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Merge remote-tracking branch 'origin/main' into feat/trigger

This commit is contained in:
lyzno1 2025-09-30 08:00:16 +08:00
parent 8106df1d7d f79d8baf63
commit 4a6903fdb4
122 changed files with 1962 additions and 451 deletions
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4
api/README.md
View File

@ -80,10 +80,10 @@
1. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
```bash
uv run celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation
uv run celery -A app.celery worker -P gevent -c 2 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation
```
Addition, if you want to debug the celery scheduled tasks, you can use the following command in another terminal:
Additionally, if you want to debug the celery scheduled tasks, you can run the following command in another terminal to start the beat service:
```bash
uv run celery -A app.celery beat

18
api/controllers/console/datasets/datasets.py
View File

@ -1,4 +1,5 @@
import flask_restx
from typing import Any, cast
from flask import request
from flask_login import current_user
from flask_restx import Resource, fields, marshal, marshal_with, reqparse
@ -31,12 +32,13 @@ from fields.dataset_fields import dataset_detail_fields, dataset_query_detail_fi
from fields.document_fields import document_status_fields
from libs.login import login_required
from models import ApiToken, Dataset, Document, DocumentSegment, UploadFile
from models.account import Account
from models.dataset import DatasetPermissionEnum
from models.provider_ids import ModelProviderID
from services.dataset_service import DatasetPermissionService, DatasetService, DocumentService
def _validate_name(name):
def _validate_name(name: str) -> str:
if not name or len(name) < 1 or len(name) > 40:
raise ValueError("Name must be between 1 to 40 characters.")
return name
@ -92,7 +94,7 @@ class DatasetListApi(Resource):
for embedding_model in embedding_models:
model_names.append(f"{embedding_model.model}:{embedding_model.provider.provider}")
data = marshal(datasets, dataset_detail_fields)
data = cast(list[dict[str, Any]], marshal(datasets, dataset_detail_fields))
for item in data:
# convert embedding_model_provider to plugin standard format
if item["indexing_technique"] == "high_quality" and item["embedding_model_provider"]:
@ -192,7 +194,7 @@ class DatasetListApi(Resource):
name=args["name"],
description=args["description"],
indexing_technique=args["indexing_technique"],
account=current_user,
account=cast(Account, current_user),
permission=DatasetPermissionEnum.ONLY_ME,
provider=args["provider"],
external_knowledge_api_id=args["external_knowledge_api_id"],
@ -224,7 +226,7 @@ class DatasetApi(Resource):
DatasetService.check_dataset_permission(dataset, current_user)
except services.errors.account.NoPermissionError as e:
raise Forbidden(str(e))
data = marshal(dataset, dataset_detail_fields)
data = cast(dict[str, Any], marshal(dataset, dataset_detail_fields))
if dataset.indexing_technique == "high_quality":
if dataset.embedding_model_provider:
provider_id = ModelProviderID(dataset.embedding_model_provider)
@ -369,7 +371,7 @@ class DatasetApi(Resource):
if dataset is None:
raise NotFound("Dataset not found.")
result_data = marshal(dataset, dataset_detail_fields)
result_data = cast(dict[str, Any], marshal(dataset, dataset_detail_fields))
tenant_id = current_user.current_tenant_id
if data.get("partial_member_list") and data.get("permission") == "partial_members":
@ -688,7 +690,7 @@ class DatasetApiKeyApi(Resource):
)
if current_key_count >= self.max_keys:
flask_restx.abort(
api.abort(
400,
message=f"Cannot create more than {self.max_keys} API keys for this resource type.",
code="max_keys_exceeded",
@ -733,7 +735,7 @@ class DatasetApiDeleteApi(Resource):
)
if key is None:
flask_restx.abort(404, message="API key not found")
api.abort(404, message="API key not found")
db.session.query(ApiToken).where(ApiToken.id == api_key_id).delete()
db.session.commit()

15
api/controllers/console/datasets/datasets_document.py
View File

@ -55,6 +55,7 @@ from fields.document_fields import (
from libs.datetime_utils import naive_utc_now
from libs.login import login_required
from models import Dataset, DatasetProcessRule, Document, DocumentSegment, UploadFile
from models.account import Account
from models.dataset import DocumentPipelineExecutionLog
from services.dataset_service import DatasetService, DocumentService
from services.entities.knowledge_entities.knowledge_entities import KnowledgeConfig
@ -418,7 +419,9 @@ class DatasetInitApi(Resource):
try:
dataset, documents, batch = DocumentService.save_document_without_dataset_id(
tenant_id=current_user.current_tenant_id, knowledge_config=knowledge_config, account=current_user
tenant_id=current_user.current_tenant_id,
knowledge_config=knowledge_config,
account=cast(Account, current_user),
)
except ProviderTokenNotInitError as ex:
raise ProviderNotInitializeError(ex.description)
@ -452,7 +455,7 @@ class DocumentIndexingEstimateApi(DocumentResource):
raise DocumentAlreadyFinishedError()
data_process_rule = document.dataset_process_rule
data_process_rule_dict = data_process_rule.to_dict()
data_process_rule_dict = data_process_rule.to_dict() if data_process_rule else {}
response = {"tokens": 0, "total_price": 0, "currency": "USD", "total_segments": 0, "preview": []}
@ -514,7 +517,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
if not documents:
return {"tokens": 0, "total_price": 0, "currency": "USD", "total_segments": 0, "preview": []}, 200
data_process_rule = documents[0].dataset_process_rule
data_process_rule_dict = data_process_rule.to_dict()
data_process_rule_dict = data_process_rule.to_dict() if data_process_rule else {}
extract_settings = []
for document in documents:
if document.indexing_status in {"completed", "error"}:
@ -753,7 +756,7 @@ class DocumentApi(DocumentResource):
}
else:
dataset_process_rules = DatasetService.get_process_rules(dataset_id)
document_process_rules = document.dataset_process_rule.to_dict()
document_process_rules = document.dataset_process_rule.to_dict() if document.dataset_process_rule else {}
data_source_info = document.data_source_detail_dict
response = {
"id": document.id,
@ -1073,7 +1076,9 @@ class DocumentRenameApi(DocumentResource):
if not current_user.is_dataset_editor:
raise Forbidden()
dataset = DatasetService.get_dataset(dataset_id)
DatasetService.check_dataset_operator_permission(current_user, dataset)
if not dataset:
raise NotFound("Dataset not found.")
DatasetService.check_dataset_operator_permission(cast(Account, current_user), dataset)
parser = reqparse.RequestParser()
parser.add_argument("name", type=str, required=True, nullable=False, location="json")
args = parser.parse_args()

18
api/controllers/console/datasets/datasets_segments.py
View File

@ -392,7 +392,12 @@ class DatasetDocumentSegmentBatchImportApi(Resource):
# send batch add segments task
redis_client.setnx(indexing_cache_key, "waiting")
batch_create_segment_to_index_task.delay(
str(job_id), upload_file_id, dataset_id, document_id, current_user.current_tenant_id, current_user.id
str(job_id),
upload_file_id,
dataset_id,
document_id,
current_user.current_tenant_id,
current_user.id,
)
except Exception as e:
return {"error": str(e)}, 500
@ -468,7 +473,8 @@ class ChildChunkAddApi(Resource):
parser.add_argument("content", type=str, required=True, nullable=False, location="json")
args = parser.parse_args()
try:
child_chunk = SegmentService.create_child_chunk(args.get("content"), segment, document, dataset)
content = args["content"]
child_chunk = SegmentService.create_child_chunk(content, segment, document, dataset)
except ChildChunkIndexingServiceError as e:
raise ChildChunkIndexingError(str(e))
return {"data": marshal(child_chunk, child_chunk_fields)}, 200
@ -557,7 +563,8 @@ class ChildChunkAddApi(Resource):
parser.add_argument("chunks", type=list, required=True, nullable=False, location="json")
args = parser.parse_args()
try:
chunks = [ChildChunkUpdateArgs(**chunk) for chunk in args.get("chunks")]
chunks_data = args["chunks"]
chunks = [ChildChunkUpdateArgs(**chunk) for chunk in chunks_data]
child_chunks = SegmentService.update_child_chunks(chunks, segment, document, dataset)
except ChildChunkIndexingServiceError as e:
raise ChildChunkIndexingError(str(e))
@ -674,9 +681,8 @@ class ChildChunkUpdateApi(Resource):
parser.add_argument("content", type=str, required=True, nullable=False, location="json")
args = parser.parse_args()
try:
child_chunk = SegmentService.update_child_chunk(
args.get("content"), child_chunk, segment, document, dataset
)
content = args["content"]
child_chunk = SegmentService.update_child_chunk(content, child_chunk, segment, document, dataset)
except ChildChunkIndexingServiceError as e:
raise ChildChunkIndexingError(str(e))
return {"data": marshal(child_chunk, child_chunk_fields)}, 200

7
api/controllers/console/datasets/external.py
View File

@ -1,3 +1,5 @@
from typing import cast
from flask import request
from flask_login import current_user
from flask_restx import Resource, fields, marshal, reqparse
@ -9,13 +11,14 @@ from controllers.console.datasets.error import DatasetNameDuplicateError
from controllers.console.wraps import account_initialization_required, setup_required
from fields.dataset_fields import dataset_detail_fields
from libs.login import login_required
from models.account import Account
from services.dataset_service import DatasetService
from services.external_knowledge_service import ExternalDatasetService
from services.hit_testing_service import HitTestingService
from services.knowledge_service import ExternalDatasetTestService
def _validate_name(name):
def _validate_name(name: str) -> str:
if not name or len(name) < 1 or len(name) > 100:
raise ValueError("Name must be between 1 to 100 characters.")
return name
@ -274,7 +277,7 @@ class ExternalKnowledgeHitTestingApi(Resource):
response = HitTestingService.external_retrieve(
dataset=dataset,
query=args["query"],
account=current_user,
account=cast(Account, current_user),
external_retrieval_model=args["external_retrieval_model"],
metadata_filtering_conditions=args["metadata_filtering_conditions"],
)

6
api/controllers/console/datasets/hit_testing_base.py
View File

@ -1,10 +1,11 @@
import logging
from typing import cast
from flask_login import current_user
from flask_restx import marshal, reqparse
from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
import services.dataset_service
import services
from controllers.console.app.error import (
CompletionRequestError,
ProviderModelCurrentlyNotSupportError,
@ -20,6 +21,7 @@ from core.errors.error import (
)
from core.model_runtime.errors.invoke import InvokeError
from fields.hit_testing_fields import hit_testing_record_fields
from models.account import Account
from services.dataset_service import DatasetService
from services.hit_testing_service import HitTestingService
@ -59,7 +61,7 @@ class DatasetsHitTestingBase:
response = HitTestingService.retrieve(
dataset=dataset,
query=args["query"],
account=current_user,
account=cast(Account, current_user),
retrieval_model=args["retrieval_model"],
external_retrieval_model=args["external_retrieval_model"],
limit=10,

3
api/controllers/console/datasets/metadata.py
View File

@ -62,6 +62,7 @@ class DatasetMetadataApi(Resource):
parser = reqparse.RequestParser()
parser.add_argument("name", type=str, required=True, nullable=False, location="json")
args = parser.parse_args()
name = args["name"]
dataset_id_str = str(dataset_id)
metadata_id_str = str(metadata_id)
@ -70,7 +71,7 @@ class DatasetMetadataApi(Resource):
raise NotFound("Dataset not found.")
DatasetService.check_dataset_permission(dataset, current_user)
metadata = MetadataService.update_metadata_name(dataset_id_str, metadata_id_str, args.get("name"))
metadata = MetadataService.update_metadata_name(dataset_id_str, metadata_id_str, name)
return metadata, 200
@setup_required

8
api/controllers/console/datasets/rag_pipeline/rag_pipeline.py
View File

@ -20,13 +20,13 @@ from services.rag_pipeline.rag_pipeline import RagPipelineService
logger = logging.getLogger(__name__)
def _validate_name(name):
def _validate_name(name: str) -> str:
if not name or len(name) < 1 or len(name) > 40:
raise ValueError("Name must be between 1 to 40 characters.")
return name
def _validate_description_length(description):
def _validate_description_length(description: str) -> str:
if len(description) > 400:
raise ValueError("Description cannot exceed 400 characters.")
return description
@ -76,7 +76,7 @@ class CustomizedPipelineTemplateApi(Resource):
)
parser.add_argument(
"description",
type=str,
type=_validate_description_length,
nullable=True,
required=False,
default="",
@ -133,7 +133,7 @@ class PublishCustomizedPipelineTemplateApi(Resource):
)
parser.add_argument(
"description",
type=str,
type=_validate_description_length,
nullable=True,
required=False,
default="",

16
api/controllers/console/datasets/rag_pipeline/rag_pipeline_datasets.py
View File

@ -1,5 +1,5 @@
from flask_login import current_user # type: ignore # type: ignore
from flask_restx import Resource, marshal, reqparse # type: ignore
from flask_login import current_user
from flask_restx import Resource, marshal, reqparse
from sqlalchemy.orm import Session
from werkzeug.exceptions import Forbidden
@ -20,18 +20,6 @@ from services.entities.knowledge_entities.rag_pipeline_entities import IconInfo,
from services.rag_pipeline.rag_pipeline_dsl_service import RagPipelineDslService
def _validate_name(name):
if not name or len(name) < 1 or len(name) > 40:
raise ValueError("Name must be between 1 to 40 characters.")
return name
def _validate_description_length(description):
if len(description) > 400:
raise ValueError("Description cannot exceed 400 characters.")
return description
@console_ns.route("/rag/pipeline/dataset")
class CreateRagPipelineDatasetApi(Resource):
@setup_required

36
api/controllers/console/datasets/rag_pipeline/rag_pipeline_draft_variable.py
View File

@ -1,5 +1,5 @@
import logging
from typing import Any, NoReturn
from typing import NoReturn
from flask import Response
from flask_restx import Resource, fields, inputs, marshal, marshal_with, reqparse
@ -11,14 +11,12 @@ from controllers.console.app.error import (
DraftWorkflowNotExist,
)
from controllers.console.app.workflow_draft_variable import (
_WORKFLOW_DRAFT_VARIABLE_FIELDS,
_WORKFLOW_DRAFT_VARIABLE_WITHOUT_VALUE_FIELDS,
_WORKFLOW_DRAFT_VARIABLE_FIELDS, # type: ignore[private-usage]
_WORKFLOW_DRAFT_VARIABLE_WITHOUT_VALUE_FIELDS, # type: ignore[private-usage]
)
from controllers.console.datasets.wraps import get_rag_pipeline
from controllers.console.wraps import account_initialization_required, setup_required
from controllers.web.error import InvalidArgumentError, NotFoundError
from core.variables.segment_group import SegmentGroup
from core.variables.segments import ArrayFileSegment, FileSegment, Segment
from core.variables.types import SegmentType
from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID, SYSTEM_VARIABLE_NODE_ID
from extensions.ext_database import db
@ -34,32 +32,6 @@ from services.workflow_draft_variable_service import WorkflowDraftVariableList,
logger = logging.getLogger(__name__)
def _convert_values_to_json_serializable_object(value: Segment) -> Any:
if isinstance(value, FileSegment):
return value.value.model_dump()
elif isinstance(value, ArrayFileSegment):
return [i.model_dump() for i in value.value]
elif isinstance(value, SegmentGroup):
return [_convert_values_to_json_serializable_object(i) for i in value.value]
else:
return value.value
def _serialize_var_value(variable: WorkflowDraftVariable) -> Any:
value = variable.get_value()
# create a copy of the value to avoid affecting the model cache.
value = value.model_copy(deep=True)
# Refresh the url signature before returning it to client.
if isinstance(value, FileSegment):
file = value.value
file.remote_url = file.generate_url()
elif isinstance(value, ArrayFileSegment):
files = value.value
for file in files:
file.remote_url = file.generate_url()
return _convert_values_to_json_serializable_object(value)
def _create_pagination_parser():
parser = reqparse.RequestParser()
parser.add_argument(
@ -104,7 +76,7 @@ def _api_prerequisite(f):
@account_initialization_required
@get_rag_pipeline
def wrapper(*args, **kwargs):
if not isinstance(current_user, Account) or not current_user.is_editor:
if not isinstance(current_user, Account) or not current_user.has_edit_permission:
raise Forbidden()
return f(*args, **kwargs)

12
api/controllers/console/tag/tags.py
View File

@ -3,7 +3,7 @@ from flask_login import current_user
from flask_restx import Resource, marshal_with, reqparse
from werkzeug.exceptions import Forbidden
from controllers.console import api
from controllers.console import console_ns
from controllers.console.wraps import account_initialization_required, setup_required
from fields.tag_fields import dataset_tag_fields
from libs.login import login_required
@ -17,6 +17,7 @@ def _validate_name(name):
return name
@console_ns.route("/tags")
class TagListApi(Resource):
@setup_required
@login_required
@ -52,6 +53,7 @@ class TagListApi(Resource):
return response, 200
@console_ns.route("/tags/<uuid:tag_id>")
class TagUpdateDeleteApi(Resource):
@setup_required
@login_required
@ -89,6 +91,7 @@ class TagUpdateDeleteApi(Resource):
return 204
@console_ns.route("/tag-bindings/create")
class TagBindingCreateApi(Resource):
@setup_required
@login_required
@ -114,6 +117,7 @@ class TagBindingCreateApi(Resource):
return {"result": "success"}, 200
@console_ns.route("/tag-bindings/remove")
class TagBindingDeleteApi(Resource):
@setup_required
@login_required
@ -133,9 +137,3 @@ class TagBindingDeleteApi(Resource):
TagService.delete_tag_binding(args)
return {"result": "success"}, 200
api.add_resource(TagListApi, "/tags")
api.add_resource(TagUpdateDeleteApi, "/tags/<uuid:tag_id>")
api.add_resource(TagBindingCreateApi, "/tag-bindings/create")
api.add_resource(TagBindingDeleteApi, "/tag-bindings/remove")

66
api/controllers/service_api/dataset/dataset.py
View File

@ -1,10 +1,10 @@
from typing import Literal
from typing import Any, Literal, cast
from flask import request
from flask_restx import marshal, reqparse
from werkzeug.exceptions import Forbidden, NotFound
import services.dataset_service
import services
from controllers.service_api import service_api_ns
from controllers.service_api.dataset.error import DatasetInUseError, DatasetNameDuplicateError, InvalidActionError
from controllers.service_api.wraps import (
@ -254,19 +254,21 @@ class DatasetListApi(DatasetApiResource):
"""Resource for creating datasets."""
