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Merge remote-tracking branch 'origin/main' into feat/support-agent-sandbox

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# Conflicts:
#	api/.env.example
#	api/uv.lock
#	web/app/components/app/create-app-modal/index.tsx
#	web/app/components/app/create-from-dsl-modal/index.tsx
#	web/app/components/apps/app-card.tsx
#	web/pnpm-lock.yaml
This commit is contained in:
yyh
2026-01-29 21:25:28 +08:00
parent ff71816373 f79512ec78
commit 6ee9078349
137 changed files with 5382 additions and 812 deletions
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2
.agents/skills/component-refactoring/SKILL.md
View File

@@ -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

4
.agents/skills/frontend-testing/SKILL.md
View File

@@ -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

2
.agents/skills/frontend-testing/references/workflow.md
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@@ -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 |
|-------|------|

1
.github/workflows/api-tests.yml vendored
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@@ -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 \

8
.github/workflows/style.yml vendored
View File

@@ -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'

33
AGENTS.md
View File

@@ -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

2
CONTRIBUTING.md
View File

@@ -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

12
Makefile
View File

@@ -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:"

1
api/.env.example
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@@ -620,6 +620,7 @@ 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

55
api/.importlinter
View File

@@ -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

5
api/configs/feature/__init__.py
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@@ -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 CliApiConfig(BaseSettings):
"""

7
api/contexts/__init__.py
View File

@@ -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"))
)

14
api/controllers/console/admin.py
View File

@@ -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,

10
api/controllers/console/datasets/datasets.py
View File

@@ -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)

149
api/controllers/console/datasets/datasets_document.py
View File

@@ -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 = (
@@ -797,6 +810,7 @@ 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 +846,7 @@ 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
@@ -1255,3 +1270,137 @@ 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)}")
# 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

37
api/controllers/console/datasets/datasets_segments.py
View File

@@ -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

43
api/controllers/console/datasets/hit_testing.py
View File

@@ -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

2
api/controllers/service_api/dataset/dataset.py
View File

@@ -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):
@@ -217,6 +218,7 @@ 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()

21
api/controllers/service_api/dataset/document.py
View File

@@ -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

1
api/core/app/apps/base_app_generate_response_converter.py
View File

@@ -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

1
api/core/entities/knowledge_entities.py
View File

@@ -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

4
api/core/file/file_manager.py
View File

@@ -123,6 +123,8 @@ def download(f: File, /):
):
return _download_file_content(f.storage_key)
elif f.transfer_method == FileTransferMethod.REMOTE_URL:
if f.remote_url is None:
raise ValueError("Missing file remote_url")
response = ssrf_proxy.get(f.remote_url, follow_redirects=True)
response.raise_for_status()
return response.content
@@ -153,6 +155,8 @@ def _download_file_content(path: str, /):
def _get_encoded_string(f: File, /):
match f.transfer_method:
case FileTransferMethod.REMOTE_URL:
if f.remote_url is None:
raise ValueError("Missing file remote_url")
response = ssrf_proxy.get(f.remote_url, follow_redirects=True)
response.raise_for_status()
data = response.content

30
api/core/helper/ssrf_proxy.py
View File

@@ -4,8 +4,10 @@ Proxy requests to avoid SSRF
import logging
import time
from typing import Any, TypeAlias
import httpx
from pydantic import TypeAdapter, ValidationError
from configs import dify_config
from core.helper.http_client_pooling import get_pooled_http_client
@@ -18,6 +20,9 @@ SSRF_DEFAULT_MAX_RETRIES = dify_config.SSRF_DEFAULT_MAX_RETRIES
BACKOFF_FACTOR = 0.5
STATUS_FORCELIST = [429, 500, 502, 503, 504]
Headers: TypeAlias = dict[str, str]
_HEADERS_ADAPTER = TypeAdapter(Headers)
_SSL_VERIFIED_POOL_KEY = "ssrf:verified"
_SSL_UNVERIFIED_POOL_KEY = "ssrf:unverified"
_SSRF_CLIENT_LIMITS = httpx.Limits(
@@ -76,7 +81,7 @@ def _get_ssrf_client(ssl_verify_enabled: bool) -> httpx.Client:
)
def _get_user_provided_host_header(headers: dict | None) -> str | None:
def _get_user_provided_host_header(headers: Headers | None) -> str | None:
"""
Extract the user-provided Host header from the headers dict.
@@ -92,7 +97,7 @@ def _get_user_provided_host_header(headers: dict | None) -> str | None:
return None
def _inject_trace_headers(headers: dict | None) -> dict:
def _inject_trace_headers(headers: Headers | None) -> Headers:
"""
Inject W3C traceparent header for distributed tracing.
@@ -125,7 +130,7 @@ def _inject_trace_headers(headers: dict | None) -> dict:
return headers
def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
def make_request(method: str, url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> httpx.Response:
# Convert requests-style allow_redirects to httpx-style follow_redirects
if "allow_redirects" in kwargs:
allow_redirects = kwargs.pop("allow_redirects")
@@ -142,10 +147,15 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
# prioritize per-call option, which can be switched on and off inside the HTTP node on the web UI
verify_option = kwargs.pop("ssl_verify", dify_config.HTTP_REQUEST_NODE_SSL_VERIFY)
if not isinstance(verify_option, bool):
raise ValueError("ssl_verify must be a boolean")
client = _get_ssrf_client(verify_option)
# Inject traceparent header for distributed tracing (when OTEL is not enabled)
headers = kwargs.get("headers") or {}
try:
headers: Headers = _HEADERS_ADAPTER.validate_python(kwargs.get("headers") or {})
except ValidationError as e:
raise ValueError("headers must be a mapping of string keys to string values") from e
headers = _inject_trace_headers(headers)
kwargs["headers"] = headers
@@ -198,25 +208,25 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
raise MaxRetriesExceededError(f"Reached maximum retries ({max_retries}) for URL {url}")
def get(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
def get(url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> httpx.Response:
return make_request("GET", url, max_retries=max_retries, **kwargs)
def post(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
def post(url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> httpx.Response:
return make_request("POST", url, max_retries=max_retries, **kwargs)
def put(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
def put(url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> httpx.Response:
return make_request("PUT", url, max_retries=max_retries, **kwargs)
def patch(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
def patch(url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> httpx.Response:
return make_request("PATCH", url, max_retries=max_retries, **kwargs)
def delete(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
def delete(url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> httpx.Response:
return make_request("DELETE", url, max_retries=max_retries, **kwargs)
def head(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
def head(url: str, max_retries: int = SSRF_DEFAULT_MAX_RETRIES, **kwargs: Any) -> httpx.Response:
return make_request("HEAD", url, max_retries=max_retries, **kwargs)

10
api/core/indexing_runner.py
View File

@@ -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,
@@ -361,6 +365,12 @@ 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)
return IndexingEstimate(total_segments=total_segments, preview=preview_texts)
def _extract(

17
api/core/llm_generator/prompts.py
View File

@@ -435,3 +435,20 @@ 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. Use the same language as the input content
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
Output only the summary text. Start summarizing now:
"""
)

78
api/core/model_runtime/model_providers/__base/ai_model.py
View File

@@ -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:
"""

73
api/core/model_runtime/model_providers/model_provider_factory.py
View File

@@ -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,

140
api/core/rag/datasource/retrieval_service.py
View File

@@ -24,7 +24,13 @@ 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
@@ -389,15 +395,15 @@ 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:
@@ -408,16 +414,39 @@ 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:
@@ -433,6 +462,7 @@ class RetrievalService:
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)
@@ -447,6 +477,7 @@ 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()
@@ -470,6 +501,7 @@ 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,
@@ -481,6 +513,40 @@ 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, [])
@@ -489,30 +555,44 @@ 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
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,
}
@@ -520,14 +600,23 @@ class RetrievalService:
else:
if segment.id not in include_segment_ids:
include_segment_ids.add(segment.id)
max_score = 0.0
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))
# 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_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,
@@ -576,9 +665,16 @@ 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_list, score=score, files=files
segment=segment,
child_chunks=child_chunks_list,
score=score,
files=files,
summary=summary_content,
)
result.append(retrieval_segment)

1
api/core/rag/embedding/retrieval.py
View File

@@ -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

1
api/core/rag/entities/citation_metadata.py
View File

@@ -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

9
api/core/rag/extractor/word_extractor.py
View File

@@ -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

12
api/core/rag/index_processor/index_processor_base.py
View File

@@ -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 +46,17 @@ 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
) -> 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.
"""
raise NotImplementedError
@abstractmethod
def load(
self,

369
api/core/rag/index_processor/processor/paragraph_index_processor.py
View File

@@ -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 +273,322 @@ 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
) -> 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)
preview.summary = summary
else:
# Fallback: try without app context (may fail)
summary, _ = self.generate_summary(tenant_id, preview.content, summary_index_setting)
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,
) -> 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
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
if not summary_prompt:
summary_prompt = DEFAULT_GENERATOR_SUMMARY_PROMPT
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

118
api/core/rag/index_processor/processor/parent_child_index_processor.py
View File

@@ -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 +356,91 @@ 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
) -> 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,
)
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,
)
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

41
api/core/rag/index_processor/processor/qa_index_processor.py
View File

@@ -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 +240,17 @@ 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
) -> 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():

29
api/core/rag/retrieval/dataset_retrieval.py
View File

@@ -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)

29
api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py
View File

@@ -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:

2
api/core/workflow/nodes/knowledge_index/entities.py
View File

@@ -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

328
api/core/workflow/nodes/knowledge_index/knowledge_index_node.py
View File

@@ -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 +71,20 @@ 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
outputs = self._get_preview_output_with_summaries(
node_data.chunk_structure,
chunks,
dataset=dataset,
indexing_technique=indexing_technique,
summary_index_setting=summary_index_setting,
)
return NodeRunResult(
status=WorkflowNodeExecutionStatus.SUCCEEDED,
inputs=variables,
@@ -148,6 +165,11 @@ 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 +185,9 @@ 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 +198,304 @@ 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,
) -> 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
"""
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,
)
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,
)
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:

3
api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
View File

@@ -419,6 +419,9 @@ 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(

3
api/core/workflow/nodes/llm/node.py
View File

@@ -1093,6 +1093,8 @@ 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)
@@ -1154,6 +1156,7 @@ 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

2
api/extensions/ext_celery.py
View File

@@ -102,6 +102,8 @@ def init_app(app: DifyApp) -> Celery:
imports = [
"tasks.async_workflow_tasks", # trigger workers
"tasks.trigger_processing_tasks", # async trigger processing
"tasks.generate_summary_index_task", # summary index generation
"tasks.regenerate_summary_index_task", # summary index regeneration
]
day = dify_config.CELERY_BEAT_SCHEDULER_TIME

9
api/fields/dataset_fields.py
View File

@@ -39,6 +39,14 @@ dataset_retrieval_model_fields = {
"score_threshold_enabled": fields.Boolean,
"score_threshold": fields.Float,
}
dataset_summary_index_fields = {
"enable": fields.Boolean,
"model_name": fields.String,
"model_provider_name": fields.String,
"summary_prompt": fields.String,
}
external_retrieval_model_fields = {
"top_k": fields.Integer,
"score_threshold": fields.Float,
@@ -83,6 +91,7 @@ dataset_detail_fields = {
"embedding_model_provider": fields.String,
"embedding_available": fields.Boolean,
"retrieval_model_dict": fields.Nested(dataset_retrieval_model_fields),
"summary_index_setting": fields.Nested(dataset_summary_index_fields),
"tags": fields.List(fields.Nested(tag_fields)),
"doc_form": fields.String,
"external_knowledge_info": fields.Nested(external_knowledge_info_fields),

9
api/fields/document_fields.py
View File

@@ -33,6 +33,11 @@ document_fields = {
"hit_count": fields.Integer,
"doc_form": fields.String,
"doc_metadata": fields.List(fields.Nested(document_metadata_fields), attribute="doc_metadata_details"),
# Summary index generation status:
# "SUMMARIZING" (when task is queued and generating)
"summary_index_status": fields.String,
# Whether this document needs summary index generation
"need_summary": fields.Boolean,
}
document_with_segments_fields = {
@@ -60,6 +65,10 @@ document_with_segments_fields = {
"completed_segments": fields.Integer,
"total_segments": fields.Integer,
"doc_metadata": fields.List(fields.Nested(document_metadata_fields), attribute="doc_metadata_details"),
# Summary index generation status:
# "SUMMARIZING" (when task is queued and generating)
"summary_index_status": fields.String,
"need_summary": fields.Boolean, # Whether this document needs summary index generation
}
dataset_and_document_fields = {

