_code(
                             tenant_id=current_tenant_id,
-                            instruction=args.instruction,
-                            model_config=args.model_config_data,
-                            code_language=args.language,
+                            args=RuleCodeGeneratePayload(
+                                instruction=args.instruction,
+                                model_config=args.model_config_data,
+                                code_language=args.language,
+                            ),
                         )
                     case _:
                         return {"error": f"invalid node type: {node_type}"}

api/controllers/console/app/message.py

@@ -7,6 +7,7 @@ from pydantic import BaseModel, Field, field_validator
 from sqlalchemy import exists, select
 from werkzeug.exceptions import InternalServerError, NotFound
 
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.app.error import (
     CompletionRequestError,
@@ -35,7 +36,6 @@ from services.errors.message import MessageNotExistsError, SuggestedQuestionsAft
 from services.message_service import MessageService
 
 logger = logging.getLogger(__name__)
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
 
 
 class ChatMessagesQuery(BaseModel):
@@ -90,13 +90,22 @@ class FeedbackExportQuery(BaseModel):
         raise ValueError("has_comment must be a boolean value")
 
 
-def reg(cls: type[BaseModel]):
-    console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+class AnnotationCountResponse(BaseModel):
+    count: int = Field(description="Number of annotations")
 
 
-reg(ChatMessagesQuery)
-reg(MessageFeedbackPayload)
-reg(FeedbackExportQuery)
+class SuggestedQuestionsResponse(BaseModel):
+    data: list[str] = Field(description="Suggested question")
+
+
+register_schema_models(
+    console_ns,
+    ChatMessagesQuery,
+    MessageFeedbackPayload,
+    FeedbackExportQuery,
+    AnnotationCountResponse,
+    SuggestedQuestionsResponse,
+)
 
 # Register models for flask_restx to avoid dict type issues in Swagger
 # Register in dependency order: base models first, then dependent models
@@ -231,7 +240,7 @@ class ChatMessageListApi(Resource):
     @marshal_with(message_infinite_scroll_pagination_model)
     @edit_permission_required
     def get(self, app_model):
-        args = ChatMessagesQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
+        args = ChatMessagesQuery.model_validate(request.args.to_dict())
 
         conversation = (
             db.session.query(Conversation)
@@ -356,7 +365,7 @@ class MessageAnnotationCountApi(Resource):
     @console_ns.response(
         200,
         "Annotation count retrieved successfully",
-        console_ns.model("AnnotationCountResponse", {"count": fields.Integer(description="Number of annotations")}),
+        console_ns.models[AnnotationCountResponse.__name__],
     )
     @get_app_model
     @setup_required
@@ -376,9 +385,7 @@ class MessageSuggestedQuestionApi(Resource):
     @console_ns.response(
         200,
         "Suggested questions retrieved successfully",
-        console_ns.model(
-            "SuggestedQuestionsResponse", {"data": fields.List(fields.String(description="Suggested question"))}
-        ),
+        console_ns.models[SuggestedQuestionsResponse.__name__],
     )
     @console_ns.response(404, "Message or conversation not found")
     @setup_required
@@ -428,7 +435,7 @@ class MessageFeedbackExportApi(Resource):
     @login_required
     @account_initialization_required
     def get(self, app_model):
-        args = FeedbackExportQuery.model_validate(request.args.to_dict(flat=True))  # type: ignore
+        args = FeedbackExportQuery.model_validate(request.args.to_dict())
 
         # Import the service function
         from services.feedback_service import FeedbackService

api/controllers/console/auth/data_source_oauth.py

@@ -2,9 +2,11 @@ import logging
 
 import httpx
 from flask import current_app, redirect, request
-from flask_restx import Resource, fields
+from flask_restx import Resource
+from pydantic import BaseModel, Field
 
 from configs import dify_config
+from controllers.common.schema import register_schema_models
 from libs.login import login_required
 from libs.oauth_data_source import NotionOAuth
 
@@ -14,6 +16,26 @@ from ..wraps import account_initialization_required, is_admin_or_owner_required,
 logger = logging.getLogger(__name__)
 
 
+class OAuthDataSourceResponse(BaseModel):
+    data: str = Field(description="Authorization URL or 'internal' for internal setup")
+
+
+class OAuthDataSourceBindingResponse(BaseModel):
+    result: str = Field(description="Operation result")
+
+
+class OAuthDataSourceSyncResponse(BaseModel):
+    result: str = Field(description="Operation result")
+
+
+register_schema_models(
+    console_ns,
+    OAuthDataSourceResponse,
+    OAuthDataSourceBindingResponse,
+    OAuthDataSourceSyncResponse,
+)
+
+
 def get_oauth_providers():
     with current_app.app_context():
         notion_oauth = NotionOAuth(
@@ -34,10 +56,7 @@ class OAuthDataSource(Resource):
     @console_ns.response(
         200,
         "Authorization URL or internal setup success",
-        console_ns.model(
-            "OAuthDataSourceResponse",
-            {"data": fields.Raw(description="Authorization URL or 'internal' for internal setup")},
-        ),
+        console_ns.models[OAuthDataSourceResponse.__name__],
     )
     @console_ns.response(400, "Invalid provider")
     @console_ns.response(403, "Admin privileges required")
@@ -101,7 +120,7 @@ class OAuthDataSourceBinding(Resource):
     @console_ns.response(
         200,
         "Data source binding success",
-        console_ns.model("OAuthDataSourceBindingResponse", {"result": fields.String(description="Operation result")}),
+        console_ns.models[OAuthDataSourceBindingResponse.__name__],
     )
     @console_ns.response(400, "Invalid provider or code")
     def get(self, provider: str):
@@ -133,7 +152,7 @@ class OAuthDataSourceSync(Resource):
     @console_ns.response(
         200,
         "Data source sync success",
-        console_ns.model("OAuthDataSourceSyncResponse", {"result": fields.String(description="Operation result")}),
+        console_ns.models[OAuthDataSourceSyncResponse.__name__],
     )
     @console_ns.response(400, "Invalid provider or sync failed")
     @setup_required

api/controllers/console/auth/forgot_password.py

@@ -2,10 +2,11 @@ import base64
 import secrets
 
 from flask import request
-from flask_restx import Resource, fields
+from flask_restx import Resource
 from pydantic import BaseModel, Field, field_validator
 from sqlalchemy.orm import Session
 
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.auth.error import (
     EmailCodeError,
@@ -48,8 +49,31 @@ class ForgotPasswordResetPayload(BaseModel):
         return valid_password(value)
 
 
-for model in (ForgotPasswordSendPayload, ForgotPasswordCheckPayload, ForgotPasswordResetPayload):
-    console_ns.schema_model(model.__name__, model.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
+class ForgotPasswordEmailResponse(BaseModel):
+    result: str = Field(description="Operation result")
+    data: str | None = Field(default=None, description="Reset token")
+    code: str | None = Field(default=None, description="Error code if account not found")
+
+
+class ForgotPasswordCheckResponse(BaseModel):
+    is_valid: bool = Field(description="Whether code is valid")
+    email: EmailStr = Field(description="Email address")
+    token: str = Field(description="New reset token")
+
+
+class ForgotPasswordResetResponse(BaseModel):
+    result: str = Field(description="Operation result")
+
+
+register_schema_models(
+    console_ns,
+    ForgotPasswordSendPayload,
+    ForgotPasswordCheckPayload,
+    ForgotPasswordResetPayload,
+    ForgotPasswordEmailResponse,
+    ForgotPasswordCheckResponse,
+    ForgotPasswordResetResponse,
+)
 
 
 @console_ns.route("/forgot-password")
@@ -60,14 +84,7 @@ class ForgotPasswordSendEmailApi(Resource):
     @console_ns.response(
         200,
         "Email sent successfully",
-        console_ns.model(
-            "ForgotPasswordEmailResponse",
-            {
-                "result": fields.String(description="Operation result"),
-                "data": fields.String(description="Reset token"),
-                "code": fields.String(description="Error code if account not found"),
-            },
-        ),
+        console_ns.models[ForgotPasswordEmailResponse.__name__],
     )
     @console_ns.response(400, "Invalid email or rate limit exceeded")
     @setup_required
@@ -106,14 +123,7 @@ class ForgotPasswordCheckApi(Resource):
     @console_ns.response(
         200,
         "Code verified successfully",
-        console_ns.model(
-            "ForgotPasswordCheckResponse",
-            {
-                "is_valid": fields.Boolean(description="Whether code is valid"),
-                "email": fields.String(description="Email address"),
-                "token": fields.String(description="New reset token"),
-            },
-        ),
+        console_ns.models[ForgotPasswordCheckResponse.__name__],
     )
     @console_ns.response(400, "Invalid code or token")
     @setup_required
@@ -163,7 +173,7 @@ class ForgotPasswordResetApi(Resource):
     @console_ns.response(
         200,
         "Password reset successfully",
-        console_ns.model("ForgotPasswordResetResponse", {"result": fields.String(description="Operation result")}),
+        console_ns.models[ForgotPasswordResetResponse.__name__],
     )
     @console_ns.response(400, "Invalid token or password mismatch")
     @setup_required

api/controllers/console/auth/oauth_server.py

@@ -155,43 +155,43 @@ class OAuthServerUserTokenApi(Resource):
             grant_type = OAuthGrantType(payload.grant_type)
         except ValueError:
             raise BadRequest("invalid grant_type")
+        match grant_type:
+            case OAuthGrantType.AUTHORIZATION_CODE:
+                if not payload.code:
+                    raise BadRequest("code is required")
 
-        if grant_type == OAuthGrantType.AUTHORIZATION_CODE:
-            if not payload.code:
-                raise BadRequest("code is required")
+                if payload.client_secret != oauth_provider_app.client_secret:
+                    raise BadRequest("client_secret is invalid")
 
-            if payload.client_secret != oauth_provider_app.client_secret:
-                raise BadRequest("client_secret is invalid")
+                if payload.redirect_uri not in oauth_provider_app.redirect_uris:
+                    raise BadRequest("redirect_uri is invalid")
 
-            if payload.redirect_uri not in oauth_provider_app.redirect_uris:
-                raise BadRequest("redirect_uri is invalid")
+                access_token, refresh_token = OAuthServerService.sign_oauth_access_token(
+                    grant_type, code=payload.code, client_id=oauth_provider_app.client_id
+                )
+                return jsonable_encoder(
+                    {
+                        "access_token": access_token,
+                        "token_type": "Bearer",
+                        "expires_in": OAUTH_ACCESS_TOKEN_EXPIRES_IN,
+                        "refresh_token": refresh_token,
+                    }
+                )
+            case OAuthGrantType.REFRESH_TOKEN:
+                if not payload.refresh_token:
+                    raise BadRequest("refresh_token is required")
 
-            access_token, refresh_token = OAuthServerService.sign_oauth_access_token(
-                grant_type, code=payload.code, client_id=oauth_provider_app.client_id
-            )
-            return jsonable_encoder(
-                {
-                    "access_token": access_token,
-                    "token_type": "Bearer",
-                    "expires_in": OAUTH_ACCESS_TOKEN_EXPIRES_IN,
-                    "refresh_token": refresh_token,
-                }
-            )
-        elif grant_type == OAuthGrantType.REFRESH_TOKEN:
-            if not payload.refresh_token:
-                raise BadRequest("refresh_token is required")
-
-            access_token, refresh_token = OAuthServerService.sign_oauth_access_token(
-                grant_type, refresh_token=payload.refresh_token, client_id=oauth_provider_app.client_id
-            )
-            return jsonable_encoder(
-                {
-                    "access_token": access_token,
-                    "token_type": "Bearer",
-                    "expires_in": OAUTH_ACCESS_TOKEN_EXPIRES_IN,
-                    "refresh_token": refresh_token,
-                }
-            )
+                access_token, refresh_token = OAuthServerService.sign_oauth_access_token(
+                    grant_type, refresh_token=payload.refresh_token, client_id=oauth_provider_app.client_id
+                )
+                return jsonable_encoder(
+                    {
+                        "access_token": access_token,
+                        "token_type": "Bearer",
+                        "expires_in": OAUTH_ACCESS_TOKEN_EXPIRES_IN,
+                        "refresh_token": refresh_token,
+                    }
+                )
 
 
 @console_ns.route("/oauth/provider/account")

api/controllers/console/datasets/data_source.py

@@ -1,6 +1,6 @@
 import json
 from collections.abc import Generator
-from typing import Any, cast
+from typing import Any, Literal, cast
 
 from flask import request
 from flask_restx import Resource, fields, marshal_with
@@ -157,9 +157,8 @@ class DataSourceApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def patch(self, binding_id, action):
+    def patch(self, binding_id, action: Literal["enable", "disable"]):
         binding_id = str(binding_id)
-        action = str(action)
         with Session(db.engine) as session:
             data_source_binding = session.execute(
                 select(DataSourceOauthBinding).filter_by(id=binding_id)
@@ -167,23 +166,24 @@ class DataSourceApi(Resource):
         if data_source_binding is None:
             raise NotFound("Data source binding not found.")
         # enable binding
-        if action == "enable":
-            if data_source_binding.disabled:
-                data_source_binding.disabled = False
-                data_source_binding.updated_at = naive_utc_now()
-                db.session.add(data_source_binding)
-                db.session.commit()
-            else:
-                raise ValueError("Data source is not disabled.")
-        # disable binding
-        if action == "disable":
-            if not data_source_binding.disabled:
-                data_source_binding.disabled = True
-                data_source_binding.updated_at = naive_utc_now()
-                db.session.add(data_source_binding)
-                db.session.commit()
-            else:
-                raise ValueError("Data source is disabled.")
+        match action:
+            case "enable":
+                if data_source_binding.disabled:
+                    data_source_binding.disabled = False
+                    data_source_binding.updated_at = naive_utc_now()
+                    db.session.add(data_source_binding)
+                    db.session.commit()
+                else:
+                    raise ValueError("Data source is not disabled.")
+            # disable binding
+            case "disable":
+                if not data_source_binding.disabled:
+                    data_source_binding.disabled = True
+                    data_source_binding.updated_at = naive_utc_now()
+                    db.session.add(data_source_binding)
+                    db.session.commit()
+                else:
+                    raise ValueError("Data source is disabled.")
         return {"result": "success"}, 200
 
 

api/controllers/console/datasets/datasets.py

@@ -148,6 +148,7 @@ class DatasetUpdatePayload(BaseModel):
     embedding_model: str | None = None
     embedding_model_provider: str | None = None
     retrieval_model: dict[str, Any] | None = None
+    summary_index_setting: dict[str, Any] | None = None
     partial_member_list: list[dict[str, str]] | None = None
     external_retrieval_model: dict[str, Any] | None = None
     external_knowledge_id: str | None = None
@@ -288,7 +289,14 @@ class DatasetListApi(Resource):
     @enterprise_license_required
     def get(self):
         current_user, current_tenant_id = current_account_with_tenant()
-        query = ConsoleDatasetListQuery.model_validate(request.args.to_dict())
+        # Convert query parameters to dict, handling list parameters correctly
+        query_params: dict[str, str | list[str]] = dict(request.args.to_dict())
+        # Handle ids and tag_ids as lists (Flask request.args.getlist returns list even for single value)
+        if "ids" in request.args:
+            query_params["ids"] = request.args.getlist("ids")
+        if "tag_ids" in request.args:
+            query_params["tag_ids"] = request.args.getlist("tag_ids")
+        query = ConsoleDatasetListQuery.model_validate(query_params)
         # provider = request.args.get("provider", default="vendor")
         if query.ids:
             datasets, total = DatasetService.get_datasets_by_ids(query.ids, current_tenant_id)

api/controllers/console/datasets/datasets_document.py

@@ -45,6 +45,7 @@ from models.dataset import DocumentPipelineExecutionLog
 from services.dataset_service import DatasetService, DocumentService
 from services.entities.knowledge_entities.knowledge_entities import KnowledgeConfig, ProcessRule, RetrievalModel
 from services.file_service import FileService
+from tasks.generate_summary_index_task import generate_summary_index_task
 
 from ..app.error import (
     ProviderModelCurrentlyNotSupportError,
@@ -103,6 +104,10 @@ class DocumentRenamePayload(BaseModel):
     name: str
 
 
+class GenerateSummaryPayload(BaseModel):
+    document_list: list[str]
+
+
 class DocumentBatchDownloadZipPayload(BaseModel):
     """Request payload for bulk downloading documents as a zip archive."""
 
@@ -125,6 +130,7 @@ register_schema_models(
     RetrievalModel,
     DocumentRetryPayload,
     DocumentRenamePayload,
+    GenerateSummaryPayload,
     DocumentBatchDownloadZipPayload,
 )
 
@@ -312,6 +318,13 @@ class DatasetDocumentListApi(Resource):
 
         paginated_documents = db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)
         documents = paginated_documents.items
+
+        DocumentService.enrich_documents_with_summary_index_status(
+            documents=documents,
+            dataset=dataset,
+            tenant_id=current_tenant_id,
+        )
+
         if fetch:
             for document in documents:
                 completed_segments = (
@@ -563,63 +576,62 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
             if document.indexing_status in {"completed", "error"}:
                 raise DocumentAlreadyFinishedError()
             data_source_info = document.data_source_info_dict
+            match document.data_source_type:
+                case "upload_file":
+                    if not data_source_info:
+                        continue
+                    file_id = data_source_info["upload_file_id"]
+                    file_detail = (
+                        db.session.query(UploadFile)
+                        .where(UploadFile.tenant_id == current_tenant_id, UploadFile.id == file_id)
+                        .first()
+                    )
 
-            if document.data_source_type == "upload_file":
-                if not data_source_info:
-                    continue
-                file_id = data_source_info["upload_file_id"]
-                file_detail = (
-                    db.session.query(UploadFile)
-                    .where(UploadFile.tenant_id == current_tenant_id, UploadFile.id == file_id)
-                    .first()
-                )
+                    if file_detail is None:
+                        raise NotFound("File not found.")
 
-                if file_detail is None:
-                    raise NotFound("File not found.")
+                    extract_setting = ExtractSetting(
+                        datasource_type=DatasourceType.FILE, upload_file=file_detail, document_model=document.doc_form
+                    )
+                    extract_settings.append(extract_setting)
+                case "notion_import":
+                    if not data_source_info:
+                        continue
+                    extract_setting = ExtractSetting(
+                        datasource_type=DatasourceType.NOTION,
+                        notion_info=NotionInfo.model_validate(
+                            {
+                                "credential_id": data_source_info.get("credential_id"),
+                                "notion_workspace_id": data_source_info["notion_workspace_id"],
+                                "notion_obj_id": data_source_info["notion_page_id"],
+                                "notion_page_type": data_source_info["type"],
+                                "tenant_id": current_tenant_id,
+                            }
+                        ),
+                        document_model=document.doc_form,
+                    )
+                    extract_settings.append(extract_setting)
+                case "website_crawl":
+                    if not data_source_info:
+                        continue
+                    extract_setting = ExtractSetting(
+                        datasource_type=DatasourceType.WEBSITE,
+                        website_info=WebsiteInfo.model_validate(
+                            {
+                                "provider": data_source_info["provider"],
+                                "job_id": data_source_info["job_id"],
+                                "url": data_source_info["url"],
+                                "tenant_id": current_tenant_id,
+                                "mode": data_source_info["mode"],
+                                "only_main_content": data_source_info["only_main_content"],
+                            }
+                        ),
+                        document_model=document.doc_form,
+                    )
+                    extract_settings.append(extract_setting)
 
-                extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.FILE, upload_file=file_detail, document_model=document.doc_form
-                )
-                extract_settings.append(extract_setting)
-
-            elif document.data_source_type == "notion_import":
-                if not data_source_info:
-                    continue
-                extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.NOTION,
-                    notion_info=NotionInfo.model_validate(
-                        {
-                            "credential_id": data_source_info.get("credential_id"),
-                            "notion_workspace_id": data_source_info["notion_workspace_id"],
-                            "notion_obj_id": data_source_info["notion_page_id"],
-                            "notion_page_type": data_source_info["type"],
-                            "tenant_id": current_tenant_id,
-                        }
-                    ),
-                    document_model=document.doc_form,
-                )
-                extract_settings.append(extract_setting)
-            elif document.data_source_type == "website_crawl":
-                if not data_source_info:
-                    continue
-                extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.WEBSITE,
-                    website_info=WebsiteInfo.model_validate(
-                        {
-                            "provider": data_source_info["provider"],
-                            "job_id": data_source_info["job_id"],
-                            "url": data_source_info["url"],
-                            "tenant_id": current_tenant_id,
-                            "mode": data_source_info["mode"],
-                            "only_main_content": data_source_info["only_main_content"],
-                        }
-                    ),
-                    document_model=document.doc_form,
-                )
-                extract_settings.append(extract_setting)
-
-            else:
-                raise ValueError("Data source type not support")
+                case _:
+                    raise ValueError("Data source type not support")
             indexing_runner = IndexingRunner()
             try:
                 response = indexing_runner.indexing_estimate(
@@ -797,6 +809,7 @@ class DocumentApi(DocumentResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
         else:
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
@@ -832,6 +845,7 @@ class DocumentApi(DocumentResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
 
         return response, 200
@@ -939,23 +953,24 @@ class DocumentProcessingApi(DocumentResource):
         if not current_user.is_dataset_editor:
             raise Forbidden()
 
-        if action == "pause":
-            if document.indexing_status != "indexing":
-                raise InvalidActionError("Document not in indexing state.")
+        match action:
+            case "pause":
+                if document.indexing_status != "indexing":
+                    raise InvalidActionError("Document not in indexing state.")
 
-            document.paused_by = current_user.id
-            document.paused_at = naive_utc_now()
-            document.is_paused = True
-            db.session.commit()
+                document.paused_by = current_user.id
+                document.paused_at = naive_utc_now()
+                document.is_paused = True
+                db.session.commit()
 
-        elif action == "resume":
-            if document.indexing_status not in {"paused", "error"}:
-                raise InvalidActionError("Document not in paused or error state.")
+            case "resume":
+                if document.indexing_status not in {"paused", "error"}:
+                    raise InvalidActionError("Document not in paused or error state.")
 
-            document.paused_by = None
-            document.paused_at = None
-            document.is_paused = False
-            db.session.commit()
+                document.paused_by = None
+                document.paused_at = None
+                document.is_paused = False
+                db.session.commit()
 
         return {"result": "success"}, 200
 
@@ -1255,3 +1270,149 @@ class DocumentPipelineExecutionLogApi(DocumentResource):
             "input_data": log.input_data,
             "datasource_node_id": log.datasource_node_id,
         }, 200
+
+
+@console_ns.route("/datasets/<uuid:dataset_id>/documents/generate-summary")
+class DocumentGenerateSummaryApi(Resource):
+    @console_ns.doc("generate_summary_for_documents")
+    @console_ns.doc(description="Generate summary index for documents")
+    @console_ns.doc(params={"dataset_id": "Dataset ID"})
+    @console_ns.expect(console_ns.models[GenerateSummaryPayload.__name__])
+    @console_ns.response(200, "Summary generation started successfully")
+    @console_ns.response(400, "Invalid request or dataset configuration")
+    @console_ns.response(403, "Permission denied")
+    @console_ns.response(404, "Dataset not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @cloud_edition_billing_rate_limit_check("knowledge")
+    def post(self, dataset_id):
+        """
+        Generate summary index for specified documents.
+
+        This endpoint checks if the dataset configuration supports summary generation
+        (indexing_technique must be 'high_quality' and summary_index_setting.enable must be true),
+        then asynchronously generates summary indexes for the provided documents.
+        """
+        current_user, _ = current_account_with_tenant()
+        dataset_id = str(dataset_id)
+
+        # Get dataset
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound("Dataset not found.")
+
+        # Check permissions
+        if not current_user.is_dataset_editor:
+            raise Forbidden()
+
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        # Validate request payload
+        payload = GenerateSummaryPayload.model_validate(console_ns.payload or {})
+        document_list = payload.document_list
+
+        if not document_list:
+            from werkzeug.exceptions import BadRequest
+
+            raise BadRequest("document_list cannot be empty.")
+
+        # Check if dataset configuration supports summary generation
+        if dataset.indexing_technique != "high_quality":
+            raise ValueError(
+                f"Summary generation is only available for 'high_quality' indexing technique. "
+                f"Current indexing technique: {dataset.indexing_technique}"
+            )
+
+        summary_index_setting = dataset.summary_index_setting
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            raise ValueError("Summary index is not enabled for this dataset. Please enable it in the dataset settings.")
+
+        # Verify all documents exist and belong to the dataset
+        documents = DocumentService.get_documents_by_ids(dataset_id, document_list)
+
+        if len(documents) != len(document_list):
+            found_ids = {doc.id for doc in documents}
+            missing_ids = set(document_list) - found_ids
+            raise NotFound(f"Some documents not found: {list(missing_ids)}")
+
+        # Update need_summary to True for documents that don't have it set
+        # This handles the case where documents were created when summary_index_setting was disabled
+        documents_to_update = [doc for doc in documents if not doc.need_summary and doc.doc_form != "qa_model"]
+
+        if documents_to_update:
+            document_ids_to_update = [str(doc.id) for doc in documents_to_update]
+            DocumentService.update_documents_need_summary(
+                dataset_id=dataset_id,
+                document_ids=document_ids_to_update,
+                need_summary=True,
+            )
+
+        # Dispatch async tasks for each document
+        for document in documents:
+            # Skip qa_model documents as they don't generate summaries
+            if document.doc_form == "qa_model":
+                logger.info("Skipping summary generation for qa_model document %s", document.id)
+                continue
+
+            # Dispatch async task
+            generate_summary_index_task.delay(dataset_id, document.id)
+            logger.info(
+                "Dispatched summary generation task for document %s in dataset %s",
+                document.id,
+                dataset_id,
+            )
+
+        return {"result": "success"}, 200
+
+
+@console_ns.route("/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/summary-status")
+class DocumentSummaryStatusApi(DocumentResource):
+    @console_ns.doc("get_document_summary_status")
+    @console_ns.doc(description="Get summary index generation status for a document")
+    @console_ns.doc(params={"dataset_id": "Dataset ID", "document_id": "Document ID"})
+    @console_ns.response(200, "Summary status retrieved successfully")
+    @console_ns.response(404, "Document not found")
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, document_id):
+        """
+        Get summary index generation status for a document.
+
+        Returns:
+        - total_segments: Total number of segments in the document
+        - summary_status: Dictionary with status counts
+          - completed: Number of summaries completed
+          - generating: Number of summaries being generated
+          - error: Number of summaries with errors
+          - not_started: Number of segments without summary records
+        - summaries: List of summary records with status and content preview
+        """
+        current_user, _ = current_account_with_tenant()
+        dataset_id = str(dataset_id)
+        document_id = str(document_id)
+
+        # Get dataset
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound("Dataset not found.")
+
+        # Check permissions
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        # Get summary status detail from service
+        from services.summary_index_service import SummaryIndexService
+
+        result = SummaryIndexService.get_document_summary_status_detail(
+            document_id=document_id,
+            dataset_id=dataset_id,
+        )
+
+        return result, 200

api/controllers/console/datasets/datasets_segments.py

@@ -41,6 +41,17 @@ from services.errors.chunk import ChildChunkIndexingError as ChildChunkIndexingS
 from tasks.batch_create_segment_to_index_task import batch_create_segment_to_index_task
 
