chore: bump version to 0.14.1 (#11784)

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

.devcontainer/post_create_command.sh

@@ -7,5 +7,6 @@ echo 'alias start-api="cd /workspaces/dify/api && poetry run python -m flask run
 echo 'alias start-worker="cd /workspaces/dify/api && poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion"' >> ~/.bashrc
 echo 'alias start-web="cd /workspaces/dify/web && npm run dev"' >> ~/.bashrc
 echo 'alias start-containers="cd /workspaces/dify/docker && docker-compose -f docker-compose.middleware.yaml -p dify up -d"' >> ~/.bashrc
+echo 'alias stop-containers="cd /workspaces/dify/docker && docker-compose -f docker-compose.middleware.yaml -p dify down"' >> ~/.bashrc
 
-source /home/vscode/.bashrc
+source /home/vscode/.bashrc

.github/pull_request_template.md

@@ -8,16 +8,9 @@ Please include a summary of the change and which issue is fixed. Please also inc
 
 # Screenshots
 
-<table>
-  <tr>
-  <td>Before: </td>
-  <td>After: </td>
-  </tr>
-  <tr>
-  <td>...</td>
-  <td>...</td>
-  </tr>
-</table>
+| Before | After |
+|--------|-------|
+| ...    | ...   |
 
 # Checklist
 

.github/workflows/expose_service_ports.sh

@@ -9,5 +9,6 @@ yq eval '.services["pgvecto-rs"].ports += ["5431:5432"]' -i docker/docker-compos
 yq eval '.services["elasticsearch"].ports += ["9200:9200"]' -i docker/docker-compose.yaml
 yq eval '.services.couchbase-server.ports += ["8091-8096:8091-8096"]' -i docker/docker-compose.yaml
 yq eval '.services.couchbase-server.ports += ["11210:11210"]' -i docker/docker-compose.yaml
+yq eval '.services.tidb.ports += ["4000:4000"]' -i docker/docker-compose.yaml
 
-echo "Ports exposed for sandbox, weaviate, qdrant, chroma, milvus, pgvector, pgvecto-rs, elasticsearch, couchbase"
+echo "Ports exposed for sandbox, weaviate, tidb, qdrant, chroma, milvus, pgvector, pgvecto-rs, elasticsearch, couchbase"

.github/workflows/style.yml

@@ -37,6 +37,7 @@ jobs:
       - name: Ruff check
         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
 

.github/workflows/vdb-tests.yml

@@ -51,7 +51,7 @@ jobs:
       - name: Expose Service Ports
         run: sh .github/workflows/expose_service_ports.sh
 
-      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale, ElasticSearch, Couchbase)
+      - name: Set up Vector Stores (TiDB, Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale, ElasticSearch, Couchbase)
         uses: hoverkraft-tech/compose-action@v2.0.2
         with:
           compose-file: |
@@ -67,6 +67,7 @@ jobs:
             pgvector
             chroma
             elasticsearch
+            tidb
 
       - name: Test Vector Stores
         run: poetry run -C api bash dev/pytest/pytest_vdb.sh

api/.env.example

@@ -56,20 +56,27 @@ DB_DATABASE=dify
 
 # Storage configuration
 # use for store upload files, private keys...
-# storage type: local, s3, aliyun-oss, azure-blob, baidu-obs, google-storage, huawei-obs, oci-storage, tencent-cos, volcengine-tos, supabase
-STORAGE_TYPE=local
-STORAGE_LOCAL_PATH=storage
+# storage type: opendal, s3, aliyun-oss, azure-blob, baidu-obs, google-storage, huawei-obs, oci-storage, tencent-cos, volcengine-tos, supabase
+STORAGE_TYPE=opendal
+
+# Apache OpenDAL storage configuration, refer to https://github.com/apache/opendal
+OPENDAL_SCHEME=fs
+OPENDAL_FS_ROOT=storage
+
+# S3 Storage configuration
 S3_USE_AWS_MANAGED_IAM=false
 S3_ENDPOINT=https://your-bucket-name.storage.s3.clooudflare.com
 S3_BUCKET_NAME=your-bucket-name
 S3_ACCESS_KEY=your-access-key
 S3_SECRET_KEY=your-secret-key
 S3_REGION=your-region
+
 # Azure Blob Storage configuration
 AZURE_BLOB_ACCOUNT_NAME=your-account-name
 AZURE_BLOB_ACCOUNT_KEY=your-account-key
 AZURE_BLOB_CONTAINER_NAME=yout-container-name
 AZURE_BLOB_ACCOUNT_URL=https://<your_account_name>.blob.core.windows.net
+
 # Aliyun oss Storage configuration
 ALIYUN_OSS_BUCKET_NAME=your-bucket-name
 ALIYUN_OSS_ACCESS_KEY=your-access-key
@@ -79,6 +86,7 @@ ALIYUN_OSS_AUTH_VERSION=v1
 ALIYUN_OSS_REGION=your-region
 # Don't start with '/'. OSS doesn't support leading slash in object names.
 ALIYUN_OSS_PATH=your-path
+
 # Google Storage configuration
 GOOGLE_STORAGE_BUCKET_NAME=yout-bucket-name
 GOOGLE_STORAGE_SERVICE_ACCOUNT_JSON_BASE64=your-google-service-account-json-base64-string
@@ -125,8 +133,8 @@ SUPABASE_URL=your-server-url
 WEB_API_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
 CONSOLE_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
 
-
-# Vector database configuration, support: weaviate, qdrant, milvus, myscale, relyt, pgvecto_rs, pgvector, pgvector, chroma, opensearch, tidb_vector, couchbase, vikingdb, upstash, lindorm, oceanbase
+# Vector database configuration
+# support: weaviate, qdrant, milvus, myscale, relyt, pgvecto_rs, pgvector, pgvector, chroma, opensearch, tidb_vector, couchbase, vikingdb, upstash, lindorm, oceanbase
 VECTOR_STORE=weaviate
 
 # Weaviate configuration
@@ -277,6 +285,7 @@ VIKINGDB_SOCKET_TIMEOUT=30
 LINDORM_URL=http://ld-*******************-proxy-search-pub.lindorm.aliyuncs.com:30070
 LINDORM_USERNAME=admin
 LINDORM_PASSWORD=admin
+USING_UGC_INDEX=False
 
 # OceanBase Vector configuration
 OCEANBASE_VECTOR_HOST=127.0.0.1
@@ -295,8 +304,7 @@ UPLOAD_VIDEO_FILE_SIZE_LIMIT=100
 UPLOAD_AUDIO_FILE_SIZE_LIMIT=50
 
 # Model configuration
-MULTIMODAL_SEND_IMAGE_FORMAT=base64
-MULTIMODAL_SEND_VIDEO_FORMAT=base64
+MULTIMODAL_SEND_FORMAT=base64
 PROMPT_GENERATION_MAX_TOKENS=512
 CODE_GENERATION_MAX_TOKENS=1024
 
@@ -381,6 +389,8 @@ LOG_FILE_BACKUP_COUNT=5
 LOG_DATEFORMAT=%Y-%m-%d %H:%M:%S
 # Log Timezone
 LOG_TZ=UTC
+# Log format
+LOG_FORMAT=%(asctime)s,%(msecs)d %(levelname)-2s [%(filename)s:%(lineno)d] %(req_id)s %(message)s
 
 # Indexing configuration
 INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH=4000
@@ -413,4 +423,7 @@ RESET_PASSWORD_TOKEN_EXPIRY_MINUTES=5
 
 CREATE_TIDB_SERVICE_JOB_ENABLED=false
 
-RETRIEVAL_TOP_N=0
+# Maximum number of submitted thread count in a ThreadPool for parallel node execution
+MAX_SUBMIT_COUNT=100
+# Lockout duration in seconds
+LOGIN_LOCKOUT_DURATION=86400

api/.ruff.toml

@@ -20,6 +20,8 @@ select = [
     "PLC0208", # iteration-over-set
     "PLC2801", # unnecessary-dunder-call
     "PLC0414", # useless-import-alias
+    "PLE0604", # invalid-all-object
+    "PLE0605", # invalid-all-format
     "PLR0402", # manual-from-import
     "PLR1711", # useless-return
     "PLR1714", # repeated-equality-comparison
@@ -28,6 +30,7 @@ select = [
     "RUF100", # unused-noqa
     "RUF101", # redirected-noqa
     "RUF200", # invalid-pyproject-toml
+    "RUF022", # unsorted-dunder-all
     "S506", # unsafe-yaml-load
     "SIM", # flake8-simplify rules
     "TRY400", # error-instead-of-exception

api/app.py

@@ -1,13 +1,30 @@
-from app_factory import create_app
-from libs import threadings_utils, version_utils
+from libs import version_utils
 
 # preparation before creating app
 version_utils.check_supported_python_version()
-threadings_utils.apply_gevent_threading_patch()
+
+
+def is_db_command():
+    import sys
+
+    if len(sys.argv) > 1 and sys.argv[0].endswith("flask") and sys.argv[1] == "db":
+        return True
+    return False
+
 
 # create app
-app = create_app()
-celery = app.extensions["celery"]
+if is_db_command():
+    from app_factory import create_migrations_app
+
+    app = create_migrations_app()
+else:
+    from app_factory import create_app
+    from libs import threadings_utils
+
+    threadings_utils.apply_gevent_threading_patch()
+
+    app = create_app()
+    celery = app.extensions["celery"]
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=5001)

api/app_factory.py

@@ -1,5 +1,4 @@
 import logging
-import os
 import time
 
 from configs import dify_config
@@ -17,15 +16,6 @@ def create_flask_app_with_configs() -> DifyApp:
     dify_app = DifyApp(__name__)
     dify_app.config.from_mapping(dify_config.model_dump())
 
-    # populate configs into system environment variables
-    for key, value in dify_app.config.items():
-        if isinstance(value, str):
-            os.environ[key] = value
-        elif isinstance(value, int | float | bool):
-            os.environ[key] = str(value)
-        elif value is None:
-            os.environ[key] = ""
-
     return dify_app
 
 
@@ -98,3 +88,14 @@ def initialize_extensions(app: DifyApp):
         end_time = time.perf_counter()
         if dify_config.DEBUG:
             logging.info(f"Loaded {short_name} ({round((end_time - start_time) * 1000, 2)} ms)")
+
+
+def create_migrations_app():
+    app = create_flask_app_with_configs()
+    from extensions import ext_database, ext_migrate
+
+    # Initialize only required extensions
+    ext_database.init_app(app)
+    ext_migrate.init_app(app)
+
+    return app

api/commands.py

@@ -259,7 +259,7 @@ def migrate_knowledge_vector_database():
     skipped_count = 0
     total_count = 0
     vector_type = dify_config.VECTOR_STORE
-    upper_colletion_vector_types = {
+    upper_collection_vector_types = {
         VectorType.MILVUS,
         VectorType.PGVECTOR,
         VectorType.RELYT,
@@ -267,7 +267,7 @@ def migrate_knowledge_vector_database():
         VectorType.ORACLE,
         VectorType.ELASTICSEARCH,
     }
-    lower_colletion_vector_types = {
+    lower_collection_vector_types = {
         VectorType.ANALYTICDB,
         VectorType.CHROMA,
         VectorType.MYSCALE,
@@ -307,7 +307,7 @@ def migrate_knowledge_vector_database():
                         continue
                 collection_name = ""
                 dataset_id = dataset.id
-                if vector_type in upper_colletion_vector_types:
+                if vector_type in upper_collection_vector_types:
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                 elif vector_type == VectorType.QDRANT:
                     if dataset.collection_binding_id:
@@ -323,7 +323,7 @@ def migrate_knowledge_vector_database():
                     else:
                         collection_name = Dataset.gen_collection_name_by_id(dataset_id)
 
-                elif vector_type in lower_colletion_vector_types:
+                elif vector_type in lower_collection_vector_types:
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
                 else:
                     raise ValueError(f"Vector store {vector_type} is not supported.")

api/configs/app_config.py

@@ -1,11 +1,51 @@
-from pydantic_settings import SettingsConfigDict
+import logging
+from typing import Any
 