1
api/fields/hit_testing_fields.py
View File

@@ -58,4 +58,5 @@ hit_testing_record_fields = {
"score": fields.Float,
"tsne_position": fields.Raw,
"files": fields.List(fields.Nested(files_fields)),
"summary": fields.String, # Summary content if retrieved via summary index
}

1
api/fields/message_fields.py
View File

@@ -36,6 +36,7 @@ class RetrieverResource(ResponseModel):
segment_position: int | None = None
index_node_hash: str | None = None
content: str | None = None
summary: str | None = None
created_at: int | None = None
@field_validator("created_at", mode="before")

1
api/fields/segment_fields.py
View File

@@ -49,4 +49,5 @@ segment_fields = {
"stopped_at": TimestampField,
"child_chunks": fields.List(fields.Nested(child_chunk_fields)),
"attachments": fields.List(fields.Nested(attachment_fields)),
"summary": fields.String, # Summary content for the segment
}

107
api/migrations/versions/2026_01_27_1815-788d3099ae3a_add_summary_index_feature.py Normal file
View File

@@ -0,0 +1,107 @@
"""add summary index feature
Revision ID: 788d3099ae3a
Revises: 9d77545f524e
Create Date: 2026-01-27 18:15:45.277928
"""
from alembic import op
import models as models
import sqlalchemy as sa
def _is_pg(conn):
return conn.dialect.name == "postgresql"
# revision identifiers, used by Alembic.
revision = '788d3099ae3a'
down_revision = '9d77545f524e'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
conn = op.get_bind()
if _is_pg(conn):
op.create_table('document_segment_summaries',
sa.Column('id', models.types.StringUUID(), nullable=False),
sa.Column('dataset_id', models.types.StringUUID(), nullable=False),
sa.Column('document_id', models.types.StringUUID(), nullable=False),
sa.Column('chunk_id', models.types.StringUUID(), nullable=False),
sa.Column('summary_content', models.types.LongText(), nullable=True),
sa.Column('summary_index_node_id', sa.String(length=255), nullable=True),
sa.Column('summary_index_node_hash', sa.String(length=255), nullable=True),
sa.Column('tokens', sa.Integer(), nullable=True),
sa.Column('status', sa.String(length=32), server_default=sa.text("'generating'"), nullable=False),
sa.Column('error', models.types.LongText(), nullable=True),
sa.Column('enabled', sa.Boolean(), server_default=sa.text('true'), nullable=False),
sa.Column('disabled_at', sa.DateTime(), nullable=True),
sa.Column('disabled_by', models.types.StringUUID(), nullable=True),
sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
sa.PrimaryKeyConstraint('id', name='document_segment_summaries_pkey')
)
with op.batch_alter_table('document_segment_summaries', schema=None) as batch_op:
batch_op.create_index('document_segment_summaries_chunk_id_idx', ['chunk_id'], unique=False)
batch_op.create_index('document_segment_summaries_dataset_id_idx', ['dataset_id'], unique=False)
batch_op.create_index('document_segment_summaries_document_id_idx', ['document_id'], unique=False)
batch_op.create_index('document_segment_summaries_status_idx', ['status'], unique=False)
with op.batch_alter_table('datasets', schema=None) as batch_op:
batch_op.add_column(sa.Column('summary_index_setting', models.types.AdjustedJSON(), nullable=True))
with op.batch_alter_table('documents', schema=None) as batch_op:
batch_op.add_column(sa.Column('need_summary', sa.Boolean(), server_default=sa.text('false'), nullable=True))
else:
# MySQL: Use compatible syntax
op.create_table(
'document_segment_summaries',
sa.Column('id', models.types.StringUUID(), nullable=False),
sa.Column('dataset_id', models.types.StringUUID(), nullable=False),
sa.Column('document_id', models.types.StringUUID(), nullable=False),
sa.Column('chunk_id', models.types.StringUUID(), nullable=False),
sa.Column('summary_content', models.types.LongText(), nullable=True),
sa.Column('summary_index_node_id', sa.String(length=255), nullable=True),
sa.Column('summary_index_node_hash', sa.String(length=255), nullable=True),
sa.Column('tokens', sa.Integer(), nullable=True),
sa.Column('status', sa.String(length=32), server_default=sa.text("'generating'"), nullable=False),
sa.Column('error', models.types.LongText(), nullable=True),
sa.Column('enabled', sa.Boolean(), server_default=sa.text('true'), nullable=False),
sa.Column('disabled_at', sa.DateTime(), nullable=True),
sa.Column('disabled_by', models.types.StringUUID(), nullable=True),
sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP'), nullable=False),
sa.PrimaryKeyConstraint('id', name='document_segment_summaries_pkey'),
)
with op.batch_alter_table('document_segment_summaries', schema=None) as batch_op:
batch_op.create_index('document_segment_summaries_chunk_id_idx', ['chunk_id'], unique=False)
batch_op.create_index('document_segment_summaries_dataset_id_idx', ['dataset_id'], unique=False)
batch_op.create_index('document_segment_summaries_document_id_idx', ['document_id'], unique=False)
batch_op.create_index('document_segment_summaries_status_idx', ['status'], unique=False)
with op.batch_alter_table('datasets', schema=None) as batch_op:
batch_op.add_column(sa.Column('summary_index_setting', models.types.AdjustedJSON(), nullable=True))
with op.batch_alter_table('documents', schema=None) as batch_op:
batch_op.add_column(sa.Column('need_summary', sa.Boolean(), server_default=sa.text('false'), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('documents', schema=None) as batch_op:
batch_op.drop_column('need_summary')
with op.batch_alter_table('datasets', schema=None) as batch_op:
batch_op.drop_column('summary_index_setting')
with op.batch_alter_table('document_segment_summaries', schema=None) as batch_op:
batch_op.drop_index('document_segment_summaries_status_idx')
batch_op.drop_index('document_segment_summaries_document_id_idx')
batch_op.drop_index('document_segment_summaries_dataset_id_idx')
batch_op.drop_index('document_segment_summaries_chunk_id_idx')
op.drop_table('document_segment_summaries')
# ### end Alembic commands ###

35
api/models/dataset.py
View File

@@ -72,6 +72,7 @@ class Dataset(Base):
keyword_number = mapped_column(sa.Integer, nullable=True, server_default=sa.text("10"))
collection_binding_id = mapped_column(StringUUID, nullable=True)
retrieval_model = mapped_column(AdjustedJSON, nullable=True)
summary_index_setting = mapped_column(AdjustedJSON, nullable=True)
built_in_field_enabled = mapped_column(sa.Boolean, nullable=False, server_default=sa.text("false"))
icon_info = mapped_column(AdjustedJSON, nullable=True)
runtime_mode = mapped_column(sa.String(255), nullable=True, server_default=sa.text("'general'"))
@@ -419,6 +420,7 @@ class Document(Base):
doc_metadata = mapped_column(AdjustedJSON, nullable=True)
doc_form = mapped_column(String(255), nullable=False, server_default=sa.text("'text_model'"))
doc_language = mapped_column(String(255), nullable=True)
need_summary: Mapped[bool | None] = mapped_column(sa.Boolean, nullable=True, server_default=sa.text("false"))
DATA_SOURCES = ["upload_file", "notion_import", "website_crawl"]
@@ -1575,3 +1577,36 @@ class SegmentAttachmentBinding(Base):
segment_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
attachment_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
created_at: Mapped[datetime] = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
class DocumentSegmentSummary(Base):
__tablename__ = "document_segment_summaries"
__table_args__ = (
sa.PrimaryKeyConstraint("id", name="document_segment_summaries_pkey"),
sa.Index("document_segment_summaries_dataset_id_idx", "dataset_id"),
sa.Index("document_segment_summaries_document_id_idx", "document_id"),
sa.Index("document_segment_summaries_chunk_id_idx", "chunk_id"),
sa.Index("document_segment_summaries_status_idx", "status"),
)
id: Mapped[str] = mapped_column(StringUUID, nullable=False, default=lambda: str(uuid4()))
dataset_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
document_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
# corresponds to DocumentSegment.id or parent chunk id
chunk_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
summary_content: Mapped[str] = mapped_column(LongText, nullable=True)
summary_index_node_id: Mapped[str] = mapped_column(String(255), nullable=True)
summary_index_node_hash: Mapped[str] = mapped_column(String(255), nullable=True)
tokens: Mapped[int | None] = mapped_column(sa.Integer, nullable=True)
status: Mapped[str] = mapped_column(String(32), nullable=False, server_default=sa.text("'generating'"))
error: Mapped[str] = mapped_column(LongText, nullable=True)
enabled: Mapped[bool] = mapped_column(sa.Boolean, nullable=False, server_default=sa.text("true"))
disabled_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
disabled_by = mapped_column(StringUUID, nullable=True)
created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.current_timestamp())
updated_at: Mapped[datetime] = mapped_column(
DateTime, nullable=False, server_default=func.current_timestamp(), onupdate=func.current_timestamp()
)
def __repr__(self):
return f"<DocumentSegmentSummary id={self.id} chunk_id={self.chunk_id} status={self.status}>"

22
api/models/model.py
View File

@@ -659,16 +659,22 @@ class AccountTrialAppRecord(Base):
return user
class ExporleBanner(Base):
class ExporleBanner(TypeBase):
__tablename__ = "exporle_banners"
__table_args__ = (sa.PrimaryKeyConstraint("id", name="exporler_banner_pkey"),)
id = mapped_column(StringUUID, server_default=sa.text("uuid_generate_v4()"))
content = mapped_column(sa.JSON, nullable=False)
link = mapped_column(String(255), nullable=False)
sort = mapped_column(sa.Integer, nullable=False)
status = mapped_column(sa.String(255), nullable=False, server_default=sa.text("'enabled'::character varying"))
created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
language = mapped_column(String(255), nullable=False, server_default=sa.text("'en-US'::character varying"))
id: Mapped[str] = mapped_column(StringUUID, server_default=sa.text("uuid_generate_v4()"), init=False)
content: Mapped[dict[str, Any]] = mapped_column(sa.JSON, nullable=False)
link: Mapped[str] = mapped_column(String(255), nullable=False)
sort: Mapped[int] = mapped_column(sa.Integer, nullable=False)
status: Mapped[str] = mapped_column(
sa.String(255), nullable=False, server_default=sa.text("'enabled'::character varying"), default="enabled"
)
created_at: Mapped[datetime] = mapped_column(
sa.DateTime, nullable=False, server_default=func.current_timestamp(), init=False
)
language: Mapped[str] = mapped_column(
String(255), nullable=False, server_default=sa.text("'en-US'::character varying"), default="en-US"
)
class OAuthProviderApp(TypeBase):

1
api/pyproject.toml
View File

@@ -181,6 +181,7 @@ dev = [
# "locust>=2.40.4", # Temporarily removed due to compatibility issues. Uncomment when resolved.
"sseclient-py>=1.8.0",
"pytest-timeout>=2.4.0",
"pytest-xdist>=3.8.0",
]
############################################################