 
+def _get_segment_with_summary(segment, dataset_id):
+    """Helper function to marshal segment and add summary information."""
+    from services.summary_index_service import SummaryIndexService
+
+    segment_dict = dict(marshal(segment, segment_fields))
+    # Query summary for this segment (only enabled summaries)
+    summary = SummaryIndexService.get_segment_summary(segment_id=segment.id, dataset_id=dataset_id)
+    segment_dict["summary"] = summary.summary_content if summary else None
+    return segment_dict
+
+
 class SegmentListQuery(BaseModel):
     limit: int = Field(default=20, ge=1, le=100)
     status: list[str] = Field(default_factory=list)
@@ -63,6 +74,7 @@ class SegmentUpdatePayload(BaseModel):
     keywords: list[str] | None = None
     regenerate_child_chunks: bool = False
     attachment_ids: list[str] | None = None
+    summary: str | None = None  # Summary content for summary index
 
 
 class BatchImportPayload(BaseModel):
@@ -181,8 +193,25 @@ class DatasetDocumentSegmentListApi(Resource):
 
         segments = db.paginate(select=query, page=page, per_page=limit, max_per_page=100, error_out=False)
 
+        # Query summaries for all segments in this page (batch query for efficiency)
+        segment_ids = [segment.id for segment in segments.items]
+        summaries = {}
+        if segment_ids:
+            from services.summary_index_service import SummaryIndexService
+
+            summary_records = SummaryIndexService.get_segments_summaries(segment_ids=segment_ids, dataset_id=dataset_id)
+            # Only include enabled summaries (already filtered by service)
+            summaries = {chunk_id: summary.summary_content for chunk_id, summary in summary_records.items()}
+
+        # Add summary to each segment
+        segments_with_summary = []
+        for segment in segments.items:
+            segment_dict = dict(marshal(segment, segment_fields))
+            segment_dict["summary"] = summaries.get(segment.id)
+            segments_with_summary.append(segment_dict)
+
         response = {
-            "data": marshal(segments.items, segment_fields),
+            "data": segments_with_summary,
             "limit": limit,
             "total": segments.total,
             "total_pages": segments.pages,
@@ -328,7 +357,7 @@ class DatasetDocumentSegmentAddApi(Resource):
         payload_dict = payload.model_dump(exclude_none=True)
         SegmentService.segment_create_args_validate(payload_dict, document)
         segment = SegmentService.create_segment(payload_dict, document, dataset)
-        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+        return {"data": _get_segment_with_summary(segment, dataset_id), "doc_form": document.doc_form}, 200
 
 
 @console_ns.route("/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>")
@@ -390,10 +419,12 @@ class DatasetDocumentSegmentUpdateApi(Resource):
         payload = SegmentUpdatePayload.model_validate(console_ns.payload or {})
         payload_dict = payload.model_dump(exclude_none=True)
         SegmentService.segment_create_args_validate(payload_dict, document)
+
+        # Update segment (summary update with change detection is handled in SegmentService.update_segment)
         segment = SegmentService.update_segment(
             SegmentUpdateArgs.model_validate(payload.model_dump(exclude_none=True)), segment, document, dataset
         )
-        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+        return {"data": _get_segment_with_summary(segment, dataset_id), "doc_form": document.doc_form}, 200
 
     @setup_required
     @login_required

api/controllers/console/datasets/hit_testing.py

@@ -1,6 +1,13 @@
-from flask_restx import Resource
+from flask_restx import Resource, fields
 
 from controllers.common.schema import register_schema_model
+from fields.hit_testing_fields import (
+    child_chunk_fields,
+    document_fields,
+    files_fields,
+    hit_testing_record_fields,
+    segment_fields,
+)
 from libs.login import login_required
 
 from .. import console_ns
@@ -14,13 +21,45 @@ from ..wraps import (
 register_schema_model(console_ns, HitTestingPayload)
 
 
+def _get_or_create_model(model_name: str, field_def):
+    """Get or create a flask_restx model to avoid dict type issues in Swagger."""
+    existing = console_ns.models.get(model_name)
+    if existing is None:
+        existing = console_ns.model(model_name, field_def)
+    return existing
+
+
+# Register models for flask_restx to avoid dict type issues in Swagger
+document_model = _get_or_create_model("HitTestingDocument", document_fields)
+
+segment_fields_copy = segment_fields.copy()
+segment_fields_copy["document"] = fields.Nested(document_model)
+segment_model = _get_or_create_model("HitTestingSegment", segment_fields_copy)
+
+child_chunk_model = _get_or_create_model("HitTestingChildChunk", child_chunk_fields)
+files_model = _get_or_create_model("HitTestingFile", files_fields)
+
+hit_testing_record_fields_copy = hit_testing_record_fields.copy()
+hit_testing_record_fields_copy["segment"] = fields.Nested(segment_model)
+hit_testing_record_fields_copy["child_chunks"] = fields.List(fields.Nested(child_chunk_model))
+hit_testing_record_fields_copy["files"] = fields.List(fields.Nested(files_model))
+hit_testing_record_model = _get_or_create_model("HitTestingRecord", hit_testing_record_fields_copy)
+
+# Response model for hit testing API
+hit_testing_response_fields = {
+    "query": fields.String,
+    "records": fields.List(fields.Nested(hit_testing_record_model)),
+}
+hit_testing_response_model = _get_or_create_model("HitTestingResponse", hit_testing_response_fields)
+
+
 @console_ns.route("/datasets/<uuid:dataset_id>/hit-testing")
 class HitTestingApi(Resource, DatasetsHitTestingBase):
     @console_ns.doc("test_dataset_retrieval")
     @console_ns.doc(description="Test dataset knowledge retrieval")
     @console_ns.doc(params={"dataset_id": "Dataset ID"})
     @console_ns.expect(console_ns.models[HitTestingPayload.__name__])
-    @console_ns.response(200, "Hit testing completed successfully")
+    @console_ns.response(200, "Hit testing completed successfully", model=hit_testing_response_model)
     @console_ns.response(404, "Dataset not found")
     @console_ns.response(400, "Invalid parameters")
     @setup_required

api/controllers/console/datasets/metadata.py

@@ -126,10 +126,11 @@ class DatasetMetadataBuiltInFieldActionApi(Resource):
             raise NotFound("Dataset not found.")
         DatasetService.check_dataset_permission(dataset, current_user)
 
-        if action == "enable":
-            MetadataService.enable_built_in_field(dataset)
-        elif action == "disable":
-            MetadataService.disable_built_in_field(dataset)
+        match action:
+            case "enable":
+                MetadataService.enable_built_in_field(dataset)
+            case "disable":
+                MetadataService.disable_built_in_field(dataset)
         return {"result": "success"}, 200
 
 

api/controllers/console/datasets/rag_pipeline/rag_pipeline_workflow.py

@@ -1,10 +1,9 @@
 import json
 import logging
 from typing import Any, Literal, cast
-from uuid import UUID
 
 from flask import abort, request
-from flask_restx import Resource, marshal_with, reqparse  # type: ignore
+from flask_restx import Resource, marshal_with  # type: ignore
 from pydantic import BaseModel, Field
 from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
@@ -38,7 +37,7 @@ from core.model_runtime.utils.encoders import jsonable_encoder
 from extensions.ext_database import db
 from factories import variable_factory
 from libs import helper
-from libs.helper import TimestampField
+from libs.helper import TimestampField, UUIDStrOrEmpty
 from libs.login import current_account_with_tenant, current_user, login_required
 from models import Account
 from models.dataset import Pipeline
@@ -110,7 +109,7 @@ class NodeIdQuery(BaseModel):
 
 
 class WorkflowRunQuery(BaseModel):
-    last_id: UUID | None = None
+    last_id: UUIDStrOrEmpty | None = None
     limit: int = Field(default=20, ge=1, le=100)
 
 
@@ -121,6 +120,10 @@ class DatasourceVariablesPayload(BaseModel):
     start_node_title: str
 
 
+class RagPipelineRecommendedPluginQuery(BaseModel):
+    type: str = "all"
+
+
 register_schema_models(
     console_ns,
     DraftWorkflowSyncPayload,
@@ -135,6 +138,7 @@ register_schema_models(
     NodeIdQuery,
     WorkflowRunQuery,
     DatasourceVariablesPayload,
+    RagPipelineRecommendedPluginQuery,
 )
 
 
@@ -975,11 +979,8 @@ class RagPipelineRecommendedPluginApi(Resource):
     @login_required
     @account_initialization_required
     def get(self):
-        parser = reqparse.RequestParser()
-        parser.add_argument("type", type=str, location="args", required=False, default="all")
-        args = parser.parse_args()
-        type = args["type"]
+        query = RagPipelineRecommendedPluginQuery.model_validate(request.args.to_dict())
 
         rag_pipeline_service = RagPipelineService()
-        recommended_plugins = rag_pipeline_service.get_recommended_plugins(type)
+        recommended_plugins = rag_pipeline_service.get_recommended_plugins(query.type)
         return recommended_plugins

api/controllers/console/explore/trial.py

@@ -9,7 +9,7 @@ import services
 from controllers.common.fields import Parameters as ParametersResponse
 from controllers.common.fields import Site as SiteResponse
 from controllers.common.schema import get_or_create_model
-from controllers.console import api, console_ns
+from controllers.console import api
 from controllers.console.app.error import (
     AppUnavailableError,
     AudioTooLargeError,
@@ -51,7 +51,7 @@ from fields.app_fields import (
     tag_fields,
 )
 from fields.dataset_fields import dataset_fields
-from fields.member_fields import build_simple_account_model
+from fields.member_fields import simple_account_fields
 from fields.workflow_fields import (
     conversation_variable_fields,
     pipeline_variable_fields,
@@ -103,7 +103,7 @@ app_detail_fields_with_site_copy["tags"] = fields.List(fields.Nested(tag_model))
 app_detail_fields_with_site_copy["site"] = fields.Nested(site_model)
 app_detail_with_site_model = get_or_create_model("TrialAppDetailWithSite", app_detail_fields_with_site_copy)
 
-simple_account_model = build_simple_account_model(console_ns)
+simple_account_model = get_or_create_model("SimpleAccount", simple_account_fields)
 conversation_variable_model = get_or_create_model("TrialConversationVariable", conversation_variable_fields)
 pipeline_variable_model = get_or_create_model("TrialPipelineVariable", pipeline_variable_fields)
 

api/controllers/console/init_validate.py

@@ -1,87 +1,74 @@
 import os
+from typing import Literal
 
 from flask import session
-from flask_restx import Resource, fields
 from pydantic import BaseModel, Field
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from configs import dify_config
+from controllers.fastopenapi import console_router
 from extensions.ext_database import db
 from models.model import DifySetup
 from services.account_service import TenantService
 
-from . import console_ns
 from .error import AlreadySetupError, InitValidateFailedError
 from .wraps import only_edition_self_hosted
 
-DEFAULT_REF_TEMPLATE_SWAGGER_2_0 = "#/definitions/{model}"
-
 
 class InitValidatePayload(BaseModel):
-    password: str = Field(..., max_length=30)
+    password: str = Field(..., max_length=30, description="Initialization password")
 
 
-console_ns.schema_model(
-    InitValidatePayload.__name__,
-    InitValidatePayload.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0),
+class InitStatusResponse(BaseModel):
+    status: Literal["finished", "not_started"] = Field(..., description="Initialization status")
+
+
+class InitValidateResponse(BaseModel):
+    result: str = Field(description="Operation result", examples=["success"])
+
+
+@console_router.get(
+    "/init",
+    response_model=InitStatusResponse,
+    tags=["console"],
 )
+def get_init_status() -> InitStatusResponse:
+    """Get initialization validation status."""
+    init_status = get_init_validate_status()
+    if init_status:
+        return InitStatusResponse(status="finished")
+    return InitStatusResponse(status="not_started")
 
 
-@console_ns.route("/init")
-class InitValidateAPI(Resource):
-    @console_ns.doc("get_init_status")
-    @console_ns.doc(description="Get initialization validation status")
-    @console_ns.response(
-        200,
-        "Success",
-        model=console_ns.model(
-            "InitStatusResponse",
-            {"status": fields.String(description="Initialization status", enum=["finished", "not_started"])},
-        ),
-    )
-    def get(self):
-        """Get initialization validation status"""
-        init_status = get_init_validate_status()
-        if init_status:
-            return {"status": "finished"}
-        return {"status": "not_started"}
+@console_router.post(
+    "/init",
+    response_model=InitValidateResponse,
+    tags=["console"],
+    status_code=201,
+)
+@only_edition_self_hosted
+def validate_init_password(payload: InitValidatePayload) -> InitValidateResponse:
+    """Validate initialization password."""
+    tenant_count = TenantService.get_tenant_count()
+    if tenant_count > 0:
+        raise AlreadySetupError()
 
-    @console_ns.doc("validate_init_password")
-    @console_ns.doc(description="Validate initialization password for self-hosted edition")
-    @console_ns.expect(console_ns.models[InitValidatePayload.__name__])
-    @console_ns.response(
-        201,
-        "Success",
-        model=console_ns.model("InitValidateResponse", {"result": fields.String(description="Operation result")}),
-    )
-    @console_ns.response(400, "Already setup or validation failed")
-    @only_edition_self_hosted
-    def post(self):
-        """Validate initialization password"""
-        # is tenant created
-        tenant_count = TenantService.get_tenant_count()
-        if tenant_count > 0:
-            raise AlreadySetupError()
+    if payload.password != os.environ.get("INIT_PASSWORD"):
+        session["is_init_validated"] = False
+        raise InitValidateFailedError()
 
-        payload = InitValidatePayload.model_validate(console_ns.payload)
-        input_password = payload.password
-
-        if input_password != os.environ.get("INIT_PASSWORD"):
-            session["is_init_validated"] = False
-            raise InitValidateFailedError()
-
-        session["is_init_validated"] = True
-        return {"result": "success"}, 201
+    session["is_init_validated"] = True
+    return InitValidateResponse(result="success")
 
 
-def get_init_validate_status():
+def get_init_validate_status() -> bool:
     if dify_config.EDITION == "SELF_HOSTED":
         if os.environ.get("INIT_PASSWORD"):
             if session.get("is_init_validated"):
                 return True
 
             with Session(db.engine) as db_session:
-                return db_session.execute(select(DifySetup)).scalar_one_or_none()
+                return db_session.execute(select(DifySetup)).scalar_one_or_none() is not None
 
     return True

api/controllers/console/remote_files.py

@@ -1,7 +1,6 @@
 import urllib.parse
 
 import httpx
-from flask_restx import Resource
 from pydantic import BaseModel, Field
 
 import services
@@ -11,7 +10,7 @@ from controllers.common.errors import (
     RemoteFileUploadError,
     UnsupportedFileTypeError,
 )
-from controllers.common.schema import register_schema_models
+from controllers.fastopenapi import console_router
 from core.file import helpers as file_helpers
 from core.helper import ssrf_proxy
 from extensions.ext_database import db
@@ -19,84 +18,74 @@ from fields.file_fields import FileWithSignedUrl, RemoteFileInfo
 from libs.login import current_account_with_tenant
 from services.file_service import FileService
 
-from . import console_ns
-
-register_schema_models(console_ns, RemoteFileInfo, FileWithSignedUrl)
-
-
-@console_ns.route("/remote-files/<path:url>")
-class RemoteFileInfoApi(Resource):
-    @console_ns.response(200, "Remote file info", console_ns.models[RemoteFileInfo.__name__])
-    def get(self, url):
-        decoded_url = urllib.parse.unquote(url)
-        resp = ssrf_proxy.head(decoded_url)
-        if resp.status_code != httpx.codes.OK:
-            # failed back to get method
-            resp = ssrf_proxy.get(decoded_url, timeout=3)
-        resp.raise_for_status()
-        info = RemoteFileInfo(
-            file_type=resp.headers.get("Content-Type", "application/octet-stream"),
-            file_length=int(resp.headers.get("Content-Length", 0)),
-        )
-        return info.model_dump(mode="json")
-
 
 class RemoteFileUploadPayload(BaseModel):
     url: str = Field(..., description="URL to fetch")
 
 
-console_ns.schema_model(
-    RemoteFileUploadPayload.__name__,
-    RemoteFileUploadPayload.model_json_schema(ref_template="#/definitions/{model}"),
+@console_router.get(
+    "/remote-files/<path:url>",
+    response_model=RemoteFileInfo,
+    tags=["console"],
 )
+def get_remote_file_info(url: str) -> RemoteFileInfo:
+    decoded_url = urllib.parse.unquote(url)
+    resp = ssrf_proxy.head(decoded_url)
+    if resp.status_code != httpx.codes.OK:
+        resp = ssrf_proxy.get(decoded_url, timeout=3)
+    resp.raise_for_status()
+    return RemoteFileInfo(
+        file_type=resp.headers.get("Content-Type", "application/octet-stream"),
+        file_length=int(resp.headers.get("Content-Length", 0)),
+    )
 
 
-@console_ns.route("/remote-files/upload")
-class RemoteFileUploadApi(Resource):
-    @console_ns.expect(console_ns.models[RemoteFileUploadPayload.__name__])
-    @console_ns.response(201, "Remote file uploaded", console_ns.models[FileWithSignedUrl.__name__])
-    def post(self):
-        args = RemoteFileUploadPayload.model_validate(console_ns.payload)
-        url = args.url
+@console_router.post(
+    "/remote-files/upload",
+    response_model=FileWithSignedUrl,
+    tags=["console"],
+    status_code=201,
+)
+def upload_remote_file(payload: RemoteFileUploadPayload) -> FileWithSignedUrl:
+    url = payload.url
 
-        try:
-            resp = ssrf_proxy.head(url=url)
-            if resp.status_code != httpx.codes.OK:
-                resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
-            if resp.status_code != httpx.codes.OK:
-                raise RemoteFileUploadError(f"Failed to fetch file from {url}: {resp.text}")
-        except httpx.RequestError as e:
-            raise RemoteFileUploadError(f"Failed to fetch file from {url}: {str(e)}")
+    try:
+        resp = ssrf_proxy.head(url=url)
+        if resp.status_code != httpx.codes.OK:
+            resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
+        if resp.status_code != httpx.codes.OK:
+            raise RemoteFileUploadError(f"Failed to fetch file from {url}: {resp.text}")
+    except httpx.RequestError as e:
+        raise RemoteFileUploadError(f"Failed to fetch file from {url}: {str(e)}")
 
-        file_info = helpers.guess_file_info_from_response(resp)
+    file_info = helpers.guess_file_info_from_response(resp)
 
-        if not FileService.is_file_size_within_limit(extension=file_info.extension, file_size=file_info.size):
-            raise FileTooLargeError
+    if not FileService.is_file_size_within_limit(extension=file_info.extension, file_size=file_info.size):
+        raise FileTooLargeError
 
-        content = resp.content if resp.request.method == "GET" else ssrf_proxy.get(url).content
+    content = resp.content if resp.request.method == "GET" else ssrf_proxy.get(url).content
 
-        try:
-            user, _ = current_account_with_tenant()
-            upload_file = FileService(db.engine).upload_file(
-                filename=file_info.filename,
-                content=content,
-                mimetype=file_info.mimetype,
-                user=user,
-                source_url=url,
-            )
-        except services.errors.file.FileTooLargeError as file_too_large_error:
-            raise FileTooLargeError(file_too_large_error.description)
-        except services.errors.file.UnsupportedFileTypeError:
-            raise UnsupportedFileTypeError()
-
-        payload = FileWithSignedUrl(
-            id=upload_file.id,
-            name=upload_file.name,
-            size=upload_file.size,
-            extension=upload_file.extension,
-            url=file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
-            mime_type=upload_file.mime_type,
-            created_by=upload_file.created_by,
-            created_at=int(upload_file.created_at.timestamp()),
+    try:
+        user, _ = current_account_with_tenant()
+        upload_file = FileService(db.engine).upload_file(
+            filename=file_info.filename,
+            content=content,
+            mimetype=file_info.mimetype,
+            user=user,
+            source_url=url,
         )
-        return payload.model_dump(mode="json"), 201
+    except services.errors.file.FileTooLargeError as file_too_large_error:
+        raise FileTooLargeError(file_too_large_error.description)
+    except services.errors.file.UnsupportedFileTypeError:
+        raise UnsupportedFileTypeError()
+
+    return FileWithSignedUrl(
+        id=upload_file.id,
+        name=upload_file.name,
+        size=upload_file.size,
+        extension=upload_file.extension,
+        url=file_helpers.get_signed_file_url(upload_file_id=upload_file.id),
+        mime_type=upload_file.mime_type,
+        created_by=upload_file.created_by,
+        created_at=int(upload_file.created_at.timestamp()),
+    )

api/controllers/console/tag/tags.py

@@ -1,18 +1,28 @@
 from typing import Literal
 
 from flask import request
-from flask_restx import Resource, marshal_with
+from flask_restx import Namespace, Resource, fields, marshal_with
 from pydantic import BaseModel, Field
 from werkzeug.exceptions import Forbidden
 
 from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.wraps import account_initialization_required, edit_permission_required, setup_required
-from fields.tag_fields import dataset_tag_fields
 from libs.login import current_account_with_tenant, login_required
 from services.recommended_app_service_extend import RecommendedAppService
 from services.tag_service import TagService
 
+dataset_tag_fields = {
+    "id": fields.String,
+    "name": fields.String,
+    "type": fields.String,
+    "binding_count": fields.String,
+}
+
+
+def build_dataset_tag_fields(api_or_ns: Namespace):
+    return api_or_ns.model("DataSetTag", dataset_tag_fields)
+
 
 class TagBasePayload(BaseModel):
     name: str = Field(description="Tag name", min_length=1, max_length=50)

api/controllers/console/workspace/account.py

@@ -12,6 +12,7 @@ from sqlalchemy.orm import Session
 
 from configs import dify_config
 from constants.languages import supported_language
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.auth.error import (
     EmailAlreadyInUseError,
@@ -37,7 +38,7 @@ from controllers.console.wraps import (
     setup_required,
 )
 from extensions.ext_database import db
-from fields.member_fields import account_fields
+from fields.member_fields import Account as AccountResponse
 from libs.datetime_utils import naive_utc_now
 from libs.helper import EmailStr, TimestampField, extract_remote_ip, timezone
 from libs.login import current_account_with_tenant, login_required
@@ -170,6 +171,12 @@ reg(ChangeEmailSendPayload)
 reg(ChangeEmailValidityPayload)
 reg(ChangeEmailResetPayload)
 reg(CheckEmailUniquePayload)
+register_schema_models(console_ns, AccountResponse)
+
+
+def _serialize_account(account) -> dict:
+    return AccountResponse.model_validate(account, from_attributes=True).model_dump(mode="json")
+
 
 integrate_fields = {
     "provider": fields.String,
@@ -236,11 +243,11 @@ class AccountProfileApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     @enterprise_license_required
     def get(self):
         current_user, _ = current_account_with_tenant()
-        return current_user
+        return _serialize_account(current_user)
 
 
 @console_ns.route("/account/name")
@@ -249,14 +256,14 @@ class AccountNameApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     def post(self):
         current_user, _ = current_account_with_tenant()
         payload = console_ns.payload or {}
         args = AccountNamePayload.model_validate(payload)
         updated_account = AccountService.update_account(current_user, name=args.name)
 
-        return updated_account
+        return _serialize_account(updated_account)
 
 
 @console_ns.route("/account/avatar")
@@ -265,7 +272,7 @@ class AccountAvatarApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     def post(self):
         current_user, _ = current_account_with_tenant()
         payload = console_ns.payload or {}
@@ -273,7 +280,7 @@ class AccountAvatarApi(Resource):
 
         updated_account = AccountService.update_account(current_user, avatar=args.avatar)
 
-        return updated_account
+        return _serialize_account(updated_account)
 
 
 @console_ns.route("/account/interface-language")
@@ -282,7 +289,7 @@ class AccountInterfaceLanguageApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     def post(self):
         current_user, _ = current_account_with_tenant()
         payload = console_ns.payload or {}
@@ -290,7 +297,7 @@ class AccountInterfaceLanguageApi(Resource):
 
         updated_account = AccountService.update_account(current_user, interface_language=args.interface_language)
 
-        return updated_account
+        return _serialize_account(updated_account)
 
 
 @console_ns.route("/account/interface-theme")
@@ -299,7 +306,7 @@ class AccountInterfaceThemeApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     def post(self):
         current_user, _ = current_account_with_tenant()
         payload = console_ns.payload or {}
@@ -307,7 +314,7 @@ class AccountInterfaceThemeApi(Resource):
 
         updated_account = AccountService.update_account(current_user, interface_theme=args.interface_theme)
 
-        return updated_account
+        return _serialize_account(updated_account)
 
 
 @console_ns.route("/account/timezone")
@@ -316,7 +323,7 @@ class AccountTimezoneApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     def post(self):
         current_user, _ = current_account_with_tenant()
         payload = console_ns.payload or {}
@@ -324,7 +331,7 @@ class AccountTimezoneApi(Resource):
 
         updated_account = AccountService.update_account(current_user, timezone=args.timezone)
 
-        return updated_account
+        return _serialize_account(updated_account)
 
 
 @console_ns.route("/account/password")
@@ -333,7 +340,7 @@ class AccountPasswordApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     def post(self):
         current_user, _ = current_account_with_tenant()
         payload = console_ns.payload or {}
@@ -344,7 +351,7 @@ class AccountPasswordApi(Resource):
         except ServiceCurrentPasswordIncorrectError:
             raise CurrentPasswordIncorrectError()
 
-        return {"result": "success"}
+        return _serialize_account(current_user)
 
 
 @console_ns.route("/account/integrates")
@@ -620,7 +627,7 @@ class ChangeEmailResetApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_fields)
+    @console_ns.response(200, "Success", console_ns.models[AccountResponse.__name__])
     def post(self):
         payload = console_ns.payload or {}
         args = ChangeEmailResetPayload.model_validate(payload)
@@ -649,7 +656,7 @@ class ChangeEmailResetApi(Resource):
             email=normalized_new_email,
         )
 
-        return updated_account
+        return _serialize_account(updated_account)
 
 
 @console_ns.route("/account/change-email/check-email-unique")

api/controllers/console/workspace/endpoint.py

@@ -1,9 +1,10 @@
 from typing import Any
 
 from flask import request
-from flask_restx import Resource, fields
+from flask_restx import Resource
 from pydantic import BaseModel, Field
 
+from controllers.common.schema import register_schema_models
 from controllers.console import console_ns
 from controllers.console.wraps import account_initialization_required, is_admin_or_owner_required, setup_required
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -38,15 +39,53 @@ class EndpointListForPluginQuery(EndpointListQuery):
     plugin_id: str
 
 
+class EndpointCreateResponse(BaseModel):
+    success: bool = Field(description="Operation success")
+
+
+class EndpointListResponse(BaseModel):
+    endpoints: list[dict[str, Any]] = Field(description="Endpoint information")
+
+
+class PluginEndpointListResponse(BaseModel):
+    endpoints: list[dict[str, Any]] = Field(description="Endpoint information")
+
+
+class EndpointDeleteResponse(BaseModel):
+    success: bool = Field(description="Operation success")
+
+
+class EndpointUpdateResponse(BaseModel):
+    success: bool = Field(description="Operation success")
+
+
+class EndpointEnableResponse(BaseModel):
+    success: bool = Field(description="Operation success")
+
+
+class EndpointDisableResponse(BaseModel):
+    success: bool = Field(description="Operation success")
+
+
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
 