args = dataset_create_parser.parse_args()
if args.get("embedding_model_provider"):
DatasetService.check_embedding_model_setting(
tenant_id, args.get("embedding_model_provider"), args.get("embedding_model")
)
embedding_model_provider = args.get("embedding_model_provider")
embedding_model = args.get("embedding_model")
if embedding_model_provider and embedding_model:
DatasetService.check_embedding_model_setting(tenant_id, embedding_model_provider, embedding_model)
retrieval_model = args.get("retrieval_model")
if (
args.get("retrieval_model")
and args.get("retrieval_model").get("reranking_model")
and args.get("retrieval_model").get("reranking_model").get("reranking_provider_name")
retrieval_model
and retrieval_model.get("reranking_model")
and retrieval_model.get("reranking_model").get("reranking_provider_name")
):
DatasetService.check_reranking_model_setting(
tenant_id,
args.get("retrieval_model").get("reranking_model").get("reranking_provider_name"),
args.get("retrieval_model").get("reranking_model").get("reranking_model_name"),
retrieval_model.get("reranking_model").get("reranking_provider_name"),
retrieval_model.get("reranking_model").get("reranking_model_name"),
)
try:
@ -317,7 +319,7 @@ class DatasetApi(DatasetApiResource):
DatasetService.check_dataset_permission(dataset, current_user)
except services.errors.account.NoPermissionError as e:
raise Forbidden(str(e))
data = marshal(dataset, dataset_detail_fields)
data = cast(dict[str, Any], marshal(dataset, dataset_detail_fields))
# check embedding setting
provider_manager = ProviderManager()
assert isinstance(current_user, Account)
@ -331,8 +333,8 @@ class DatasetApi(DatasetApiResource):
for embedding_model in embedding_models:
model_names.append(f"{embedding_model.model}:{embedding_model.provider.provider}")
if data["indexing_technique"] == "high_quality":
item_model = f"{data['embedding_model']}:{data['embedding_model_provider']}"
if data.get("indexing_technique") == "high_quality":
item_model = f"{data.get('embedding_model')}:{data.get('embedding_model_provider')}"
if item_model in model_names:
data["embedding_available"] = True
else:
@ -341,7 +343,9 @@ class DatasetApi(DatasetApiResource):
data["embedding_available"] = True
# force update search method to keyword_search if indexing_technique is economic
data["retrieval_model_dict"]["search_method"] = "keyword_search"
retrieval_model_dict = data.get("retrieval_model_dict")
if retrieval_model_dict:
retrieval_model_dict["search_method"] = "keyword_search"
if data.get("permission") == "partial_members":
part_users_list = DatasetPermissionService.get_dataset_partial_member_list(dataset_id_str)
@ -372,19 +376,24 @@ class DatasetApi(DatasetApiResource):
data = request.get_json()
# check embedding model setting
if data.get("indexing_technique") == "high_quality" or data.get("embedding_model_provider"):
DatasetService.check_embedding_model_setting(
dataset.tenant_id, data.get("embedding_model_provider"), data.get("embedding_model")
)
embedding_model_provider = data.get("embedding_model_provider")
embedding_model = data.get("embedding_model")
if data.get("indexing_technique") == "high_quality" or embedding_model_provider:
if embedding_model_provider and embedding_model:
DatasetService.check_embedding_model_setting(
dataset.tenant_id, embedding_model_provider, embedding_model
)
retrieval_model = data.get("retrieval_model")
if (
data.get("retrieval_model")
and data.get("retrieval_model").get("reranking_model")
and data.get("retrieval_model").get("reranking_model").get("reranking_provider_name")
retrieval_model
and retrieval_model.get("reranking_model")
and retrieval_model.get("reranking_model").get("reranking_provider_name")
):
DatasetService.check_reranking_model_setting(
dataset.tenant_id,
data.get("retrieval_model").get("reranking_model").get("reranking_provider_name"),
data.get("retrieval_model").get("reranking_model").get("reranking_model_name"),
retrieval_model.get("reranking_model").get("reranking_provider_name"),
retrieval_model.get("reranking_model").get("reranking_model_name"),
)
# The role of the current user in the ta table must be admin, owner, editor, or dataset_operator
@ -397,7 +406,7 @@ class DatasetApi(DatasetApiResource):
if dataset is None:
raise NotFound("Dataset not found.")
result_data = marshal(dataset, dataset_detail_fields)
result_data = cast(dict[str, Any], marshal(dataset, dataset_detail_fields))
assert isinstance(current_user, Account)
tenant_id = current_user.current_tenant_id
@ -591,9 +600,10 @@ class DatasetTagsApi(DatasetApiResource):
args = tag_update_parser.parse_args()
args["type"] = "knowledge"
tag = TagService.update_tags(args, args.get("tag_id"))
tag_id = args["tag_id"]
tag = TagService.update_tags(args, tag_id)
binding_count = TagService.get_tag_binding_count(args.get("tag_id"))
binding_count = TagService.get_tag_binding_count(tag_id)
response = {"id": tag.id, "name": tag.name, "type": tag.type, "binding_count": binding_count}
@ -616,7 +626,7 @@ class DatasetTagsApi(DatasetApiResource):
if not current_user.has_edit_permission:
raise Forbidden()
args = tag_delete_parser.parse_args()
TagService.delete_tag(args.get("tag_id"))
TagService.delete_tag(args["tag_id"])
return 204

31
api/controllers/service_api/dataset/document.py
View File

@ -108,19 +108,21 @@ class DocumentAddByTextApi(DatasetApiResource):
if text is None or name is None:
raise ValueError("Both 'text' and 'name' must be non-null values.")
if args.get("embedding_model_provider"):
DatasetService.check_embedding_model_setting(
tenant_id, args.get("embedding_model_provider"), args.get("embedding_model")
)
embedding_model_provider = args.get("embedding_model_provider")
embedding_model = args.get("embedding_model")
if embedding_model_provider and embedding_model:
DatasetService.check_embedding_model_setting(tenant_id, embedding_model_provider, embedding_model)
retrieval_model = args.get("retrieval_model")
if (
args.get("retrieval_model")
and args.get("retrieval_model").get("reranking_model")
and args.get("retrieval_model").get("reranking_model").get("reranking_provider_name")
retrieval_model
and retrieval_model.get("reranking_model")
and retrieval_model.get("reranking_model").get("reranking_provider_name")
):
DatasetService.check_reranking_model_setting(
tenant_id,
args.get("retrieval_model").get("reranking_model").get("reranking_provider_name"),
args.get("retrieval_model").get("reranking_model").get("reranking_model_name"),
retrieval_model.get("reranking_model").get("reranking_provider_name"),
retrieval_model.get("reranking_model").get("reranking_model_name"),
)
if not current_user:
@ -187,15 +189,16 @@ class DocumentUpdateByTextApi(DatasetApiResource):
if not dataset:
raise ValueError("Dataset does not exist.")
retrieval_model = args.get("retrieval_model")
if (
args.get("retrieval_model")
and args.get("retrieval_model").get("reranking_model")
and args.get("retrieval_model").get("reranking_model").get("reranking_provider_name")
retrieval_model
and retrieval_model.get("reranking_model")
and retrieval_model.get("reranking_model").get("reranking_provider_name")
):
DatasetService.check_reranking_model_setting(
tenant_id,
args.get("retrieval_model").get("reranking_model").get("reranking_provider_name"),
args.get("retrieval_model").get("reranking_model").get("reranking_model_name"),
retrieval_model.get("reranking_model").get("reranking_provider_name"),
retrieval_model.get("reranking_model").get("reranking_model_name"),
)
# indexing_technique is already set in dataset since this is an update

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

@ -106,7 +106,7 @@ class DatasetMetadataServiceApi(DatasetApiResource):
raise NotFound("Dataset not found.")
DatasetService.check_dataset_permission(dataset, current_user)
metadata = MetadataService.update_metadata_name(dataset_id_str, metadata_id_str, args.get("name"))
metadata = MetadataService.update_metadata_name(dataset_id_str, metadata_id_str, args["name"])
return marshal(metadata, dataset_metadata_fields), 200
@service_api_ns.doc("delete_dataset_metadata")

4
api/core/tools/utils/dataset_retriever/dataset_retriever_base_tool.py
View File

@ -18,6 +18,10 @@ class DatasetRetrieverBaseTool(BaseModel, ABC):
retriever_from: str
model_config = ConfigDict(arbitrary_types_allowed=True)
def run(self, query: str) -> str:
"""Use the tool."""
return self._run(query)
@abstractmethod
def _run(self, query: str) -> str:
"""Use the tool.

2
api/core/tools/utils/dataset_retriever_tool.py
View File

@ -124,7 +124,7 @@ class DatasetRetrieverTool(Tool):
yield self.create_text_message(text="please input query")
else:
# invoke dataset retriever tool
result = self.retrieval_tool._run(query=query)
result = self.retrieval_tool.run(query=query)
yield self.create_text_message(text=result)
def validate_credentials(

72
api/core/tools/utils/parser.py
View File

@ -2,6 +2,7 @@ import re
from json import dumps as json_dumps
from json import loads as json_loads
from json.decoder import JSONDecodeError
from typing import Any
from flask import request
from requests import get
@ -127,34 +128,34 @@ class ApiBasedToolSchemaParser:
if "allOf" in prop_dict:
del prop_dict["allOf"]
# parse body parameters
if "schema" in interface["operation"]["requestBody"]["content"][content_type]:
body_schema = interface["operation"]["requestBody"]["content"][content_type]["schema"]
required = body_schema.get("required", [])
properties = body_schema.get("properties", {})
for name, property in properties.items():
tool = ToolParameter(
name=name,
label=I18nObject(en_US=name, zh_Hans=name),
human_description=I18nObject(
en_US=property.get("description", ""), zh_Hans=property.get("description", "")
),
type=ToolParameter.ToolParameterType.STRING,
required=name in required,
form=ToolParameter.ToolParameterForm.LLM,
llm_description=property.get("description", ""),
default=property.get("default", None),
placeholder=I18nObject(
en_US=property.get("description", ""), zh_Hans=property.get("description", "")
),
)
# parse body parameters
if "schema" in interface["operation"]["requestBody"]["content"][content_type]:
body_schema = interface["operation"]["requestBody"]["content"][content_type]["schema"]
required = body_schema.get("required", [])
properties = body_schema.get("properties", {})
for name, property in properties.items():
tool = ToolParameter(
name=name,
label=I18nObject(en_US=name, zh_Hans=name),
human_description=I18nObject(
en_US=property.get("description", ""), zh_Hans=property.get("description", "")
),
type=ToolParameter.ToolParameterType.STRING,
required=name in required,
form=ToolParameter.ToolParameterForm.LLM,
llm_description=property.get("description", ""),
default=property.get("default", None),
placeholder=I18nObject(
en_US=property.get("description", ""), zh_Hans=property.get("description", "")
),
)
# check if there is a type
typ = ApiBasedToolSchemaParser._get_tool_parameter_type(property)
if typ:
tool.type = typ
# check if there is a type
typ = ApiBasedToolSchemaParser._get_tool_parameter_type(property)
if typ:
tool.type = typ
parameters.append(tool)
parameters.append(tool)
# check if parameters is duplicated
parameters_count = {}
@ -241,7 +242,9 @@ class ApiBasedToolSchemaParser:
return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
@staticmethod
def parse_swagger_to_openapi(swagger: dict, extra_info: dict | None = None, warning: dict | None = None):
def parse_swagger_to_openapi(
swagger: dict, extra_info: dict | None = None, warning: dict | None = None
) -> dict[str, Any]:
warning = warning or {}
"""
parse swagger to openapi
@ -257,7 +260,7 @@ class ApiBasedToolSchemaParser:
if len(servers) == 0:
raise ToolApiSchemaError("No server found in the swagger yaml.")
openapi = {
converted_openapi: dict[str, Any] = {
"openapi": "3.0.0",
"info": {
"title": info.get("title", "Swagger"),
@ -275,7 +278,7 @@ class ApiBasedToolSchemaParser:
# convert paths
for path, path_item in swagger["paths"].items():
openapi["paths"][path] = {}
converted_openapi["paths"][path] = {}
for method, operation in path_item.items():
if "operationId" not in operation:
raise ToolApiSchemaError(f"No operationId found in operation {method} {path}.")
@ -286,7 +289,7 @@ class ApiBasedToolSchemaParser:
if warning is not None:
warning["missing_summary"] = f"No summary or description found in operation {method} {path}."
openapi["paths"][path][method] = {
converted_openapi["paths"][path][method] = {
"operationId": operation["operationId"],
"summary": operation.get("summary", ""),
"description": operation.get("description", ""),
@ -295,13 +298,14 @@ class ApiBasedToolSchemaParser:
}
if "requestBody" in operation:
openapi["paths"][path][method]["requestBody"] = operation["requestBody"]
converted_openapi["paths"][path][method]["requestBody"] = operation["requestBody"]
# convert definitions
for name, definition in swagger["definitions"].items():
openapi["components"]["schemas"][name] = definition
if "definitions" in swagger:
for name, definition in swagger["definitions"].items():
converted_openapi["components"]["schemas"][name] = definition
return openapi
return converted_openapi
@staticmethod
def parse_openai_plugin_json_to_tool_bundle(

19
api/core/workflow/entities/variable_pool.py
View File

@ -184,11 +184,22 @@ class VariablePool(BaseModel):
"""Extract the actual value from an ObjectSegment."""
return obj.value if isinstance(obj, ObjectSegment) else obj
def _get_nested_attribute(self, obj: Mapping[str, Any], attr: str):
"""Get a nested attribute from a dictionary-like object."""
if not isinstance(obj, dict):
def _get_nested_attribute(self, obj: Mapping[str, Any], attr: str) -> Segment | None:
"""
Get a nested attribute from a dictionary-like object.
Args:
obj: The dictionary-like object to search.
attr: The key to look up.
Returns:
Segment | None:
The corresponding Segment built from the attribute value if the key exists,
otherwise None.
"""
if not isinstance(obj, dict) or attr not in obj:
return None
return obj.get(attr)
return variable_factory.build_segment(obj.get(attr))
def remove(self, selector: Sequence[str], /):
"""

49
api/core/workflow/nodes/iteration/iteration_node.py
View File

@ -10,6 +10,8 @@ from typing_extensions import TypeIs
from core.variables import IntegerVariable, NoneSegment
from core.variables.segments import ArrayAnySegment, ArraySegment
from core.variables.variables import VariableUnion
from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID
from core.workflow.entities import VariablePool
from core.workflow.enums import (
ErrorStrategy,
@ -217,6 +219,13 @@ class IterationNode(Node):
graph_engine=graph_engine,
)
# Sync conversation variables after each iteration completes
self._sync_conversation_variables_from_snapshot(
self._extract_conversation_variable_snapshot(
variable_pool=graph_engine.graph_runtime_state.variable_pool
)
)
# Update the total tokens from this iteration
self.graph_runtime_state.total_tokens += graph_engine.graph_runtime_state.total_tokens
iter_run_map[str(index)] = (datetime.now(UTC).replace(tzinfo=None) - iter_start_at).total_seconds()
@ -235,7 +244,10 @@ class IterationNode(Node):
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Submit all iteration tasks
future_to_index: dict[Future[tuple[datetime, list[GraphNodeEventBase], object | None, int]], int] = {}
future_to_index: dict[
Future[tuple[datetime, list[GraphNodeEventBase], object | None, int, dict[str, VariableUnion]]],
int,
] = {}
for index, item in enumerate(iterator_list_value):
yield IterationNextEvent(index=index)
future = executor.submit(
@ -252,7 +264,7 @@ class IterationNode(Node):
index = future_to_index[future]
try:
result = future.result()
iter_start_at, events, output_value, tokens_used = result
iter_start_at, events, output_value, tokens_used, conversation_snapshot = result
# Update outputs at the correct index
outputs[index] = output_value
@ -264,6 +276,9 @@ class IterationNode(Node):
self.graph_runtime_state.total_tokens += tokens_used
iter_run_map[str(index)] = (datetime.now(UTC).replace(tzinfo=None) - iter_start_at).total_seconds()
# Sync conversation variables after iteration completion
self._sync_conversation_variables_from_snapshot(conversation_snapshot)
except Exception as e:
# Handle errors based on error_handle_mode
match self._node_data.error_handle_mode:
@ -288,7 +303,7 @@ class IterationNode(Node):
item: object,
flask_app: Flask,
context_vars: contextvars.Context,
) -> tuple[datetime, list[GraphNodeEventBase], object | None, int]:
) -> tuple[datetime, list[GraphNodeEventBase], object | None, int, dict[str, VariableUnion]]:
"""Execute a single iteration in parallel mode and return results."""
