Merge branch 'main' into feat/support-extractor-tools

api/.env.example

@@ -202,6 +202,20 @@ TIDB_VECTOR_USER=xxx.root
 TIDB_VECTOR_PASSWORD=xxxxxx
 TIDB_VECTOR_DATABASE=dify
 
+# Tidb on qdrant configuration
+TIDB_ON_QDRANT_URL=http://127.0.0.1
+TIDB_ON_QDRANT_API_KEY=dify
+TIDB_ON_QDRANT_CLIENT_TIMEOUT=20
+TIDB_ON_QDRANT_GRPC_ENABLED=false
+TIDB_ON_QDRANT_GRPC_PORT=6334
+TIDB_PUBLIC_KEY=dify
+TIDB_PRIVATE_KEY=dify
+TIDB_API_URL=http://127.0.0.1
+TIDB_IAM_API_URL=http://127.0.0.1
+TIDB_REGION=regions/aws-us-east-1
+TIDB_PROJECT_ID=dify
+TIDB_SPEND_LIMIT=100
+
 # Chroma configuration
 CHROMA_HOST=127.0.0.1
 CHROMA_PORT=8000
@@ -249,6 +263,14 @@ VIKINGDB_SCHEMA=http
 VIKINGDB_CONNECTION_TIMEOUT=30
 VIKINGDB_SOCKET_TIMEOUT=30
 
+# OceanBase Vector configuration
+OCEANBASE_VECTOR_HOST=127.0.0.1
+OCEANBASE_VECTOR_PORT=2881
+OCEANBASE_VECTOR_USER=root@test
+OCEANBASE_VECTOR_PASSWORD=
+OCEANBASE_VECTOR_DATABASE=test
+OCEANBASE_MEMORY_LIMIT=6G
+
 # Upload configuration
 UPLOAD_FILE_SIZE_LIMIT=15
 UPLOAD_FILE_BATCH_LIMIT=5

api/Dockerfile

@@ -55,7 +55,12 @@ RUN apt-get update \
     && echo "deb http://deb.debian.org/debian testing main" > /etc/apt/sources.list \
     && apt-get update \
     # For Security
-    && apt-get install -y --no-install-recommends zlib1g=1:1.3.dfsg+really1.3.1-1 expat=2.6.3-1 libldap-2.5-0=2.5.18+dfsg-3+b1 perl=5.40.0-6 libsqlite3-0=3.46.1-1 \
+    && apt-get install -y --no-install-recommends expat=2.6.3-2 libldap-2.5-0=2.5.18+dfsg-3+b1 perl=5.40.0-6 libsqlite3-0=3.46.1-1 \
+    && if [ "$(dpkg --print-architecture)" = "amd64" ]; then \
+        apt-get install -y --no-install-recommends zlib1g=1:1.3.dfsg+really1.3.1-1+b1; \
+    else \
+        apt-get install -y --no-install-recommends zlib1g=1:1.3.dfsg+really1.3.1-1; \
+    fi \
     # install a chinese font to support the use of tools like matplotlib
     && apt-get install -y fonts-noto-cjk \
     && apt-get autoremove -y \

api/commands.py

@@ -279,6 +279,7 @@ def migrate_knowledge_vector_database():
         VectorType.VIKINGDB,
         VectorType.UPSTASH,
         VectorType.COUCHBASE,
+        VectorType.OCEANBASE,
     }
     page = 1
     while True:

api/configs/middleware/__init__.py

@@ -16,11 +16,13 @@ from configs.middleware.storage.supabase_storage_config import SupabaseStorageCo
 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.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
@@ -257,5 +259,7 @@ class MiddlewareConfig(
     VikingDBConfig,
     UpstashConfig,
     TidbOnQdrantConfig,
+    OceanBaseVectorConfig,
+    BaiduVectorDBConfig,
 ):
     pass

api/configs/middleware/vdb/oceanbase_config.py

@@ -0,0 +1,35 @@
+from typing import Optional
+
+from pydantic import Field, PositiveInt
+from pydantic_settings import BaseSettings
+
+
+class OceanBaseVectorConfig(BaseSettings):
+    """
+    Configuration settings for OceanBase Vector database
+    """
+
+    OCEANBASE_VECTOR_HOST: Optional[str] = Field(
+        description="Hostname or IP address of the OceanBase Vector server (e.g. 'localhost')",
+        default=None,
+    )
+
+    OCEANBASE_VECTOR_PORT: Optional[PositiveInt] = Field(
+        description="Port number on which the OceanBase Vector server is listening (default is 2881)",
+        default=2881,
+    )
+
+    OCEANBASE_VECTOR_USER: Optional[str] = Field(
+        description="Username for authenticating with the OceanBase Vector database",
+        default=None,
+    )
+
+    OCEANBASE_VECTOR_PASSWORD: Optional[str] = Field(
+        description="Password for authenticating with the OceanBase Vector database",
+        default=None,
+    )
+
+    OCEANBASE_VECTOR_DATABASE: Optional[str] = Field(
+        description="Name of the OceanBase Vector database to connect to",
+        default=None,
+    )

