merge main

.github/pull_request_template.md

@@ -12,7 +12,7 @@ Please delete options that are not relevant.
 - [ ] New feature (non-breaking change which adds functionality)
 - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
 - [ ] This change requires a documentation update, included: [Dify Document](https://github.com/langgenius/dify-docs)
-- [ ] Improvement，including but not limited to code refactoring, performance optimization, and UI/UX improvement
+- [ ] Improvement, including but not limited to code refactoring, performance optimization, and UI/UX improvement
 - [ ] Dependency upgrade
 
 # How Has This Been Tested?

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

@@ -0,0 +1,148 @@
+from typing import IO, Optional
+
+from xinference_client.client.restful.restful_client import Client, RESTfulAudioModelHandle
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
+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.speech2text_model import Speech2TextModel
+
+
+class XinferenceSpeech2TextModel(Speech2TextModel):
+    """
+    Model class for Xinference speech to text model.
+    """
+
+    def _invoke(self, model: str, credentials: dict,
+                file: IO[bytes], user: Optional[str] = None) \
+            -> str:
+        """
+        Invoke speech2text model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param file: audio file
+        :param user: unique user id
+        :return: text for given audio file
+        """
+        return self._speech2text_invoke(model, credentials, file)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            if "/" in credentials['model_uid'] or "?" in credentials['model_uid'] or "#" in credentials['model_uid']:
+                raise CredentialsValidateFailedError("model_uid should not contain /, ?, or #")
+
+            audio_file_path = self._get_demo_file_path()
+
+            with open(audio_file_path, 'rb') as audio_file:
+                self.invoke(model, credentials, audio_file)
+        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 error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [
+                InvokeConnectionError
+            ],
+            InvokeServerUnavailableError: [
+                InvokeServerUnavailableError
+            ],
+            InvokeRateLimitError: [
+                InvokeRateLimitError
+            ],
+            InvokeAuthorizationError: [
+                InvokeAuthorizationError
+            ],
+            InvokeBadRequestError: [
+                InvokeBadRequestError,
+                KeyError,
+                ValueError
+            ]
+        }
+
+    def _speech2text_invoke(
+        self, 
+        model: str, 
+        credentials: dict, 
+        file: IO[bytes],
+        language: Optional[str] = None,
+        prompt: Optional[str] = None,
+        response_format: Optional[str] = "json",
+        temperature: Optional[float] = 0,
+    ) -> str:
+        """
+        Invoke speech2text model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param file: The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpe            g,mpga, m4a, ogg, wav, or webm.
+        :param language: The language of the input audio. Supplying the input language in ISO-639-1
+        :param prompt: An optional text to guide the model's style or continue a previous audio segment.
+            The prompt should match the audio language.
+        :param response_format: The format of the transcript output, in one of these options: json, text, srt, verbose            _json, or vtt.
+        :param temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output mor            e random,while lower values like 0.2 will make it more focused and deterministic.If set to 0, the model wi            ll use log probability to automatically increase the temperature until certain thresholds are hit.
+        :return: text for given audio file
+        """
+        if credentials['server_url'].endswith('/'):
+            credentials['server_url'] = credentials['server_url'][:-1]
+
+        # initialize client
+        client = Client(
+            base_url=credentials['server_url']
+        )
+
+        xinference_client = client.get_model(model_uid=credentials['model_uid'])
+
+        if not isinstance(xinference_client, RESTfulAudioModelHandle):
+            raise InvokeBadRequestError('please check model type, the model you want to invoke is not a audio model')
+        
+        response = xinference_client.transcriptions(
+            audio=file,
+            language = language,
+            prompt = prompt,
+            response_format = response_format,
+            temperature = temperature
+        )
+
+        return response["text"]
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+            used to define customizable model schema
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(
+                en_US=model
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.SPEECH2TEXT,
+            model_properties={ },
+            parameter_rules=[]
+        )
+
+        return entity

api/core/model_runtime/model_providers/xinference/xinference.yaml

@@ -16,6 +16,7 @@ supported_model_types:
   - llm
   - text-embedding
   - rerank
+  - speech2text
 configurate_methods:
   - customizable-model
 model_credential_schema:

api/core/rag/datasource/keyword/jieba/jieba.py

@@ -8,6 +8,7 @@ from core.rag.datasource.keyword.jieba.jieba_keyword_table_handler import JiebaK
 from core.rag.datasource.keyword.keyword_base import BaseKeyword
 from core.rag.models.document import Document
 from extensions.ext_database import db
+from extensions.ext_redis import redis_client
 from models.dataset import Dataset, DatasetKeywordTable, DocumentSegment
 