-from configs.deploy import DeploymentConfig
-from configs.enterprise import EnterpriseFeatureConfig
-from configs.extra import ExtraServiceConfig
-from configs.feature import FeatureConfig
-from configs.middleware import MiddlewareConfig
-from configs.packaging import PackagingInfo
+from pydantic.fields import FieldInfo
+from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict
+
+from .deploy import DeploymentConfig
+from .enterprise import EnterpriseFeatureConfig
+from .extra import ExtraServiceConfig
+from .feature import FeatureConfig
+from .middleware import MiddlewareConfig
+from .packaging import PackagingInfo
+from .remote_settings_sources import RemoteSettingsSource, RemoteSettingsSourceConfig, RemoteSettingsSourceName
+from .remote_settings_sources.apollo import ApolloSettingsSource
+
+logger = logging.getLogger(__name__)
+
+
+class RemoteSettingsSourceFactory(PydanticBaseSettingsSource):
+    def __init__(self, settings_cls: type[BaseSettings]):
+        super().__init__(settings_cls)
+
+    def get_field_value(self, field: FieldInfo, field_name: str) -> tuple[Any, str, bool]:
+        raise NotImplementedError
+
+    def __call__(self) -> dict[str, Any]:
+        current_state = self.current_state
+        remote_source_name = current_state.get("REMOTE_SETTINGS_SOURCE_NAME")
+        if not remote_source_name:
+            return {}
+
+        remote_source: RemoteSettingsSource | None = None
+        match remote_source_name:
+            case RemoteSettingsSourceName.APOLLO:
+                remote_source = ApolloSettingsSource(current_state)
+            case _:
+                logger.warning(f"Unsupported remote source: {remote_source_name}")
+                return {}
+
+        d: dict[str, Any] = {}
+
+        for field_name, field in self.settings_cls.model_fields.items():
+            field_value, field_key, value_is_complex = remote_source.get_field_value(field, field_name)
+            field_value = remote_source.prepare_field_value(field_name, field, field_value, value_is_complex)
+            if field_value is not None:
+                d[field_key] = field_value
+
+        return d
 
 
 class DifyConfig(
@@ -19,6 +59,8 @@ class DifyConfig(
     MiddlewareConfig,
     # Extra service configs
     ExtraServiceConfig,
+    # Remote source configs
+    RemoteSettingsSourceConfig,
     # Enterprise feature configs
     # **Before using, please contact business@dify.ai by email to inquire about licensing matters.**
     EnterpriseFeatureConfig,
@@ -35,3 +77,20 @@ class DifyConfig(
     # please consider to arrange it in the proper config group of existed or added
     # for better readability and maintainability.
     # Thanks for your concentration and consideration.
+
+    @classmethod
+    def settings_customise_sources(
+        cls,
+        settings_cls: type[BaseSettings],
+        init_settings: PydanticBaseSettingsSource,
+        env_settings: PydanticBaseSettingsSource,
+        dotenv_settings: PydanticBaseSettingsSource,
+        file_secret_settings: PydanticBaseSettingsSource,
+    ) -> tuple[PydanticBaseSettingsSource, ...]:
+        return (
+            init_settings,
+            env_settings,
+            RemoteSettingsSourceFactory(settings_cls),
+            dotenv_settings,
+            file_secret_settings,
+        )

api/configs/feature/__init__.py

@@ -439,6 +439,17 @@ class WorkflowConfig(BaseSettings):
     )
 
 
+class WorkflowNodeExecutionConfig(BaseSettings):
+    """
+    Configuration for workflow node execution
+    """
+
+    MAX_SUBMIT_COUNT: PositiveInt = Field(
+        description="Maximum number of submitted thread count in a ThreadPool for parallel node execution",
+        default=100,
+    )
+
+
 class AuthConfig(BaseSettings):
     """
     Configuration for authentication and OAuth
@@ -474,6 +485,11 @@ class AuthConfig(BaseSettings):
         default=60,
     )
 
+    LOGIN_LOCKOUT_DURATION: PositiveInt = Field(
+        description="Time (in seconds) a user must wait before retrying login after exceeding the rate limit.",
+        default=86400,
+    )
+
 
 class ModerationConfig(BaseSettings):
     """
@@ -626,8 +642,6 @@ class DataSetConfig(BaseSettings):
         default=30,
     )
 
-    RETRIEVAL_TOP_N: int = Field(description="number of retrieval top_n", default=0)
-
 
 class WorkspaceConfig(BaseSettings):
     """
@@ -651,14 +665,9 @@ class IndexingConfig(BaseSettings):
     )
 
 
-class VisionFormatConfig(BaseSettings):
-    MULTIMODAL_SEND_IMAGE_FORMAT: Literal["base64", "url"] = Field(
-        description="Format for sending images in multimodal contexts ('base64' or 'url'), default is base64",
-        default="base64",
-    )
-
-    MULTIMODAL_SEND_VIDEO_FORMAT: Literal["base64", "url"] = Field(
-        description="Format for sending videos in multimodal contexts ('base64' or 'url'), default is base64",
+class MultiModalTransferConfig(BaseSettings):
+    MULTIMODAL_SEND_FORMAT: Literal["base64", "url"] = Field(
+        description="Format for sending files in multimodal contexts ('base64' or 'url'), default is base64",
         default="base64",
     )
 
@@ -764,19 +773,20 @@ class FeatureConfig(
     FileAccessConfig,
     FileUploadConfig,
     HttpConfig,
-    VisionFormatConfig,
     InnerAPIConfig,
     IndexingConfig,
     LoggingConfig,
     MailConfig,
     ModelLoadBalanceConfig,
     ModerationConfig,
+    MultiModalTransferConfig,
     PositionConfig,
     RagEtlConfig,
     SecurityConfig,
     ToolConfig,
     UpdateConfig,
     WorkflowConfig,
+    WorkflowNodeExecutionConfig,
     WorkspaceConfig,
     LoginConfig,
     # hosted services config

api/configs/middleware/__init__.py

@@ -1,54 +1,69 @@
-from typing import Any, Optional
+from typing import Any, Literal, Optional
 from urllib.parse import quote_plus
 
 from pydantic import Field, NonNegativeInt, PositiveFloat, PositiveInt, computed_field
 from pydantic_settings import BaseSettings
 
-from configs.middleware.cache.redis_config import RedisConfig
-from configs.middleware.storage.aliyun_oss_storage_config import AliyunOSSStorageConfig
-from configs.middleware.storage.amazon_s3_storage_config import S3StorageConfig
-from configs.middleware.storage.azure_blob_storage_config import AzureBlobStorageConfig
-from configs.middleware.storage.baidu_obs_storage_config import BaiduOBSStorageConfig
-from configs.middleware.storage.google_cloud_storage_config import GoogleCloudStorageConfig
-from configs.middleware.storage.huawei_obs_storage_config import HuaweiCloudOBSStorageConfig
-from configs.middleware.storage.oci_storage_config import OCIStorageConfig
-from configs.middleware.storage.supabase_storage_config import SupabaseStorageConfig
-from configs.middleware.storage.tencent_cos_storage_config import TencentCloudCOSStorageConfig
-from configs.middleware.storage.volcengine_tos_storage_config import VolcengineTOSStorageConfig
-from configs.middleware.vdb.analyticdb_config import AnalyticdbConfig
-from configs.middleware.vdb.baidu_vector_config import BaiduVectorDBConfig
-from configs.middleware.vdb.chroma_config import ChromaConfig
-from configs.middleware.vdb.couchbase_config import CouchbaseConfig
-from configs.middleware.vdb.elasticsearch_config import ElasticsearchConfig
-from configs.middleware.vdb.lindorm_config import LindormConfig
-from configs.middleware.vdb.milvus_config import MilvusConfig
-from configs.middleware.vdb.myscale_config import MyScaleConfig
-from configs.middleware.vdb.oceanbase_config import OceanBaseVectorConfig
-from configs.middleware.vdb.opensearch_config import OpenSearchConfig
-from configs.middleware.vdb.oracle_config import OracleConfig
-from configs.middleware.vdb.pgvector_config import PGVectorConfig
-from configs.middleware.vdb.pgvectors_config import PGVectoRSConfig
-from configs.middleware.vdb.qdrant_config import QdrantConfig
-from configs.middleware.vdb.relyt_config import RelytConfig
-from configs.middleware.vdb.tencent_vector_config import TencentVectorDBConfig
-from configs.middleware.vdb.tidb_on_qdrant_config import TidbOnQdrantConfig
-from configs.middleware.vdb.tidb_vector_config import TiDBVectorConfig
-from configs.middleware.vdb.upstash_config import UpstashConfig
-from configs.middleware.vdb.vikingdb_config import VikingDBConfig
-from configs.middleware.vdb.weaviate_config import WeaviateConfig
+from .cache.redis_config import RedisConfig
+from .storage.aliyun_oss_storage_config import AliyunOSSStorageConfig
+from .storage.amazon_s3_storage_config import S3StorageConfig
+from .storage.azure_blob_storage_config import AzureBlobStorageConfig
+from .storage.baidu_obs_storage_config import BaiduOBSStorageConfig
+from .storage.google_cloud_storage_config import GoogleCloudStorageConfig
+from .storage.huawei_obs_storage_config import HuaweiCloudOBSStorageConfig
+from .storage.oci_storage_config import OCIStorageConfig
+from .storage.opendal_storage_config import OpenDALStorageConfig
+from .storage.supabase_storage_config import SupabaseStorageConfig
+from .storage.tencent_cos_storage_config import TencentCloudCOSStorageConfig
+from .storage.volcengine_tos_storage_config import VolcengineTOSStorageConfig
+from .vdb.analyticdb_config import AnalyticdbConfig
+from .vdb.baidu_vector_config import BaiduVectorDBConfig
+from .vdb.chroma_config import ChromaConfig
+from .vdb.couchbase_config import CouchbaseConfig
+from .vdb.elasticsearch_config import ElasticsearchConfig
+from .vdb.lindorm_config import LindormConfig
+from .vdb.milvus_config import MilvusConfig
+from .vdb.myscale_config import MyScaleConfig
+from .vdb.oceanbase_config import OceanBaseVectorConfig
+from .vdb.opensearch_config import OpenSearchConfig
+from .vdb.oracle_config import OracleConfig
+from .vdb.pgvector_config import PGVectorConfig
+from .vdb.pgvectors_config import PGVectoRSConfig
+from .vdb.qdrant_config import QdrantConfig
+from .vdb.relyt_config import RelytConfig
+from .vdb.tencent_vector_config import TencentVectorDBConfig
+from .vdb.tidb_on_qdrant_config import TidbOnQdrantConfig
+from .vdb.tidb_vector_config import TiDBVectorConfig
+from .vdb.upstash_config import UpstashConfig
+from .vdb.vikingdb_config import VikingDBConfig
+from .vdb.weaviate_config import WeaviateConfig
 
 
 class StorageConfig(BaseSettings):
-    STORAGE_TYPE: str = Field(
+    STORAGE_TYPE: Literal[
+        "opendal",
+        "s3",
+        "aliyun-oss",
+        "azure-blob",
+        "baidu-obs",
+        "google-storage",
+        "huawei-obs",
+        "oci-storage",
+        "tencent-cos",
+        "volcengine-tos",
+        "supabase",
+        "local",
+    ] = Field(
         description="Type of storage to use."
-        " Options: 'local', 's3', 'aliyun-oss', 'azure-blob', 'baidu-obs', 'google-storage', 'huawei-obs', "
-        "'oci-storage', 'tencent-cos', 'volcengine-tos', 'supabase'. Default is 'local'.",
-        default="local",
+        " Options: 'opendal', '(deprecated) local', 's3', 'aliyun-oss', 'azure-blob', 'baidu-obs', 'google-storage', "
+        "'huawei-obs', 'oci-storage', 'tencent-cos', 'volcengine-tos', 'supabase'. Default is 'opendal'.",
+        default="opendal",
     )
 
     STORAGE_LOCAL_PATH: str = Field(
         description="Path for local storage when STORAGE_TYPE is set to 'local'.",
         default="storage",
+        deprecated=True,
     )
 
 
@@ -73,7 +88,7 @@ class KeywordStoreConfig(BaseSettings):
     )
 
 
-class DatabaseConfig:
+class DatabaseConfig(BaseSettings):
     DB_HOST: str = Field(
         description="Hostname or IP address of the database server.",
         default="localhost",
@@ -235,6 +250,7 @@ class MiddlewareConfig(
     GoogleCloudStorageConfig,
     HuaweiCloudOBSStorageConfig,
     OCIStorageConfig,
+    OpenDALStorageConfig,
     S3StorageConfig,
     SupabaseStorageConfig,
     TencentCloudCOSStorageConfig,

api/configs/middleware/storage/baidu_obs_storage_config.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import Field
+from pydantic_settings import BaseSettings
 
 
-class BaiduOBSStorageConfig(BaseModel):
+class BaiduOBSStorageConfig(BaseSettings):
     """
     Configuration settings for Baidu Object Storage Service (OBS)
     """

api/configs/middleware/storage/huawei_obs_storage_config.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import Field
+from pydantic_settings import BaseSettings
 
 
-class HuaweiCloudOBSStorageConfig(BaseModel):
+class HuaweiCloudOBSStorageConfig(BaseSettings):
     """
     Configuration settings for Huawei Cloud Object Storage Service (OBS)
     """

api/configs/middleware/storage/opendal_storage_config.py

@@ -0,0 +1,9 @@
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class OpenDALStorageConfig(BaseSettings):
+    OPENDAL_SCHEME: str = Field(
+        default="fs",
+        description="OpenDAL scheme.",
+    )

api/configs/middleware/storage/supabase_storage_config.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import Field
+from pydantic_settings import BaseSettings
 
 
-class SupabaseStorageConfig(BaseModel):
+class SupabaseStorageConfig(BaseSettings):
     """
     Configuration settings for Supabase Object Storage Service
     """

api/configs/middleware/storage/volcengine_tos_storage_config.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import Field
+from pydantic_settings import BaseSettings
 