272
api/services/dataset_service.py
View File

@@ -89,6 +89,7 @@ from tasks.disable_segments_from_index_task import disable_segments_from_index_t
from tasks.document_indexing_update_task import document_indexing_update_task
from tasks.enable_segments_to_index_task import enable_segments_to_index_task
from tasks.recover_document_indexing_task import recover_document_indexing_task
from tasks.regenerate_summary_index_task import regenerate_summary_index_task
from tasks.remove_document_from_index_task import remove_document_from_index_task
from tasks.retry_document_indexing_task import retry_document_indexing_task
from tasks.sync_website_document_indexing_task import sync_website_document_indexing_task
@@ -211,6 +212,7 @@ class DatasetService:
embedding_model_provider: str | None = None,
embedding_model_name: str | None = None,
retrieval_model: RetrievalModel | None = None,
summary_index_setting: dict | None = None,
):
# check if dataset name already exists
if db.session.query(Dataset).filter_by(name=name, tenant_id=tenant_id).first():
@@ -253,6 +255,8 @@ class DatasetService:
dataset.retrieval_model = retrieval_model.model_dump() if retrieval_model else None
dataset.permission = permission or DatasetPermissionEnum.ONLY_ME
dataset.provider = provider
if summary_index_setting is not None:
dataset.summary_index_setting = summary_index_setting
db.session.add(dataset)
db.session.flush()
@@ -476,6 +480,11 @@ class DatasetService:
if external_retrieval_model:
dataset.retrieval_model = external_retrieval_model
# Update summary index setting if provided
summary_index_setting = data.get("summary_index_setting", None)
if summary_index_setting is not None:
dataset.summary_index_setting = summary_index_setting
# Update basic dataset properties
dataset.name = data.get("name", dataset.name)
dataset.description = data.get("description", dataset.description)
@@ -564,6 +573,9 @@ class DatasetService:
# update Retrieval model
if data.get("retrieval_model"):
filtered_data["retrieval_model"] = data["retrieval_model"]
# update summary index setting
if data.get("summary_index_setting"):
filtered_data["summary_index_setting"] = data.get("summary_index_setting")
# update icon info
if data.get("icon_info"):
filtered_data["icon_info"] = data.get("icon_info")
@@ -572,12 +584,27 @@ class DatasetService:
db.session.query(Dataset).filter_by(id=dataset.id).update(filtered_data)
db.session.commit()
# Reload dataset to get updated values
db.session.refresh(dataset)
# update pipeline knowledge base node data
DatasetService._update_pipeline_knowledge_base_node_data(dataset, user.id)
# Trigger vector index task if indexing technique changed
if action:
deal_dataset_vector_index_task.delay(dataset.id, action)
# If embedding_model changed, also regenerate summary vectors
if action == "update":
regenerate_summary_index_task.delay(
dataset.id,
regenerate_reason="embedding_model_changed",
regenerate_vectors_only=True,
)
# Note: summary_index_setting changes do not trigger automatic regeneration of existing summaries.
# The new setting will only apply to:
# 1. New documents added after the setting change
# 2. Manual summary generation requests
return dataset
@@ -616,6 +643,7 @@ class DatasetService:
knowledge_index_node_data["chunk_structure"] = dataset.chunk_structure
knowledge_index_node_data["indexing_technique"] = dataset.indexing_technique # pyright: ignore[reportAttributeAccessIssue]
knowledge_index_node_data["keyword_number"] = dataset.keyword_number
knowledge_index_node_data["summary_index_setting"] = dataset.summary_index_setting
node["data"] = knowledge_index_node_data
updated = True
except Exception:
@@ -854,6 +882,54 @@ class DatasetService:
)
filtered_data["collection_binding_id"] = dataset_collection_binding.id
@staticmethod
def _check_summary_index_setting_model_changed(dataset: Dataset, data: dict[str, Any]) -> bool:
"""
Check if summary_index_setting model (model_name or model_provider_name) has changed.
Args:
dataset: Current dataset object
data: Update data dictionary
Returns:
bool: True if summary model changed, False otherwise
"""
# Check if summary_index_setting is being updated
if "summary_index_setting" not in data or data.get("summary_index_setting") is None:
return False
new_summary_setting = data.get("summary_index_setting")
old_summary_setting = dataset.summary_index_setting
# If new setting is disabled, no need to regenerate
if not new_summary_setting or not new_summary_setting.get("enable"):
return False
# If old setting doesn't exist, no need to regenerate (no existing summaries to regenerate)
# Note: This task only regenerates existing summaries, not generates new ones
if not old_summary_setting:
return False
# Compare model_name and model_provider_name
old_model_name = old_summary_setting.get("model_name")
old_model_provider = old_summary_setting.get("model_provider_name")
new_model_name = new_summary_setting.get("model_name")
new_model_provider = new_summary_setting.get("model_provider_name")
# Check if model changed
if old_model_name != new_model_name or old_model_provider != new_model_provider:
logger.info(
"Summary index setting model changed for dataset %s: old=%s/%s, new=%s/%s",
dataset.id,
old_model_provider,
old_model_name,
new_model_provider,
new_model_name,
)
return True
return False
@staticmethod
def update_rag_pipeline_dataset_settings(
session: Session, dataset: Dataset, knowledge_configuration: KnowledgeConfiguration, has_published: bool = False
@@ -889,6 +965,9 @@ class DatasetService:
else:
raise ValueError("Invalid index method")
dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
# Update summary_index_setting if provided
if knowledge_configuration.summary_index_setting is not None:
dataset.summary_index_setting = knowledge_configuration.summary_index_setting
session.add(dataset)
else:
if dataset.chunk_structure and dataset.chunk_structure != knowledge_configuration.chunk_structure:
@@ -994,6 +1073,9 @@ class DatasetService:
if dataset.keyword_number != knowledge_configuration.keyword_number:
dataset.keyword_number = knowledge_configuration.keyword_number
dataset.retrieval_model = knowledge_configuration.retrieval_model.model_dump()
# Update summary_index_setting if provided
if knowledge_configuration.summary_index_setting is not None:
dataset.summary_index_setting = knowledge_configuration.summary_index_setting
session.add(dataset)
session.commit()
if action:
@@ -1314,6 +1396,50 @@ class DocumentService:
upload_file = DocumentService._get_upload_file_for_upload_file_document(document)
return file_helpers.get_signed_file_url(upload_file_id=upload_file.id, as_attachment=True)
@staticmethod
def enrich_documents_with_summary_index_status(
documents: Sequence[Document],
dataset: Dataset,
tenant_id: str,
) -> None:
"""
Enrich documents with summary_index_status based on dataset summary index settings.
This method calculates and sets the summary_index_status for each document that needs summary.
Documents that don't need summary or when summary index is disabled will have status set to None.
Args:
documents: List of Document instances to enrich
dataset: Dataset instance containing summary_index_setting
tenant_id: Tenant ID for summary status lookup
"""
# Check if dataset has summary index enabled
has_summary_index = dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True
# Filter documents that need summary calculation
documents_need_summary = [doc for doc in documents if doc.need_summary is True]
document_ids_need_summary = [str(doc.id) for doc in documents_need_summary]
# Calculate summary_index_status for documents that need summary (only if dataset summary index is enabled)
summary_status_map: dict[str, str | None] = {}
if has_summary_index and document_ids_need_summary:
from services.summary_index_service import SummaryIndexService
summary_status_map = SummaryIndexService.get_documents_summary_index_status(
document_ids=document_ids_need_summary,
dataset_id=dataset.id,
tenant_id=tenant_id,
)
# Add summary_index_status to each document
for document in documents:
if has_summary_index and document.need_summary is True:
# Get status from map, default to None (not queued yet)
document.summary_index_status = summary_status_map.get(str(document.id)) # type: ignore[attr-defined]
else:
# Return null if summary index is not enabled or document doesn't need summary
document.summary_index_status = None # type: ignore[attr-defined]
@staticmethod
def prepare_document_batch_download_zip(
*,
@@ -1964,6 +2090,8 @@ class DocumentService:
DuplicateDocumentIndexingTaskProxy(
dataset.tenant_id, dataset.id, duplicate_document_ids
).delay()
# Note: Summary index generation is triggered in document_indexing_task after indexing completes
# to ensure segments are available. See tasks/document_indexing_task.py
except LockNotOwnedError:
pass
@@ -2268,6 +2396,11 @@ class DocumentService:
name: str,
batch: str,
):
# Set need_summary based on dataset's summary_index_setting
need_summary = False
if dataset.summary_index_setting and dataset.summary_index_setting.get("enable") is True:
need_summary = True
document = Document(
tenant_id=dataset.tenant_id,
dataset_id=dataset.id,
@@ -2281,6 +2414,7 @@ class DocumentService:
created_by=account.id,
doc_form=document_form,
doc_language=document_language,
need_summary=need_summary,
)
doc_metadata = {}
if dataset.built_in_field_enabled:
@@ -2505,6 +2639,7 @@ class DocumentService:
embedding_model_provider=knowledge_config.embedding_model_provider,
collection_binding_id=dataset_collection_binding_id,
retrieval_model=retrieval_model.model_dump() if retrieval_model else None,
summary_index_setting=knowledge_config.summary_index_setting,
is_multimodal=knowledge_config.is_multimodal,
)
@@ -2686,6 +2821,14 @@ class DocumentService:
if not isinstance(args["process_rule"]["rules"]["segmentation"]["max_tokens"], int):
raise ValueError("Process rule segmentation max_tokens is invalid")
# valid summary index setting
summary_index_setting = args["process_rule"].get("summary_index_setting")
if summary_index_setting and summary_index_setting.get("enable"):
if "model_name" not in summary_index_setting or not summary_index_setting["model_name"]:
raise ValueError("Summary index model name is required")
if "model_provider_name" not in summary_index_setting or not summary_index_setting["model_provider_name"]:
raise ValueError("Summary index model provider name is required")
@staticmethod
def batch_update_document_status(
dataset: Dataset, document_ids: list[str], action: Literal["enable", "disable", "archive", "un_archive"], user
@@ -3154,6 +3297,35 @@ class SegmentService:
if args.enabled or keyword_changed:
# update segment vector index
VectorService.update_segment_vector(args.keywords, segment, dataset)
# update summary index if summary is provided and has changed
if args.summary is not None:
# When user manually provides summary, allow saving even if summary_index_setting doesn't exist
# summary_index_setting is only needed for LLM generation, not for manual summary vectorization
# Vectorization uses dataset.embedding_model, which doesn't require summary_index_setting
if dataset.indexing_technique == "high_quality":
# Query existing summary from database
from models.dataset import DocumentSegmentSummary
existing_summary = (
db.session.query(DocumentSegmentSummary)
.where(
DocumentSegmentSummary.chunk_id == segment.id,
DocumentSegmentSummary.dataset_id == dataset.id,
)
.first()
)
# Check if summary has changed
existing_summary_content = existing_summary.summary_content if existing_summary else None
if existing_summary_content != args.summary:
# Summary has changed, update it
from services.summary_index_service import SummaryIndexService
try:
SummaryIndexService.update_summary_for_segment(segment, dataset, args.summary)
except Exception:
logger.exception("Failed to update summary for segment %s", segment.id)
# Don't fail the entire update if summary update fails
else:
segment_hash = helper.generate_text_hash(content)
tokens = 0
@@ -3228,6 +3400,73 @@ class SegmentService:
elif document.doc_form in (IndexStructureType.PARAGRAPH_INDEX, IndexStructureType.QA_INDEX):
# update segment vector index
VectorService.update_segment_vector(args.keywords, segment, dataset)
# Handle summary index when content changed
if dataset.indexing_technique == "high_quality":
from models.dataset import DocumentSegmentSummary
existing_summary = (
db.session.query(DocumentSegmentSummary)
.where(
DocumentSegmentSummary.chunk_id == segment.id,
DocumentSegmentSummary.dataset_id == dataset.id,
)
.first()
)
if args.summary is None:
# User didn't provide summary, auto-regenerate if segment previously had summary
# Auto-regeneration only happens if summary_index_setting exists and enable is True
if (
existing_summary
and dataset.summary_index_setting
and dataset.summary_index_setting.get("enable") is True
):
# Segment previously had summary, regenerate it with new content
from services.summary_index_service import SummaryIndexService
try:
SummaryIndexService.generate_and_vectorize_summary(
segment, dataset, dataset.summary_index_setting
)
logger.info("Auto-regenerated summary for segment %s after content change", segment.id)
except Exception:
logger.exception("Failed to auto-regenerate summary for segment %s", segment.id)
# Don't fail the entire update if summary regeneration fails
else:
# User provided summary, check if it has changed
# Manual summary updates are allowed even if summary_index_setting doesn't exist
existing_summary_content = existing_summary.summary_content if existing_summary else None
if existing_summary_content != args.summary:
# Summary has changed, use user-provided summary
from services.summary_index_service import SummaryIndexService
try:
SummaryIndexService.update_summary_for_segment(segment, dataset, args.summary)
logger.info("Updated summary for segment %s with user-provided content", segment.id)
except Exception:
logger.exception("Failed to update summary for segment %s", segment.id)
# Don't fail the entire update if summary update fails
else:
# Summary hasn't changed, regenerate based on new content
# Auto-regeneration only happens if summary_index_setting exists and enable is True
if (
existing_summary
and dataset.summary_index_setting
and dataset.summary_index_setting.get("enable") is True
):
from services.summary_index_service import SummaryIndexService
try:
SummaryIndexService.generate_and_vectorize_summary(
segment, dataset, dataset.summary_index_setting
)
logger.info(
"Regenerated summary for segment %s after content change (summary unchanged)",
segment.id,
)
except Exception:
logger.exception("Failed to regenerate summary for segment %s", segment.id)
# Don't fail the entire update if summary regeneration fails
# update multimodel vector index
VectorService.update_multimodel_vector(segment, args.attachment_ids or [], dataset)
except Exception as e:
@@ -3616,6 +3855,39 @@ class SegmentService:
)
return result if isinstance(result, DocumentSegment) else None
@classmethod
def get_segments_by_document_and_dataset(
cls,
document_id: str,
dataset_id: str,
status: str | None = None,
enabled: bool | None = None,
) -> Sequence[DocumentSegment]:
"""
Get segments for a document in a dataset with optional filtering.
Args:
document_id: Document ID
dataset_id: Dataset ID
status: Optional status filter (e.g., "completed")
enabled: Optional enabled filter (True/False)
Returns:
Sequence of DocumentSegment instances
"""
query = select(DocumentSegment).where(
DocumentSegment.document_id == document_id,
DocumentSegment.dataset_id == dataset_id,
)
if status is not None:
query = query.where(DocumentSegment.status == status)
if enabled is not None:
query = query.where(DocumentSegment.enabled == enabled)
return db.session.scalars(query).all()
class DatasetCollectionBindingService:
@classmethod

