chore: update version to 0.15.2 in packaging and docker configurations (#12940)

Signed-off-by: -LAN- <laipz8200@outlook.com>

.github/actions/setup-poetry/action.yml

@@ -8,7 +8,7 @@ inputs:
   poetry-version:
     description: Poetry version to set up
     required: true
-    default: '1.8.4'
+    default: '2.0.1'
   poetry-lockfile:
     description: Path to the Poetry lockfile to restore cache from
     required: true

.github/workflows/api-tests.yml

@@ -42,25 +42,23 @@ jobs:
         run: poetry install -C api --with dev
 
       - name: Check dependencies in pyproject.toml
-        run: poetry run -C api bash dev/pytest/pytest_artifacts.sh
+        run: poetry run -P api bash dev/pytest/pytest_artifacts.sh
 
       - name: Run Unit tests
-        run: poetry run -C api bash dev/pytest/pytest_unit_tests.sh
+        run: poetry run -P api bash dev/pytest/pytest_unit_tests.sh
 
       - name: Run ModelRuntime
-        run: poetry run -C api bash dev/pytest/pytest_model_runtime.sh
+        run: poetry run -P api bash dev/pytest/pytest_model_runtime.sh
 
       - name: Run dify config tests
-        run: poetry run -C api python dev/pytest/pytest_config_tests.py
+        run: poetry run -P api python dev/pytest/pytest_config_tests.py
 
       - name: Run Tool
-        run: poetry run -C api bash dev/pytest/pytest_tools.sh
+        run: poetry run -P api bash dev/pytest/pytest_tools.sh
 
       - name: Run mypy
         run: |
-          pushd api
-          poetry run python -m mypy --install-types --non-interactive .
-          popd
+          poetry run -C api python -m mypy --install-types --non-interactive .
 
       - name: Set up dotenvs
         run: |
@@ -80,4 +78,4 @@ jobs:
             ssrf_proxy
 
       - name: Run Workflow
-        run: poetry run -C api bash dev/pytest/pytest_workflow.sh
+        run: poetry run -P api bash dev/pytest/pytest_workflow.sh

.github/workflows/style.yml

@@ -38,12 +38,12 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: |
           poetry run -C api ruff --version
-          poetry run -C api ruff check ./api
-          poetry run -C api ruff format --check ./api
+          poetry run -C api ruff check ./
+          poetry run -C api ruff format --check ./
 
       - name: Dotenv check
         if: steps.changed-files.outputs.any_changed == 'true'
-        run: poetry run -C api dotenv-linter ./api/.env.example ./web/.env.example
+        run: poetry run -P api dotenv-linter ./api/.env.example ./web/.env.example
 
       - name: Lint hints
         if: failure()
@@ -82,6 +82,33 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: yarn run lint
 
+  docker-compose-template:
+    name: Docker Compose Template
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Check changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v45
+        with:
+          files: |
+            docker/generate_docker_compose
+            docker/.env.example
+            docker/docker-compose-template.yaml
+            docker/docker-compose.yaml
+
+      - name: Generate Docker Compose
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: |
+          cd docker
+          ./generate_docker_compose
+
+      - name: Check for changes
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: git diff --exit-code
 
   superlinter:
     name: SuperLinter

.github/workflows/vdb-tests.yml

@@ -70,4 +70,4 @@ jobs:
             tidb
 
       - name: Test Vector Stores
-        run: poetry run -C api bash dev/pytest/pytest_vdb.sh
+        run: poetry run -P api bash dev/pytest/pytest_vdb.sh

api/.ruff.toml

@@ -53,10 +53,12 @@ ignore = [
     "FURB152", # math-constant
     "UP007", # non-pep604-annotation
     "UP032", # f-string
+    "UP045", # non-pep604-annotation-optional
     "B005", # strip-with-multi-characters
     "B006", # mutable-argument-default
     "B007", # unused-loop-control-variable
     "B026", # star-arg-unpacking-after-keyword-arg
+    "B903", # class-as-data-structure
     "B904", # raise-without-from-inside-except
     "B905", # zip-without-explicit-strict
     "N806", # non-lowercase-variable-in-function

api/Dockerfile

@@ -4,7 +4,7 @@ FROM python:3.12-slim-bookworm AS base
 WORKDIR /app/api
 
 # Install Poetry
-ENV POETRY_VERSION=1.8.4
+ENV POETRY_VERSION=2.0.1
 
 # if you located in China, you can use aliyun mirror to speed up
 # RUN pip install --no-cache-dir poetry==${POETRY_VERSION} -i https://mirrors.aliyun.com/pypi/simple/

api/README.md

@@ -79,5 +79,5 @@
 2. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`
 
    ```bash
-   poetry run -C api bash dev/pytest/pytest_all_tests.sh
+   poetry run -P api bash dev/pytest/pytest_all_tests.sh
    ```

api/configs/feature/__init__.py

@@ -146,7 +146,7 @@ class EndpointConfig(BaseSettings):
     )
 
     CONSOLE_WEB_URL: str = Field(
-        description="Base URL for the console web interface," "used for frontend references and CORS configuration",
+        description="Base URL for the console web interface,used for frontend references and CORS configuration",
         default="",
     )
 

api/configs/feature/hosted_service/__init__.py

@@ -181,7 +181,7 @@ class HostedFetchAppTemplateConfig(BaseSettings):
     """
 
     HOSTED_FETCH_APP_TEMPLATES_MODE: str = Field(
-        description="Mode for fetching app templates: remote, db, or builtin" " default to remote,",
+        description="Mode for fetching app templates: remote, db, or builtin default to remote,",
         default="remote",
     )
 

api/configs/middleware/vdb/milvus_config.py

@@ -33,3 +33,9 @@ class MilvusConfig(BaseSettings):
         description="Name of the Milvus database to connect to (default is 'default')",
         default="default",
     )
+
+    MILVUS_ENABLE_HYBRID_SEARCH: bool = Field(
+        description="Enable hybrid search features (requires Milvus >= 2.5.0). Set to false for compatibility with "
+        "older versions",
+        default=True,
+    )

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.14.2",
+        default="0.15.2",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/admin.py

@@ -56,7 +56,7 @@ class InsertExploreAppListApi(Resource):
 
         app = App.query.filter(App.id == args["app_id"]).first()
         if not app:
-            raise NotFound(f'App \'{args["app_id"]}\' is not found')
+            raise NotFound(f"App '{args['app_id']}' is not found")
 
         site = app.site
         if not site:

api/controllers/console/app/audio.py

@@ -22,7 +22,7 @@ from controllers.console.wraps import account_initialization_required, setup_req
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from libs.login import login_required
-from models.model import AppMode
+from models import App, AppMode
 from services.audio_service import AudioService
 from services.errors.audio import (
     AudioTooLargeServiceError,
@@ -79,7 +79,7 @@ class ChatMessageTextApi(Resource):
     @login_required
     @account_initialization_required
     @get_app_model
-    def post(self, app_model):
+    def post(self, app_model: App):
         from werkzeug.exceptions import InternalServerError
 
         try:
@@ -98,9 +98,13 @@ class ChatMessageTextApi(Resource):
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
+                if text_to_speech is None:
+                    raise ValueError("TTS is not enabled")
                 voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
+                    if app_model.app_model_config is None:
+                        raise ValueError("AppModelConfig not found")
                     voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None

api/controllers/console/datasets/datasets.py

@@ -52,12 +52,12 @@ class DatasetListApi(Resource):
         # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
-
+        include_all = request.args.get("include_all", default="false").lower() == "true"
         if ids:
             datasets, total = DatasetService.get_datasets_by_ids(ids, current_user.current_tenant_id)
         else:
             datasets, total = DatasetService.get_datasets(
-                page, limit, current_user.current_tenant_id, current_user, search, tag_ids
+                page, limit, current_user.current_tenant_id, current_user, search, tag_ids, include_all
             )
 
         # check embedding setting
@@ -457,7 +457,7 @@ class DatasetIndexingEstimateApi(Resource):
             )
         except LLMBadRequestError:
             raise ProviderNotInitializeError(
-                "No Embedding Model available. Please configure a valid provider " "in the Settings -> Model Provider."
+                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -619,8 +619,7 @@ class DatasetRetrievalSettingApi(Resource):
         vector_type = dify_config.VECTOR_STORE
         match vector_type:
             case (
-                VectorType.MILVUS
-                | VectorType.RELYT
+                VectorType.RELYT
                 | VectorType.PGVECTOR
                 | VectorType.TIDB_VECTOR
                 | VectorType.CHROMA
@@ -640,10 +639,12 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
+                | VectorType.ELASTICSEARCH_JA
                 | VectorType.PGVECTOR
                 | VectorType.TIDB_ON_QDRANT
                 | VectorType.LINDORM
                 | VectorType.COUCHBASE
+                | VectorType.MILVUS
             ):
                 return {
                     "retrieval_method": [
@@ -683,6 +684,7 @@ class DatasetRetrievalSettingMockApi(Resource):
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
+                | VectorType.ELASTICSEARCH_JA
                 | VectorType.COUCHBASE
                 | VectorType.PGVECTOR
                 | VectorType.LINDORM

api/controllers/console/datasets/datasets_document.py

@@ -257,7 +257,8 @@ class DatasetDocumentListApi(Resource):
         parser.add_argument("original_document_id", type=str, required=False, location="json")
         parser.add_argument("doc_form", type=str, default="text_model", required=False, nullable=False, location="json")
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
-
+        parser.add_argument("embedding_model", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("embedding_model_provider", type=str, required=False, nullable=True, location="json")
         parser.add_argument(
             "doc_language", type=str, default="English", required=False, nullable=False, location="json"
         )
@@ -349,8 +350,7 @@ class DatasetInitApi(Resource):
                 )
             except InvokeAuthorizationError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -525,8 +525,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                 return response.model_dump(), 200
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/console/datasets/datasets_segments.py

@@ -168,8 +168,7 @@ class DatasetDocumentSegmentApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -217,8 +216,7 @@ class DatasetDocumentSegmentAddApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -267,8 +265,7 @@ class DatasetDocumentSegmentUpdateApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -368,9 +365,9 @@ class DatasetDocumentSegmentBatchImportApi(Resource):
             result = []
             for index, row in df.iterrows():
                 if document.doc_form == "qa_model":
-                    data = {"content": row[0], "answer": row[1]}
+                    data = {"content": row.iloc[0], "answer": row.iloc[1]}
                 else:
-                    data = {"content": row[0]}
+                    data = {"content": row.iloc[0]}
                 result.append(data)
             if len(result) == 0:
                 raise ValueError("The CSV file is empty.")
@@ -437,8 +434,7 @@ class ChildChunkAddApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/console/explore/conversation.py

@@ -32,7 +32,7 @@ class ConversationListApi(InstalledAppResource):
 
         pinned = None
         if "pinned" in args and args["pinned"] is not None:
-            pinned = True if args["pinned"] == "true" else False
+            pinned = args["pinned"] == "true"
 
         try:
             with Session(db.engine) as session:

api/controllers/service_api/__init__.py

@@ -7,4 +7,4 @@ api = ExternalApi(bp)
 
 from . import index
 from .app import app, audio, completion, conversation, file, message, workflow
-from .dataset import dataset, document, hit_testing, segment
+from .dataset import dataset, document, hit_testing, segment, upload_file

api/controllers/service_api/dataset/dataset.py

@@ -31,8 +31,11 @@ class DatasetListApi(DatasetApiResource):
         # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
+        include_all = request.args.get("include_all", default="false").lower() == "true"
 
-        datasets, total = DatasetService.get_datasets(page, limit, tenant_id, current_user, search, tag_ids)
+        datasets, total = DatasetService.get_datasets(
+            page, limit, tenant_id, current_user, search, tag_ids, include_all
+        )
         # check embedding setting
         provider_manager = ProviderManager()
         configurations = provider_manager.get_configurations(tenant_id=current_user.current_tenant_id)

api/controllers/service_api/dataset/segment.py

@@ -53,8 +53,7 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -95,8 +94,7 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -175,8 +173,7 @@ class DatasetSegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/service_api/dataset/upload_file.py

@@ -0,0 +1,54 @@
+from werkzeug.exceptions import NotFound
+
+from controllers.service_api import api
+from controllers.service_api.wraps import (
+    DatasetApiResource,
+)
+from core.file import helpers as file_helpers
+from extensions.ext_database import db
+from models.dataset import Dataset
+from models.model import UploadFile
+from services.dataset_service import DocumentService
+
+
+class UploadFileApi(DatasetApiResource):
+    def get(self, tenant_id, dataset_id, document_id):
+        """Get upload file."""
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset.id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        # check upload file
+        if document.data_source_type != "upload_file":
+            raise ValueError(f"Document data source type ({document.data_source_type}) is not upload_file.")
+        data_source_info = document.data_source_info_dict
+        if data_source_info and "upload_file_id" in data_source_info:
+            file_id = data_source_info["upload_file_id"]
+            upload_file = db.session.query(UploadFile).filter(UploadFile.id == file_id).first()
+            if not upload_file:
+                raise NotFound("UploadFile not found.")
+        else:
+            raise ValueError("Upload file id not found in document data source info.")
+
+        url = file_helpers.get_signed_file_url(upload_file_id=upload_file.id)
+        return {
+            "id": upload_file.id,
+            "name": upload_file.name,
+            "size": upload_file.size,
+            "extension": upload_file.extension,
+            "url": url,
+            "download_url": f"{url}&as_attachment=true",
+            "mime_type": upload_file.mime_type,
+            "created_by": upload_file.created_by,
+            "created_at": upload_file.created_at.timestamp(),
+        }, 200
+
+
+api.add_resource(UploadFileApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/upload-file")

api/controllers/service_api/wraps.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable
-from datetime import UTC, datetime
+from datetime import UTC, datetime, timedelta
 from enum import Enum
 from functools import wraps
 from typing import Optional
@@ -8,6 +8,8 @@ from flask import current_app, request
 from flask_login import user_logged_in  # type: ignore
 from flask_restful import Resource  # type: ignore
 from pydantic import BaseModel
+from sqlalchemy import select, update
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, Unauthorized
 
 from extensions.ext_database import db
@@ -174,7 +176,7 @@ def validate_dataset_token(view=None):
     return decorator
 
 
-def validate_and_get_api_token(scope=None):
+def validate_and_get_api_token(scope: str | None = None):
     """
     Validate and get API token.
     """
@@ -188,20 +190,29 @@ def validate_and_get_api_token(scope=None):
     if auth_scheme != "bearer":
         raise Unauthorized("Authorization scheme must be 'Bearer'")
 