 
-class VolcengineTOSStorageConfig(BaseModel):
+class VolcengineTOSStorageConfig(BaseSettings):
     """
     Configuration settings for Volcengine Tinder Object Storage (TOS)
     """

api/configs/middleware/vdb/analyticdb_config.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field, PositiveInt
+from pydantic import Field, PositiveInt
+from pydantic_settings import BaseSettings
 
 
-class AnalyticdbConfig(BaseModel):
+class AnalyticdbConfig(BaseSettings):
     """
     Configuration for connecting to Alibaba Cloud AnalyticDB for PostgreSQL.
     Refer to the following documentation for details on obtaining credentials:

api/configs/middleware/vdb/couchbase_config.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import Field
+from pydantic_settings import BaseSettings
 
 
-class CouchbaseConfig(BaseModel):
+class CouchbaseConfig(BaseSettings):
     """
     Couchbase configs
     """

api/configs/middleware/vdb/lindorm_config.py

@@ -21,3 +21,14 @@ class LindormConfig(BaseSettings):
         description="Lindorm password",
         default=None,
     )
+    DEFAULT_INDEX_TYPE: Optional[str] = Field(
+        description="Lindorm Vector Index Type, hnsw or flat is available in dify",
+        default="hnsw",
+    )
+    DEFAULT_DISTANCE_TYPE: Optional[str] = Field(
+        description="Vector Distance Type, support l2, cosinesimil, innerproduct", default="l2"
+    )
+    USING_UGC_INDEX: Optional[bool] = Field(
+        description="Using UGC index will store the same type of Index in a single index but can retrieve separately.",
+        default=False,
+    )

api/configs/middleware/vdb/myscale_config.py

@@ -1,7 +1,8 @@
-from pydantic import BaseModel, Field, PositiveInt
+from pydantic import Field, PositiveInt
+from pydantic_settings import BaseSettings
 
 
-class MyScaleConfig(BaseModel):
+class MyScaleConfig(BaseSettings):
     """
     Configuration settings for MyScale vector database
     """

api/configs/middleware/vdb/vikingdb_config.py

@@ -1,9 +1,10 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import Field
+from pydantic_settings import BaseSettings
 
 
-class VikingDBConfig(BaseModel):
+class VikingDBConfig(BaseSettings):
     """
     Configuration for connecting to Volcengine VikingDB.
     Refer to the following documentation for details on obtaining credentials:

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.12.1",
+        default="0.14.1",
     )
 
     COMMIT_SHA: str = Field(

api/configs/remote_settings_sources/__init__.py

@@ -0,0 +1,17 @@
+from typing import Optional
+
+from pydantic import Field
+
+from .apollo import ApolloSettingsSourceInfo
+from .base import RemoteSettingsSource
+from .enums import RemoteSettingsSourceName
+
+
+class RemoteSettingsSourceConfig(ApolloSettingsSourceInfo):
+    REMOTE_SETTINGS_SOURCE_NAME: RemoteSettingsSourceName | str = Field(
+        description="name of remote config source",
+        default="",
+    )
+
+
+__all__ = ["RemoteSettingsSource", "RemoteSettingsSourceConfig", "RemoteSettingsSourceName"]

api/configs/remote_settings_sources/apollo/__init__.py

@@ -0,0 +1,55 @@
+from collections.abc import Mapping
+from typing import Any, Optional
+
+from pydantic import Field
+from pydantic.fields import FieldInfo
+from pydantic_settings import BaseSettings
+
+from configs.remote_settings_sources.base import RemoteSettingsSource
+
+from .client import ApolloClient
+
+
+class ApolloSettingsSourceInfo(BaseSettings):
+    """
+    Packaging build information
+    """
+
+    APOLLO_APP_ID: Optional[str] = Field(
+        description="apollo app_id",
+        default=None,
+    )
+
+    APOLLO_CLUSTER: Optional[str] = Field(
+        description="apollo cluster",
+        default=None,
+    )
+
+    APOLLO_CONFIG_URL: Optional[str] = Field(
+        description="apollo config url",
+        default=None,
+    )
+
+    APOLLO_NAMESPACE: Optional[str] = Field(
+        description="apollo namespace",
+        default=None,
+    )
+
+
+class ApolloSettingsSource(RemoteSettingsSource):
+    def __init__(self, configs: Mapping[str, Any]):
+        self.client = ApolloClient(
+            app_id=configs["APOLLO_APP_ID"],
+            cluster=configs["APOLLO_CLUSTER"],
+            config_url=configs["APOLLO_CONFIG_URL"],
+            start_hot_update=False,
+            _notification_map={configs["APOLLO_NAMESPACE"]: -1},
+        )
+        self.namespace = configs["APOLLO_NAMESPACE"]
+        self.remote_configs = self.client.get_all_dicts(self.namespace)
+
+    def get_field_value(self, field: FieldInfo, field_name: str) -> tuple[Any, str, bool]:
+        if not isinstance(self.remote_configs, dict):
+            raise ValueError(f"remote configs is not dict, but {type(self.remote_configs)}")
+        field_value = self.remote_configs.get(field_name)
+        return field_value, field_name, False

api/configs/remote_settings_sources/apollo/client.py

@@ -0,0 +1,303 @@
+import hashlib
+import json
+import logging
+import os
+import threading
+import time
+from pathlib import Path
+
+from .python_3x import http_request, makedirs_wrapper
+from .utils import (
+    CONFIGURATIONS,
+    NAMESPACE_NAME,
+    NOTIFICATION_ID,
+    get_value_from_dict,
+    init_ip,
+    no_key_cache_key,
+    signature,
+    url_encode_wrapper,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class ApolloClient:
+    def __init__(
+        self,
+        config_url,
+        app_id,
+        cluster="default",
+        secret="",
+        start_hot_update=True,
+        change_listener=None,
+        _notification_map=None,
+    ):
+        # Core routing parameters
+        self.config_url = config_url
+        self.cluster = cluster
+        self.app_id = app_id
+
+        # Non-core parameters
+        self.ip = init_ip()
+        self.secret = secret
+
+        # Check the parameter variables
+
+        # Private control variables
+        self._cycle_time = 5
+        self._stopping = False
+        self._cache = {}
+        self._no_key = {}
+        self._hash = {}
+        self._pull_timeout = 75
+        self._cache_file_path = os.path.expanduser("~") + "/.dify/config/remote-settings/apollo/cache/"
+        self._long_poll_thread = None
+        self._change_listener = change_listener  # "add" "delete" "update"
+        if _notification_map is None:
+            _notification_map = {"application": -1}
+        self._notification_map = _notification_map
+        self.last_release_key = None
+        # Private startup method
+        self._path_checker()
+        if start_hot_update:
+            self._start_hot_update()
+
+        # start the heartbeat thread
+        heartbeat = threading.Thread(target=self._heart_beat)
+        heartbeat.daemon = True
+        heartbeat.start()
+
+    def get_json_from_net(self, namespace="application"):
+        url = "{}/configs/{}/{}/{}?releaseKey={}&ip={}".format(
+            self.config_url, self.app_id, self.cluster, namespace, "", self.ip
+        )
+        try:
+            code, body = http_request(url, timeout=3, headers=self._sign_headers(url))
+            if code == 200:
+                if not body:
+                    logger.error(f"get_json_from_net load configs failed, body is {body}")
+                    return None
+                data = json.loads(body)
+                data = data["configurations"]
+                return_data = {CONFIGURATIONS: data}
+                return return_data
+            else:
+                return None
+        except Exception:
+            logger.exception("an error occurred in get_json_from_net")
+            return None
+
+    def get_value(self, key, default_val=None, namespace="application"):
+        try:
+            # read memory configuration
+            namespace_cache = self._cache.get(namespace)
+            val = get_value_from_dict(namespace_cache, key)
+            if val is not None:
+                return val
+
+            no_key = no_key_cache_key(namespace, key)
+            if no_key in self._no_key:
+                return default_val
+
+            # read the network configuration
+            namespace_data = self.get_json_from_net(namespace)
+            val = get_value_from_dict(namespace_data, key)
+            if val is not None:
+                self._update_cache_and_file(namespace_data, namespace)
+                return val
+
+            # read the file configuration
+            namespace_cache = self._get_local_cache(namespace)
+            val = get_value_from_dict(namespace_cache, key)
+            if val is not None:
+                self._update_cache_and_file(namespace_cache, namespace)
+                return val
+
+            # If all of them are not obtained, the default value is returned
+            # and the local cache is set to None
+            self._set_local_cache_none(namespace, key)
+            return default_val
+        except Exception:
+            logger.exception("get_value has error, [key is %s], [namespace is %s]", key, namespace)
+            return default_val
+
+    # Set the key of a namespace to none, and do not set default val
+    # to ensure the real-time correctness of the function call.
+    # If the user does not have the same default val twice
+    # and the default val is used here, there may be a problem.
+    def _set_local_cache_none(self, namespace, key):
+        no_key = no_key_cache_key(namespace, key)
+        self._no_key[no_key] = key
+
+    def _start_hot_update(self):
+        self._long_poll_thread = threading.Thread(target=self._listener)
+        # When the asynchronous thread is started, the daemon thread will automatically exit
+        # when the main thread is launched.
+        self._long_poll_thread.daemon = True
+        self._long_poll_thread.start()
+
+    def stop(self):
+        self._stopping = True
+        logger.info("Stopping listener...")
+
+    # Call the set callback function, and if it is abnormal, try it out
+    def _call_listener(self, namespace, old_kv, new_kv):
+        if self._change_listener is None:
+            return
+        if old_kv is None:
+            old_kv = {}
+        if new_kv is None:
+            new_kv = {}
+        try:
+            for key in old_kv:
+                new_value = new_kv.get(key)
+                old_value = old_kv.get(key)
+                if new_value is None:
+                    # If newValue is empty, it means key, and the value is deleted.
+                    self._change_listener("delete", namespace, key, old_value)
+                    continue
+                if new_value != old_value:
+                    self._change_listener("update", namespace, key, new_value)
+                    continue
+            for key in new_kv:
+                new_value = new_kv.get(key)
+                old_value = old_kv.get(key)
+                if old_value is None:
+                    self._change_listener("add", namespace, key, new_value)
+        except BaseException as e:
+            logger.warning(str(e))
+
+    def _path_checker(self):
+        if not os.path.isdir(self._cache_file_path):
+            makedirs_wrapper(self._cache_file_path)
+
+    # update the local cache and file cache
+    def _update_cache_and_file(self, namespace_data, namespace="application"):
+        # update the local cache
+        self._cache[namespace] = namespace_data
+        # update the file cache
+        new_string = json.dumps(namespace_data)
+        new_hash = hashlib.md5(new_string.encode("utf-8")).hexdigest()
+        if self._hash.get(namespace) == new_hash:
+            pass
+        else:
+            file_path = Path(self._cache_file_path) / f"{self.app_id}_configuration_{namespace}.txt"
+            file_path.write_text(new_string)
+            self._hash[namespace] = new_hash
+
+    # get the configuration from the local file
+    def _get_local_cache(self, namespace="application"):
+        cache_file_path = os.path.join(self._cache_file_path, f"{self.app_id}_configuration_{namespace}.txt")
+        if os.path.isfile(cache_file_path):
+            with open(cache_file_path) as f:
+                result = json.loads(f.readline())
+            return result
+        return {}
+
+    def _long_poll(self):
+        notifications = []
+        for key in self._cache:
+            namespace_data = self._cache[key]
+            notification_id = -1
+            if NOTIFICATION_ID in namespace_data:
+                notification_id = self._cache[key][NOTIFICATION_ID]
+            notifications.append({NAMESPACE_NAME: key, NOTIFICATION_ID: notification_id})
+        try:
+            # if the length is 0 it is returned directly
+            if len(notifications) == 0:
+                return
+            url = "{}/notifications/v2".format(self.config_url)
+            params = {
+                "appId": self.app_id,
+                "cluster": self.cluster,
+                "notifications": json.dumps(notifications, ensure_ascii=False),
+            }
+            param_str = url_encode_wrapper(params)
+            url = url + "?" + param_str
+            code, body = http_request(url, self._pull_timeout, headers=self._sign_headers(url))
+            http_code = code
+            if http_code == 304:
+                logger.debug("No change, loop...")
+                return
+            if http_code == 200:
+                if not body:
+                    logger.error(f"_long_poll load configs failed,body is {body}")
+                    return
+                data = json.loads(body)
+                for entry in data:
+                    namespace = entry[NAMESPACE_NAME]
+                    n_id = entry[NOTIFICATION_ID]
+                    logger.info("%s has changes: notificationId=%d", namespace, n_id)
+                    self._get_net_and_set_local(namespace, n_id, call_change=True)
+                    return
+            else:
+                logger.warning("Sleep...")
+        except Exception as e:
+            logger.warning(str(e))
+
+    def _get_net_and_set_local(self, namespace, n_id, call_change=False):
+        namespace_data = self.get_json_from_net(namespace)
+        if not namespace_data:
+            return
+        namespace_data[NOTIFICATION_ID] = n_id
+        old_namespace = self._cache.get(namespace)
+        self._update_cache_and_file(namespace_data, namespace)
+        if self._change_listener is not None and call_change and old_namespace:
+            old_kv = old_namespace.get(CONFIGURATIONS)
+            new_kv = namespace_data.get(CONFIGURATIONS)
+            self._call_listener(namespace, old_kv, new_kv)
+
+    def _listener(self):
+        logger.info("start long_poll")
+        while not self._stopping:
+            self._long_poll()
+            time.sleep(self._cycle_time)
+        logger.info("stopped, long_poll")
+
+    # add the need for endorsement to the header
+    def _sign_headers(self, url):
+        headers = {}
+        if self.secret == "":
+            return headers
+        uri = url[len(self.config_url) : len(url)]
+        time_unix_now = str(int(round(time.time() * 1000)))
+        headers["Authorization"] = "Apollo " + self.app_id + ":" + signature(time_unix_now, uri, self.secret)
+        headers["Timestamp"] = time_unix_now
+        return headers
+
+    def _heart_beat(self):
+        while not self._stopping:
+            for namespace in self._notification_map:
+                self._do_heart_beat(namespace)
+            time.sleep(60 * 10)  # 10分钟
+
+    def _do_heart_beat(self, namespace):
+        url = "{}/configs/{}/{}/{}?ip={}".format(self.config_url, self.app_id, self.cluster, namespace, self.ip)
+        try:
+            code, body = http_request(url, timeout=3, headers=self._sign_headers(url))
+            if code == 200:
+                if not body:
+                    logger.error(f"_do_heart_beat load configs failed,body is {body}")
+                    return None
+                data = json.loads(body)
+                if self.last_release_key == data["releaseKey"]:
+                    return None
+                self.last_release_key = data["releaseKey"]
+                data = data["configurations"]
+                self._update_cache_and_file(data, namespace)
+            else:
+                return None
+        except Exception:
+            logger.exception("an error occurred in _do_heart_beat")
+            return None
+
+    def get_all_dicts(self, namespace):
+        namespace_data = self._cache.get(namespace)
+        if namespace_data is None:
+            net_namespace_data = self.get_json_from_net(namespace)
+            if not net_namespace_data:
+                return namespace_data
+            namespace_data = net_namespace_data.get(CONFIGURATIONS)
+            if namespace_data:
+                self._update_cache_and_file(namespace_data, namespace)
+        return namespace_data