-reg(EndpointCreatePayload)
-reg(EndpointIdPayload)
-reg(EndpointUpdatePayload)
-reg(EndpointListQuery)
-reg(EndpointListForPluginQuery)
+register_schema_models(
+    console_ns,
+    EndpointCreatePayload,
+    EndpointIdPayload,
+    EndpointUpdatePayload,
+    EndpointListQuery,
+    EndpointListForPluginQuery,
+    EndpointCreateResponse,
+    EndpointListResponse,
+    PluginEndpointListResponse,
+    EndpointDeleteResponse,
+    EndpointUpdateResponse,
+    EndpointEnableResponse,
+    EndpointDisableResponse,
+)
 
 
 @console_ns.route("/workspaces/current/endpoints/create")
@@ -57,7 +96,7 @@ class EndpointCreateApi(Resource):
     @console_ns.response(
         200,
         "Endpoint created successfully",
-        console_ns.model("EndpointCreateResponse", {"success": fields.Boolean(description="Operation success")}),
+        console_ns.models[EndpointCreateResponse.__name__],
     )
     @console_ns.response(403, "Admin privileges required")
     @setup_required
@@ -91,9 +130,7 @@ class EndpointListApi(Resource):
     @console_ns.response(
         200,
         "Success",
-        console_ns.model(
-            "EndpointListResponse", {"endpoints": fields.List(fields.Raw(description="Endpoint information"))}
-        ),
+        console_ns.models[EndpointListResponse.__name__],
     )
     @setup_required
     @login_required
@@ -126,9 +163,7 @@ class EndpointListForSinglePluginApi(Resource):
     @console_ns.response(
         200,
         "Success",
-        console_ns.model(
-            "PluginEndpointListResponse", {"endpoints": fields.List(fields.Raw(description="Endpoint information"))}
-        ),
+        console_ns.models[PluginEndpointListResponse.__name__],
     )
     @setup_required
     @login_required
@@ -163,7 +198,7 @@ class EndpointDeleteApi(Resource):
     @console_ns.response(
         200,
         "Endpoint deleted successfully",
-        console_ns.model("EndpointDeleteResponse", {"success": fields.Boolean(description="Operation success")}),
+        console_ns.models[EndpointDeleteResponse.__name__],
     )
     @console_ns.response(403, "Admin privileges required")
     @setup_required
@@ -190,7 +225,7 @@ class EndpointUpdateApi(Resource):
     @console_ns.response(
         200,
         "Endpoint updated successfully",
-        console_ns.model("EndpointUpdateResponse", {"success": fields.Boolean(description="Operation success")}),
+        console_ns.models[EndpointUpdateResponse.__name__],
     )
     @console_ns.response(403, "Admin privileges required")
     @setup_required
@@ -221,7 +256,7 @@ class EndpointEnableApi(Resource):
     @console_ns.response(
         200,
         "Endpoint enabled successfully",
-        console_ns.model("EndpointEnableResponse", {"success": fields.Boolean(description="Operation success")}),
+        console_ns.models[EndpointEnableResponse.__name__],
     )
     @console_ns.response(403, "Admin privileges required")
     @setup_required
@@ -248,7 +283,7 @@ class EndpointDisableApi(Resource):
     @console_ns.response(
         200,
         "Endpoint disabled successfully",
-        console_ns.model("EndpointDisableResponse", {"success": fields.Boolean(description="Operation success")}),
+        console_ns.models[EndpointDisableResponse.__name__],
     )
     @console_ns.response(403, "Admin privileges required")
     @setup_required

api/controllers/console/workspace/members.py

@@ -1,12 +1,12 @@
 from urllib import parse
 
 from flask import abort, request
-from flask_restx import Resource, fields, marshal_with
-from pydantic import BaseModel, Field
+from flask_restx import Resource
+from pydantic import BaseModel, Field, TypeAdapter
 
 import services
 from configs import dify_config
-from controllers.common.schema import get_or_create_model, register_enum_models
+from controllers.common.schema import register_enum_models, register_schema_models
 from controllers.console import console_ns
 from controllers.console.auth.error import (
     CannotTransferOwnerToSelfError,
@@ -25,7 +25,7 @@ from controllers.console.wraps import (
     setup_required,
 )
 from extensions.ext_database import db
-from fields.member_fields import account_with_role_fields, account_with_role_list_fields
+from fields.member_fields import AccountWithRole, AccountWithRoleList
 from libs.helper import extract_remote_ip
 from libs.login import current_account_with_tenant, login_required
 from models.account import Account, TenantAccountRole
@@ -69,12 +69,7 @@ reg(OwnerTransferEmailPayload)
 reg(OwnerTransferCheckPayload)
 reg(OwnerTransferPayload)
 register_enum_models(console_ns, TenantAccountRole)
-
-account_with_role_model = get_or_create_model("AccountWithRole", account_with_role_fields)
-
-account_with_role_list_fields_copy = account_with_role_list_fields.copy()
-account_with_role_list_fields_copy["accounts"] = fields.List(fields.Nested(account_with_role_model))
-account_with_role_list_model = get_or_create_model("AccountWithRoleList", account_with_role_list_fields_copy)
+register_schema_models(console_ns, AccountWithRole, AccountWithRoleList)
 
 
 @console_ns.route("/workspaces/current/members")
@@ -84,13 +79,15 @@ class MemberListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_with_role_list_model)
+    @console_ns.response(200, "Success", console_ns.models[AccountWithRoleList.__name__])
     def get(self):
         current_user, _ = current_account_with_tenant()
         if not current_user.current_tenant:
             raise ValueError("No current tenant")
         members = TenantService.get_tenant_members(current_user.current_tenant)
-        return {"result": "success", "accounts": members}, 200
+        member_models = TypeAdapter(list[AccountWithRole]).validate_python(members, from_attributes=True)
+        response = AccountWithRoleList(accounts=member_models)
+        return response.model_dump(mode="json"), 200
 
 
 @console_ns.route("/workspaces/current/members/invite-email")
@@ -235,13 +232,15 @@ class DatasetOperatorMemberListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_with_role_list_model)
+    @console_ns.response(200, "Success", console_ns.models[AccountWithRoleList.__name__])
     def get(self):
         current_user, _ = current_account_with_tenant()
         if not current_user.current_tenant:
             raise ValueError("No current tenant")
         members = TenantService.get_dataset_operator_members(current_user.current_tenant)
-        return {"result": "success", "accounts": members}, 200
+        member_models = TypeAdapter(list[AccountWithRole]).validate_python(members, from_attributes=True)
+        response = AccountWithRoleList(accounts=member_models)
+        return response.model_dump(mode="json"), 200
 
 
 @console_ns.route("/workspaces/current/members/send-owner-transfer-confirm-email")

api/controllers/service_api/app/annotation.py

@@ -1,16 +1,16 @@
 from typing import Literal
 
 from flask import request
-from flask_restx import Namespace, Resource, fields
+from flask_restx import Resource
 from flask_restx.api import HTTPStatus
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, TypeAdapter
 
 from controllers.common.schema import register_schema_models
 from controllers.console.wraps import edit_permission_required
 from controllers.service_api import service_api_ns
 from controllers.service_api.wraps import validate_app_token
 from extensions.ext_redis import redis_client
-from fields.annotation_fields import annotation_fields, build_annotation_model
+from fields.annotation_fields import Annotation, AnnotationList
 from models.model import ApiToken, App  # extend - 密钥额度限制，新增ApiToken
 from services.annotation_service import AppAnnotationService
 
@@ -26,7 +26,9 @@ class AnnotationReplyActionPayload(BaseModel):
     embedding_model_name: str = Field(description="Embedding model name")
 
 
-register_schema_models(service_api_ns, AnnotationCreatePayload, AnnotationReplyActionPayload)
+register_schema_models(
+    service_api_ns, AnnotationCreatePayload, AnnotationReplyActionPayload, Annotation, AnnotationList
+)
 
 
 @service_api_ns.route("/apps/annotation-reply/<string:action>")
@@ -45,10 +47,11 @@ class AnnotationReplyActionApi(Resource):
     def post(self, app_model: App, action: Literal["enable", "disable"]):
         """Enable or disable annotation reply feature."""
         args = AnnotationReplyActionPayload.model_validate(service_api_ns.payload or {}).model_dump()
-        if action == "enable":
-            result = AppAnnotationService.enable_app_annotation(args, app_model.id)
-        elif action == "disable":
-            result = AppAnnotationService.disable_app_annotation(app_model.id)
+        match action:
+            case "enable":
+                result = AppAnnotationService.enable_app_annotation(args, app_model.id)
+            case "disable":
+                result = AppAnnotationService.disable_app_annotation(app_model.id)
         return result, 200
 
 
@@ -82,23 +85,6 @@ class AnnotationReplyActionStatusApi(Resource):
         return {"job_id": job_id, "job_status": job_status, "error_msg": error_msg}, 200
 
 
-# Define annotation list response model
-annotation_list_fields = {
-    "data": fields.List(fields.Nested(annotation_fields)),
-    "has_more": fields.Boolean,
-    "limit": fields.Integer,
-    "total": fields.Integer,
-    "page": fields.Integer,
-}
-
-
-def build_annotation_list_model(api_or_ns: Namespace):
-    """Build the annotation list model for the API or Namespace."""
-    copied_annotation_list_fields = annotation_list_fields.copy()
-    copied_annotation_list_fields["data"] = fields.List(fields.Nested(build_annotation_model(api_or_ns)))
-    return api_or_ns.model("AnnotationList", copied_annotation_list_fields)
-
-
 @service_api_ns.route("/apps/annotations")
 class AnnotationListApi(Resource):
     @service_api_ns.doc("list_annotations")
@@ -109,8 +95,12 @@ class AnnotationListApi(Resource):
             401: "Unauthorized - invalid API token",
         }
     )
+    @service_api_ns.response(
+        200,
+        "Annotations retrieved successfully",
+        service_api_ns.models[AnnotationList.__name__],
+    )
     @validate_app_token
-    @service_api_ns.marshal_with(build_annotation_list_model(service_api_ns))
     def get(self, app_model: App, api_token: ApiToken):  # extend - 密钥额度限制，新增api_token
         """List annotations for the application."""
         page = request.args.get("page", default=1, type=int)
@@ -118,13 +108,15 @@ class AnnotationListApi(Resource):
         keyword = request.args.get("keyword", default="", type=str)
 
         annotation_list, total = AppAnnotationService.get_annotation_list_by_app_id(app_model.id, page, limit, keyword)
-        return {
-            "data": annotation_list,
-            "has_more": len(annotation_list) == limit,
-            "limit": limit,
-            "total": total,
-            "page": page,
-        }
+        annotation_models = TypeAdapter(list[Annotation]).validate_python(annotation_list, from_attributes=True)
+        response = AnnotationList(
+            data=annotation_models,
+            has_more=len(annotation_list) == limit,
+            limit=limit,
+            total=total,
+            page=page,
+        )
+        return response.model_dump(mode="json")
 
     @service_api_ns.expect(service_api_ns.models[AnnotationCreatePayload.__name__])
     @service_api_ns.doc("create_annotation")
@@ -135,13 +127,18 @@ class AnnotationListApi(Resource):
             401: "Unauthorized - invalid API token",
         }
     )
+    @service_api_ns.response(
+        HTTPStatus.CREATED,
+        "Annotation created successfully",
+        service_api_ns.models[Annotation.__name__],
+    )
     @validate_app_token
-    @service_api_ns.marshal_with(build_annotation_model(service_api_ns), code=HTTPStatus.CREATED)
     def post(self, app_model: App):
         """Create a new annotation."""
         args = AnnotationCreatePayload.model_validate(service_api_ns.payload or {}).model_dump()
         annotation = AppAnnotationService.insert_app_annotation_directly(args, app_model.id)
-        return annotation, 201
+        response = Annotation.model_validate(annotation, from_attributes=True)
+        return response.model_dump(mode="json"), HTTPStatus.CREATED
 
 
 @service_api_ns.route("/apps/annotations/<uuid:annotation_id>")
@@ -158,14 +155,19 @@ class AnnotationUpdateDeleteApi(Resource):
             404: "Annotation not found",
         }
     )
+    @service_api_ns.response(
+        200,
+        "Annotation updated successfully",
+        service_api_ns.models[Annotation.__name__],
+    )
     @validate_app_token
     @edit_permission_required
-    @service_api_ns.marshal_with(build_annotation_model(service_api_ns))
     def put(self, app_model: App, annotation_id: str):
         """Update an existing annotation."""
         args = AnnotationCreatePayload.model_validate(service_api_ns.payload or {}).model_dump()
         annotation = AppAnnotationService.update_app_annotation_directly(args, app_model.id, annotation_id)
-        return annotation
+        response = Annotation.model_validate(annotation, from_attributes=True)
+        return response.model_dump(mode="json")
 
     @service_api_ns.doc("delete_annotation")
     @service_api_ns.doc(description="Delete an annotation")

api/controllers/service_api/app/completion.py

@@ -30,6 +30,7 @@ from core.errors.error import (
 from core.helper.trace_id_helper import get_external_trace_id
 from core.model_runtime.errors.invoke import InvokeError
 from libs import helper
+from libs.helper import UUIDStrOrEmpty
 from models.model import ApiToken, App, AppMode, EndUser  # extend: 密钥额度限制，新增ApiToken
 from services.app_generate_service import AppGenerateService
 from services.app_generate_service_extend import AppGenerateServiceExtend  # extend: 密钥额度限制，新增ApiToken
@@ -53,7 +54,7 @@ class ChatRequestPayload(BaseModel):
     query: str
     files: list[dict[str, Any]] | None = None
     response_mode: Literal["blocking", "streaming"] | None = None
-    conversation_id: str | None = Field(default=None, description="Conversation UUID")
+    conversation_id: UUIDStrOrEmpty | None = Field(default=None, description="Conversation UUID")
     retriever_from: str = Field(default="dev")
     auto_generate_name: bool = Field(default=True, description="Auto generate conversation name")
     workflow_id: str | None = Field(default=None, description="Workflow ID for advanced chat")

api/controllers/service_api/app/conversation.py

@@ -1,5 +1,4 @@
 from typing import Any, Literal
-from uuid import UUID
 
 from flask import request
 from flask_restx import Resource
@@ -23,12 +22,13 @@ from fields.conversation_variable_fields import (
     build_conversation_variable_infinite_scroll_pagination_model,
     build_conversation_variable_model,
 )
+from libs.helper import UUIDStrOrEmpty
 from models.model import ApiToken, App, AppMode, EndUser  # extend - 密钥额度限制，新增api_token,否则上传文件会报错
 from services.conversation_service import ConversationService
 
 
 class ConversationListQuery(BaseModel):
-    last_id: UUID | None = Field(default=None, description="Last conversation ID for pagination")
+    last_id: UUIDStrOrEmpty | None = Field(default=None, description="Last conversation ID for pagination")
     limit: int = Field(default=20, ge=1, le=100, description="Number of conversations to return")
     sort_by: Literal["created_at", "-created_at", "updated_at", "-updated_at"] = Field(
         default="-updated_at", description="Sort order for conversations"
@@ -48,7 +48,7 @@ class ConversationRenamePayload(BaseModel):
 
 
 class ConversationVariablesQuery(BaseModel):
-    last_id: UUID | None = Field(default=None, description="Last variable ID for pagination")
+    last_id: UUIDStrOrEmpty | None = Field(default=None, description="Last variable ID for pagination")
     limit: int = Field(default=20, ge=1, le=100, description="Number of variables to return")
     variable_name: str | None = Field(
         default=None, description="Filter variables by name", min_length=1, max_length=255

api/controllers/service_api/app/message.py

@@ -1,6 +1,5 @@
 import logging
 from typing import Literal
-from uuid import UUID
 
 from flask import request
 from flask_restx import Resource
@@ -15,6 +14,7 @@ from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate
 from core.app.entities.app_invoke_entities import InvokeFrom
 from fields.conversation_fields import ResultResponse
 from fields.message_fields import MessageInfiniteScrollPagination, MessageListItem
+from libs.helper import UUIDStrOrEmpty
 from models.model import ApiToken, App, AppMode, EndUser  # extend - 密钥额度限制，新增ApiToken
 from services.errors.message import (
     FirstMessageNotExistsError,
@@ -27,8 +27,8 @@ logger = logging.getLogger(__name__)
 
 
 class MessageListQuery(BaseModel):
-    conversation_id: UUID
-    first_id: UUID | None = None
+    conversation_id: UUIDStrOrEmpty
+    first_id: UUIDStrOrEmpty | None = None
     limit: int = Field(default=20, ge=1, le=100, description="Number of messages to return")
 
 

api/controllers/service_api/dataset/dataset.py

@@ -17,7 +17,7 @@ from controllers.service_api.wraps import (
 from core.model_runtime.entities.model_entities import ModelType
 from core.provider_manager import ProviderManager
 from fields.dataset_fields import dataset_detail_fields
-from fields.tag_fields import build_dataset_tag_fields
+from fields.tag_fields import DataSetTag
 from libs.login import current_user
 from models.account import Account
 from models.dataset import DatasetPermissionEnum
@@ -46,6 +46,7 @@ class DatasetCreatePayload(BaseModel):
     retrieval_model: RetrievalModel | None = None
     embedding_model: str | None = None
     embedding_model_provider: str | None = None
+    summary_index_setting: dict | None = None
 
 
 class DatasetUpdatePayload(BaseModel):
@@ -113,6 +114,7 @@ register_schema_models(
     TagBindingPayload,
     TagUnbindingPayload,
     DatasetListQuery,
+    DataSetTag,
 )
 
 
@@ -217,6 +219,7 @@ class DatasetListApi(DatasetApiResource):
                 embedding_model_provider=payload.embedding_model_provider,
                 embedding_model_name=payload.embedding_model,
                 retrieval_model=payload.retrieval_model,
+                summary_index_setting=payload.summary_index_setting,
             )
         except services.errors.dataset.DatasetNameDuplicateError:
             raise DatasetNameDuplicateError()
@@ -478,15 +481,14 @@ class DatasetTagsApi(DatasetApiResource):
             401: "Unauthorized - invalid API token",
         }
     )
-    @service_api_ns.marshal_with(build_dataset_tag_fields(service_api_ns))
     def get(self, _):
         """Get all knowledge type tags."""
         assert isinstance(current_user, Account)
         cid = current_user.current_tenant_id
         assert cid is not None
         tags = TagService.get_tags("knowledge", cid)
-
-        return tags, 200
+        tag_models = TypeAdapter(list[DataSetTag]).validate_python(tags, from_attributes=True)
+        return [tag.model_dump(mode="json") for tag in tag_models], 200
 
     @service_api_ns.expect(service_api_ns.models[TagCreatePayload.__name__])
     @service_api_ns.doc("create_dataset_tag")
@@ -498,7 +500,6 @@ class DatasetTagsApi(DatasetApiResource):
             403: "Forbidden - insufficient permissions",
         }
     )
-    @service_api_ns.marshal_with(build_dataset_tag_fields(service_api_ns))
     def post(self, _):
         """Add a knowledge type tag."""
         assert isinstance(current_user, Account)
@@ -508,7 +509,9 @@ class DatasetTagsApi(DatasetApiResource):
         payload = TagCreatePayload.model_validate(service_api_ns.payload or {})
         tag = TagService.save_tags({"name": payload.name, "type": "knowledge"})
 
-        response = {"id": tag.id, "name": tag.name, "type": tag.type, "binding_count": 0}
+        response = DataSetTag.model_validate(
+            {"id": tag.id, "name": tag.name, "type": tag.type, "binding_count": 0}
+        ).model_dump(mode="json")
         return response, 200
 
     @service_api_ns.expect(service_api_ns.models[TagUpdatePayload.__name__])
@@ -521,7 +524,6 @@ class DatasetTagsApi(DatasetApiResource):
             403: "Forbidden - insufficient permissions",
         }
     )
-    @service_api_ns.marshal_with(build_dataset_tag_fields(service_api_ns))
     def patch(self, _):
         assert isinstance(current_user, Account)
         if not (current_user.has_edit_permission or current_user.is_dataset_editor):
@@ -534,8 +536,9 @@ class DatasetTagsApi(DatasetApiResource):
 
         binding_count = TagService.get_tag_binding_count(tag_id)
 
-        response = {"id": tag.id, "name": tag.name, "type": tag.type, "binding_count": binding_count}
-
+        response = DataSetTag.model_validate(
+            {"id": tag.id, "name": tag.name, "type": tag.type, "binding_count": binding_count}
+        ).model_dump(mode="json")
         return response, 200
 
     @service_api_ns.expect(service_api_ns.models[TagDeletePayload.__name__])

api/controllers/service_api/dataset/document.py

@@ -45,6 +45,7 @@ from services.entities.knowledge_entities.knowledge_entities import (
     Segmentation,
 )
 from services.file_service import FileService
+from services.summary_index_service import SummaryIndexService
 
 
 class DocumentTextCreatePayload(BaseModel):
@@ -508,6 +509,12 @@ class DocumentListApi(DatasetApiResource):
         )
         documents = paginated_documents.items
 
+        DocumentService.enrich_documents_with_summary_index_status(
+            documents=documents,
+            dataset=dataset,
+            tenant_id=tenant_id,
+        )
+
         response = {
             "data": marshal(documents, document_fields),
             "has_more": len(documents) == query_params.limit,
@@ -612,6 +619,16 @@ class DocumentApi(DatasetApiResource):
         if metadata not in self.METADATA_CHOICES:
             raise InvalidMetadataError(f"Invalid metadata value: {metadata}")
 
+        # Calculate summary_index_status if needed
+        summary_index_status = None
+        has_summary_index = dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True
+        if has_summary_index and document.need_summary is True:
+            summary_index_status = SummaryIndexService.get_document_summary_index_status(
+                document_id=document_id,
+                dataset_id=dataset_id,
+                tenant_id=tenant_id,
+            )
+
         if metadata == "only":
             response = {"id": document.id, "doc_type": document.doc_type, "doc_metadata": document.doc_metadata_details}
         elif metadata == "without":
@@ -646,6 +663,8 @@ class DocumentApi(DatasetApiResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "summary_index_status": summary_index_status,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
         else:
             dataset_process_rules = DatasetService.get_process_rules(dataset_id)
@@ -681,6 +700,8 @@ class DocumentApi(DatasetApiResource):
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
                 "doc_language": document.doc_language,
+                "summary_index_status": summary_index_status,
+                "need_summary": document.need_summary if document.need_summary is not None else False,
             }
 
         return response

api/controllers/service_api/dataset/hit_testing.py

@@ -1,7 +1,10 @@
-from controllers.console.datasets.hit_testing_base import DatasetsHitTestingBase
+from controllers.common.schema import register_schema_model
+from controllers.console.datasets.hit_testing_base import DatasetsHitTestingBase, HitTestingPayload
 from controllers.service_api import service_api_ns
 from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_rate_limit_check
 
+register_schema_model(service_api_ns, HitTestingPayload)
+
 
 @service_api_ns.route("/datasets/<uuid:dataset_id>/hit-testing", "/datasets/<uuid:dataset_id>/retrieve")
 class HitTestingApi(DatasetApiResource, DatasetsHitTestingBase):
@@ -15,6 +18,7 @@ class HitTestingApi(DatasetApiResource, DatasetsHitTestingBase):
             404: "Dataset not found",
         }
     )
+    @service_api_ns.expect(service_api_ns.models[HitTestingPayload.__name__])
     @cloud_edition_billing_rate_limit_check("knowledge", "dataset")
     def post(self, tenant_id, dataset_id):
         """Perform hit testing on a dataset.

api/controllers/service_api/dataset/metadata.py

@@ -168,10 +168,11 @@ class DatasetMetadataBuiltInFieldActionServiceApi(DatasetApiResource):
             raise NotFound("Dataset not found.")
         DatasetService.check_dataset_permission(dataset, current_user)
 
-        if action == "enable":
-            MetadataService.enable_built_in_field(dataset)
-        elif action == "disable":
-            MetadataService.disable_built_in_field(dataset)
+        match action:
+            case "enable":
+                MetadataService.enable_built_in_field(dataset)
+            case "disable":
+                MetadataService.disable_built_in_field(dataset)
         return {"result": "success"}, 200
 
 

api/controllers/service_api/wraps.py

@@ -126,14 +126,14 @@ def validate_app_token(view: Callable[P, R] | None = None, *, fetch_user_arg: Fe
 
             # If caller needs end-user context, attach EndUser to current_user
             if fetch_user_arg:
-                if fetch_user_arg.fetch_from == WhereisUserArg.QUERY:
-                    user_id = request.args.get("user")
-                elif fetch_user_arg.fetch_from == WhereisUserArg.JSON:
-                    user_id = request.get_json().get("user")
-                elif fetch_user_arg.fetch_from == WhereisUserArg.FORM:
-                    user_id = request.form.get("user")
-                else:
-                    user_id = None
+                user_id = None
+                match fetch_user_arg.fetch_from:
+                    case WhereisUserArg.QUERY:
+                        user_id = request.args.get("user")
+                    case WhereisUserArg.JSON:
+                        user_id = request.get_json().get("user")
+                    case WhereisUserArg.FORM:
+                        user_id = request.form.get("user")
 
                 if not user_id and fetch_user_arg.required:
                     raise ValueError("Arg user must be provided.")

api/core/app/app_config/easy_ui_based_app/agent/manager.py

@@ -14,16 +14,17 @@ class AgentConfigManager:
             agent_dict = config.get("agent_mode", {})
             agent_strategy = agent_dict.get("strategy", "cot")
 