with preserve_flask_contexts(flask_app=flask_app, context_vars=context_vars):
iter_start_at = datetime.now(UTC).replace(tzinfo=None)
@ -307,8 +322,17 @@ class IterationNode(Node):
# Get the output value from the temporary outputs list
output_value = outputs_temp[0] if outputs_temp else None
conversation_snapshot = self._extract_conversation_variable_snapshot(
variable_pool=graph_engine.graph_runtime_state.variable_pool
)
return iter_start_at, events, output_value, graph_engine.graph_runtime_state.total_tokens
return (
iter_start_at,
events,
output_value,
graph_engine.graph_runtime_state.total_tokens,
conversation_snapshot,
)
def _handle_iteration_success(
self,
@ -430,6 +454,23 @@ class IterationNode(Node):
return variable_mapping
def _extract_conversation_variable_snapshot(self, *, variable_pool: VariablePool) -> dict[str, VariableUnion]:
conversation_variables = variable_pool.variable_dictionary.get(CONVERSATION_VARIABLE_NODE_ID, {})
return {name: variable.model_copy(deep=True) for name, variable in conversation_variables.items()}
def _sync_conversation_variables_from_snapshot(self, snapshot: dict[str, VariableUnion]) -> None:
parent_pool = self.graph_runtime_state.variable_pool
parent_conversations = parent_pool.variable_dictionary.get(CONVERSATION_VARIABLE_NODE_ID, {})
current_keys = set(parent_conversations.keys())
snapshot_keys = set(snapshot.keys())
for removed_key in current_keys - snapshot_keys:
parent_pool.remove((CONVERSATION_VARIABLE_NODE_ID, removed_key))
for name, variable in snapshot.items():
parent_pool.add((CONVERSATION_VARIABLE_NODE_ID, name), variable)
def _append_iteration_info_to_event(
self,
event: GraphNodeEventBase,

1
api/extensions/ext_celery.py
View File

@ -147,6 +147,7 @@ def init_app(app: DifyApp) -> Celery:
}
if dify_config.ENABLE_CHECK_UPGRADABLE_PLUGIN_TASK and dify_config.MARKETPLACE_ENABLED:
imports.append("schedule.check_upgradable_plugin_task")
imports.append("tasks.process_tenant_plugin_autoupgrade_check_task")
beat_schedule["check_upgradable_plugin_task"] = {
"task": "schedule.check_upgradable_plugin_task.check_upgradable_plugin_task",
"schedule": crontab(minute="*/15"),

2
api/factories/variable_factory.py
View File

@ -142,6 +142,8 @@ def build_segment(value: Any, /) -> Segment:
# below
if value is None:
return NoneSegment()
if isinstance(value, Segment):
return value
if isinstance(value, str):
return StringSegment(value=value)
if isinstance(value, bool):

2
api/libs/external_api.py
View File

@ -94,7 +94,7 @@ def register_external_error_handlers(api: Api):
got_request_exception.send(current_app, exception=e)
status_code = 500
data = getattr(e, "data", {"message": http_status_message(status_code)})
data: dict[str, Any] = getattr(e, "data", {"message": http_status_message(status_code)})
# 🔒 Normalize non-mapping data (e.g., if someone set e.data = Response)
if not isinstance(data, dict):

6
api/libs/gmpy2_pkcs10aep_cipher.py
View File

@ -27,7 +27,7 @@ import gmpy2 # type: ignore
from Crypto import Random
from Crypto.Signature.pss import MGF1
from Crypto.Util.number import bytes_to_long, ceil_div, long_to_bytes
from Crypto.Util.py3compat import _copy_bytes, bord
from Crypto.Util.py3compat import bord
from Crypto.Util.strxor import strxor
@ -72,7 +72,7 @@ class PKCS1OAepCipher:
else:
self._mgf = lambda x, y: MGF1(x, y, self._hashObj)
self._label = _copy_bytes(None, None, label)
self._label = bytes(label)
self._randfunc = randfunc
def can_encrypt(self):
@ -120,7 +120,7 @@ class PKCS1OAepCipher:
# Step 2b
ps = b"\x00" * ps_len
# Step 2c
db = lHash + ps + b"\x01" + _copy_bytes(None, None, message)
db = lHash + ps + b"\x01" + bytes(message)
# Step 2d
ros = self._randfunc(hLen)
# Step 2e

4
api/libs/sendgrid.py
View File

@ -14,7 +14,7 @@ class SendGridClient:
def send(self, mail: dict):
logger.debug("Sending email with SendGrid")
_to = ""
try:
_to = mail["to"]
@ -28,7 +28,7 @@ class SendGridClient:
content = Content("text/html", mail["html"])
sg_mail = Mail(from_email, to_email, subject, content)
mail_json = sg_mail.get()
response = sg.client.mail.send.post(request_body=mail_json) # ty: ignore [call-non-callable]
response = sg.client.mail.send.post(request_body=mail_json) # type: ignore
logger.debug(response.status_code)
logger.debug(response.body)
logger.debug(response.headers)

6
api/pyproject.toml
View File

@ -1,6 +1,6 @@
[project]
name = "dify-api"
version = "1.9.0"
version = "1.9.1"
requires-python = ">=3.11,<3.13"
dependencies = [
@ -181,7 +181,7 @@ dev = [
storage = [
"azure-storage-blob==12.13.0",
"bce-python-sdk~=0.9.23",
"cos-python-sdk-v5==1.9.30",
"cos-python-sdk-v5==1.9.38",
"esdk-obs-python==3.24.6.1",
"google-cloud-storage==2.16.0",
"opendal~=0.46.0",
@ -208,7 +208,7 @@ vdb = [
"couchbase~=4.3.0",
"elasticsearch==8.14.0",
"opensearch-py==2.4.0",
"oracledb==3.0.0",
"oracledb==3.3.0",
"pgvecto-rs[sqlalchemy]~=0.2.1",
"pgvector==0.2.5",
"pymilvus~=2.5.0",

4
api/pyrightconfig.json
View File

@ -6,11 +6,7 @@
"migrations/",
"core/rag",
"extensions",
"libs",
"controllers/console/datasets",
"controllers/service_api/dataset",
"core/ops",
"core/tools",
"core/model_runtime",
"core/workflow/nodes",
"core/app/app_config/easy_ui_based_app/dataset"

4
api/schedule/check_upgradable_plugin_task.py
View File

@ -6,7 +6,7 @@ import click
import app
from extensions.ext_database import db
from models.account import TenantPluginAutoUpgradeStrategy
from tasks.process_tenant_plugin_autoupgrade_check_task import process_tenant_plugin_autoupgrade_check_task
from tasks import process_tenant_plugin_autoupgrade_check_task as check_task
AUTO_UPGRADE_MINIMAL_CHECKING_INTERVAL = 15 * 60 # 15 minutes
MAX_CONCURRENT_CHECK_TASKS = 20
@ -43,7 +43,7 @@ def check_upgradable_plugin_task():
for i in range(0, total_strategies, MAX_CONCURRENT_CHECK_TASKS):
batch_strategies = strategies[i : i + MAX_CONCURRENT_CHECK_TASKS]
for strategy in batch_strategies:
process_tenant_plugin_autoupgrade_check_task.delay(
check_task.process_tenant_plugin_autoupgrade_check_task.delay(
strategy.tenant_id,
strategy.strategy_setting,
strategy.upgrade_time_of_day,

108
api/tasks/process_tenant_plugin_autoupgrade_check_task.py
View File

@ -1,5 +1,5 @@
import json
import operator
import traceback
import typing
import click
@ -9,38 +9,106 @@ from core.helper import marketplace
from core.helper.marketplace import MarketplacePluginDeclaration
from core.plugin.entities.plugin import PluginInstallationSource
from core.plugin.impl.plugin import PluginInstaller
from extensions.ext_redis import redis_client
from models.account import TenantPluginAutoUpgradeStrategy
RETRY_TIMES_OF_ONE_PLUGIN_IN_ONE_TENANT = 3
CACHE_REDIS_KEY_PREFIX = "plugin_autoupgrade_check_task:cached_plugin_manifests:"
CACHE_REDIS_TTL = 60 * 15 # 15 minutes
cached_plugin_manifests: dict[str, typing.Union[MarketplacePluginDeclaration, None]] = {}
def _get_redis_cache_key(plugin_id: str) -> str:
"""Generate Redis cache key for plugin manifest."""
return f"{CACHE_REDIS_KEY_PREFIX}{plugin_id}"
def _get_cached_manifest(plugin_id: str) -> typing.Union[MarketplacePluginDeclaration, None, bool]:
"""
Get cached plugin manifest from Redis.
Returns:
- MarketplacePluginDeclaration: if found in cache
- None: if cached as not found (marketplace returned no result)
- False: if not in cache at all
"""
try:
key = _get_redis_cache_key(plugin_id)
cached_data = redis_client.get(key)
if cached_data is None:
return False
cached_json = json.loads(cached_data)
if cached_json is None:
return None
return MarketplacePluginDeclaration.model_validate(cached_json)
except Exception:
return False
def _set_cached_manifest(plugin_id: str, manifest: typing.Union[MarketplacePluginDeclaration, None]) -> None:
"""
Cache plugin manifest in Redis.
Args:
plugin_id: The plugin ID
manifest: The manifest to cache, or None if not found in marketplace
"""
try:
key = _get_redis_cache_key(plugin_id)
if manifest is None:
# Cache the fact that this plugin was not found
redis_client.setex(key, CACHE_REDIS_TTL, json.dumps(None))
else:
# Cache the manifest data
redis_client.setex(key, CACHE_REDIS_TTL, manifest.model_dump_json())
except Exception:
# If Redis fails, continue without caching
# traceback.print_exc()
pass
def marketplace_batch_fetch_plugin_manifests(
plugin_ids_plain_list: list[str],
) -> list[MarketplacePluginDeclaration]:
global cached_plugin_manifests
# return marketplace.batch_fetch_plugin_manifests(plugin_ids_plain_list)
not_included_plugin_ids = [
plugin_id for plugin_id in plugin_ids_plain_list if plugin_id not in cached_plugin_manifests
]
if not_included_plugin_ids:
manifests = marketplace.batch_fetch_plugin_manifests_ignore_deserialization_error(not_included_plugin_ids)
"""Fetch plugin manifests with Redis caching support."""
cached_manifests: dict[str, typing.Union[MarketplacePluginDeclaration, None]] = {}
not_cached_plugin_ids: list[str] = []
# Check Redis cache for each plugin
for plugin_id in plugin_ids_plain_list:
cached_result = _get_cached_manifest(plugin_id)
if cached_result is False:
# Not in cache, need to fetch
not_cached_plugin_ids.append(plugin_id)
else:
# Either found manifest or cached as None (not found in marketplace)
# At this point, cached_result is either MarketplacePluginDeclaration or None
if isinstance(cached_result, bool):
# This should never happen due to the if condition above, but for type safety
continue
cached_manifests[plugin_id] = cached_result
# Fetch uncached plugins from marketplace
if not_cached_plugin_ids:
manifests = marketplace.batch_fetch_plugin_manifests_ignore_deserialization_error(not_cached_plugin_ids)
# Cache the fetched manifests
for manifest in manifests:
cached_plugin_manifests[manifest.plugin_id] = manifest
cached_manifests[manifest.plugin_id] = manifest
_set_cached_manifest(manifest.plugin_id, manifest)
if (
len(manifests) == 0
): # this indicates that the plugin not found in marketplace, should set None in cache to prevent future check
for plugin_id in not_included_plugin_ids:
cached_plugin_manifests[plugin_id] = None
# Cache plugins that were not found in marketplace
fetched_plugin_ids = {manifest.plugin_id for manifest in manifests}
for plugin_id in not_cached_plugin_ids:
if plugin_id not in fetched_plugin_ids:
cached_manifests[plugin_id] = None
_set_cached_manifest(plugin_id, None)
# Build result list from cached manifests
result: list[MarketplacePluginDeclaration] = []
for plugin_id in plugin_ids_plain_list:
final_manifest = cached_plugin_manifests.get(plugin_id)
if final_manifest is not None:
result.append(final_manifest)
cached_manifest: typing.Union[MarketplacePluginDeclaration, None] = cached_manifests.get(plugin_id)
if cached_manifest is not None:
result.append(cached_manifest)
return result
@ -157,10 +225,10 @@ def process_tenant_plugin_autoupgrade_check_task(
)
except Exception as e:
click.echo(click.style(f"Error when upgrading plugin: {e}", fg="red"))
traceback.print_exc()
# traceback.print_exc()
break
except Exception as e:
click.echo(click.style(f"Error when checking upgradable plugin: {e}", fg="red"))
traceback.print_exc()
# traceback.print_exc()
return

316
api/tests/fixtures/workflow/update-conversation-variable-in-iteration.yml vendored Normal file
View File

@ -0,0 +1,316 @@
app:
description: 'This chatflow receives a sys.query, writes it into the `answer` variable,
and then outputs the `answer` variable.
`answer` is a conversation variable with a blank default value; it will be updated
in an iteration node.
if this chatflow works correctly, it will output the `sys.query` as the same.'
icon: 🤖
icon_background: '#FFEAD5'
mode: advanced-chat
name: update-conversation-variable-in-iteration
use_icon_as_answer_icon: false
dependencies: []
kind: app
version: 0.4.0
workflow:
conversation_variables:
- description: ''
id: c30af82d-b2ec-417d-a861-4dd78584faa4
name: answer
selector:
- conversation
- answer
value: ''
value_type: string
environment_variables: []
features:
file_upload:
allowed_file_extensions:
- .JPG
- .JPEG
- .PNG
- .GIF
- .WEBP
- .SVG
allowed_file_types:
- image
allowed_file_upload_methods:
- local_file
- remote_url
enabled: false
fileUploadConfig:
audio_file_size_limit: 50
batch_count_limit: 5
file_size_limit: 15
image_file_size_limit: 10
video_file_size_limit: 100
workflow_file_upload_limit: 10
image:
enabled: false
number_limits: 3
transfer_methods:
- local_file
- remote_url
number_limits: 3
opening_statement: ''
retriever_resource:
enabled: true
sensitive_word_avoidance:
enabled: false
speech_to_text:
enabled: false
suggested_questions: []
suggested_questions_after_answer:
enabled: false
text_to_speech:
enabled: false
language: ''
voice: ''
graph:
edges:
- data:
isInIteration: false
isInLoop: false
sourceType: start
targetType: code
id: 1759032354471-source-1759032363865-target
source: '1759032354471'
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target: '1759032363865'
targetHandle: target
type: custom
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target: '1759032379989'
targetHandle: target
type: custom
zIndex: 0
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source: 1759032379989start
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sourceType: iteration
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source: '1759032394460'
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target: '1759032476318'
targetHandle: target
type: custom
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positionAbsolute:
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selected: false
sourcePosition: right
targetPosition: left
type: custom
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type: code
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x: 332
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sourcePosition: right
targetPosition: left
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error_handle_mode: terminated
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is_parallel: false
iterator_input_type: array[number]
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output_selector:
- '1759032476318'
- result
output_type: array[string]
parallel_nums: 10
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start_node_id: 1759032379989start
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type: iteration
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height: 204
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x: 634
y: 302
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sourcePosition: right
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title: ''
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selectable: false
sourcePosition: right
targetPosition: left
type: custom-iteration-start
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- data:
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isInLoop: false
items:
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operation: over-write
value:
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write_mode: over-write
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type: code
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parentId: '1759032379989'
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targetPosition: left
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viewport:
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y: 85.20000000000005
zoom: 0.7
rag_pipeline_variables: []

113
api/tests/unit_tests/core/workflow/entities/test_variable_pool.py Normal file
View File

@ -0,0 +1,113 @@
from core.variables.segments import (
BooleanSegment,
IntegerSegment,
NoneSegment,
StringSegment,
)
from core.workflow.entities.variable_pool import VariablePool
class TestVariablePoolGetAndNestedAttribute:
#
# _get_nested_attribute tests
#
def test__get_nested_attribute_existing_key(self):
pool = VariablePool.empty()
obj = {"a": 123}
segment = pool._get_nested_attribute(obj, "a")
assert segment is not None
assert segment.value == 123
def test__get_nested_attribute_missing_key(self):
pool = VariablePool.empty()
obj = {"a": 123}
segment = pool._get_nested_attribute(obj, "b")
assert segment is None
def test__get_nested_attribute_non_dict(self):
pool = VariablePool.empty()
obj = ["not", "a", "dict"]
segment = pool._get_nested_attribute(obj, "a")
assert segment is None
def test__get_nested_attribute_with_none_value(self):
pool = VariablePool.empty()
obj = {"a": None}
segment = pool._get_nested_attribute(obj, "a")
assert segment is not None
assert isinstance(segment, NoneSegment)
def test__get_nested_attribute_with_empty_string(self):
pool = VariablePool.empty()
obj = {"a": ""}
segment = pool._get_nested_attribute(obj, "a")
assert segment is not None
assert isinstance(segment, StringSegment)
assert segment.value == ""
#
# get tests
#
def test_get_simple_variable(self):
pool = VariablePool.empty()
pool.add(("node1", "var1"), "value1")
segment = pool.get(("node1", "var1"))
assert segment is not None
assert segment.value == "value1"
def test_get_missing_variable(self):
pool = VariablePool.empty()
result = pool.get(("node1", "unknown"))
assert result is None
def test_get_with_too_short_selector(self):
pool = VariablePool.empty()
result = pool.get(("only_node",))
assert result is None
def test_get_nested_object_attribute(self):
pool = VariablePool.empty()
obj_value = {"inner": "hello"}
pool.add(("node1", "obj"), obj_value)
# simulate selector with nested attr
segment = pool.get(("node1", "obj", "inner"))
assert segment is not None
assert segment.value == "hello"
def test_get_nested_object_missing_attribute(self):
pool = VariablePool.empty()
obj_value = {"inner": "hello"}
pool.add(("node1", "obj"), obj_value)
result = pool.get(("node1", "obj", "not_exist"))
assert result is None
def test_get_nested_object_attribute_with_falsy_values(self):
pool = VariablePool.empty()
obj_value = {
"inner_none": None,
"inner_empty": "",
"inner_zero": 0,
"inner_false": False,
}
pool.add(("node1", "obj"), obj_value)
segment_none = pool.get(("node1", "obj", "inner_none"))
assert segment_none is not None
assert isinstance(segment_none, NoneSegment)
segment_empty = pool.get(("node1", "obj", "inner_empty"))
assert segment_empty is not None
assert isinstance(segment_empty, StringSegment)
assert segment_empty.value == ""
segment_zero = pool.get(("node1", "obj", "inner_zero"))
assert segment_zero is not None
assert isinstance(segment_zero, IntegerSegment)
assert segment_zero.value == 0
segment_false = pool.get(("node1", "obj", "inner_false"))
assert segment_false is not None
assert isinstance(segment_false, BooleanSegment)
assert segment_false.value is False

41
api/tests/unit_tests/core/workflow/graph_engine/test_update_conversation_variable_iteration.py Normal file
View File

@ -0,0 +1,41 @@
"""Validate conversation variable updates inside an iteration workflow.
This test uses the ``update-conversation-variable-in-iteration`` fixture, which
routes ``sys.query`` into the conversation variable ``answer`` from within an
iteration container. The workflow should surface that updated conversation
variable in the final answer output.
Code nodes in the fixture are mocked because their concrete outputs are not
relevant to verifying variable propagation semantics.