2
api/services/entities/knowledge_entities/knowledge_entities.py
View File

@@ -119,6 +119,7 @@ class KnowledgeConfig(BaseModel):
data_source: DataSource | None = None
process_rule: ProcessRule | None = None
retrieval_model: RetrievalModel | None = None
summary_index_setting: dict | None = None
doc_form: str = "text_model"
doc_language: str = "English"
embedding_model: str | None = None
@@ -141,6 +142,7 @@ class SegmentUpdateArgs(BaseModel):
regenerate_child_chunks: bool = False
enabled: bool | None = None
attachment_ids: list[str] | None = None
summary: str | None = None # Summary content for summary index
class ChildChunkUpdateArgs(BaseModel):

2
api/services/entities/knowledge_entities/rag_pipeline_entities.py
View File

@@ -116,6 +116,8 @@ class KnowledgeConfiguration(BaseModel):
embedding_model: str = ""
keyword_number: int | None = 10
retrieval_model: RetrievalSetting
# add summary index setting
summary_index_setting: dict | None = None
@field_validator("embedding_model_provider", mode="before")
@classmethod

6
api/services/rag_pipeline/rag_pipeline_dsl_service.py
View File

@@ -343,6 +343,9 @@ class RagPipelineDslService:
dataset.embedding_model_provider = knowledge_configuration.embedding_model_provider
elif knowledge_configuration.indexing_technique == "economy":
dataset.keyword_number = knowledge_configuration.keyword_number
# Update summary_index_setting if provided
if knowledge_configuration.summary_index_setting is not None:
dataset.summary_index_setting = knowledge_configuration.summary_index_setting
dataset.pipeline_id = pipeline.id
self._session.add(dataset)
self._session.commit()
@@ -477,6 +480,9 @@ class RagPipelineDslService:
dataset.embedding_model_provider = knowledge_configuration.embedding_model_provider
elif knowledge_configuration.indexing_technique == "economy":
dataset.keyword_number = knowledge_configuration.keyword_number
# Update summary_index_setting if provided
if knowledge_configuration.summary_index_setting is not None:
dataset.summary_index_setting = knowledge_configuration.summary_index_setting
dataset.pipeline_id = pipeline.id
self._session.add(dataset)
self._session.commit()

1432
api/services/summary_index_service.py Normal file
View File

File diff suppressed because it is too large Load Diff

13
api/tasks/add_document_to_index_task.py
View File

@@ -118,6 +118,19 @@ def add_document_to_index_task(dataset_document_id: str):
)
session.commit()
# Enable summary indexes for all segments in this document
from services.summary_index_service import SummaryIndexService
segment_ids_list = [segment.id for segment in segments]
if segment_ids_list:
try:
SummaryIndexService.enable_summaries_for_segments(
dataset=dataset,
segment_ids=segment_ids_list,
)
except Exception as e:
logger.warning("Failed to enable summaries for document %s: %s", dataset_document.id, str(e))
end_at = time.perf_counter()
logger.info(
click.style(f"Document added to index: {dataset_document.id} latency: {end_at - start_at}", fg="green")

4
api/tasks/batch_clean_document_task.py
View File

@@ -50,7 +50,9 @@ def batch_clean_document_task(document_ids: list[str], dataset_id: str, doc_form
if segments:
index_node_ids = [segment.index_node_id for segment in segments]
index_processor = IndexProcessorFactory(doc_form).init_index_processor()
index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
index_processor.clean(
dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
)
for segment in segments:
image_upload_file_ids = get_image_upload_file_ids(segment.content)

4
api/tasks/clean_document_task.py
View File

@@ -51,7 +51,9 @@ def clean_document_task(document_id: str, dataset_id: str, doc_form: str, file_i
if segments:
index_node_ids = [segment.index_node_id for segment in segments]
index_processor = IndexProcessorFactory(doc_form).init_index_processor()
index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
index_processor.clean(
dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
)
for segment in segments:
image_upload_file_ids = get_image_upload_file_ids(segment.content)

4
api/tasks/clean_notion_document_task.py
View File

@@ -42,7 +42,9 @@ def clean_notion_document_task(document_ids: list[str], dataset_id: str):
).all()
index_node_ids = [segment.index_node_id for segment in segments]
index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=True)
index_processor.clean(
dataset, index_node_ids, with_keywords=True, delete_child_chunks=True, delete_summaries=True
)
segment_ids = [segment.id for segment in segments]
segment_delete_stmt = delete(DocumentSegment).where(DocumentSegment.id.in_(segment_ids))
session.execute(segment_delete_stmt)

2
api/tasks/delete_segment_from_index_task.py
View File

@@ -47,6 +47,7 @@ def delete_segment_from_index_task(
doc_form = dataset_document.doc_form
# Proceed with index cleanup using the index_node_ids directly
# For actual deletion, we should delete summaries (not just disable them)
index_processor = IndexProcessorFactory(doc_form).init_index_processor()
index_processor.clean(
dataset,
@@ -54,6 +55,7 @@ def delete_segment_from_index_task(
with_keywords=True,
delete_child_chunks=True,
precomputed_child_node_ids=child_node_ids,
delete_summaries=True, # Actually delete summaries when segment is deleted
)
if dataset.is_multimodal:
# delete segment attachment binding

12
api/tasks/disable_segment_from_index_task.py
View File

@@ -60,6 +60,18 @@ def disable_segment_from_index_task(segment_id: str):
index_processor = IndexProcessorFactory(index_type).init_index_processor()
index_processor.clean(dataset, [segment.index_node_id])
# Disable summary index for this segment
from services.summary_index_service import SummaryIndexService
try:
SummaryIndexService.disable_summaries_for_segments(
dataset=dataset,
segment_ids=[segment.id],
disabled_by=segment.disabled_by,
)
except Exception as e:
logger.warning("Failed to disable summary for segment %s: %s", segment.id, str(e))
end_at = time.perf_counter()
logger.info(
click.style(

15
api/tasks/disable_segments_from_index_task.py
View File

@@ -68,6 +68,21 @@ def disable_segments_from_index_task(segment_ids: list, dataset_id: str, documen
index_node_ids.extend(attachment_ids)
index_processor.clean(dataset, index_node_ids, with_keywords=True, delete_child_chunks=False)
# Disable summary indexes for these segments
from services.summary_index_service import SummaryIndexService
segment_ids_list = [segment.id for segment in segments]
try:
# Get disabled_by from first segment (they should all have the same disabled_by)
disabled_by = segments[0].disabled_by if segments else None
SummaryIndexService.disable_summaries_for_segments(
dataset=dataset,
segment_ids=segment_ids_list,
disabled_by=disabled_by,
)
except Exception as e:
logger.warning("Failed to disable summaries for segments: %s", str(e))
end_at = time.perf_counter()
logger.info(click.style(f"Segments removed from index latency: {end_at - start_at}", fg="green"))
except Exception:

73
api/tasks/document_indexing_task.py
View File

@@ -14,6 +14,7 @@ from enums.cloud_plan import CloudPlan
from libs.datetime_utils import naive_utc_now
from models.dataset import Dataset, Document
from services.feature_service import FeatureService
from tasks.generate_summary_index_task import generate_summary_index_task
logger = logging.getLogger(__name__)
@@ -99,6 +100,78 @@ def _document_indexing(dataset_id: str, document_ids: Sequence[str]):
indexing_runner.run(documents)
end_at = time.perf_counter()
logger.info(click.style(f"Processed dataset: {dataset_id} latency: {end_at - start_at}", fg="green"))
# Trigger summary index generation for completed documents if enabled
# Only generate for high_quality indexing technique and when summary_index_setting is enabled
# Re-query dataset to get latest summary_index_setting (in case it was updated)
dataset = session.query(Dataset).where(Dataset.id == dataset_id).first()
if not dataset:
logger.warning("Dataset %s not found after indexing", dataset_id)
return
if dataset.indexing_technique == "high_quality":
summary_index_setting = dataset.summary_index_setting
if summary_index_setting and summary_index_setting.get("enable"):
# expire all session to get latest document's indexing status
session.expire_all()
# Check each document's indexing status and trigger summary generation if completed
for document_id in document_ids:
# Re-query document to get latest status (IndexingRunner may have updated it)
document = (
session.query(Document)
.where(Document.id == document_id, Document.dataset_id == dataset_id)
.first()
)
if document:
logger.info(
"Checking document %s for summary generation: status=%s, doc_form=%s, need_summary=%s",
document_id,
document.indexing_status,
document.doc_form,
document.need_summary,
)
if (
document.indexing_status == "completed"
and document.doc_form != "qa_model"
and document.need_summary is True
):
try:
generate_summary_index_task.delay(dataset.id, document_id, None)
logger.info(
"Queued summary index generation task for document %s in dataset %s "
"after indexing completed",
document_id,
dataset.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 summary task queuing fails
else:
logger.info(
"Skipping summary generation for document %s: "
"status=%s, doc_form=%s, need_summary=%s",
document_id,
document.indexing_status,
document.doc_form,
document.need_summary,
)
else:
logger.warning("Document %s not found after indexing", document_id)
else:
logger.info(
"Summary index generation skipped for dataset %s: summary_index_setting.enable=%s",
dataset.id,
summary_index_setting.get("enable") if summary_index_setting else None,
)
else:
logger.info(
"Summary index generation skipped for dataset %s: indexing_technique=%s (not 'high_quality')",
dataset.id,
dataset.indexing_technique,
)
except DocumentIsPausedError as ex:
logger.info(click.style(str(ex), fg="yellow"))
except Exception:

11
api/tasks/enable_segment_to_index_task.py
View File

@@ -106,6 +106,17 @@ def enable_segment_to_index_task(segment_id: str):
# save vector index
index_processor.load(dataset, [document], multimodal_documents=multimodel_documents)
# Enable summary index for this segment
from services.summary_index_service import SummaryIndexService
try:
SummaryIndexService.enable_summaries_for_segments(
dataset=dataset,
segment_ids=[segment.id],
)
except Exception as e:
logger.warning("Failed to enable summary for segment %s: %s", segment.id, str(e))
end_at = time.perf_counter()
logger.info(click.style(f"Segment enabled to index: {segment.id} latency: {end_at - start_at}", fg="green"))
except Exception as e:

12
api/tasks/enable_segments_to_index_task.py
View File

@@ -106,6 +106,18 @@ def enable_segments_to_index_task(segment_ids: list, dataset_id: str, document_i
# save vector index
index_processor.load(dataset, documents, multimodal_documents=multimodal_documents)
# Enable summary indexes for these segments
from services.summary_index_service import SummaryIndexService
segment_ids_list = [segment.id for segment in segments]
try:
SummaryIndexService.enable_summaries_for_segments(
dataset=dataset,
segment_ids=segment_ids_list,
)
except Exception as e:
logger.warning("Failed to enable summaries for segments: %s", str(e))
end_at = time.perf_counter()
logger.info(click.style(f"Segments enabled to index latency: {end_at - start_at}", fg="green"))
except Exception as e:

119
api/tasks/generate_summary_index_task.py Normal file
View File

@@ -0,0 +1,119 @@
"""Async task for generating summary indexes."""
import logging
import time
import click
from celery import shared_task
from core.db.session_factory import session_factory
from models.dataset import Dataset, DocumentSegment
from models.dataset import Document as DatasetDocument
from services.summary_index_service import SummaryIndexService
logger = logging.getLogger(__name__)
@shared_task(queue="dataset")
def generate_summary_index_task(dataset_id: str, document_id: str, segment_ids: list[str] | None = None):
"""
Async generate summary index for document segments.
Args:
dataset_id: Dataset ID
document_id: Document ID
segment_ids: Optional list of specific segment IDs to process. If None, process all segments.
Usage:
generate_summary_index_task.delay(dataset_id, document_id)
generate_summary_index_task.delay(dataset_id, document_id, segment_ids)
"""
logger.info(
click.style(
f"Start generating summary index for document {document_id} in dataset {dataset_id}",
fg="green",
)
)
start_at = time.perf_counter()
try:
with session_factory.create_session() as session:
dataset = session.query(Dataset).where(Dataset.id == dataset_id).first()
if not dataset:
logger.error(click.style(f"Dataset not found: {dataset_id}", fg="red"))
return
document = session.query(DatasetDocument).where(DatasetDocument.id == document_id).first()
if not document:
logger.error(click.style(f"Document not found: {document_id}", fg="red"))
return
# Check if document needs summary
if not document.need_summary:
logger.info(
click.style(
f"Skipping summary generation for document {document_id}: need_summary is False",
fg="cyan",
)
)
return
# Only generate summary index for high_quality indexing technique
if dataset.indexing_technique != "high_quality":
logger.info(
click.style(
f"Skipping summary generation for dataset {dataset_id}: "
f"indexing_technique is {dataset.indexing_technique}, not 'high_quality'",
fg="cyan",
)
)
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"):
logger.info(
click.style(
f"Summary index is disabled for dataset {dataset_id}",
fg="cyan",
)
)
return
# Determine if only parent chunks should be processed
only_parent_chunks = dataset.chunk_structure == "parent_child_index"
# Generate summaries
summary_records = SummaryIndexService.generate_summaries_for_document(
dataset=dataset,
document=document,
summary_index_setting=summary_index_setting,
segment_ids=segment_ids,
only_parent_chunks=only_parent_chunks,
)
end_at = time.perf_counter()
logger.info(
click.style(
f"Summary index generation completed for document {document_id}: "
f"{len(summary_records)} summaries generated, latency: {end_at - start_at}",
fg="green",
)
)
except Exception as e:
logger.exception("Failed to generate summary index for document %s", document_id)
# Update document segments with error status if needed
if segment_ids:
error_message = f"Summary generation failed: {str(e)}"
with session_factory.create_session() as session:
session.query(DocumentSegment).filter(
DocumentSegment.id.in_(segment_ids),
DocumentSegment.dataset_id == dataset_id,
).update(
{
DocumentSegment.error: error_message,
},
synchronize_session=False,
)
session.commit()

315
api/tasks/regenerate_summary_index_task.py Normal file
View File

@@ -0,0 +1,315 @@
"""Task for regenerating summary indexes when dataset settings change."""
import logging
import time
from collections import defaultdict
import click
from celery import shared_task
from sqlalchemy import or_, select
from core.db.session_factory import session_factory
from models.dataset import Dataset, DocumentSegment, DocumentSegmentSummary
from models.dataset import Document as DatasetDocument
from services.summary_index_service import SummaryIndexService
logger = logging.getLogger(__name__)
@shared_task(queue="dataset")
def regenerate_summary_index_task(
dataset_id: str,
regenerate_reason: str = "summary_model_changed",
regenerate_vectors_only: bool = False,
):
"""
Regenerate summary indexes for all documents in a dataset.
This task is triggered when:
1. summary_index_setting model changes (regenerate_reason="summary_model_changed")
- Regenerates summary content and vectors for all existing summaries
2. embedding_model changes (regenerate_reason="embedding_model_changed")
- Only regenerates vectors for existing summaries (keeps summary content)
Args:
dataset_id: Dataset ID
regenerate_reason: Reason for regeneration ("summary_model_changed" or "embedding_model_changed")
regenerate_vectors_only: If True, only regenerate vectors without regenerating summary content
"""
logger.info(
click.style(
f"Start regenerate summary index for dataset {dataset_id}, reason: {regenerate_reason}",
fg="green",
)
)
start_at = time.perf_counter()
try:
with session_factory.create_session() as session:
dataset = session.query(Dataset).filter_by(id=dataset_id).first()
if not dataset:
logger.error(click.style(f"Dataset not found: {dataset_id}", fg="red"))
return
# Only regenerate summary index for high_quality indexing technique
if dataset.indexing_technique != "high_quality":
logger.info(
click.style(
f"Skipping summary regeneration for dataset {dataset_id}: "
f"indexing_technique is {dataset.indexing_technique}, not 'high_quality'",
fg="cyan",
)
)
return
# Check if summary index is enabled (only for summary_model change)
# For embedding_model change, we still re-vectorize existing summaries even if setting is disabled
summary_index_setting = dataset.summary_index_setting
if not regenerate_vectors_only:
# For summary_model change, require summary_index_setting to be enabled
if not summary_index_setting or not summary_index_setting.get("enable"):
logger.info(
click.style(
f"Summary index is disabled for dataset {dataset_id}",
fg="cyan",
)
)
return
total_segments_processed = 0
total_segments_failed = 0
if regenerate_vectors_only:
# For embedding_model change: directly query all segments with existing summaries
# Don't require document indexing_status == "completed"
# Include summaries with status "completed" or "error" (if they have content)
segments_with_summaries = (
session.query(DocumentSegment, DocumentSegmentSummary)
.join(
DocumentSegmentSummary,
DocumentSegment.id == DocumentSegmentSummary.chunk_id,
)
.join(
DatasetDocument,
DocumentSegment.document_id == DatasetDocument.id,
)
.where(
DocumentSegment.dataset_id == dataset_id,
DocumentSegment.status == "completed", # Segment must be completed
DocumentSegment.enabled == True,
DocumentSegmentSummary.dataset_id == dataset_id,
DocumentSegmentSummary.summary_content.isnot(None), # Must have summary content
# Include completed summaries or error summaries (with content)
or_(
DocumentSegmentSummary.status == "completed",
DocumentSegmentSummary.status == "error",
),
DatasetDocument.enabled == True, # Document must be enabled
DatasetDocument.archived == False, # Document must not be archived
DatasetDocument.doc_form != "qa_model", # Skip qa_model documents
)
.order_by(DocumentSegment.document_id.asc(), DocumentSegment.position.asc())
.all()
)
if not segments_with_summaries:
logger.info(
click.style(
f"No segments with summaries found for re-vectorization in dataset {dataset_id}",
fg="cyan",
)
)
return
logger.info(
"Found %s segments with summaries for re-vectorization in dataset %s",
len(segments_with_summaries),
dataset_id,
)
# Group by document for logging
segments_by_document = defaultdict(list)
for segment, summary_record in segments_with_summaries:
segments_by_document[segment.document_id].append((segment, summary_record))
logger.info(
"Segments grouped into %s documents for re-vectorization",
len(segments_by_document),
)
for document_id, segment_summary_pairs in segments_by_document.items():
logger.info(
"Re-vectorizing summaries for %s segments in document %s",
len(segment_summary_pairs),
document_id,
)
for segment, summary_record in segment_summary_pairs:
try:
# Delete old vector
if summary_record.summary_index_node_id:
try:
from core.rag.datasource.vdb.vector_factory import Vector
vector = Vector(dataset)
vector.delete_by_ids([summary_record.summary_index_node_id])
except Exception as e:
logger.warning(
"Failed to delete old summary vector for segment %s: %s",
segment.id,
str(e),
)
# Re-vectorize with new embedding model
SummaryIndexService.vectorize_summary(summary_record, segment, dataset)
session.commit()
total_segments_processed += 1
except Exception as e:
logger.error(
"Failed to re-vectorize summary for segment %s: %s",
segment.id,
str(e),
exc_info=True,
)
total_segments_failed += 1
# Update summary record with error status
summary_record.status = "error"
summary_record.error = f"Re-vectorization failed: {str(e)}"
session.add(summary_record)
session.commit()
continue
else:
# For summary_model change: require document indexing_status == "completed"
# Get all documents with completed indexing status
dataset_documents = session.scalars(
select(DatasetDocument).where(
DatasetDocument.dataset_id == dataset_id,
DatasetDocument.indexing_status == "completed",
DatasetDocument.enabled == True,
DatasetDocument.archived == False,
)
).all()
if not dataset_documents:
logger.info(
click.style(
f"No documents found for summary regeneration in dataset {dataset_id}",
fg="cyan",
)
)
return
logger.info(
"Found %s documents for summary regeneration in dataset %s",
len(dataset_documents),
dataset_id,
)
for dataset_document in dataset_documents:
# Skip qa_model documents
if dataset_document.doc_form == "qa_model":
continue
try:
# Get all segments with existing summaries
segments = (
session.query(DocumentSegment)
.join(
DocumentSegmentSummary,
DocumentSegment.id == DocumentSegmentSummary.chunk_id,
)
.where(
DocumentSegment.document_id == dataset_document.id,
DocumentSegment.dataset_id == dataset_id,
DocumentSegment.status == "completed",
DocumentSegment.enabled == True,
DocumentSegmentSummary.dataset_id == dataset_id,
)
.order_by(DocumentSegment.position.asc())
.all()
)
if not segments:
continue
logger.info(
"Regenerating summaries for %s segments in document %s",
len(segments),
dataset_document.id,
)
for segment in segments:
summary_record = None
try:
# Get existing summary record
summary_record = (
session.query(DocumentSegmentSummary)
.filter_by(
chunk_id=segment.id,
dataset_id=dataset_id,
)
.first()
)
if not summary_record:
logger.warning("Summary record not found for segment %s, skipping", segment.id)
continue
# Regenerate both summary content and vectors (for summary_model change)
SummaryIndexService.generate_and_vectorize_summary(
segment, dataset, summary_index_setting
)
session.commit()
total_segments_processed += 1
except Exception as e:
logger.error(
"Failed to regenerate summary for segment %s: %s",
segment.id,
str(e),
exc_info=True,
)
total_segments_failed += 1
# Update summary record with error status
if summary_record:
summary_record.status = "error"
summary_record.error = f"Regeneration failed: {str(e)}"
session.add(summary_record)
session.commit()
continue
except Exception as e:
logger.error(
"Failed to process document %s for summary regeneration: %s",
dataset_document.id,
str(e),
exc_info=True,
)
continue
end_at = time.perf_counter()
if regenerate_vectors_only:
logger.info(
click.style(
f"Summary re-vectorization completed for dataset {dataset_id}: "
f"{total_segments_processed} segments processed successfully, "
f"{total_segments_failed} segments failed, "
f"latency: {end_at - start_at:.2f}s",
fg="green",
)
)
else:
logger.info(
click.style(
f"Summary index regeneration completed for dataset {dataset_id}: "
f"{total_segments_processed} segments processed successfully, "
f"{total_segments_failed} segments failed, "
f"latency: {end_at - start_at:.2f}s",
fg="green",
)
)
except Exception:
logger.exception("Regenerate summary index failed for dataset %s", dataset_id)

15
api/tasks/remove_document_from_index_task.py
View File

@@ -46,6 +46,21 @@ def remove_document_from_index_task(document_id: str):
index_processor = IndexProcessorFactory(document.doc_form).init_index_processor()
segments = session.scalars(select(DocumentSegment).where(DocumentSegment.document_id == document.id)).all()
# Disable summary indexes for all segments in this document
from services.summary_index_service import SummaryIndexService
segment_ids_list = [segment.id for segment in segments]
if segment_ids_list:
try:
SummaryIndexService.disable_summaries_for_segments(
dataset=dataset,
segment_ids=segment_ids_list,
disabled_by=document.disabled_by,
)
except Exception as e:
logger.warning("Failed to disable summaries for document %s: %s", document.id, str(e))
index_node_ids = [segment.index_node_id for segment in segments]
if index_node_ids:
try:

17
api/tests/unit_tests/conftest.py
View File

@@ -3,6 +3,7 @@ from unittest.mock import MagicMock, patch
import pytest
from flask import Flask
from sqlalchemy import create_engine
# Getting the absolute path of the current file's directory
ABS_PATH = os.path.dirname(os.path.abspath(__file__))
@@ -36,6 +37,7 @@ import sys
sys.path.insert(0, PROJECT_DIR)
from core.db.session_factory import configure_session_factory, session_factory
from extensions import ext_redis
@@ -102,3 +104,18 @@ def reset_secret_key():
yield
finally:
dify_config.SECRET_KEY = original
@pytest.fixture(scope="session")
def _unit_test_engine():
engine = create_engine("sqlite:///:memory:")
yield engine
engine.dispose()
@pytest.fixture(autouse=True)
def _configure_session_factory(_unit_test_engine):
try:
session_factory.get_session_maker()
except RuntimeError:
configure_session_factory(_unit_test_engine, expire_on_commit=False)

7
api/tests/unit_tests/controllers/console/app/test_app_response_models.py
View File

@@ -31,6 +31,13 @@ def _load_app_module():
def schema_model(self, name, schema):
self.models[name] = schema
return schema
def model(self, name, model_dict=None, **kwargs):
"""Register a model with the namespace (flask-restx compatibility)."""
if model_dict is not None:
self.models[name] = model_dict
return model_dict
def _decorator(self, obj):
return obj

38
api/tests/unit_tests/core/rag/extractor/test_word_extractor.py
View File

@@ -1,7 +1,9 @@
"""Primarily used for testing merged cell scenarios"""
import io
import os
import tempfile
from pathlib import Path
from types import SimpleNamespace
from docx import Document
@@ -56,6 +58,42 @@ def test_parse_row():
assert extractor._parse_row(row, {}, 3) == gt[idx]
def test_init_downloads_via_ssrf_proxy(monkeypatch):
doc = Document()
doc.add_paragraph("hello")
buf = io.BytesIO()
doc.save(buf)
docx_bytes = buf.getvalue()
calls: list[tuple[str, object]] = []
class FakeResponse:
status_code = 200
content = docx_bytes
def close(self) -> None:
calls.append(("close", None))
def fake_get(url: str, **kwargs):
calls.append(("get", (url, kwargs)))
return FakeResponse()
monkeypatch.setattr(we, "ssrf_proxy", SimpleNamespace(get=fake_get))
extractor = WordExtractor("https://example.com/test.docx", "tenant_id", "user_id")
try:
assert calls
assert calls[0][0] == "get"
url, kwargs = calls[0][1]
assert url == "https://example.com/test.docx"
assert kwargs.get("timeout") is None
assert extractor.web_path == "https://example.com/test.docx"
assert extractor.file_path != extractor.web_path
assert Path(extractor.file_path).read_bytes() == docx_bytes
finally:
extractor.temp_file.close()
def test_extract_images_from_docx(monkeypatch):
external_bytes = b"ext-bytes"
internal_bytes = b"int-bytes"

9
api/tests/unit_tests/services/test_dataset_service_update_dataset.py
View File

@@ -138,6 +138,7 @@ class TestDatasetServiceUpdateDataset:
"services.dataset_service.DatasetCollectionBindingService.get_dataset_collection_binding"
) as mock_get_binding,
patch("services.dataset_service.deal_dataset_vector_index_task") as mock_task,
patch("services.dataset_service.regenerate_summary_index_task") as mock_regenerate_task,
patch(
"services.dataset_service.current_user", create_autospec(Account, instance=True)
) as mock_current_user,
@@ -147,6 +148,7 @@ class TestDatasetServiceUpdateDataset:
"model_manager": mock_model_manager,
"get_binding": mock_get_binding,
"task": mock_task,
"regenerate_task": mock_regenerate_task,
"current_user": mock_current_user,
}
@@ -549,6 +551,13 @@ class TestDatasetServiceUpdateDataset:
# Verify vector index task was triggered
mock_internal_provider_dependencies["task"].delay.assert_called_once_with("dataset-123", "update")
# Verify regenerate summary index task was triggered (when embedding_model changes)
mock_internal_provider_dependencies["regenerate_task"].delay.assert_called_once_with(
"dataset-123",
regenerate_reason="embedding_model_changed",
regenerate_vectors_only=True,
)
# Verify return value
assert result == dataset

24
api/ty.toml
View File

@@ -1,11 +1,33 @@
[src]
exclude = [
# TODO: enable when violations fixed
# deps groups (A1/A2/B/C/D/E)
# A1: foundational runtime typing / provider plumbing
"core/mcp/session",
"core/model_runtime/model_providers",
"core/workflow/nodes/protocols.py",
"libs/gmpy2_pkcs10aep_cipher.py",
# A2: workflow engine/nodes
"core/workflow",
"core/app/workflow",
"core/helper/code_executor",
# B: app runner + prompt
"core/prompt",
"core/app/apps/base_app_runner.py",
"core/app/apps/workflow_app_runner.py",
# C: services/controllers/fields/libs
"services",
"controllers/console/app",
"controllers/console/explore",
"controllers/console/datasets",
"controllers/console/workspace",
"controllers/service_api/wraps.py",
"fields/conversation_fields.py",
"libs/external_api.py",
# D: observability + integrations
"core/ops",
"extensions",
# E: vector DB integrations
"core/rag/datasource/vdb",
# non-producition or generated code
"migrations",
"tests",

340
api/uv.lock generated
View File

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{ url = "https://files.pythonhosted.org/packages/ad/0c/186829654f5bfd9a028f6648e9caeb11271960a61de97484627d24443f91/ty-0.0.14-py3-none-win_arm64.whl", hash = "sha256:b6facdbe9b740cb2c15293a1d178e22ffc600653646452632541d01c36d5e378", size = 9885831, upload-time = "2026-01-27T00:57:49.747Z" },
]
[[package]]
@@ -6739,11 +6761,11 @@ wheels = [
[[package]]
name = "types-pexpect"
version = "4.9.0.20250916"
version = "4.9.0.20260127"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/0c/e6/cc43e306dc7de14ec7861c24ac4957f688741ae39ae685049695d796b587/types_pexpect-4.9.0.20250916.tar.gz", hash = "sha256:69e5fed6199687a730a572de780a5749248a4c5df2ff1521e194563475c9928d", size = 13322, upload-time = "2025-09-16T02:49:25.61Z" }
sdist = { url = "https://files.pythonhosted.org/packages/2e/32/7e03a07e16f79a404d6200ed6bdfcc320d0fb833436a5c6895a1403dedb7/types_pexpect-4.9.0.20260127.tar.gz", hash = "sha256:f8d43efc24251a8e533c71ea9be03d19bb5d08af096d561611697af9720cba7f", size = 13461, upload-time = "2026-01-27T03:28:30.923Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/aa/6d/7740e235a9fb2570968da7d386d7feb511ce68cd23472402ff8cdf7fc78f/types_pexpect-4.9.0.20250916-py3-none-any.whl", hash = "sha256:7fa43cb96042ac58bc74f7c28e5d85782be0ee01344149886849e9d90936fe8a", size = 17057, upload-time = "2025-09-16T02:49:24.546Z" },
{ url = "https://files.pythonhosted.org/packages/8a/d9/7ac5c9aa5a89a1a64cd835ae348227f4939406d826e461b85b690a8ba1c2/types_pexpect-4.9.0.20260127-py3-none-any.whl", hash = "sha256:69216c0ebf0fe45ad2900823133959b027e9471e24fc3f2e4c7b00605555da5f", size = 17078, upload-time = "2026-01-27T03:28:29.848Z" },
]
[[package]]
@@ -7331,11 +7353,11 @@ wheels = [
[[package]]
name = "wcwidth"
version = "0.3.2"
version = "0.5.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/05/07/0b5bcc9812b1b2fd331cc88289ef4d47d428afdbbf0216bb7d53942d93d6/wcwidth-0.3.2.tar.gz", hash = "sha256:d469b3059dab6b1077def5923ed0a8bf5738bd4a1a87f686d5e2de455354c4ad", size = 233633, upload-time = "2026-01-23T21:08:52.451Z" }
sdist = { url = "https://files.pythonhosted.org/packages/64/6e/62daec357285b927e82263a81f3b4c1790215bc77c42530ce4a69d501a43/wcwidth-0.5.0.tar.gz", hash = "sha256:f89c103c949a693bf563377b2153082bf58e309919dfb7f27b04d862a0089333", size = 246585, upload-time = "2026-01-27T01:31:44.942Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/72/c6/1452e716c5af065c018f75d42ca97517a04ac6aae4133722e0424649a07c/wcwidth-0.3.2-py3-none-any.whl", hash = "sha256:817abc6a89e47242a349b5d100cbd244301690d6d8d2ec6335f26fe6640a6315", size = 86280, upload-time = "2026-01-23T21:08:51.362Z" },
{ url = "https://files.pythonhosted.org/packages/f2/3e/45583b67c2ff08ad5a582d316fcb2f11d6cf0a50c7707ac09d212d25bc98/wcwidth-0.5.0-py3-none-any.whl", hash = "sha256:1efe1361b83b0ff7877b81ba57c8562c99cf812158b778988ce17ec061095695", size = 93772, upload-time = "2026-01-27T01:31:43.432Z" },
]
[[package]]

10
dev/pytest/pytest_unit_tests.sh
View File

@@ -5,6 +5,12 @@ SCRIPT_DIR="$(dirname "$(realpath "$0")")"
cd "$SCRIPT_DIR/../.."
PYTEST_TIMEOUT="${PYTEST_TIMEOUT:-20}"
PYTEST_XDIST_ARGS="${PYTEST_XDIST_ARGS:--n auto}"
# libs
pytest --timeout "${PYTEST_TIMEOUT}" api/tests/unit_tests
# Run most tests in parallel (excluding controllers which have import conflicts with xdist)
# Controller tests have module-level side effects (Flask route registration) that cause
# race conditions when imported concurrently by multiple pytest-xdist workers.
pytest --timeout "${PYTEST_TIMEOUT}" ${PYTEST_XDIST_ARGS} api/tests/unit_tests --ignore=api/tests/unit_tests/controllers
# Run controller tests sequentially to avoid import race conditions
pytest --timeout "${PYTEST_TIMEOUT}" api/tests/unit_tests/controllers

1
docker/.env.example
View File

@@ -1388,6 +1388,7 @@ PLUGIN_DAEMON_PORT=5002
PLUGIN_DAEMON_KEY=lYkiYYT6owG+71oLerGzA7GXCgOT++6ovaezWAjpCjf+Sjc3ZtU+qUEi
PLUGIN_DAEMON_URL=http://plugin_daemon:5002
PLUGIN_MAX_PACKAGE_SIZE=52428800
PLUGIN_MODEL_SCHEMA_CACHE_TTL=3600
PLUGIN_PPROF_ENABLED=false
PLUGIN_DEBUGGING_HOST=0.0.0.0

1
docker/docker-compose.yaml
View File

@@ -591,6 +591,7 @@ x-shared-env: &shared-api-worker-env
PLUGIN_DAEMON_KEY: ${PLUGIN_DAEMON_KEY:-lYkiYYT6owG+71oLerGzA7GXCgOT++6ovaezWAjpCjf+Sjc3ZtU+qUEi}
PLUGIN_DAEMON_URL: ${PLUGIN_DAEMON_URL:-http://plugin_daemon:5002}
PLUGIN_MAX_PACKAGE_SIZE: ${PLUGIN_MAX_PACKAGE_SIZE:-52428800}
PLUGIN_MODEL_SCHEMA_CACHE_TTL: ${PLUGIN_MODEL_SCHEMA_CACHE_TTL:-3600}
PLUGIN_PPROF_ENABLED: ${PLUGIN_PPROF_ENABLED:-false}
PLUGIN_DEBUGGING_HOST: ${PLUGIN_DEBUGGING_HOST:-0.0.0.0}
PLUGIN_DEBUGGING_PORT: ${PLUGIN_DEBUGGING_PORT:-5003}

6
web/AGENTS.md
View File

@@ -1,5 +1,9 @@
## Frontend Workflow
- Refer to the `./docs/test.md` and `./docs/lint.md` for detailed frontend workflow instructions.
## Automated Test Generation
- Use `web/testing/testing.md` as the canonical instruction set for generating frontend automated tests.
- Use `./docs/test.md` as the canonical instruction set for generating frontend automated tests.
- When proposing or saving tests, re-read that document and follow every requirement.
- All frontend tests MUST also comply with the `frontend-testing` skill. Treat the skill as a mandatory constraint, not optional guidance.