 
@@ -121,26 +122,28 @@ class Jieba(BaseKeyword):
         db.session.commit()
 
     def _get_dataset_keyword_table(self) -> Optional[dict]:
-        dataset_keyword_table = self.dataset.dataset_keyword_table
-        if dataset_keyword_table:
-            if dataset_keyword_table.keyword_table_dict:
-                return dataset_keyword_table.keyword_table_dict['__data__']['table']
-        else:
-            dataset_keyword_table = DatasetKeywordTable(
-                dataset_id=self.dataset.id,
-                keyword_table=json.dumps({
-                    '__type__': 'keyword_table',
-                    '__data__': {
-                        "index_id": self.dataset.id,
-                        "summary": None,
-                        "table": {}
-                    }
-                }, cls=SetEncoder)
-            )
-            db.session.add(dataset_keyword_table)
-            db.session.commit()
+        lock_name = 'keyword_indexing_lock_{}'.format(self.dataset.id)
+        with redis_client.lock(lock_name, timeout=20):
+            dataset_keyword_table = self.dataset.dataset_keyword_table
+            if dataset_keyword_table:
+                if dataset_keyword_table.keyword_table_dict:
+                    return dataset_keyword_table.keyword_table_dict['__data__']['table']
+            else:
+                dataset_keyword_table = DatasetKeywordTable(
+                    dataset_id=self.dataset.id,
+                    keyword_table=json.dumps({
+                        '__type__': 'keyword_table',
+                        '__data__': {
+                            "index_id": self.dataset.id,
+                            "summary": None,
+                            "table": {}
+                        }
+                    }, cls=SetEncoder)
+                )
+                db.session.add(dataset_keyword_table)
+                db.session.commit()
 
-        return {}
+            return {}
 
     def _add_text_to_keyword_table(self, keyword_table: dict, id: str, keywords: list[str]) -> dict:
         for keyword in keywords:

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

@@ -8,6 +8,7 @@ from pymilvus import MilvusClient, MilvusException, connections
 from core.rag.datasource.vdb.field import Field
 from core.rag.datasource.vdb.vector_base import BaseVector
 from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
 
 logger = logging.getLogger(__name__)
 
@@ -61,17 +62,7 @@ class MilvusVector(BaseVector):
             'params': {"M": 8, "efConstruction": 64}
         }
         metadatas = [d.metadata for d in texts]
-
-        # Grab the existing collection if it exists
-        from pymilvus import utility
-        alias = uuid4().hex
-        if self._client_config.secure:
-            uri = "https://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        else:
-            uri = "http://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        connections.connect(alias=alias, uri=uri, user=self._client_config.user, password=self._client_config.password)
-        if not utility.has_collection(self._collection_name, using=alias):
-            self.create_collection(embeddings, metadatas, index_params)
+        self.create_collection(embeddings, metadatas, index_params)
         self.add_texts(texts, embeddings)
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
@@ -187,46 +178,60 @@ class MilvusVector(BaseVector):
 
     def create_collection(
             self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
-    ) -> str:
-        from pymilvus import CollectionSchema, DataType, FieldSchema
-        from pymilvus.orm.types import infer_dtype_bydata
+    ):
+        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            if redis_client.get(collection_exist_cache_key):
+                return
+            # Grab the existing collection if it exists
+            from pymilvus import utility
+            alias = uuid4().hex
+            if self._client_config.secure:
+                uri = "https://" + str(self._client_config.host) + ":" + str(self._client_config.port)
+            else:
+                uri = "http://" + str(self._client_config.host) + ":" + str(self._client_config.port)
+            connections.connect(alias=alias, uri=uri, user=self._client_config.user,
+                                password=self._client_config.password)
+            if not utility.has_collection(self._collection_name, using=alias):
+                from pymilvus import CollectionSchema, DataType, FieldSchema
+                from pymilvus.orm.types import infer_dtype_bydata
 