-    api_token = (
-        db.session.query(ApiToken)
-        .filter(
-            ApiToken.token == auth_token,
-            ApiToken.type == scope,
+    current_time = datetime.now(UTC).replace(tzinfo=None)
+    cutoff_time = current_time - timedelta(minutes=1)
+    with Session(db.engine, expire_on_commit=False) as session:
+        update_stmt = (
+            update(ApiToken)
+            .where(
+                ApiToken.token == auth_token,
+                (ApiToken.last_used_at.is_(None) | (ApiToken.last_used_at < cutoff_time)),
+                ApiToken.type == scope,
+            )
+            .values(last_used_at=current_time)
+            .returning(ApiToken)
         )
-        .first()
-    )
+        result = session.execute(update_stmt)
+        api_token = result.scalar_one_or_none()
 
-    if not api_token:
-        raise Unauthorized("Access token is invalid")
-
-    api_token.last_used_at = datetime.now(UTC).replace(tzinfo=None)
-    db.session.commit()
+        if not api_token:
+            stmt = select(ApiToken).where(ApiToken.token == auth_token, ApiToken.type == scope)
+            api_token = session.scalar(stmt)
+            if not api_token:
+                raise Unauthorized("Access token is invalid")
+        else:
+            session.commit()
 
     return api_token
 
@@ -229,7 +240,7 @@ def create_or_update_end_user_for_user_id(app_model: App, user_id: Optional[str]
             tenant_id=app_model.tenant_id,
             app_id=app_model.id,
             type="service_api",
-            is_anonymous=True if user_id == "DEFAULT-USER" else False,
+            is_anonymous=user_id == "DEFAULT-USER",
             session_id=user_id,
         )
         db.session.add(end_user)

api/controllers/web/conversation.py

@@ -39,7 +39,7 @@ class ConversationListApi(WebApiResource):
 
         pinned = None
         if "pinned" in args and args["pinned"] is not None:
-            pinned = True if args["pinned"] == "true" else False
+            pinned = args["pinned"] == "true"
 
         try:
             with Session(db.engine) as session:

api/core/agent/cot_agent_runner.py

@@ -172,7 +172,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
 
             self.save_agent_thought(
                 agent_thought=agent_thought,
-                tool_name=scratchpad.action.action_name if scratchpad.action else "",
+                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
                 tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
                 tool_invoke_meta={},
                 thought=scratchpad.thought or "",

api/core/app/apps/base_app_queue_manager.py

@@ -167,8 +167,7 @@ class AppQueueManager:
         else:
             if isinstance(data, DeclarativeMeta) or hasattr(data, "_sa_instance_state"):
                 raise TypeError(
-                    "Critical Error: Passing SQLAlchemy Model instances "
-                    "that cause thread safety issues is not allowed."
+                    "Critical Error: Passing SQLAlchemy Model instances that cause thread safety issues is not allowed."
                 )
 
 

api/core/app/apps/message_based_app_generator.py

@@ -89,6 +89,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             Conversation.id == conversation_id,
             Conversation.app_id == app_model.id,
             Conversation.status == "normal",
+            Conversation.is_deleted.is_(False),
         ]
 
         if isinstance(user, Account):

api/core/app/task_pipeline/message_cycle_manage.py

@@ -145,7 +145,7 @@ class MessageCycleManage:
 
             # get extension
             if "." in message_file.url:
-                extension = f'.{message_file.url.split(".")[-1]}'
+                extension = f".{message_file.url.split('.')[-1]}"
                 if len(extension) > 10:
                     extension = ".bin"
             else:

api/core/external_data_tool/api/api.py

@@ -62,8 +62,9 @@ class ApiExternalDataTool(ExternalDataTool):
 
         if not api_based_extension:
             raise ValueError(
-                "[External data tool] API query failed, variable: {}, "
-                "error: api_based_extension_id is invalid".format(self.variable)
+                "[External data tool] API query failed, variable: {}, error: api_based_extension_id is invalid".format(
+                    self.variable
+                )
             )
 
         # decrypt api_key

api/core/file/models.py

@@ -90,7 +90,7 @@ class File(BaseModel):
     def markdown(self) -> str:
         url = self.generate_url()
         if self.type == FileType.IMAGE:
-            text = f'![{self.filename or ""}]({url})'
+            text = f"![{self.filename or ''}]({url})"
         else:
             text = f"[{self.filename or url}]({url})"
 

api/core/indexing_runner.py

@@ -530,7 +530,6 @@ class IndexingRunner:
         # chunk nodes by chunk size
         indexing_start_at = time.perf_counter()
         tokens = 0
-        chunk_size = 10
         if dataset_document.doc_form != IndexType.PARENT_CHILD_INDEX:
             # create keyword index
             create_keyword_thread = threading.Thread(
@@ -539,11 +538,22 @@ class IndexingRunner:
             )
             create_keyword_thread.start()
 
+        max_workers = 10
         if dataset.indexing_technique == "high_quality":
-            with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
+            with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
                 futures = []
-                for i in range(0, len(documents), chunk_size):
-                    chunk_documents = documents[i : i + chunk_size]
+
+                # Distribute documents into multiple groups based on the hash values of page_content
+                # This is done to prevent multiple threads from processing the same document,
+                # Thereby avoiding potential database insertion deadlocks
+                document_groups: list[list[Document]] = [[] for _ in range(max_workers)]
+                for document in documents:
+                    hash = helper.generate_text_hash(document.page_content)
+                    group_index = int(hash, 16) % max_workers
+                    document_groups[group_index].append(document)
+                for chunk_documents in document_groups:
+                    if len(chunk_documents) == 0:
+                        continue
                     futures.append(
                         executor.submit(
                             self._process_chunk,

api/core/llm_generator/prompts.py

@@ -131,7 +131,7 @@ JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE = (
 SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
     "Please help me predict the three most likely questions that human would ask, "
     "and keeping each question under 20 characters.\n"
-    "MAKE SURE your output is the SAME language as the Assistant's latest response"
+    "MAKE SURE your output is the SAME language as the Assistant's latest response. "
     "The output must be an array in JSON format following the specified schema:\n"
     '["question1","question2","question3"]\n'
 )

api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py

@@ -1,13 +1,11 @@
-from concurrent.futures import ProcessPoolExecutor
-from os.path import abspath, dirname, join
+import logging
 from threading import Lock
-from typing import Any, cast
+from typing import Any
 
-from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer  # type: ignore
+logger = logging.getLogger(__name__)
 
 _tokenizer: Any = None
 _lock = Lock()
-_executor = ProcessPoolExecutor(max_workers=1)
 
 
 class GPT2Tokenizer:
@@ -17,22 +15,37 @@ class GPT2Tokenizer:
         use gpt2 tokenizer to get num tokens
         """
         _tokenizer = GPT2Tokenizer.get_encoder()
-        tokens = _tokenizer.encode(text, verbose=False)
+        tokens = _tokenizer.encode(text)
         return len(tokens)
 
     @staticmethod
     def get_num_tokens(text: str) -> int:
-        future = _executor.submit(GPT2Tokenizer._get_num_tokens_by_gpt2, text)
-        result = future.result()
-        return cast(int, result)
+        # Because this process needs more cpu resource, we turn this back before we find a better way to handle it.
+        #
+        # future = _executor.submit(GPT2Tokenizer._get_num_tokens_by_gpt2, text)
+        # result = future.result()
+        # return cast(int, result)
+        return GPT2Tokenizer._get_num_tokens_by_gpt2(text)
 
     @staticmethod
     def get_encoder() -> Any:
         global _tokenizer, _lock
         with _lock:
             if _tokenizer is None:
-                base_path = abspath(__file__)
-                gpt2_tokenizer_path = join(dirname(base_path), "gpt2")
-                _tokenizer = TransformerGPT2Tokenizer.from_pretrained(gpt2_tokenizer_path)
+                # Try to use tiktoken to get the tokenizer because it is faster
+                #
+                try:
+                    import tiktoken
+
+                    _tokenizer = tiktoken.get_encoding("gpt2")
+                except Exception:
+                    from os.path import abspath, dirname, join
+
+                    from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer  # type: ignore
+
+                    base_path = abspath(__file__)
+                    gpt2_tokenizer_path = join(dirname(base_path), "gpt2")
+                    _tokenizer = TransformerGPT2Tokenizer.from_pretrained(gpt2_tokenizer_path)
+                    logger.info("Fallback to Transformers' GPT-2 tokenizer from tiktoken")
 
             return _tokenizer

api/core/model_runtime/model_providers/azure_openai/llm/llm.py

@@ -108,7 +108,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         ai_model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
 
         if not ai_model_entity:
-            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
+            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
 
         try:
             client = AzureOpenAI(**self._to_credential_kwargs(credentials))

api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py

@@ -130,7 +130,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             raise CredentialsValidateFailedError("Base Model Name is required")
 
         if not self._get_ai_model_entity(credentials["base_model_name"], model):
-            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
+            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
 
         try:
             credentials_kwargs = self._to_credential_kwargs(credentials)

api/core/model_runtime/model_providers/bedrock/rerank/rerank.py

@@ -70,7 +70,7 @@ class BedrockRerankModel(RerankModel):
         rerankingConfiguration = {
             "type": "BEDROCK_RERANKING_MODEL",
             "bedrockRerankingConfiguration": {
-                "numberOfResults": top_n,
+                "numberOfResults": min(top_n, len(text_sources)),
                 "modelConfiguration": {
                     "modelArn": model_package_arn,
                 },

api/core/model_runtime/model_providers/deepseek/llm/_position.yaml

@@ -1,2 +1,3 @@
 - deepseek-chat
 - deepseek-coder
+- deepseek-reasoner

api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml

@@ -10,7 +10,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 128000
+  context_size: 64000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/deepseek/llm/deepseek-coder.yaml

@@ -10,7 +10,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 128000
+  context_size: 64000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/deepseek/llm/deepseek-reasoner.yaml

@@ -0,0 +1,21 @@
+model: deepseek-reasoner
+label:
+  zh_Hans: deepseek-reasoner
+  en_US: deepseek-reasoner
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 8192
+    default: 4096
+pricing:
+  input: "4"
+  output: "16"
+  unit: "0.000001"
+  currency: RMB

api/core/model_runtime/model_providers/deepseek/llm/llm.py

@@ -24,9 +24,6 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
-        # {"response_format": "xx"} need convert to {"response_format": {"type": "xx"}}
-        if "response_format" in model_parameters:
-            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:

api/core/model_runtime/model_providers/google/llm/_position.yaml

@@ -1,5 +1,6 @@
 - gemini-2.0-flash-exp
 - gemini-2.0-flash-thinking-exp-1219
+- gemini-2.0-flash-thinking-exp-01-21
 - gemini-1.5-pro
 - gemini-1.5-pro-latest
 - gemini-1.5-pro-001

api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-thinking-exp-01-21.yaml

@@ -0,0 +1,39 @@
+model: gemini-2.0-flash-thinking-exp-01-21
+label:
+  en_US: Gemini 2.0 Flash Thinking Exp 01-21
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/gpustack/gpustack.yaml

@@ -9,6 +9,8 @@ supported_model_types:
   - llm
   - text-embedding
   - rerank
+  - speech2text
+  - tts
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -118,3 +120,19 @@ model_credential_schema:
           label:
             en_US: Not Support
             zh_Hans: 不支持
+    - variable: voices
+      show_on:
+        - variable: __model_type
+          value: tts
+      label:
+        en_US: Available Voices (comma-separated)
+        zh_Hans: 可用声音（用英文逗号分隔）
+      type: text-input
+      required: false
+      default: "Chinese Female"
+      placeholder:
+        en_US: "Chinese Female, Chinese Male, Japanese Male, Cantonese Female, English Female, English Male, Korean Female"
+        zh_Hans: "Chinese Female, Chinese Male, Japanese Male, Cantonese Female, English Female, English Male, Korean Female"
+      help:
+        en_US: "List voice names separated by commas. First voice will be used as default."
+        zh_Hans: "用英文逗号分隔的声音列表。第一个声音将作为默认值。"

api/core/model_runtime/model_providers/gpustack/llm/llm.py

@@ -1,7 +1,5 @@
 from collections.abc import Generator
 
-from yarl import URL
-
 from core.model_runtime.entities.llm_entities import LLMResult
 from core.model_runtime.entities.message_entities import (
     PromptMessage,
@@ -24,9 +22,10 @@ class GPUStackLanguageModel(OAIAPICompatLargeLanguageModel):
         stream: bool = True,
         user: str | None = None,
     ) -> LLMResult | Generator:
+        compatible_credentials = self._get_compatible_credentials(credentials)
         return super()._invoke(
             model,
-            credentials,
+            compatible_credentials,
             prompt_messages,
             model_parameters,
             tools,
@@ -36,10 +35,15 @@ class GPUStackLanguageModel(OAIAPICompatLargeLanguageModel):
         )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials)
-        super().validate_credentials(model, credentials)
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        super().validate_credentials(model, compatible_credentials)
+
+    def _get_compatible_credentials(self, credentials: dict) -> dict:
+        credentials = credentials.copy()
+        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
+        credentials["endpoint_url"] = f"{base_url}/v1-openai"
+        return credentials
 
     @staticmethod
     def _add_custom_parameters(credentials: dict) -> None:
-        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"]) / "v1-openai")
         credentials["mode"] = "chat"

api/core/model_runtime/model_providers/gpustack/speech2text/speech2text.py

@@ -0,0 +1,43 @@
+from typing import IO, Optional
+
+from core.model_runtime.model_providers.openai_api_compatible.speech2text.speech2text import OAICompatSpeech2TextModel
+
+
+class GPUStackSpeech2TextModel(OAICompatSpeech2TextModel):
+    """
+    Model class for GPUStack Speech to text model.
+    """
+
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
+        """
+        Invoke speech2text model
+        :param model: model name
+        :param credentials: model credentials
+        :param file: audio file
+        :param user: unique user id
+        :return: text for given audio file
+        """
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        return super()._invoke(model, compatible_credentials, file)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        """
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        super().validate_credentials(model, compatible_credentials)
+
+    def _get_compatible_credentials(self, credentials: dict) -> dict:
+        """
+        Get compatible credentials
+
+        :param credentials: model credentials
+        :return: compatible credentials
+        """
+        compatible_credentials = credentials.copy()
+        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
+        compatible_credentials["endpoint_url"] = f"{base_url}/v1-openai"
+        return compatible_credentials

api/core/model_runtime/model_providers/gpustack/text_embedding/text_embedding.py

@@ -1,7 +1,5 @@
 from typing import Optional
 
-from yarl import URL
-
 from core.entities.embedding_type import EmbeddingInputType
 from core.model_runtime.entities.text_embedding_entities import (
     TextEmbeddingResult,
@@ -24,12 +22,15 @@ class GPUStackTextEmbeddingModel(OAICompatEmbeddingModel):
         user: Optional[str] = None,
         input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
-        return super()._invoke(model, credentials, texts, user, input_type)
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        return super()._invoke(model, compatible_credentials, texts, user, input_type)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials)
-        super().validate_credentials(model, credentials)
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        super().validate_credentials(model, compatible_credentials)
 