api/configs/remote_settings_sources/apollo/python_3x.py

@@ -0,0 +1,41 @@
+import logging
+import os
+import ssl
+import urllib.request
+from urllib import parse
+from urllib.error import HTTPError
+
+# Create an SSL context that allows for a lower level of security
+ssl_context = ssl.create_default_context()
+ssl_context.set_ciphers("HIGH:!DH:!aNULL")
+ssl_context.check_hostname = False
+ssl_context.verify_mode = ssl.CERT_NONE
+
+# Create an opener object and pass in a custom SSL context
+opener = urllib.request.build_opener(urllib.request.HTTPSHandler(context=ssl_context))
+
+urllib.request.install_opener(opener)
+
+logger = logging.getLogger(__name__)
+
+
+def http_request(url, timeout, headers={}):
+    try:
+        request = urllib.request.Request(url, headers=headers)
+        res = urllib.request.urlopen(request, timeout=timeout)
+        body = res.read().decode("utf-8")
+        return res.code, body
+    except HTTPError as e:
+        if e.code == 304:
+            logger.warning("http_request error,code is 304, maybe you should check secret")
+            return 304, None
+        logger.warning("http_request error,code is %d, msg is %s", e.code, e.msg)
+        raise e
+
+
+def url_encode(params):
+    return parse.urlencode(params)
+
+
+def makedirs_wrapper(path):
+    os.makedirs(path, exist_ok=True)

api/configs/remote_settings_sources/apollo/utils.py

@@ -0,0 +1,51 @@
+import hashlib
+import socket
+
+from .python_3x import url_encode
+
+# define constants
+CONFIGURATIONS = "configurations"
+NOTIFICATION_ID = "notificationId"
+NAMESPACE_NAME = "namespaceName"
+
+
+# add timestamps uris and keys
+def signature(timestamp, uri, secret):
+    import base64
+    import hmac
+
+    string_to_sign = "" + timestamp + "\n" + uri
+    hmac_code = hmac.new(secret.encode(), string_to_sign.encode(), hashlib.sha1).digest()
+    return base64.b64encode(hmac_code).decode()
+
+
+def url_encode_wrapper(params):
+    return url_encode(params)
+
+
+def no_key_cache_key(namespace, key):
+    return "{}{}{}".format(namespace, len(namespace), key)
+
+
+# Returns whether the obtained value is obtained, and None if it does not
+def get_value_from_dict(namespace_cache, key):
+    if namespace_cache:
+        kv_data = namespace_cache.get(CONFIGURATIONS)
+        if kv_data is None:
+            return None
+        if key in kv_data:
+            return kv_data[key]
+    return None
+
+
+def init_ip():
+    ip = ""
+    s = None
+    try:
+        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+        s.connect(("8.8.8.8", 53))
+        ip = s.getsockname()[0]
+    finally:
+        if s:
+            s.close()
+    return ip

api/configs/remote_settings_sources/base.py

@@ -0,0 +1,15 @@
+from collections.abc import Mapping
+from typing import Any
+
+from pydantic.fields import FieldInfo
+
+
+class RemoteSettingsSource:
+    def __init__(self, configs: Mapping[str, Any]):
+        pass
+
+    def get_field_value(self, field: FieldInfo, field_name: str) -> tuple[Any, str, bool]:
+        raise NotImplementedError
+
+    def prepare_field_value(self, field_name: str, field: FieldInfo, value: Any, value_is_complex: bool) -> Any:
+        return value

api/configs/remote_settings_sources/enums.py

@@ -0,0 +1,5 @@
+from enum import StrEnum
+
+
+class RemoteSettingsSourceName(StrEnum):
+    APOLLO = "apollo"

api/constants/__init__.py

@@ -14,11 +14,11 @@ AUDIO_EXTENSIONS.extend([ext.upper() for ext in AUDIO_EXTENSIONS])
 
 
 if dify_config.ETL_TYPE == "Unstructured":
-    DOCUMENT_EXTENSIONS = ["txt", "markdown", "md", "pdf", "html", "htm", "xlsx", "xls"]
+    DOCUMENT_EXTENSIONS = ["txt", "markdown", "md", "mdx", "pdf", "html", "htm", "xlsx", "xls"]
     DOCUMENT_EXTENSIONS.extend(("docx", "csv", "eml", "msg", "pptx", "xml", "epub"))
     if dify_config.UNSTRUCTURED_API_URL:
         DOCUMENT_EXTENSIONS.append("ppt")
     DOCUMENT_EXTENSIONS.extend([ext.upper() for ext in DOCUMENT_EXTENSIONS])
 else:
-    DOCUMENT_EXTENSIONS = ["txt", "markdown", "md", "pdf", "html", "htm", "xlsx", "xls", "docx", "csv"]
+    DOCUMENT_EXTENSIONS = ["txt", "markdown", "md", "mdx", "pdf", "html", "htm", "xlsx", "xls", "docx", "csv"]
     DOCUMENT_EXTENSIONS.extend([ext.upper() for ext in DOCUMENT_EXTENSIONS])

api/controllers/console/app/model_config.py

@@ -65,7 +65,7 @@ class ModelConfigResource(Resource):
                         provider_type=agent_tool_entity.provider_type,
                         identity_id=f"AGENT.{app_model.id}",
                     )
-                except Exception as e:
+                except Exception:
                     continue
 
                 # get decrypted parameters
@@ -97,7 +97,7 @@ class ModelConfigResource(Resource):
                             app_id=app_model.id,
                             agent_tool=agent_tool_entity,
                         )
-                    except Exception as e:
+                    except Exception:
                         continue
 
                 manager = ToolParameterConfigurationManager(

api/controllers/console/app/ops_trace.py

@@ -1,4 +1,5 @@
 from flask_restful import Resource, reqparse
+from werkzeug.exceptions import BadRequest
 
 from controllers.console import api
 from controllers.console.app.error import TracingConfigCheckError, TracingConfigIsExist, TracingConfigNotExist
@@ -26,7 +27,7 @@ class TraceAppConfigApi(Resource):
                 return {"has_not_configured": True}
             return trace_config
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
     @setup_required
     @login_required
@@ -48,7 +49,7 @@ class TraceAppConfigApi(Resource):
                 raise TracingConfigCheckError()
             return result
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
     @setup_required
     @login_required
@@ -68,7 +69,7 @@ class TraceAppConfigApi(Resource):
                 raise TracingConfigNotExist()
             return {"result": "success"}
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
     @setup_required
     @login_required
@@ -85,7 +86,7 @@ class TraceAppConfigApi(Resource):
                 raise TracingConfigNotExist()
             return {"result": "success"}
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
 
 api.add_resource(TraceAppConfigApi, "/apps/<uuid:app_id>/trace-config")

api/controllers/console/app/workflow.py

@@ -100,11 +100,11 @@ class DraftWorkflowApi(Resource):
         try:
             environment_variables_list = args.get("environment_variables") or []
             environment_variables = [
-                variable_factory.build_variable_from_mapping(obj) for obj in environment_variables_list
+                variable_factory.build_environment_variable_from_mapping(obj) for obj in environment_variables_list
             ]
             conversation_variables_list = args.get("conversation_variables") or []
             conversation_variables = [
-                variable_factory.build_variable_from_mapping(obj) for obj in conversation_variables_list
+                variable_factory.build_conversation_variable_from_mapping(obj) for obj in conversation_variables_list
             ]
             workflow = workflow_service.sync_draft_workflow(
                 app_model=app_model,
@@ -382,7 +382,7 @@ class DefaultBlockConfigApi(Resource):
         filters = None
         if args.get("q"):
             try:
-                filters = json.loads(args.get("q"))
+                filters = json.loads(args.get("q", ""))
             except json.JSONDecodeError:
                 raise ValueError("Invalid filters")
 

api/controllers/console/datasets/datasets_document.py

@@ -948,7 +948,7 @@ class DocumentRetryApi(DocumentResource):
                 if document.indexing_status == "completed":
                     raise DocumentAlreadyFinishedError()
                 retry_documents.append(document)
-            except Exception as e:
+            except Exception:
                 logging.exception(f"Failed to retry document, document id: {document_id}")
                 continue
         # retry document

api/controllers/console/explore/installed_app.py

@@ -1,5 +1,6 @@
 from datetime import UTC, datetime
 
+from flask import request
 from flask_login import current_user
 from flask_restful import Resource, inputs, marshal_with, reqparse
 from sqlalchemy import and_
@@ -20,8 +21,17 @@ class InstalledAppsListApi(Resource):
     @account_initialization_required
     @marshal_with(installed_app_list_fields)
     def get(self):
+        app_id = request.args.get("app_id", default=None, type=str)
         current_tenant_id = current_user.current_tenant_id
-        installed_apps = db.session.query(InstalledApp).filter(InstalledApp.tenant_id == current_tenant_id).all()
+
+        if app_id:
+            installed_apps = (
+                db.session.query(InstalledApp)
+                .filter(and_(InstalledApp.tenant_id == current_tenant_id, InstalledApp.app_id == app_id))
+                .all()
+            )
+        else:
+            installed_apps = db.session.query(InstalledApp).filter(InstalledApp.tenant_id == current_tenant_id).all()
 
         current_user.role = TenantService.get_user_role(current_user, current_user.current_tenant)
         installed_apps = [

api/controllers/console/explore/recommended_app.py

@@ -4,6 +4,7 @@ from flask_restful import Resource, fields, marshal_with, reqparse
 from constants.languages import languages
 from controllers.console import api
 from controllers.console.wraps import account_initialization_required
+from libs.helper import AppIconUrlField
 from libs.login import login_required
 from services.recommended_app_service import RecommendedAppService
 
@@ -12,6 +13,7 @@ app_fields = {
     "name": fields.String,
     "mode": fields.String,
     "icon": fields.String,
+    "icon_url": AppIconUrlField,
     "icon_background": fields.String,
 }
 

api/controllers/console/files.py

@@ -1,6 +1,7 @@
 from flask import request
 from flask_login import current_user
 from flask_restful import Resource, marshal_with
+from werkzeug.exceptions import Forbidden
 
 import services
 from configs import dify_config
@@ -58,6 +59,9 @@ class FileApi(Resource):
         if not file.filename:
             raise FilenameNotExistsError
 
+        if source == "datasets" and not current_user.is_dataset_editor:
+            raise Forbidden()
+
         if source not in ("datasets", None):
             source = None
 

api/controllers/console/workspace/tool_providers.py

@@ -368,6 +368,7 @@ class ToolWorkflowProviderCreateApi(Resource):
             description=args["description"],
             parameters=args["parameters"],
             privacy_policy=args["privacy_policy"],
+            labels=args["labels"],
         )
 
 

api/core/app/apps/README.md

@@ -2,7 +2,7 @@
 
 Due to the presence of tasks in App Runner that require long execution times, such as LLM generation and external requests, Flask-Sqlalchemy's strategy for database connection pooling is to allocate one connection (transaction) per request. This approach keeps a connection occupied even during non-DB tasks, leading to the inability to acquire new connections during high concurrency requests due to multiple long-running tasks.
 