"""
from .test_mock_config import MockConfigBuilder
from .test_table_runner import TableTestRunner, WorkflowTestCase
def test_update_conversation_variable_in_iteration():
fixture_name = "update-conversation-variable-in-iteration"
user_query = "ensure conversation variable syncs"
mock_config = (
MockConfigBuilder()
.with_node_output("1759032363865", {"result": [1]})
.with_node_output("1759032476318", {"result": ""})
.build()
)
case = WorkflowTestCase(
fixture_path=fixture_name,
use_auto_mock=True,
mock_config=mock_config,
query=user_query,
expected_outputs={"answer": user_query},
description="Conversation variable updated within iteration should flow to answer output.",
)
runner = TableTestRunner()
result = runner.run_test_case(case)
assert result.success, f"Workflow execution failed: {result.error}"
assert result.actual_outputs is not None
assert result.actual_outputs.get("answer") == user_query

37
api/uv.lock
View File

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version = "1.9.38"
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{ name = "six" },
{ name = "xmltodict" },
]
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[[package]]
name = "couchbase"
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[[package]]
name = "dify-api"
version = "1.9.0"
version = "1.9.1"
source = { virtual = "." }
dependencies = [
{ name = "arize-phoenix-otel" },
@ -1639,7 +1642,7 @@ dev = [
storage = [
{ name = "azure-storage-blob", specifier = "==12.13.0" },
{ name = "bce-python-sdk", specifier = "~=0.9.23" },
{ name = "cos-python-sdk-v5", specifier = "==1.9.30" },
{ name = "cos-python-sdk-v5", specifier = "==1.9.38" },
{ name = "esdk-obs-python", specifier = "==3.24.6.1" },
{ name = "google-cloud-storage", specifier = "==2.16.0" },
{ name = "opendal", specifier = "~=0.46.0" },
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{ name = "elasticsearch", specifier = "==8.14.0" },
{ name = "mo-vector", specifier = "~=0.1.13" },
{ name = "opensearch-py", specifier = "==2.4.0" },
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{ name = "oracledb", specifier = "==3.3.0" },
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{ name = "pgvector", specifier = "==0.2.5" },
{ name = "pymilvus", specifier = "~=2.5.0" },
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[[package]]

2
docker/.env.example
View File

@ -45,7 +45,7 @@ APP_WEB_URL=
# Recommendation: use a dedicated domain (e.g., https://upload.example.com).
# Alternatively, use http://<your-ip>:5001 or http://api:5001,
# ensuring port 5001 is externally accessible (see docker-compose.yaml).
FILES_URL=http://api:5001
FILES_URL=
# INTERNAL_FILES_URL is used for plugin daemon communication within Docker network.
# Set this to the internal Docker service URL for proper plugin file access.

8
docker/docker-compose-template.yaml
View File

@ -2,7 +2,7 @@ x-shared-env: &shared-api-worker-env
services:
# API service
api:
image: langgenius/dify-api:1.9.0
image: langgenius/dify-api:1.9.1
restart: always
environment:
# Use the shared environment variables.
@ -31,7 +31,7 @@ services:
# worker service
# The Celery worker for processing all queues (dataset, workflow, mail, etc.)
worker:
image: langgenius/dify-api:1.9.0
image: langgenius/dify-api:1.9.1
restart: always
environment:
# Use the shared environment variables.
@ -58,7 +58,7 @@ services:
# worker_beat service
# Celery beat for scheduling periodic tasks.
worker_beat:
image: langgenius/dify-api:1.9.0
image: langgenius/dify-api:1.9.1
restart: always
environment:
# Use the shared environment variables.
@ -76,7 +76,7 @@ services:
# Frontend web application.
web:
image: langgenius/dify-web:1.9.0
image: langgenius/dify-web:1.9.1
restart: always
environment:
CONSOLE_API_URL: ${CONSOLE_API_URL:-}

10
docker/docker-compose.yaml
View File

@ -10,7 +10,7 @@ x-shared-env: &shared-api-worker-env
SERVICE_API_URL: ${SERVICE_API_URL:-}
APP_API_URL: ${APP_API_URL:-}
APP_WEB_URL: ${APP_WEB_URL:-}
FILES_URL: ${FILES_URL:-http://api:5001}
FILES_URL: ${FILES_URL:-}
INTERNAL_FILES_URL: ${INTERNAL_FILES_URL:-}
LANG: ${LANG:-en_US.UTF-8}
LC_ALL: ${LC_ALL:-en_US.UTF-8}
@ -604,7 +604,7 @@ x-shared-env: &shared-api-worker-env
services:
# API service
api:
image: langgenius/dify-api:1.9.0
image: langgenius/dify-api:1.9.1
restart: always
environment:
# Use the shared environment variables.
@ -633,7 +633,7 @@ services:
# worker service
# The Celery worker for processing the queue.
worker:
image: langgenius/dify-api:1.9.0
image: langgenius/dify-api:1.9.1
restart: always
environment:
# Use the shared environment variables.
@ -660,7 +660,7 @@ services:
# worker_beat service
# Celery beat for scheduling periodic tasks.
worker_beat:
image: langgenius/dify-api:1.9.0
image: langgenius/dify-api:1.9.1
restart: always
environment:
# Use the shared environment variables.
@ -678,7 +678,7 @@ services:
# Frontend web application.
web:
image: langgenius/dify-web:1.9.0
image: langgenius/dify-web:1.9.1
restart: always
environment:
CONSOLE_API_URL: ${CONSOLE_API_URL:-}

2
sdks/python-client/dify_client/__init__.py
View File

@ -4,6 +4,7 @@ from dify_client.client import (
DifyClient,
KnowledgeBaseClient,
WorkflowClient,
WorkspaceClient,
)
__all__ = [
@ -12,4 +13,5 @@ __all__ = [
"DifyClient",
"KnowledgeBaseClient",
"WorkflowClient",
"WorkspaceClient",
]

223
sdks/python-client/dify_client/client.py
View File

@ -1,5 +1,6 @@
import json
from typing import Literal
from typing import Literal, Union, Dict, List, Any, Optional, IO
import requests
@ -49,6 +50,18 @@ class DifyClient:
params = {"user": user}
return self._send_request("GET", "/meta", params=params)
def get_app_info(self):
"""Get basic application information including name, description, tags, and mode."""
return self._send_request("GET", "/info")
def get_app_site_info(self):
"""Get application site information."""
return self._send_request("GET", "/site")
def get_file_preview(self, file_id: str):
"""Get file preview by file ID."""
return self._send_request("GET", f"/files/{file_id}/preview")
class CompletionClient(DifyClient):
def create_completion_message(
@ -144,6 +157,51 @@ class ChatClient(DifyClient):
files = {"file": audio_file}
return self._send_request_with_files("POST", "/audio-to-text", data, files)
# Annotation APIs
def annotation_reply_action(
self,
action: Literal["enable", "disable"],
score_threshold: float,
embedding_provider_name: str,
embedding_model_name: str,
):
"""Enable or disable annotation reply feature."""
# Backend API requires these fields to be non-None values
if score_threshold is None or embedding_provider_name is None or embedding_model_name is None:
raise ValueError("score_threshold, embedding_provider_name, and embedding_model_name cannot be None")
data = {
"score_threshold": score_threshold,
"embedding_provider_name": embedding_provider_name,
"embedding_model_name": embedding_model_name,
}
return self._send_request("POST", f"/apps/annotation-reply/{action}", json=data)
def get_annotation_reply_status(self, action: Literal["enable", "disable"], job_id: str):
"""Get the status of an annotation reply action job."""
return self._send_request("GET", f"/apps/annotation-reply/{action}/status/{job_id}")
def list_annotations(self, page: int = 1, limit: int = 20, keyword: str = ""):
"""List annotations for the application."""
params = {"page": page, "limit": limit}
if keyword:
params["keyword"] = keyword
return self._send_request("GET", "/apps/annotations", params=params)
def create_annotation(self, question: str, answer: str):
"""Create a new annotation."""
data = {"question": question, "answer": answer}
return self._send_request("POST", "/apps/annotations", json=data)
def update_annotation(self, annotation_id: str, question: str, answer: str):
"""Update an existing annotation."""
data = {"question": question, "answer": answer}
return self._send_request("PUT", f"/apps/annotations/{annotation_id}", json=data)
def delete_annotation(self, annotation_id: str):
"""Delete an annotation."""
return self._send_request("DELETE", f"/apps/annotations/{annotation_id}")
class WorkflowClient(DifyClient):
def run(self, inputs: dict, response_mode: Literal["blocking", "streaming"] = "streaming", user: str = "abc-123"):
@ -157,6 +215,55 @@ class WorkflowClient(DifyClient):
def get_result(self, workflow_run_id):
return self._send_request("GET", f"/workflows/run/{workflow_run_id}")
def get_workflow_logs(
self,
keyword: str = None,
status: Literal["succeeded", "failed", "stopped"] | None = None,
page: int = 1,
limit: int = 20,
created_at__before: str = None,
created_at__after: str = None,
created_by_end_user_session_id: str = None,
created_by_account: str = None,
):
"""Get workflow execution logs with optional filtering."""
params = {"page": page, "limit": limit}
if keyword:
params["keyword"] = keyword
if status:
params["status"] = status
if created_at__before:
params["created_at__before"] = created_at__before
if created_at__after:
params["created_at__after"] = created_at__after
if created_by_end_user_session_id:
params["created_by_end_user_session_id"] = created_by_end_user_session_id
if created_by_account:
params["created_by_account"] = created_by_account
return self._send_request("GET", "/workflows/logs", params=params)
def run_specific_workflow(
self,
workflow_id: str,
inputs: dict,
response_mode: Literal["blocking", "streaming"] = "streaming",
user: str = "abc-123",
):
"""Run a specific workflow by workflow ID."""
data = {"inputs": inputs, "response_mode": response_mode, "user": user}
return self._send_request(
"POST", f"/workflows/{workflow_id}/run", data, stream=True if response_mode == "streaming" else False
)
class WorkspaceClient(DifyClient):
"""Client for workspace-related operations."""
def get_available_models(self, model_type: str):
"""Get available models by model type."""
url = f"/workspaces/current/models/model-types/{model_type}"
return self._send_request("GET", url)
class KnowledgeBaseClient(DifyClient):
def __init__(
@ -443,3 +550,117 @@ class KnowledgeBaseClient(DifyClient):
data = {"segment": segment_data}
url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/segments/{segment_id}"
return self._send_request("POST", url, json=data, **kwargs)
# Advanced Knowledge Base APIs
def hit_testing(
self, query: str, retrieval_model: Dict[str, Any] = None, external_retrieval_model: Dict[str, Any] = None
):
"""Perform hit testing on the dataset."""
data = {"query": query}
if retrieval_model:
data["retrieval_model"] = retrieval_model
if external_retrieval_model:
data["external_retrieval_model"] = external_retrieval_model
url = f"/datasets/{self._get_dataset_id()}/hit-testing"
return self._send_request("POST", url, json=data)
def get_dataset_metadata(self):
"""Get dataset metadata."""
url = f"/datasets/{self._get_dataset_id()}/metadata"
return self._send_request("GET", url)
def create_dataset_metadata(self, metadata_data: Dict[str, Any]):
"""Create dataset metadata."""
url = f"/datasets/{self._get_dataset_id()}/metadata"
return self._send_request("POST", url, json=metadata_data)
def update_dataset_metadata(self, metadata_id: str, metadata_data: Dict[str, Any]):
"""Update dataset metadata."""
url = f"/datasets/{self._get_dataset_id()}/metadata/{metadata_id}"
return self._send_request("PATCH", url, json=metadata_data)
def get_built_in_metadata(self):
"""Get built-in metadata."""
url = f"/datasets/{self._get_dataset_id()}/metadata/built-in"
return self._send_request("GET", url)
def manage_built_in_metadata(self, action: str, metadata_data: Dict[str, Any] = None):
"""Manage built-in metadata with specified action."""
data = metadata_data or {}
url = f"/datasets/{self._get_dataset_id()}/metadata/built-in/{action}"
return self._send_request("POST", url, json=data)
def update_documents_metadata(self, operation_data: List[Dict[str, Any]]):
"""Update metadata for multiple documents."""
url = f"/datasets/{self._get_dataset_id()}/documents/metadata"
data = {"operation_data": operation_data}
return self._send_request("POST", url, json=data)
# Dataset Tags APIs
def list_dataset_tags(self):
"""List all dataset tags."""
return self._send_request("GET", "/datasets/tags")
def bind_dataset_tags(self, tag_ids: List[str]):
"""Bind tags to dataset."""
data = {"tag_ids": tag_ids, "target_id": self._get_dataset_id()}
return self._send_request("POST", "/datasets/tags/binding", json=data)
def unbind_dataset_tag(self, tag_id: str):
"""Unbind a single tag from dataset."""
data = {"tag_id": tag_id, "target_id": self._get_dataset_id()}
return self._send_request("POST", "/datasets/tags/unbinding", json=data)
def get_dataset_tags(self):
"""Get tags for current dataset."""
url = f"/datasets/{self._get_dataset_id()}/tags"
return self._send_request("GET", url)
# RAG Pipeline APIs
def get_datasource_plugins(self, is_published: bool = True):
"""Get datasource plugins for RAG pipeline."""
params = {"is_published": is_published}
url = f"/datasets/{self._get_dataset_id()}/pipeline/datasource-plugins"
return self._send_request("GET", url, params=params)
def run_datasource_node(
self,
node_id: str,
inputs: Dict[str, Any],
datasource_type: str,
is_published: bool = True,
credential_id: str = None,
):
"""Run a datasource node in RAG pipeline."""
data = {"inputs": inputs, "datasource_type": datasource_type, "is_published": is_published}
if credential_id:
data["credential_id"] = credential_id
url = f"/datasets/{self._get_dataset_id()}/pipeline/datasource/nodes/{node_id}/run"
return self._send_request("POST", url, json=data, stream=True)
def run_rag_pipeline(
self,
inputs: Dict[str, Any],
datasource_type: str,
datasource_info_list: List[Dict[str, Any]],
start_node_id: str,
is_published: bool = True,
response_mode: Literal["streaming", "blocking"] = "blocking",
):
"""Run RAG pipeline."""
data = {
"inputs": inputs,
"datasource_type": datasource_type,
"datasource_info_list": datasource_info_list,
"start_node_id": start_node_id,
"is_published": is_published,
"response_mode": response_mode,
}
url = f"/datasets/{self._get_dataset_id()}/pipeline/run"
return self._send_request("POST", url, json=data, stream=response_mode == "streaming")
def upload_pipeline_file(self, file_path: str):
"""Upload file for RAG pipeline."""
with open(file_path, "rb") as f:
files = {"file": f}
return self._send_request_with_files("POST", "/datasets/pipeline/file-upload", {}, files)

416
sdks/python-client/tests/test_new_apis.py Normal file
View File

@ -0,0 +1,416 @@
#!/usr/bin/env python3
"""
Test suite for the new Service API functionality in the Python SDK.
This test validates the implementation of the missing Service API endpoints
that were added to the Python SDK to achieve complete coverage.
"""
import unittest
from unittest.mock import Mock, patch, MagicMock
import json
from dify_client import (
DifyClient,
ChatClient,
WorkflowClient,
KnowledgeBaseClient,
WorkspaceClient,
)
class TestNewServiceAPIs(unittest.TestCase):
"""Test cases for new Service API implementations."""
def setUp(self):
"""Set up test fixtures."""
self.api_key = "test-api-key"
self.base_url = "https://api.dify.ai/v1"
@patch("dify_client.client.requests.request")
def test_app_info_apis(self, mock_request):
"""Test application info APIs."""
mock_response = Mock()
mock_response.json.return_value = {
"name": "Test App",
"description": "Test Description",
"tags": ["test", "api"],
"mode": "chat",
"author_name": "Test Author",
}
mock_request.return_value = mock_response
client = DifyClient(self.api_key, self.base_url)
# Test get_app_info
result = client.get_app_info()
mock_request.assert_called_with(
"GET",
f"{self.base_url}/info",
json=None,
params=None,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
# Test get_app_site_info
client.get_app_site_info()
mock_request.assert_called_with(
"GET",
f"{self.base_url}/site",
json=None,
params=None,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
# Test get_file_preview
file_id = "test-file-id"
client.get_file_preview(file_id)
mock_request.assert_called_with(
"GET",
f"{self.base_url}/files/{file_id}/preview",
json=None,
params=None,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
@patch("dify_client.client.requests.request")
def test_annotation_apis(self, mock_request):
"""Test annotation APIs."""
mock_response = Mock()
mock_response.json.return_value = {"result": "success"}
mock_request.return_value = mock_response
client = ChatClient(self.api_key, self.base_url)
# Test annotation_reply_action - enable
client.annotation_reply_action(
action="enable",
score_threshold=0.8,
embedding_provider_name="openai",
embedding_model_name="text-embedding-ada-002",
)
mock_request.assert_called_with(
"POST",
f"{self.base_url}/apps/annotation-reply/enable",
json={
"score_threshold": 0.8,
"embedding_provider_name": "openai",
"embedding_model_name": "text-embedding-ada-002",
},
params=None,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
# Test annotation_reply_action - disable (now requires same fields as enable)
client.annotation_reply_action(
action="disable",
score_threshold=0.5,
embedding_provider_name="openai",
embedding_model_name="text-embedding-ada-002",
)
# Test annotation_reply_action with score_threshold=0 (edge case)
client.annotation_reply_action(
action="enable",
score_threshold=0.0, # This should work and not raise ValueError
embedding_provider_name="openai",
embedding_model_name="text-embedding-ada-002",
)
# Test get_annotation_reply_status
client.get_annotation_reply_status("enable", "job-123")
# Test list_annotations
client.list_annotations(page=1, limit=20, keyword="test")
# Test create_annotation
client.create_annotation("Test question?", "Test answer.")
# Test update_annotation
client.update_annotation("annotation-123", "Updated question?", "Updated answer.")