-        # Determine embedding dim
-        dim = len(embeddings[0])
-        fields = []
-        if metadatas:
-            fields.append(FieldSchema(Field.METADATA_KEY.value, DataType.JSON, max_length=65_535))
+                # Determine embedding dim
+                dim = len(embeddings[0])
+                fields = []
+                if metadatas:
+                    fields.append(FieldSchema(Field.METADATA_KEY.value, DataType.JSON, max_length=65_535))
 
-        # Create the text field
-        fields.append(
-            FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535)
-        )
-        # Create the primary key field
-        fields.append(
-            FieldSchema(
-                Field.PRIMARY_KEY.value, DataType.INT64, is_primary=True, auto_id=True
-            )
-        )
-        # Create the vector field, supports binary or float vectors
-        fields.append(
-            FieldSchema(Field.VECTOR.value, infer_dtype_bydata(embeddings[0]), dim=dim)
-        )
+                # Create the text field
+                fields.append(
+                    FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535)
+                )
+                # Create the primary key field
+                fields.append(
+                    FieldSchema(
+                        Field.PRIMARY_KEY.value, DataType.INT64, is_primary=True, auto_id=True
+                    )
+                )
+                # Create the vector field, supports binary or float vectors
+                fields.append(
+                    FieldSchema(Field.VECTOR.value, infer_dtype_bydata(embeddings[0]), dim=dim)
+                )
 
-        # Create the schema for the collection
-        schema = CollectionSchema(fields)
+                # Create the schema for the collection
+                schema = CollectionSchema(fields)
 
-        for x in schema.fields:
-            self._fields.append(x.name)
-        # Since primary field is auto-id, no need to track it
-        self._fields.remove(Field.PRIMARY_KEY.value)
-
-        # Create the collection
-        collection_name = self._collection_name
-        self._client.create_collection_with_schema(collection_name=collection_name,
-                                                   schema=schema, index_param=index_params,
-                                                   consistency_level=self._consistency_level)
-        return collection_name
+                for x in schema.fields:
+                    self._fields.append(x.name)
+                # Since primary field is auto-id, no need to track it
+                self._fields.remove(Field.PRIMARY_KEY.value)
 
+                # Create the collection
+                collection_name = self._collection_name
+                self._client.create_collection_with_schema(collection_name=collection_name,
+                                                           schema=schema, index_param=index_params,
+                                                           consistency_level=self._consistency_level)
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
     def _init_client(self, config) -> MilvusClient:
         if config.secure:
             uri = "https://" + str(config.host) + ":" + str(config.port)

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

@@ -20,6 +20,7 @@ from qdrant_client.local.qdrant_local import QdrantLocal
 from core.rag.datasource.vdb.field import Field
 from core.rag.datasource.vdb.vector_base import BaseVector
 from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
 
 if TYPE_CHECKING:
     from qdrant_client import grpc  # noqa
@@ -77,6 +78,17 @@ class QdrantVector(BaseVector):
             vector_size = len(embeddings[0])
             # get collection name
             collection_name = self._collection_name
+            # create collection
+            self.create_collection(collection_name, vector_size)
+
+            self.add_texts(texts, embeddings, **kwargs)
+
+    def create_collection(self, collection_name: str, vector_size: int):
+        lock_name = 'vector_indexing_lock_{}'.format(collection_name)
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            if redis_client.get(collection_exist_cache_key):
+                return
             collection_name = collection_name or uuid.uuid4().hex
             all_collection_name = []
             collections_response = self._client.get_collections()
@@ -84,40 +96,35 @@ class QdrantVector(BaseVector):
             for collection in collection_list:
                 all_collection_name.append(collection.name)
             if collection_name not in all_collection_name:
-                # create collection
-                self.create_collection(collection_name, vector_size)
+                from qdrant_client.http import models as rest
+                vectors_config = rest.VectorParams(
+                    size=vector_size,
+                    distance=rest.Distance[self._distance_func],
+                )
+                hnsw_config = HnswConfigDiff(m=0, payload_m=16, ef_construct=100, full_scan_threshold=10000,
+                                             max_indexing_threads=0, on_disk=False)
+                self._client.recreate_collection(
+                    collection_name=collection_name,
+                    vectors_config=vectors_config,
+                    hnsw_config=hnsw_config,
+                    timeout=int(self._client_config.timeout),
+                )
 