-            if agent_strategy == "function_call":
-                strategy = AgentEntity.Strategy.FUNCTION_CALLING
-            elif agent_strategy in {"cot", "react"}:
-                strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
-            else:
-                # old configs, try to detect default strategy
-                if config["model"]["provider"] == "openai":
+            match agent_strategy:
+                case "function_call":
                     strategy = AgentEntity.Strategy.FUNCTION_CALLING
-                else:
+                case "cot" | "react":
                     strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
+                case _:
+                    # old configs, try to detect default strategy
+                    if config["model"]["provider"] == "openai":
+                        strategy = AgentEntity.Strategy.FUNCTION_CALLING
+                    else:
+                        strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
 
             agent_tools = []
             for tool in agent_dict.get("tools", []):

api/core/app/apps/base_app_generate_response_converter.py

@@ -79,6 +79,7 @@ class AppGenerateResponseConverter(ABC):
                         "document_name": resource["document_name"],
                         "score": resource["score"],
                         "content": resource["content"],
+                        "summary": resource.get("summary"),
                     }
                 )
             metadata["retriever_resources"] = updated_resources

api/core/app/apps/common/workflow_response_converter.py

@@ -250,7 +250,7 @@ class WorkflowResponseConverter:
             data=WorkflowFinishStreamResponse.Data(
                 id=run_id,
                 workflow_id=workflow_id,
-                status=status.value,
+                status=status,
                 outputs=encoded_outputs,
                 error=error,
                 elapsed_time=elapsed_time,
@@ -340,13 +340,13 @@ class WorkflowResponseConverter:
         metadata = self._merge_metadata(event.execution_metadata, snapshot)
 
         if isinstance(event, QueueNodeSucceededEvent):
-            status = WorkflowNodeExecutionStatus.SUCCEEDED.value
+            status = WorkflowNodeExecutionStatus.SUCCEEDED
             error_message = event.error
         elif isinstance(event, QueueNodeFailedEvent):
-            status = WorkflowNodeExecutionStatus.FAILED.value
+            status = WorkflowNodeExecutionStatus.FAILED
             error_message = event.error
         else:
-            status = WorkflowNodeExecutionStatus.EXCEPTION.value
+            status = WorkflowNodeExecutionStatus.EXCEPTION
             error_message = event.error
 
         return NodeFinishStreamResponse(
@@ -413,7 +413,7 @@ class WorkflowResponseConverter:
                 process_data_truncated=process_data_truncated,
                 outputs=outputs,
                 outputs_truncated=outputs_truncated,
-                status=WorkflowNodeExecutionStatus.RETRY.value,
+                status=WorkflowNodeExecutionStatus.RETRY,
                 error=event.error,
                 elapsed_time=elapsed_time,
                 execution_metadata=metadata,

api/core/app/apps/pipeline/pipeline_generator.py

@@ -120,7 +120,7 @@ class PipelineGenerator(BaseAppGenerator):
                 raise ValueError("Pipeline dataset is required")
         inputs: Mapping[str, Any] = args["inputs"]
         start_node_id: str = args["start_node_id"]
-        datasource_type: str = args["datasource_type"]
+        datasource_type = DatasourceProviderType(args["datasource_type"])
         datasource_info_list: list[Mapping[str, Any]] = self._format_datasource_info_list(
             datasource_type, args["datasource_info_list"], pipeline, workflow, start_node_id, user
         )
@@ -660,7 +660,7 @@ class PipelineGenerator(BaseAppGenerator):
         tenant_id: str,
         dataset_id: str,
         built_in_field_enabled: bool,
-        datasource_type: str,
+        datasource_type: DatasourceProviderType,
         datasource_info: Mapping[str, Any],
         created_from: str,
         position: int,
@@ -668,17 +668,17 @@ class PipelineGenerator(BaseAppGenerator):
         batch: str,
         document_form: str,
     ):
-        if datasource_type == "local_file":
-            name = datasource_info.get("name", "untitled")
-        elif datasource_type == "online_document":
-            name = datasource_info.get("page", {}).get("page_name", "untitled")
-        elif datasource_type == "website_crawl":
-            name = datasource_info.get("title", "untitled")
-        elif datasource_type == "online_drive":
-            name = datasource_info.get("name", "untitled")
-        else:
-            raise ValueError(f"Unsupported datasource type: {datasource_type}")
-
+        match datasource_type:
+            case DatasourceProviderType.LOCAL_FILE:
+                name = datasource_info.get("name", "untitled")
+            case DatasourceProviderType.ONLINE_DOCUMENT:
+                name = datasource_info.get("page", {}).get("page_name", "untitled")
+            case DatasourceProviderType.WEBSITE_CRAWL:
+                name = datasource_info.get("title", "untitled")
+            case DatasourceProviderType.ONLINE_DRIVE:
+                name = datasource_info.get("name", "untitled")
+            case _:
+                raise ValueError(f"Unsupported datasource type: {datasource_type}")
         document = Document(
             tenant_id=tenant_id,
             dataset_id=dataset_id,
@@ -706,7 +706,7 @@ class PipelineGenerator(BaseAppGenerator):
 
     def _format_datasource_info_list(
         self,
-        datasource_type: str,
+        datasource_type: DatasourceProviderType,
         datasource_info_list: list[Mapping[str, Any]],
         pipeline: Pipeline,
         workflow: Workflow,
@@ -716,7 +716,7 @@ class PipelineGenerator(BaseAppGenerator):
         """
         Format datasource info list.
         """
-        if datasource_type == "online_drive":
+        if datasource_type == DatasourceProviderType.ONLINE_DRIVE:
             all_files: list[Mapping[str, Any]] = []
             datasource_node_data = None
             datasource_nodes = workflow.graph_dict.get("nodes", [])

api/core/app/entities/task_entities.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.workflow.entities import AgentNodeStrategyInit
-from core.workflow.enums import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from core.workflow.enums import WorkflowExecutionStatus, WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 
 
 class AnnotationReplyAccount(BaseModel):
@@ -223,7 +223,7 @@ class WorkflowFinishStreamResponse(StreamResponse):
 
         id: str
         workflow_id: str
-        status: str
+        status: WorkflowExecutionStatus
         outputs: Mapping[str, Any] | None = None
         error: str | None = None
         elapsed_time: float
@@ -311,7 +311,7 @@ class NodeFinishStreamResponse(StreamResponse):
         process_data_truncated: bool = False
         outputs: Mapping[str, Any] | None = None
         outputs_truncated: bool = True
-        status: str
+        status: WorkflowNodeExecutionStatus
         error: str | None = None
         elapsed_time: float
         execution_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] | None = None
@@ -375,7 +375,7 @@ class NodeRetryStreamResponse(StreamResponse):
         process_data_truncated: bool = False
         outputs: Mapping[str, Any] | None = None
         outputs_truncated: bool = False
-        status: str
+        status: WorkflowNodeExecutionStatus
         error: str | None = None
         elapsed_time: float
         execution_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] | None = None
@@ -719,7 +719,7 @@ class WorkflowAppBlockingResponse(AppBlockingResponse):
 
         id: str
         workflow_id: str
-        status: str
+        status: WorkflowExecutionStatus
         outputs: Mapping[str, Any] | None = None
         error: str | None = None
         elapsed_time: float

api/core/entities/knowledge_entities.py

@@ -3,6 +3,7 @@ from pydantic import BaseModel, Field, field_validator
 
 class PreviewDetail(BaseModel):
     content: str
+    summary: str | None = None
     child_chunks: list[str] | None = None
 
 

api/core/helper/code_executor/code_node_provider.py

@@ -47,15 +47,16 @@ class CodeNodeProvider(BaseModel, ABC):
 
     @classmethod
     def get_default_config(cls) -> DefaultConfig:
-        return {
-            "type": "code",
-            "config": {
-                "variables": [
-                    {"variable": "arg1", "value_selector": []},
-                    {"variable": "arg2", "value_selector": []},
-                ],
-                "code_language": cls.get_language(),
-                "code": cls.get_default_code(),
-                "outputs": {"result": {"type": "string", "children": None}},
-            },
+        variables: list[VariableConfig] = [
+            {"variable": "arg1", "value_selector": []},
+            {"variable": "arg2", "value_selector": []},
+        ]
+        outputs: dict[str, OutputConfig] = {"result": {"type": "string", "children": None}}
+
+        config: CodeConfig = {
+            "variables": variables,
+            "code_language": cls.get_language(),
+            "code": cls.get_default_code(),
+            "outputs": outputs,
         }
+        return {"type": "code", "config": config}

api/core/indexing_runner.py

@@ -311,14 +311,18 @@ class IndexingRunner:
         qa_preview_texts: list[QAPreviewDetail] = []
 
         total_segments = 0
+        # doc_form represents the segmentation method (general, parent-child, QA)
         index_type = doc_form
         index_processor = IndexProcessorFactory(index_type).init_index_processor()
+        # one extract_setting is one source document
         for extract_setting in extract_settings:
             # extract
             processing_rule = DatasetProcessRule(
                 mode=tmp_processing_rule["mode"], rules=json.dumps(tmp_processing_rule["rules"])
             )
+            # Extract document content
             text_docs = index_processor.extract(extract_setting, process_rule_mode=tmp_processing_rule["mode"])
+            # Cleaning and segmentation
             documents = index_processor.transform(
                 text_docs,
                 current_user=None,
@@ -361,75 +365,82 @@ class IndexingRunner:
 
         if doc_form and doc_form == "qa_model":
             return IndexingEstimate(total_segments=total_segments * 20, qa_preview=qa_preview_texts, preview=[])
+
+        # Generate summary preview
+        summary_index_setting = tmp_processing_rule.get("summary_index_setting")
+        if summary_index_setting and summary_index_setting.get("enable") and preview_texts:
+            preview_texts = index_processor.generate_summary_preview(
+                tenant_id, preview_texts, summary_index_setting, doc_language
+            )
+
         return IndexingEstimate(total_segments=total_segments, preview=preview_texts)
 
     def _extract(
         self, index_processor: BaseIndexProcessor, dataset_document: DatasetDocument, process_rule: dict
     ) -> list[Document]:
-        # load file
-        if dataset_document.data_source_type not in {"upload_file", "notion_import", "website_crawl"}:
-            return []
-
         data_source_info = dataset_document.data_source_info_dict
         text_docs = []
-        if dataset_document.data_source_type == "upload_file":
-            if not data_source_info or "upload_file_id" not in data_source_info:
-                raise ValueError("no upload file found")
-            stmt = select(UploadFile).where(UploadFile.id == data_source_info["upload_file_id"])
-            file_detail = db.session.scalars(stmt).one_or_none()
+        match dataset_document.data_source_type:
+            case "upload_file":
+                if not data_source_info or "upload_file_id" not in data_source_info:
+                    raise ValueError("no upload file found")
+                stmt = select(UploadFile).where(UploadFile.id == data_source_info["upload_file_id"])
+                file_detail = db.session.scalars(stmt).one_or_none()
 
-            if file_detail:
+                if file_detail:
+                    extract_setting = ExtractSetting(
+                        datasource_type=DatasourceType.FILE,
+                        upload_file=file_detail,
+                        document_model=dataset_document.doc_form,
+                    )
+                    text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
+            case "notion_import":
+                if (
+                    not data_source_info
+                    or "notion_workspace_id" not in data_source_info
+                    or "notion_page_id" not in data_source_info
+                ):
+                    raise ValueError("no notion import info found")
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.FILE,
-                    upload_file=file_detail,
+                    datasource_type=DatasourceType.NOTION,
+                    notion_info=NotionInfo.model_validate(
+                        {
+                            "credential_id": data_source_info.get("credential_id"),
+                            "notion_workspace_id": data_source_info["notion_workspace_id"],
+                            "notion_obj_id": data_source_info["notion_page_id"],
+                            "notion_page_type": data_source_info["type"],
+                            "document": dataset_document,
+                            "tenant_id": dataset_document.tenant_id,
+                        }
+                    ),
                     document_model=dataset_document.doc_form,
                 )
                 text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
-        elif dataset_document.data_source_type == "notion_import":
-            if (
-                not data_source_info
-                or "notion_workspace_id" not in data_source_info
-                or "notion_page_id" not in data_source_info
-            ):
-                raise ValueError("no notion import info found")
-            extract_setting = ExtractSetting(
-                datasource_type=DatasourceType.NOTION,
-                notion_info=NotionInfo.model_validate(
-                    {
-                        "credential_id": data_source_info.get("credential_id"),
-                        "notion_workspace_id": data_source_info["notion_workspace_id"],
-                        "notion_obj_id": data_source_info["notion_page_id"],
-                        "notion_page_type": data_source_info["type"],
-                        "document": dataset_document,
-                        "tenant_id": dataset_document.tenant_id,
-                    }
-                ),
-                document_model=dataset_document.doc_form,
-            )
-            text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
-        elif dataset_document.data_source_type == "website_crawl":
-            if (
-                not data_source_info
-                or "provider" not in data_source_info
-                or "url" not in data_source_info
-                or "job_id" not in data_source_info
-            ):
-                raise ValueError("no website import info found")
-            extract_setting = ExtractSetting(
-                datasource_type=DatasourceType.WEBSITE,
-                website_info=WebsiteInfo.model_validate(
-                    {
-                        "provider": data_source_info["provider"],
-                        "job_id": data_source_info["job_id"],
-                        "tenant_id": dataset_document.tenant_id,
-                        "url": data_source_info["url"],
-                        "mode": data_source_info["mode"],
-                        "only_main_content": data_source_info["only_main_content"],
-                    }
-                ),
-                document_model=dataset_document.doc_form,
-            )
-            text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
+            case "website_crawl":
+                if (
+                    not data_source_info
+                    or "provider" not in data_source_info
+                    or "url" not in data_source_info
+                    or "job_id" not in data_source_info
+                ):
+                    raise ValueError("no website import info found")
+                extract_setting = ExtractSetting(
+                    datasource_type=DatasourceType.WEBSITE,
+                    website_info=WebsiteInfo.model_validate(
+                        {
+                            "provider": data_source_info["provider"],
+                            "job_id": data_source_info["job_id"],
+                            "tenant_id": dataset_document.tenant_id,
+                            "url": data_source_info["url"],
+                            "mode": data_source_info["mode"],
+                            "only_main_content": data_source_info["only_main_content"],
+                        }
+                    ),
+                    document_model=dataset_document.doc_form,
+                )
+                text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
+            case _:
+                return []
         # update document status to splitting
         self._update_document_index_status(
             document_id=dataset_document.id,

api/core/llm_generator/entities.py

@@ -0,0 +1,20 @@
+"""Shared payload models for LLM generator helpers and controllers."""
+
+from pydantic import BaseModel, Field
+
+from core.app.app_config.entities import ModelConfig
+
+
+class RuleGeneratePayload(BaseModel):
+    instruction: str = Field(..., description="Rule generation instruction")
+    model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")
+    no_variable: bool = Field(default=False, description="Whether to exclude variables")
+
+
+class RuleCodeGeneratePayload(RuleGeneratePayload):
+    code_language: str = Field(default="javascript", description="Programming language for code generation")
+
+
+class RuleStructuredOutputPayload(BaseModel):
+    instruction: str = Field(..., description="Structured output generation instruction")
+    model_config_data: ModelConfig = Field(..., alias="model_config", description="Model configuration")

api/core/llm_generator/llm_generator.py

@@ -6,6 +6,8 @@ from typing import Protocol, cast
 
 import json_repair
 
+from core.app.app_config.entities import ModelConfig
+from core.llm_generator.entities import RuleCodeGeneratePayload, RuleGeneratePayload, RuleStructuredOutputPayload
 from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
 from core.llm_generator.prompts import (
@@ -151,19 +153,19 @@ class LLMGenerator:
         return questions
 
     @classmethod
-    def generate_rule_config(cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool):
+    def generate_rule_config(cls, tenant_id: str, args: RuleGeneratePayload):
         output_parser = RuleConfigGeneratorOutputParser()
 
         error = ""
         error_step = ""
         rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
-        model_parameters = model_config.get("completion_params", {})
-        if no_variable:
+        model_parameters = args.model_config_data.completion_params
+        if args.no_variable:
             prompt_template = PromptTemplateParser(WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
 
             prompt_generate = prompt_template.format(
                 inputs={
-                    "TASK_DESCRIPTION": instruction,
+                    "TASK_DESCRIPTION": args.instruction,
                 },
                 remove_template_variables=False,
             )
@@ -175,8 +177,8 @@ class LLMGenerator:
             model_instance = model_manager.get_model_instance(
                 tenant_id=tenant_id,
                 model_type=ModelType.LLM,
-                provider=model_config.get("provider", ""),
-                model=model_config.get("name", ""),
+                provider=args.model_config_data.provider,
+                model=args.model_config_data.name,
             )
 
             try:
@@ -190,7 +192,7 @@ class LLMGenerator:
                 error = str(e)
                 error_step = "generate rule config"
             except Exception as e:
-                logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
+                logger.exception("Failed to generate rule config, model: %s", args.model_config_data.name)
                 rule_config["error"] = str(e)
 
             rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -209,7 +211,7 @@ class LLMGenerator:
         # format the prompt_generate_prompt
         prompt_generate_prompt = prompt_template.format(
             inputs={
-                "TASK_DESCRIPTION": instruction,
+                "TASK_DESCRIPTION": args.instruction,
             },
             remove_template_variables=False,
         )
@@ -220,8 +222,8 @@ class LLMGenerator:
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
-            model=model_config.get("name", ""),
+            provider=args.model_config_data.provider,
+            model=args.model_config_data.name,
         )
 
         try:
@@ -250,7 +252,7 @@ class LLMGenerator:
             # the second step to generate the task_parameter and task_statement
             statement_generate_prompt = statement_template.format(
                 inputs={
-                    "TASK_DESCRIPTION": instruction,
+                    "TASK_DESCRIPTION": args.instruction,
                     "INPUT_TEXT": prompt_content.message.get_text_content(),
                 },
                 remove_template_variables=False,
@@ -276,7 +278,7 @@ class LLMGenerator:
                 error_step = "generate conversation opener"
 
         except Exception as e:
-            logger.exception("Failed to generate rule config, model: %s", model_config.get("name"))
+            logger.exception("Failed to generate rule config, model: %s", args.model_config_data.name)
             rule_config["error"] = str(e)
 
         rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -284,16 +286,20 @@ class LLMGenerator:
         return rule_config
 
     @classmethod
-    def generate_code(cls, tenant_id: str, instruction: str, model_config: dict, code_language: str = "javascript"):
-        if code_language == "python":
+    def generate_code(
+        cls,
+        tenant_id: str,
+        args: RuleCodeGeneratePayload,
+    ):
+        if args.code_language == "python":
             prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
         else:
             prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
 
         prompt = prompt_template.format(
             inputs={
-                "INSTRUCTION": instruction,
-                "CODE_LANGUAGE": code_language,
+                "INSTRUCTION": args.instruction,
+                "CODE_LANGUAGE": args.code_language,
             },
             remove_template_variables=False,
         )
@@ -302,28 +308,28 @@ class LLMGenerator:
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
-            model=model_config.get("name", ""),
+            provider=args.model_config_data.provider,
+            model=args.model_config_data.name,
         )
 
         prompt_messages = [UserPromptMessage(content=prompt)]
-        model_parameters = model_config.get("completion_params", {})
+        model_parameters = args.model_config_data.completion_params
         try:
             response: LLMResult = model_instance.invoke_llm(
                 prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
             )
 
             generated_code = response.message.get_text_content()
-            return {"code": generated_code, "language": code_language, "error": ""}
+            return {"code": generated_code, "language": args.code_language, "error": ""}
 
         except InvokeError as e:
             error = str(e)
-            return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
+            return {"code": "", "language": args.code_language, "error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
             logger.exception(
-                "Failed to invoke LLM model, model: %s, language: %s", model_config.get("name"), code_language
+                "Failed to invoke LLM model, model: %s, language: %s", args.model_config_data.name, args.code_language
             )
-            return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
+            return {"code": "", "language": args.code_language, "error": f"An unexpected error occurred: {str(e)}"}
 
     @classmethod
     def generate_qa_document(cls, tenant_id: str, query, document_language: str):
@@ -353,20 +359,20 @@ class LLMGenerator:
         return answer.strip()
 
     @classmethod
-    def generate_structured_output(cls, tenant_id: str, instruction: str, model_config: dict):
+    def generate_structured_output(cls, tenant_id: str, args: RuleStructuredOutputPayload):
         model_manager = ModelManager()
         model_instance = model_manager.get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
-            model=model_config.get("name", ""),
+            provider=args.model_config_data.provider,
+            model=args.model_config_data.name,
         )
 
         prompt_messages = [
             SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
-            UserPromptMessage(content=instruction),
+            UserPromptMessage(content=args.instruction),
         ]
-        model_parameters = model_config.get("model_parameters", {})
+        model_parameters = args.model_config_data.completion_params
 
         try:
             response: LLMResult = model_instance.invoke_llm(
@@ -390,12 +396,17 @@ class LLMGenerator:
             error = str(e)
             return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
         except Exception as e:
-            logger.exception("Failed to invoke LLM model, model: %s", model_config.get("name"))
+            logger.exception("Failed to invoke LLM model, model: %s", args.model_config_data.name)
             return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
 
     @staticmethod
     def instruction_modify_legacy(
-        tenant_id: str, flow_id: str, current: str, instruction: str, model_config: dict, ideal_output: str | None
+        tenant_id: str,
+        flow_id: str,
+        current: str,
+        instruction: str,
+        model_config: ModelConfig,
+        ideal_output: str | None,
     ):
         last_run: Message | None = (
             db.session.query(Message).where(Message.app_id == flow_id).order_by(Message.created_at.desc()).first()
@@ -434,7 +445,7 @@ class LLMGenerator:
         node_id: str,
         current: str,
         instruction: str,
-        model_config: dict,
+        model_config: ModelConfig,
         ideal_output: str | None,
         workflow_service: WorkflowServiceInterface,
     ):
@@ -505,7 +516,7 @@ class LLMGenerator:
     @staticmethod
     def __instruction_modify_common(
         tenant_id: str,
-        model_config: dict,
+        model_config: ModelConfig,
         last_run: dict | None,
         current: str | None,
         error_message: str | None,
@@ -526,8 +537,8 @@ class LLMGenerator:
         model_instance = ModelManager().get_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider", ""),
-            model=model_config.get("name", ""),
+            provider=model_config.provider,
+            model=model_config.name,
         )
         match node_type:
             case "llm" | "agent":
@@ -570,7 +581,5 @@ class LLMGenerator:
             error = str(e)
             return {"error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
-            logger.exception(
-                "Failed to invoke LLM model, model: %s", json.dumps(model_config.get("name")), exc_info=True
-            )
+            logger.exception("Failed to invoke LLM model, model: %s", json.dumps(model_config.name), exc_info=True)
             return {"error": f"An unexpected error occurred: {str(e)}"}

api/core/llm_generator/prompts.py

@@ -434,3 +434,22 @@ INSTRUCTION_GENERATE_TEMPLATE_PROMPT = """The output of this prompt is not as ex
 You should edit the prompt according to the IDEAL OUTPUT."""
 
 INSTRUCTION_GENERATE_TEMPLATE_CODE = """Please fix the errors in the {{#error_message#}}."""
+
+DEFAULT_GENERATOR_SUMMARY_PROMPT = (
+    """Summarize the following content. Extract only the key information and main points. """
+    """Remove redundant details.
+
+Requirements:
+1. Write a concise summary in plain text
+2. You must write in {language}. No language other than {language} should be used.
+3. Focus on important facts, concepts, and details
+4. If images are included, describe their key information
+5. Do not use words like "好的", "ok", "I understand", "This text discusses", "The content mentions"
+6. Write directly without extra words
+7. If there is not enough content to generate a meaningful summary, 
+   return an empty string without any explanation or prompt
+
+Output only the summary text. Start summarizing now:
+
+"""
+)

api/core/mcp/session/base_session.py

@@ -347,7 +347,7 @@ class BaseSession(
                         message.message.root.model_dump(by_alias=True, mode="json", exclude_none=True)
                     )
 
-                    responder = RequestResponder(
+                    responder = RequestResponder[ReceiveRequestT, SendResultT](
                         request_id=message.message.root.id,
                         request_meta=validated_request.root.params.meta if validated_request.root.params else None,
                         request=validated_request,

api/core/model_runtime/entities/defaults.py

@@ -88,7 +88,7 @@ PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
     DefaultParameterName.MAX_TOKENS: {
         "label": {
             "en_US": "Max Tokens",
-            "zh_Hans": "最大标记",
+            "zh_Hans": "最大 Token 数",
         },
         "type": "int",
         "help": {

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -1,10 +1,11 @@
 import decimal
 import hashlib
-from threading import Lock
+import logging
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, ValidationError
+from redis import RedisError
 
-import contexts
+from configs import dify_config
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.defaults import PARAMETER_RULE_TEMPLATE
 from core.model_runtime.entities.model_entities import (
@@ -24,6 +25,9 @@ from core.model_runtime.errors.invoke import (
     InvokeServerUnavailableError,
 )
 from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
+from extensions.ext_redis import redis_client
+
+logger = logging.getLogger(__name__)
 
 
 class AIModel(BaseModel):
@@ -144,34 +148,60 @@ class AIModel(BaseModel):
 
         plugin_model_manager = PluginModelClient()
         cache_key = f"{self.tenant_id}:{self.plugin_id}:{self.provider_name}:{self.model_type.value}:{model}"
-        # sort credentials
         sorted_credentials = sorted(credentials.items()) if credentials else []
         cache_key += ":".join([hashlib.md5(f"{k}:{v}".encode()).hexdigest() for k, v in sorted_credentials])
 
+        cached_schema_json = None
         try:
-            contexts.plugin_model_schemas.get()
-        except LookupError:
-            contexts.plugin_model_schemas.set({})
-            contexts.plugin_model_schema_lock.set(Lock())
-
-        with contexts.plugin_model_schema_lock.get():
-            if cache_key in contexts.plugin_model_schemas.get():
-                return contexts.plugin_model_schemas.get()[cache_key]
-
-            schema = plugin_model_manager.get_model_schema(
-                tenant_id=self.tenant_id,
-                user_id="unknown",
-                plugin_id=self.plugin_id,
-                provider=self.provider_name,
-                model_type=self.model_type.value,
-                model=model,
-                credentials=credentials or {},
+            cached_schema_json = redis_client.get(cache_key)
+        except (RedisError, RuntimeError) as exc:
+            logger.warning(
+                "Failed to read plugin model schema cache for model %s: %s",
+                model,
+                str(exc),
+                exc_info=True,
             )
+        if cached_schema_json:
+            try:
+                return AIModelEntity.model_validate_json(cached_schema_json)
+            except ValidationError:
+                logger.warning(
+                    "Failed to validate cached plugin model schema for model %s",
+                    model,
+                    exc_info=True,
+                )
+                try:
+                    redis_client.delete(cache_key)
+                except (RedisError, RuntimeError) as exc:
+                    logger.warning(
+                        "Failed to delete invalid plugin model schema cache for model %s: %s",
+                        model,
+                        str(exc),
+                        exc_info=True,
+                    )
 
-            if schema:
-                contexts.plugin_model_schemas.get()[cache_key] = schema
+        schema = plugin_model_manager.get_model_schema(
+            tenant_id=self.tenant_id,
+            user_id="unknown",
+            plugin_id=self.plugin_id,
+            provider=self.provider_name,
+            model_type=self.model_type.value,
+            model=model,
+            credentials=credentials or {},
+        )
 
-            return schema
+        if schema:
+            try:
+                redis_client.setex(cache_key, dify_config.PLUGIN_MODEL_SCHEMA_CACHE_TTL, schema.model_dump_json())
+            except (RedisError, RuntimeError) as exc:
+                logger.warning(
+                    "Failed to write plugin model schema cache for model %s: %s",
+                    model,
+                    str(exc),
+                    exc_info=True,
+                )
+
+        return schema
 
     def get_customizable_model_schema_from_credentials(self, model: str, credentials: dict) -> AIModelEntity | None:
         """

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -92,6 +92,10 @@ def _build_llm_result_from_first_chunk(
     Build a single `LLMResult` from the first returned chunk.
 