2
web/README.md
View File

@@ -109,6 +109,8 @@ Open [http://localhost:6006](http://localhost:6006) with your browser to see the
If your IDE is VSCode, rename `web/.vscode/settings.example.json` to `web/.vscode/settings.json` for lint code setting.
Then follow the [Lint Documentation](./docs/lint.md) to lint the code.
## Test
We use [Vitest](https://vitest.dev/) and [React Testing Library](https://testing-library.com/docs/react-testing-library/intro/) for Unit Testing.

8
web/app/components/app-sidebar/app-info.tsx
View File

@@ -33,6 +33,7 @@ import { fetchWorkflowDraft } from '@/service/workflow'
import { AppModeEnum } from '@/types/app'
import { getRedirection } from '@/utils/app-redirection'
import { cn } from '@/utils/classnames'
import { downloadBlob } from '@/utils/download'
import AppIcon from '../base/app-icon'
import AppOperations from './app-operations'
@@ -161,13 +162,8 @@ const AppInfo = ({ expand, onlyShowDetail = false, openState = false, onDetailEx
appID: appDetail.id,
include,
})
const a = document.createElement('a')
const file = new Blob([data], { type: 'application/yaml' })
const url = URL.createObjectURL(file)
a.href = url
a.download = `${appDetail.name}.yml`
a.click()
URL.revokeObjectURL(url)
downloadBlob({ data: file, fileName: `${appDetail.name}.yml` })
}
catch {
notify({ type: 'error', message: t('exportFailed', { ns: 'app' }) })

8
web/app/components/app-sidebar/dataset-info/dropdown.tsx
View File

@@ -11,6 +11,7 @@ import { datasetDetailQueryKeyPrefix, useInvalidDatasetList } from '@/service/kn
import { useInvalid } from '@/service/use-base'
import { useExportPipelineDSL } from '@/service/use-pipeline'
import { cn } from '@/utils/classnames'
import { downloadBlob } from '@/utils/download'
import ActionButton from '../../base/action-button'
import Confirm from '../../base/confirm'
import { PortalToFollowElem, PortalToFollowElemContent, PortalToFollowElemTrigger } from '../../base/portal-to-follow-elem'
@@ -64,13 +65,8 @@ const DropDown = ({
pipelineId: pipeline_id,
include,
})
const a = document.createElement('a')
const file = new Blob([data], { type: 'application/yaml' })
const url = URL.createObjectURL(file)
a.href = url
a.download = `${name}.pipeline`
a.click()
URL.revokeObjectURL(url)
downloadBlob({ data: file, fileName: `${name}.pipeline` })
}
catch {
Toast.notify({ type: 'error', message: t('exportFailed', { ns: 'app' }) })

15
web/app/components/app-sidebar/toggle-button.tsx
View File

@@ -4,7 +4,7 @@ import { useTranslation } from 'react-i18next'
import { cn } from '@/utils/classnames'
import Button from '../base/button'
import Tooltip from '../base/tooltip'
import { getKeyboardKeyNameBySystem } from '../workflow/utils'
import ShortcutsName from '../workflow/shortcuts-name'
type TooltipContentProps = {
expand: boolean
@@ -20,18 +20,7 @@ const TooltipContent = ({
return (
<div className="flex items-center gap-x-1">
<span className="system-xs-medium px-0.5 text-text-secondary">{expand ? t('sidebar.collapseSidebar', { ns: 'layout' }) : t('sidebar.expandSidebar', { ns: 'layout' })}</span>
<div className="flex items-center gap-x-0.5">
{
TOGGLE_SHORTCUT.map(key => (
<span
key={key}
className="system-kbd inline-flex items-center justify-center rounded-[4px] bg-components-kbd-bg-gray px-1 text-text-tertiary"
>
{getKeyboardKeyNameBySystem(key)}
</span>
))
}
</div>
<ShortcutsName keys={TOGGLE_SHORTCUT} textColor="secondary" />
</div>
)
}

16
web/app/components/app/annotation/header-opts/index.tsx
View File

@@ -21,6 +21,7 @@ import { LanguagesSupported } from '@/i18n-config/language'
import { clearAllAnnotations, fetchExportAnnotationList } from '@/service/annotation'
import { cn } from '@/utils/classnames'
import { downloadBlob } from '@/utils/download'
import Button from '../../../base/button'
import AddAnnotationModal from '../add-annotation-modal'
import BatchAddModal from '../batch-add-annotation-modal'
@@ -56,28 +57,23 @@ const HeaderOptions: FC<Props> = ({
)
const JSONLOutput = () => {
const a = document.createElement('a')
const content = listTransformer(list).join('\n')
const file = new Blob([content], { type: 'application/jsonl' })
const url = URL.createObjectURL(file)
a.href = url
a.download = `annotations-${locale}.jsonl`
a.click()
URL.revokeObjectURL(url)
downloadBlob({ data: file, fileName: `annotations-${locale}.jsonl` })
}
const fetchList = async () => {
const fetchList = React.useCallback(async () => {
const { data }: any = await fetchExportAnnotationList(appId)
setList(data as AnnotationItemBasic[])
}
}, [appId])
useEffect(() => {
fetchList()
}, [])
}, [fetchList])
useEffect(() => {
if (controlUpdateList)
fetchList()
}, [controlUpdateList])
}, [controlUpdateList, fetchList])
const [showBulkImportModal, setShowBulkImportModal] = useState(false)
const [showClearConfirm, setShowClearConfirm] = useState(false)

11
web/app/components/app/app-publisher/index.tsx
View File

@@ -57,7 +57,8 @@ import Divider from '../../base/divider'
import Loading from '../../base/loading'
import Toast from '../../base/toast'
import Tooltip from '../../base/tooltip'
import { getKeyboardKeyCodeBySystem, getKeyboardKeyNameBySystem } from '../../workflow/utils'
import ShortcutsName from '../../workflow/shortcuts-name'
import { getKeyboardKeyCodeBySystem } from '../../workflow/utils'
import AccessControl from '../app-access-control'
import PublishWithMultipleModel from './publish-with-multiple-model'
import SuggestedAction from './suggested-action'
@@ -410,13 +411,7 @@ const AppPublisher = ({
: (
<div className="flex gap-1">
<span>{t('common.publishUpdate', { ns: 'workflow' })}</span>
<div className="flex gap-0.5">
{PUBLISH_SHORTCUT.map(key => (
<span key={key} className="system-kbd h-4 w-4 rounded-[4px] bg-components-kbd-bg-white text-text-primary-on-surface">
{getKeyboardKeyNameBySystem(key)}
</span>
))}
</div>
<ShortcutsName keys={PUBLISH_SHORTCUT} bgColor="white" />
</div>
)
}

6
web/app/components/app/configuration/config-var/index.spec.tsx
View File

@@ -2,7 +2,7 @@ import type { ReactNode } from 'react'
import type { IConfigVarProps } from './index'
import type { ExternalDataTool } from '@/models/common'
import type { PromptVariable } from '@/models/debug'
import { act, fireEvent, render, screen } from '@testing-library/react'
import { act, fireEvent, render, screen, waitFor } from '@testing-library/react'
import * as React from 'react'
import { vi } from 'vitest'
import Toast from '@/app/components/base/toast'
@@ -240,7 +240,9 @@ describe('ConfigVar', () => {
const saveButton = await screen.findByRole('button', { name: 'common.operation.save' })
fireEvent.click(saveButton)
expect(onPromptVariablesChange).toHaveBeenCalledTimes(1)
await waitFor(() => {
expect(onPromptVariablesChange).toHaveBeenCalledTimes(1)
})
})
it('should show error when variable key is duplicated', async () => {

9
web/app/components/app/create-app-modal/index.tsx
View File

@@ -2,8 +2,7 @@
import type { AppIconSelection } from '../../base/app-icon-picker'
import type { RuntimeMode } from '@/types/app'
import { RiArrowRightLine, RiArrowRightSLine, RiCheckLine, RiCommandLine, RiCornerDownLeftLine, RiExchange2Fill } from '@remixicon/react'
import { RiArrowRightLine, RiArrowRightSLine, RiCheckLine, RiExchange2Fill } from '@remixicon/react'
import { useDebounceFn, useKeyPress } from 'ahooks'
import Image from 'next/image'
import { useRouter } from 'next/navigation'
@@ -32,6 +31,7 @@ import { getRedirection } from '@/utils/app-redirection'
import { cn } from '@/utils/classnames'
import { basePath } from '@/utils/var'
import AppIconPicker from '../../base/app-icon-picker'
import ShortcutsName from '../../workflow/shortcuts-name'
type CreateAppProps = {
onSuccess: () => void
@@ -342,10 +342,7 @@ function CreateApp({ onClose, onSuccess, onCreateFromTemplate, defaultAppMode }:
<Button onClick={onClose}>{t('newApp.Cancel', { ns: 'app' })}</Button>
<Button disabled={isAppsFull || !name} className="gap-1" variant="primary" onClick={handleCreateApp}>
<span>{t('newApp.Create', { ns: 'app' })}</span>
<div className="flex gap-0.5">
<RiCommandLine size={14} className="system-kbd rounded-sm bg-components-kbd-bg-white p-0.5" />
<RiCornerDownLeftLine size={14} className="system-kbd rounded-sm bg-components-kbd-bg-white p-0.5" />
</div>
<ShortcutsName keys={['ctrl', '↵']} bgColor="white" />
</Button>
</div>
</div>

5
web/app/components/app/create-from-dsl-modal/index.tsx
View File

@@ -29,6 +29,7 @@ import {
} from '@/service/apps'
import { getRedirection } from '@/utils/app-redirection'
import { cn } from '@/utils/classnames'
import ShortcutsName from '../../workflow/shortcuts-name'
import DSLConfirmModal from './dsl-confirm-modal'
import Uploader from './uploader'
@@ -327,8 +328,10 @@ const CreateFromDSLModal = ({ show, onSuccess, onClose, activeTab = CreateFromDS
disabled={buttonDisabled}
variant="primary"
onClick={handleCreateApp}
className="gap-1"
>
{t('newApp.import', { ns: 'app' })}
<span>{t('newApp.import', { ns: 'app' })}</span>
<ShortcutsName keys={['ctrl', '↵']} bgColor="white" />
</Button>
</div>
</div>

10
web/app/components/apps/app-card.tsx
View File

@@ -35,6 +35,7 @@ import { fetchWorkflowDraft } from '@/service/workflow'
import { AppModeEnum } from '@/types/app'
import { getRedirection } from '@/utils/app-redirection'
import { cn } from '@/utils/classnames'
import { downloadBlob } from '@/utils/download'
import { formatTime } from '@/utils/time'
import { basePath } from '@/utils/var'
@@ -172,13 +173,8 @@ const AppCard = ({ app, onRefresh, onlineUsers = [] }: AppCardProps) => {
appID: app.id,
include,
})
const a = document.createElement('a')
const file = new Blob([data], { type: 'application/yaml' })
const url = URL.createObjectURL(file)
a.href = url
a.download = `${app.name}.${isDownLoadBundle ? 'zip' : 'yaml'}`
a.click()
URL.revokeObjectURL(url)
downloadBlob({ data: file, fileName: `${app.name}.yml` })
}
catch {
notify({
@@ -363,7 +359,7 @@ const AppCard = ({ app, onRefresh, onlineUsers = [] }: AppCardProps) => {
dateFormat: `${t('segment.dateTimeFormat', { ns: 'datasetDocuments' })}`,
})
return `${t('segment.editedAt', { ns: 'datasetDocuments' })} ${timeText}`
}, [app.updated_at, app.created_at])
}, [app.updated_at, app.created_at, t])
const onlineUserAvatars = useMemo(() => {
if (!onlineUsers.length)

1
web/app/components/apps/index.tsx
View File

@@ -105,6 +105,7 @@ const Apps = () => {
{isShowTryAppPanel && (
<TryApp
appId={currentTryAppParams?.appId || ''}
app={currentTryAppParams?.app}
category={currentTryAppParams?.app?.category}
onClose={hideTryAppPanel}
onCreate={handleShowFromTryApp}