-            self.add_texts(texts, embeddings, **kwargs)
-
-    def create_collection(self, collection_name: str, vector_size: int):
-        from qdrant_client.http import models as rest
-        vectors_config = rest.VectorParams(
-            size=vector_size,
-            distance=rest.Distance[self._distance_func],
-        )
-        hnsw_config = HnswConfigDiff(m=0, payload_m=16, ef_construct=100, full_scan_threshold=10000,
-                                     max_indexing_threads=0, on_disk=False)
-        self._client.recreate_collection(
-            collection_name=collection_name,
-            vectors_config=vectors_config,
-            hnsw_config=hnsw_config,
-            timeout=int(self._client_config.timeout),
-        )
-
-        # create payload index
-        self._client.create_payload_index(collection_name, Field.GROUP_KEY.value,
-                                          field_schema=PayloadSchemaType.KEYWORD,
-                                          field_type=PayloadSchemaType.KEYWORD)
-        # creat full text index
-        text_index_params = TextIndexParams(
-            type=TextIndexType.TEXT,
-            tokenizer=TokenizerType.MULTILINGUAL,
-            min_token_len=2,
-            max_token_len=20,
-            lowercase=True
-        )
-        self._client.create_payload_index(collection_name, Field.CONTENT_KEY.value,
-                                          field_schema=text_index_params)
+                # create payload index
+                self._client.create_payload_index(collection_name, Field.GROUP_KEY.value,
+                                                  field_schema=PayloadSchemaType.KEYWORD,
+                                                  field_type=PayloadSchemaType.KEYWORD)
+                # creat full text index
+                text_index_params = TextIndexParams(
+                    type=TextIndexType.TEXT,
+                    tokenizer=TokenizerType.MULTILINGUAL,
+                    min_token_len=2,
+                    max_token_len=20,
+                    lowercase=True
+                )
+                self._client.create_payload_index(collection_name, Field.CONTENT_KEY.value,
+                                                  field_schema=text_index_params)
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
         uuids = self._get_uuids(documents)

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -8,6 +8,7 @@ from pydantic import BaseModel, root_validator
 from core.rag.datasource.vdb.field import Field
 from core.rag.datasource.vdb.vector_base import BaseVector
 from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
 from models.dataset import Dataset
 
 
@@ -79,16 +80,23 @@ class WeaviateVector(BaseVector):
         }
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
-
-        schema = self._default_schema(self._collection_name)
-
-        # check whether the index already exists
-        if not self._client.schema.contains(schema):
-            # create collection
-            self._client.schema.create_class(schema)
+        # create collection
+        self._create_collection()
         # create vector
         self.add_texts(texts, embeddings)
 
+    def _create_collection(self):
+        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            if redis_client.get(collection_exist_cache_key):
+                return
+            schema = self._default_schema(self._collection_name)
+            if not self._client.schema.contains(schema):
+                # create collection
+                self._client.schema.create_class(schema)
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
         uuids = self._get_uuids(documents)
         texts = [d.page_content for d in documents]

api/core/tools/utils/parser.py

@@ -1,10 +1,12 @@
 
 import re
 import uuid
+from json import dumps as json_dumps
 from json import loads as json_loads
+from json.decoder import JSONDecodeError
 
 from requests import get
-from yaml import FullLoader, load
+from yaml import YAMLError, safe_load
 
 from core.tools.entities.common_entities import I18nObject
 from core.tools.entities.tool_bundle import ApiBasedToolBundle
@@ -184,27 +186,11 @@ class ApiBasedToolSchemaParser:
         warning = warning if warning is not None else {}
         extra_info = extra_info if extra_info is not None else {}
 
-        openapi: dict = load(yaml, Loader=FullLoader)
+        openapi: dict = safe_load(yaml)
         if openapi is None:
             raise ToolApiSchemaError('Invalid openapi yaml.')
         return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
     