-    @staticmethod
-    def _add_custom_parameters(credentials: dict) -> None:
-        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"]) / "v1-openai")
+    def _get_compatible_credentials(self, credentials: dict) -> dict:
+        credentials = credentials.copy()
+        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
+        credentials["endpoint_url"] = f"{base_url}/v1-openai"
+        return credentials

api/core/model_runtime/model_providers/gpustack/tts/tts.py

@@ -0,0 +1,57 @@
+from typing import Any, Optional
+
+from core.model_runtime.model_providers.openai_api_compatible.tts.tts import OAICompatText2SpeechModel
+
+
+class GPUStackText2SpeechModel(OAICompatText2SpeechModel):
+    """
+    Model class for GPUStack Text to Speech model.
+    """
+
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ) -> Any:
+        """
+        Invoke text2speech model
+
+        :param model: model name
+        :param tenant_id: user tenant id
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model timbre
+        :param user: unique user id
+        :return: text translated to audio file
+        """
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        return super()._invoke(
+            model=model,
+            tenant_id=tenant_id,
+            credentials=compatible_credentials,
+            content_text=content_text,
+            voice=voice,
+            user=user,
+        )
+
+    def validate_credentials(self, model: str, credentials: dict, user: Optional[str] = None) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :param user: unique user id
+        """
+        compatible_credentials = self._get_compatible_credentials(credentials)
+        super().validate_credentials(model, compatible_credentials)
+
+    def _get_compatible_credentials(self, credentials: dict) -> dict:
+        """
+        Get compatible credentials
+
+        :param credentials: model credentials
+        :return: compatible credentials
+        """
+        compatible_credentials = credentials.copy()
+        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
+        compatible_credentials["endpoint_url"] = f"{base_url}/v1-openai"
+
+        return compatible_credentials

api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py

@@ -162,9 +162,9 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel
     @staticmethod
     def _check_endpoint_url_model_repository_name(credentials: dict, model_name: str):
         try:
-            url = f'{HUGGINGFACE_ENDPOINT_API}{credentials["huggingface_namespace"]}'
+            url = f"{HUGGINGFACE_ENDPOINT_API}{credentials['huggingface_namespace']}"
             headers = {
-                "Authorization": f'Bearer {credentials["huggingfacehub_api_token"]}',
+                "Authorization": f"Bearer {credentials['huggingfacehub_api_token']}",
                 "Content-Type": "application/json",
             }
 

api/core/model_runtime/model_providers/minimax/llm/llm.py

@@ -34,6 +34,7 @@ from core.model_runtime.model_providers.minimax.llm.types import MinimaxMessage
 
 class MinimaxLargeLanguageModel(LargeLanguageModel):
     model_apis = {
+        "minimax-text-01": MinimaxChatCompletionPro,
         "abab7-chat-preview": MinimaxChatCompletionPro,
         "abab6.5t-chat": MinimaxChatCompletionPro,
         "abab6.5s-chat": MinimaxChatCompletionPro,

api/core/model_runtime/model_providers/minimax/llm/minimax-text-01.yaml

@@ -0,0 +1,46 @@
+model: minimax-text-01
+label:
+  en_US: Minimax-Text-01
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1000192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0.01
+    max: 1
+    default: 0.1
+  - name: top_p
+    use_template: top_p
+    min: 0.01
+    max: 1
+    default: 0.95
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 1000192
+  - name: mask_sensitive_info
+    type: boolean
+    default: true
+    label:
+      zh_Hans: 隐私保护
+      en_US: Moderate
+    help:
+      zh_Hans: 对输出中易涉及隐私问题的文本信息进行打码，目前包括但不限于邮箱、域名、链接、证件号、家庭住址等，默认true，即开启打码
+      en_US: Mask the sensitive info of the generated content, such as email/domain/link/address/phone/id..
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+pricing:
+  input: '0.001'
+  output: '0.008'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/moonshot/llm/llm.py

@@ -44,9 +44,6 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         self._add_custom_parameters(credentials)
         self._add_function_call(model, credentials)
         user = user[:32] if user else None
-        # {"response_format": "json_object"} need convert to {"response_format": {"type": "json_object"}}
-        if "response_format" in model_parameters:
-            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:

api/core/model_runtime/model_providers/openai/llm/llm.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import re
 from collections.abc import Generator
 from typing import Any, Optional, Union, cast
 
@@ -621,11 +622,19 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         prompt_messages = self._clear_illegal_prompt_messages(model, prompt_messages)
 
         # o1 compatibility
+        block_as_stream = False
         if model.startswith("o1"):
             if "max_tokens" in model_parameters:
                 model_parameters["max_completion_tokens"] = model_parameters["max_tokens"]
                 del model_parameters["max_tokens"]
 
+            if re.match(r"^o1(-\d{4}-\d{2}-\d{2})?$", model):
+                if stream:
+                    block_as_stream = True
+                    stream = False
+                    if "stream_options" in extra_model_kwargs:
+                        del extra_model_kwargs["stream_options"]
+
             if "stop" in extra_model_kwargs:
                 del extra_model_kwargs["stop"]
 
@@ -642,7 +651,45 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         if stream:
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
 
-        return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
+        block_result = self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
+
+        if block_as_stream:
+            return self._handle_chat_block_as_stream_response(block_result, prompt_messages, stop)
+
+        return block_result
+
+    def _handle_chat_block_as_stream_response(
+        self,
+        block_result: LLMResult,
+        prompt_messages: list[PromptMessage],
+        stop: Optional[list[str]] = None,
+    ) -> Generator[LLMResultChunk, None, None]:
+        """
+        Handle llm chat response
+        :param model: model name
+        :param credentials: credentials
+        :param response: response
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return: llm response chunk generator
+        """
+        text = block_result.message.content
+        text = cast(str, text)
+
+        if stop:
+            text = self.enforce_stop_tokens(text, stop)
+
+        yield LLMResultChunk(
+            model=block_result.model,
+            prompt_messages=prompt_messages,
+            system_fingerprint=block_result.system_fingerprint,
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=block_result.message,
+                finish_reason="stop",
+                usage=block_result.usage,
+            ),
+        )
 
     def _handle_chat_generate_response(
         self,

api/core/model_runtime/model_providers/openrouter/llm/claude-3-5-sonnet.yaml

@@ -7,6 +7,7 @@ features:
   - vision
   - tool-call
   - stream-tool-call
+  - document
 model_properties:
   mode: chat
   context_size: 200000

api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml

@@ -7,6 +7,8 @@
 - Qwen/Qwen2.5-Coder-7B-Instruct
 - Qwen/Qwen2-VL-72B-Instruct
 - Qwen/Qwen2-1.5B-Instruct
+- Qwen/Qwen2.5-72B-Instruct-128K
+- Vendor-A/Qwen/Qwen2.5-72B-Instruct
 - Pro/Qwen/Qwen2-VL-7B-Instruct
 - OpenGVLab/InternVL2-26B
 - Pro/OpenGVLab/InternVL2-8B

api/core/model_runtime/model_providers/siliconflow/llm/llm.py

@@ -29,9 +29,6 @@ class SiliconflowLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
-        # {"response_format": "json_object"} need convert to {"response_format": {"type": "json_object"}}
-        if "response_format" in model_parameters:
-            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:

api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-72b-instruct-128k.yaml

@@ -0,0 +1,51 @@
+model: Qwen/Qwen2.5-72B-Instruct-128K
+label:
+  en_US: Qwen/Qwen2.5-72B-Instruct-128K
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 512
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
+      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: Response Format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '4.13'
+  output: '4.13'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-72b-instruct-vendorA.yaml

@@ -0,0 +1,51 @@
+model: Vendor-A/Qwen/Qwen2.5-72B-Instruct
+label:
+  en_US: Vendor-A/Qwen/Qwen2.5-72B-Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 512
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
+      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: Response Format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '1.00'
+  output: '1.00'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-72b-instruct.yaml

@@ -15,7 +15,7 @@ parameter_rules:
     type: int
     default: 512
     min: 1
-    max: 8192
+    max: 4096
     help:
       zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
       en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.

api/core/model_runtime/model_providers/siliconflow/tts/fish-speech-1.5.yaml

@@ -0,0 +1,37 @@
+model: fishaudio/fish-speech-1.5
+model_type: tts
+model_properties:
+  default_voice: 'fishaudio/fish-speech-1.5:alex'
+  voices:
+    - mode: "fishaudio/fish-speech-1.5:alex"
+      name: "Alex（男声）"
+      language: [ "zh-Hans", "en-US" ]
+    - mode: "fishaudio/fish-speech-1.5:benjamin"
+      name: "Benjamin（男声）"
+      language: [ "zh-Hans", "en-US" ]
+    - mode: "fishaudio/fish-speech-1.5:charles"
+      name: "Charles（男声）"
+      language: [ "zh-Hans", "en-US" ]
+    - mode: "fishaudio/fish-speech-1.5:david"
+      name: "David（男声）"
+      language: [ "zh-Hans", "en-US" ]
+    - mode: "fishaudio/fish-speech-1.5:anna"
+      name: "Anna（女声）"
+      language: [ "zh-Hans", "en-US" ]
+    - mode: "fishaudio/fish-speech-1.5:bella"
+      name: "Bella（女声）"
+      language: [ "zh-Hans", "en-US" ]
+    - mode: "fishaudio/fish-speech-1.5:claire"
+      name: "Claire（女声）"
+      language: [ "zh-Hans", "en-US" ]
+    - mode: "fishaudio/fish-speech-1.5:diana"
+      name: "Diana（女声）"
+      language: [ "zh-Hans", "en-US" ]
+  audio_type: 'mp3'
+  max_workers: 5
+  # stream: false
+pricing:
+  input: '0.015'
+  output: '0'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/spark/llm/_client.py

@@ -21,7 +21,7 @@ class SparkLLMClient:
             domain = api_domain
 
         model_api_configs = {
-            "spark-lite": {"version": "v1.1", "chat_domain": "general"},
+            "spark-lite": {"version": "v1.1", "chat_domain": "lite"},
             "spark-pro": {"version": "v3.1", "chat_domain": "generalv3"},
             "spark-pro-128k": {"version": "pro-128k", "chat_domain": "pro-128k"},
             "spark-max": {"version": "v3.5", "chat_domain": "generalv3.5"},

api/core/model_runtime/model_providers/tongyi/llm/llm.py

@@ -257,8 +257,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         for index, response in enumerate(responses):
             if response.status_code not in {200, HTTPStatus.OK}:
                 raise ServiceUnavailableError(
-                    f"Failed to invoke model {model}, status code: {response.status_code}, "
-                    f"message: {response.message}"
+                    f"Failed to invoke model {model}, status code: {response.status_code}, message: {response.message}"
                 )
 
             resp_finish_reason = response.output.choices[0].finish_reason

api/core/model_runtime/model_providers/triton_inference_server/llm/llm.py

@@ -146,7 +146,7 @@ class TritonInferenceAILargeLanguageModel(LargeLanguageModel):
             elif credentials["completion_type"] == "completion":
                 completion_type = LLMMode.COMPLETION.value
             else:
-                raise ValueError(f'completion_type {credentials["completion_type"]} is not supported')
+                raise ValueError(f"completion_type {credentials['completion_type']} is not supported")
 
         entity = AIModelEntity(
             model=model,

api/core/model_runtime/model_providers/volcengine_maas/llm/models.py

@@ -18,72 +18,93 @@ class ModelConfig(BaseModel):
 
 
 configs: dict[str, ModelConfig] = {
+    "Doubao-1.5-vision-pro-32k": ModelConfig(
+        properties=ModelProperties(context_size=32768, max_tokens=12288, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.VISION],
+    ),
+    "Doubao-1.5-pro-32k": ModelConfig(
+        properties=ModelProperties(context_size=32768, max_tokens=12288, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
+    ),
+    "Doubao-1.5-lite-32k": ModelConfig(
+        properties=ModelProperties(context_size=32768, max_tokens=12288, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
+    ),
+    "Doubao-1.5-pro-256k": ModelConfig(
+        properties=ModelProperties(context_size=262144, max_tokens=12288, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
+    ),
     "Doubao-vision-pro-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.VISION],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.VISION],
     ),
     "Doubao-vision-lite-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.VISION],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.VISION],
     ),
     "Doubao-pro-4k": ModelConfig(
         properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Doubao-lite-4k": ModelConfig(
         properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Doubao-pro-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Doubao-lite-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Doubao-pro-256k": ModelConfig(
         properties=ModelProperties(context_size=262144, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[],
+        features=[ModelFeature.AGENT_THOUGHT],
     ),
     "Doubao-pro-128k": ModelConfig(
         properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Doubao-lite-128k": ModelConfig(
-        properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT), features=[]
+        properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
     ),
     "Skylark2-pro-4k": ModelConfig(
-        properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT), features=[]
+        properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
     ),
     "Llama3-8B": ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT), features=[]
+        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
     ),
     "Llama3-70B": ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT), features=[]
+        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
     ),
     "Moonshot-v1-8k": ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Moonshot-v1-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=16384, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Moonshot-v1-128k": ModelConfig(
         properties=ModelProperties(context_size=131072, max_tokens=65536, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "GLM3-130B": ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "GLM3-130B-Fin": ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL],
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL],
     ),
     "Mistral-7B": ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=2048, mode=LLMMode.CHAT), features=[]
+        properties=ModelProperties(context_size=8192, max_tokens=2048, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
     ),
 }
 

api/core/model_runtime/model_providers/volcengine_maas/volcengine_maas.yaml

@@ -118,6 +118,30 @@ model_credential_schema:
       type: select
       required: true
       options:
+        - label:
+            en_US: Doubao-1.5-vision-pro-32k
+          value: Doubao-1.5-vision-pro-32k
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: Doubao-1.5-pro-32k
+          value: Doubao-1.5-pro-32k
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: Doubao-1.5-lite-32k
+          value: Doubao-1.5-lite-32k
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: Doubao-1.5-pro-256k
+          value: Doubao-1.5-pro-256k
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: Doubao-vision-pro-32k
           value: Doubao-vision-pro-32k