4
web/app/components/base/file-uploader/file-uploader-in-attachment/file-item.tsx
View File

@@ -15,11 +15,11 @@ import ImagePreview from '@/app/components/base/image-uploader/image-preview'
import ProgressCircle from '@/app/components/base/progress-bar/progress-circle'
import { SupportUploadFileTypes } from '@/app/components/workflow/types'
import { cn } from '@/utils/classnames'
import { downloadUrl } from '@/utils/download'
import { formatFileSize } from '@/utils/format'
import FileImageRender from '../file-image-render'
import FileTypeIcon from '../file-type-icon'
import {
downloadFile,
fileIsUploaded,
getFileAppearanceType,
getFileExtension,
@@ -140,7 +140,7 @@ const FileInAttachmentItem = ({
showDownloadAction && (
<ActionButton onClick={(e) => {
e.stopPropagation()
downloadFile(url || base64Url || '', name)
downloadUrl({ url: url || base64Url || '', fileName: name, target: '_blank' })
}}
>
<RiDownloadLine className="h-4 w-4" />

4
web/app/components/base/file-uploader/file-uploader-in-chat-input/file-image-item.tsx
View File

@@ -8,9 +8,9 @@ import Button from '@/app/components/base/button'
import { ReplayLine } from '@/app/components/base/icons/src/vender/other'
import ImagePreview from '@/app/components/base/image-uploader/image-preview'
import ProgressCircle from '@/app/components/base/progress-bar/progress-circle'
import { downloadUrl } from '@/utils/download'
import FileImageRender from '../file-image-render'
import {
downloadFile,
fileIsUploaded,
} from '../utils'
@@ -85,7 +85,7 @@ const FileImageItem = ({
className="absolute bottom-0.5 right-0.5 flex h-6 w-6 items-center justify-center rounded-lg bg-components-actionbar-bg shadow-md"
onClick={(e) => {
e.stopPropagation()
downloadFile(download_url || '', name)
downloadUrl({ url: download_url || '', fileName: name, target: '_blank' })
}}
>
<RiDownloadLine className="h-4 w-4 text-text-tertiary" />

4
web/app/components/base/file-uploader/file-uploader-in-chat-input/file-item.tsx
View File

@@ -12,10 +12,10 @@ import VideoPreview from '@/app/components/base/file-uploader/video-preview'
import { ReplayLine } from '@/app/components/base/icons/src/vender/other'
import ProgressCircle from '@/app/components/base/progress-bar/progress-circle'
import { cn } from '@/utils/classnames'
import { downloadUrl } from '@/utils/download'
import { formatFileSize } from '@/utils/format'
import FileTypeIcon from '../file-type-icon'
import {
downloadFile,
fileIsUploaded,
getFileAppearanceType,
getFileExtension,
@@ -100,7 +100,7 @@ const FileItem = ({
className="absolute -right-1 -top-1 hidden group-hover/file-item:flex"
onClick={(e) => {
e.stopPropagation()
downloadFile(download_url || '', name)
downloadUrl({ url: download_url || '', fileName: name, target: '_blank' })
}}
>
<RiDownloadLine className="h-3.5 w-3.5 text-text-tertiary" />

72
web/app/components/base/file-uploader/utils.spec.ts
View File

@@ -1,4 +1,3 @@
import type { MockInstance } from 'vitest'
import mime from 'mime'
import { SupportUploadFileTypes } from '@/app/components/workflow/types'
import { upload } from '@/service/base'
@@ -6,7 +5,6 @@ import { TransferMethod } from '@/types/app'
import { FILE_EXTS } from '../prompt-editor/constants'
import { FileAppearanceTypeEnum } from './types'
import {
downloadFile,
fileIsUploaded,
fileUpload,
getFileAppearanceType,
@@ -782,74 +780,4 @@ describe('file-uploader utils', () => {
} as any)).toBe(true)
})
})
describe('downloadFile', () => {
let mockAnchor: HTMLAnchorElement
let createElementMock: MockInstance
let appendChildMock: MockInstance
let removeChildMock: MockInstance
beforeEach(() => {
// Mock createElement and appendChild
mockAnchor = {
href: '',
download: '',
style: { display: '' },
target: '',
title: '',
click: vi.fn(),
} as unknown as HTMLAnchorElement
createElementMock = vi.spyOn(document, 'createElement').mockReturnValue(mockAnchor as any)
appendChildMock = vi.spyOn(document.body, 'appendChild').mockImplementation((node: Node) => {
return node
})
removeChildMock = vi.spyOn(document.body, 'removeChild').mockImplementation((node: Node) => {
return node
})
})
afterEach(() => {
vi.resetAllMocks()
})
it('should create and trigger download with correct attributes', () => {
const url = 'https://example.com/test.pdf'
const filename = 'test.pdf'
downloadFile(url, filename)
// Verify anchor element was created with correct properties
expect(createElementMock).toHaveBeenCalledWith('a')
expect(mockAnchor.href).toBe(url)
expect(mockAnchor.download).toBe(filename)
expect(mockAnchor.style.display).toBe('none')
expect(mockAnchor.target).toBe('_blank')
expect(mockAnchor.title).toBe(filename)
// Verify DOM operations
expect(appendChildMock).toHaveBeenCalledWith(mockAnchor)
expect(mockAnchor.click).toHaveBeenCalled()
expect(removeChildMock).toHaveBeenCalledWith(mockAnchor)
})
it('should handle empty filename', () => {
const url = 'https://example.com/test.pdf'
const filename = ''
downloadFile(url, filename)
expect(mockAnchor.download).toBe('')
expect(mockAnchor.title).toBe('')
})
it('should handle empty url', () => {
const url = ''
const filename = 'test.pdf'
downloadFile(url, filename)
expect(mockAnchor.href).toBe('')
})
})
})

12
web/app/components/base/file-uploader/utils.ts
View File

@@ -249,15 +249,3 @@ export const fileIsUploaded = (file: FileEntity) => {
if (file.transferMethod === TransferMethod.remote_url && file.progress === 100)
return true
}
export const downloadFile = (url: string, filename: string) => {
const anchor = document.createElement('a')
anchor.href = url
anchor.download = filename
anchor.style.display = 'none'
anchor.target = '_blank'
anchor.title = filename
document.body.appendChild(anchor)
anchor.click()
document.body.removeChild(anchor)
}

36
web/app/components/base/image-uploader/image-preview.tsx
View File

@@ -8,6 +8,7 @@ import { createPortal } from 'react-dom'
import { useHotkeys } from 'react-hotkeys-hook'
import Toast from '@/app/components/base/toast'
import Tooltip from '@/app/components/base/tooltip'
import { downloadUrl } from '@/utils/download'
type ImagePreviewProps = {
url: string
@@ -60,27 +61,14 @@ const ImagePreview: FC<ImagePreviewProps> = ({
const downloadImage = () => {
// Open in a new window, considering the case when the page is inside an iframe
if (url.startsWith('http') || url.startsWith('https')) {
const a = document.createElement('a')
a.href = url
a.target = '_blank'
a.download = title
a.click()
}
else if (url.startsWith('data:image')) {
// Base64 image
const a = document.createElement('a')
a.href = url
a.target = '_blank'
a.download = title
a.click()
}
else {
Toast.notify({
type: 'error',
message: `Unable to open image: ${url}`,
})
if (url.startsWith('http') || url.startsWith('https') || url.startsWith('data:image')) {
downloadUrl({ url, fileName: title, target: '_blank' })
return
}
Toast.notify({
type: 'error',
message: `Unable to open image: ${url}`,
})
}
const zoomIn = () => {
@@ -135,12 +123,7 @@ const ImagePreview: FC<ImagePreviewProps> = ({
catch (err) {
console.error('Failed to copy image:', err)
const link = document.createElement('a')
link.href = url
link.download = `${title}.png`
document.body.appendChild(link)
link.click()
document.body.removeChild(link)
downloadUrl({ url, fileName: `${title}.png` })
Toast.notify({
type: 'info',
@@ -215,6 +198,7 @@ const ImagePreview: FC<ImagePreviewProps> = ({
tabIndex={-1}
>
{ }
{/* eslint-disable-next-line next/no-img-element */}
<img
ref={imgRef}
alt={title}

10
web/app/components/base/qrcode/index.tsx
View File

@@ -8,6 +8,7 @@ import { useEffect, useRef, useState } from 'react'
import { useTranslation } from 'react-i18next'
import ActionButton from '@/app/components/base/action-button'
import Tooltip from '@/app/components/base/tooltip'
import { downloadUrl } from '@/utils/download'
type Props = {
content: string
@@ -40,11 +41,10 @@ const ShareQRCode = ({ content }: Props) => {
}, [isShow])
const downloadQR = () => {
const canvas = document.getElementsByTagName('canvas')[0]
const link = document.createElement('a')
link.download = 'qrcode.png'
link.href = canvas.toDataURL()
link.click()
const canvas = qrCodeRef.current?.querySelector('canvas')
if (!(canvas instanceof HTMLCanvasElement))
return
downloadUrl({ url: canvas.toDataURL(), fileName: 'qrcode.png' })
}
const handlePanelClick = (event: React.MouseEvent) => {

11
web/app/components/datasets/common/document-status-with-action/index-failed.spec.tsx
View File

@@ -179,8 +179,10 @@ describe('RetryButton (IndexFailed)', () => {
}, false),
)
// Delay the response to test loading state
mockRetryErrorDocs.mockImplementation(() => new Promise(resolve => setTimeout(() => resolve({ result: 'success' }), 100)))
let resolveRetry: ((value: { result: 'success' }) => void) | undefined
mockRetryErrorDocs.mockImplementation(() => new Promise((resolve) => {
resolveRetry = resolve
}))
render(<RetryButton datasetId="test-dataset" />)
@@ -193,6 +195,11 @@ describe('RetryButton (IndexFailed)', () => {
expect(button).toHaveClass('cursor-not-allowed')
expect(button).toHaveClass('text-text-disabled')
})
resolveRetry?.({ result: 'success' })
await waitFor(() => {
expect(mockRefetch).toHaveBeenCalled()
})
})
})

13
web/app/components/datasets/create-from-pipeline/list/template-card/index.spec.tsx
View File

@@ -23,9 +23,10 @@ vi.mock('@/app/components/base/toast', () => ({
},
}))
// Mock downloadFile utility
vi.mock('@/utils/format', () => ({
downloadFile: vi.fn(),
// Mock download utilities
vi.mock('@/utils/download', () => ({
downloadBlob: vi.fn(),
downloadUrl: vi.fn(),
}))
// Capture Confirm callbacks
@@ -502,8 +503,8 @@ describe('TemplateCard', () => {
})
})
it('should call downloadFile on successful export', async () => {
const { downloadFile } = await import('@/utils/format')
it('should call downloadBlob on successful export', async () => {
const { downloadBlob } = await import('@/utils/download')
mockExportPipelineDSL.mockImplementation((_id, callbacks) => {
callbacks.onSuccess({ data: 'yaml_content' })
return Promise.resolve()
@@ -514,7 +515,7 @@ describe('TemplateCard', () => {
fireEvent.click(exportButton)
await waitFor(() => {
expect(downloadFile).toHaveBeenCalledWith(expect.objectContaining({
expect(downloadBlob).toHaveBeenCalledWith(expect.objectContaining({
fileName: 'Test Pipeline.pipeline',
}))
})

7
web/app/components/datasets/create-from-pipeline/list/template-card/index.tsx
View File

@@ -16,7 +16,7 @@ import {
useInvalidCustomizedTemplateList,
usePipelineTemplateById,
} from '@/service/use-pipeline'
import { downloadFile } from '@/utils/format'
import { downloadBlob } from '@/utils/download'
import Actions from './actions'
import Content from './content'
import Details from './details'
@@ -108,10 +108,7 @@ const TemplateCard = ({
await exportPipelineDSL(pipeline.id, {
onSuccess: (res) => {
const blob = new Blob([res.data], { type: 'application/yaml' })
downloadFile({
data: blob,
fileName: `${pipeline.name}.pipeline`,
})
downloadBlob({ data: blob, fileName: `${pipeline.name}.pipeline` })
Toast.notify({
type: 'success',
message: t('exportDSL.successTip', { ns: 'datasetPipeline' }),

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