# Test delete_annotation
client.delete_annotation("annotation-123")
# Verify all calls were made (8 calls: enable + disable + enable with 0.0 + 5 other operations)
self.assertEqual(mock_request.call_count, 8)
@patch("dify_client.client.requests.request")
def test_knowledge_base_advanced_apis(self, mock_request):
"""Test advanced knowledge base APIs."""
mock_response = Mock()
mock_response.json.return_value = {"result": "success"}
mock_request.return_value = mock_response
dataset_id = "test-dataset-id"
client = KnowledgeBaseClient(self.api_key, self.base_url, dataset_id)
# Test hit_testing
client.hit_testing("test query", {"type": "vector"})
mock_request.assert_called_with(
"POST",
f"{self.base_url}/datasets/{dataset_id}/hit-testing",
json={"query": "test query", "retrieval_model": {"type": "vector"}},
params=None,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
# Test metadata operations
client.get_dataset_metadata()
client.create_dataset_metadata({"key": "value"})
client.update_dataset_metadata("meta-123", {"key": "new_value"})
client.get_built_in_metadata()
client.manage_built_in_metadata("enable", {"type": "built_in"})
client.update_documents_metadata([{"document_id": "doc1", "metadata": {"key": "value"}}])
# Test tag operations
client.list_dataset_tags()
client.bind_dataset_tags(["tag1", "tag2"])
client.unbind_dataset_tag("tag1")
client.get_dataset_tags()
# Verify multiple calls were made
self.assertGreater(mock_request.call_count, 5)
@patch("dify_client.client.requests.request")
def test_rag_pipeline_apis(self, mock_request):
"""Test RAG pipeline APIs."""
mock_response = Mock()
mock_response.json.return_value = {"result": "success"}
mock_request.return_value = mock_response
dataset_id = "test-dataset-id"
client = KnowledgeBaseClient(self.api_key, self.base_url, dataset_id)
# Test get_datasource_plugins
client.get_datasource_plugins(is_published=True)
mock_request.assert_called_with(
"GET",
f"{self.base_url}/datasets/{dataset_id}/pipeline/datasource-plugins",
json=None,
params={"is_published": True},
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
# Test run_datasource_node
client.run_datasource_node(
node_id="node-123",
inputs={"param": "value"},
datasource_type="online_document",
is_published=True,
credential_id="cred-123",
)
# Test run_rag_pipeline with blocking mode
client.run_rag_pipeline(
inputs={"query": "test"},
datasource_type="online_document",
datasource_info_list=[{"id": "ds1"}],
start_node_id="start-node",
is_published=True,
response_mode="blocking",
)
# Test run_rag_pipeline with streaming mode
client.run_rag_pipeline(
inputs={"query": "test"},
datasource_type="online_document",
datasource_info_list=[{"id": "ds1"}],
start_node_id="start-node",
is_published=True,
response_mode="streaming",
)
self.assertEqual(mock_request.call_count, 4)
@patch("dify_client.client.requests.request")
def test_workspace_apis(self, mock_request):
"""Test workspace APIs."""
mock_response = Mock()
mock_response.json.return_value = {
"data": [{"name": "gpt-3.5-turbo", "type": "llm"}, {"name": "gpt-4", "type": "llm"}]
}
mock_request.return_value = mock_response
client = WorkspaceClient(self.api_key, self.base_url)
# Test get_available_models
result = client.get_available_models("llm")
mock_request.assert_called_with(
"GET",
f"{self.base_url}/workspaces/current/models/model-types/llm",
json=None,
params=None,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
@patch("dify_client.client.requests.request")
def test_workflow_advanced_apis(self, mock_request):
"""Test advanced workflow APIs."""
mock_response = Mock()
mock_response.json.return_value = {"result": "success"}
mock_request.return_value = mock_response
client = WorkflowClient(self.api_key, self.base_url)
# Test get_workflow_logs
client.get_workflow_logs(keyword="test", status="succeeded", page=1, limit=20)
mock_request.assert_called_with(
"GET",
f"{self.base_url}/workflows/logs",
json=None,
params={"page": 1, "limit": 20, "keyword": "test", "status": "succeeded"},
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
},
stream=False,
)
# Test get_workflow_logs with additional filters
client.get_workflow_logs(
keyword="test",
status="succeeded",
page=1,
limit=20,
created_at__before="2024-01-01",
created_at__after="2023-01-01",
created_by_account="user123",
)
# Test run_specific_workflow
client.run_specific_workflow(
workflow_id="workflow-123", inputs={"param": "value"}, response_mode="streaming", user="user-123"
)
self.assertEqual(mock_request.call_count, 3)
def test_error_handling(self):
"""Test error handling for required parameters."""
client = ChatClient(self.api_key, self.base_url)
# Test annotation_reply_action with missing required parameters would be a TypeError now
# since parameters are required in method signature
with self.assertRaises(TypeError):
client.annotation_reply_action("enable")
# Test annotation_reply_action with explicit None values should raise ValueError
with self.assertRaises(ValueError) as context:
client.annotation_reply_action("enable", None, "provider", "model")
self.assertIn("cannot be None", str(context.exception))
# Test KnowledgeBaseClient without dataset_id
kb_client = KnowledgeBaseClient(self.api_key, self.base_url)
with self.assertRaises(ValueError) as context:
kb_client.hit_testing("test query")
self.assertIn("dataset_id is not set", str(context.exception))
@patch("dify_client.client.open")
@patch("dify_client.client.requests.request")
def test_file_upload_apis(self, mock_request, mock_open):
"""Test file upload APIs."""
mock_response = Mock()
mock_response.json.return_value = {"result": "success"}
mock_request.return_value = mock_response
mock_file = MagicMock()
mock_open.return_value.__enter__.return_value = mock_file
dataset_id = "test-dataset-id"
client = KnowledgeBaseClient(self.api_key, self.base_url, dataset_id)
# Test upload_pipeline_file
client.upload_pipeline_file("/path/to/test.pdf")
mock_open.assert_called_with("/path/to/test.pdf", "rb")
mock_request.assert_called_once()
def test_comprehensive_coverage(self):
"""Test that all previously missing APIs are now implemented."""
# Test DifyClient methods
dify_methods = ["get_app_info", "get_app_site_info", "get_file_preview"]
client = DifyClient(self.api_key)
for method in dify_methods:
self.assertTrue(hasattr(client, method), f"DifyClient missing method: {method}")
# Test ChatClient annotation methods
chat_methods = [
"annotation_reply_action",
"get_annotation_reply_status",
"list_annotations",
"create_annotation",
"update_annotation",
"delete_annotation",
]
chat_client = ChatClient(self.api_key)
for method in chat_methods:
self.assertTrue(hasattr(chat_client, method), f"ChatClient missing method: {method}")
# Test WorkflowClient advanced methods
workflow_methods = ["get_workflow_logs", "run_specific_workflow"]
workflow_client = WorkflowClient(self.api_key)
for method in workflow_methods:
self.assertTrue(hasattr(workflow_client, method), f"WorkflowClient missing method: {method}")
# Test KnowledgeBaseClient advanced methods
kb_methods = [
"hit_testing",
"get_dataset_metadata",
"create_dataset_metadata",
"update_dataset_metadata",
"get_built_in_metadata",
"manage_built_in_metadata",
"update_documents_metadata",
"list_dataset_tags",
"bind_dataset_tags",
"unbind_dataset_tag",
"get_dataset_tags",
"get_datasource_plugins",
"run_datasource_node",
"run_rag_pipeline",
"upload_pipeline_file",
]
kb_client = KnowledgeBaseClient(self.api_key)
for method in kb_methods:
self.assertTrue(hasattr(kb_client, method), f"KnowledgeBaseClient missing method: {method}")
# Test WorkspaceClient methods
workspace_methods = ["get_available_models"]
workspace_client = WorkspaceClient(self.api_key)
for method in workspace_methods:
self.assertTrue(hasattr(workspace_client, method), f"WorkspaceClient missing method: {method}")
if __name__ == "__main__":
unittest.main()

4
web/app/components/app/annotation/index.tsx
View File

@ -38,7 +38,7 @@ const Annotation: FC<Props> = (props) => {
const [isShowEdit, setIsShowEdit] = useState(false)
const [annotationConfig, setAnnotationConfig] = useState<AnnotationReplyConfig | null>(null)
const [isChatApp] = useState(appDetail.mode !== 'completion')
const [controlRefreshSwitch, setControlRefreshSwitch] = useState(Date.now())
const [controlRefreshSwitch, setControlRefreshSwitch] = useState(() => Date.now())
const { plan, enableBilling } = useProviderContext()
const isAnnotationFull = enableBilling && plan.usage.annotatedResponse >= plan.total.annotatedResponse
const [isShowAnnotationFullModal, setIsShowAnnotationFullModal] = useState(false)
@ -48,7 +48,7 @@ const Annotation: FC<Props> = (props) => {
const [list, setList] = useState<AnnotationItem[]>([])
const [total, setTotal] = useState(0)
const [isLoading, setIsLoading] = useState(false)
const [controlUpdateList, setControlUpdateList] = useState(Date.now())
const [controlUpdateList, setControlUpdateList] = useState(() => Date.now())
const [currItem, setCurrItem] = useState<AnnotationItem | null>(null)
const [isShowViewModal, setIsShowViewModal] = useState(false)
const [selectedIds, setSelectedIds] = useState<string[]>([])

2
web/app/components/app/configuration/config-prompt/prompt-editor-height-resize-wrap.tsx
View File

@ -25,7 +25,7 @@ const PromptEditorHeightResizeWrap: FC<Props> = ({
}) => {
const [clientY, setClientY] = useState(0)
const [isResizing, setIsResizing] = useState(false)
const [prevUserSelectStyle, setPrevUserSelectStyle] = useState(getComputedStyle(document.body).userSelect)
const [prevUserSelectStyle, setPrevUserSelectStyle] = useState(() => getComputedStyle(document.body).userSelect)
const [oldHeight, setOldHeight] = useState(height)
const handleStartResize = useCallback((e: React.MouseEvent<HTMLElement>) => {

2
web/app/components/app/configuration/config-var/config-modal/index.tsx
View File

@ -53,7 +53,7 @@ const ConfigModal: FC<IConfigModalProps> = ({
}) => {
const { modelConfig } = useContext(ConfigContext)
const { t } = useTranslation()
const [tempPayload, setTempPayload] = useState<InputVar>(payload || getNewVarInWorkflow('') as any)
const [tempPayload, setTempPayload] = useState<InputVar>(() => payload || getNewVarInWorkflow('') as any)
const { type, label, variable, options, max_length } = tempPayload
const modalRef = useRef<HTMLDivElement>(null)
const appDetail = useAppStore(state => state.appDetail)

31
web/app/components/app/configuration/dataset-config/index.tsx
View File

@ -65,13 +65,40 @@ const DatasetConfig: FC = () => {
const onRemove = (id: string) => {
const filteredDataSets = dataSet.filter(item => item.id !== id)
setDataSet(filteredDataSets)
const retrievalConfig = getMultipleRetrievalConfig(datasetConfigs as any, filteredDataSets, dataSet, {
const { datasets, retrieval_model, score_threshold_enabled, ...restConfigs } = datasetConfigs
const {
top_k,
score_threshold,
reranking_model,
reranking_mode,
weights,
reranking_enable,
} = restConfigs
const oldRetrievalConfig = {
top_k,
score_threshold,
reranking_model: (reranking_model.reranking_provider_name && reranking_model.reranking_model_name) ? {
provider: reranking_model.reranking_provider_name,
model: reranking_model.reranking_model_name,
} : undefined,
reranking_mode,
weights,
reranking_enable,
}
const retrievalConfig = getMultipleRetrievalConfig(oldRetrievalConfig, filteredDataSets, dataSet, {
provider: currentRerankProvider?.provider,
model: currentRerankModel?.model,
})
setDatasetConfigs({
...(datasetConfigs as any),
...datasetConfigsRef.current,
...retrievalConfig,
reranking_model: {
reranking_provider_name: retrievalConfig?.reranking_model?.provider || '',
reranking_model_name: retrievalConfig?.reranking_model?.model || '',
},
retrieval_model,
score_threshold_enabled,
datasets,
})
const {
allExternal,

27
web/app/components/app/configuration/dataset-config/params-config/config-content.tsx
View File

@ -30,11 +30,11 @@ import { noop } from 'lodash-es'
type Props = {
datasetConfigs: DatasetConfigs
onChange: (configs: DatasetConfigs, isRetrievalModeChange?: boolean) => void
selectedDatasets?: DataSet[]
isInWorkflow?: boolean
singleRetrievalModelConfig?: ModelConfig
onSingleRetrievalModelChange?: (config: ModelConfig) => void
onSingleRetrievalModelParamsChange?: (config: ModelConfig) => void
selectedDatasets?: DataSet[]
}
const ConfigContent: FC<Props> = ({
@ -61,22 +61,28 @@ const ConfigContent: FC<Props> = ({
const {
modelList: rerankModelList,
currentModel: validDefaultRerankModel,
currentProvider: validDefaultRerankProvider,
} = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
/**
* If reranking model is set and is valid, use the reranking model
* Otherwise, check if the default reranking model is valid
*/
const {
currentModel: currentRerankModel,
} = useCurrentProviderAndModel(
rerankModelList,
{
provider: datasetConfigs.reranking_model?.reranking_provider_name,
model: datasetConfigs.reranking_model?.reranking_model_name,
provider: datasetConfigs.reranking_model?.reranking_provider_name || validDefaultRerankProvider?.provider || '',
model: datasetConfigs.reranking_model?.reranking_model_name || validDefaultRerankModel?.model || '',
},
)
const rerankModel = useMemo(() => {
return {
provider_name: datasetConfigs?.reranking_model?.reranking_provider_name ?? '',
model_name: datasetConfigs?.reranking_model?.reranking_model_name ?? '',
provider_name: datasetConfigs.reranking_model?.reranking_provider_name ?? '',
model_name: datasetConfigs.reranking_model?.reranking_model_name ?? '',
}
}, [datasetConfigs.reranking_model])
@ -135,7 +141,7 @@ const ConfigContent: FC<Props> = ({
})
}
const model = singleRetrievalConfig
const model = singleRetrievalConfig // Legacy code, for compatibility, have to keep it
const rerankingModeOptions = [
{
@ -158,7 +164,7 @@ const ConfigContent: FC<Props> = ({
const canManuallyToggleRerank = useMemo(() => {
return (selectedDatasetsMode.allInternal && selectedDatasetsMode.allEconomic)
|| selectedDatasetsMode.allExternal
|| selectedDatasetsMode.allExternal
}, [selectedDatasetsMode.allEconomic, selectedDatasetsMode.allExternal, selectedDatasetsMode.allInternal])
const showRerankModel = useMemo(() => {
@ -168,7 +174,7 @@ const ConfigContent: FC<Props> = ({
return datasetConfigs.reranking_enable
}, [datasetConfigs.reranking_enable, canManuallyToggleRerank])
const handleDisabledSwitchClick = useCallback((enable: boolean) => {
const handleManuallyToggleRerank = useCallback((enable: boolean) => {
if (!currentRerankModel && enable)
Toast.notify({ type: 'error', message: t('workflow.errorMsg.rerankModelRequired') })
onChange({
@ -255,12 +261,11 @@ const ConfigContent: FC<Props> = ({
<div className='mt-2'>
<div className='flex items-center'>
{
selectedDatasetsMode.allEconomic && !selectedDatasetsMode.mixtureInternalAndExternal && (
canManuallyToggleRerank && (
<Switch
size='md'
defaultValue={showRerankModel}
disabled={!canManuallyToggleRerank}
onChange={handleDisabledSwitchClick}
onChange={handleManuallyToggleRerank}
/>
)
}

4
web/app/components/app/configuration/hooks/use-advanced-prompt-config.ts
View File

@ -35,8 +35,8 @@ const useAdvancedPromptConfig = ({
setStop,
}: Param) => {
const isAdvancedPrompt = promptMode === PromptMode.advanced
const [chatPromptConfig, setChatPromptConfig] = useState<ChatPromptConfig>(clone(DEFAULT_CHAT_PROMPT_CONFIG))
const [completionPromptConfig, setCompletionPromptConfig] = useState<CompletionPromptConfig>(clone(DEFAULT_COMPLETION_PROMPT_CONFIG))
const [chatPromptConfig, setChatPromptConfig] = useState<ChatPromptConfig>(() => clone(DEFAULT_CHAT_PROMPT_CONFIG))
const [completionPromptConfig, setCompletionPromptConfig] = useState<CompletionPromptConfig>(() => clone(DEFAULT_COMPLETION_PROMPT_CONFIG))
const currentAdvancedPrompt = (() => {
if (!isAdvancedPrompt)

32
web/app/components/app/configuration/index.tsx
View File

@ -284,18 +284,28 @@ const Configuration: FC = () => {
setRerankSettingModalOpen(true)
const { datasets, retrieval_model, score_threshold_enabled, ...restConfigs } = datasetConfigs
const {
top_k,
score_threshold,
reranking_model,
reranking_mode,
weights,
reranking_enable,
} = restConfigs
const retrievalConfig = getMultipleRetrievalConfig({
top_k: restConfigs.top_k,
score_threshold: restConfigs.score_threshold,
reranking_model: restConfigs.reranking_model && {
provider: restConfigs.reranking_model.reranking_provider_name,
model: restConfigs.reranking_model.reranking_model_name,
},
reranking_mode: restConfigs.reranking_mode,
weights: restConfigs.weights,
reranking_enable: restConfigs.reranking_enable,
}, newDatasets, dataSets, {
const oldRetrievalConfig = {
top_k,
score_threshold,
reranking_model: (reranking_model.reranking_provider_name && reranking_model.reranking_model_name) ? {
provider: reranking_model.reranking_provider_name,
model: reranking_model.reranking_model_name,
} : undefined,
reranking_mode,
weights,
reranking_enable,
}
const retrievalConfig = getMultipleRetrievalConfig(oldRetrievalConfig, newDatasets, dataSets, {
provider: currentRerankProvider?.provider,