api/configs/middleware/vdb/tidb_on_qdrant_config.py

@@ -63,3 +63,8 @@ class TidbOnQdrantConfig(BaseSettings):
         description="Tidb project id",
         default=None,
     )
+
+    TIDB_SPEND_LIMIT: Optional[int] = Field(
+        description="Tidb spend limit",
+        default=100,
+    )

api/controllers/console/datasets/datasets.py

@@ -628,6 +628,7 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.BAIDU
                 | VectorType.VIKINGDB
                 | VectorType.UPSTASH
+                | VectorType.OCEANBASE
             ):
                 return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
             case (
@@ -669,6 +670,7 @@ class DatasetRetrievalSettingMockApi(Resource):
                 | VectorType.BAIDU
                 | VectorType.VIKINGDB
                 | VectorType.UPSTASH
+                | VectorType.OCEANBASE
             ):
                 return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
             case (

api/controllers/console/version.py

@@ -3,6 +3,7 @@ import logging
 
 import requests
 from flask_restful import Resource, reqparse
+from packaging import version
 
 from configs import dify_config
 
@@ -47,43 +48,15 @@ class VersionApi(Resource):
 
 
 def _has_new_version(*, latest_version: str, current_version: str) -> bool:
-    def parse_version(version: str) -> tuple:
-        # Split version into parts and pre-release suffix if any
-        parts = version.split("-")
-        version_parts = parts[0].split(".")
-        pre_release = parts[1] if len(parts) > 1 else None
+    try:
+        latest = version.parse(latest_version)
+        current = version.parse(current_version)
 
-        # Validate version format
-        if len(version_parts) != 3:
-            raise ValueError(f"Invalid version format: {version}")
-
-        try:
-            # Convert version parts to integers
-            major, minor, patch = map(int, version_parts)
-            return (major, minor, patch, pre_release)
-        except ValueError:
-            raise ValueError(f"Invalid version format: {version}")
-
-    latest = parse_version(latest_version)
-    current = parse_version(current_version)
-
-    # Compare major, minor, and patch versions
-    for latest_part, current_part in zip(latest[:3], current[:3]):
-        if latest_part > current_part:
-            return True
-        elif latest_part < current_part:
-            return False
-
-    # If versions are equal, check pre-release suffixes
-    if latest[3] is None and current[3] is not None:
-        return True
-    elif latest[3] is not None and current[3] is None:
+        # Compare versions
+        return latest > current
+    except version.InvalidVersion:
+        logging.warning(f"Invalid version format: latest={latest_version}, current={current_version}")
         return False
-    elif latest[3] is not None and current[3] is not None:
-        # Simple string comparison for pre-release versions
-        return latest[3] > current[3]
-
-    return False
 
 
 api.add_resource(VersionApi, "/version")

api/controllers/service_api/dataset/document.py

@@ -230,7 +230,7 @@ class DocumentUpdateByFileApi(DatasetApiResource):
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
         document = documents[0]
-        documents_and_batch_fields = {"document": marshal(document, document_fields), "batch": batch}
+        documents_and_batch_fields = {"document": marshal(document, document_fields), "batch": document.batch}
         return documents_and_batch_fields, 200
 
 
@@ -331,10 +331,26 @@ class DocumentIndexingStatusApi(DatasetApiResource):
         return data
 
 
-api.add_resource(DocumentAddByTextApi, "/datasets/<uuid:dataset_id>/document/create_by_text")
-api.add_resource(DocumentAddByFileApi, "/datasets/<uuid:dataset_id>/document/create_by_file")
-api.add_resource(DocumentUpdateByTextApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update_by_text")
-api.add_resource(DocumentUpdateByFileApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update_by_file")
+api.add_resource(
+    DocumentAddByTextApi,
+    "/datasets/<uuid:dataset_id>/document/create_by_text",
+    "/datasets/<uuid:dataset_id>/document/create-by-text",
+)
+api.add_resource(
+    DocumentAddByFileApi,
+    "/datasets/<uuid:dataset_id>/document/create_by_file",
+    "/datasets/<uuid:dataset_id>/document/create-by-file",
+)
+api.add_resource(
+    DocumentUpdateByTextApi,
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update_by_text",
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update-by-text",
+)
+api.add_resource(
+    DocumentUpdateByFileApi,
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update_by_file",
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update-by-file",
+)
 api.add_resource(DocumentDeleteApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>")
 api.add_resource(DocumentListApi, "/datasets/<uuid:dataset_id>/documents")
 api.add_resource(DocumentIndexingStatusApi, "/datasets/<uuid:dataset_id>/documents/<string:batch>/indexing-status")

api/controllers/service_api/dataset/hit_testing.py

@@ -14,4 +14,4 @@ class HitTestingApi(DatasetApiResource, DatasetsHitTestingBase):
         return self.perform_hit_testing(dataset, args)
 