-Therefore, the database operations in App Runner and Task Pipeline must ensure connections are closed immediately after use, and it's better to pass IDs rather than Model objects to avoid deattach errors.
+Therefore, the database operations in App Runner and Task Pipeline must ensure connections are closed immediately after use, and it's better to pass IDs rather than Model objects to avoid detach errors.
 
 Examples:
 

api/core/app/apps/advanced_chat/app_generator.py

@@ -3,7 +3,7 @@ import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping
-from typing import Any, Optional, Union
+from typing import Any, Literal, Optional, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -36,6 +36,29 @@ logger = logging.getLogger(__name__)
 class AdvancedChatAppGenerator(MessageBasedAppGenerator):
     _dialogue_count: int
 
+    @overload
+    def generate(
+        self,
+        app_model: App,
+        workflow: Workflow,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: Literal[True],
+    ) -> Generator[str, None, None]: ...
+
+    @overload
+    def generate(
+        self,
+        app_model: App,
+        workflow: Workflow,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: Literal[False],
+    ) -> Mapping[str, Any]: ...
+
+    @overload
     def generate(
         self,
         app_model: App,
@@ -44,7 +67,17 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         args: Mapping[str, Any],
         invoke_from: InvokeFrom,
         streaming: bool = True,
-    ) -> Mapping[str, Any] | Generator[str, None, None]:
+    ) -> Union[Mapping[str, Any], Generator[str, None, None]]: ...
+
+    def generate(
+        self,
+        app_model: App,
+        workflow: Workflow,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: bool = True,
+    ):
         """
         Generate App response.
 

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -19,6 +19,7 @@ from core.app.entities.queue_entities import (
     QueueIterationNextEvent,
     QueueIterationStartEvent,
     QueueMessageReplaceEvent,
+    QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
     QueueNodeStartedEvent,
@@ -31,6 +32,7 @@ from core.app.entities.queue_entities import (
     QueueStopEvent,
     QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
+    QueueWorkflowPartialSuccessEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
@@ -127,7 +129,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
         self._conversation_name_generate_thread = None
         self._recorded_files: list[Mapping[str, Any]] = []
-        self.total_tokens: int = 0
 
     def process(self):
         """
@@ -318,7 +319,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
                 if response:
                     yield response
-            elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent):
+            elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
                 workflow_node_execution = self._handle_workflow_node_execution_failed(event)
 
                 response = self._workflow_node_finish_to_stream_response(
@@ -361,8 +362,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 if not workflow_run:
                     raise Exception("Workflow run not initialized.")
 
-                # FIXME for issue #11221 quick fix maybe have a better solution
-                self.total_tokens += event.metadata.get("total_tokens", 0) if event.metadata else 0
                 yield self._workflow_iteration_completed_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
@@ -376,7 +375,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 workflow_run = self._handle_workflow_run_success(
                     workflow_run=workflow_run,
                     start_at=graph_runtime_state.start_at,
-                    total_tokens=graph_runtime_state.total_tokens or self.total_tokens,
+                    total_tokens=graph_runtime_state.total_tokens,
                     total_steps=graph_runtime_state.node_run_steps,
                     outputs=event.outputs,
                     conversation_id=self._conversation.id,
@@ -387,6 +386,29 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )
 
+                self._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
+            elif isinstance(event, QueueWorkflowPartialSuccessEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                if not graph_runtime_state:
+                    raise Exception("Graph runtime state not initialized.")
+
+                workflow_run = self._handle_workflow_run_partial_success(
+                    workflow_run=workflow_run,
+                    start_at=graph_runtime_state.start_at,
+                    total_tokens=graph_runtime_state.total_tokens,
+                    total_steps=graph_runtime_state.node_run_steps,
+                    outputs=event.outputs,
+                    exceptions_count=event.exceptions_count,
+                    conversation_id=None,
+                    trace_manager=trace_manager,
+                )
+
+                yield self._workflow_finish_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
+                )
+
                 self._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
             elif isinstance(event, QueueWorkflowFailedEvent):
                 if not workflow_run:
@@ -404,6 +426,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                     error=event.error,
                     conversation_id=self._conversation.id,
                     trace_manager=trace_manager,
+                    exceptions_count=event.exceptions_count,
                 )
 
                 yield self._workflow_finish_to_stream_response(

api/core/app/apps/agent_chat/app_generator.py

@@ -2,7 +2,7 @@ import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping
-from typing import Any, Union
+from typing import Any, Literal, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -28,6 +28,39 @@ logger = logging.getLogger(__name__)
 
 
 class AgentChatAppGenerator(MessageBasedAppGenerator):
+    @overload
+    def generate(
+        self,
+        *,
+        app_model: App,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: Literal[True],
+    ) -> Generator[str, None, None]: ...
+
+    @overload
+    def generate(
+        self,
+        *,
+        app_model: App,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: Literal[False],
+    ) -> Mapping[str, Any]: ...
+
+    @overload
+    def generate(
+        self,
+        *,
+        app_model: App,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: bool,
+    ) -> Mapping[str, Any] | Generator[str, None, None]: ...
+
     def generate(
         self,
         *,
@@ -36,7 +69,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
         args: Mapping[str, Any],
         invoke_from: InvokeFrom,
         streaming: bool = True,
-    ) -> Mapping[str, Any] | Generator[str, None, None]:
+    ):
         """
         Generate App response.
 

api/core/app/apps/base_app_generate_response_converter.py

@@ -82,7 +82,7 @@ class AppGenerateResponseConverter(ABC):
             for resource in metadata["retriever_resources"]:
                 updated_resources.append(
                     {
-                        "segment_id": resource["segment_id"],
+                        "segment_id": resource.get("segment_id", ""),
                         "position": resource["position"],
                         "document_name": resource["document_name"],
                         "score": resource["score"],

api/core/app/apps/chat/app_generator.py

@@ -1,7 +1,7 @@
 import logging
 import threading
 import uuid
-from collections.abc import Generator
+from collections.abc import Generator, Mapping
 from typing import Any, Literal, Union, overload
 
 from flask import Flask, current_app
@@ -34,9 +34,9 @@ class ChatAppGenerator(MessageBasedAppGenerator):
         self,
         app_model: App,
         user: Union[Account, EndUser],
-        args: Any,
+        args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        stream: Literal[True] = True,
+        streaming: Literal[True],
     ) -> Generator[str, None, None]: ...
 
     @overload
@@ -44,19 +44,29 @@ class ChatAppGenerator(MessageBasedAppGenerator):
         self,
         app_model: App,
         user: Union[Account, EndUser],
-        args: Any,
+        args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        stream: Literal[False] = False,
-    ) -> dict: ...
+        streaming: Literal[False],
+    ) -> Mapping[str, Any]: ...
+
+    @overload
+    def generate(
+        self,
+        app_model: App,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: bool,
+    ) -> Union[Mapping[str, Any], Generator[str, None, None]]: ...
 
     def generate(
         self,
         app_model: App,
         user: Union[Account, EndUser],
-        args: Any,
+        args: Mapping[str, Any],
         invoke_from: InvokeFrom,
         streaming: bool = True,
-    ) -> Union[dict, Generator[str, None, None]]:
+    ):
         """
         Generate App response.
 

api/core/app/apps/completion/app_generator.py

@@ -1,7 +1,7 @@
 import logging
 import threading
 import uuid
-from collections.abc import Generator
+from collections.abc import Generator, Mapping
 from typing import Any, Literal, Union, overload
 
 from flask import Flask, current_app
@@ -34,9 +34,9 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
         self,
         app_model: App,
         user: Union[Account, EndUser],
-        args: dict,
+        args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        stream: Literal[True] = True,
+        streaming: Literal[True],
     ) -> Generator[str, None, None]: ...
 
     @overload
@@ -44,14 +44,29 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
         self,
         app_model: App,
         user: Union[Account, EndUser],
-        args: dict,
+        args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        stream: Literal[False] = False,
-    ) -> dict: ...
+        streaming: Literal[False],
+    ) -> Mapping[str, Any]: ...
+
+    @overload
+    def generate(
+        self,
+        app_model: App,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: bool,
+    ) -> Mapping[str, Any] | Generator[str, None, None]: ...
 
     def generate(
-        self, app_model: App, user: Union[Account, EndUser], args: Any, invoke_from: InvokeFrom, streaming: bool = True
-    ) -> Union[dict, Generator[str, None, None]]:
+        self,
+        app_model: App,
+        user: Union[Account, EndUser],
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: bool = True,
+    ):
         """
         Generate App response.
 

api/core/app/apps/workflow/app_generator.py

@@ -3,7 +3,7 @@ import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, Optional, Union
+from typing import Any, Literal, Optional, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -30,6 +30,35 @@ logger = logging.getLogger(__name__)
 
 
 class WorkflowAppGenerator(BaseAppGenerator):
+    @overload
+    def generate(
+        self,
+        *,
+        app_model: App,
+        workflow: Workflow,
+        user: Account | EndUser,
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: Literal[True],
+        call_depth: int = 0,
+        workflow_thread_pool_id: Optional[str] = None,
+    ) -> Generator[str, None, None]: ...
+
+    @overload
+    def generate(
+        self,
+        *,
+        app_model: App,
+        workflow: Workflow,
+        user: Account | EndUser,
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: Literal[False],
+        call_depth: int = 0,
+        workflow_thread_pool_id: Optional[str] = None,
+    ) -> Mapping[str, Any]: ...
+
+    @overload
     def generate(
         self,
         *,
@@ -41,7 +70,20 @@ class WorkflowAppGenerator(BaseAppGenerator):
         streaming: bool = True,
         call_depth: int = 0,
         workflow_thread_pool_id: Optional[str] = None,
-    ) -> Mapping[str, Any] | Generator[str, None, None]:
+    ) -> Mapping[str, Any] | Generator[str, None, None]: ...
+
+    def generate(
+        self,
+        *,
+        app_model: App,
+        workflow: Workflow,
+        user: Account | EndUser,
+        args: Mapping[str, Any],
+        invoke_from: InvokeFrom,
+        streaming: bool = True,
+        call_depth: int = 0,
+        workflow_thread_pool_id: Optional[str] = None,
+    ):
         files: Sequence[Mapping[str, Any]] = args.get("files") or []
 
         # parse files

api/core/app/apps/workflow/app_queue_manager.py

@@ -6,6 +6,7 @@ from core.app.entities.queue_entities import (
     QueueMessageEndEvent,
     QueueStopEvent,
     QueueWorkflowFailedEvent,
+    QueueWorkflowPartialSuccessEvent,
     QueueWorkflowSucceededEvent,
     WorkflowQueueMessage,
 )
@@ -34,7 +35,8 @@ class WorkflowAppQueueManager(AppQueueManager):
             | QueueErrorEvent
             | QueueMessageEndEvent
             | QueueWorkflowSucceededEvent
-            | QueueWorkflowFailedEvent,
+            | QueueWorkflowFailedEvent
+            | QueueWorkflowPartialSuccessEvent,
         ):
             self.stop_listen()
 

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -15,6 +15,7 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
+    QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
     QueueNodeStartedEvent,
@@ -26,6 +27,7 @@ from core.app.entities.queue_entities import (
     QueueStopEvent,
     QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
+    QueueWorkflowPartialSuccessEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
@@ -106,7 +108,6 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
 
         self._task_state = WorkflowTaskState()
         self._wip_workflow_node_executions = {}
-        self.total_tokens: int = 0
 
     def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
         """
@@ -258,36 +259,36 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
 
                 workflow_node_execution = self._handle_node_execution_start(workflow_run=workflow_run, event=event)
 
-                response = self._workflow_node_start_to_stream_response(
+                node_start_response = self._workflow_node_start_to_stream_response(
                     event=event,
                     task_id=self._application_generate_entity.task_id,
                     workflow_node_execution=workflow_node_execution,
                 )
 
-                if response:
-                    yield response
+                if node_start_response:
+                    yield node_start_response
             elif isinstance(event, QueueNodeSucceededEvent):
                 workflow_node_execution = self._handle_workflow_node_execution_success(event)
 
-                response = self._workflow_node_finish_to_stream_response(
+                node_success_response = self._workflow_node_finish_to_stream_response(
                     event=event,
                     task_id=self._application_generate_entity.task_id,
                     workflow_node_execution=workflow_node_execution,
                 )
 
-                if response:
-                    yield response
-            elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent):
+                if node_success_response:
+                    yield node_success_response
+            elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
                 workflow_node_execution = self._handle_workflow_node_execution_failed(event)
 
-                response = self._workflow_node_finish_to_stream_response(
+                node_failed_response = self._workflow_node_finish_to_stream_response(
                     event=event,
                     task_id=self._application_generate_entity.task_id,
                     workflow_node_execution=workflow_node_execution,
                 )
 
-                if response:
-                    yield response
+                if node_failed_response:
+                    yield node_failed_response
             elif isinstance(event, QueueParallelBranchRunStartedEvent):
                 if not workflow_run:
                     raise Exception("Workflow run not initialized.")
@@ -320,8 +321,6 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 if not workflow_run:
                     raise Exception("Workflow run not initialized.")
 