api/core/model_runtime/model_providers/wenxin/_common.py

@@ -41,15 +41,15 @@ class BaiduAccessToken:
         resp = response.json()
         if "error" in resp:
             if resp["error"] == "invalid_client":
-                raise InvalidAPIKeyError(f'Invalid API key or secret key: {resp["error_description"]}')
+                raise InvalidAPIKeyError(f"Invalid API key or secret key: {resp['error_description']}")
             elif resp["error"] == "unknown_error":
-                raise InternalServerError(f'Internal server error: {resp["error_description"]}')
+                raise InternalServerError(f"Internal server error: {resp['error_description']}")
             elif resp["error"] == "invalid_request":
-                raise BadRequestError(f'Bad request: {resp["error_description"]}')
+                raise BadRequestError(f"Bad request: {resp['error_description']}")
             elif resp["error"] == "rate_limit_exceeded":
-                raise RateLimitReachedError(f'Rate limit reached: {resp["error_description"]}')
+                raise RateLimitReachedError(f"Rate limit reached: {resp['error_description']}")
             else:
-                raise Exception(f'Unknown error: {resp["error_description"]}')
+                raise Exception(f"Unknown error: {resp['error_description']}")
 
         return resp["access_token"]
 

api/core/model_runtime/model_providers/xinference/llm/llm.py

@@ -406,7 +406,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             elif credentials["completion_type"] == "completion":
                 completion_type = LLMMode.COMPLETION.value
             else:
-                raise ValueError(f'completion_type {credentials["completion_type"]} is not supported')
+                raise ValueError(f"completion_type {credentials['completion_type']} is not supported")
         else:
             extra_args = XinferenceHelper.get_xinference_extra_parameter(
                 server_url=credentials["server_url"],
@@ -472,7 +472,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         api_key = credentials.get("api_key") or "abc"
 
         client = OpenAI(
-            base_url=f'{credentials["server_url"]}/v1',
+            base_url=f"{credentials['server_url']}/v1",
             api_key=api_key,
             max_retries=int(credentials.get("max_retries") or DEFAULT_MAX_RETRIES),
             timeout=int(credentials.get("invoke_timeout") or DEFAULT_INVOKE_TIMEOUT),

api/core/model_runtime/schema_validators/common_validator.py

@@ -87,6 +87,6 @@ class CommonValidator:
             if value.lower() not in {"true", "false"}:
                 raise ValueError(f"Variable {credential_form_schema.variable} should be true or false")
 
-            value = True if value.lower() == "true" else False
+            value = value.lower() == "true"
 
         return value

api/core/ops/entities/config_entity.py

@@ -6,6 +6,7 @@ from pydantic import BaseModel, ValidationInfo, field_validator
 class TracingProviderEnum(Enum):
     LANGFUSE = "langfuse"
     LANGSMITH = "langsmith"
+    OPIK = "opik"
 
 
 class BaseTracingConfig(BaseModel):
@@ -56,5 +57,36 @@ class LangSmithConfig(BaseTracingConfig):
         return v
 
 
+class OpikConfig(BaseTracingConfig):
+    """
+    Model class for Opik tracing config.
+    """
+
+    api_key: str | None = None
+    project: str | None = None
+    workspace: str | None = None
+    url: str = "https://www.comet.com/opik/api/"
+
+    @field_validator("project")
+    @classmethod
+    def project_validator(cls, v, info: ValidationInfo):
+        if v is None or v == "":
+            v = "Default Project"
+
+        return v
+
+    @field_validator("url")
+    @classmethod
+    def url_validator(cls, v, info: ValidationInfo):
+        if v is None or v == "":
+            v = "https://www.comet.com/opik/api/"
+        if not v.startswith(("https://", "http://")):
+            raise ValueError("url must start with https:// or http://")
+        if not v.endswith("/api/"):
+            raise ValueError("url should ends with /api/")
+
+        return v
+
+
 OPS_FILE_PATH = "ops_trace/"
 OPS_TRACE_FAILED_KEY = "FAILED_OPS_TRACE"

api/core/ops/opik_trace/opik_trace.py

@@ -0,0 +1,469 @@
+import json
+import logging
+import os
+import uuid
+from datetime import datetime, timedelta
+from typing import Optional, cast
+
+from opik import Opik, Trace
+from opik.id_helpers import uuid4_to_uuid7
+
+from core.ops.base_trace_instance import BaseTraceInstance
+from core.ops.entities.config_entity import OpikConfig
+from core.ops.entities.trace_entity import (
+    BaseTraceInfo,
+    DatasetRetrievalTraceInfo,
+    GenerateNameTraceInfo,
+    MessageTraceInfo,
+    ModerationTraceInfo,
+    SuggestedQuestionTraceInfo,
+    ToolTraceInfo,
+    TraceTaskName,
+    WorkflowTraceInfo,
+)
+from extensions.ext_database import db
+from models.model import EndUser, MessageFile
+from models.workflow import WorkflowNodeExecution
+
+logger = logging.getLogger(__name__)
+
+
+def wrap_dict(key_name, data):
+    """Make sure that the input data is a dict"""
+    if not isinstance(data, dict):
+        return {key_name: data}
+
+    return data
+
+
+def wrap_metadata(metadata, **kwargs):
+    """Add common metatada to all Traces and Spans"""
+    metadata["created_from"] = "dify"
+
+    metadata.update(kwargs)
+
+    return metadata
+
+
+def prepare_opik_uuid(user_datetime: Optional[datetime], user_uuid: Optional[str]):
+    """Opik needs UUIDv7 while Dify uses UUIDv4 for identifier of most
+    messages and objects. The type-hints of BaseTraceInfo indicates that
+    objects start_time and message_id could be null which means we cannot map
+    it to a UUIDv7. Given that we have no way to identify that object
+    uniquely, generate a new random one UUIDv7 in that case.
+    """
+
+    if user_datetime is None:
+        user_datetime = datetime.now()
+
+    if user_uuid is None:
+        user_uuid = str(uuid.uuid4())
+
+    return uuid4_to_uuid7(user_datetime, user_uuid)
+
+
+class OpikDataTrace(BaseTraceInstance):
+    def __init__(
+        self,
+        opik_config: OpikConfig,
+    ):
+        super().__init__(opik_config)
+        self.opik_client = Opik(
+            project_name=opik_config.project,
+            workspace=opik_config.workspace,
+            host=opik_config.url,
+            api_key=opik_config.api_key,
+        )
+        self.project = opik_config.project
+        self.file_base_url = os.getenv("FILES_URL", "http://127.0.0.1:5001")
+
+    def trace(self, trace_info: BaseTraceInfo):
+        if isinstance(trace_info, WorkflowTraceInfo):
+            self.workflow_trace(trace_info)
+        if isinstance(trace_info, MessageTraceInfo):
+            self.message_trace(trace_info)
+        if isinstance(trace_info, ModerationTraceInfo):
+            self.moderation_trace(trace_info)
+        if isinstance(trace_info, SuggestedQuestionTraceInfo):
+            self.suggested_question_trace(trace_info)
+        if isinstance(trace_info, DatasetRetrievalTraceInfo):
+            self.dataset_retrieval_trace(trace_info)
+        if isinstance(trace_info, ToolTraceInfo):
+            self.tool_trace(trace_info)
+        if isinstance(trace_info, GenerateNameTraceInfo):
+            self.generate_name_trace(trace_info)
+
+    def workflow_trace(self, trace_info: WorkflowTraceInfo):
+        dify_trace_id = trace_info.workflow_run_id
+        opik_trace_id = prepare_opik_uuid(trace_info.start_time, dify_trace_id)
+        workflow_metadata = wrap_metadata(
+            trace_info.metadata, message_id=trace_info.message_id, workflow_app_log_id=trace_info.workflow_app_log_id
+        )
+        root_span_id = None
+
+        if trace_info.message_id:
+            dify_trace_id = trace_info.message_id
+            opik_trace_id = prepare_opik_uuid(trace_info.start_time, dify_trace_id)
+
+            trace_data = {
+                "id": opik_trace_id,
+                "name": TraceTaskName.MESSAGE_TRACE.value,
+                "start_time": trace_info.start_time,
+                "end_time": trace_info.end_time,
+                "metadata": workflow_metadata,
+                "input": wrap_dict("input", trace_info.workflow_run_inputs),
+                "output": wrap_dict("output", trace_info.workflow_run_outputs),
+                "tags": ["message", "workflow"],
+                "project_name": self.project,
+            }
+            self.add_trace(trace_data)
+
+            root_span_id = prepare_opik_uuid(trace_info.start_time, trace_info.workflow_run_id)
+            span_data = {
+                "id": root_span_id,
+                "parent_span_id": None,
+                "trace_id": opik_trace_id,
+                "name": TraceTaskName.WORKFLOW_TRACE.value,
+                "input": wrap_dict("input", trace_info.workflow_run_inputs),
+                "output": wrap_dict("output", trace_info.workflow_run_outputs),
+                "start_time": trace_info.start_time,
+                "end_time": trace_info.end_time,
+                "metadata": workflow_metadata,
+                "tags": ["workflow"],
+                "project_name": self.project,
+            }
+            self.add_span(span_data)
+        else:
+            trace_data = {
+                "id": opik_trace_id,
+                "name": TraceTaskName.MESSAGE_TRACE.value,
+                "start_time": trace_info.start_time,
+                "end_time": trace_info.end_time,
+                "metadata": workflow_metadata,
+                "input": wrap_dict("input", trace_info.workflow_run_inputs),
+                "output": wrap_dict("output", trace_info.workflow_run_outputs),
+                "tags": ["workflow"],
+                "project_name": self.project,
+            }
+            self.add_trace(trace_data)
+
+        # through workflow_run_id get all_nodes_execution
+        workflow_nodes_execution_id_records = (
+            db.session.query(WorkflowNodeExecution.id)
+            .filter(WorkflowNodeExecution.workflow_run_id == trace_info.workflow_run_id)
+            .all()
+        )
+
+        for node_execution_id_record in workflow_nodes_execution_id_records:
+            node_execution = (
+                db.session.query(
+                    WorkflowNodeExecution.id,
+                    WorkflowNodeExecution.tenant_id,
+                    WorkflowNodeExecution.app_id,
+                    WorkflowNodeExecution.title,
+                    WorkflowNodeExecution.node_type,
+                    WorkflowNodeExecution.status,
+                    WorkflowNodeExecution.inputs,
+                    WorkflowNodeExecution.outputs,
+                    WorkflowNodeExecution.created_at,
+                    WorkflowNodeExecution.elapsed_time,
+                    WorkflowNodeExecution.process_data,
+                    WorkflowNodeExecution.execution_metadata,
+                )
+                .filter(WorkflowNodeExecution.id == node_execution_id_record.id)
+                .first()
+            )
+
+            if not node_execution:
+                continue
+
+            node_execution_id = node_execution.id
+            tenant_id = node_execution.tenant_id
+            app_id = node_execution.app_id
+            node_name = node_execution.title
+            node_type = node_execution.node_type
+            status = node_execution.status
+            if node_type == "llm":
+                inputs = (
+                    json.loads(node_execution.process_data).get("prompts", {}) if node_execution.process_data else {}
+                )
+            else:
+                inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
+            outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
+            created_at = node_execution.created_at or datetime.now()
+            elapsed_time = node_execution.elapsed_time
+            finished_at = created_at + timedelta(seconds=elapsed_time)
+
+            execution_metadata = (
+                json.loads(node_execution.execution_metadata) if node_execution.execution_metadata else {}
+            )
+            metadata = execution_metadata.copy()
+            metadata.update(
+                {
+                    "workflow_run_id": trace_info.workflow_run_id,
+                    "node_execution_id": node_execution_id,
+                    "tenant_id": tenant_id,
+                    "app_id": app_id,
+                    "app_name": node_name,
+                    "node_type": node_type,
+                    "status": status,
+                }
+            )
+
+            process_data = json.loads(node_execution.process_data) if node_execution.process_data else {}
+
+            provider = None
+            model = None
+            total_tokens = 0
+            completion_tokens = 0
+            prompt_tokens = 0
+
+            if process_data and process_data.get("model_mode") == "chat":
+                run_type = "llm"
+                provider = process_data.get("model_provider", None)
+                model = process_data.get("model_name", "")
+                metadata.update(
+                    {
+                        "ls_provider": provider,
+                        "ls_model_name": model,
+                    }
+                )
+
+                try:
+                    if outputs.get("usage"):
+                        total_tokens = outputs["usage"].get("total_tokens", 0)
+                        prompt_tokens = outputs["usage"].get("prompt_tokens", 0)
+                        completion_tokens = outputs["usage"].get("completion_tokens", 0)
+                except Exception:
+                    logger.error("Failed to extract usage", exc_info=True)
+
+            else:
+                run_type = "tool"
+
+            parent_span_id = trace_info.workflow_app_log_id or trace_info.workflow_run_id
+
+            if not total_tokens:
+                total_tokens = execution_metadata.get("total_tokens", 0)
+
+            span_data = {
+                "trace_id": opik_trace_id,
+                "id": prepare_opik_uuid(created_at, node_execution_id),
+                "parent_span_id": prepare_opik_uuid(trace_info.start_time, parent_span_id),
+                "name": node_type,
+                "type": run_type,
+                "start_time": created_at,
+                "end_time": finished_at,
+                "metadata": wrap_metadata(metadata),
+                "input": wrap_dict("input", inputs),
+                "output": wrap_dict("output", outputs),
+                "tags": ["node_execution"],
+                "project_name": self.project,
+                "usage": {
+                    "total_tokens": total_tokens,
+                    "completion_tokens": completion_tokens,
+                    "prompt_tokens": prompt_tokens,
+                },
+                "model": model,
+                "provider": provider,
+            }
+
+            self.add_span(span_data)
+
+    def message_trace(self, trace_info: MessageTraceInfo):
+        # get message file data
+        file_list = cast(list[str], trace_info.file_list) or []
+        message_file_data: Optional[MessageFile] = trace_info.message_file_data
+
+        if message_file_data is not None:
+            file_url = f"{self.file_base_url}/{message_file_data.url}" if message_file_data else ""
+            file_list.append(file_url)
+
+        message_data = trace_info.message_data
+        if message_data is None:
+            return
+
+        metadata = trace_info.metadata
+        message_id = trace_info.message_id
+
+        user_id = message_data.from_account_id
+        metadata["user_id"] = user_id
+        metadata["file_list"] = file_list
+
+        if message_data.from_end_user_id:
+            end_user_data: Optional[EndUser] = (
+                db.session.query(EndUser).filter(EndUser.id == message_data.from_end_user_id).first()
+            )
+            if end_user_data is not None:
+                end_user_id = end_user_data.session_id
+                metadata["end_user_id"] = end_user_id
+
+        trace_data = {
+            "id": prepare_opik_uuid(trace_info.start_time, message_id),
+            "name": TraceTaskName.MESSAGE_TRACE.value,
+            "start_time": trace_info.start_time,
+            "end_time": trace_info.end_time,
+            "metadata": wrap_metadata(metadata),
+            "input": trace_info.inputs,
+            "output": message_data.answer,
+            "tags": ["message", str(trace_info.conversation_mode)],
+            "project_name": self.project,
+        }
+        trace = self.add_trace(trace_data)
+
+        span_data = {
+            "trace_id": trace.id,
+            "name": "llm",
+            "type": "llm",
+            "start_time": trace_info.start_time,
+            "end_time": trace_info.end_time,
+            "metadata": wrap_metadata(metadata),
+            "input": {"input": trace_info.inputs},
+            "output": {"output": message_data.answer},
+            "tags": ["llm", str(trace_info.conversation_mode)],
+            "usage": {
+                "completion_tokens": trace_info.answer_tokens,
+                "prompt_tokens": trace_info.message_tokens,
+                "total_tokens": trace_info.total_tokens,
+            },
+            "project_name": self.project,
+        }
+        self.add_span(span_data)
+
+    def moderation_trace(self, trace_info: ModerationTraceInfo):
+        if trace_info.message_data is None:
+            return
+
+        start_time = trace_info.start_time or trace_info.message_data.created_at
+
+        span_data = {
+            "trace_id": prepare_opik_uuid(start_time, trace_info.message_id),
+            "name": TraceTaskName.MODERATION_TRACE.value,
+            "type": "tool",
+            "start_time": start_time,
+            "end_time": trace_info.end_time or trace_info.message_data.updated_at,
+            "metadata": wrap_metadata(trace_info.metadata),
+            "input": wrap_dict("input", trace_info.inputs),
+            "output": {
+                "action": trace_info.action,
+                "flagged": trace_info.flagged,
+                "preset_response": trace_info.preset_response,
+                "inputs": trace_info.inputs,
+            },
+            "tags": ["moderation"],
+        }
+
+        self.add_span(span_data)
+
+    def suggested_question_trace(self, trace_info: SuggestedQuestionTraceInfo):
+        message_data = trace_info.message_data
+        if message_data is None:
+            return
+
+        start_time = trace_info.start_time or message_data.created_at
+
+        span_data = {
+            "trace_id": prepare_opik_uuid(start_time, trace_info.message_id),
+            "name": TraceTaskName.SUGGESTED_QUESTION_TRACE.value,
+            "type": "tool",
+            "start_time": start_time,
+            "end_time": trace_info.end_time or message_data.updated_at,
+            "metadata": wrap_metadata(trace_info.metadata),
+            "input": wrap_dict("input", trace_info.inputs),
+            "output": wrap_dict("output", trace_info.suggested_question),
+            "tags": ["suggested_question"],
+        }
+
+        self.add_span(span_data)
+
+    def dataset_retrieval_trace(self, trace_info: DatasetRetrievalTraceInfo):
+        if trace_info.message_data is None:
+            return
+
+        start_time = trace_info.start_time or trace_info.message_data.created_at
+
+        span_data = {
+            "trace_id": prepare_opik_uuid(start_time, trace_info.message_id),
+            "name": TraceTaskName.DATASET_RETRIEVAL_TRACE.value,
+            "type": "tool",
+            "start_time": start_time,
+            "end_time": trace_info.end_time or trace_info.message_data.updated_at,
+            "metadata": wrap_metadata(trace_info.metadata),
+            "input": wrap_dict("input", trace_info.inputs),
+            "output": {"documents": trace_info.documents},
+            "tags": ["dataset_retrieval"],
+        }
+
+        self.add_span(span_data)
+
+    def tool_trace(self, trace_info: ToolTraceInfo):
+        span_data = {
+            "trace_id": prepare_opik_uuid(trace_info.start_time, trace_info.message_id),
+            "name": trace_info.tool_name,
+            "type": "tool",
+            "start_time": trace_info.start_time,
+            "end_time": trace_info.end_time,
+            "metadata": wrap_metadata(trace_info.metadata),
+            "input": wrap_dict("input", trace_info.tool_inputs),
+            "output": wrap_dict("output", trace_info.tool_outputs),
+            "tags": ["tool", trace_info.tool_name],
+        }
+
+        self.add_span(span_data)
+
+    def generate_name_trace(self, trace_info: GenerateNameTraceInfo):
+        trace_data = {
+            "id": prepare_opik_uuid(trace_info.start_time, trace_info.message_id),
+            "name": TraceTaskName.GENERATE_NAME_TRACE.value,
+            "start_time": trace_info.start_time,
+            "end_time": trace_info.end_time,
+            "metadata": wrap_metadata(trace_info.metadata),
+            "input": trace_info.inputs,
+            "output": trace_info.outputs,
+            "tags": ["generate_name"],
+            "project_name": self.project,
+        }
+
+        trace = self.add_trace(trace_data)
+
+        span_data = {
+            "trace_id": trace.id,
+            "name": TraceTaskName.GENERATE_NAME_TRACE.value,
+            "start_time": trace_info.start_time,
+            "end_time": trace_info.end_time,
+            "metadata": wrap_metadata(trace_info.metadata),
+            "input": wrap_dict("input", trace_info.inputs),
+            "output": wrap_dict("output", trace_info.outputs),
+            "tags": ["generate_name"],
+        }
+
+        self.add_span(span_data)
+
+    def add_trace(self, opik_trace_data: dict) -> Trace:
+        try:
+            trace = self.opik_client.trace(**opik_trace_data)
+            logger.debug("Opik Trace created successfully")
+            return trace
+        except Exception as e:
+            raise ValueError(f"Opik Failed to create trace: {str(e)}")
+
+    def add_span(self, opik_span_data: dict):
+        try:
+            self.opik_client.span(**opik_span_data)
+            logger.debug("Opik Span created successfully")
+        except Exception as e:
+            raise ValueError(f"Opik Failed to create span: {str(e)}")
+
+    def api_check(self):
+        try:
+            self.opik_client.auth_check()
+            return True
+        except Exception as e:
+            logger.info(f"Opik API check failed: {str(e)}", exc_info=True)
+            raise ValueError(f"Opik API check failed: {str(e)}")
+
+    def get_project_url(self):
+        try:
+            return self.opik_client.get_project_url(project_name=self.project)
+        except Exception as e:
+            logger.info(f"Opik get run url failed: {str(e)}", exc_info=True)
+            raise ValueError(f"Opik get run url failed: {str(e)}")