model: currentRerankModel?.model,
})

9
web/app/components/base/chat/chat/index.tsx
View File

@ -160,8 +160,13 @@ const Chat: FC<ChatProps> = ({
})
useEffect(() => {
window.addEventListener('resize', debounce(handleWindowResize))
return () => window.removeEventListener('resize', handleWindowResize)
const debouncedHandler = debounce(handleWindowResize, 200)
window.addEventListener('resize', debouncedHandler)
return () => {
window.removeEventListener('resize', debouncedHandler)
debouncedHandler.cancel()
}
}, [handleWindowResize])
useEffect(() => {

20
web/app/components/base/chat/chat/loading-anim/style.module.css
View File

@ -1,6 +1,6 @@
.dot-flashing {
position: relative;
animation: 1s infinite linear alternate;
animation: dot-flashing 1s infinite linear alternate;
animation-delay: 0.5s;
}
@ -10,7 +10,7 @@
display: inline-block;
position: absolute;
top: 0;
animation: 1s infinite linear alternate;
animation: dot-flashing 1s infinite linear alternate;
}
.dot-flashing::before {
@ -51,15 +51,21 @@
border-radius: 50%;
background-color: #667085;
color: #667085;
animation-name: dot-flashing;
animation: dot-flashing 1s infinite linear alternate;
}
.text {
animation-delay: 0.5s;
}
.text::before {
left: -7px;
animation-delay: 0s;
}
.text::after {
left: 7px;
animation-delay: 1s;
}
.avatar,
@ -70,13 +76,19 @@
border-radius: 50%;
background-color: #155EEF;
color: #155EEF;
animation-name: dot-flashing-avatar;
animation: dot-flashing-avatar 1s infinite linear alternate;
}
.avatar {
animation-delay: 0.5s;
}
.avatar::before {
left: -5px;
animation-delay: 0s;
}
.avatar::after {
left: 5px;
animation-delay: 1s;
}

4
web/app/components/base/date-and-time-picker/date-picker/index.tsx
View File

@ -56,8 +56,8 @@ const DatePicker = ({
const [currentDate, setCurrentDate] = useState(inputValue || defaultValue)
const [selectedDate, setSelectedDate] = useState(inputValue)
const [selectedMonth, setSelectedMonth] = useState((inputValue || defaultValue).month())
const [selectedYear, setSelectedYear] = useState((inputValue || defaultValue).year())
const [selectedMonth, setSelectedMonth] = useState(() => (inputValue || defaultValue).month())
const [selectedYear, setSelectedYear] = useState(() => (inputValue || defaultValue).year())
useEffect(() => {
const handleClickOutside = (event: MouseEvent) => {

2
web/app/components/base/date-and-time-picker/time-picker/index.tsx
View File

@ -29,7 +29,7 @@ const TimePicker = ({
const [isOpen, setIsOpen] = useState(false)
const containerRef = useRef<HTMLDivElement>(null)
const isInitial = useRef(true)
const [selectedTime, setSelectedTime] = useState(value ? getDateWithTimezone({ timezone, date: value }) : undefined)
const [selectedTime, setSelectedTime] = useState(() => value ? getDateWithTimezone({ timezone, date: value }) : undefined)
useEffect(() => {
const handleClickOutside = (event: MouseEvent) => {

2
web/app/components/base/markdown-blocks/think-block.tsx
View File

@ -37,7 +37,7 @@ const removeEndThink = (children: any): any => {
const useThinkTimer = (children: any) => {
const { isResponding } = useChatContext()
const [startTime] = useState(Date.now())
const [startTime] = useState(() => Date.now())
const [elapsedTime, setElapsedTime] = useState(0)
const [isComplete, setIsComplete] = useState(false)
const timerRef = useRef<NodeJS.Timeout>()

2
web/app/components/base/notion-page-selector/base.tsx
View File

@ -93,7 +93,7 @@ const NotionPageSelector = ({
const defaultSelectedPagesId = useMemo(() => {
return [...Array.from(pagesMapAndSelectedPagesId[1]), ...(value || [])]
}, [pagesMapAndSelectedPagesId, value])
const [selectedPagesId, setSelectedPagesId] = useState<Set<string>>(new Set(defaultSelectedPagesId))
const [selectedPagesId, setSelectedPagesId] = useState<Set<string>>(() => new Set(defaultSelectedPagesId))
useEffect(() => {
setSelectedPagesId(new Set(defaultSelectedPagesId))

2
web/app/components/base/tab-slider/index.tsx
View File

@ -21,7 +21,7 @@ const TabSlider: FC<TabSliderProps> = ({
onChange,
options,
}) => {
const [activeIndex, setActiveIndex] = useState(options.findIndex(option => option.value === value))
const [activeIndex, setActiveIndex] = useState(() => options.findIndex(option => option.value === value))
const [sliderStyle, setSliderStyle] = useState({})
const { data: pluginList } = useInstalledPluginList()

2
web/app/components/custom/custom-web-app-brand/index.tsx
View File

@ -38,7 +38,7 @@ const CustomWebAppBrand = () => {
isCurrentWorkspaceManager,
} = useAppContext()
const [fileId, setFileId] = useState('')
const [imgKey, setImgKey] = useState(Date.now())
const [imgKey, setImgKey] = useState(() => Date.now())
const [uploadProgress, setUploadProgress] = useState(0)
const systemFeatures = useGlobalPublicStore(s => s.systemFeatures)
const isSandbox = enableBilling && plan.type === Plan.sandbox

4
web/app/components/datasets/common/retrieval-method-config/index.tsx
View File

@ -40,7 +40,7 @@ const RetrievalMethodConfig: FC<Props> = ({
onChange({
...value,
search_method: retrieveMethod,
...(!value.reranking_model.reranking_model_name
...((!value.reranking_model.reranking_model_name || !value.reranking_model.reranking_provider_name)
? {
reranking_model: {
reranking_provider_name: isRerankDefaultModelValid ? rerankDefaultModel?.provider?.provider ?? '' : '',
@ -57,7 +57,7 @@ const RetrievalMethodConfig: FC<Props> = ({
onChange({
...value,
search_method: retrieveMethod,
...(!value.reranking_model.reranking_model_name
...((!value.reranking_model.reranking_model_name || !value.reranking_model.reranking_provider_name)
? {
reranking_model: {
reranking_provider_name: isRerankDefaultModelValid ? rerankDefaultModel?.provider?.provider ?? '' : '',

4
web/app/components/datasets/common/retrieval-param-config/index.tsx
View File

@ -54,7 +54,7 @@ const RetrievalParamConfig: FC<Props> = ({
},
)
const handleDisabledSwitchClick = useCallback((enable: boolean) => {
const handleToggleRerankEnable = useCallback((enable: boolean) => {
if (enable && !currentModel)
Toast.notify({ type: 'error', message: t('workflow.errorMsg.rerankModelRequired') })
onChange({
@ -119,7 +119,7 @@ const RetrievalParamConfig: FC<Props> = ({
<Switch
size='md'
defaultValue={value.reranking_enable}
onChange={handleDisabledSwitchClick}
onChange={handleToggleRerankEnable}
/>
)}
<div className='flex items-center'>

2
web/app/components/header/account-setting/data-source-page-new/install-from-marketplace.tsx
View File

@ -52,7 +52,7 @@ const InstallFromMarketplace = ({
<div className='flex items-center justify-between'>
<div className='system-md-semibold flex cursor-pointer items-center gap-1 text-text-primary' onClick={() => setCollapse(!collapse)}>
<RiArrowDownSLine className={cn('h-4 w-4', collapse && '-rotate-90')} />
{t('common.modelProvider.installProvider')}
{t('common.modelProvider.installDataSourceProvider')}
</div>
<div className='mb-2 flex items-center pt-2'>
<span className='system-sm-regular pr-1 text-text-tertiary'>{t('common.modelProvider.discoverMore')}</span>

9
web/app/components/header/account-setting/model-provider-page/hooks.ts
View File

@ -323,15 +323,18 @@ export const useRefreshModel = () => {
const { eventEmitter } = useEventEmitterContextContext()
const updateModelProviders = useUpdateModelProviders()
const updateModelList = useUpdateModelList()
const handleRefreshModel = useCallback((provider: ModelProvider, configurationMethod: ConfigurationMethodEnum, CustomConfigurationModelFixedFields?: CustomConfigurationModelFixedFields) => {
const handleRefreshModel = useCallback((
provider: ModelProvider,
CustomConfigurationModelFixedFields?: CustomConfigurationModelFixedFields,
refreshModelList?: boolean,
) => {
updateModelProviders()
provider.supported_model_types.forEach((type) => {
updateModelList(type)
})
if (configurationMethod === ConfigurationMethodEnum.customizableModel
&& provider.custom_configuration.status === CustomConfigurationStatusEnum.active) {
if (refreshModelList && provider.custom_configuration.status === CustomConfigurationStatusEnum.active) {
eventEmitter?.emit({
type: UPDATE_MODEL_PROVIDER_CUSTOM_MODEL_LIST,
payload: provider.provider,

6
web/app/components/header/account-setting/model-provider-page/model-auth/hooks/use-auth.ts
View File

@ -90,7 +90,7 @@ export const useAuth = (
type: 'success',
message: t('common.api.actionSuccess'),
})
handleRefreshModel(provider, configurationMethod, undefined)
handleRefreshModel(provider, undefined, true)
}
finally {
handleSetDoingAction(false)
@ -125,7 +125,7 @@ export const useAuth = (
type: 'success',
message: t('common.api.actionSuccess'),
})
handleRefreshModel(provider, configurationMethod, undefined)
handleRefreshModel(provider, undefined, true)
onRemove?.(pendingOperationCredentialId.current ?? '')
closeConfirmDelete()
}
@ -147,7 +147,7 @@ export const useAuth = (
if (res.result === 'success') {
notify({ type: 'success', message: t('common.actionMsg.modifiedSuccessfully') })
handleRefreshModel(provider, configurationMethod, undefined)
handleRefreshModel(provider, undefined, !payload.credential_id)
}
}
finally {

2
web/app/components/header/account-setting/model-provider-page/provider-added-card/model-load-balancing-modal.tsx
View File

@ -159,7 +159,7 @@ const ModelLoadBalancingModal = ({
)
if (res.result === 'success') {
notify({ type: 'success', message: t('common.actionMsg.modifiedSuccessfully') })
handleRefreshModel(provider, configurateMethod, currentCustomConfigurationModelFixedFields)
handleRefreshModel(provider, currentCustomConfigurationModelFixedFields, false)
onSave?.(provider.provider)
onClose?.()
}

2
web/app/components/header/maintenance-notice.tsx
View File

@ -6,7 +6,7 @@ import { useLanguage } from '@/app/components/header/account-setting/model-provi
const MaintenanceNotice = () => {
const locale = useLanguage()
const [showNotice, setShowNotice] = useState(localStorage.getItem('hide-maintenance-notice') !== '1')
const [showNotice, setShowNotice] = useState(() => localStorage.getItem('hide-maintenance-notice') !== '1')
const handleJumpNotice = () => {
window.open(NOTICE_I18N.href, '_blank')
}

2
web/app/components/signin/countdown.tsx
View File

@ -12,7 +12,7 @@ type CountdownProps = {
export default function Countdown({ onResend }: CountdownProps) {
const { t } = useTranslation()
const [leftTime, setLeftTime] = useState(Number(localStorage.getItem(COUNT_DOWN_KEY) || COUNT_DOWN_TIME_MS))
const [leftTime, setLeftTime] = useState(() => Number(localStorage.getItem(COUNT_DOWN_KEY) || COUNT_DOWN_TIME_MS))
const [time] = useCountDown({
leftTime,
onEnd: () => {

2
web/app/components/tools/mcp/modal.tsx
View File

@ -65,7 +65,7 @@ const MCPModal = ({
const originalServerID = data?.server_identifier
const [url, setUrl] = React.useState(data?.server_url || '')
const [name, setName] = React.useState(data?.name || '')
const [appIcon, setAppIcon] = useState<AppIconSelection>(getIcon(data))
const [appIcon, setAppIcon] = useState<AppIconSelection>(() => getIcon(data))
const [showAppIconPicker, setShowAppIconPicker] = useState(false)
const [serverIdentifier, setServerIdentifier] = React.useState(data?.server_identifier || '')
const [timeout, setMcpTimeout] = React.useState(data?.timeout || 30)

12
web/app/components/tools/provider-list.tsx
View File

@ -17,7 +17,7 @@ import CardMoreInfo from '@/app/components/plugins/card/card-more-info'
import PluginDetailPanel from '@/app/components/plugins/plugin-detail-panel'
import MCPList from './mcp'
import { useAllToolProviders } from '@/service/use-tools'
import { useInstalledPluginList, useInvalidateInstalledPluginList } from '@/service/use-plugins'
import { useCheckInstalled, useInvalidateInstalledPluginList } from '@/service/use-plugins'
import { useGlobalPublicStore } from '@/context/global-public-context'
import { ToolTypeEnum } from '../workflow/block-selector/types'
import { useMarketplace } from './marketplace/hooks'
@ -77,12 +77,14 @@ const ProviderList = () => {
const currentProvider = useMemo<Collection | undefined>(() => {
return filteredCollectionList.find(collection => collection.id === currentProviderId)
}, [currentProviderId, filteredCollectionList])
const { data: pluginList } = useInstalledPluginList()
const { data: checkedInstalledData } = useCheckInstalled({
pluginIds: currentProvider?.plugin_id ? [currentProvider.plugin_id] : [],
enabled: !!currentProvider?.plugin_id,
})
const invalidateInstalledPluginList = useInvalidateInstalledPluginList()
const currentPluginDetail = useMemo(() => {
const detail = pluginList?.plugins.find(plugin => plugin.plugin_id === currentProvider?.plugin_id)
return detail
}, [currentProvider?.plugin_id, pluginList?.plugins])
return checkedInstalledData?.plugins?.[0]
}, [checkedInstalledData])
const toolListTailRef = useRef<HTMLDivElement>(null)
const showMarketplacePanel = useCallback(() => {

2
web/app/components/workflow/nodes/_base/hooks/use-resize-panel.ts
View File

@ -33,7 +33,7 @@ export const useResizePanel = (params?: UseResizePanelParams) => {
const initContainerWidthRef = useRef(0)
const initContainerHeightRef = useRef(0)
const isResizingRef = useRef(false)
const [prevUserSelectStyle, setPrevUserSelectStyle] = useState(getComputedStyle(document.body).userSelect)
const [prevUserSelectStyle, setPrevUserSelectStyle] = useState(() => getComputedStyle(document.body).userSelect)
const handleStartResize = useCallback((e: MouseEvent) => {
initXRef.current = e.clientX

2
web/app/components/workflow/nodes/http/hooks/use-key-value-list.ts
View File

@ -16,7 +16,7 @@ const strToKeyValueList = (value: string) => {
}
const useKeyValueList = (value: string, onChange: (value: string) => void, noFilter?: boolean) => {
const [list, doSetList] = useState<KeyValue[]>(value ? strToKeyValueList(value) : [])
const [list, doSetList] = useState<KeyValue[]>(() => value ? strToKeyValueList(value) : [])
const setList = (l: KeyValue[]) => {
doSetList(l.map((item) => {
return {

5
web/app/components/workflow/nodes/knowledge-base/components/option-card.tsx
View File

@ -86,7 +86,10 @@ const OptionCard = memo(({
readonly && 'cursor-not-allowed',
wrapperClassName && (typeof wrapperClassName === 'function' ? wrapperClassName(isActive) : wrapperClassName),
)}
onClick={() => !readonly && enableSelect && id && onClick?.(id)}
onClick={(e) => {
e.stopPropagation()
!readonly && enableSelect && id && onClick?.(id)
}}
>
<div className={cn(
'relative flex rounded-t-xl p-2',

33
web/app/components/workflow/nodes/knowledge-base/default.ts
View File

@ -2,6 +2,7 @@ import type { NodeDefault } from '../../types'
import type { KnowledgeBaseNodeType } from './types'
import { genNodeMetaData } from '@/app/components/workflow/utils'
import { BlockEnum } from '@/app/components/workflow/types'
import { IndexingType } from '@/app/components/datasets/create/step-two'
const metaData = genNodeMetaData({
sort: 3.1,
@ -27,8 +28,17 @@ const nodeDefault: NodeDefault<KnowledgeBaseNodeType> = {
chunk_structure,
indexing_technique,
retrieval_model,
embedding_model,
embedding_model_provider,
index_chunk_variable_selector,
} = payload
const {
search_method,
reranking_enable,
reranking_model,
} = retrieval_model || {}
if (!chunk_structure) {
return {
isValid: false,
@ -36,6 +46,13 @@ const nodeDefault: NodeDefault<KnowledgeBaseNodeType> = {
}
}
if (index_chunk_variable_selector.length === 0) {
return {
isValid: false,
errorMessage: t('workflow.nodes.knowledgeBase.chunksVariableIsRequired'),
}
}
if (!indexing_technique) {
return {
isValid: false,
@ -43,13 +60,27 @@ const nodeDefault: NodeDefault<KnowledgeBaseNodeType> = {
}
}
if (!retrieval_model || !retrieval_model.search_method) {
if (indexing_technique === IndexingType.QUALIFIED && (!embedding_model || !embedding_model_provider)) {
return {
isValid: false,
errorMessage: t('workflow.nodes.knowledgeBase.embeddingModelIsRequired'),
}
}
if (!retrieval_model || !search_method) {
return {
isValid: false,
errorMessage: t('workflow.nodes.knowledgeBase.retrievalSettingIsRequired'),
}
}
if (reranking_enable && (!reranking_model || !reranking_model.reranking_provider_name || !reranking_model.reranking_model_name)) {
return {
isValid: false,
errorMessage: t('workflow.nodes.knowledgeBase.rerankingModelIsRequired'),
}
}
return {
isValid: true,
errorMessage: '',

85
web/app/components/workflow/nodes/knowledge-base/hooks/use-config.ts
View File

@ -9,13 +9,17 @@ import {
ChunkStructureEnum,
IndexMethodEnum,
RetrievalSearchMethodEnum,
WeightedScoreEnum,
} from '../types'
import type {
HybridSearchModeEnum,
KnowledgeBaseNodeType,
RerankingModel,
} from '../types'
import {
HybridSearchModeEnum,
} from '../types'
import { isHighQualitySearchMethod } from '../utils'
import { DEFAULT_WEIGHTED_SCORE, RerankingModeEnum } from '@/models/datasets'
export const useConfig = (id: string) => {
const store = useStoreApi()
@ -35,6 +39,25 @@ export const useConfig = (id: string) => {
})
}, [id, handleNodeDataUpdateWithSyncDraft])
const getDefaultWeights = useCallback(({
embeddingModel,
embeddingModelProvider,
}: {
embeddingModel: string