     This is used for `stream=False` because the plugin side may still implement the response via a chunked stream.
+
+    Note:
+        This function always drains the `chunks` iterator after reading the first chunk to ensure any underlying
+        streaming resources are released (e.g., HTTP connections owned by the plugin runtime).
     """
     content = ""
     content_list: list[PromptMessageContentUnionTypes] = []
@@ -99,18 +103,25 @@ def _build_llm_result_from_first_chunk(
     system_fingerprint: str | None = None
     tools_calls: list[AssistantPromptMessage.ToolCall] = []
 
-    first_chunk = next(chunks, None)
-    if first_chunk is not None:
-        if isinstance(first_chunk.delta.message.content, str):
-            content += first_chunk.delta.message.content
-        elif isinstance(first_chunk.delta.message.content, list):
-            content_list.extend(first_chunk.delta.message.content)
+    try:
+        first_chunk = next(chunks, None)
+        if first_chunk is not None:
+            if isinstance(first_chunk.delta.message.content, str):
+                content += first_chunk.delta.message.content
+            elif isinstance(first_chunk.delta.message.content, list):
+                content_list.extend(first_chunk.delta.message.content)
 
-        if first_chunk.delta.message.tool_calls:
-            _increase_tool_call(first_chunk.delta.message.tool_calls, tools_calls)
+            if first_chunk.delta.message.tool_calls:
+                _increase_tool_call(first_chunk.delta.message.tool_calls, tools_calls)
 
-        usage = first_chunk.delta.usage or LLMUsage.empty_usage()
-        system_fingerprint = first_chunk.system_fingerprint
+            usage = first_chunk.delta.usage or LLMUsage.empty_usage()
+            system_fingerprint = first_chunk.system_fingerprint
+    finally:
+        try:
+            for _ in chunks:
+                pass
+        except Exception:
+            logger.debug("Failed to drain non-stream plugin chunk iterator.", exc_info=True)
 
     return LLMResult(
         model=model,
@@ -283,7 +294,7 @@ class LargeLanguageModel(AIModel):
             # TODO
             raise self._transform_invoke_error(e)
 
-        if stream and isinstance(result, Generator):
+        if stream and not isinstance(result, LLMResult):
             return self._invoke_result_generator(
                 model=model,
                 result=result,

api/core/model_runtime/model_providers/model_provider_factory.py

@@ -5,7 +5,11 @@ import logging
 from collections.abc import Sequence
 from threading import Lock
 
+from pydantic import ValidationError
+from redis import RedisError
+
 import contexts
+from configs import dify_config
 from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
 from core.model_runtime.entities.provider_entities import ProviderConfig, ProviderEntity, SimpleProviderEntity
 from core.model_runtime.model_providers.__base.ai_model import AIModel
@@ -18,6 +22,7 @@ from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.model_runtime.schema_validators.model_credential_schema_validator import ModelCredentialSchemaValidator
 from core.model_runtime.schema_validators.provider_credential_schema_validator import ProviderCredentialSchemaValidator
 from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
+from extensions.ext_redis import redis_client
 from models.provider_ids import ModelProviderID
 
 logger = logging.getLogger(__name__)
@@ -175,34 +180,60 @@ class ModelProviderFactory:
         """
         plugin_id, provider_name = self.get_plugin_id_and_provider_name_from_provider(provider)
         cache_key = f"{self.tenant_id}:{plugin_id}:{provider_name}:{model_type.value}:{model}"
-        # sort credentials
         sorted_credentials = sorted(credentials.items()) if credentials else []
         cache_key += ":".join([hashlib.md5(f"{k}:{v}".encode()).hexdigest() for k, v in sorted_credentials])
 
+        cached_schema_json = None
         try:
-            contexts.plugin_model_schemas.get()
-        except LookupError:
-            contexts.plugin_model_schemas.set({})
-            contexts.plugin_model_schema_lock.set(Lock())
-
-        with contexts.plugin_model_schema_lock.get():
-            if cache_key in contexts.plugin_model_schemas.get():
-                return contexts.plugin_model_schemas.get()[cache_key]
-
-            schema = self.plugin_model_manager.get_model_schema(
-                tenant_id=self.tenant_id,
-                user_id="unknown",
-                plugin_id=plugin_id,
-                provider=provider_name,
-                model_type=model_type.value,
-                model=model,
-                credentials=credentials or {},
+            cached_schema_json = redis_client.get(cache_key)
+        except (RedisError, RuntimeError) as exc:
+            logger.warning(
+                "Failed to read plugin model schema cache for model %s: %s",
+                model,
+                str(exc),
+                exc_info=True,
             )
+        if cached_schema_json:
+            try:
+                return AIModelEntity.model_validate_json(cached_schema_json)
+            except ValidationError:
+                logger.warning(
+                    "Failed to validate cached plugin model schema for model %s",
+                    model,
+                    exc_info=True,
+                )
+                try:
+                    redis_client.delete(cache_key)
+                except (RedisError, RuntimeError) as exc:
+                    logger.warning(
+                        "Failed to delete invalid plugin model schema cache for model %s: %s",
+                        model,
+                        str(exc),
+                        exc_info=True,
+                    )
 
-            if schema:
-                contexts.plugin_model_schemas.get()[cache_key] = schema
+        schema = self.plugin_model_manager.get_model_schema(
+            tenant_id=self.tenant_id,
+            user_id="unknown",
+            plugin_id=plugin_id,
+            provider=provider_name,
+            model_type=model_type.value,
+            model=model,
+            credentials=credentials or {},
+        )
 
-            return schema
+        if schema:
+            try:
+                redis_client.setex(cache_key, dify_config.PLUGIN_MODEL_SCHEMA_CACHE_TTL, schema.model_dump_json())
+            except (RedisError, RuntimeError) as exc:
+                logger.warning(
+                    "Failed to write plugin model schema cache for model %s: %s",
+                    model,
+                    str(exc),
+                    exc_info=True,
+                )
+
+        return schema
 
     def get_models(
         self,
@@ -283,6 +314,8 @@ class ModelProviderFactory:
         elif model_type == ModelType.TTS:
             return TTSModel.model_validate(init_params)
 
+        raise ValueError(f"Unsupported model type: {model_type}")
+
     def get_provider_icon(self, provider: str, icon_type: str, lang: str) -> tuple[bytes, str]:
         """
         Get provider icon

api/core/rag/datasource/retrieval_service.py

@@ -23,7 +23,13 @@ from core.rag.rerank.rerank_type import RerankMode
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.signature import sign_upload_file
 from extensions.ext_database import db
-from models.dataset import ChildChunk, Dataset, DocumentSegment, SegmentAttachmentBinding
+from models.dataset import (
+    ChildChunk,
+    Dataset,
+    DocumentSegment,
+    DocumentSegmentSummary,
+    SegmentAttachmentBinding,
+)
 from models.dataset import Document as DatasetDocument
 from models.model import UploadFile
 from services.external_knowledge_service import ExternalDatasetService
@@ -378,15 +384,15 @@ class RetrievalService:
                 .all()
             }
 
-            records = []
-            include_segment_ids = set()
-            segment_child_map = {}
-
             valid_dataset_documents = {}
             image_doc_ids: list[Any] = []
             child_index_node_ids = []
             index_node_ids = []
             doc_to_document_map = {}
+            summary_segment_ids = set()  # Track segments retrieved via summary
+            summary_score_map: dict[str, float] = {}  # Map original_chunk_id to summary score
+
+            # First pass: collect all document IDs and identify summary documents
             for document in documents:
                 document_id = document.metadata.get("document_id")
                 if document_id not in dataset_documents:
@@ -397,16 +403,39 @@ class RetrievalService:
                     continue
                 valid_dataset_documents[document_id] = dataset_document
 
+                doc_id = document.metadata.get("doc_id") or ""
+                doc_to_document_map[doc_id] = document
+
+                # Check if this is a summary document
+                is_summary = document.metadata.get("is_summary", False)
+                if is_summary:
+                    # For summary documents, find the original chunk via original_chunk_id
+                    original_chunk_id = document.metadata.get("original_chunk_id")
+                    if original_chunk_id:
+                        summary_segment_ids.add(original_chunk_id)
+                        # Save summary's score for later use
+                        summary_score = document.metadata.get("score")
+                        if summary_score is not None:
+                            try:
+                                summary_score_float = float(summary_score)
+                                # If the same segment has multiple summary hits, take the highest score
+                                if original_chunk_id not in summary_score_map:
+                                    summary_score_map[original_chunk_id] = summary_score_float
+                                else:
+                                    summary_score_map[original_chunk_id] = max(
+                                        summary_score_map[original_chunk_id], summary_score_float
+                                    )
+                            except (ValueError, TypeError):
+                                # Skip invalid score values
+                                pass
+                    continue  # Skip adding to other lists for summary documents
+
                 if dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
-                    doc_id = document.metadata.get("doc_id") or ""
-                    doc_to_document_map[doc_id] = document
                     if document.metadata.get("doc_type") == DocType.IMAGE:
                         image_doc_ids.append(doc_id)
                     else:
                         child_index_node_ids.append(doc_id)
                 else:
-                    doc_id = document.metadata.get("doc_id") or ""
-                    doc_to_document_map[doc_id] = document
                     if document.metadata.get("doc_type") == DocType.IMAGE:
                         image_doc_ids.append(doc_id)
                     else:
@@ -419,9 +448,10 @@ class RetrievalService:
             segment_ids = []
             index_node_segments: list[DocumentSegment] = []
             segments: list[DocumentSegment] = []
-            attachment_map = {}
-            child_chunk_map: dict[Any, Any] = {}
-            doc_segment_map = {}
+            attachment_map: dict[str, list[dict[str, Any]]] = {}
+            child_chunk_map: dict[str, list[ChildChunk]] = {}
+            doc_segment_map: dict[str, list[str]] = {}
+            segment_summary_map: dict[str, str] = {}  # Map segment_id to summary content
 
             with session_factory.create_session() as session:
                 attachments = cls.get_segment_attachment_infos(image_doc_ids, session)
@@ -436,6 +466,7 @@ class RetrievalService:
                         doc_segment_map[attachment["segment_id"]].append(attachment["attachment_id"])
                     else:
                         doc_segment_map[attachment["segment_id"]] = [attachment["attachment_id"]]
+
                 child_chunk_stmt = select(ChildChunk).where(ChildChunk.index_node_id.in_(child_index_node_ids))
                 child_index_nodes = session.execute(child_chunk_stmt).scalars().all()
 
@@ -459,6 +490,7 @@ class RetrievalService:
                     index_node_segments = session.execute(document_segment_stmt).scalars().all()  # type: ignore
                     for index_node_segment in index_node_segments:
                         doc_segment_map[index_node_segment.id] = [index_node_segment.index_node_id]
+
                 if segment_ids:
                     document_segment_stmt = select(DocumentSegment).where(
                         DocumentSegment.enabled == True,
@@ -470,6 +502,40 @@ class RetrievalService:
                 if index_node_segments:
                     segments.extend(index_node_segments)
 
+                # Handle summary documents: query segments by original_chunk_id
+                if summary_segment_ids:
+                    summary_segment_ids_list = list(summary_segment_ids)
+                    summary_segment_stmt = select(DocumentSegment).where(
+                        DocumentSegment.enabled == True,
+                        DocumentSegment.status == "completed",
+                        DocumentSegment.id.in_(summary_segment_ids_list),
+                    )
+                    summary_segments = session.execute(summary_segment_stmt).scalars().all()  # type: ignore
+                    segments.extend(summary_segments)
+                    # Add summary segment IDs to segment_ids for summary query
+                    for seg in summary_segments:
+                        if seg.id not in segment_ids:
+                            segment_ids.append(seg.id)
+
+                # Batch query summaries for segments retrieved via summary (only enabled summaries)
+                if summary_segment_ids:
+                    summaries = (
+                        session.query(DocumentSegmentSummary)
+                        .filter(
+                            DocumentSegmentSummary.chunk_id.in_(list(summary_segment_ids)),
+                            DocumentSegmentSummary.status == "completed",
+                            DocumentSegmentSummary.enabled == True,  # Only retrieve enabled summaries
+                        )
+                        .all()
+                    )
+                    for summary in summaries:
+                        if summary.summary_content:
+                            segment_summary_map[summary.chunk_id] = summary.summary_content
+
+            include_segment_ids = set()
+            segment_child_map: dict[str, dict[str, Any]] = {}
+            records: list[dict[str, Any]] = []
+
             for segment in segments:
                 child_chunks: list[ChildChunk] = child_chunk_map.get(segment.id, [])
                 attachment_infos: list[dict[str, Any]] = attachment_map.get(segment.id, [])
@@ -478,45 +544,68 @@ class RetrievalService:
                 if ds_dataset_document and ds_dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
                     if segment.id not in include_segment_ids:
                         include_segment_ids.add(segment.id)
+                        # Check if this segment was retrieved via summary
+                        # Use summary score as base score if available, otherwise 0.0
+                        max_score = summary_score_map.get(segment.id, 0.0)
+
                         if child_chunks or attachment_infos:
                             child_chunk_details = []
-                            max_score = 0.0
                             for child_chunk in child_chunks:
-                                document = doc_to_document_map[child_chunk.index_node_id]
+                                child_document: Document | None = doc_to_document_map.get(child_chunk.index_node_id)
+                                if child_document:
+                                    child_score = child_document.metadata.get("score", 0.0)
+                                else:
+                                    child_score = 0.0
                                 child_chunk_detail = {
                                     "id": child_chunk.id,
                                     "content": child_chunk.content,
                                     "position": child_chunk.position,
-                                    "score": document.metadata.get("score", 0.0) if document else 0.0,
+                                    "score": child_score,
                                 }
                                 child_chunk_details.append(child_chunk_detail)
-                                max_score = max(max_score, document.metadata.get("score", 0.0) if document else 0.0)
+                                max_score = max(max_score, child_score)
                             for attachment_info in attachment_infos:
-                                file_document = doc_to_document_map[attachment_info["id"]]
-                                max_score = max(
-                                    max_score, file_document.metadata.get("score", 0.0) if file_document else 0.0
-                                )
+                                file_document = doc_to_document_map.get(attachment_info["id"])
+                                if file_document:
+                                    max_score = max(max_score, file_document.metadata.get("score", 0.0))
 
                             map_detail = {
                                 "max_score": max_score,
                                 "child_chunks": child_chunk_details,
                             }
                             segment_child_map[segment.id] = map_detail
-                        record = {
+                        else:
+                            # No child chunks or attachments, use summary score if available
+                            summary_score = summary_score_map.get(segment.id)
+                            if summary_score is not None:
+                                segment_child_map[segment.id] = {
+                                    "max_score": summary_score,
+                                    "child_chunks": [],
+                                }
+                        record: dict[str, Any] = {
                             "segment": segment,
                         }
                         records.append(record)
                 else:
                     if segment.id not in include_segment_ids:
                         include_segment_ids.add(segment.id)
-                        max_score = 0.0
-                        document = doc_to_document_map.get(segment.index_node_id)
-                        if document:
-                            max_score = max(max_score, document.metadata.get("score", 0.0))
+
+                        # Check if this segment was retrieved via summary
+                        # Use summary score if available (summary retrieval takes priority)
+                        max_score = summary_score_map.get(segment.id, 0.0)
+
+                        # If not retrieved via summary, use original segment's score
+                        if segment.id not in summary_score_map:
+                            segment_document = doc_to_document_map.get(segment.index_node_id)
+                            if segment_document:
+                                max_score = max(max_score, segment_document.metadata.get("score", 0.0))
+
+                        # Also consider attachment scores
                         for attachment_info in attachment_infos:
-                            file_document = doc_to_document_map.get(attachment_info["id"])
-                            if file_document:
-                                max_score = max(max_score, file_document.metadata.get("score", 0.0))
+                            file_doc = doc_to_document_map.get(attachment_info["id"])
+                            if file_doc:
+                                max_score = max(max_score, file_doc.metadata.get("score", 0.0))
+
                         record = {
                             "segment": segment,
                             "score": max_score,
@@ -557,9 +646,16 @@ class RetrievalService:
                     else None
                 )
 
+                # Extract summary if this segment was retrieved via summary
+                summary_content = segment_summary_map.get(segment.id)
+
                 # Create RetrievalSegments object
                 retrieval_segment = RetrievalSegments(
-                    segment=segment, child_chunks=child_chunks, score=score, files=files
+                    segment=segment,
+                    child_chunks=child_chunks,
+                    score=score,
+                    files=files,
+                    summary=summary_content,
                 )
                 result.append(retrieval_segment)
 

api/core/rag/datasource/vdb/qdrant/qdrant_vector.py

@@ -391,46 +391,78 @@ class QdrantVector(BaseVector):
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        """Return docs most similar by bm25.
+        """Return docs most similar by full-text search.
+
+        Searches each keyword separately and merges results to ensure documents
+        matching ANY keyword are returned (OR logic). Results are capped at top_k.
+
+        Args:
+            query: Search query text. Multi-word queries are split into keywords,
+                   with each keyword searched separately. Limited to 10 keywords.
+            **kwargs: Additional search parameters (top_k, document_ids_filter)
+
         Returns:
-            List of documents most similar to the query text and distance for each.
+            List of up to top_k unique documents matching any query keyword.
         """
         from qdrant_client.http import models
 
-        scroll_filter = models.Filter(
-            must=[
-                models.FieldCondition(
-                    key="group_id",
-                    match=models.MatchValue(value=self._group_id),
-                ),
-                models.FieldCondition(
-                    key="page_content",
-                    match=models.MatchText(text=query),
-                ),
-            ]
-        )
+        # Build base must conditions (AND logic) for metadata filters
+        base_must_conditions: list = [
+            models.FieldCondition(
+                key="group_id",
+                match=models.MatchValue(value=self._group_id),
+            ),
+        ]
+
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
-            if scroll_filter.must:
-                scroll_filter.must.append(
-                    models.FieldCondition(
-                        key="metadata.document_id",
-                        match=models.MatchAny(any=document_ids_filter),
-                    )
+            base_must_conditions.append(
+                models.FieldCondition(
+                    key="metadata.document_id",
+                    match=models.MatchAny(any=document_ids_filter),
                 )
-        response = self._client.scroll(
-            collection_name=self._collection_name,
-            scroll_filter=scroll_filter,
-            limit=kwargs.get("top_k", 2),
-            with_payload=True,
-            with_vectors=True,
-        )
-        results = response[0]
-        documents = []
-        for result in results:
-            if result:
-                document = self._document_from_scored_point(result, Field.CONTENT_KEY, Field.METADATA_KEY)
-                documents.append(document)
+            )
+
+        # Split query into keywords, deduplicate and limit to prevent DoS
+        keywords = list(dict.fromkeys(kw.strip() for kw in query.strip().split() if kw.strip()))[:10]
+
+        if not keywords:
+            return []
+
+        top_k = kwargs.get("top_k", 2)
+        seen_ids: set[str | int] = set()
+        documents: list[Document] = []
+
+        # Search each keyword separately and merge results.
+        # This ensures each keyword gets its own search, preventing one keyword's
+        # results from completely overshadowing another's due to scroll ordering.
+        for keyword in keywords:
+            scroll_filter = models.Filter(
+                must=[
+                    *base_must_conditions,
+                    models.FieldCondition(
+                        key="page_content",
+                        match=models.MatchText(text=keyword),
+                    ),
+                ]
+            )
+
+            response = self._client.scroll(
+                collection_name=self._collection_name,
+                scroll_filter=scroll_filter,
+                limit=top_k,
+                with_payload=True,
+                with_vectors=True,
+            )
+            results = response[0]
+
+            for result in results:
+                if result and result.id not in seen_ids:
+                    seen_ids.add(result.id)
+                    document = self._document_from_scored_point(result, Field.CONTENT_KEY, Field.METADATA_KEY)
+                    documents.append(document)
+                    if len(documents) >= top_k:
+                        return documents
 
         return documents
 

api/core/rag/embedding/retrieval.py

@@ -20,3 +20,4 @@ class RetrievalSegments(BaseModel):
     child_chunks: list[RetrievalChildChunk] | None = None
     score: float | None = None
     files: list[dict[str, str | int]] | None = None
+    summary: str | None = None  # Summary content if retrieved via summary index

api/core/rag/entities/citation_metadata.py

@@ -22,3 +22,4 @@ class RetrievalSourceMetadata(BaseModel):
     doc_metadata: dict[str, Any] | None = None
     title: str | None = None
     files: list[dict[str, Any]] | None = None
+    summary: str | None = None

api/core/rag/extractor/word_extractor.py

@@ -1,4 +1,7 @@
-"""Abstract interface for document loader implementations."""
+"""Word (.docx) document extractor used for RAG ingestion.
+
+Supports local file paths and remote URLs (downloaded via `core.helper.ssrf_proxy`).
+"""
 
 import logging
 import mimetypes
@@ -8,7 +11,6 @@ import tempfile
 import uuid
 from urllib.parse import urlparse
 
-import httpx
 from docx import Document as DocxDocument
 from docx.oxml.ns import qn
 from docx.text.run import Run
@@ -44,7 +46,7 @@ class WordExtractor(BaseExtractor):
 
         # If the file is a web path, download it to a temporary file, and use that
         if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
-            response = httpx.get(self.file_path, timeout=None)
+            response = ssrf_proxy.get(self.file_path)
 
             if response.status_code != 200:
                 response.close()
@@ -55,6 +57,7 @@ class WordExtractor(BaseExtractor):
             self.temp_file = tempfile.NamedTemporaryFile()  # noqa SIM115
             try:
                 self.temp_file.write(response.content)
+                self.temp_file.flush()
             finally:
                 response.close()
             self.file_path = self.temp_file.name

api/core/rag/index_processor/index_processor_base.py

@@ -13,6 +13,7 @@ from urllib.parse import unquote, urlparse
 import httpx
 
 from configs import dify_config
+from core.entities.knowledge_entities import PreviewDetail
 from core.helper import ssrf_proxy
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.index_processor.constant.doc_type import DocType
@@ -45,6 +46,27 @@ class BaseIndexProcessor(ABC):
     def transform(self, documents: list[Document], current_user: Account | None = None, **kwargs) -> list[Document]:
         raise NotImplementedError
 
+    @abstractmethod
+    def generate_summary_preview(
+        self,
+        tenant_id: str,
+        preview_texts: list[PreviewDetail],
+        summary_index_setting: dict,
+        doc_language: str | None = None,
+    ) -> list[PreviewDetail]:
+        """
+        For each segment in preview_texts, generate a summary using LLM and attach it to the segment.
+        The summary can be stored in a new attribute, e.g., summary.
+        This method should be implemented by subclasses.
+
+        Args:
+            tenant_id: Tenant ID
+            preview_texts: List of preview details to generate summaries for
+            summary_index_setting: Summary index configuration
+            doc_language: Optional document language to ensure summary is generated in the correct language
+        """
+        raise NotImplementedError
+
     @abstractmethod
     def load(
         self,

api/core/rag/index_processor/processor/paragraph_index_processor.py

@@ -1,9 +1,27 @@
 """Paragraph index processor."""
 
+import logging
+import re
 import uuid
 from collections.abc import Mapping
-from typing import Any
+from typing import Any, cast
 
+logger = logging.getLogger(__name__)
+
+from core.entities.knowledge_entities import PreviewDetail
+from core.file import File, FileTransferMethod, FileType, file_manager
+from core.llm_generator.prompts import DEFAULT_GENERATOR_SUMMARY_PROMPT
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
+from core.model_runtime.entities.message_entities import (
+    ImagePromptMessageContent,
+    PromptMessage,
+    PromptMessageContentUnionTypes,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import ModelFeature, ModelType
+from core.provider_manager import ProviderManager
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.keyword.keyword_factory import Keyword
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -17,12 +35,17 @@ from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import AttachmentDocument, Document, MultimodalGeneralStructureChunk
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
+from core.workflow.nodes.llm import llm_utils
+from extensions.ext_database import db
+from factories.file_factory import build_from_mapping
 from libs import helper
+from models import UploadFile
 from models.account import Account
-from models.dataset import Dataset, DatasetProcessRule
+from models.dataset import Dataset, DatasetProcessRule, DocumentSegment, SegmentAttachmentBinding
 from models.dataset import Document as DatasetDocument
 from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import Rule
+from services.summary_index_service import SummaryIndexService
 