api/core/ops/ops_trace_manager.py

@@ -17,6 +17,7 @@ from core.ops.entities.config_entity import (
     OPS_FILE_PATH,
     LangfuseConfig,
     LangSmithConfig,
+    OpikConfig,
     TracingProviderEnum,
 )
 from core.ops.entities.trace_entity import (
@@ -32,6 +33,7 @@ from core.ops.entities.trace_entity import (
 )
 from core.ops.langfuse_trace.langfuse_trace import LangFuseDataTrace
 from core.ops.langsmith_trace.langsmith_trace import LangSmithDataTrace
+from core.ops.opik_trace.opik_trace import OpikDataTrace
 from core.ops.utils import get_message_data
 from extensions.ext_database import db
 from extensions.ext_storage import storage
@@ -52,6 +54,12 @@ provider_config_map: dict[str, dict[str, Any]] = {
         "other_keys": ["project", "endpoint"],
         "trace_instance": LangSmithDataTrace,
     },
+    TracingProviderEnum.OPIK.value: {
+        "config_class": OpikConfig,
+        "secret_keys": ["api_key"],
+        "other_keys": ["project", "url", "workspace"],
+        "trace_instance": OpikDataTrace,
+    },
 }
 
 

api/core/provider_manager.py

@@ -22,7 +22,12 @@ from core.helper import encrypter
 from core.helper.model_provider_cache import ProviderCredentialsCache, ProviderCredentialsCacheType
 from core.helper.position_helper import is_filtered
 from core.model_runtime.entities.model_entities import ModelType
-from core.model_runtime.entities.provider_entities import CredentialFormSchema, FormType, ProviderEntity
+from core.model_runtime.entities.provider_entities import (
+    ConfigurateMethod,
+    CredentialFormSchema,
+    FormType,
+    ProviderEntity,
+)
 from core.model_runtime.model_providers import model_provider_factory
 from extensions import ext_hosting_provider
 from extensions.ext_database import db
@@ -835,11 +840,18 @@ class ProviderManager:
         :return:
         """
         # Get provider model credential secret variables
-        model_credential_secret_variables = self._extract_secret_variables(
-            provider_entity.model_credential_schema.credential_form_schemas
-            if provider_entity.model_credential_schema
-            else []
-        )
+        if ConfigurateMethod.PREDEFINED_MODEL in provider_entity.configurate_methods:
+            model_credential_secret_variables = self._extract_secret_variables(
+                provider_entity.provider_credential_schema.credential_form_schemas
+                if provider_entity.provider_credential_schema
+                else []
+            )
+        else:
+            model_credential_secret_variables = self._extract_secret_variables(
+                provider_entity.model_credential_schema.credential_form_schemas
+                if provider_entity.model_credential_schema
+                else []
+            )
 
         model_settings: list[ModelSettings] = []
         if not provider_model_settings:

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_ja_vector.py

@@ -0,0 +1,104 @@
+import json
+import logging
+from typing import Any, Optional
+
+from flask import current_app
+
+from core.rag.datasource.vdb.elasticsearch.elasticsearch_vector import (
+    ElasticSearchConfig,
+    ElasticSearchVector,
+    ElasticSearchVectorFactory,
+)
+from core.rag.datasource.vdb.field import Field
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.embedding.embedding_base import Embeddings
+from extensions.ext_redis import redis_client
+from models.dataset import Dataset
+
+logger = logging.getLogger(__name__)
+
+
+class ElasticSearchJaVector(ElasticSearchVector):
+    def create_collection(
+        self,
+        embeddings: list[list[float]],
+        metadatas: Optional[list[dict[Any, Any]]] = None,
+        index_params: Optional[dict] = None,
+    ):
+        lock_name = f"vector_indexing_lock_{self._collection_name}"
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
+            if redis_client.get(collection_exist_cache_key):
+                logger.info(f"Collection {self._collection_name} already exists.")
+                return
+
+            if not self._client.indices.exists(index=self._collection_name):
+                dim = len(embeddings[0])
+                settings = {
+                    "analysis": {
+                        "analyzer": {
+                            "ja_analyzer": {
+                                "type": "custom",
+                                "char_filter": [
+                                    "icu_normalizer",
+                                    "kuromoji_iteration_mark",
+                                ],
+                                "tokenizer": "kuromoji_tokenizer",
+                                "filter": [
+                                    "kuromoji_baseform",
+                                    "kuromoji_part_of_speech",
+                                    "ja_stop",
+                                    "kuromoji_number",
+                                    "kuromoji_stemmer",
+                                ],
+                            }
+                        }
+                    }
+                }
+                mappings = {
+                    "properties": {
+                        Field.CONTENT_KEY.value: {
+                            "type": "text",
+                            "analyzer": "ja_analyzer",
+                            "search_analyzer": "ja_analyzer",
+                        },
+                        Field.VECTOR.value: {  # Make sure the dimension is correct here
+                            "type": "dense_vector",
+                            "dims": dim,
+                            "index": True,
+                            "similarity": "cosine",
+                        },
+                        Field.METADATA_KEY.value: {
+                            "type": "object",
+                            "properties": {
+                                "doc_id": {"type": "keyword"}  # Map doc_id to keyword type
+                            },
+                        },
+                    }
+                }
+                self._client.indices.create(index=self._collection_name, settings=settings, mappings=mappings)
+
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+
+class ElasticSearchJaVectorFactory(ElasticSearchVectorFactory):
+    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> ElasticSearchJaVector:
+        if dataset.index_struct_dict:
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
+            collection_name = class_prefix
+        else:
+            dataset_id = dataset.id
+            collection_name = Dataset.gen_collection_name_by_id(dataset_id)
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.ELASTICSEARCH, collection_name))
+
+        config = current_app.config
+        return ElasticSearchJaVector(
+            index_name=collection_name,
+            config=ElasticSearchConfig(
+                host=config.get("ELASTICSEARCH_HOST", "localhost"),
+                port=config.get("ELASTICSEARCH_PORT", 9200),
+                username=config.get("ELASTICSEARCH_USERNAME", ""),
+                password=config.get("ELASTICSEARCH_PASSWORD", ""),
+            ),
+            attributes=[],
+        )

api/core/rag/datasource/vdb/field.py

@@ -6,6 +6,8 @@ class Field(Enum):
     METADATA_KEY = "metadata"
     GROUP_KEY = "group_id"
     VECTOR = "vector"
+    # Sparse Vector aims to support full text search
+    SPARSE_VECTOR = "sparse_vector"
     TEXT_KEY = "text"
     PRIMARY_KEY = "id"
     DOC_ID = "metadata.doc_id"

api/core/rag/datasource/vdb/lindorm/lindorm_vector.py

@@ -258,7 +258,7 @@ class LindormVectorStore(BaseVector):
             hnsw_ef_construction = kwargs.pop("hnsw_ef_construction", 500)
             ivfpq_m = kwargs.pop("ivfpq_m", dimension)
             nlist = kwargs.pop("nlist", 1000)
-            centroids_use_hnsw = kwargs.pop("centroids_use_hnsw", True if nlist >= 5000 else False)
+            centroids_use_hnsw = kwargs.pop("centroids_use_hnsw", nlist >= 5000)
             centroids_hnsw_m = kwargs.pop("centroids_hnsw_m", 24)
             centroids_hnsw_ef_construct = kwargs.pop("centroids_hnsw_ef_construct", 500)
             centroids_hnsw_ef_search = kwargs.pop("centroids_hnsw_ef_search", 100)
@@ -305,7 +305,7 @@ def default_text_mapping(dimension: int, method_name: str, **kwargs: Any) -> dic
     if method_name == "ivfpq":
         ivfpq_m = kwargs["ivfpq_m"]
         nlist = kwargs["nlist"]
-        centroids_use_hnsw = True if nlist > 10000 else False
+        centroids_use_hnsw = nlist > 10000
         centroids_hnsw_m = 24
         centroids_hnsw_ef_construct = 500
         centroids_hnsw_ef_search = 100

api/core/rag/datasource/vdb/milvus/milvus_vector.py

@@ -2,6 +2,7 @@ import json
 import logging
 from typing import Any, Optional
 
+from packaging import version
 from pydantic import BaseModel, model_validator
 from pymilvus import MilvusClient, MilvusException  # type: ignore
 from pymilvus.milvus_client import IndexParams  # type: ignore
@@ -20,16 +21,25 @@ logger = logging.getLogger(__name__)
 
 
 class MilvusConfig(BaseModel):
-    uri: str
-    token: Optional[str] = None
-    user: str
-    password: str
-    batch_size: int = 100
-    database: str = "default"
+    """
+    Configuration class for Milvus connection.
+    """
+
+    uri: str  # Milvus server URI
+    token: Optional[str] = None  # Optional token for authentication
+    user: str  # Username for authentication
+    password: str  # Password for authentication
+    batch_size: int = 100  # Batch size for operations
+    database: str = "default"  # Database name
+    enable_hybrid_search: bool = False  # Flag to enable hybrid search
 