embeddingModelProvider: string
}) => {
return {
vector_setting: {
vector_weight: DEFAULT_WEIGHTED_SCORE.other.semantic,
embedding_provider_name: embeddingModelProvider || '',
embedding_model_name: embeddingModel,
},
keyword_setting: {
keyword_weight: DEFAULT_WEIGHTED_SCORE.other.keyword,
},
}
}, [])
const handleChunkStructureChange = useCallback((chunkStructure: ChunkStructureEnum) => {
const nodeData = getNodeData()
const {
@ -80,39 +103,72 @@ export const useConfig = (id: string) => {
embeddingModelProvider: string
}) => {
const nodeData = getNodeData()
handleNodeDataUpdate({
const defaultWeights = getDefaultWeights({
embeddingModel,
embeddingModelProvider,
})
const changeData = {
embedding_model: embeddingModel,
embedding_model_provider: embeddingModelProvider,
retrieval_model: {
...nodeData?.data.retrieval_model,
vector_setting: {
...nodeData?.data.retrieval_model.vector_setting,
embedding_provider_name: embeddingModelProvider,
embedding_model_name: embeddingModel,
},
},
})
}, [getNodeData, handleNodeDataUpdate])
}
if (changeData.retrieval_model.weights) {
changeData.retrieval_model = {
...changeData.retrieval_model,
weights: {
...changeData.retrieval_model.weights,
vector_setting: {
...changeData.retrieval_model.weights.vector_setting,
embedding_provider_name: embeddingModelProvider,
embedding_model_name: embeddingModel,
},
},
}
}
else {
changeData.retrieval_model = {
...changeData.retrieval_model,
weights: defaultWeights,
}
}
handleNodeDataUpdate(changeData)
}, [getNodeData, getDefaultWeights, handleNodeDataUpdate])
const handleRetrievalSearchMethodChange = useCallback((searchMethod: RetrievalSearchMethodEnum) => {
const nodeData = getNodeData()
handleNodeDataUpdate({
const changeData = {
retrieval_model: {
...nodeData?.data.retrieval_model,
search_method: searchMethod,
reranking_mode: nodeData?.data.retrieval_model.reranking_mode || RerankingModeEnum.RerankingModel,
},
})
}
if (searchMethod === RetrievalSearchMethodEnum.hybrid) {
changeData.retrieval_model = {
...changeData.retrieval_model,
reranking_enable: changeData.retrieval_model.reranking_mode === RerankingModeEnum.RerankingModel,
}
}
handleNodeDataUpdate(changeData)
}, [getNodeData, handleNodeDataUpdate])
const handleHybridSearchModeChange = useCallback((hybridSearchMode: HybridSearchModeEnum) => {
const nodeData = getNodeData()
const defaultWeights = getDefaultWeights({
embeddingModel: nodeData?.data.embedding_model || '',
embeddingModelProvider: nodeData?.data.embedding_model_provider || '',
})
handleNodeDataUpdate({
retrieval_model: {
...nodeData?.data.retrieval_model,
reranking_mode: hybridSearchMode,
reranking_enable: hybridSearchMode === HybridSearchModeEnum.RerankingModel,
weights: nodeData?.data.retrieval_model.weights || defaultWeights,
},
})
}, [getNodeData, handleNodeDataUpdate])
}, [getNodeData, getDefaultWeights, handleNodeDataUpdate])
const handleRerankingModelEnabledChange = useCallback((rerankingModelEnabled: boolean) => {
const nodeData = getNodeData()
@ -130,11 +186,10 @@ export const useConfig = (id: string) => {
retrieval_model: {
...nodeData?.data.retrieval_model,
weights: {
weight_type: 'weighted_score',
weight_type: WeightedScoreEnum.Customized,
vector_setting: {
...nodeData?.data.retrieval_model.weights?.vector_setting,
vector_weight: weightedScore.value[0],
embedding_provider_name: '',
embedding_model_name: '',
},
keyword_setting: {
keyword_weight: weightedScore.value[1],

100
web/app/components/workflow/nodes/knowledge-retrieval/components/retrieval-config.tsx
View File

@ -1,6 +1,6 @@
'use client'
import type { FC } from 'react'
import React, { useCallback, useState } from 'react'
import React, { useCallback, useMemo } from 'react'
import { RiEqualizer2Line } from '@remixicon/react'
import { useTranslation } from 'react-i18next'
import type { MultipleRetrievalConfig, SingleRetrievalConfig } from '../types'
@ -14,8 +14,6 @@ import {
import ConfigRetrievalContent from '@/app/components/app/configuration/dataset-config/params-config/config-content'
import { RETRIEVE_TYPE } from '@/types/app'
import { DATASET_DEFAULT } from '@/config'
import { useModelListAndDefaultModelAndCurrentProviderAndModel } from '@/app/components/header/account-setting/model-provider-page/hooks'
import { ModelTypeEnum } from '@/app/components/header/account-setting/model-provider-page/declarations'
import Button from '@/app/components/base/button'
import type { DatasetConfigs } from '@/models/debug'
import type { DataSet } from '@/models/datasets'
@ -32,8 +30,8 @@ type Props = {
onSingleRetrievalModelChange?: (config: ModelConfig) => void
onSingleRetrievalModelParamsChange?: (config: ModelConfig) => void
readonly?: boolean
openFromProps?: boolean
onOpenFromPropsChange?: (openFromProps: boolean) => void
rerankModalOpen: boolean
onRerankModelOpenChange: (open: boolean) => void
selectedDatasets: DataSet[]
}
@ -45,26 +43,52 @@ const RetrievalConfig: FC<Props> = ({
onSingleRetrievalModelChange,
onSingleRetrievalModelParamsChange,
readonly,
openFromProps,
onOpenFromPropsChange,
rerankModalOpen,
onRerankModelOpenChange,
selectedDatasets,
}) => {
const { t } = useTranslation()
const [open, setOpen] = useState(false)
const mergedOpen = openFromProps !== undefined ? openFromProps : open
const { retrieval_mode, multiple_retrieval_config } = payload
const handleOpen = useCallback((newOpen: boolean) => {
setOpen(newOpen)
onOpenFromPropsChange?.(newOpen)
}, [onOpenFromPropsChange])
onRerankModelOpenChange(newOpen)
}, [onRerankModelOpenChange])
const {
currentProvider: validRerankDefaultProvider,
currentModel: validRerankDefaultModel,
} = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
const datasetConfigs = useMemo(() => {
const {
reranking_model,
top_k,
score_threshold,
reranking_mode,
weights,
reranking_enable,
} = multiple_retrieval_config || {}
return {
retrieval_model: retrieval_mode,
reranking_model: (reranking_model?.provider && reranking_model?.model)
? {
reranking_provider_name: reranking_model?.provider,
reranking_model_name: reranking_model?.model,
}
: {
reranking_provider_name: '',
reranking_model_name: '',
},
top_k: top_k || DATASET_DEFAULT.top_k,
score_threshold_enabled: !(score_threshold === undefined || score_threshold === null),
score_threshold,
datasets: {
datasets: [],
},
reranking_mode,
weights,
reranking_enable,
}
}, [retrieval_mode, multiple_retrieval_config])
const { multiple_retrieval_config } = payload
const handleChange = useCallback((configs: DatasetConfigs, isRetrievalModeChange?: boolean) => {
// Legacy code, for compatibility, have to keep it
if (isRetrievalModeChange) {
onRetrievalModeChange(configs.retrieval_model)
return
@ -72,13 +96,11 @@ const RetrievalConfig: FC<Props> = ({
onMultipleRetrievalConfigChange({
top_k: configs.top_k,
score_threshold: configs.score_threshold_enabled ? (configs.score_threshold ?? DATASET_DEFAULT.score_threshold) : null,
reranking_model: payload.retrieval_mode === RETRIEVE_TYPE.oneWay
reranking_model: retrieval_mode === RETRIEVE_TYPE.oneWay
? undefined
// eslint-disable-next-line sonarjs/no-nested-conditional
: (!configs.reranking_model?.reranking_provider_name
? {
provider: validRerankDefaultProvider?.provider || '',
model: validRerankDefaultModel?.model || '',
}
? undefined
: {
provider: configs.reranking_model?.reranking_provider_name,
model: configs.reranking_model?.reranking_model_name,
@ -87,11 +109,11 @@ const RetrievalConfig: FC<Props> = ({
weights: configs.weights,
reranking_enable: configs.reranking_enable,
})
}, [onMultipleRetrievalConfigChange, payload.retrieval_mode, validRerankDefaultProvider, validRerankDefaultModel, onRetrievalModeChange])
}, [onMultipleRetrievalConfigChange, retrieval_mode, onRetrievalModeChange])
return (
<PortalToFollowElem
open={mergedOpen}
open={rerankModalOpen}
onOpenChange={handleOpen}
placement='bottom-end'
offset={{
@ -102,14 +124,14 @@ const RetrievalConfig: FC<Props> = ({
onClick={() => {
if (readonly)
return
handleOpen(!mergedOpen)
handleOpen(!rerankModalOpen)
}}
>
<Button
variant='ghost'
size='small'
disabled={readonly}
className={cn(open && 'bg-components-button-ghost-bg-hover')}
className={cn(rerankModalOpen && 'bg-components-button-ghost-bg-hover')}
>
<RiEqualizer2Line className='mr-1 h-3.5 w-3.5' />
{t('dataset.retrievalSettings')}
@ -118,35 +140,13 @@ const RetrievalConfig: FC<Props> = ({
<PortalToFollowElemContent style={{ zIndex: 1001 }}>
<div className='w-[404px] rounded-2xl border border-components-panel-border bg-components-panel-bg px-4 pb-4 pt-3 shadow-xl'>
<ConfigRetrievalContent
datasetConfigs={
{
retrieval_model: payload.retrieval_mode,
reranking_model: multiple_retrieval_config?.reranking_model?.provider
? {
reranking_provider_name: multiple_retrieval_config.reranking_model?.provider,
reranking_model_name: multiple_retrieval_config.reranking_model?.model,
}
: {
reranking_provider_name: '',
reranking_model_name: '',
},
top_k: multiple_retrieval_config?.top_k || DATASET_DEFAULT.top_k,
score_threshold_enabled: !(multiple_retrieval_config?.score_threshold === undefined || multiple_retrieval_config.score_threshold === null),
score_threshold: multiple_retrieval_config?.score_threshold,
datasets: {
datasets: [],
},
reranking_mode: multiple_retrieval_config?.reranking_mode,
weights: multiple_retrieval_config?.weights,
reranking_enable: multiple_retrieval_config?.reranking_enable,
}
}
datasetConfigs={datasetConfigs}
onChange={handleChange}
selectedDatasets={selectedDatasets}
isInWorkflow
singleRetrievalModelConfig={singleRetrievalModelConfig}
onSingleRetrievalModelChange={onSingleRetrievalModelChange}
onSingleRetrievalModelParamsChange={onSingleRetrievalModelParamsChange}
selectedDatasets={selectedDatasets}
/>
</div>
</PortalToFollowElemContent>

4
web/app/components/workflow/nodes/knowledge-retrieval/default.ts
View File

@ -1,6 +1,6 @@
import type { NodeDefault } from '../../types'
import type { KnowledgeRetrievalNodeType } from './types'
import { checkoutRerankModelConfigedInRetrievalSettings } from './utils'
import { checkoutRerankModelConfiguredInRetrievalSettings } from './utils'
import { DATASET_DEFAULT } from '@/config'
import { RETRIEVE_TYPE } from '@/types/app'
import { genNodeMetaData } from '@/app/components/workflow/utils'
@ -36,7 +36,7 @@ const nodeDefault: NodeDefault<KnowledgeRetrievalNodeType> = {
const { _datasets, multiple_retrieval_config, retrieval_mode } = payload
if (retrieval_mode === RETRIEVE_TYPE.multiWay) {
const checked = checkoutRerankModelConfigedInRetrievalSettings(_datasets || [], multiple_retrieval_config)
const checked = checkoutRerankModelConfiguredInRetrievalSettings(_datasets || [], multiple_retrieval_config)
if (!errorMessages && !checked)
errorMessages = t(`${i18nPrefix}.errorMsg.fieldRequired`, { field: t(`${i18nPrefix}.errorMsg.fields.rerankModel`) })

10
web/app/components/workflow/nodes/knowledge-retrieval/panel.tsx
View File

@ -1,7 +1,6 @@
import type { FC } from 'react'
import {
memo,
useCallback,
useMemo,
} from 'react'
import { intersectionBy } from 'lodash-es'
@ -53,10 +52,6 @@ const Panel: FC<NodePanelProps<KnowledgeRetrievalNodeType>> = ({
availableNumberNodesWithParent,
} = useConfig(id, data)
const handleOpenFromPropsChange = useCallback((openFromProps: boolean) => {
setRerankModelOpen(openFromProps)
}, [setRerankModelOpen])
const metadataList = useMemo(() => {
return intersectionBy(...selectedDatasets.filter((dataset) => {
return !!dataset.doc_metadata
@ -68,7 +63,6 @@ const Panel: FC<NodePanelProps<KnowledgeRetrievalNodeType>> = ({
return (
<div className='pt-2'>
<div className='space-y-4 px-4 pb-2'>
{/* {JSON.stringify(inputs, null, 2)} */}
<Field
title={t(`${i18nPrefix}.queryVariable`)}
required
@ -100,8 +94,8 @@ const Panel: FC<NodePanelProps<KnowledgeRetrievalNodeType>> = ({
onSingleRetrievalModelChange={handleModelChanged as any}
onSingleRetrievalModelParamsChange={handleCompletionParamsChange}
readonly={readOnly || !selectedDatasets.length}
openFromProps={rerankModelOpen}
onOpenFromPropsChange={handleOpenFromPropsChange}
rerankModalOpen={rerankModelOpen}
onRerankModelOpenChange={setRerankModelOpen}
selectedDatasets={selectedDatasets}
/>
{!readOnly && (<div className='h-3 w-px bg-divider-regular'></div>)}

6
web/app/components/workflow/nodes/knowledge-retrieval/use-config.ts
View File

@ -204,10 +204,11 @@ const useConfig = (id: string, payload: KnowledgeRetrievalNodeType) => {
const handleMultipleRetrievalConfigChange = useCallback((newConfig: MultipleRetrievalConfig) => {
const newInputs = produce(inputs, (draft) => {
draft.multiple_retrieval_config = getMultipleRetrievalConfig(newConfig!, selectedDatasets, selectedDatasets, {
const newMultipleRetrievalConfig = getMultipleRetrievalConfig(newConfig!, selectedDatasets, selectedDatasets, {
provider: currentRerankProvider?.provider,
model: currentRerankModel?.model,
})
draft.multiple_retrieval_config = newMultipleRetrievalConfig
})
setInputs(newInputs)
}, [inputs, setInputs, selectedDatasets, currentRerankModel, currentRerankProvider])
@ -254,10 +255,11 @@ const useConfig = (id: string, payload: KnowledgeRetrievalNodeType) => {
if (payload.retrieval_mode === RETRIEVE_TYPE.multiWay && newDatasets.length > 0) {
const multipleRetrievalConfig = draft.multiple_retrieval_config
draft.multiple_retrieval_config = getMultipleRetrievalConfig(multipleRetrievalConfig!, newDatasets, selectedDatasets, {
const newMultipleRetrievalConfig = getMultipleRetrievalConfig(multipleRetrievalConfig!, newDatasets, selectedDatasets, {
provider: currentRerankProvider?.provider,
model: currentRerankModel?.model,
})
draft.multiple_retrieval_config = newMultipleRetrievalConfig
}
})
updateDatasetsDetail(newDatasets)

132
web/app/components/workflow/nodes/knowledge-retrieval/utils.ts
View File

@ -10,6 +10,7 @@ import type {
import {
DEFAULT_WEIGHTED_SCORE,
RerankingModeEnum,
WeightedScoreEnum,
} from '@/models/datasets'
import { RETRIEVE_METHOD } from '@/types/app'
import { DATASET_DEFAULT } from '@/config'
@ -93,10 +94,12 @@ export const getMultipleRetrievalConfig = (
multipleRetrievalConfig: MultipleRetrievalConfig,
selectedDatasets: DataSet[],
originalDatasets: DataSet[],
validRerankModel?: { provider?: string; model?: string },
fallbackRerankModel?: { provider?: string; model?: string }, // fallback rerank model
) => {
const shouldSetWeightDefaultValue = xorBy(selectedDatasets, originalDatasets, 'id').length > 0
const rerankModelIsValid = validRerankModel?.provider && validRerankModel?.model
// Check if the selected datasets are different from the original datasets
const isDatasetsChanged = xorBy(selectedDatasets, originalDatasets, 'id').length > 0
// Check if the rerank model is valid
const isFallbackRerankModelValid = !!(fallbackRerankModel?.provider && fallbackRerankModel?.model)
const {
allHighQuality,
@ -125,14 +128,16 @@ export const getMultipleRetrievalConfig = (
reranking_mode,
reranking_model,
weights,
reranking_enable: ((allInternal && allEconomic) || allExternal) ? reranking_enable : shouldSetWeightDefaultValue,
reranking_enable,
}
const setDefaultWeights = () => {
result.weights = {
weight_type: WeightedScoreEnum.Customized,
vector_setting: {
vector_weight: allHighQualityVectorSearch
? DEFAULT_WEIGHTED_SCORE.allHighQualityVectorSearch.semantic
// eslint-disable-next-line sonarjs/no-nested-conditional
: allHighQualityFullTextSearch
? DEFAULT_WEIGHTED_SCORE.allHighQualityFullTextSearch.semantic
: DEFAULT_WEIGHTED_SCORE.other.semantic,
@ -142,6 +147,7 @@ export const getMultipleRetrievalConfig = (
keyword_setting: {
keyword_weight: allHighQualityVectorSearch
? DEFAULT_WEIGHTED_SCORE.allHighQualityVectorSearch.keyword
// eslint-disable-next-line sonarjs/no-nested-conditional
: allHighQualityFullTextSearch
? DEFAULT_WEIGHTED_SCORE.allHighQualityFullTextSearch.keyword
: DEFAULT_WEIGHTED_SCORE.other.keyword,
@ -149,65 +155,106 @@ export const getMultipleRetrievalConfig = (
}
}
if (allEconomic || mixtureHighQualityAndEconomic || inconsistentEmbeddingModel || allExternal || mixtureInternalAndExternal) {
/**
* In this case, user can manually toggle reranking
* So should keep the reranking_enable value
* But the default reranking_model should be set
*/
if ((allEconomic && allInternal) || allExternal) {
result.reranking_mode = RerankingModeEnum.RerankingModel
if (!result.reranking_model?.provider || !result.reranking_model?.model) {
if (rerankModelIsValid) {
result.reranking_enable = reranking_enable !== false
result.reranking_model = {
provider: validRerankModel?.provider || '',
model: validRerankModel?.model || '',
}
}
else {
result.reranking_model = {
provider: '',
model: '',
}
// Need to check if the reranking model should be set to default when first time initialized
if ((!result.reranking_model?.provider || !result.reranking_model?.model) && isFallbackRerankModelValid) {
result.reranking_model = {
provider: fallbackRerankModel.provider || '',
model: fallbackRerankModel.model || '',
}
}
else {
result.reranking_enable = reranking_enable !== false
}
result.reranking_enable = reranking_enable
}
/**