-    @staticmethod
-    def parse_openapi_json_to_tool_bundle(json: str, extra_info: dict = None, warning: dict = None) -> list[ApiBasedToolBundle]:
-        """
-            parse openapi yaml to tool bundle
-
-            :param yaml: the yaml string
-            :return: the tool bundle
-        """
-        warning = warning if warning is not None else {}
-        extra_info = extra_info if extra_info is not None else {}
-
-        openapi: dict = json_loads(json)
-        if openapi is None:
-            raise ToolApiSchemaError('Invalid openapi json.')
-        return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
-    
     @staticmethod
     def parse_swagger_to_openapi(swagger: dict, extra_info: dict = None, warning: dict = None) -> dict:
         """
@@ -271,38 +257,6 @@ class ApiBasedToolSchemaParser:
 
         return openapi
 
-    @staticmethod
-    def parse_swagger_yaml_to_tool_bundle(yaml: str, extra_info: dict = None, warning: dict = None) -> list[ApiBasedToolBundle]:
-        """
-            parse swagger yaml to tool bundle
-
-            :param yaml: the yaml string
-            :return: the tool bundle
-        """
-        warning = warning if warning is not None else {}
-        extra_info = extra_info if extra_info is not None else {}
-
-        swagger: dict = load(yaml, Loader=FullLoader)
-
-        openapi = ApiBasedToolSchemaParser.parse_swagger_to_openapi(swagger, extra_info=extra_info, warning=warning)
-        return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
-
-    @staticmethod
-    def parse_swagger_json_to_tool_bundle(json: str, extra_info: dict = None, warning: dict = None) -> list[ApiBasedToolBundle]:
-        """
-            parse swagger yaml to tool bundle
-
-            :param yaml: the yaml string
-            :return: the tool bundle
-        """
-        warning = warning if warning is not None else {}
-        extra_info = extra_info if extra_info is not None else {}
-
-        swagger: dict = json_loads(json)
-
-        openapi = ApiBasedToolSchemaParser.parse_swagger_to_openapi(swagger, extra_info=extra_info, warning=warning)
-        return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
-
     @staticmethod
     def parse_openai_plugin_json_to_tool_bundle(json: str, extra_info: dict = None, warning: dict = None) -> list[ApiBasedToolBundle]:
         """
@@ -346,40 +300,50 @@ class ApiBasedToolSchemaParser:
         warning = warning if warning is not None else {}
         extra_info = extra_info if extra_info is not None else {}
 
-        json_possible = False
         content = content.strip()
+        loaded_content = None
+        json_error = None
+        yaml_error = None
+        
+        try:
+            loaded_content = json_loads(content)
+        except JSONDecodeError as e:
+            json_error = e
 
-        if content.startswith('{') and content.endswith('}'):
-            json_possible = True
+        if loaded_content is None:
+            try:
+                loaded_content = safe_load(content)
+            except YAMLError as e:
+                yaml_error = e
+        if loaded_content is None:
+            raise ToolApiSchemaError(f'Invalid api schema, schema is neither json nor yaml. json error: {str(json_error)}, yaml error: {str(yaml_error)}')
 
-        if json_possible:
-            try:
-                return ApiBasedToolSchemaParser.parse_openapi_json_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
-                    ApiProviderSchemaType.OPENAPI.value
-            except:
-                pass
+        swagger_error = None
+        openapi_error = None
+        openapi_plugin_error = None
+        schema_type = None
+        
+        try:
+            openapi = ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(loaded_content, extra_info=extra_info, warning=warning)
+            schema_type = ApiProviderSchemaType.OPENAPI.value
+            return openapi, schema_type
+        except ToolApiSchemaError as e:
+            openapi_error = e
+        
+        # openai parse error, fallback to swagger
+        try:
+            converted_swagger = ApiBasedToolSchemaParser.parse_swagger_to_openapi(loaded_content, extra_info=extra_info, warning=warning)
+            schema_type = ApiProviderSchemaType.SWAGGER.value
+            return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(converted_swagger, extra_info=extra_info, warning=warning), schema_type
+        except ToolApiSchemaError as e:
+            swagger_error = e
+        
+        # swagger parse error, fallback to openai plugin
+        try:
+            openapi_plugin = ApiBasedToolSchemaParser.parse_openai_plugin_json_to_tool_bundle(json_dumps(loaded_content), extra_info=extra_info, warning=warning)
+            return openapi_plugin, ApiProviderSchemaType.OPENAI_PLUGIN.value
+        except ToolNotSupportedError as e:
+            # maybe it's not plugin at all
+            openapi_plugin_error = e
 