     @model_validator(mode="before")
     @classmethod
     def validate_config(cls, values: dict) -> dict:
+        """
+        Validate the configuration values.
+        Raises ValueError if required fields are missing.
+        """
         if not values.get("uri"):
             raise ValueError("config MILVUS_URI is required")
         if not values.get("user"):
@@ -39,6 +49,9 @@ class MilvusConfig(BaseModel):
         return values
 
     def to_milvus_params(self):
+        """
+        Convert the configuration to a dictionary of Milvus connection parameters.
+        """
         return {
             "uri": self.uri,
             "token": self.token,
@@ -49,26 +62,57 @@ class MilvusConfig(BaseModel):
 
 
 class MilvusVector(BaseVector):
+    """
+    Milvus vector storage implementation.
+    """
+
     def __init__(self, collection_name: str, config: MilvusConfig):
         super().__init__(collection_name)
         self._client_config = config
         self._client = self._init_client(config)
-        self._consistency_level = "Session"
-        self._fields: list[str] = []
+        self._consistency_level = "Session"  # Consistency level for Milvus operations
+        self._fields: list[str] = []  # List of fields in the collection
+        self._hybrid_search_enabled = self._check_hybrid_search_support()  # Check if hybrid search is supported
+
+    def _check_hybrid_search_support(self) -> bool:
+        """
+        Check if the current Milvus version supports hybrid search.
+        Returns True if the version is >= 2.5.0, otherwise False.
+        """
+        if not self._client_config.enable_hybrid_search:
+            return False
+
+        try:
+            milvus_version = self._client.get_server_version()
+            return version.parse(milvus_version).base_version >= version.parse("2.5.0").base_version
+        except Exception as e:
+            logger.warning(f"Failed to check Milvus version: {str(e)}. Disabling hybrid search.")
+            return False
 
     def get_type(self) -> str:
+        """
+        Get the type of vector storage (Milvus).
+        """
         return VectorType.MILVUS
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        """
+        Create a collection and add texts with embeddings.
+        """
         index_params = {"metric_type": "IP", "index_type": "HNSW", "params": {"M": 8, "efConstruction": 64}}
         metadatas = [d.metadata if d.metadata is not None else {} for d in texts]
         self.create_collection(embeddings, metadatas, index_params)
         self.add_texts(texts, embeddings)
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        """
+        Add texts and their embeddings to the collection.
+        """
         insert_dict_list = []
         for i in range(len(documents)):
             insert_dict = {
+                # Do not need to insert the sparse_vector field separately, as the text_bm25_emb
+                # function will automatically convert the native text into a sparse vector for us.
                 Field.CONTENT_KEY.value: documents[i].page_content,
                 Field.VECTOR.value: embeddings[i],
                 Field.METADATA_KEY.value: documents[i].metadata,
@@ -76,12 +120,11 @@ class MilvusVector(BaseVector):
             insert_dict_list.append(insert_dict)
         # Total insert count
         total_count = len(insert_dict_list)
-
         pks: list[str] = []
 
         for i in range(0, total_count, 1000):
-            batch_insert_list = insert_dict_list[i : i + 1000]
             # Insert into the collection.
+            batch_insert_list = insert_dict_list[i : i + 1000]
             try:
                 ids = self._client.insert(collection_name=self._collection_name, data=batch_insert_list)
                 pks.extend(ids)
@@ -91,6 +134,9 @@ class MilvusVector(BaseVector):
         return pks
 
     def get_ids_by_metadata_field(self, key: str, value: str):
+        """
+        Get document IDs by metadata field key and value.
+        """
         result = self._client.query(
             collection_name=self._collection_name, filter=f'metadata["{key}"] == "{value}"', output_fields=["id"]
         )
@@ -100,12 +146,18 @@ class MilvusVector(BaseVector):
             return None
 
     def delete_by_metadata_field(self, key: str, value: str):
+        """
+        Delete documents by metadata field key and value.
+        """
         if self._client.has_collection(self._collection_name):
             ids = self.get_ids_by_metadata_field(key, value)
             if ids:
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        """
+        Delete documents by their IDs.
+        """
         if self._client.has_collection(self._collection_name):
             result = self._client.query(
                 collection_name=self._collection_name, filter=f'metadata["doc_id"] in {ids}', output_fields=["id"]
@@ -115,10 +167,16 @@ class MilvusVector(BaseVector):
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete(self) -> None:
+        """
+        Delete the entire collection.
+        """
         if self._client.has_collection(self._collection_name):
             self._client.drop_collection(self._collection_name, None)
 
     def text_exists(self, id: str) -> bool:
+        """
+        Check if a text with the given ID exists in the collection.
+        """
         if not self._client.has_collection(self._collection_name):
             return False
 
@@ -128,32 +186,80 @@ class MilvusVector(BaseVector):
 
         return len(result) > 0
 
+    def field_exists(self, field: str) -> bool:
+        """
+        Check if a field exists in the collection.
+        """
+        return field in self._fields
+
+    def _process_search_results(
+        self, results: list[Any], output_fields: list[str], score_threshold: float = 0.0
+    ) -> list[Document]:
+        """
+        Common method to process search results
+
+        :param results: Search results
+        :param output_fields: Fields to be output
+        :param score_threshold: Score threshold for filtering
+        :return: List of documents
+        """
+        docs = []
+        for result in results[0]:
+            metadata = result["entity"].get(output_fields[1], {})
+            metadata["score"] = result["distance"]
+
+            if result["distance"] > score_threshold:
+                doc = Document(page_content=result["entity"].get(output_fields[0], ""), metadata=metadata)
+                docs.append(doc)
+
+        return docs
+
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-        # Set search parameters.
+        """
+        Search for documents by vector similarity.
+        """
         results = self._client.search(
             collection_name=self._collection_name,
             data=[query_vector],
+            anns_field=Field.VECTOR.value,
             limit=kwargs.get("top_k", 4),
             output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
         )
-        # Organize results.
-        docs = []
-        for result in results[0]:
-            metadata = result["entity"].get(Field.METADATA_KEY.value)
-            metadata["score"] = result["distance"]
-            score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if result["distance"] > score_threshold:
-                doc = Document(page_content=result["entity"].get(Field.CONTENT_KEY.value), metadata=metadata)
-                docs.append(doc)
-        return docs
+
+        return self._process_search_results(
+            results,
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            score_threshold=float(kwargs.get("score_threshold") or 0.0),
+        )
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        # milvus/zilliz doesn't support bm25 search
-        return []
+        """
+        Search for documents by full-text search (if hybrid search is enabled).
+        """
+        if not self._hybrid_search_enabled or not self.field_exists(Field.SPARSE_VECTOR.value):
+            logger.warning("Full-text search is not supported in current Milvus version (requires >= 2.5.0)")
+            return []
+
+        results = self._client.search(
+            collection_name=self._collection_name,
+            data=[query],
+            anns_field=Field.SPARSE_VECTOR.value,
+            limit=kwargs.get("top_k", 4),
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+        )
+
+        return self._process_search_results(
+            results,
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            score_threshold=float(kwargs.get("score_threshold") or 0.0),
+        )
 
     def create_collection(
         self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
     ):
+        """
+        Create a new collection in Milvus with the specified schema and index parameters.
+        """
         lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
             collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
@@ -161,7 +267,7 @@ class MilvusVector(BaseVector):
                 return
             # Grab the existing collection if it exists
             if not self._client.has_collection(self._collection_name):
-                from pymilvus import CollectionSchema, DataType, FieldSchema  # type: ignore
+                from pymilvus import CollectionSchema, DataType, FieldSchema, Function, FunctionType  # type: ignore
                 from pymilvus.orm.types import infer_dtype_bydata  # type: ignore
 
                 # Determine embedding dim
@@ -170,16 +276,36 @@ class MilvusVector(BaseVector):
                 if metadatas:
                     fields.append(FieldSchema(Field.METADATA_KEY.value, DataType.JSON, max_length=65_535))
 
-                # Create the text field
-                fields.append(FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535))
+                # Create the text field, enable_analyzer will be set True to support milvus automatically
+                # transfer text to sparse_vector, reference: https://milvus.io/docs/full-text-search.md
+                fields.append(
+                    FieldSchema(
+                        Field.CONTENT_KEY.value,
+                        DataType.VARCHAR,
+                        max_length=65_535,
+                        enable_analyzer=self._hybrid_search_enabled,
+                    )
+                )
                 # Create the primary key field
                 fields.append(FieldSchema(Field.PRIMARY_KEY.value, DataType.INT64, is_primary=True, auto_id=True))
                 # Create the vector field, supports binary or float vectors
                 fields.append(FieldSchema(Field.VECTOR.value, infer_dtype_bydata(embeddings[0]), dim=dim))
+                # Create Sparse Vector Index for the collection
+                if self._hybrid_search_enabled:
+                    fields.append(FieldSchema(Field.SPARSE_VECTOR.value, DataType.SPARSE_FLOAT_VECTOR))
 
-                # Create the schema for the collection
                 schema = CollectionSchema(fields)
 
+                # Create custom function to support text to sparse vector by BM25
+                if self._hybrid_search_enabled:
+                    bm25_function = Function(
+                        name="text_bm25_emb",
+                        input_field_names=[Field.CONTENT_KEY.value],
+                        output_field_names=[Field.SPARSE_VECTOR.value],
+                        function_type=FunctionType.BM25,
+                    )
+                    schema.add_function(bm25_function)
+
                 for x in schema.fields:
                     self._fields.append(x.name)
                 # Since primary field is auto-id, no need to track it
@@ -189,10 +315,15 @@ class MilvusVector(BaseVector):
                 index_params_obj = IndexParams()
                 index_params_obj.add_index(field_name=Field.VECTOR.value, **index_params)
 
+                # Create Sparse Vector Index for the collection
+                if self._hybrid_search_enabled:
+                    index_params_obj.add_index(
+                        field_name=Field.SPARSE_VECTOR.value, index_type="AUTOINDEX", metric_type="BM25"
+                    )
+
                 # Create the collection
-                collection_name = self._collection_name
                 self._client.create_collection(
-                    collection_name=collection_name,
+                    collection_name=self._collection_name,
                     schema=schema,
                     index_params=index_params_obj,
                     consistency_level=self._consistency_level,
@@ -200,12 +331,22 @@ class MilvusVector(BaseVector):
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def _init_client(self, config) -> MilvusClient:
+        """
+        Initialize and return a Milvus client.
+        """
         client = MilvusClient(uri=config.uri, user=config.user, password=config.password, db_name=config.database)
         return client
 
 
 class MilvusVectorFactory(AbstractVectorFactory):
+    """
+    Factory class for creating MilvusVector instances.
+    """
+
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> MilvusVector:
+        """
+        Initialize a MilvusVector instance for the given dataset.
+        """
         if dataset.index_struct_dict:
             class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix
@@ -222,5 +363,6 @@ class MilvusVectorFactory(AbstractVectorFactory):
                 user=dify_config.MILVUS_USER or "",
                 password=dify_config.MILVUS_PASSWORD or "",
                 database=dify_config.MILVUS_DATABASE or "",
+                enable_hybrid_search=dify_config.MILVUS_ENABLE_HYBRID_SEARCH or False,
             ),
         )

api/core/rag/datasource/vdb/tidb_on_qdrant/tidb_on_qdrant_vector.py

@@ -409,27 +409,27 @@ class TidbOnQdrantVectorFactory(AbstractVectorFactory):
             db.session.query(TidbAuthBinding).filter(TidbAuthBinding.tenant_id == dataset.tenant_id).one_or_none()
         )
         if not tidb_auth_binding:
-            idle_tidb_auth_binding = (
-                db.session.query(TidbAuthBinding)
-                .filter(TidbAuthBinding.active == False, TidbAuthBinding.status == "ACTIVE")
-                .limit(1)
-                .one_or_none()
-            )
-            if idle_tidb_auth_binding:
-                idle_tidb_auth_binding.active = True
-                idle_tidb_auth_binding.tenant_id = dataset.tenant_id
-                db.session.commit()
-                TIDB_ON_QDRANT_API_KEY = f"{idle_tidb_auth_binding.account}:{idle_tidb_auth_binding.password}"
-            else:
-                with redis_client.lock("create_tidb_serverless_cluster_lock", timeout=900):
-                    tidb_auth_binding = (
+            with redis_client.lock("create_tidb_serverless_cluster_lock", timeout=900):
+                tidb_auth_binding = (
+                    db.session.query(TidbAuthBinding)
+                    .filter(TidbAuthBinding.tenant_id == dataset.tenant_id)
+                    .one_or_none()
+                )
+                if tidb_auth_binding:
+                    TIDB_ON_QDRANT_API_KEY = f"{tidb_auth_binding.account}:{tidb_auth_binding.password}"
+
+                else:
+                    idle_tidb_auth_binding = (
                         db.session.query(TidbAuthBinding)
-                        .filter(TidbAuthBinding.tenant_id == dataset.tenant_id)
+                        .filter(TidbAuthBinding.active == False, TidbAuthBinding.status == "ACTIVE")
+                        .limit(1)
                         .one_or_none()
                     )
-                    if tidb_auth_binding:
-                        TIDB_ON_QDRANT_API_KEY = f"{tidb_auth_binding.account}:{tidb_auth_binding.password}"
-
+                    if idle_tidb_auth_binding:
+                        idle_tidb_auth_binding.active = True
+                        idle_tidb_auth_binding.tenant_id = dataset.tenant_id
+                        db.session.commit()
+                        TIDB_ON_QDRANT_API_KEY = f"{idle_tidb_auth_binding.account}:{idle_tidb_auth_binding.password}"
                     else:
                         new_cluster = TidbService.create_tidb_serverless_cluster(
                             dify_config.TIDB_PROJECT_ID or "",
@@ -451,7 +451,6 @@ class TidbOnQdrantVectorFactory(AbstractVectorFactory):
                         db.session.add(new_tidb_auth_binding)
                         db.session.commit()
                         TIDB_ON_QDRANT_API_KEY = f"{new_tidb_auth_binding.account}:{new_tidb_auth_binding.password}"
-
         else:
             TIDB_ON_QDRANT_API_KEY = f"{tidb_auth_binding.account}:{tidb_auth_binding.password}"
 