* In this case, reranking_enable must be true
* And if rerank model is not set, should set the default rerank model
*/
if (mixtureHighQualityAndEconomic || inconsistentEmbeddingModel || mixtureInternalAndExternal) {
result.reranking_mode = RerankingModeEnum.RerankingModel
// Need to check if the reranking model should be set to default when first time initialized
if ((!result.reranking_model?.provider || !result.reranking_model?.model) && isFallbackRerankModelValid) {
result.reranking_model = {
provider: fallbackRerankModel.provider || '',
model: fallbackRerankModel.model || '',
}
}
result.reranking_enable = true
}
/**
* In this case, user can choose to use weighted score or rerank model
* But if the reranking_mode is not initialized, should set the default rerank model and reranking_enable to true
* and set reranking_mode to reranking_model
*/
if (allHighQuality && !inconsistentEmbeddingModel && allInternal) {
// If not initialized, check if the default rerank model is valid
if (!reranking_mode) {
if (validRerankModel?.provider && validRerankModel?.model) {
if (isFallbackRerankModelValid) {
result.reranking_mode = RerankingModeEnum.RerankingModel
result.reranking_enable = reranking_enable !== false
result.reranking_enable = true
result.reranking_model = {
provider: validRerankModel.provider,
model: validRerankModel.model,
provider: fallbackRerankModel.provider || '',
model: fallbackRerankModel.model || '',
}
}
else {
result.reranking_mode = RerankingModeEnum.WeightedScore
result.reranking_enable = false
setDefaultWeights()
}
}
if (reranking_mode === RerankingModeEnum.WeightedScore && !weights)
setDefaultWeights()
if (reranking_mode === RerankingModeEnum.WeightedScore && weights && shouldSetWeightDefaultValue) {
if (rerankModelIsValid) {
result.reranking_mode = RerankingModeEnum.RerankingModel
result.reranking_enable = reranking_enable !== false
// After initialization, if datasets has no change, make sure the config has correct value
if (reranking_mode === RerankingModeEnum.WeightedScore) {
result.reranking_enable = false
if (!weights)
setDefaultWeights()
}
if (reranking_mode === RerankingModeEnum.RerankingModel) {
if ((!result.reranking_model?.provider || !result.reranking_model?.model) && isFallbackRerankModelValid) {
result.reranking_model = {
provider: validRerankModel.provider || '',
model: validRerankModel.model || '',
provider: fallbackRerankModel.provider || '',
model: fallbackRerankModel.model || '',
}
}
result.reranking_enable = true
}
// Need to check if reranking_mode should be set to reranking_model when datasets changed
if (reranking_mode === RerankingModeEnum.WeightedScore && weights && isDatasetsChanged) {
if ((result.reranking_model?.provider && result.reranking_model?.model) || isFallbackRerankModelValid) {
result.reranking_mode = RerankingModeEnum.RerankingModel
result.reranking_enable = true
// eslint-disable-next-line sonarjs/nested-control-flow
if ((!result.reranking_model?.provider || !result.reranking_model?.model) && isFallbackRerankModelValid) {
result.reranking_model = {
provider: fallbackRerankModel.provider || '',
model: fallbackRerankModel.model || '',
}
}
}
else {
setDefaultWeights()
}
}
if (reranking_mode === RerankingModeEnum.RerankingModel && !rerankModelIsValid && shouldSetWeightDefaultValue) {
// Need to switch to weighted score when reranking model is not valid and datasets changed
if (
reranking_mode === RerankingModeEnum.RerankingModel
&& (!result.reranking_model?.provider || !result.reranking_model?.model)
&& !isFallbackRerankModelValid
&& isDatasetsChanged
) {
result.reranking_mode = RerankingModeEnum.WeightedScore
result.reranking_enable = false
setDefaultWeights()
}
}
@ -215,7 +262,7 @@ export const getMultipleRetrievalConfig = (
return result
}
export const checkoutRerankModelConfigedInRetrievalSettings = (
export const checkoutRerankModelConfiguredInRetrievalSettings = (
datasets: DataSet[],
multipleRetrievalConfig?: MultipleRetrievalConfig,
) => {
@ -225,6 +272,7 @@ export const checkoutRerankModelConfigedInRetrievalSettings = (
const {
allEconomic,
allExternal,
allInternal,
} = getSelectedDatasetsMode(datasets)
const {
@ -233,12 +281,8 @@ export const checkoutRerankModelConfigedInRetrievalSettings = (
reranking_model,
} = multipleRetrievalConfig
if (reranking_mode === RerankingModeEnum.RerankingModel && (!reranking_model?.provider || !reranking_model?.model)) {
if ((allEconomic || allExternal) && !reranking_enable)
return true
return false
}
if (reranking_mode === RerankingModeEnum.RerankingModel && (!reranking_model?.provider || !reranking_model?.model))
return ((allEconomic && allInternal) || allExternal) && !reranking_enable
return true
}

2
web/app/components/workflow/nodes/llm/components/json-schema-config-modal/json-schema-config.tsx
View File

@ -55,7 +55,7 @@ const JsonSchemaConfig: FC<JsonSchemaConfigProps> = ({
const docLink = useDocLink()
const [currentTab, setCurrentTab] = useState(SchemaView.VisualEditor)
const [jsonSchema, setJsonSchema] = useState(defaultSchema || DEFAULT_SCHEMA)
const [json, setJson] = useState(JSON.stringify(jsonSchema, null, 2))
const [json, setJson] = useState(() => JSON.stringify(jsonSchema, null, 2))
const [btnWidth, setBtnWidth] = useState(0)
const [parseError, setParseError] = useState<Error | null>(null)
const [validationError, setValidationError] = useState<string>('')

2
web/app/components/workflow/nodes/question-classifier/components/class-item.tsx
View File

@ -34,7 +34,7 @@ const ClassItem: FC<Props> = ({
filterVar,
}) => {
const { t } = useTranslation()
const [instanceId, setInstanceId] = useState(uniqueId())
const [instanceId, setInstanceId] = useState(() => uniqueId())
useEffect(() => {
setInstanceId(`${nodeId}-${uniqueId()}`)

2
web/app/components/workflow/variable-inspect/value-content.tsx
View File

@ -69,7 +69,7 @@ const ValueContent = ({
const [json, setJson] = useState('')
const [parseError, setParseError] = useState<Error | null>(null)
const [validationError, setValidationError] = useState<string>('')
const [fileValue, setFileValue] = useState<any>(formatFileValue(currentVar))
const [fileValue, setFileValue] = useState<any>(() => formatFileValue(currentVar))
const { run: debounceValueChange } = useDebounceFn(handleValueChange, { wait: 500 })

4
web/app/components/workflow/workflow-preview/index.tsx
View File

@ -68,8 +68,8 @@ const WorkflowPreview = ({
viewport,
className,
}: WorkflowPreviewProps) => {
const [nodesData, setNodesData] = useState(initialNodes(nodes, edges))
const [edgesData, setEdgesData] = useState(initialEdges(edges, nodes))
const [nodesData, setNodesData] = useState(() => initialNodes(nodes, edges))
const [edgesData, setEdgesData] = useState(() => initialEdges(edges, nodes))
const onNodesChange = useCallback(
(changes: NodeChange[]) => setNodesData(nds => applyNodeChanges(changes, nds)),

2
web/app/signin/invite-settings/page.tsx
View File

@ -30,7 +30,7 @@ export default function InviteSettingsPage() {
const { setLocaleOnClient } = useContext(I18n)
const [name, setName] = useState('')
const [language, setLanguage] = useState(LanguagesSupported[0])
const [timezone, setTimezone] = useState(Intl.DateTimeFormat().resolvedOptions().timeZone || 'America/Los_Angeles')
const [timezone, setTimezone] = useState(() => Intl.DateTimeFormat().resolvedOptions().timeZone || 'America/Los_Angeles')
const checkParams = {
url: '/activate/check',

1
web/i18n/de-DE/common.ts
View File

@ -501,6 +501,7 @@ const translation = {
customModelCredentialsDeleteTip: 'Anmeldeinformationen werden verwendet und können nicht gelöscht werden',
},
parametersInvalidRemoved: 'Einige Parameter sind ungültig und wurden entfernt.',
installDataSourceProvider: 'Datenquellenanbieter installieren',
},
dataSource: {
add: 'Eine Datenquelle hinzufügen',

3
web/i18n/de-DE/workflow.ts
View File

@ -942,6 +942,9 @@ const translation = {
chunkIsRequired: 'Chunk-Struktur ist erforderlich',
chunksInput: 'Stücke',
chunksInputTip: 'Die Eingangsvariable des Wissensbasis-Knotens sind Chunks. Der Variablentyp ist ein Objekt mit einem spezifischen JSON-Schema, das konsistent mit der ausgewählten Chunk-Struktur sein muss.',
embeddingModelIsRequired: 'Ein Einbettungsmodell ist erforderlich',
chunksVariableIsRequired: 'Die Variable \'Chunks\' ist erforderlich',
rerankingModelIsRequired: 'Ein Reranking-Modell ist erforderlich',
},
},
tracing: {

1
web/i18n/en-US/common.ts
View File

@ -497,6 +497,7 @@ const translation = {
toBeConfigured: 'To be configured',
configureTip: 'Set up api-key or add model to use',
installProvider: 'Install model providers',
installDataSourceProvider: 'Install data source providers',
discoverMore: 'Discover more in ',
emptyProviderTitle: 'Model provider not set up',
emptyProviderTip: 'Please install a model provider first.',

3
web/i18n/en-US/workflow.ts
View File

@ -1028,7 +1028,10 @@ const translation = {
aboutRetrieval: 'about retrieval method.',
chunkIsRequired: 'Chunk structure is required',
indexMethodIsRequired: 'Index method is required',
chunksVariableIsRequired: 'Chunks variable is required',
embeddingModelIsRequired: 'Embedding model is required',
retrievalSettingIsRequired: 'Retrieval setting is required',
rerankingModelIsRequired: 'Reranking model is required',
},
triggerSchedule: {
title: 'Schedule',

1
web/i18n/es-ES/common.ts
View File

@ -505,6 +505,7 @@ const translation = {
editModelCredential: 'Editar credencial de modelo',
},
parametersInvalidRemoved: 'Algunos parámetros son inválidos y han sido eliminados',
installDataSourceProvider: 'Instalar proveedores de fuentes de datos',
},
dataSource: {
add: 'Agregar una fuente de datos',

3
web/i18n/es-ES/workflow.ts
View File

@ -942,6 +942,9 @@ const translation = {
chunkIsRequired: 'Se requiere una estructura de fragmentos',
chunksInput: 'Trozo',
chunksInputTip: 'La variable de entrada del nodo de la base de conocimientos es Chunks. El tipo de variable es un objeto con un esquema JSON específico que debe ser consistente con la estructura del fragmento seleccionado.',
embeddingModelIsRequired: 'Se requiere un modelo de incrustación',
rerankingModelIsRequired: 'Se requiere un modelo de reordenamiento',
chunksVariableIsRequired: 'La variable Chunks es obligatoria',
},
},
tracing: {

1
web/i18n/fa-IR/common.ts
View File

@ -505,6 +505,7 @@ const translation = {
customModelCredentialsDeleteTip: 'اعتبار در حال استفاده است و قابل حذف نیست',
},
parametersInvalidRemoved: 'برخی پارامترها نامعتبر هستند و حذف شده‌اند',
installDataSourceProvider: 'نصب ارائه‌دهندگان منبع داده',
},
dataSource: {
add: 'افزودن منبع داده',

3
web/i18n/fa-IR/workflow.ts
View File

@ -942,6 +942,9 @@ const translation = {
chooseChunkStructure: 'یک ساختار تکه ای را انتخاب کنید',
chunksInput: 'تکه‌ها',
chunksInputTip: 'متغیر ورودی گره پایگاه دانش چانک‌ها است. نوع متغیر یک شیء با یک طرح JSON خاص است که باید با ساختار چانک انتخاب شده سازگار باشد.',
embeddingModelIsRequired: 'مدل جاسازی مورد نیاز است',
chunksVariableIsRequired: 'متغیر Chunks الزامی است',
rerankingModelIsRequired: 'مدل رتبه‌بندی مجدد مورد نیاز است',
},
},
tracing: {

1
web/i18n/fr-FR/common.ts
View File

@ -502,6 +502,7 @@ const translation = {
editModelCredential: 'Modifier les informations d’identification du modèle',
},
parametersInvalidRemoved: 'Certains paramètres sont invalides et ont été supprimés.',
installDataSourceProvider: 'Installer les fournisseurs de sources de données',
},
dataSource: {
add: 'Ajouter une source de données',

3
web/i18n/fr-FR/workflow.ts
View File

@ -942,6 +942,9 @@ const translation = {
retrievalSettingIsRequired: 'Le paramètre de récupération est requis',
chunksInput: 'Morceaux',
chunksInputTip: 'La variable d\'entrée du nœud de la base de connaissances est Chunks. Le type de variable est un objet avec un schéma JSON spécifique qui doit être cohérent avec la structure de morceau sélectionnée.',
rerankingModelIsRequired: 'Un modèle de rerankage est requis',
embeddingModelIsRequired: 'Un modèle d\'intégration est requis',
chunksVariableIsRequired: 'La variable Chunks est requise',
},
},
tracing: {

1
web/i18n/hi-IN/common.ts
View File

@ -521,6 +521,7 @@ const translation = {
editModelCredential: 'मॉडल की क्रेडेंशियल संपादित करें',
},
parametersInvalidRemoved: 'कुछ पैरामीटर अमान्य हैं और हटा दिए गए हैं',
installDataSourceProvider: 'डेटा स्रोत प्रदाताओं को स्थापित करें',
},
dataSource: {
add: 'डेटा स्रोत जोड़ें',

3
web/i18n/hi-IN/workflow.ts
View File

@ -962,6 +962,9 @@ const translation = {
chooseChunkStructure: 'एक चंक संरचना चुनें',
chunksInput: 'टुकड़े',
chunksInputTip: 'ज्ञान आधार नोड का इनपुट वेरिएबल चंक्स है। वेरिएबल प्रकार एक ऑब्जेक्ट है जिसमें एक विशेष JSON स्कीमा है जो चयनित चंक संरचना के साथ सुसंगत होना चाहिए।',
chunksVariableIsRequired: 'Chunks चर आवश्यक है',
embeddingModelIsRequired: 'एम्बेडिंग मॉडल आवश्यक है',
rerankingModelIsRequired: 'पुनः क्रमांकन मॉडल की आवश्यकता है',
},
},
tracing: {

1
web/i18n/id-ID/common.ts
View File

@ -501,6 +501,7 @@ const translation = {
callTimes: 'Waktu panggilan',
getFreeTokens: 'Dapatkan Token gratis',
parametersInvalidRemoved: 'Beberapa parameter tidak valid dan telah dihapus',
installDataSourceProvider: 'Pasang penyedia sumber data',
},
dataSource: {
notion: {

3
web/i18n/id-ID/workflow.ts
View File

@ -919,6 +919,9 @@ const translation = {
chunkStructure: 'Struktur Potongan',
chunksInput: 'Potongan',
chunksInputTip: 'Variabel input dari node basis pengetahuan adalah Chunks. Tipe variabel adalah objek dengan Skema JSON tertentu yang harus konsisten dengan struktur chunk yang dipilih.',
chunksVariableIsRequired: 'Variabel Chunks diperlukan',
rerankingModelIsRequired: 'Model reranking diperlukan',
embeddingModelIsRequired: 'Model embedding diperlukan',
},
},
tracing: {},

1
web/i18n/it-IT/common.ts
View File

@ -527,6 +527,7 @@ const translation = {
editModelCredential: 'Modificare le credenziali del modello',
},
parametersInvalidRemoved: 'Alcuni parametri non sono validi e sono stati rimossi.',
installDataSourceProvider: 'Installa i fornitori di sorgenti dati',
},
dataSource: {
add: 'Aggiungi una fonte di dati',

3
web/i18n/it-IT/workflow.ts
View File

@ -968,6 +968,9 @@ const translation = {
retrievalSettingIsRequired: 'È richiesta l\'impostazione di recupero',
chunksInputTip: 'La variabile di input del nodo della base di conoscenza è Chunks. Il tipo di variabile è un oggetto con uno specifico schema JSON che deve essere coerente con la struttura del chunk selezionato.',
chunksInput: 'Pezzetti',
chunksVariableIsRequired: 'La variabile Chunks è richiesta',
rerankingModelIsRequired: 'È richiesto un modello di riordinamento',
embeddingModelIsRequired: 'È necessario un modello di embedding',
},
},
tracing: {

1
web/i18n/ja-JP/common.ts
View File

@ -485,6 +485,7 @@ const translation = {
emptyProviderTitle: 'モデルプロバイダーが設定されていません',
discoverMore: 'もっと発見する',
installProvider: 'モデルプロバイダーをインストールする',
installDataSourceProvider: 'データソースプロバイダーをインストールする',
configureTip: 'API キーを設定するか、使用するモデルを追加してください',
toBeConfigured: '設定中',
emptyProviderTip: '最初にモデルプロバイダーをインストールしてください。',

3
web/i18n/ja-JP/workflow.ts
View File

@ -982,6 +982,9 @@ const translation = {
indexMethodIsRequired: 'インデックスメソッドが必要です',
chunksInput: 'チャンク',
chunksInputTip: '知識ベースノードの入力変数はチャンクです。変数のタイプは、選択されたチャンク構造と一貫性のある特定のJSONスキーマを持つオブジェクトです。',
chunksVariableIsRequired: 'Chunks変数は必須です',
embeddingModelIsRequired: '埋め込みモデルが必要です',
rerankingModelIsRequired: '再ランキングモデルが必要です',
},
triggerSchedule: {
frequency: {

1
web/i18n/ko-KR/common.ts
View File

@ -497,6 +497,7 @@ const translation = {
customModelCredentialsDeleteTip: '자격 증명이 사용 중이며 삭제할 수 없습니다.',
},
parametersInvalidRemoved: '일부 매개변수가 유효하지 않아 제거되었습니다.',
installDataSourceProvider: '데이터 소스 공급자 설치',
},
dataSource: {
add: '데이터 소스 추가하기',

3
web/i18n/ko-KR/workflow.ts
View File

@ -990,6 +990,9 @@ const translation = {
retrievalSettingIsRequired: '검색 설정이 필요합니다.',
chunksInput: '청크',
chunksInputTip: '지식 기반 노드의 입력 변수는 Chunks입니다. 변수 유형은 선택된 청크 구조와 일치해야 하는 특정 JSON 스키마를 가진 객체입니다.',
chunksVariableIsRequired: 'Chunks 변수는 필수입니다',
embeddingModelIsRequired: '임베딩 모델이 필요합니다',
rerankingModelIsRequired: '재순위 모델이 필요합니다',
},
},
tracing: {

1
web/i18n/pl-PL/common.ts
View File

@ -514,6 +514,7 @@ const translation = {
editModelCredential: 'Edytowanie poświadczeń modelu',
},
parametersInvalidRemoved: 'Niektóre parametry są nieprawidłowe i zostały usunięte.',
installDataSourceProvider: 'Zainstaluj dostawców źródeł danych',
},
dataSource: {
add: 'Dodaj źródło danych',

3
web/i18n/pl-PL/workflow.ts
View File

@ -942,6 +942,9 @@ const translation = {
chunkIsRequired: 'Wymagana jest struktura porcji',
chunksInput: 'Kawałki',
chunksInputTip: 'Zmienna wejściowa węzła bazy wiedzy to Chunks. Typ zmiennej to obiekt z określonym schematem JSON, który musi być zgodny z wybraną strukturą chunk.',
embeddingModelIsRequired: 'Wymagany jest model osadzania',
chunksVariableIsRequired: 'Wymagana jest zmienna Chunks',
rerankingModelIsRequired: 'Wymagany jest model ponownego rankingu',
},
},
tracing: {

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