 
 class ParagraphIndexProcessor(BaseIndexProcessor):
@@ -108,6 +131,29 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
                 keyword.add_texts(documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                segments = (
+                    db.session.query(DocumentSegment)
+                    .filter(
+                        DocumentSegment.dataset_id == dataset.id,
+                        DocumentSegment.index_node_id.in_(node_ids),
+                    )
+                    .all()
+                )
+                segment_ids = [segment.id for segment in segments]
+                if segment_ids:
+                    SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             if node_ids:
@@ -227,3 +273,347 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
             }
         else:
             raise ValueError("Chunks is not a list")
+
+    def generate_summary_preview(
+        self,
+        tenant_id: str,
+        preview_texts: list[PreviewDetail],
+        summary_index_setting: dict,
+        doc_language: str | None = None,
+    ) -> list[PreviewDetail]:
+        """
+        For each segment, concurrently call generate_summary to generate a summary
+        and write it to the summary attribute of PreviewDetail.
+        In preview mode (indexing-estimate), if any summary generation fails, the method will raise an exception.
+        """
+        import concurrent.futures
+
+        from flask import current_app
+
+        # Capture Flask app context for worker threads
+        flask_app = None
+        try:
+            flask_app = current_app._get_current_object()  # type: ignore
+        except RuntimeError:
+            logger.warning("No Flask application context available, summary generation may fail")
+
+        def process(preview: PreviewDetail) -> None:
+            """Generate summary for a single preview item."""
+            if flask_app:
+                # Ensure Flask app context in worker thread
+                with flask_app.app_context():
+                    summary, _ = self.generate_summary(
+                        tenant_id, preview.content, summary_index_setting, document_language=doc_language
+                    )
+                    preview.summary = summary
+            else:
+                # Fallback: try without app context (may fail)
+                summary, _ = self.generate_summary(
+                    tenant_id, preview.content, summary_index_setting, document_language=doc_language
+                )
+                preview.summary = summary
+
+        # Generate summaries concurrently using ThreadPoolExecutor
+        # Set a reasonable timeout to prevent hanging (60 seconds per chunk, max 5 minutes total)
+        timeout_seconds = min(300, 60 * len(preview_texts))
+        errors: list[Exception] = []
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=min(10, len(preview_texts))) as executor:
+            futures = [executor.submit(process, preview) for preview in preview_texts]
+            # Wait for all tasks to complete with timeout
+            done, not_done = concurrent.futures.wait(futures, timeout=timeout_seconds)
+
+            # Cancel tasks that didn't complete in time
+            if not_done:
+                timeout_error_msg = (
+                    f"Summary generation timeout: {len(not_done)} chunks did not complete within {timeout_seconds}s"
+                )
+                logger.warning("%s. Cancelling remaining tasks...", timeout_error_msg)
+                # In preview mode, timeout is also an error
+                errors.append(TimeoutError(timeout_error_msg))
+                for future in not_done:
+                    future.cancel()
+                # Wait a bit for cancellation to take effect
+                concurrent.futures.wait(not_done, timeout=5)
+
+            # Collect exceptions from completed futures
+            for future in done:
+                try:
+                    future.result()  # This will raise any exception that occurred
+                except Exception as e:
+                    logger.exception("Error in summary generation future")
+                    errors.append(e)
+
+        # In preview mode (indexing-estimate), if there are any errors, fail the request
+        if errors:
+            error_messages = [str(e) for e in errors]
+            error_summary = (
+                f"Failed to generate summaries for {len(errors)} chunk(s). "
+                f"Errors: {'; '.join(error_messages[:3])}"  # Show first 3 errors
+            )
+            if len(errors) > 3:
+                error_summary += f" (and {len(errors) - 3} more)"
+            logger.error("Summary generation failed in preview mode: %s", error_summary)
+            raise ValueError(error_summary)
+
+        return preview_texts
+
+    @staticmethod
+    def generate_summary(
+        tenant_id: str,
+        text: str,
+        summary_index_setting: dict | None = None,
+        segment_id: str | None = None,
+        document_language: str | None = None,
+    ) -> tuple[str, LLMUsage]:
+        """
+        Generate summary for the given text using ModelInstance.invoke_llm and the default or custom summary prompt,
+        and supports vision models by including images from the segment attachments or text content.
+
+        Args:
+            tenant_id: Tenant ID
+            text: Text content to summarize
+            summary_index_setting: Summary index configuration
+            segment_id: Optional segment ID to fetch attachments from SegmentAttachmentBinding table
+            document_language: Optional document language (e.g., "Chinese", "English")
+                to ensure summary is generated in the correct language
+
+        Returns:
+            Tuple of (summary_content, llm_usage) where llm_usage is LLMUsage object
+        """
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            raise ValueError("summary_index_setting is required and must be enabled to generate summary.")
+
+        model_name = summary_index_setting.get("model_name")
+        model_provider_name = summary_index_setting.get("model_provider_name")
+        summary_prompt = summary_index_setting.get("summary_prompt")
+
+        if not model_name or not model_provider_name:
+            raise ValueError("model_name and model_provider_name are required in summary_index_setting")
+
+        # Import default summary prompt
+        is_default_prompt = False
+        if not summary_prompt:
+            summary_prompt = DEFAULT_GENERATOR_SUMMARY_PROMPT
+            is_default_prompt = True
+
+        # Format prompt with document language only for default prompt
+        # Custom prompts are used as-is to avoid interfering with user-defined templates
+        # If document_language is provided, use it; otherwise, use "the same language as the input content"
+        # This is especially important for image-only chunks where text is empty or minimal
+        if is_default_prompt:
+            language_for_prompt = document_language or "the same language as the input content"
+            try:
+                summary_prompt = summary_prompt.format(language=language_for_prompt)
+            except KeyError:
+                # If default prompt doesn't have {language} placeholder, use it as-is
+                pass
+
+        provider_manager = ProviderManager()
+        provider_model_bundle = provider_manager.get_provider_model_bundle(
+            tenant_id, model_provider_name, ModelType.LLM
+        )
+        model_instance = ModelInstance(provider_model_bundle, model_name)
+
+        # Get model schema to check if vision is supported
+        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
+        supports_vision = model_schema and model_schema.features and ModelFeature.VISION in model_schema.features
+
+        # Extract images if model supports vision
+        image_files = []
+        if supports_vision:
+            # First, try to get images from SegmentAttachmentBinding (preferred method)
+            if segment_id:
+                image_files = ParagraphIndexProcessor._extract_images_from_segment_attachments(tenant_id, segment_id)
+
+            # If no images from attachments, fall back to extracting from text
+            if not image_files:
+                image_files = ParagraphIndexProcessor._extract_images_from_text(tenant_id, text)
+
+        # Build prompt messages
+        prompt_messages = []
+
+        if image_files:
+            # If we have images, create a UserPromptMessage with both text and images
+            prompt_message_contents: list[PromptMessageContentUnionTypes] = []
+
+            # Add images first
+            for file in image_files:
+                try:
+                    file_content = file_manager.to_prompt_message_content(
+                        file, image_detail_config=ImagePromptMessageContent.DETAIL.LOW
+                    )
+                    prompt_message_contents.append(file_content)
+                except Exception as e:
+                    logger.warning("Failed to convert image file to prompt message content: %s", str(e))
+                    continue
+
+            # Add text content
+            if prompt_message_contents:  # Only add text if we successfully added images
+                prompt_message_contents.append(TextPromptMessageContent(data=f"{summary_prompt}\n{text}"))
+                prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+            else:
+                # If image conversion failed, fall back to text-only
+                prompt = f"{summary_prompt}\n{text}"
+                prompt_messages.append(UserPromptMessage(content=prompt))
+        else:
+            # No images, use simple text prompt
+            prompt = f"{summary_prompt}\n{text}"
+            prompt_messages.append(UserPromptMessage(content=prompt))
+
+        result = model_instance.invoke_llm(
+            prompt_messages=cast(list[PromptMessage], prompt_messages), model_parameters={}, stream=False
+        )
+
+        # Type assertion: when stream=False, invoke_llm returns LLMResult, not Generator
+        if not isinstance(result, LLMResult):
+            raise ValueError("Expected LLMResult when stream=False")
+
+        summary_content = getattr(result.message, "content", "")
+        usage = result.usage
+
+        # Deduct quota for summary generation (same as workflow nodes)
+        try:
+            llm_utils.deduct_llm_quota(tenant_id=tenant_id, model_instance=model_instance, usage=usage)
+        except Exception as e:
+            # Log but don't fail summary generation if quota deduction fails
+            logger.warning("Failed to deduct quota for summary generation: %s", str(e))
+
+        return summary_content, usage
+
+    @staticmethod
+    def _extract_images_from_text(tenant_id: str, text: str) -> list[File]:
+        """
+        Extract images from markdown text and convert them to File objects.
+
+        Args:
+            tenant_id: Tenant ID
+            text: Text content that may contain markdown image links
+
+        Returns:
+            List of File objects representing images found in the text
+        """
+        # Extract markdown images using regex pattern
+        pattern = r"!\[.*?\]\((.*?)\)"
+        images = re.findall(pattern, text)
+
+        if not images:
+            return []
+
+        upload_file_id_list = []
+
+        for image in images:
+            # For data before v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/image-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For data after v0.10.0
+            pattern = r"/files/([a-f0-9\-]+)/file-preview(?:\?.*?)?"
+            match = re.search(pattern, image)
+            if match:
+                upload_file_id = match.group(1)
+                upload_file_id_list.append(upload_file_id)
+                continue
+
+            # For tools directory - direct file formats (e.g., .png, .jpg, etc.)
+            pattern = r"/files/tools/([a-f0-9\-]+)\.([a-zA-Z0-9]+)(?:\?[^\s\)\"\']*)?"
+            match = re.search(pattern, image)
+            if match:
+                # Tool files are handled differently, skip for now
+                continue
+
+        if not upload_file_id_list:
+            return []
+
+        # Get unique IDs for database query
+        unique_upload_file_ids = list(set(upload_file_id_list))
+        upload_files = (
+            db.session.query(UploadFile)
+            .where(UploadFile.id.in_(unique_upload_file_ids), UploadFile.tenant_id == tenant_id)
+            .all()
+        )
+
+        # Create File objects from UploadFile records
+        file_objects = []
+        for upload_file in upload_files:
+            # Only process image files
+            if not upload_file.mime_type or "image" not in upload_file.mime_type:
+                continue
+
+            mapping = {
+                "upload_file_id": upload_file.id,
+                "transfer_method": FileTransferMethod.LOCAL_FILE.value,
+                "type": FileType.IMAGE.value,
+            }
+
+            try:
+                file_obj = build_from_mapping(
+                    mapping=mapping,
+                    tenant_id=tenant_id,
+                )
+                file_objects.append(file_obj)
+            except Exception as e:
+                logger.warning("Failed to create File object from UploadFile %s: %s", upload_file.id, str(e))
+                continue
+
+        return file_objects
+
+    @staticmethod
+    def _extract_images_from_segment_attachments(tenant_id: str, segment_id: str) -> list[File]:
+        """
+        Extract images from SegmentAttachmentBinding table (preferred method).
+        This matches how DatasetRetrieval gets segment attachments.
+
+        Args:
+            tenant_id: Tenant ID
+            segment_id: Segment ID to fetch attachments for
+
+        Returns:
+            List of File objects representing images found in segment attachments
+        """
+        from sqlalchemy import select
+
+        # Query attachments from SegmentAttachmentBinding table
+        attachments_with_bindings = db.session.execute(
+            select(SegmentAttachmentBinding, UploadFile)
+            .join(UploadFile, UploadFile.id == SegmentAttachmentBinding.attachment_id)
+            .where(
+                SegmentAttachmentBinding.segment_id == segment_id,
+                SegmentAttachmentBinding.tenant_id == tenant_id,
+            )
+        ).all()
+
+        if not attachments_with_bindings:
+            return []
+
+        file_objects = []
+        for _, upload_file in attachments_with_bindings:
+            # Only process image files
+            if not upload_file.mime_type or "image" not in upload_file.mime_type:
+                continue
+
+            try:
+                # Create File object directly (similar to DatasetRetrieval)
+                file_obj = File(
+                    id=upload_file.id,
+                    filename=upload_file.name,
+                    extension="." + upload_file.extension,
+                    mime_type=upload_file.mime_type,
+                    tenant_id=tenant_id,
+                    type=FileType.IMAGE,
+                    transfer_method=FileTransferMethod.LOCAL_FILE,
+                    remote_url=upload_file.source_url,
+                    related_id=upload_file.id,
+                    size=upload_file.size,
+                    storage_key=upload_file.key,
+                )
+                file_objects.append(file_obj)
+            except Exception as e:
+                logger.warning("Failed to create File object from UploadFile %s: %s", upload_file.id, str(e))
+                continue
+
+        return file_objects

api/core/rag/index_processor/processor/parent_child_index_processor.py

@@ -1,11 +1,14 @@
 """Paragraph index processor."""
 
 import json
+import logging
 import uuid
 from collections.abc import Mapping
 from typing import Any
 
 from configs import dify_config
+from core.db.session_factory import session_factory
+from core.entities.knowledge_entities import PreviewDetail
 from core.model_manager import ModelInstance
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -25,6 +28,9 @@ from models.dataset import ChildChunk, Dataset, DatasetProcessRule, DocumentSegm
 from models.dataset import Document as DatasetDocument
 from services.account_service import AccountService
 from services.entities.knowledge_entities.knowledge_entities import ParentMode, Rule
+from services.summary_index_service import SummaryIndexService
+
+logger = logging.getLogger(__name__)
 
 
 class ParentChildIndexProcessor(BaseIndexProcessor):
@@ -135,6 +141,30 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
         # node_ids is segment's node_ids
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                with session_factory.create_session() as session:
+                    segments = (
+                        session.query(DocumentSegment)
+                        .filter(
+                            DocumentSegment.dataset_id == dataset.id,
+                            DocumentSegment.index_node_id.in_(node_ids),
+                        )
+                        .all()
+                    )
+                    segment_ids = [segment.id for segment in segments]
+                    if segment_ids:
+                        SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         if dataset.indexing_technique == "high_quality":
             delete_child_chunks = kwargs.get("delete_child_chunks") or False
             precomputed_child_node_ids = kwargs.get("precomputed_child_node_ids")
@@ -326,3 +356,97 @@ class ParentChildIndexProcessor(BaseIndexProcessor):
             "preview": preview,
             "total_segments": len(parent_childs.parent_child_chunks),
         }
+
+    def generate_summary_preview(
+        self,
+        tenant_id: str,
+        preview_texts: list[PreviewDetail],
+        summary_index_setting: dict,
+        doc_language: str | None = None,
+    ) -> list[PreviewDetail]:
+        """
+        For each parent chunk in preview_texts, concurrently call generate_summary to generate a summary
+        and write it to the summary attribute of PreviewDetail.
+        In preview mode (indexing-estimate), if any summary generation fails, the method will raise an exception.
+
+        Note: For parent-child structure, we only generate summaries for parent chunks.
+        """
+        import concurrent.futures
+
+        from flask import current_app
+
+        # Capture Flask app context for worker threads
+        flask_app = None
+        try:
+            flask_app = current_app._get_current_object()  # type: ignore
+        except RuntimeError:
+            logger.warning("No Flask application context available, summary generation may fail")
+
+        def process(preview: PreviewDetail) -> None:
+            """Generate summary for a single preview item (parent chunk)."""
+            from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
+
+            if flask_app:
+                # Ensure Flask app context in worker thread
+                with flask_app.app_context():
+                    summary, _ = ParagraphIndexProcessor.generate_summary(
+                        tenant_id=tenant_id,
+                        text=preview.content,
+                        summary_index_setting=summary_index_setting,
+                        document_language=doc_language,
+                    )
+                    preview.summary = summary
+            else:
+                # Fallback: try without app context (may fail)
+                summary, _ = ParagraphIndexProcessor.generate_summary(
+                    tenant_id=tenant_id,
+                    text=preview.content,
+                    summary_index_setting=summary_index_setting,
+                    document_language=doc_language,
+                )
+                preview.summary = summary
+
+        # Generate summaries concurrently using ThreadPoolExecutor
+        # Set a reasonable timeout to prevent hanging (60 seconds per chunk, max 5 minutes total)
+        timeout_seconds = min(300, 60 * len(preview_texts))
+        errors: list[Exception] = []
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=min(10, len(preview_texts))) as executor:
+            futures = [executor.submit(process, preview) for preview in preview_texts]
+            # Wait for all tasks to complete with timeout
+            done, not_done = concurrent.futures.wait(futures, timeout=timeout_seconds)
+
+            # Cancel tasks that didn't complete in time
+            if not_done:
+                timeout_error_msg = (
+                    f"Summary generation timeout: {len(not_done)} chunks did not complete within {timeout_seconds}s"
+                )
+                logger.warning("%s. Cancelling remaining tasks...", timeout_error_msg)
+                # In preview mode, timeout is also an error
+                errors.append(TimeoutError(timeout_error_msg))
+                for future in not_done:
+                    future.cancel()
+                # Wait a bit for cancellation to take effect
+                concurrent.futures.wait(not_done, timeout=5)
+
+            # Collect exceptions from completed futures
+            for future in done:
+                try:
+                    future.result()  # This will raise any exception that occurred
+                except Exception as e:
+                    logger.exception("Error in summary generation future")
+                    errors.append(e)
+
+        # In preview mode (indexing-estimate), if there are any errors, fail the request
+        if errors:
+            error_messages = [str(e) for e in errors]
+            error_summary = (
+                f"Failed to generate summaries for {len(errors)} chunk(s). "
+                f"Errors: {'; '.join(error_messages[:3])}"  # Show first 3 errors
+            )
+            if len(errors) > 3:
+                error_summary += f" (and {len(errors) - 3} more)"
+            logger.error("Summary generation failed in preview mode: %s", error_summary)
+            raise ValueError(error_summary)
+
+        return preview_texts

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -11,6 +11,8 @@ import pandas as pd
 from flask import Flask, current_app
 from werkzeug.datastructures import FileStorage
 
+from core.db.session_factory import session_factory
+from core.entities.knowledge_entities import PreviewDetail
 from core.llm_generator.llm_generator import LLMGenerator
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.retrieval_service import RetrievalService
@@ -25,9 +27,10 @@ from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.text_processing_utils import remove_leading_symbols
 from libs import helper
 from models.account import Account
-from models.dataset import Dataset
+from models.dataset import Dataset, DocumentSegment
 from models.dataset import Document as DatasetDocument
 from services.entities.knowledge_entities.knowledge_entities import Rule
+from services.summary_index_service import SummaryIndexService
 
 logger = logging.getLogger(__name__)
 
@@ -144,6 +147,31 @@ class QAIndexProcessor(BaseIndexProcessor):
                 vector.create_multimodal(multimodal_documents)
 
     def clean(self, dataset: Dataset, node_ids: list[str] | None, with_keywords: bool = True, **kwargs):
+        # Note: Summary indexes are now disabled (not deleted) when segments are disabled.
+        # This method is called for actual deletion scenarios (e.g., when segment is deleted).
+        # For disable operations, disable_summaries_for_segments is called directly in the task.
+        # Note: qa_model doesn't generate summaries, but we clean them for completeness
+        # Only delete summaries if explicitly requested (e.g., when segment is actually deleted)
+        delete_summaries = kwargs.get("delete_summaries", False)
+        if delete_summaries:
+            if node_ids:
+                # Find segments by index_node_id
+                with session_factory.create_session() as session:
+                    segments = (
+                        session.query(DocumentSegment)
+                        .filter(
+                            DocumentSegment.dataset_id == dataset.id,
+                            DocumentSegment.index_node_id.in_(node_ids),
+                        )
+                        .all()
+                    )
+                    segment_ids = [segment.id for segment in segments]
+                    if segment_ids:
+                        SummaryIndexService.delete_summaries_for_segments(dataset, segment_ids)
+            else:
+                # Delete all summaries for the dataset
+                SummaryIndexService.delete_summaries_for_segments(dataset, None)
+
         vector = Vector(dataset)
         if node_ids:
             vector.delete_by_ids(node_ids)
@@ -212,6 +240,21 @@ class QAIndexProcessor(BaseIndexProcessor):
             "total_segments": len(qa_chunks.qa_chunks),
         }
 
+    def generate_summary_preview(
+        self,
+        tenant_id: str,
+        preview_texts: list[PreviewDetail],
+        summary_index_setting: dict,
+        doc_language: str | None = None,
+    ) -> list[PreviewDetail]:
+        """
+        QA model doesn't generate summaries, so this method returns preview_texts unchanged.
+
+        Note: QA model uses question-answer pairs, which don't require summary generation.
+        """
+        # QA model doesn't generate summaries, return as-is
+        return preview_texts
+
     def _format_qa_document(self, flask_app: Flask, tenant_id: str, document_node, all_qa_documents, document_language):
         format_documents = []
         if document_node.page_content is None or not document_node.page_content.strip():

api/core/rag/retrieval/dataset_retrieval.py

@@ -236,20 +236,24 @@ class DatasetRetrieval:
             if records:
                 for record in records:
                     segment = record.segment
+                    # Build content: if summary exists, add it before the segment content
                     if segment.answer:
-                        document_context_list.append(
-                            DocumentContext(
-                                content=f"question:{segment.get_sign_content()} answer:{segment.answer}",
-                                score=record.score,
-                            )
-                        )
+                        segment_content = f"question:{segment.get_sign_content()} answer:{segment.answer}"
                     else:
-                        document_context_list.append(
-                            DocumentContext(
-                                content=segment.get_sign_content(),
-                                score=record.score,
-                            )
+                        segment_content = segment.get_sign_content()
+
+                    # If summary exists, prepend it to the content
+                    if record.summary:
+                        final_content = f"{record.summary}\n{segment_content}"
+                    else:
+                        final_content = segment_content
+
+                    document_context_list.append(
+                        DocumentContext(
+                            content=final_content,
+                            score=record.score,
                         )
+                    )
                     if vision_enabled:
                         attachments_with_bindings = db.session.execute(
                             select(SegmentAttachmentBinding, UploadFile)
@@ -316,6 +320,9 @@ class DatasetRetrieval:
                                 source.content = f"question:{segment.content} \nanswer:{segment.answer}"
                             else:
                                 source.content = segment.content
+                            # Add summary if this segment was retrieved via summary
+                            if hasattr(record, "summary") and record.summary:
+                                source.summary = record.summary
                             retrieval_resource_list.append(source)
         if hit_callback and retrieval_resource_list:
             retrieval_resource_list = sorted(retrieval_resource_list, key=lambda x: x.score or 0.0, reverse=True)

api/core/schemas/registry.py

@@ -35,6 +35,7 @@ class SchemaRegistry:
                     registry.load_all_versions()
 
                     cls._default_instance = registry
+            return cls._default_instance
 
         return cls._default_instance
 

api/core/tools/tool_manager.py

@@ -226,16 +226,13 @@ class ToolManager:
                 raise ToolProviderNotFoundError(f"builtin tool {tool_name} not found")
 
             if not provider_controller.need_credentials:
-                return cast(
-                    BuiltinTool,
-                    builtin_tool.fork_tool_runtime(
-                        runtime=ToolRuntime(
-                            tenant_id=tenant_id,
-                            credentials={},
-                            invoke_from=invoke_from,
-                            tool_invoke_from=tool_invoke_from,
-                        )
-                    ),
+                return builtin_tool.fork_tool_runtime(
+                    runtime=ToolRuntime(
+                        tenant_id=tenant_id,
+                        credentials={},
+                        invoke_from=invoke_from,
+                        tool_invoke_from=tool_invoke_from,
+                    )
                 )
             builtin_provider = None
             if isinstance(provider_controller, PluginToolProviderController):
@@ -337,18 +334,15 @@ class ToolManager:
                 decrypted_credentials = refreshed_credentials.credentials
                 cache.delete()
 
-            return cast(
-                BuiltinTool,
-                builtin_tool.fork_tool_runtime(
-                    runtime=ToolRuntime(
-                        tenant_id=tenant_id,
-                        credentials=dict(decrypted_credentials),
-                        credential_type=CredentialType.of(builtin_provider.credential_type),
-                        runtime_parameters={},
-                        invoke_from=invoke_from,
-                        tool_invoke_from=tool_invoke_from,
-                    )
-                ),
+            return builtin_tool.fork_tool_runtime(
+                runtime=ToolRuntime(
+                    tenant_id=tenant_id,
+                    credentials=dict(decrypted_credentials),
+                    credential_type=CredentialType.of(builtin_provider.credential_type),
+                    runtime_parameters={},
+                    invoke_from=invoke_from,
+                    tool_invoke_from=tool_invoke_from,
+                )
             )
 
         elif provider_type == ToolProviderType.API:

api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py

@@ -169,20 +169,24 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                 if records:
                     for record in records:
                         segment = record.segment
+                        # Build content: if summary exists, add it before the segment content
                         if segment.answer:
-                            document_context_list.append(
-                                DocumentContext(
-                                    content=f"question:{segment.get_sign_content()} answer:{segment.answer}",
-                                    score=record.score,
-                                )
-                            )
+                            segment_content = f"question:{segment.get_sign_content()} answer:{segment.answer}"
                         else:
-                            document_context_list.append(
-                                DocumentContext(
-                                    content=segment.get_sign_content(),
-                                    score=record.score,
-                                )
+                            segment_content = segment.get_sign_content()
+
+                        # If summary exists, prepend it to the content
+                        if record.summary:
+                            final_content = f"{record.summary}\n{segment_content}"
+                        else:
+                            final_content = segment_content
+
+                        document_context_list.append(
+                            DocumentContext(
+                                content=final_content,
+                                score=record.score,
                             )
+                        )
 
                     if self.return_resource:
                         for record in records:
@@ -216,6 +220,9 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                                     source.content = f"question:{segment.content} \nanswer:{segment.answer}"
                                 else:
                                     source.content = segment.content
+                                # Add summary if this segment was retrieved via summary
+                                if hasattr(record, "summary") and record.summary:
+                                    source.summary = record.summary
                                 retrieval_resource_list.append(source)
 
             if self.return_resource and retrieval_resource_list:

api/core/tools/utils/workflow_configuration_sync.py

@@ -7,11 +7,6 @@ from core.workflow.nodes.base.entities import OutputVariableEntity
 
 
 class WorkflowToolConfigurationUtils:
-    @classmethod
-    def check_parameter_configurations(cls, configurations: list[Mapping[str, Any]]):
-        for configuration in configurations:
-            WorkflowToolParameterConfiguration.model_validate(configuration)
-
     @classmethod
     def get_workflow_graph_variables(cls, graph: Mapping[str, Any]) -> Sequence[VariableEntity]:
         """

api/core/trigger/debug/event_bus.py

@@ -23,8 +23,8 @@ class TriggerDebugEventBus:
     """
 
     # LUA_SELECT: Atomic poll or register for event
-    # KEYS[1] = trigger_debug_inbox:{tenant_id}:{address_id}
-    # KEYS[2] = trigger_debug_waiting_pool:{tenant_id}:...
+    # KEYS[1] = trigger_debug_inbox:{<tenant_id>}:<address_id>
+    # KEYS[2] = trigger_debug_waiting_pool:{<tenant_id>}:...
     # ARGV[1] = address_id
     LUA_SELECT = (
         "local v=redis.call('GET',KEYS[1]);"
@@ -35,7 +35,7 @@ class TriggerDebugEventBus:
     )
 
     # LUA_DISPATCH: Dispatch event to all waiting addresses
-    # KEYS[1] = trigger_debug_waiting_pool:{tenant_id}:...
+    # KEYS[1] = trigger_debug_waiting_pool:{<tenant_id>}:...
     # ARGV[1] = tenant_id
     # ARGV[2] = event_json
     LUA_DISPATCH = (
@@ -43,7 +43,7 @@ class TriggerDebugEventBus:
         "if #a==0 then return 0 end;"
         "redis.call('DEL',KEYS[1]);"
         "for i=1,#a do "
-        f"redis.call('SET','trigger_debug_inbox:'..ARGV[1]..':'..a[i],ARGV[2],'EX',{TRIGGER_DEBUG_EVENT_TTL});"
+        f"redis.call('SET','trigger_debug_inbox:{{'..ARGV[1]..'}}'..':'..a[i],ARGV[2],'EX',{TRIGGER_DEBUG_EVENT_TTL});"
         "end;"
         "return #a"
     )
@@ -108,7 +108,7 @@ class TriggerDebugEventBus:
             Event object if available, None otherwise
         """
         address_id: str = hashlib.sha256(f"{user_id}|{app_id}|{node_id}".encode()).hexdigest()
-        address: str = f"trigger_debug_inbox:{tenant_id}:{address_id}"
+        address: str = f"trigger_debug_inbox:{{{tenant_id}}}:{address_id}"
 
         try:
             event_data = redis_client.eval(

api/core/trigger/debug/events.py

@@ -42,7 +42,7 @@ def build_webhook_pool_key(tenant_id: str, app_id: str, node_id: str) -> str:
         app_id: App ID
         node_id: Node ID
     """
-    return f"{TriggerDebugPoolKey.WEBHOOK}:{tenant_id}:{app_id}:{node_id}"
+    return f"{TriggerDebugPoolKey.WEBHOOK}:{{{tenant_id}}}:{app_id}:{node_id}"
 
 
 class PluginTriggerDebugEvent(BaseDebugEvent):
@@ -64,4 +64,4 @@ def build_plugin_pool_key(tenant_id: str, provider_id: str, subscription_id: str
         provider_id: Provider ID
         subscription_id: Subscription ID
     """
-    return f"{TriggerDebugPoolKey.PLUGIN}:{tenant_id}:{str(provider_id)}:{subscription_id}:{name}"
+    return f"{TriggerDebugPoolKey.PLUGIN}:{{{tenant_id}}}:{str(provider_id)}:{subscription_id}:{name}"

api/core/workflow/graph/graph.py

@@ -5,15 +5,20 @@ from collections import defaultdict
 from collections.abc import Mapping, Sequence
 from typing import Protocol, cast, final
 
+from pydantic import TypeAdapter
+
+from core.workflow.entities.graph_config import NodeConfigDict
 from core.workflow.enums import ErrorStrategy, NodeExecutionType, NodeState, NodeType
 from core.workflow.nodes.base.node import Node
-from libs.typing import is_str, is_str_dict
+from libs.typing import is_str
 
 from .edge import Edge
 from .validation import get_graph_validator
 
 logger = logging.getLogger(__name__)
 
+_ListNodeConfigDict = TypeAdapter(list[NodeConfigDict])
+
 
 class NodeFactory(Protocol):
     """
@@ -23,7 +28,7 @@ class NodeFactory(Protocol):
     allowing for different node creation strategies while maintaining type safety.
     """
 
-    def create_node(self, node_config: dict[str, object]) -> Node:
+    def create_node(self, node_config: NodeConfigDict) -> Node:
         """
         Create a Node instance from node configuration data.
 