api/core/rag/datasource/vdb/vector_factory.py

@@ -90,6 +90,12 @@ class Vector:
                 from core.rag.datasource.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
 
                 return ElasticSearchVectorFactory
+            case VectorType.ELASTICSEARCH_JA:
+                from core.rag.datasource.vdb.elasticsearch.elasticsearch_ja_vector import (
+                    ElasticSearchJaVectorFactory,
+                )
+
+                return ElasticSearchJaVectorFactory
             case VectorType.TIDB_VECTOR:
                 from core.rag.datasource.vdb.tidb_vector.tidb_vector import TiDBVectorFactory
 

api/core/rag/datasource/vdb/vector_type.py

@@ -16,6 +16,7 @@ class VectorType(StrEnum):
     TENCENT = "tencent"
     ORACLE = "oracle"
     ELASTICSEARCH = "elasticsearch"
+    ELASTICSEARCH_JA = "elasticsearch-ja"
     LINDORM = "lindorm"
     COUCHBASE = "couchbase"
     BAIDU = "baidu"

api/core/rag/extractor/firecrawl/firecrawl_app.py

@@ -31,7 +31,7 @@ class FirecrawlApp:
                     "markdown": data.get("markdown"),
                 }
             else:
-                raise Exception(f'Failed to scrape URL. Error: {response_data["error"]}')
+                raise Exception(f"Failed to scrape URL. Error: {response_data['error']}")
 
         elif response.status_code in {402, 409, 500}:
             error_message = response.json().get("error", "Unknown error occurred")

api/core/rag/extractor/notion_extractor.py

@@ -358,8 +358,7 @@ class NotionExtractor(BaseExtractor):
 
         if not data_source_binding:
             raise Exception(
-                f"No notion data source binding found for tenant {tenant_id} "
-                f"and notion workspace {notion_workspace_id}"
+                f"No notion data source binding found for tenant {tenant_id} and notion workspace {notion_workspace_id}"
             )
 
         return cast(str, data_source_binding.access_token)

api/core/rag/extractor/pdf_extractor.py

@@ -23,7 +23,6 @@ class PdfExtractor(BaseExtractor):
         self._file_cache_key = file_cache_key
 
     def extract(self) -> list[Document]:
-        plaintext_file_key = ""
         plaintext_file_exists = False
         if self._file_cache_key:
             try:
@@ -39,8 +38,8 @@ class PdfExtractor(BaseExtractor):
         text = "\n\n".join(text_list)
 
         # save plaintext file for caching
-        if not plaintext_file_exists and plaintext_file_key:
-            storage.save(plaintext_file_key, text.encode("utf-8"))
+        if not plaintext_file_exists and self._file_cache_key:
+            storage.save(self._file_cache_key, text.encode("utf-8"))
 
         return documents
 

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

@@ -112,7 +112,7 @@ class QAIndexProcessor(BaseIndexProcessor):
             df = pd.read_csv(file)
             text_docs = []
             for index, row in df.iterrows():
-                data = Document(page_content=row[0], metadata={"answer": row[1]})
+                data = Document(page_content=row.iloc[0], metadata={"answer": row.iloc[1]})
                 text_docs.append(data)
             if len(text_docs) == 0:
                 raise ValueError("The CSV file is empty.")

api/core/tools/provider/builtin/aippt/tools/aippt.py

@@ -127,7 +127,7 @@ class AIPPTGenerateToolAdapter:
 
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to create task: {response.get("msg")}')
+            raise Exception(f"Failed to create task: {response.get('msg')}")
 
         return response.get("data", {}).get("id")
 
@@ -222,7 +222,7 @@ class AIPPTGenerateToolAdapter:
         elif model == "wenxin":
             response = response.json()
             if response.get("code") != 0:
-                raise Exception(f'Failed to generate content: {response.get("msg")}')
+                raise Exception(f"Failed to generate content: {response.get('msg')}")
 
             return response.get("data", "")
 
@@ -254,7 +254,7 @@ class AIPPTGenerateToolAdapter:
 
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to generate ppt: {response.get("msg")}')
+            raise Exception(f"Failed to generate ppt: {response.get('msg')}")
 
         id = response.get("data", {}).get("id")
         cover_url = response.get("data", {}).get("cover_url")
@@ -270,7 +270,7 @@ class AIPPTGenerateToolAdapter:
 
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to generate ppt: {response.get("msg")}')
+            raise Exception(f"Failed to generate ppt: {response.get('msg')}")
 
         export_code = response.get("data")
         if not export_code:
@@ -290,7 +290,7 @@ class AIPPTGenerateToolAdapter:
 
             response = response.json()
             if response.get("code") != 0:
-                raise Exception(f'Failed to generate ppt: {response.get("msg")}')
+                raise Exception(f"Failed to generate ppt: {response.get('msg')}")
 
             if response.get("msg") == "导出中":
                 current_iteration += 1
@@ -343,7 +343,7 @@ class AIPPTGenerateToolAdapter:
             raise Exception(f"Failed to connect to aippt: {response.text}")
         response = response.json()
         if response.get("code") != 0:
-            raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
+            raise Exception(f"Failed to connect to aippt: {response.get('msg')}")
 
         token = response.get("data", {}).get("token")
         expire = response.get("data", {}).get("time_expire")
@@ -379,7 +379,7 @@ class AIPPTGenerateToolAdapter:
                 if cls._style_cache[key]["expire"] < now:
                     del cls._style_cache[key]
 
-            key = f'{credentials["aippt_access_key"]}#@#{user_id}'
+            key = f"{credentials['aippt_access_key']}#@#{user_id}"
             if key in cls._style_cache:
                 return cls._style_cache[key]["colors"], cls._style_cache[key]["styles"]
 
@@ -396,11 +396,11 @@ class AIPPTGenerateToolAdapter:
         response = response.json()
 
         if response.get("code") != 0:
-            raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
+            raise Exception(f"Failed to connect to aippt: {response.get('msg')}")
 
         colors = [
             {
-                "id": f'id-{item.get("id")}',
+                "id": f"id-{item.get('id')}",
                 "name": item.get("name"),
                 "en_name": item.get("en_name", item.get("name")),
             }
@@ -408,7 +408,7 @@ class AIPPTGenerateToolAdapter:
         ]
         styles = [
             {
-                "id": f'id-{item.get("id")}',
+                "id": f"id-{item.get('id')}",
                 "name": item.get("title"),
             }
             for item in response.get("data", {}).get("suit_style") or []
@@ -454,7 +454,7 @@ class AIPPTGenerateToolAdapter:
         response = response.json()
 
         if response.get("code") != 0:
-            raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
+            raise Exception(f"Failed to connect to aippt: {response.get('msg')}")
 
         if len(response.get("data", {}).get("list") or []) > 0:
             return response.get("data", {}).get("list")[0].get("id")

api/core/tools/provider/builtin/aws/tools/bedrock_retrieve.py

@@ -14,14 +14,38 @@ class BedrockRetrieveTool(BuiltinTool):
     topk: int = None
 
     def _bedrock_retrieve(
-        self, query_input: str, knowledge_base_id: str, num_results: int, metadata_filter: Optional[dict] = None
+        self,
+        query_input: str,
+        knowledge_base_id: str,
+        num_results: int,
+        search_type: str,
+        rerank_model_id: str,
+        metadata_filter: Optional[dict] = None,
     ):
         try:
             retrieval_query = {"text": query_input}
 
-            retrieval_configuration = {"vectorSearchConfiguration": {"numberOfResults": num_results}}
+            if search_type not in ["HYBRID", "SEMANTIC"]:
+                raise RuntimeException("search_type should be HYBRID or SEMANTIC")
 
-            # Add metadata filter to retrieval configuration if present
+            retrieval_configuration = {
+                "vectorSearchConfiguration": {"numberOfResults": num_results, "overrideSearchType": search_type}
+            }
+
+            if rerank_model_id != "default":
+                model_for_rerank_arn = f"arn:aws:bedrock:us-west-2::foundation-model/{rerank_model_id}"
+                rerankingConfiguration = {
+                    "bedrockRerankingConfiguration": {
+                        "numberOfRerankedResults": num_results,
+                        "modelConfiguration": {"modelArn": model_for_rerank_arn},
+                    },
+                    "type": "BEDROCK_RERANKING_MODEL",
+                }
+
+                retrieval_configuration["vectorSearchConfiguration"]["rerankingConfiguration"] = rerankingConfiguration
+                retrieval_configuration["vectorSearchConfiguration"]["numberOfResults"] = num_results * 5
+
+            # 如果有元数据过滤条件，则添加到检索配置中
             if metadata_filter:
                 retrieval_configuration["vectorSearchConfiguration"]["filter"] = metadata_filter
 
@@ -77,15 +101,20 @@ class BedrockRetrieveTool(BuiltinTool):
             if not query:
                 return self.create_text_message("Please input query")
 
-            # Get metadata filter conditions (if they exist)
+            # 获取元数据过滤条件（如果存在）
             metadata_filter_str = tool_parameters.get("metadata_filter")
             metadata_filter = json.loads(metadata_filter_str) if metadata_filter_str else None
 
+            search_type = tool_parameters.get("search_type")
+            rerank_model_id = tool_parameters.get("rerank_model_id")
+
             line = 4
             retrieved_docs = self._bedrock_retrieve(
                 query_input=query,
                 knowledge_base_id=self.knowledge_base_id,
                 num_results=self.topk,
+                search_type=search_type,
+                rerank_model_id=rerank_model_id,
                 metadata_filter=metadata_filter,
             )
 
@@ -109,7 +138,7 @@ class BedrockRetrieveTool(BuiltinTool):
         if not parameters.get("query"):
             raise ValueError("query is required")
 
-        # Optional: Validate if metadata filter is a valid JSON string (if provided)
+        # 可选：可以验证元数据过滤条件是否为有效的 JSON 字符串（如果提供）
         metadata_filter_str = parameters.get("metadata_filter")
         if metadata_filter_str and not isinstance(json.loads(metadata_filter_str), dict):
             raise ValueError("metadata_filter must be a valid JSON object")

api/core/tools/provider/builtin/aws/tools/bedrock_retrieve.yaml

@@ -59,6 +59,57 @@ parameters:
     max: 10
     default: 5
 
+  - name: search_type
+    type: select
+    required: false
+    label:
+      en_US: search type
+      zh_Hans: 搜索类型
+      pt_BR: search type
+    human_description:
+      en_US: search type
+      zh_Hans: 搜索类型
+      pt_BR: search type
+    llm_description: search type
+    default: SEMANTIC
+    options:
+      - value: SEMANTIC
+        label:
+          en_US: SEMANTIC
+          zh_Hans: 语义搜索
+      - value: HYBRID
+        label:
+          en_US: HYBRID
+          zh_Hans: 混合搜索
+    form: form
+
+  - name: rerank_model_id
+    type: select
+    required: false
+    label:
+      en_US: rerank model id
+      zh_Hans: 重拍模型ID
+      pt_BR: rerank model id
+    human_description:
+      en_US: rerank model id
+      zh_Hans: 重拍模型ID
+      pt_BR: rerank model id
+    llm_description: rerank model id
+    options:
+      - value: default
+        label:
+          en_US: default
+          zh_Hans: 默认
+      - value: cohere.rerank-v3-5:0
+        label:
+          en_US: cohere.rerank-v3-5:0
+          zh_Hans: cohere.rerank-v3-5:0
+      - value: amazon.rerank-v1:0
+        label:
+          en_US: amazon.rerank-v1:0
+          zh_Hans: amazon.rerank-v1:0
+    form: form
+
   - name: aws_region
     type: string
     required: false

api/core/tools/provider/builtin/aws/tools/nova_reel.py

@@ -229,8 +229,7 @@ class NovaReelTool(BuiltinTool):
 
         if async_mode:
             return self.create_text_message(
-                f"Video generation started.\nInvocation ARN: {invocation_arn}\n"
-                f"Video will be available at: {video_uri}"
+                f"Video generation started.\nInvocation ARN: {invocation_arn}\nVideo will be available at: {video_uri}"
             )
 
         return self._wait_for_completion(bedrock, s3_client, invocation_arn)

api/core/tools/provider/builtin/baidu_translate/tools/fieldtranslate.py

@@ -65,7 +65,7 @@ class BaiduFieldTranslateTool(BuiltinTool, BaiduTranslateToolBase):
             if "trans_result" in result:
                 result_text = result["trans_result"][0]["dst"]
             else:
-                result_text = f'{result["error_code"]}: {result["error_msg"]}'
+                result_text = f"{result['error_code']}: {result['error_msg']}"
 
             return self.create_text_message(str(result_text))
         except requests.RequestException as e:

api/core/tools/provider/builtin/baidu_translate/tools/language.py

@@ -52,7 +52,7 @@ class BaiduLanguageTool(BuiltinTool, BaiduTranslateToolBase):
 
             result_text = ""
             if result["error_code"] != 0:
-                result_text = f'{result["error_code"]}: {result["error_msg"]}'
+                result_text = f"{result['error_code']}: {result['error_msg']}"
             else:
                 result_text = result["data"]["src"]
                 result_text = self.mapping_result(description_language, result_text)

api/core/tools/provider/builtin/baidu_translate/tools/translate.py

@@ -58,7 +58,7 @@ class BaiduTranslateTool(BuiltinTool, BaiduTranslateToolBase):
             if "trans_result" in result:
                 result_text = result["trans_result"][0]["dst"]
             else:
-                result_text = f'{result["error_code"]}: {result["error_msg"]}'
+                result_text = f"{result['error_code']}: {result['error_msg']}"
 
             return self.create_text_message(str(result_text))
         except requests.RequestException as e:

api/core/tools/provider/builtin/bing/tools/bing_web_search.py

@@ -30,7 +30,7 @@ class BingSearchTool(BuiltinTool):
         headers = {"Ocp-Apim-Subscription-Key": subscription_key, "Accept-Language": accept_language}
 
         query = quote(query)
-        server_url = f'{server_url}?q={query}&mkt={market_code}&count={limit}&responseFilter={",".join(filters)}'
+        server_url = f"{server_url}?q={query}&mkt={market_code}&count={limit}&responseFilter={','.join(filters)}"
         response = get(server_url, headers=headers)
 