-            try:
-                return ApiBasedToolSchemaParser.parse_swagger_json_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
-                    ApiProviderSchemaType.SWAGGER.value
-            except:
-                pass
-            try:
-                return ApiBasedToolSchemaParser.parse_openai_plugin_json_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
-                    ApiProviderSchemaType.OPENAI_PLUGIN.value
-            except:
-                pass
-        else:
-            try:
-                return ApiBasedToolSchemaParser.parse_openapi_yaml_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
-                    ApiProviderSchemaType.OPENAPI.value
-            except:
-                pass
-
-            try:
-                return ApiBasedToolSchemaParser.parse_swagger_yaml_to_tool_bundle(content, extra_info=extra_info, warning=warning), \
-                    ApiProviderSchemaType.SWAGGER.value
-            except:
-                pass
-
-        raise ToolApiSchemaError('Invalid api schema.')
+        raise ToolApiSchemaError(f'Invalid api schema, openapi error: {str(openapi_error)}, swagger error: {str(swagger_error)}, openapi plugin error: {str(openapi_plugin_error)}')

api/extensions/ext_celery.py

@@ -50,7 +50,7 @@ def init_app(app: Flask) -> Celery:
         },
         'clean_unused_datasets_task': {
             'task': 'schedule.clean_unused_datasets_task.clean_unused_datasets_task',
-            'schedule': timedelta(days=7),
+            'schedule': timedelta(minutes=3),
         }
     }
     celery_app.conf.update(

api/requirements.txt

@@ -48,7 +48,7 @@ dashscope[tokenizer]~=1.14.0
 huggingface_hub~=0.16.4
 transformers~=4.31.0
 pandas==1.5.3
-xinference-client==0.8.4
+xinference-client==0.9.4
 safetensors==0.3.2
 zhipuai==1.0.7
 werkzeug~=3.0.1
@@ -73,4 +73,5 @@ yarl~=1.9.4
 twilio==9.0.0
 qrcode~=7.4.2
 azure-storage-blob==12.9.0
-azure-identity==1.15.0
+azure-identity==1.15.0
+lxml==5.1.0

api/services/tools_manage_service.py

@@ -1,4 +1,5 @@
 import json
+import logging
 
 from httpx import get
 
@@ -24,6 +25,8 @@ from models.tools import ApiToolProvider, BuiltinToolProvider
 from services.model_provider_service import ModelProviderService
 from services.tools_transform_service import ToolTransformService
 
+logger = logging.getLogger(__name__)
+
 
 class ToolManageService:
     @staticmethod
@@ -257,6 +260,7 @@ class ToolManageService:
             # try to parse schema, avoid SSRF attack
             ToolManageService.parser_api_schema(schema)
         except Exception as e:
+            logger.error(f"parse api schema error: {str(e)}")
             raise ValueError('invalid schema, please check the url you provided')
         
         return {
@@ -683,4 +687,4 @@ class ToolManageService:
 
             result.append(user_provider)
 
-        return result
+        return result

web/app/components/app/configuration/config/agent/agent-tools/choose-tool/index.tsx

@@ -59,7 +59,7 @@ const ChooseTool: FC<Props> = ({
                 provider_type: collection.type,
                 provider_name: collection.name,
                 tool_name: tool.name,
-                tool_label: tool.label[locale],
+                tool_label: tool.label[locale] || tool.label[locale.replaceAll('-', '_')],
                 tool_parameters: parameters,
                 enabled: true,
               })

web/i18n/en-US/app-annotation.ts

@@ -4,7 +4,7 @@ const translation = {
   editBy: 'Answer edited by {{author}}',
   noData: {
     title: 'No annotations',
-    description: 'You can edit annotations in app debuggiung, or import annotations in bulk here for high-quality response.',
+    description: 'You can edit annotations during app debugging or import annotations in bulk here for a high-quality response.',
   },
   table: {
     header: {