@@ -63,28 +68,24 @@ class Graph:
         self.root_node = root_node
 
     @classmethod
-    def _parse_node_configs(cls, node_configs: list[dict[str, object]]) -> dict[str, dict[str, object]]:
+    def _parse_node_configs(cls, node_configs: list[NodeConfigDict]) -> dict[str, NodeConfigDict]:
         """
         Parse node configurations and build a mapping of node IDs to configs.
 
         :param node_configs: list of node configuration dictionaries
         :return: mapping of node ID to node config
         """
-        node_configs_map: dict[str, dict[str, object]] = {}
+        node_configs_map: dict[str, NodeConfigDict] = {}
 
         for node_config in node_configs:
-            node_id = node_config.get("id")
-            if not node_id or not isinstance(node_id, str):
-                continue
-
-            node_configs_map[node_id] = node_config
+            node_configs_map[node_config["id"]] = node_config
 
         return node_configs_map
 
     @classmethod
     def _find_root_node_id(
         cls,
-        node_configs_map: Mapping[str, Mapping[str, object]],
+        node_configs_map: Mapping[str, NodeConfigDict],
         edge_configs: Sequence[Mapping[str, object]],
         root_node_id: str | None = None,
     ) -> str:
@@ -113,10 +114,8 @@ class Graph:
         # Prefer START node if available
         start_node_id = None
         for nid in root_candidates:
-            node_data = node_configs_map[nid].get("data")
-            if not is_str_dict(node_data):
-                continue
-            node_type = node_data.get("type")
+            node_data = node_configs_map[nid]["data"]
+            node_type = node_data["type"]
             if not isinstance(node_type, str):
                 continue
             if NodeType(node_type).is_start_node:
@@ -176,7 +175,7 @@ class Graph:
     @classmethod
     def _create_node_instances(
         cls,
-        node_configs_map: dict[str, dict[str, object]],
+        node_configs_map: dict[str, NodeConfigDict],
         node_factory: NodeFactory,
     ) -> dict[str, Node]:
         """
@@ -303,7 +302,7 @@ class Graph:
         node_configs = graph_config.get("nodes", [])
 
         edge_configs = cast(list[dict[str, object]], edge_configs)
-        node_configs = cast(list[dict[str, object]], node_configs)
+        node_configs = _ListNodeConfigDict.validate_python(node_configs)
 
         if not node_configs:
             raise ValueError("Graph must have at least one node")

api/core/workflow/graph_engine/graph_engine.py

@@ -46,7 +46,6 @@ from .graph_traversal import EdgeProcessor, SkipPropagator
 from .layers.base import GraphEngineLayer
 from .orchestration import Dispatcher, ExecutionCoordinator
 from .protocols.command_channel import CommandChannel
-from .ready_queue import ReadyQueue
 from .worker_management import WorkerPool
 
 if TYPE_CHECKING:
@@ -90,7 +89,7 @@ class GraphEngine:
         self._graph_execution.workflow_id = workflow_id
 
         # === Execution Queues ===
-        self._ready_queue = cast(ReadyQueue, self._graph_runtime_state.ready_queue)
+        self._ready_queue = self._graph_runtime_state.ready_queue
 
         # Queue for events generated during execution
         self._event_queue: queue.Queue[GraphNodeEventBase] = queue.Queue()

api/core/workflow/graph_engine/response_coordinator/coordinator.py

@@ -15,10 +15,10 @@ from uuid import uuid4
 from pydantic import BaseModel, Field
 
 from core.workflow.enums import NodeExecutionType, NodeState
-from core.workflow.graph import Graph
 from core.workflow.graph_events import NodeRunStreamChunkEvent, NodeRunSucceededEvent
 from core.workflow.nodes.base.template import TextSegment, VariableSegment
 from core.workflow.runtime import VariablePool
+from core.workflow.runtime.graph_runtime_state import GraphProtocol
 
 from .path import Path
 from .session import ResponseSession
@@ -75,7 +75,7 @@ class ResponseStreamCoordinator:
     Ensures ordered streaming of responses based on upstream node outputs and constants.
     """
 
-    def __init__(self, variable_pool: "VariablePool", graph: "Graph") -> None:
+    def __init__(self, variable_pool: "VariablePool", graph: GraphProtocol) -> None:
         """
         Initialize coordinator with variable pool.
 

api/core/workflow/graph_engine/response_coordinator/session.py

@@ -10,10 +10,10 @@ from __future__ import annotations
 from dataclasses import dataclass
 
 from core.workflow.nodes.answer.answer_node import AnswerNode
-from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.base.template import Template
 from core.workflow.nodes.end.end_node import EndNode
 from core.workflow.nodes.knowledge_index import KnowledgeIndexNode
+from core.workflow.runtime.graph_runtime_state import NodeProtocol
 
 
 @dataclass
@@ -29,21 +29,26 @@ class ResponseSession:
     index: int = 0  # Current position in the template segments
 
     @classmethod
-    def from_node(cls, node: Node) -> ResponseSession:
+    def from_node(cls, node: NodeProtocol) -> ResponseSession:
         """
-        Create a ResponseSession from an AnswerNode or EndNode.
+        Create a ResponseSession from a response-capable node.
+
+        The parameter is typed as `NodeProtocol` because the graph is exposed behind a protocol at the runtime layer,
+        but at runtime this must be an `AnswerNode`, `EndNode`, or `KnowledgeIndexNode` that provides:
+        - `id: str`
+        - `get_streaming_template() -> Template`
 
         Args:
-            node: Must be either an AnswerNode or EndNode instance
+            node: Node from the materialized workflow graph.
 
         Returns:
             ResponseSession configured with the node's streaming template
 
         Raises:
-            TypeError: If node is not an AnswerNode or EndNode
+            TypeError: If node is not a supported response node type.
         """
         if not isinstance(node, AnswerNode | EndNode | KnowledgeIndexNode):
-            raise TypeError
+            raise TypeError("ResponseSession.from_node only supports AnswerNode, EndNode, or KnowledgeIndexNode")
         return cls(
             node_id=node.id,
             template=node.get_streaming_template(),

api/core/workflow/nodes/agent/agent_node.py

@@ -192,32 +192,33 @@ class AgentNode(Node[AgentNodeData]):
                 result[parameter_name] = None
                 continue
             agent_input = node_data.agent_parameters[parameter_name]
-            if agent_input.type == "variable":
-                variable = variable_pool.get(agent_input.value)  # type: ignore
-                if variable is None:
-                    raise AgentVariableNotFoundError(str(agent_input.value))
-                parameter_value = variable.value
-            elif agent_input.type in {"mixed", "constant"}:
-                # variable_pool.convert_template expects a string template,
-                # but if passing a dict, convert to JSON string first before rendering
-                try:
-                    if not isinstance(agent_input.value, str):
-                        parameter_value = json.dumps(agent_input.value, ensure_ascii=False)
-                    else:
+            match agent_input.type:
+                case "variable":
+                    variable = variable_pool.get(agent_input.value)  # type: ignore
+                    if variable is None:
+                        raise AgentVariableNotFoundError(str(agent_input.value))
+                    parameter_value = variable.value
+                case "mixed" | "constant":
+                    # variable_pool.convert_template expects a string template,
+                    # but if passing a dict, convert to JSON string first before rendering
+                    try:
+                        if not isinstance(agent_input.value, str):
+                            parameter_value = json.dumps(agent_input.value, ensure_ascii=False)
+                        else:
+                            parameter_value = str(agent_input.value)
+                    except TypeError:
                         parameter_value = str(agent_input.value)
-                except TypeError:
-                    parameter_value = str(agent_input.value)
-                segment_group = variable_pool.convert_template(parameter_value)
-                parameter_value = segment_group.log if for_log else segment_group.text
-                # variable_pool.convert_template returns a string,
-                # so we need to convert it back to a dictionary
-                try:
-                    if not isinstance(agent_input.value, str):
-                        parameter_value = json.loads(parameter_value)
-                except json.JSONDecodeError:
-                    parameter_value = parameter_value
-            else:
-                raise AgentInputTypeError(agent_input.type)
+                    segment_group = variable_pool.convert_template(parameter_value)
+                    parameter_value = segment_group.log if for_log else segment_group.text
+                    # variable_pool.convert_template returns a string,
+                    # so we need to convert it back to a dictionary
+                    try:
+                        if not isinstance(agent_input.value, str):
+                            parameter_value = json.loads(parameter_value)
+                    except json.JSONDecodeError:
+                        parameter_value = parameter_value
+                case _:
+                    raise AgentInputTypeError(agent_input.type)
             value = parameter_value
             if parameter.type == "array[tools]":
                 value = cast(list[dict[str, Any]], value)
@@ -374,12 +375,13 @@ class AgentNode(Node[AgentNodeData]):
         result: dict[str, Any] = {}
         for parameter_name in typed_node_data.agent_parameters:
             input = typed_node_data.agent_parameters[parameter_name]
-            if input.type in ["mixed", "constant"]:
-                selectors = VariableTemplateParser(str(input.value)).extract_variable_selectors()
-                for selector in selectors:
-                    result[selector.variable] = selector.value_selector
-            elif input.type == "variable":
-                result[parameter_name] = input.value
+            match input.type:
+                case "mixed" | "constant":
+                    selectors = VariableTemplateParser(str(input.value)).extract_variable_selectors()
+                    for selector in selectors:
+                        result[selector.variable] = selector.value_selector
+                case "variable":
+                    result[parameter_name] = input.value
 
         result = {node_id + "." + key: value for key, value in result.items()}
 

api/core/workflow/nodes/code/entities.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Literal, Self
+from typing import Annotated, Literal
 
 from pydantic import AfterValidator, BaseModel
 
@@ -34,7 +34,7 @@ class CodeNodeData(BaseNodeData):
 
     class Output(BaseModel):
         type: Annotated[SegmentType, AfterValidator(_validate_type)]
-        children: dict[str, Self] | None = None
+        children: dict[str, "CodeNodeData.Output"] | None = None
 
     class Dependency(BaseModel):
         name: str

api/core/workflow/nodes/datasource/datasource_node.py

@@ -69,11 +69,13 @@ class DatasourceNode(Node[DatasourceNodeData]):
         if datasource_type is None:
             raise DatasourceNodeError("Datasource type is not set")
 
+        datasource_type = DatasourceProviderType.value_of(datasource_type)
+
         datasource_runtime = DatasourceManager.get_datasource_runtime(
             provider_id=f"{node_data.plugin_id}/{node_data.provider_name}",
             datasource_name=node_data.datasource_name or "",
             tenant_id=self.tenant_id,
-            datasource_type=DatasourceProviderType.value_of(datasource_type),
+            datasource_type=datasource_type,
         )
         datasource_info["icon"] = datasource_runtime.get_icon_url(self.tenant_id)
 
@@ -268,15 +270,18 @@ class DatasourceNode(Node[DatasourceNodeData]):
         if typed_node_data.datasource_parameters:
             for parameter_name in typed_node_data.datasource_parameters:
                 input = typed_node_data.datasource_parameters[parameter_name]
-                if input.type == "mixed":
-                    assert isinstance(input.value, str)
-                    selectors = VariableTemplateParser(input.value).extract_variable_selectors()
-                    for selector in selectors:
-                        result[selector.variable] = selector.value_selector
-                elif input.type == "variable":
-                    result[parameter_name] = input.value
-                elif input.type == "constant":
-                    pass
+                match input.type:
+                    case "mixed":
+                        assert isinstance(input.value, str)
+                        selectors = VariableTemplateParser(input.value).extract_variable_selectors()
+                        for selector in selectors:
+                            result[selector.variable] = selector.value_selector
+                    case "variable":
+                        result[parameter_name] = input.value
+                    case "constant":
+                        pass
+                    case None:
+                        pass
 
             result = {node_id + "." + key: value for key, value in result.items()}
 
@@ -306,99 +311,107 @@ class DatasourceNode(Node[DatasourceNodeData]):
         variables: dict[str, Any] = {}
 
         for message in message_stream:
-            if message.type in {
-                DatasourceMessage.MessageType.IMAGE_LINK,
-                DatasourceMessage.MessageType.BINARY_LINK,
-                DatasourceMessage.MessageType.IMAGE,
-            }:
-                assert isinstance(message.message, DatasourceMessage.TextMessage)
+            match message.type:
+                case (
+                    DatasourceMessage.MessageType.IMAGE_LINK
+                    | DatasourceMessage.MessageType.BINARY_LINK
+                    | DatasourceMessage.MessageType.IMAGE
+                ):
+                    assert isinstance(message.message, DatasourceMessage.TextMessage)
 
-                url = message.message.text
-                transfer_method = FileTransferMethod.TOOL_FILE
+                    url = message.message.text
+                    transfer_method = FileTransferMethod.TOOL_FILE
 
-                datasource_file_id = str(url).split("/")[-1].split(".")[0]
+                    datasource_file_id = str(url).split("/")[-1].split(".")[0]
 
-                with Session(db.engine) as session:
-                    stmt = select(ToolFile).where(ToolFile.id == datasource_file_id)
-                    datasource_file = session.scalar(stmt)
-                    if datasource_file is None:
-                        raise ToolFileError(f"Tool file {datasource_file_id} does not exist")
+                    with Session(db.engine) as session:
+                        stmt = select(ToolFile).where(ToolFile.id == datasource_file_id)
+                        datasource_file = session.scalar(stmt)
+                        if datasource_file is None:
+                            raise ToolFileError(f"Tool file {datasource_file_id} does not exist")
 
-                mapping = {
-                    "tool_file_id": datasource_file_id,
-                    "type": file_factory.get_file_type_by_mime_type(datasource_file.mimetype),
-                    "transfer_method": transfer_method,
-                    "url": url,
-                }
-                file = file_factory.build_from_mapping(
-                    mapping=mapping,
-                    tenant_id=self.tenant_id,
-                )
-                files.append(file)
-            elif message.type == DatasourceMessage.MessageType.BLOB:
-                # get tool file id
-                assert isinstance(message.message, DatasourceMessage.TextMessage)
-                assert message.meta
-
-                datasource_file_id = message.message.text.split("/")[-1].split(".")[0]
-                with Session(db.engine) as session:
-                    stmt = select(ToolFile).where(ToolFile.id == datasource_file_id)
-                    datasource_file = session.scalar(stmt)
-                    if datasource_file is None:
-                        raise ToolFileError(f"datasource file {datasource_file_id} not exists")
-
-                mapping = {
-                    "tool_file_id": datasource_file_id,
-                    "transfer_method": FileTransferMethod.TOOL_FILE,
-                }
-
-                files.append(
-                    file_factory.build_from_mapping(
+                    mapping = {
+                        "tool_file_id": datasource_file_id,
+                        "type": file_factory.get_file_type_by_mime_type(datasource_file.mimetype),
+                        "transfer_method": transfer_method,
+                        "url": url,
+                    }
+                    file = file_factory.build_from_mapping(
                         mapping=mapping,
                         tenant_id=self.tenant_id,
                     )
-                )
-            elif message.type == DatasourceMessage.MessageType.TEXT:
-                assert isinstance(message.message, DatasourceMessage.TextMessage)
-                text += message.message.text
-                yield StreamChunkEvent(
-                    selector=[self._node_id, "text"],
-                    chunk=message.message.text,
-                    is_final=False,
-                )
-            elif message.type == DatasourceMessage.MessageType.JSON:
-                assert isinstance(message.message, DatasourceMessage.JsonMessage)
-                json.append(message.message.json_object)
-            elif message.type == DatasourceMessage.MessageType.LINK:
-                assert isinstance(message.message, DatasourceMessage.TextMessage)
-                stream_text = f"Link: {message.message.text}\n"
-                text += stream_text
-                yield StreamChunkEvent(
-                    selector=[self._node_id, "text"],
-                    chunk=stream_text,
-                    is_final=False,
-                )
-            elif message.type == DatasourceMessage.MessageType.VARIABLE:
-                assert isinstance(message.message, DatasourceMessage.VariableMessage)
-                variable_name = message.message.variable_name
-                variable_value = message.message.variable_value
-                if message.message.stream:
-                    if not isinstance(variable_value, str):
-                        raise ValueError("When 'stream' is True, 'variable_value' must be a string.")
-                    if variable_name not in variables:
-                        variables[variable_name] = ""
-                    variables[variable_name] += variable_value
+                    files.append(file)
+                case DatasourceMessage.MessageType.BLOB:
+                    # get tool file id
+                    assert isinstance(message.message, DatasourceMessage.TextMessage)
+                    assert message.meta
 
+                    datasource_file_id = message.message.text.split("/")[-1].split(".")[0]
+                    with Session(db.engine) as session:
+                        stmt = select(ToolFile).where(ToolFile.id == datasource_file_id)
+                        datasource_file = session.scalar(stmt)
+                        if datasource_file is None:
+                            raise ToolFileError(f"datasource file {datasource_file_id} not exists")
+
+                    mapping = {
+                        "tool_file_id": datasource_file_id,
+                        "transfer_method": FileTransferMethod.TOOL_FILE,
+                    }
+
+                    files.append(
+                        file_factory.build_from_mapping(
+                            mapping=mapping,
+                            tenant_id=self.tenant_id,
+                        )
+                    )
+                case DatasourceMessage.MessageType.TEXT:
+                    assert isinstance(message.message, DatasourceMessage.TextMessage)
+                    text += message.message.text
                     yield StreamChunkEvent(
-                        selector=[self._node_id, variable_name],
-                        chunk=variable_value,
+                        selector=[self._node_id, "text"],
+                        chunk=message.message.text,
                         is_final=False,
                     )
-                else:
-                    variables[variable_name] = variable_value
-            elif message.type == DatasourceMessage.MessageType.FILE:
-                assert message.meta is not None
-                files.append(message.meta["file"])
+                case DatasourceMessage.MessageType.JSON:
+                    assert isinstance(message.message, DatasourceMessage.JsonMessage)
+                    json.append(message.message.json_object)
+                case DatasourceMessage.MessageType.LINK:
+                    assert isinstance(message.message, DatasourceMessage.TextMessage)
+                    stream_text = f"Link: {message.message.text}\n"
+                    text += stream_text
+                    yield StreamChunkEvent(
+                        selector=[self._node_id, "text"],
+                        chunk=stream_text,
+                        is_final=False,
+                    )
+                case DatasourceMessage.MessageType.VARIABLE:
+                    assert isinstance(message.message, DatasourceMessage.VariableMessage)
+                    variable_name = message.message.variable_name
+                    variable_value = message.message.variable_value
+                    if message.message.stream:
+                        if not isinstance(variable_value, str):
+                            raise ValueError("When 'stream' is True, 'variable_value' must be a string.")
+                        if variable_name not in variables:
+                            variables[variable_name] = ""
+                        variables[variable_name] += variable_value
+
+                        yield StreamChunkEvent(
+                            selector=[self._node_id, variable_name],
+                            chunk=variable_value,
+                            is_final=False,
+                        )
+                    else:
+                        variables[variable_name] = variable_value
+                case DatasourceMessage.MessageType.FILE:
+                    assert message.meta is not None
+                    files.append(message.meta["file"])
+                case (
+                    DatasourceMessage.MessageType.BLOB_CHUNK
+                    | DatasourceMessage.MessageType.LOG
+                    | DatasourceMessage.MessageType.RETRIEVER_RESOURCES
+                ):
+                    pass
+
         # mark the end of the stream
         yield StreamChunkEvent(
             selector=[self._node_id, "text"],

api/core/workflow/nodes/http_request/executor.py

@@ -2,7 +2,7 @@ import base64
 import json
 import secrets
 import string
-from collections.abc import Mapping
+from collections.abc import Callable, Mapping
 from copy import deepcopy
 from typing import Any, Literal
 from urllib.parse import urlencode, urlparse
@@ -11,9 +11,9 @@ import httpx
 from json_repair import repair_json
 
 from configs import dify_config
-from core.file import file_manager
 from core.file.enums import FileTransferMethod
-from core.helper import ssrf_proxy
+from core.file.file_manager import file_manager as default_file_manager
+from core.helper.ssrf_proxy import ssrf_proxy
 from core.variables.segments import ArrayFileSegment, FileSegment
 from core.workflow.runtime import VariablePool
 
@@ -79,8 +79,8 @@ class Executor:
         timeout: HttpRequestNodeTimeout,
         variable_pool: VariablePool,
         max_retries: int = dify_config.SSRF_DEFAULT_MAX_RETRIES,
-        http_client: HttpClientProtocol = ssrf_proxy,
-        file_manager: FileManagerProtocol = file_manager,
+        http_client: HttpClientProtocol | None = None,
+        file_manager: FileManagerProtocol | None = None,
     ):
         # If authorization API key is present, convert the API key using the variable pool
         if node_data.authorization.type == "api-key":
@@ -107,8 +107,8 @@ class Executor:
         self.data = None
         self.json = None
         self.max_retries = max_retries
-        self._http_client = http_client
-        self._file_manager = file_manager
+        self._http_client = http_client or ssrf_proxy
+        self._file_manager = file_manager or default_file_manager
 
         # init template
         self.variable_pool = variable_pool
@@ -336,7 +336,7 @@ class Executor:
         """
         do http request depending on api bundle
         """
-        _METHOD_MAP = {
+        _METHOD_MAP: dict[str, Callable[..., httpx.Response]] = {
             "get": self._http_client.get,
             "head": self._http_client.head,
             "post": self._http_client.post,
@@ -348,7 +348,7 @@ class Executor:
         if method_lc not in _METHOD_MAP:
             raise InvalidHttpMethodError(f"Invalid http method {self.method}")
 
-        request_args = {
+        request_args: dict[str, Any] = {
             "data": self.data,
             "files": self.files,
             "json": self.json,
@@ -361,14 +361,13 @@ class Executor:
         }
         # request_args = {k: v for k, v in request_args.items() if v is not None}
         try:
-            response: httpx.Response = _METHOD_MAP[method_lc](
+            response = _METHOD_MAP[method_lc](
                 url=self.url,
                 **request_args,
                 max_retries=self.max_retries,
             )
         except (self._http_client.max_retries_exceeded_error, self._http_client.request_error) as e:
             raise HttpRequestNodeError(str(e)) from e
-        # FIXME: fix type ignore, this maybe httpx type issue
         return response
 
     def invoke(self) -> Response:

api/core/workflow/nodes/http_request/node.py

@@ -4,8 +4,9 @@ from collections.abc import Callable, Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
 from configs import dify_config
-from core.file import File, FileTransferMethod, file_manager
-from core.helper import ssrf_proxy
+from core.file import File, FileTransferMethod
+from core.file.file_manager import file_manager as default_file_manager
+from core.helper.ssrf_proxy import ssrf_proxy
 from core.tools.tool_file_manager import ToolFileManager
 from core.variables.segments import ArrayFileSegment
 from core.workflow.enums import NodeType, WorkflowNodeExecutionStatus
@@ -47,9 +48,9 @@ class HttpRequestNode(Node[HttpRequestNodeData]):
         graph_init_params: "GraphInitParams",
         graph_runtime_state: "GraphRuntimeState",
         *,
-        http_client: HttpClientProtocol = ssrf_proxy,
+        http_client: HttpClientProtocol | None = None,
         tool_file_manager_factory: Callable[[], ToolFileManager] = ToolFileManager,
-        file_manager: FileManagerProtocol = file_manager,
+        file_manager: FileManagerProtocol | None = None,
     ) -> None:
         super().__init__(
             id=id,
@@ -57,9 +58,9 @@ class HttpRequestNode(Node[HttpRequestNodeData]):
             graph_init_params=graph_init_params,
             graph_runtime_state=graph_runtime_state,
         )
-        self._http_client = http_client
+        self._http_client = http_client or ssrf_proxy
         self._tool_file_manager_factory = tool_file_manager_factory
-        self._file_manager = file_manager
+        self._file_manager = file_manager or default_file_manager
 
     @classmethod
     def get_default_config(cls, filters: Mapping[str, object] | None = None) -> Mapping[str, object]:

api/core/workflow/nodes/iteration/iteration_node.py

@@ -397,7 +397,7 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
             return outputs
 
         # Check if all non-None outputs are lists
-        non_none_outputs = [output for output in outputs if output is not None]
+        non_none_outputs: list[object] = [output for output in outputs if output is not None]
         if not non_none_outputs:
             return outputs
 

api/core/workflow/nodes/knowledge_index/entities.py

@@ -158,3 +158,5 @@ class KnowledgeIndexNodeData(BaseNodeData):
     type: str = "knowledge-index"
     chunk_structure: str
     index_chunk_variable_selector: list[str]
+    indexing_technique: str | None = None
+    summary_index_setting: dict | None = None

api/core/workflow/nodes/knowledge_index/knowledge_index_node.py

@@ -1,9 +1,11 @@
+import concurrent.futures
 import datetime
 import logging
 import time
 from collections.abc import Mapping
 from typing import Any
 
+from flask import current_app
 from sqlalchemy import func, select
 
 from core.app.entities.app_invoke_entities import InvokeFrom
@@ -16,7 +18,9 @@ from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.base.template import Template
 from core.workflow.runtime import VariablePool
 from extensions.ext_database import db
-from models.dataset import Dataset, Document, DocumentSegment
+from models.dataset import Dataset, Document, DocumentSegment, DocumentSegmentSummary
+from services.summary_index_service import SummaryIndexService
+from tasks.generate_summary_index_task import generate_summary_index_task
 
 from .entities import KnowledgeIndexNodeData
 from .exc import (
@@ -67,7 +71,29 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
         # index knowledge
         try:
             if is_preview:
-                outputs = self._get_preview_output(node_data.chunk_structure, chunks)
+                # Preview mode: generate summaries for chunks directly without saving to database
+                # Format preview and generate summaries on-the-fly
+                # Get indexing_technique and summary_index_setting from node_data (workflow graph config)
+                # or fallback to dataset if not available in node_data
+                indexing_technique = node_data.indexing_technique or dataset.indexing_technique
+                summary_index_setting = node_data.summary_index_setting or dataset.summary_index_setting
+
+                # Try to get document language if document_id is available
+                doc_language = None
+                document_id = variable_pool.get(["sys", SystemVariableKey.DOCUMENT_ID])
+                if document_id:
+                    document = db.session.query(Document).filter_by(id=document_id.value).first()
+                    if document and document.doc_language:
+                        doc_language = document.doc_language
+
+                outputs = self._get_preview_output_with_summaries(
+                    node_data.chunk_structure,
+                    chunks,
+                    dataset=dataset,
+                    indexing_technique=indexing_technique,
+                    summary_index_setting=summary_index_setting,
+                    doc_language=doc_language,
+                )
                 return NodeRunResult(
                     status=WorkflowNodeExecutionStatus.SUCCEEDED,
                     inputs=variables,
@@ -148,6 +174,11 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
             )
             .scalar()
         )
+        # Update need_summary based on dataset's summary_index_setting
+        if dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True:
+            document.need_summary = True
+        else:
+            document.need_summary = False
         db.session.add(document)
         # update document segment status
         db.session.query(DocumentSegment).where(
@@ -163,6 +194,9 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
 
         db.session.commit()
 
+        # Generate summary index if enabled
+        self._handle_summary_index_generation(dataset, document, variable_pool)
+
         return {
             "dataset_id": ds_id_value,
             "dataset_name": dataset_name_value,
@@ -173,9 +207,308 @@ class KnowledgeIndexNode(Node[KnowledgeIndexNodeData]):
             "display_status": "completed",
         }
 