         if response.status_code != 200:
@@ -47,23 +47,23 @@ class BingSearchTool(BuiltinTool):
             results = []
             if search_results:
                 for result in search_results:
-                    url = f': {result["url"]}' if "url" in result else ""
-                    results.append(self.create_text_message(text=f'{result["name"]}{url}'))
+                    url = f": {result['url']}" if "url" in result else ""
+                    results.append(self.create_text_message(text=f"{result['name']}{url}"))
 
             if entities:
                 for entity in entities:
-                    url = f': {entity["url"]}' if "url" in entity else ""
-                    results.append(self.create_text_message(text=f'{entity.get("name", "")}{url}'))
+                    url = f": {entity['url']}" if "url" in entity else ""
+                    results.append(self.create_text_message(text=f"{entity.get('name', '')}{url}"))
 
             if news:
                 for news_item in news:
-                    url = f': {news_item["url"]}' if "url" in news_item else ""
-                    results.append(self.create_text_message(text=f'{news_item.get("name", "")}{url}'))
+                    url = f": {news_item['url']}" if "url" in news_item else ""
+                    results.append(self.create_text_message(text=f"{news_item.get('name', '')}{url}"))
 
             if related_searches:
                 for related in related_searches:
-                    url = f': {related["displayText"]}' if "displayText" in related else ""
-                    results.append(self.create_text_message(text=f'{related.get("displayText", "")}{url}'))
+                    url = f": {related['displayText']}" if "displayText" in related else ""
+                    results.append(self.create_text_message(text=f"{related.get('displayText', '')}{url}"))
 
             return results
         elif result_type == "json":
@@ -106,29 +106,29 @@ class BingSearchTool(BuiltinTool):
             text = ""
             if search_results:
                 for i, result in enumerate(search_results):
-                    text += f'{i + 1}: {result.get("name", "")} - {result.get("snippet", "")}\n'
+                    text += f"{i + 1}: {result.get('name', '')} - {result.get('snippet', '')}\n"
 
             if computation and "expression" in computation and "value" in computation:
                 text += "\nComputation:\n"
-                text += f'{computation["expression"]} = {computation["value"]}\n'
+                text += f"{computation['expression']} = {computation['value']}\n"
 
             if entities:
                 text += "\nEntities:\n"
                 for entity in entities:
-                    url = f'- {entity["url"]}' if "url" in entity else ""
-                    text += f'{entity.get("name", "")}{url}\n'
+                    url = f"- {entity['url']}" if "url" in entity else ""
+                    text += f"{entity.get('name', '')}{url}\n"
 
             if news:
                 text += "\nNews:\n"
                 for news_item in news:
-                    url = f'- {news_item["url"]}' if "url" in news_item else ""
-                    text += f'{news_item.get("name", "")}{url}\n'
+                    url = f"- {news_item['url']}" if "url" in news_item else ""
+                    text += f"{news_item.get('name', '')}{url}\n"
 
             if related_searches:
                 text += "\n\nRelated Searches:\n"
                 for related in related_searches:
-                    url = f'- {related["webSearchUrl"]}' if "webSearchUrl" in related else ""
-                    text += f'{related.get("displayText", "")}{url}\n'
+                    url = f"- {related['webSearchUrl']}" if "webSearchUrl" in related else ""
+                    text += f"{related.get('displayText', '')}{url}\n"
 
             return self.create_text_message(text=self.summary(user_id=user_id, content=text))
 

api/core/tools/provider/builtin/did/did_appx.py

@@ -83,5 +83,5 @@ class DIDApp:
             if status["status"] == "done":
                 return status
             elif status["status"] == "error" or status["status"] == "rejected":
-                raise HTTPError(f'Talks {id} failed: {status["status"]} {status.get("error", {}).get("description")}')
+                raise HTTPError(f"Talks {id} failed: {status['status']} {status.get('error', {}).get('description')}")
             time.sleep(poll_interval)

api/core/tools/provider/builtin/email/tools/send.py

@@ -20,33 +20,33 @@ class SendEmailToolParameters(BaseModel):
     encrypt_method: str
 
 
-def send_mail(parmas: SendEmailToolParameters):
+def send_mail(params: SendEmailToolParameters):
     timeout = 60
     msg = MIMEMultipart("alternative")
-    msg["From"] = parmas.email_account
-    msg["To"] = parmas.sender_to
-    msg["Subject"] = parmas.subject
-    msg.attach(MIMEText(parmas.email_content, "plain"))
-    msg.attach(MIMEText(parmas.email_content, "html"))
+    msg["From"] = params.email_account
+    msg["To"] = params.sender_to
+    msg["Subject"] = params.subject
+    msg.attach(MIMEText(params.email_content, "plain"))
+    msg.attach(MIMEText(params.email_content, "html"))
 
     ctx = ssl.create_default_context()
 
-    if parmas.encrypt_method.upper() == "SSL":
+    if params.encrypt_method.upper() == "SSL":
         try:
-            with smtplib.SMTP_SSL(parmas.smtp_server, parmas.smtp_port, context=ctx, timeout=timeout) as server:
-                server.login(parmas.email_account, parmas.email_password)
-                server.sendmail(parmas.email_account, parmas.sender_to, msg.as_string())
+            with smtplib.SMTP_SSL(params.smtp_server, params.smtp_port, context=ctx, timeout=timeout) as server:
+                server.login(params.email_account, params.email_password)
+                server.sendmail(params.email_account, params.sender_to, msg.as_string())
                 return True
         except Exception as e:
             logging.exception("send email failed")
             return False
     else:  # NONE or TLS
         try:
-            with smtplib.SMTP(parmas.smtp_server, parmas.smtp_port, timeout=timeout) as server:
-                if parmas.encrypt_method.upper() == "TLS":
+            with smtplib.SMTP(params.smtp_server, params.smtp_port, timeout=timeout) as server:
+                if params.encrypt_method.upper() == "TLS":
                     server.starttls(context=ctx)
-                server.login(parmas.email_account, parmas.email_password)
-                server.sendmail(parmas.email_account, parmas.sender_to, msg.as_string())
+                server.login(params.email_account, params.email_password)
+                server.sendmail(params.email_account, params.sender_to, msg.as_string())
                 return True
         except Exception as e:
             logging.exception("send email failed")

api/core/tools/provider/builtin/firecrawl/firecrawl_appx.py

@@ -74,7 +74,7 @@ class FirecrawlApp:
         if response is None:
             raise HTTPError("Failed to initiate crawl after multiple retries")
         elif response.get("success") == False:
-            raise HTTPError(f'Failed to crawl: {response.get("error")}')
+            raise HTTPError(f"Failed to crawl: {response.get('error')}")
         job_id: str = response["id"]
         if wait:
             return self._monitor_job_status(job_id=job_id, poll_interval=poll_interval)
@@ -100,7 +100,7 @@ class FirecrawlApp:
             if status["status"] == "completed":
                 return status
             elif status["status"] == "failed":
-                raise HTTPError(f'Job {job_id} failed: {status["error"]}')
+                raise HTTPError(f"Job {job_id} failed: {status['error']}")
             time.sleep(poll_interval)
 
 

api/core/tools/provider/builtin/gaode/tools/gaode_weather.py

@@ -37,8 +37,9 @@ class GaodeRepositoriesTool(BuiltinTool):
                     CityCode = City_data["districts"][0]["adcode"]
                     weatherInfo_response = s.request(
                         method="GET",
-                        url="{url}/weather/weatherInfo?city={citycode}&extensions=all&key={apikey}&output=json"
-                        "".format(url=api_domain, citycode=CityCode, apikey=self.runtime.credentials.get("api_key")),
+                        url="{url}/weather/weatherInfo?city={citycode}&extensions=all&key={apikey}&output=json".format(
+                            url=api_domain, citycode=CityCode, apikey=self.runtime.credentials.get("api_key")
+                        ),
                     )
                     weatherInfo_data = weatherInfo_response.json()
                     if weatherInfo_response.status_code == 200 and weatherInfo_data.get("info") == "OK":

api/core/tools/provider/builtin/gitlab/tools/gitlab_files.py

@@ -11,19 +11,21 @@ class GitlabFilesTool(BuiltinTool):
     def _invoke(
         self, user_id: str, tool_parameters: dict[str, Any]
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        project = tool_parameters.get("project", "")
         repository = tool_parameters.get("repository", "")
+        project = tool_parameters.get("project", "")
         branch = tool_parameters.get("branch", "")
         path = tool_parameters.get("path", "")
+        file_path = tool_parameters.get("file_path", "")
 
-        if not project and not repository:
-            return self.create_text_message("Either project or repository is required")
+        if not repository and not project:
+            return self.create_text_message("Either repository or project is required")
         if not branch:
             return self.create_text_message("Branch is required")
-        if not path:
-            return self.create_text_message("Path is required")
+        if not path and not file_path:
+            return self.create_text_message("Either path or file_path is required")
 
         access_token = self.runtime.credentials.get("access_tokens")
+        headers = {"PRIVATE-TOKEN": access_token}
         site_url = self.runtime.credentials.get("site_url")
 
         if "access_tokens" not in self.runtime.credentials or not self.runtime.credentials.get("access_tokens"):
@@ -31,33 +33,45 @@ class GitlabFilesTool(BuiltinTool):
         if "site_url" not in self.runtime.credentials or not self.runtime.credentials.get("site_url"):
             site_url = "https://gitlab.com"
 
-        # Get file content
         if repository:
-            result = self.fetch_files(site_url, access_token, repository, branch, path, is_repository=True)
+            # URL encode the repository path
+            identifier = urllib.parse.quote(repository, safe="")
         else:
-            result = self.fetch_files(site_url, access_token, project, branch, path, is_repository=False)
+            identifier = self.get_project_id(site_url, access_token, project)
+            if not identifier:
+                raise Exception(f"Project '{project}' not found.)")
 
-        return [self.create_json_message(item) for item in result]
+        # Get file content
+        if path:
+            results = self.fetch_files(site_url, headers, identifier, branch, path)
+            return [self.create_json_message(item) for item in results]
+        else:
+            result = self.fetch_file(site_url, headers, identifier, branch, file_path)
+            return [self.create_json_message(result)]
+
+    @staticmethod
+    def fetch_file(
+        site_url: str,
+        headers: dict[str, str],
+        identifier: str,
+        branch: str,
+        path: str,
+    ) -> dict[str, Any]:
+        encoded_file_path = urllib.parse.quote(path, safe="")
+        file_url = f"{site_url}/api/v4/projects/{identifier}/repository/files/{encoded_file_path}/raw?ref={branch}"
+
+        file_response = requests.get(file_url, headers=headers)
+        file_response.raise_for_status()
+        file_content = file_response.text
+        return {"path": path, "branch": branch, "content": file_content}
 
     def fetch_files(
-        self, site_url: str, access_token: str, identifier: str, branch: str, path: str, is_repository: bool
+        self, site_url: str, headers: dict[str, str], identifier: str, branch: str, path: str
     ) -> list[dict[str, Any]]:
-        domain = site_url
-        headers = {"PRIVATE-TOKEN": access_token}
         results = []
 
         try:
-            if is_repository:
-                # URL encode the repository path
-                encoded_identifier = urllib.parse.quote(identifier, safe="")
-                tree_url = f"{domain}/api/v4/projects/{encoded_identifier}/repository/tree?path={path}&ref={branch}"
-            else:
-                # Get project ID from project name
-                project_id = self.get_project_id(site_url, access_token, identifier)
-                if not project_id:
-                    return self.create_text_message(f"Project '{identifier}' not found.")
-                tree_url = f"{domain}/api/v4/projects/{project_id}/repository/tree?path={path}&ref={branch}"
-
+            tree_url = f"{site_url}/api/v4/projects/{identifier}/repository/tree?path={path}&ref={branch}"
             response = requests.get(tree_url, headers=headers)
             response.raise_for_status()
             items = response.json()
@@ -65,26 +79,10 @@ class GitlabFilesTool(BuiltinTool):
             for item in items:
                 item_path = item["path"]
                 if item["type"] == "tree":  # It's a directory
-                    results.extend(
-                        self.fetch_files(site_url, access_token, identifier, branch, item_path, is_repository)
-                    )
+                    results.extend(self.fetch_files(site_url, headers, identifier, branch, item_path))
                 else:  # It's a file
-                    encoded_item_path = urllib.parse.quote(item_path, safe="")
-                    if is_repository:
-                        file_url = (
-                            f"{domain}/api/v4/projects/{encoded_identifier}/repository/files"
-                            f"/{encoded_item_path}/raw?ref={branch}"
-                        )
-                    else:
-                        file_url = (
-                            f"{domain}/api/v4/projects/{project_id}/repository/files"
-                            f"{encoded_item_path}/raw?ref={branch}"
-                        )
-
-                    file_response = requests.get(file_url, headers=headers)
-                    file_response.raise_for_status()
-                    file_content = file_response.text
-                    results.append({"path": item_path, "branch": branch, "content": file_content})
+                    result = self.fetch_file(site_url, headers, identifier, branch, item_path)
+                    results.append(result)
         except requests.RequestException as e:
             print(f"Error fetching data from GitLab: {e}")
 

api/core/tools/provider/builtin/gitlab/tools/gitlab_files.yaml

@@ -29,7 +29,7 @@ parameters:
       zh_Hans: 项目
     human_description:
       en_US: project
-      zh_Hans: 项目
+      zh_Hans: 项目(和仓库路径二选一,都填写以仓库路径优先)
     llm_description: Project for GitLab
     form: llm
   - name: branch
@@ -45,12 +45,21 @@ parameters:
     form: llm
   - name: path
     type: string
-    required: true
     label:
       en_US: path
-      zh_Hans: 文件路径
+      zh_Hans: 文件夹
     human_description:
       en_US: path
+      zh_Hans: 文件夹
+    llm_description: Dir path for GitLab
+    form: llm
+  - name: file_path
+    type: string
+    label:
+      en_US: file_path
       zh_Hans: 文件路径
+    human_description:
+      en_US: file_path
+      zh_Hans: 文件路径(和文件夹二选一,都填写以文件夹优先)
     llm_description: File path for GitLab
     form: llm

api/core/tools/provider/builtin/hap/tools/list_worksheet_records.py

@@ -110,7 +110,7 @@ class ListWorksheetRecordsTool(BuiltinTool):
                             result["rows"].append(self.get_row_field_value(row, schema))
                         return self.create_text_message(json.dumps(result, ensure_ascii=False))
                     else:
-                        result_text = f"Found {result['total']} rows in worksheet \"{worksheet_name}\"."
+                        result_text = f'Found {result["total"]} rows in worksheet "{worksheet_name}".'
                         if result["total"] > 0:
                             result_text += (
                                 f" The following are {min(limit, result['total'])}"