-                # FIXME for issue #11221 quick fix maybe have a better solution
-                self.total_tokens += event.metadata.get("total_tokens", 0) if event.metadata else 0
                 yield self._workflow_iteration_completed_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
@@ -335,7 +334,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 workflow_run = self._handle_workflow_run_success(
                     workflow_run=workflow_run,
                     start_at=graph_runtime_state.start_at,
-                    total_tokens=graph_runtime_state.total_tokens or self.total_tokens,
+                    total_tokens=graph_runtime_state.total_tokens,
                     total_steps=graph_runtime_state.node_run_steps,
                     outputs=event.outputs,
                     conversation_id=None,
@@ -345,6 +344,30 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 # save workflow app log
                 self._save_workflow_app_log(workflow_run)
 
+                yield self._workflow_finish_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
+                )
+            elif isinstance(event, QueueWorkflowPartialSuccessEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                if not graph_runtime_state:
+                    raise Exception("Graph runtime state not initialized.")
+
+                workflow_run = self._handle_workflow_run_partial_success(
+                    workflow_run=workflow_run,
+                    start_at=graph_runtime_state.start_at,
+                    total_tokens=graph_runtime_state.total_tokens,
+                    total_steps=graph_runtime_state.node_run_steps,
+                    outputs=event.outputs,
+                    exceptions_count=event.exceptions_count,
+                    conversation_id=None,
+                    trace_manager=trace_manager,
+                )
+
+                # save workflow app log
+                self._save_workflow_app_log(workflow_run)
+
                 yield self._workflow_finish_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )
@@ -354,7 +377,6 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
 
                 if not graph_runtime_state:
                     raise Exception("Graph runtime state not initialized.")
-
                 workflow_run = self._handle_workflow_run_failed(
                     workflow_run=workflow_run,
                     start_at=graph_runtime_state.start_at,
@@ -366,6 +388,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                     error=event.error if isinstance(event, QueueWorkflowFailedEvent) else event.get_stop_reason(),
                     conversation_id=None,
                     trace_manager=trace_manager,
+                    exceptions_count=event.exceptions_count if isinstance(event, QueueWorkflowFailedEvent) else 0,
                 )
 
                 # save workflow app log

api/core/app/apps/workflow_app_runner.py

@@ -8,6 +8,7 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
+    QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
     QueueNodeStartedEvent,
@@ -18,6 +19,7 @@ from core.app.entities.queue_entities import (
     QueueRetrieverResourcesEvent,
     QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
+    QueueWorkflowPartialSuccessEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
@@ -25,6 +27,7 @@ from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.graph_engine.entities.event import (
     GraphEngineEvent,
     GraphRunFailedEvent,
+    GraphRunPartialSucceededEvent,
     GraphRunStartedEvent,
     GraphRunSucceededEvent,
     IterationRunFailedEvent,
@@ -32,6 +35,7 @@ from core.workflow.graph_engine.entities.event import (
     IterationRunStartedEvent,
     IterationRunSucceededEvent,
     NodeInIterationFailedEvent,
+    NodeRunExceptionEvent,
     NodeRunFailedEvent,
     NodeRunRetrieverResourceEvent,
     NodeRunStartedEvent,
@@ -43,7 +47,7 @@ from core.workflow.graph_engine.entities.event import (
 )
 from core.workflow.graph_engine.entities.graph import Graph
 from core.workflow.nodes import NodeType
-from core.workflow.nodes.node_mapping import node_type_classes_mapping
+from core.workflow.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
 from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
 from models.model import App
@@ -138,7 +142,8 @@ class WorkflowBasedAppRunner(AppRunner):
 
         # Get node class
         node_type = NodeType(iteration_node_config.get("data", {}).get("type"))
-        node_cls = node_type_classes_mapping[node_type]
+        node_version = iteration_node_config.get("data", {}).get("version", "1")
+        node_cls = NODE_TYPE_CLASSES_MAPPING[node_type][node_version]
 
         # init variable pool
         variable_pool = VariablePool(
@@ -175,8 +180,12 @@ class WorkflowBasedAppRunner(AppRunner):
             )
         elif isinstance(event, GraphRunSucceededEvent):
             self._publish_event(QueueWorkflowSucceededEvent(outputs=event.outputs))
+        elif isinstance(event, GraphRunPartialSucceededEvent):
+            self._publish_event(
+                QueueWorkflowPartialSuccessEvent(outputs=event.outputs, exceptions_count=event.exceptions_count)
+            )
         elif isinstance(event, GraphRunFailedEvent):
-            self._publish_event(QueueWorkflowFailedEvent(error=event.error))
+            self._publish_event(QueueWorkflowFailedEvent(error=event.error, exceptions_count=event.exceptions_count))
         elif isinstance(event, NodeRunStartedEvent):
             self._publish_event(
                 QueueNodeStartedEvent(
@@ -252,6 +261,36 @@ class WorkflowBasedAppRunner(AppRunner):
                     in_iteration_id=event.in_iteration_id,
                 )
             )
+        elif isinstance(event, NodeRunExceptionEvent):
+            self._publish_event(
+                QueueNodeExceptionEvent(
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_data=event.node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.route_node_state.start_at,
+                    inputs=event.route_node_state.node_run_result.inputs
+                    if event.route_node_state.node_run_result
+                    else {},
+                    process_data=event.route_node_state.node_run_result.process_data
+                    if event.route_node_state.node_run_result
+                    else {},
+                    outputs=event.route_node_state.node_run_result.outputs
+                    if event.route_node_state.node_run_result
+                    else {},
+                    error=event.route_node_state.node_run_result.error
+                    if event.route_node_state.node_run_result and event.route_node_state.node_run_result.error
+                    else "Unknown error",
+                    execution_metadata=event.route_node_state.node_run_result.metadata
+                    if event.route_node_state.node_run_result
+                    else {},
+                    in_iteration_id=event.in_iteration_id,
+                )
+            )
         elif isinstance(event, NodeInIterationFailedEvent):
             self._publish_event(
                 QueueNodeInIterationFailedEvent(

api/core/app/entities/queue_entities.py

@@ -2,7 +2,7 @@ from datetime import datetime
 from enum import Enum, StrEnum
 from typing import Any, Optional
 
-from pydantic import BaseModel, field_validator
+from pydantic import BaseModel
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.workflow.entities.node_entities import NodeRunMetadataKey
@@ -25,12 +25,14 @@ class QueueEvent(StrEnum):
     WORKFLOW_STARTED = "workflow_started"
     WORKFLOW_SUCCEEDED = "workflow_succeeded"
     WORKFLOW_FAILED = "workflow_failed"
+    WORKFLOW_PARTIAL_SUCCEEDED = "workflow_partial_succeeded"
     ITERATION_START = "iteration_start"
     ITERATION_NEXT = "iteration_next"
     ITERATION_COMPLETED = "iteration_completed"
     NODE_STARTED = "node_started"
     NODE_SUCCEEDED = "node_succeeded"
     NODE_FAILED = "node_failed"
+    NODE_EXCEPTION = "node_exception"
     RETRIEVER_RESOURCES = "retriever_resources"
     ANNOTATION_REPLY = "annotation_reply"
     AGENT_THOUGHT = "agent_thought"
@@ -113,18 +115,6 @@ class QueueIterationNextEvent(AppQueueEvent):
     output: Optional[Any] = None  # output for the current iteration
     duration: Optional[float] = None
 
-    @field_validator("output", mode="before")
-    @classmethod
-    def set_output(cls, v):
-        """
-        Set output
-        """
-        if v is None:
-            return None
-        if isinstance(v, int | float | str | bool | dict | list):
-            return v
-        raise ValueError("output must be a valid type")
-
 
 class QueueIterationCompletedEvent(AppQueueEvent):
     """
@@ -249,6 +239,17 @@ class QueueWorkflowFailedEvent(AppQueueEvent):
 
     event: QueueEvent = QueueEvent.WORKFLOW_FAILED
     error: str
+    exceptions_count: int
+
+
+class QueueWorkflowPartialSuccessEvent(AppQueueEvent):
+    """
+    QueueWorkflowFailedEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.WORKFLOW_PARTIAL_SUCCEEDED
+    exceptions_count: int
+    outputs: Optional[dict[str, Any]] = None
 
 
 class QueueNodeStartedEvent(AppQueueEvent):
@@ -343,6 +344,37 @@ class QueueNodeInIterationFailedEvent(AppQueueEvent):
     error: str
 
 
+class QueueNodeExceptionEvent(AppQueueEvent):
+    """
+    QueueNodeExceptionEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.NODE_EXCEPTION
+
+    node_execution_id: str
+    node_id: str
+    node_type: NodeType
+    node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+    start_at: datetime
+
+    inputs: Optional[dict[str, Any]] = None
+    process_data: Optional[dict[str, Any]] = None
+    outputs: Optional[dict[str, Any]] = None
+    execution_metadata: Optional[dict[NodeRunMetadataKey, Any]] = None
+
+    error: str
+
+
 class QueueNodeFailedEvent(AppQueueEvent):
     """
     QueueNodeFailedEvent entity

api/core/app/entities/task_entities.py

@@ -213,6 +213,7 @@ class WorkflowFinishStreamResponse(StreamResponse):
         created_by: Optional[dict] = None
         created_at: int
         finished_at: int
+        exceptions_count: Optional[int] = 0
         files: Optional[Sequence[Mapping[str, Any]]] = []
 
     event: StreamEvent = StreamEvent.WORKFLOW_FINISHED

api/core/app/features/rate_limiting/rate_limit.py

@@ -110,7 +110,7 @@ class RateLimitGenerator:
             raise StopIteration
         try:
             return next(self.generator)
-        except StopIteration:
+        except Exception:
             self.close()
             raise
 

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -12,6 +12,7 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
+    QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
     QueueNodeStartedEvent,
@@ -164,6 +165,55 @@ class WorkflowCycleManage:
 
         return workflow_run
 
+    def _handle_workflow_run_partial_success(
+        self,
+        workflow_run: WorkflowRun,
+        start_at: float,
+        total_tokens: int,
+        total_steps: int,
+        outputs: Mapping[str, Any] | None = None,
+        exceptions_count: int = 0,
+        conversation_id: Optional[str] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
+    ) -> WorkflowRun:
+        """
+        Workflow run success
+        :param workflow_run: workflow run
+        :param start_at: start time
+        :param total_tokens: total tokens
+        :param total_steps: total steps
+        :param outputs: outputs
+        :param conversation_id: conversation id
+        :return:
+        """
+        workflow_run = self._refetch_workflow_run(workflow_run.id)
+
+        outputs = WorkflowEntry.handle_special_values(outputs)
+
+        workflow_run.status = WorkflowRunStatus.PARTIAL_SUCCESSED.value
+        workflow_run.outputs = json.dumps(outputs or {})
+        workflow_run.elapsed_time = time.perf_counter() - start_at
+        workflow_run.total_tokens = total_tokens
+        workflow_run.total_steps = total_steps
+        workflow_run.finished_at = datetime.now(UTC).replace(tzinfo=None)
+        workflow_run.exceptions_count = exceptions_count
+        db.session.commit()
+        db.session.refresh(workflow_run)
+
+        if trace_manager:
+            trace_manager.add_trace_task(
+                TraceTask(
+                    TraceTaskName.WORKFLOW_TRACE,
+                    workflow_run=workflow_run,
+                    conversation_id=conversation_id,
+                    user_id=trace_manager.user_id,
+                )
+            )
+
+        db.session.close()
+
+        return workflow_run
+
     def _handle_workflow_run_failed(
         self,
         workflow_run: WorkflowRun,
@@ -174,6 +224,7 @@ class WorkflowCycleManage:
         error: str,
         conversation_id: Optional[str] = None,
         trace_manager: Optional[TraceQueueManager] = None,
+        exceptions_count: int = 0,
     ) -> WorkflowRun:
         """
         Workflow run failed
@@ -193,7 +244,7 @@ class WorkflowCycleManage:
         workflow_run.total_tokens = total_tokens
         workflow_run.total_steps = total_steps
         workflow_run.finished_at = datetime.now(UTC).replace(tzinfo=None)
-
+        workflow_run.exceptions_count = exceptions_count
         db.session.commit()
 
         running_workflow_node_executions = (
@@ -220,9 +271,9 @@ class WorkflowCycleManage:
 
         db.session.close()
 