-    def _get_preview_output(self, chunk_structure: str, chunks: Any) -> Mapping[str, Any]:
+    def _handle_summary_index_generation(
+        self,
+        dataset: Dataset,
+        document: Document,
+        variable_pool: VariablePool,
+    ) -> None:
+        """
+        Handle summary index generation based on mode (debug/preview or production).
+
+        Args:
+            dataset: Dataset containing the document
+            document: Document to generate summaries for
+            variable_pool: Variable pool to check invoke_from
+        """
+        # Only generate summary index for high_quality indexing technique
+        if dataset.indexing_technique != "high_quality":
+            return
+
+        # Check if summary index is enabled
+        summary_index_setting = dataset.summary_index_setting
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            return
+
+        # Skip qa_model documents
+        if document.doc_form == "qa_model":
+            return
+
+        # Determine if in preview/debug mode
+        invoke_from = variable_pool.get(["sys", SystemVariableKey.INVOKE_FROM])
+        is_preview = invoke_from and invoke_from.value == InvokeFrom.DEBUGGER
+
+        if is_preview:
+            try:
+                # Query segments that need summary generation
+                query = db.session.query(DocumentSegment).filter_by(
+                    dataset_id=dataset.id,
+                    document_id=document.id,
+                    status="completed",
+                    enabled=True,
+                )
+                segments = query.all()
+
+                if not segments:
+                    logger.info("No segments found for document %s", document.id)
+                    return
+
+                # Filter segments based on mode
+                segments_to_process = []
+                for segment in segments:
+                    # Skip if summary already exists
+                    existing_summary = (
+                        db.session.query(DocumentSegmentSummary)
+                        .filter_by(chunk_id=segment.id, dataset_id=dataset.id, status="completed")
+                        .first()
+                    )
+                    if existing_summary:
+                        continue
+
+                    # For parent-child mode, all segments are parent chunks, so process all
+                    segments_to_process.append(segment)
+
+                if not segments_to_process:
+                    logger.info("No segments need summary generation for document %s", document.id)
+                    return
+
+                # Use ThreadPoolExecutor for concurrent generation
+                flask_app = current_app._get_current_object()  # type: ignore
+                max_workers = min(10, len(segments_to_process))  # Limit to 10 workers
+
+                def process_segment(segment: DocumentSegment) -> None:
+                    """Process a single segment in a thread with Flask app context."""
+                    with flask_app.app_context():
+                        try:
+                            SummaryIndexService.generate_and_vectorize_summary(segment, dataset, summary_index_setting)
+                        except Exception:
+                            logger.exception(
+                                "Failed to generate summary for segment %s",
+                                segment.id,
+                            )
+                            # Continue processing other segments
+
+                with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+                    futures = [executor.submit(process_segment, segment) for segment in segments_to_process]
+                    # Wait for all tasks to complete
+                    concurrent.futures.wait(futures)
+
+                logger.info(
+                    "Successfully generated summary index for %s segments in document %s",
+                    len(segments_to_process),
+                    document.id,
+                )
+            except Exception:
+                logger.exception("Failed to generate summary index for document %s", document.id)
+                # Don't fail the entire indexing process if summary generation fails
+        else:
+            # Production mode: asynchronous generation
+            logger.info(
+                "Queuing summary index generation task for document %s (production mode)",
+                document.id,
+            )
+            try:
+                generate_summary_index_task.delay(dataset.id, document.id, None)
+                logger.info("Summary index generation task queued for document %s", document.id)
+            except Exception:
+                logger.exception(
+                    "Failed to queue summary index generation task for document %s",
+                    document.id,
+                )
+                # Don't fail the entire indexing process if task queuing fails
+
+    def _get_preview_output_with_summaries(
+        self,
+        chunk_structure: str,
+        chunks: Any,
+        dataset: Dataset,
+        indexing_technique: str | None = None,
+        summary_index_setting: dict | None = None,
+        doc_language: str | None = None,
+    ) -> Mapping[str, Any]:
+        """
+        Generate preview output with summaries for chunks in preview mode.
+        This method generates summaries on-the-fly without saving to database.
+
+        Args:
+            chunk_structure: Chunk structure type
+            chunks: Chunks to generate preview for
+            dataset: Dataset object (for tenant_id)
+            indexing_technique: Indexing technique from node config or dataset
+            summary_index_setting: Summary index setting from node config or dataset
+            doc_language: Optional document language to ensure summary is generated in the correct language
+        """
         index_processor = IndexProcessorFactory(chunk_structure).init_index_processor()
-        return index_processor.format_preview(chunks)
+        preview_output = index_processor.format_preview(chunks)
+
+        # Check if summary index is enabled
+        if indexing_technique != "high_quality":
+            return preview_output
+
+        if not summary_index_setting or not summary_index_setting.get("enable"):
+            return preview_output
+
+        # Generate summaries for chunks
+        if "preview" in preview_output and isinstance(preview_output["preview"], list):
+            chunk_count = len(preview_output["preview"])
+            logger.info(
+                "Generating summaries for %s chunks in preview mode (dataset: %s)",
+                chunk_count,
+                dataset.id,
+            )
+            # Use ParagraphIndexProcessor's generate_summary method
+            from core.rag.index_processor.processor.paragraph_index_processor import ParagraphIndexProcessor
+
+            # Get Flask app for application context in worker threads
+            flask_app = None
+            try:
+                flask_app = current_app._get_current_object()  # type: ignore
+            except RuntimeError:
+                logger.warning("No Flask application context available, summary generation may fail")
+
+            def generate_summary_for_chunk(preview_item: dict) -> None:
+                """Generate summary for a single chunk."""
+                if "content" in preview_item:
+                    # Set Flask application context in worker thread
+                    if flask_app:
+                        with flask_app.app_context():
+                            summary, _ = ParagraphIndexProcessor.generate_summary(
+                                tenant_id=dataset.tenant_id,
+                                text=preview_item["content"],
+                                summary_index_setting=summary_index_setting,
+                                document_language=doc_language,
+                            )
+                            if summary:
+                                preview_item["summary"] = summary
+                    else:
+                        # Fallback: try without app context (may fail)
+                        summary, _ = ParagraphIndexProcessor.generate_summary(
+                            tenant_id=dataset.tenant_id,
+                            text=preview_item["content"],
+                            summary_index_setting=summary_index_setting,
+                            document_language=doc_language,
+                        )
+                        if summary:
+                            preview_item["summary"] = summary
+
+            # Generate summaries concurrently using ThreadPoolExecutor
+            # Set a reasonable timeout to prevent hanging (60 seconds per chunk, max 5 minutes total)
+            timeout_seconds = min(300, 60 * len(preview_output["preview"]))
+            errors: list[Exception] = []
+
+            with concurrent.futures.ThreadPoolExecutor(max_workers=min(10, len(preview_output["preview"]))) as executor:
+                futures = [
+                    executor.submit(generate_summary_for_chunk, preview_item)
+                    for preview_item in preview_output["preview"]
+                ]
+                # Wait for all tasks to complete with timeout
+                done, not_done = concurrent.futures.wait(futures, timeout=timeout_seconds)
+
+                # Cancel tasks that didn't complete in time
+                if not_done:
+                    timeout_error_msg = (
+                        f"Summary generation timeout: {len(not_done)} chunks did not complete within {timeout_seconds}s"
+                    )
+                    logger.warning("%s. Cancelling remaining tasks...", timeout_error_msg)
+                    # In preview mode, timeout is also an error
+                    errors.append(TimeoutError(timeout_error_msg))
+                    for future in not_done:
+                        future.cancel()
+                    # Wait a bit for cancellation to take effect
+                    concurrent.futures.wait(not_done, timeout=5)
+
+                # Collect exceptions from completed futures
+                for future in done:
+                    try:
+                        future.result()  # This will raise any exception that occurred
+                    except Exception as e:
+                        logger.exception("Error in summary generation future")
+                        errors.append(e)
+
+            # In preview mode, if there are any errors, fail the request
+            if errors:
+                error_messages = [str(e) for e in errors]
+                error_summary = (
+                    f"Failed to generate summaries for {len(errors)} chunk(s). "
+                    f"Errors: {'; '.join(error_messages[:3])}"  # Show first 3 errors
+                )
+                if len(errors) > 3:
+                    error_summary += f" (and {len(errors) - 3} more)"
+                logger.error("Summary generation failed in preview mode: %s", error_summary)
+                raise KnowledgeIndexNodeError(error_summary)
+
+            completed_count = sum(1 for item in preview_output["preview"] if item.get("summary") is not None)
+            logger.info(
+                "Completed summary generation for preview chunks: %s/%s succeeded",
+                completed_count,
+                len(preview_output["preview"]),
+            )
+
+        return preview_output
+
+    def _get_preview_output(
+        self,
+        chunk_structure: str,
+        chunks: Any,
+        dataset: Dataset | None = None,
+        variable_pool: VariablePool | None = None,
+    ) -> Mapping[str, Any]:
+        index_processor = IndexProcessorFactory(chunk_structure).init_index_processor()
+        preview_output = index_processor.format_preview(chunks)
+
+        # If dataset is provided, try to enrich preview with summaries
+        if dataset and variable_pool:
+            document_id = variable_pool.get(["sys", SystemVariableKey.DOCUMENT_ID])
+            if document_id:
+                document = db.session.query(Document).filter_by(id=document_id.value).first()
+                if document:
+                    # Query summaries for this document
+                    summaries = (
+                        db.session.query(DocumentSegmentSummary)
+                        .filter_by(
+                            dataset_id=dataset.id,
+                            document_id=document.id,
+                            status="completed",
+                            enabled=True,
+                        )
+                        .all()
+                    )
+
+                    if summaries:
+                        # Create a map of segment content to summary for matching
+                        # Use content matching as chunks in preview might not be indexed yet
+                        summary_by_content = {}
+                        for summary in summaries:
+                            segment = (
+                                db.session.query(DocumentSegment)
+                                .filter_by(id=summary.chunk_id, dataset_id=dataset.id)
+                                .first()
+                            )
+                            if segment:
+                                # Normalize content for matching (strip whitespace)
+                                normalized_content = segment.content.strip()
+                                summary_by_content[normalized_content] = summary.summary_content
+
+                        # Enrich preview with summaries by content matching
+                        if "preview" in preview_output and isinstance(preview_output["preview"], list):
+                            matched_count = 0
+                            for preview_item in preview_output["preview"]:
+                                if "content" in preview_item:
+                                    # Normalize content for matching
+                                    normalized_chunk_content = preview_item["content"].strip()
+                                    if normalized_chunk_content in summary_by_content:
+                                        preview_item["summary"] = summary_by_content[normalized_chunk_content]
+                                        matched_count += 1
+
+                            if matched_count > 0:
+                                logger.info(
+                                    "Enriched preview with %s existing summaries (dataset: %s, document: %s)",
+                                    matched_count,
+                                    dataset.id,
+                                    document.id,
+                                )
+
+        return preview_output
 
     @classmethod
     def version(cls) -> str:

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -303,33 +303,34 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
         elif str(node_data.retrieval_mode) == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE:
             if node_data.multiple_retrieval_config is None:
                 raise ValueError("multiple_retrieval_config is required")
-            if node_data.multiple_retrieval_config.reranking_mode == "reranking_model":
-                if node_data.multiple_retrieval_config.reranking_model:
-                    reranking_model = {
-                        "reranking_provider_name": node_data.multiple_retrieval_config.reranking_model.provider,
-                        "reranking_model_name": node_data.multiple_retrieval_config.reranking_model.model,
-                    }
-                else:
+            match node_data.multiple_retrieval_config.reranking_mode:
+                case "reranking_model":
+                    if node_data.multiple_retrieval_config.reranking_model:
+                        reranking_model = {
+                            "reranking_provider_name": node_data.multiple_retrieval_config.reranking_model.provider,
+                            "reranking_model_name": node_data.multiple_retrieval_config.reranking_model.model,
+                        }
+                    else:
+                        reranking_model = None
+                    weights = None
+                case "weighted_score":
+                    if node_data.multiple_retrieval_config.weights is None:
+                        raise ValueError("weights is required")
                     reranking_model = None
-                weights = None
-            elif node_data.multiple_retrieval_config.reranking_mode == "weighted_score":
-                if node_data.multiple_retrieval_config.weights is None:
-                    raise ValueError("weights is required")
-                reranking_model = None
-                vector_setting = node_data.multiple_retrieval_config.weights.vector_setting
-                weights = {
-                    "vector_setting": {
-                        "vector_weight": vector_setting.vector_weight,
-                        "embedding_provider_name": vector_setting.embedding_provider_name,
-                        "embedding_model_name": vector_setting.embedding_model_name,
-                    },
-                    "keyword_setting": {
-                        "keyword_weight": node_data.multiple_retrieval_config.weights.keyword_setting.keyword_weight
-                    },
-                }
-            else:
-                reranking_model = None
-                weights = None
+                    vector_setting = node_data.multiple_retrieval_config.weights.vector_setting
+                    weights = {
+                        "vector_setting": {
+                            "vector_weight": vector_setting.vector_weight,
+                            "embedding_provider_name": vector_setting.embedding_provider_name,
+                            "embedding_model_name": vector_setting.embedding_model_name,
+                        },
+                        "keyword_setting": {
+                            "keyword_weight": node_data.multiple_retrieval_config.weights.keyword_setting.keyword_weight
+                        },
+                    }
+                case _:
+                    reranking_model = None
+                    weights = None
             all_documents = dataset_retrieval.multiple_retrieve(
                 app_id=self.app_id,
                 tenant_id=self.tenant_id,
@@ -419,6 +420,9 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
                             source["content"] = f"question:{segment.get_sign_content()} \nanswer:{segment.answer}"
                         else:
                             source["content"] = segment.get_sign_content()
+                        # Add summary if available
+                        if record.summary:
+                            source["summary"] = record.summary
                         retrieval_resource_list.append(source)
         if retrieval_resource_list:
             retrieval_resource_list = sorted(
@@ -450,73 +454,74 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
         )
         filters: list[Any] = []
         metadata_condition = None
-        if node_data.metadata_filtering_mode == "disabled":
-            return None, None, usage
-        elif node_data.metadata_filtering_mode == "automatic":
-            automatic_metadata_filters, automatic_usage = self._automatic_metadata_filter_func(
-                dataset_ids, query, node_data
-            )
-            usage = self._merge_usage(usage, automatic_usage)
-            if automatic_metadata_filters:
-                conditions = []
-                for sequence, filter in enumerate(automatic_metadata_filters):
-                    DatasetRetrieval.process_metadata_filter_func(
-                        sequence,
-                        filter.get("condition", ""),
-                        filter.get("metadata_name", ""),
-                        filter.get("value"),
-                        filters,
-                    )
-                    conditions.append(
-                        Condition(
-                            name=filter.get("metadata_name"),  # type: ignore
-                            comparison_operator=filter.get("condition"),  # type: ignore
-                            value=filter.get("value"),
-                        )
-                    )
-                metadata_condition = MetadataCondition(
-                    logical_operator=node_data.metadata_filtering_conditions.logical_operator
-                    if node_data.metadata_filtering_conditions
-                    else "or",
-                    conditions=conditions,
+        match node_data.metadata_filtering_mode:
+            case "disabled":
+                return None, None, usage
+            case "automatic":
+                automatic_metadata_filters, automatic_usage = self._automatic_metadata_filter_func(
+                    dataset_ids, query, node_data
                 )
-        elif node_data.metadata_filtering_mode == "manual":
-            if node_data.metadata_filtering_conditions:
-                conditions = []
-                for sequence, condition in enumerate(node_data.metadata_filtering_conditions.conditions):  # type: ignore
-                    metadata_name = condition.name
-                    expected_value = condition.value
-                    if expected_value is not None and condition.comparison_operator not in ("empty", "not empty"):
-                        if isinstance(expected_value, str):
-                            expected_value = self.graph_runtime_state.variable_pool.convert_template(
-                                expected_value
-                            ).value[0]
-                            if expected_value.value_type in {"number", "integer", "float"}:
-                                expected_value = expected_value.value
-                            elif expected_value.value_type == "string":
-                                expected_value = re.sub(r"[\r\n\t]+", " ", expected_value.text).strip()
-                            else:
-                                raise ValueError("Invalid expected metadata value type")
-                    conditions.append(
-                        Condition(
-                            name=metadata_name,
-                            comparison_operator=condition.comparison_operator,
-                            value=expected_value,
+                usage = self._merge_usage(usage, automatic_usage)
+                if automatic_metadata_filters:
+                    conditions = []
+                    for sequence, filter in enumerate(automatic_metadata_filters):
+                        DatasetRetrieval.process_metadata_filter_func(
+                            sequence,
+                            filter.get("condition", ""),
+                            filter.get("metadata_name", ""),
+                            filter.get("value"),
+                            filters,
                         )
+                        conditions.append(
+                            Condition(
+                                name=filter.get("metadata_name"),  # type: ignore
+                                comparison_operator=filter.get("condition"),  # type: ignore
+                                value=filter.get("value"),
+                            )
+                        )
+                    metadata_condition = MetadataCondition(
+                        logical_operator=node_data.metadata_filtering_conditions.logical_operator
+                        if node_data.metadata_filtering_conditions
+                        else "or",
+                        conditions=conditions,
                     )
-                    filters = DatasetRetrieval.process_metadata_filter_func(
-                        sequence,
-                        condition.comparison_operator,
-                        metadata_name,
-                        expected_value,
-                        filters,
+            case "manual":
+                if node_data.metadata_filtering_conditions:
+                    conditions = []
+                    for sequence, condition in enumerate(node_data.metadata_filtering_conditions.conditions):  # type: ignore
+                        metadata_name = condition.name
+                        expected_value = condition.value
+                        if expected_value is not None and condition.comparison_operator not in ("empty", "not empty"):
+                            if isinstance(expected_value, str):
+                                expected_value = self.graph_runtime_state.variable_pool.convert_template(
+                                    expected_value
+                                ).value[0]
+                                if expected_value.value_type in {"number", "integer", "float"}:
+                                    expected_value = expected_value.value
+                                elif expected_value.value_type == "string":
+                                    expected_value = re.sub(r"[\r\n\t]+", " ", expected_value.text).strip()
+                                else:
+                                    raise ValueError("Invalid expected metadata value type")
+                        conditions.append(
+                            Condition(
+                                name=metadata_name,
+                                comparison_operator=condition.comparison_operator,
+                                value=expected_value,
+                            )
+                        )
+                        filters = DatasetRetrieval.process_metadata_filter_func(
+                            sequence,
+                            condition.comparison_operator,
+                            metadata_name,
+                            expected_value,
+                            filters,
+                        )
+                    metadata_condition = MetadataCondition(
+                        logical_operator=node_data.metadata_filtering_conditions.logical_operator,
+                        conditions=conditions,
                     )
-                metadata_condition = MetadataCondition(
-                    logical_operator=node_data.metadata_filtering_conditions.logical_operator,
-                    conditions=conditions,
-                )
-        else:
-            raise ValueError("Invalid metadata filtering mode")
+            case _:
+                raise ValueError("Invalid metadata filtering mode")
         if filters:
             if (
                 node_data.metadata_filtering_conditions

api/core/workflow/nodes/list_operator/node.py

@@ -196,13 +196,13 @@ def _get_file_extract_string_func(*, key: str) -> Callable[[File], str]:
         case "name":
             return lambda x: x.filename or ""
         case "type":
-            return lambda x: x.type
+            return lambda x: str(x.type)
         case "extension":
             return lambda x: x.extension or ""
         case "mime_type":
             return lambda x: x.mime_type or ""
         case "transfer_method":
-            return lambda x: x.transfer_method
+            return lambda x: str(x.transfer_method)
         case "url":
             return lambda x: x.remote_url or ""
         case "related_id":
@@ -276,7 +276,6 @@ def _get_boolean_filter_func(*, condition: FilterOperator, value: bool) -> Calla
 
 
 def _get_file_filter_func(*, key: str, condition: str, value: str | Sequence[str]) -> Callable[[File], bool]:
-    extract_func: Callable[[File], Any]
     if key in {"name", "extension", "mime_type", "url", "related_id"} and isinstance(value, str):
         extract_func = _get_file_extract_string_func(key=key)
         return lambda x: _get_string_filter_func(condition=condition, value=value)(extract_func(x))
@@ -284,8 +283,8 @@ def _get_file_filter_func(*, key: str, condition: str, value: str | Sequence[str
         extract_func = _get_file_extract_string_func(key=key)
         return lambda x: _get_sequence_filter_func(condition=condition, value=value)(extract_func(x))
     elif key == "size" and isinstance(value, str):
-        extract_func = _get_file_extract_number_func(key=key)
-        return lambda x: _get_number_filter_func(condition=condition, value=float(value))(extract_func(x))
+        extract_number = _get_file_extract_number_func(key=key)
+        return lambda x: _get_number_filter_func(condition=condition, value=float(value))(extract_number(x))
     else:
         raise InvalidKeyError(f"Invalid key: {key}")
 

api/core/workflow/nodes/llm/node.py

@@ -685,6 +685,8 @@ class LLMNode(Node[LLMNodeData]):
                         if "content" not in item:
                             raise InvalidContextStructureError(f"Invalid context structure: {item}")
 
+                        if item.get("summary"):
+                            context_str += item["summary"] + "\n"
                         context_str += item["content"] + "\n"
 
                         retriever_resource = self._convert_to_original_retriever_resource(item)
@@ -746,6 +748,7 @@ class LLMNode(Node[LLMNodeData]):
                 page=metadata.get("page"),
                 doc_metadata=metadata.get("doc_metadata"),
                 files=context_dict.get("files"),
+                summary=context_dict.get("summary"),
             )
 
             return source
@@ -849,18 +852,16 @@ class LLMNode(Node[LLMNodeData]):
             # Insert histories into the prompt
             prompt_content = prompt_messages[0].content
             # For issue #11247 - Check if prompt content is a string or a list
-            prompt_content_type = type(prompt_content)
-            if prompt_content_type == str:
+            if isinstance(prompt_content, str):
                 prompt_content = str(prompt_content)
                 if "#histories#" in prompt_content:
                     prompt_content = prompt_content.replace("#histories#", memory_text)
                 else:
                     prompt_content = memory_text + "\n" + prompt_content
                 prompt_messages[0].content = prompt_content
-            elif prompt_content_type == list:
-                prompt_content = prompt_content if isinstance(prompt_content, list) else []
+            elif isinstance(prompt_content, list):
                 for content_item in prompt_content:
-                    if content_item.type == PromptMessageContentType.TEXT:
+                    if isinstance(content_item, TextPromptMessageContent):
                         if "#histories#" in content_item.data:
                             content_item.data = content_item.data.replace("#histories#", memory_text)
                         else:
@@ -870,13 +871,12 @@ class LLMNode(Node[LLMNodeData]):
 
             # Add current query to the prompt message
             if sys_query:
-                if prompt_content_type == str:
+                if isinstance(prompt_content, str):
                     prompt_content = str(prompt_messages[0].content).replace("#sys.query#", sys_query)
                     prompt_messages[0].content = prompt_content
-                elif prompt_content_type == list:
-                    prompt_content = prompt_content if isinstance(prompt_content, list) else []
+                elif isinstance(prompt_content, list):
                     for content_item in prompt_content:
-                        if content_item.type == PromptMessageContentType.TEXT:
+                        if isinstance(content_item, TextPromptMessageContent):
                             content_item.data = sys_query + "\n" + content_item.data
                 else:
                     raise ValueError("Invalid prompt content type")
@@ -1030,14 +1030,14 @@ class LLMNode(Node[LLMNodeData]):
         if typed_node_data.prompt_config:
             enable_jinja = False
 
-            if isinstance(prompt_template, list):
+            if isinstance(prompt_template, LLMNodeCompletionModelPromptTemplate):
+                if prompt_template.edition_type == "jinja2":
+                    enable_jinja = True
+            else:
                 for prompt in prompt_template:
                     if prompt.edition_type == "jinja2":
                         enable_jinja = True
                         break
-            else:
-                if prompt_template.edition_type == "jinja2":
-                    enable_jinja = True
 
             if enable_jinja:
                 for variable_selector in typed_node_data.prompt_config.jinja2_variables or []:

api/core/workflow/nodes/protocols.py

@@ -1,4 +1,4 @@
-from typing import Protocol
+from typing import Any, Protocol
 
 import httpx
 
@@ -12,17 +12,17 @@ class HttpClientProtocol(Protocol):
     @property
     def request_error(self) -> type[Exception]: ...
 
-    def get(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+    def get(self, url: str, max_retries: int = ..., **kwargs: Any) -> httpx.Response: ...
 
-    def head(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+    def head(self, url: str, max_retries: int = ..., **kwargs: Any) -> httpx.Response: ...
 
-    def post(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+    def post(self, url: str, max_retries: int = ..., **kwargs: Any) -> httpx.Response: ...
 
-    def put(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+    def put(self, url: str, max_retries: int = ..., **kwargs: Any) -> httpx.Response: ...
 
-    def delete(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+    def delete(self, url: str, max_retries: int = ..., **kwargs: Any) -> httpx.Response: ...
 
-    def patch(self, url: str, max_retries: int = ..., **kwargs: object) -> httpx.Response: ...
+    def patch(self, url: str, max_retries: int = ..., **kwargs: Any) -> httpx.Response: ...
 
 
 class FileManagerProtocol(Protocol):

api/core/workflow/nodes/tool/entities.py

@@ -54,8 +54,8 @@ class ToolNodeData(BaseNodeData, ToolEntity):
                 for val in value:
                     if not isinstance(val, str):
                         raise ValueError("value must be a list of strings")
-            elif typ == "constant" and not isinstance(value, str | int | float | bool | dict):
-                raise ValueError("value must be a string, int, float, bool or dict")
+            elif typ == "constant" and not isinstance(value, (allowed_types := (str, int, float, bool, dict, list))):
+                raise ValueError(f"value must be one of: {', '.join(t.__name__ for t in allowed_types)}")
             return typ
 
     tool_parameters: dict[str, ToolInput]

api/core/workflow/nodes/tool/tool_node.py

@@ -482,16 +482,17 @@ class ToolNode(Node[ToolNodeData]):
         result = {}
         for parameter_name in typed_node_data.tool_parameters:
             input = typed_node_data.tool_parameters[parameter_name]
-            if input.type == "mixed":
-                assert isinstance(input.value, str)
-                selectors = VariableTemplateParser(input.value).extract_variable_selectors()
-                for selector in selectors:
-                    result[selector.variable] = selector.value_selector
-            elif input.type == "variable":
-                selector_key = ".".join(input.value)
-                result[f"#{selector_key}#"] = input.value
-            elif input.type == "constant":
-                pass
+            match input.type:
+                case "mixed":
+                    assert isinstance(input.value, str)
+                    selectors = VariableTemplateParser(input.value).extract_variable_selectors()
+                    for selector in selectors:
+                        result[selector.variable] = selector.value_selector
+                case "variable":
+                    selector_key = ".".join(input.value)
+                    result[f"#{selector_key}#"] = input.value
+                case "constant":
+                    pass
 
         result = {node_id + "." + key: value for key, value in result.items()}