-        with Session(db.engine, expire_on_commit=False) as session:
-            session.add(workflow_run)
-            session.refresh(workflow_run)
+        # with Session(db.engine, expire_on_commit=False) as session:
+        #     session.add(workflow_run)
+        #     session.refresh(workflow_run)
 
         if trace_manager:
             trace_manager.add_trace_task(
@@ -318,7 +369,7 @@ class WorkflowCycleManage:
         return workflow_node_execution
 
     def _handle_workflow_node_execution_failed(
-        self, event: QueueNodeFailedEvent | QueueNodeInIterationFailedEvent
+        self, event: QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent
     ) -> WorkflowNodeExecution:
         """
         Workflow node execution failed
@@ -337,7 +388,11 @@ class WorkflowCycleManage:
         )
         db.session.query(WorkflowNodeExecution).filter(WorkflowNodeExecution.id == workflow_node_execution.id).update(
             {
-                WorkflowNodeExecution.status: WorkflowNodeExecutionStatus.FAILED.value,
+                WorkflowNodeExecution.status: (
+                    WorkflowNodeExecutionStatus.FAILED.value
+                    if not isinstance(event, QueueNodeExceptionEvent)
+                    else WorkflowNodeExecutionStatus.EXCEPTION.value
+                ),
                 WorkflowNodeExecution.error: event.error,
                 WorkflowNodeExecution.inputs: json.dumps(inputs) if inputs else None,
                 WorkflowNodeExecution.process_data: json.dumps(process_data) if process_data else None,
@@ -351,8 +406,11 @@ class WorkflowCycleManage:
         db.session.commit()
         db.session.close()
         process_data = WorkflowEntry.handle_special_values(event.process_data)
-
-        workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
+        workflow_node_execution.status = (
+            WorkflowNodeExecutionStatus.FAILED.value
+            if not isinstance(event, QueueNodeExceptionEvent)
+            else WorkflowNodeExecutionStatus.EXCEPTION.value
+        )
         workflow_node_execution.error = event.error
         workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
         workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
@@ -433,6 +491,7 @@ class WorkflowCycleManage:
                 created_at=int(workflow_run.created_at.timestamp()),
                 finished_at=int(workflow_run.finished_at.timestamp()),
                 files=self._fetch_files_from_node_outputs(workflow_run.outputs_dict),
+                exceptions_count=workflow_run.exceptions_count,
             ),
         )
 
@@ -483,7 +542,10 @@ class WorkflowCycleManage:
 
     def _workflow_node_finish_to_stream_response(
         self,
-        event: QueueNodeSucceededEvent | QueueNodeFailedEvent | QueueNodeInIterationFailedEvent,
+        event: QueueNodeSucceededEvent
+        | QueueNodeFailedEvent
+        | QueueNodeInIterationFailedEvent
+        | QueueNodeExceptionEvent,
         task_id: str,
         workflow_node_execution: WorkflowNodeExecution,
     ) -> Optional[NodeFinishStreamResponse]:

api/core/file/__init__.py

@@ -7,13 +7,13 @@ from .models import (
 )
 
 __all__ = [
+    "FILE_MODEL_IDENTITY",
+    "ArrayFileAttribute",
+    "File",
+    "FileAttribute",
+    "FileBelongsTo",
+    "FileTransferMethod",
     "FileType",
     "FileUploadConfig",
-    "FileTransferMethod",
-    "FileBelongsTo",
-    "File",
     "ImageConfig",
-    "FileAttribute",
-    "ArrayFileAttribute",
-    "FILE_MODEL_IDENTITY",
 ]

api/core/file/file_manager.py

@@ -42,39 +42,31 @@ def to_prompt_message_content(
     *,
     image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
 ):
-    match f.type:
-        case FileType.IMAGE:
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-            if dify_config.MULTIMODAL_SEND_IMAGE_FORMAT == "url":
-                data = _to_url(f)
-            else:
-                data = _to_base64_data_string(f)
+    if f.extension is None:
+        raise ValueError("Missing file extension")
+    if f.mime_type is None:
+        raise ValueError("Missing file mime_type")
 
-            return ImagePromptMessageContent(data=data, detail=image_detail_config)
-        case FileType.AUDIO:
-            encoded_string = _get_encoded_string(f)
-            if f.extension is None:
-                raise ValueError("Missing file extension")
-            return AudioPromptMessageContent(data=encoded_string, format=f.extension.lstrip("."))
-        case FileType.VIDEO:
-            if dify_config.MULTIMODAL_SEND_VIDEO_FORMAT == "url":
-                data = _to_url(f)
-            else:
-                data = _to_base64_data_string(f)
-            if f.extension is None:
-                raise ValueError("Missing file extension")
-            return VideoPromptMessageContent(data=data, format=f.extension.lstrip("."))
-        case FileType.DOCUMENT:
-            data = _get_encoded_string(f)
-            if f.mime_type is None:
-                raise ValueError("Missing file mime_type")
-            return DocumentPromptMessageContent(
-                encode_format="base64",
-                mime_type=f.mime_type,
-                data=data,
-            )
-        case _:
-            raise ValueError(f"file type {f.type} is not supported")
+    params = {
+        "base64_data": _get_encoded_string(f) if dify_config.MULTIMODAL_SEND_FORMAT == "base64" else "",
+        "url": _to_url(f) if dify_config.MULTIMODAL_SEND_FORMAT == "url" else "",
+        "format": f.extension.removeprefix("."),
+        "mime_type": f.mime_type,
+    }
+    if f.type == FileType.IMAGE:
+        params["detail"] = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+    prompt_class_map = {
+        FileType.IMAGE: ImagePromptMessageContent,
+        FileType.AUDIO: AudioPromptMessageContent,
+        FileType.VIDEO: VideoPromptMessageContent,
+        FileType.DOCUMENT: DocumentPromptMessageContent,
+    }
+
+    try:
+        return prompt_class_map[f.type](**params)
+    except KeyError:
+        raise ValueError(f"file type {f.type} is not supported")
 
 
 def download(f: File, /):
@@ -128,11 +120,6 @@ def _get_encoded_string(f: File, /):
     return encoded_string
 
 
-def _to_base64_data_string(f: File, /):
-    encoded_string = _get_encoded_string(f)
-    return f"data:{f.mime_type};base64,{encoded_string}"
-
-
 def _to_url(f: File, /):
     if f.transfer_method == FileTransferMethod.REMOTE_URL:
         if f.remote_url is None:
@@ -141,7 +128,7 @@ def _to_url(f: File, /):
     elif f.transfer_method == FileTransferMethod.LOCAL_FILE:
         if f.related_id is None:
             raise ValueError("Missing file related_id")
-        return helpers.get_signed_file_url(upload_file_id=f.related_id)
+        return f.remote_url or helpers.get_signed_file_url(upload_file_id=f.related_id)
     elif f.transfer_method == FileTransferMethod.TOOL_FILE:
         # add sign url
         if f.related_id is None or f.extension is None:

api/core/helper/ssrf_proxy.py

@@ -24,6 +24,12 @@ BACKOFF_FACTOR = 0.5
 STATUS_FORCELIST = [429, 500, 502, 503, 504]
 
 
+class MaxRetriesExceededError(Exception):
+    """Raised when the maximum number of retries is exceeded."""
+
+    pass
+
+
 def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
     if "allow_redirects" in kwargs:
         allow_redirects = kwargs.pop("allow_redirects")
@@ -64,7 +70,7 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
         if retries <= max_retries:
             time.sleep(BACKOFF_FACTOR * (2 ** (retries - 1)))
 
-    raise Exception(f"Reached maximum retries ({max_retries}) for URL {url}")
+    raise MaxRetriesExceededError(f"Reached maximum retries ({max_retries}) for URL {url}")
 
 
 def get(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):

api/core/model_runtime/docs/zh_Hans/interfaces.md

@@ -91,7 +91,7 @@ class XinferenceProvider(Provider):
       """
   ```
 
-  也可以直接抛出对应Erros，并做如下定义，这样在之后的调用中可以直接抛出`InvokeConnectionError`等异常。
+  也可以直接抛出对应 Errors，并做如下定义，这样在之后的调用中可以直接抛出`InvokeConnectionError`等异常。
   
     ```python
     @property

api/core/model_runtime/entities/__init__.py

@@ -18,25 +18,25 @@ from .message_entities import (
 from .model_entities import ModelPropertyKey
 
 __all__ = [
-    "ImagePromptMessageContent",
-    "VideoPromptMessageContent",
-    "PromptMessage",
-    "PromptMessageRole",
-    "LLMUsage",
-    "ModelPropertyKey",
     "AssistantPromptMessage",
-    "PromptMessage",
-    "PromptMessageContent",
-    "PromptMessageRole",
-    "SystemPromptMessage",
-    "TextPromptMessageContent",
-    "UserPromptMessage",
-    "PromptMessageTool",
-    "ToolPromptMessage",
-    "PromptMessageContentType",
+    "AudioPromptMessageContent",
+    "DocumentPromptMessageContent",
+    "ImagePromptMessageContent",
     "LLMResult",
     "LLMResultChunk",
     "LLMResultChunkDelta",
-    "AudioPromptMessageContent",
-    "DocumentPromptMessageContent",
+    "LLMUsage",
+    "ModelPropertyKey",
+    "PromptMessage",
+    "PromptMessage",
+    "PromptMessageContent",
+    "PromptMessageContentType",
+    "PromptMessageRole",
+    "PromptMessageRole",
+    "PromptMessageTool",
+    "SystemPromptMessage",
+    "TextPromptMessageContent",
+    "ToolPromptMessage",
+    "UserPromptMessage",
+    "VideoPromptMessageContent",
 ]

api/core/model_runtime/entities/message_entities.py

@@ -1,9 +1,9 @@
 from abc import ABC
 from collections.abc import Sequence
 from enum import Enum, StrEnum
-from typing import Literal, Optional
+from typing import Optional
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, computed_field, field_validator
 
 
 class PromptMessageRole(Enum):
@@ -67,7 +67,6 @@ class PromptMessageContent(BaseModel):
     """
 
     type: PromptMessageContentType
-    data: str
 
 
 class TextPromptMessageContent(PromptMessageContent):
@@ -76,21 +75,35 @@ class TextPromptMessageContent(PromptMessageContent):
     """
 
     type: PromptMessageContentType = PromptMessageContentType.TEXT
+    data: str
 
 
-class VideoPromptMessageContent(PromptMessageContent):
+class MultiModalPromptMessageContent(PromptMessageContent):
+    """
+    Model class for multi-modal prompt message content.
+    """
+
+    type: PromptMessageContentType
+    format: str = Field(..., description="the format of multi-modal file")
+    base64_data: str = Field("", description="the base64 data of multi-modal file")
+    url: str = Field("", description="the url of multi-modal file")
+    mime_type: str = Field(..., description="the mime type of multi-modal file")
+
+    @computed_field(return_type=str)
+    @property
+    def data(self):
+        return self.url or f"data:{self.mime_type};base64,{self.base64_data}"
+
+
+class VideoPromptMessageContent(MultiModalPromptMessageContent):
     type: PromptMessageContentType = PromptMessageContentType.VIDEO
-    data: str = Field(..., description="Base64 encoded video data")
-    format: str = Field(..., description="Video format")
 
 
-class AudioPromptMessageContent(PromptMessageContent):
+class AudioPromptMessageContent(MultiModalPromptMessageContent):
     type: PromptMessageContentType = PromptMessageContentType.AUDIO
-    data: str = Field(..., description="Base64 encoded audio data")
-    format: str = Field(..., description="Audio format")
 
 
-class ImagePromptMessageContent(PromptMessageContent):
+class ImagePromptMessageContent(MultiModalPromptMessageContent):
     """
     Model class for image prompt message content.
     """
@@ -103,11 +116,8 @@ class ImagePromptMessageContent(PromptMessageContent):
     detail: DETAIL = DETAIL.LOW
 
 
-class DocumentPromptMessageContent(PromptMessageContent):
+class DocumentPromptMessageContent(MultiModalPromptMessageContent):
     type: PromptMessageContentType = PromptMessageContentType.DOCUMENT
-    encode_format: Literal["base64"]
-    mime_type: str
-    data: str
 
 
 class PromptMessage(ABC, BaseModel):

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

@@ -1,5 +1,4 @@
 import base64
-import io
 import json
 from collections.abc import Generator, Sequence
 from typing import Optional, Union, cast
@@ -18,7 +17,6 @@ from anthropic.types import (
 )
 from anthropic.types.beta.tools import ToolsBetaMessage
 from httpx import Timeout
-from PIL import Image
 
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities import (
@@ -498,22 +496,19 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                                 sub_messages.append(sub_message_dict)
                             elif message_content.type == PromptMessageContentType.IMAGE:
                                 message_content = cast(ImagePromptMessageContent, message_content)
-                                if not message_content.data.startswith("data:"):
+                                if not message_content.base64_data:
                                     # fetch image data from url
                                     try:
-                                        image_content = requests.get(message_content.data).content
-                                        with Image.open(io.BytesIO(image_content)) as img:
-                                            mime_type = f"image/{img.format.lower()}"
+                                        image_content = requests.get(message_content.url).content
                                         base64_data = base64.b64encode(image_content).decode("utf-8")
                                     except Exception as ex:
                                         raise ValueError(
                                             f"Failed to fetch image data from url {message_content.data}, {ex}"
                                         )
                                 else:
-                                    data_split = message_content.data.split(";base64,")
-                                    mime_type = data_split[0].replace("data:", "")
-                                    base64_data = data_split[1]
+                                    base64_data = message_content.base64_data
 
+                                mime_type = message_content.mime_type
                                 if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
                                     raise ValueError(
                                         f"Unsupported image type {mime_type}, "
@@ -534,7 +529,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                                 sub_message_dict = {
                                     "type": "document",
                                     "source": {
-                                        "type": message_content.encode_format,
+                                        "type": "base64",
                                         "media_type": message_content.mime_type,
                                         "data": message_content.data,
                                     },

api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml

@@ -86,6 +86,9 @@ model_credential_schema:
         - label:
             en_US: '2024-06-01'
           value: '2024-06-01'
+        - label:
+            en_US: '2024-10-21'
+          value: '2024-10-21'
       placeholder:
         zh_Hans: 在此选择您的 API 版本
         en_US: Select your API Version here

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

@@ -10,6 +10,7 @@ from core.model_runtime.entities.llm_entities import (
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     PromptMessage,
+    PromptMessageContentType,
     PromptMessageTool,
     SystemPromptMessage,
     ToolPromptMessage,
@@ -105,7 +106,11 @@ class BaichuanLanguageModel(LargeLanguageModel):
             if isinstance(message.content, str):
                 message_dict = {"role": "user", "content": message.content}
             else:
-                raise ValueError("User message content must be str")
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        message_dict = {"role": "user", "content": message_content.data}
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        raise ValueError("Content object type not support image_url")
         elif isinstance(message, AssistantPromptMessage):
             message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}

api/core/model_runtime/model_providers/bedrock/bedrock.yaml

@@ -16,6 +16,7 @@ help:
 supported_model_types:
   - llm
   - text-embedding
+  - rerank
 configurate_methods:
   - predefined-model
 provider_credential_schema:

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

@@ -0,0 +1,21 @@
+import boto3
+from botocore.config import Config
+
+
+def get_bedrock_client(service_name, credentials=None):
+    client_config = Config(region_name=credentials["aws_region"])
+    aws_access_key_id = credentials["aws_access_key_id"]
+    aws_secret_access_key = credentials["aws_secret_access_key"]
+    if aws_access_key_id and aws_secret_access_key:
+        # use aksk to call bedrock
+        client = boto3.client(
+            service_name=service_name,
+            config=client_config,
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+        )
+    else:
+        # use iam without aksk to call
+        client = boto3.client(service_name=service_name, config=client_config)
+
+    return client

api/core/model_runtime/model_providers/bedrock/llm/amazon.nova-lite-v1.yaml

@@ -0,0 +1,53 @@
+model: amazon.nova-lite-v1:0
+label:
+  en_US: Nova Lite V1
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.00006'
+  output: '0.00024'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/amazon.nova-micro-v1.yaml

@@ -0,0 +1,52 @@
+model: amazon.nova-micro-v1:0
+label:
+  en_US: Nova Micro V1
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.000035'
+  output: '0.00014'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/amazon.nova-pro-v1.yaml

@@ -0,0 +1,53 @@
+model: amazon.nova-pro-v1:0
+label:
+  en_US: Nova Pro V1
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.0008'
+  output: '0.0032'
+  unit: '0.001'
+  currency: USD

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

@@ -40,6 +40,7 @@ from core.model_runtime.errors.invoke import (
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+from core.model_runtime.model_providers.bedrock.get_bedrock_client import get_bedrock_client
 
 logger = logging.getLogger(__name__)
 ANTHROPIC_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
@@ -70,6 +71,8 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         {"prefix": "cohere.command-r", "support_system_prompts": True, "support_tool_use": True},
         {"prefix": "amazon.titan", "support_system_prompts": False, "support_tool_use": False},
         {"prefix": "ai21.jamba-1-5", "support_system_prompts": True, "support_tool_use": False},
+        {"prefix": "amazon.nova", "support_system_prompts": True, "support_tool_use": False},
+        {"prefix": "us.amazon.nova", "support_system_prompts": True, "support_tool_use": False},
     ]
 
     @staticmethod
@@ -171,13 +174,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param stream: is stream response
         :return: full response or stream response chunk generator result
         """
-        bedrock_client = boto3.client(
-            service_name="bedrock-runtime",
-            aws_access_key_id=credentials.get("aws_access_key_id"),
-            aws_secret_access_key=credentials.get("aws_secret_access_key"),
-            region_name=credentials["aws_region"],
-        )
-
+        bedrock_client = get_bedrock_client("bedrock-runtime", credentials)
         system, prompt_message_dicts = self._convert_converse_prompt_messages(prompt_messages)
         inference_config, additional_model_fields = self._convert_converse_api_model_parameters(model_parameters, stop)
 
@@ -194,6 +191,13 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         if model_info["support_tool_use"] and tools:
             parameters["toolConfig"] = self._convert_converse_tool_config(tools=tools)
         try:
+            # for issue #10976
+            conversations_list = parameters["messages"]
+            # if two consecutive user messages found, combine them into one message
+            for i in range(len(conversations_list) - 2, -1, -1):
+                if conversations_list[i]["role"] == conversations_list[i + 1]["role"]:
+                    conversations_list[i]["content"].extend(conversations_list.pop(i + 1)["content"])
+
             if stream:
                 response = bedrock_client.converse_stream(**parameters)
                 return self._handle_converse_stream_response(

api/core/model_runtime/model_providers/bedrock/llm/us.amazon.nova-lite-v1.yaml

@@ -0,0 +1,53 @@
+model: us.amazon.nova-lite-v1:0
+label:
+  en_US: Nova Lite V1 (US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.00006'
+  output: '0.00024'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/us.amazon.nova-micro-v1.yaml

@@ -0,0 +1,52 @@
+model: us.amazon.nova-micro-v1:0
+label:
+  en_US: Nova Micro V1 (US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.000035'
+  output: '0.00014'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/us.amazon.nova-pro-v1.yaml

@@ -0,0 +1,53 @@
+model: us.amazon.nova-pro-v1:0
+label:
+  en_US: Nova Pro V1 (US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.0008'
+  output: '0.0032'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/rerank/_position.yaml

@@ -0,0 +1,2 @@
+- amazon.rerank-v1
+- cohere.rerank-v3-5

api/core/model_runtime/model_providers/bedrock/rerank/amazon.rerank-v1.yaml

@@ -0,0 +1,4 @@
+model: amazon.rerank-v1:0
+model_type: rerank
+model_properties:
+  context_size: 5120

api/core/model_runtime/model_providers/bedrock/rerank/cohere.rerank-v3-5.yaml

@@ -0,0 +1,4 @@
+model: cohere.rerank-v3-5:0
+model_type: rerank
+model_properties:
+  context_size: 5120

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

@@ -0,0 +1,139 @@
+from typing import Optional
+
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+from core.model_runtime.model_providers.bedrock.get_bedrock_client import get_bedrock_client
+
+
+class BedrockRerankModel(RerankModel):
+    """
+    Model class for Cohere rerank model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n
+        :param user: unique user id
+        :return: rerank result
+        """
+
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=docs)
+
+        # initialize client
+        bedrock_runtime = get_bedrock_client("bedrock-agent-runtime", credentials)
+        queries = [{"type": "TEXT", "textQuery": {"text": query}}]
+        text_sources = []
+        for text in docs:
+            text_sources.append(
+                {
+                    "type": "INLINE",
+                    "inlineDocumentSource": {
+                        "type": "TEXT",
+                        "textDocument": {
+                            "text": text,
+                        },
+                    },
+                }
+            )
+        modelId = model
+        region = credentials["aws_region"]
+        model_package_arn = f"arn:aws:bedrock:{region}::foundation-model/{modelId}"
+        rerankingConfiguration = {
+            "type": "BEDROCK_RERANKING_MODEL",
+            "bedrockRerankingConfiguration": {
+                "numberOfResults": top_n,
+                "modelConfiguration": {
+                    "modelArn": model_package_arn,
+                },
+            },
+        }
+        response = bedrock_runtime.rerank(
+            queries=queries, sources=text_sources, rerankingConfiguration=rerankingConfiguration
+        )
+
+        rerank_documents = []
+        for idx, result in enumerate(response["results"]):
+            # format document
+            index = result["index"]
+            rerank_document = RerankDocument(
+                index=index,
+                text=docs[index],
+                score=result["relevanceScore"],
+            )
+
+            # score threshold check
+            if score_threshold is not None:
+                if rerank_document.score >= score_threshold:
+                    rerank_documents.append(rerank_document)
+            else:
+                rerank_documents.append(rerank_document)
+
+        return RerankResult(model=model, docs=rerank_documents)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self.invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8,
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the ermd = genai.GenerativeModel(model) error type thrown to the caller
+        The value is the md = genai.GenerativeModel(model) error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke emd = genai.GenerativeModel(model) error mapping
+        """
+        return {
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [],
+            InvokeBadRequestError: [],
+        }

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

@@ -3,8 +3,6 @@ import logging
 import time
 from typing import Optional
 
-import boto3
-from botocore.config import Config
 from botocore.exceptions import (
     ClientError,
     EndpointConnectionError,
@@ -25,6 +23,7 @@ from core.model_runtime.errors.invoke import (
     InvokeServerUnavailableError,
 )
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+from core.model_runtime.model_providers.bedrock.get_bedrock_client import get_bedrock_client
 
 logger = logging.getLogger(__name__)
 
@@ -48,14 +47,7 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         :param input_type: input type
         :return: embeddings result
         """
-        client_config = Config(region_name=credentials["aws_region"])
-
-        bedrock_runtime = boto3.client(
-            service_name="bedrock-runtime",
-            config=client_config,
-            aws_access_key_id=credentials.get("aws_access_key_id"),
-            aws_secret_access_key=credentials.get("aws_secret_access_key"),
-        )
+        bedrock_runtime = get_bedrock_client("bedrock-runtime", credentials)
 
         embeddings = []
         token_usage = 0

api/core/model_runtime/model_providers/cohere/rerank/_position.yaml

@@ -2,3 +2,4 @@
 - rerank-english-v3.0
 - rerank-multilingual-v2.0
 - rerank-multilingual-v3.0
+- rerank-v3.5

api/core/model_runtime/model_providers/cohere/rerank/rerank-v3.5.yaml

@@ -0,0 +1,4 @@
+model: rerank-v3.5
+model_type: rerank
+model_properties:
+  context_size: 5120

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

@@ -24,6 +24,9 @@ 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/gitee_ai/llm/InternVL2-8B.yaml

@@ -0,0 +1,93 @@
+model: InternVL2-8B
+label:
+  en_US: InternVL2-8B
+model_type: llm
+features:
+  - vision
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
+      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/InternVL2.5-26B.yaml

@@ -0,0 +1,93 @@
+model: InternVL2.5-26B
+label:
+  en_US: InternVL2.5-26B
+model_type: llm
+features:
+  - vision
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
+      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

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

@@ -6,3 +6,5 @@
 - deepseek-coder-33B-instruct-chat
 - deepseek-coder-33B-instruct-completions
 - codegeex4-all-9b
+- InternVL2.5-26B
+- InternVL2-8B

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

@@ -29,18 +29,26 @@ class GiteeAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials, model, model_parameters)
-        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+        return super()._invoke(
+            GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model),
+            credentials,
+            prompt_messages,
+            model_parameters,
+            tools,
+            stop,
+            stream,
+            user,
+        )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials, None)
-        super().validate_credentials(model, credentials)
+        self._add_custom_parameters(credentials, model, None)
+        super().validate_credentials(GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model), credentials)
 
-    def _add_custom_parameters(self, credentials: dict, model: Optional[str]) -> None:
+    def _add_custom_parameters(self, credentials: dict, model: Optional[str], model_parameters: dict) -> None:
         if model is None:
             model = "Qwen2-72B-Instruct"
 
-        model_identity = GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model)
-        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model_identity}/"
+        credentials["endpoint_url"] = "https://ai.gitee.com/v1"
         if model.endswith("completions"):
             credentials["mode"] = LLMMode.COMPLETION.value
         else:

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

@@ -1,3 +1,4 @@
+- gemini-2.0-flash-exp
 - gemini-1.5-pro
 - gemini-1.5-pro-latest
 - gemini-1.5-pro-001
@@ -11,6 +12,8 @@
 - gemini-1.5-flash-exp-0827
 - gemini-1.5-flash-8b-exp-0827
 - gemini-1.5-flash-8b-exp-0924
+- gemini-exp-1206
+- gemini-exp-1121
 - gemini-exp-1114
 - gemini-pro
 - gemini-pro-vision

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152