 
-api.add_resource(HitTestingApi, "/datasets/<uuid:dataset_id>/hit-testing")
+api.add_resource(HitTestingApi, "/datasets/<uuid:dataset_id>/hit-testing", "/datasets/<uuid:dataset_id>/retrieve")

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

@@ -37,6 +37,17 @@ def _get_max_tokens(default: int, min_val: int, max_val: int) -> ParameterRule:
     return rule
 
 
+def _get_o1_max_tokens(default: int, min_val: int, max_val: int) -> ParameterRule:
+    rule = ParameterRule(
+        name="max_completion_tokens",
+        **PARAMETER_RULE_TEMPLATE[DefaultParameterName.MAX_TOKENS],
+    )
+    rule.default = default
+    rule.min = min_val
+    rule.max = max_val
+    return rule
+
+
 class AzureBaseModel(BaseModel):
     base_model_name: str
     entity: AIModelEntity
@@ -1098,14 +1109,6 @@ LLM_BASE_MODELS = [
                 ModelPropertyKey.CONTEXT_SIZE: 128000,
             },
             parameter_rules=[
-                ParameterRule(
-                    name="temperature",
-                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
-                ),
-                ParameterRule(
-                    name="top_p",
-                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
-                ),
                 ParameterRule(
                     name="response_format",
                     label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
@@ -1116,7 +1119,7 @@ LLM_BASE_MODELS = [
                     required=False,
                     options=["text", "json_object"],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=32768),
+                _get_o1_max_tokens(default=512, min_val=1, max_val=32768),
             ],
             pricing=PriceConfig(
                 input=15.00,
@@ -1143,14 +1146,6 @@ LLM_BASE_MODELS = [
                 ModelPropertyKey.CONTEXT_SIZE: 128000,
             },
             parameter_rules=[
-                ParameterRule(
-                    name="temperature",
-                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
-                ),
-                ParameterRule(
-                    name="top_p",
-                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
-                ),
                 ParameterRule(
                     name="response_format",
                     label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
@@ -1161,7 +1156,7 @@ LLM_BASE_MODELS = [
                     required=False,
                     options=["text", "json_object"],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=65536),
+                _get_o1_max_tokens(default=512, min_val=1, max_val=65536),
             ],
             pricing=PriceConfig(
                 input=3.00,

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-standard-256k.yaml

@@ -1,7 +1,7 @@
-model: hunyuan-standard-256k
+model: hunyuan-standard-256K
 label:
-  zh_Hans: hunyuan-standard-256k
-  en_US: hunyuan-standard-256k
+  zh_Hans: hunyuan-standard-256K
+  en_US: hunyuan-standard-256K
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/vessl_ai/_assets/icon_s_en.svg

@@ -0,0 +1,3 @@
+<svg width="1200" height="925" viewBox="0 0 1200 925" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M780.152 250.999L907.882 462.174C907.882 462.174 880.925 510.854 867.43 535.21C834.845 594.039 764.171 612.49 710.442 508.333L420.376 0H0L459.926 803.307C552.303 964.663 787.366 964.663 879.743 803.307C989.874 610.952 1089.87 441.97 1200 249.646L1052.28 0H639.519L780.152 250.999Z" fill="#3366FF"/>
+</svg>

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

@@ -0,0 +1,83 @@
+from decimal import Decimal
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.llm_entities import LLMMode
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    DefaultParameterName,
+    FetchFrom,
+    ModelPropertyKey,
+    ModelType,
+    ParameterRule,
+    ParameterType,
+    PriceConfig,
+)
+from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
+
+
+class VesslAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        features = []
+
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.LLM,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            features=features,
+            model_properties={
+                ModelPropertyKey.MODE: credentials.get("mode"),
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name=DefaultParameterName.TEMPERATURE.value,
+                    label=I18nObject(en_US="Temperature"),
+                    type=ParameterType.FLOAT,
+                    default=float(credentials.get("temperature", 0.7)),
+                    min=0,
+                    max=2,
+                    precision=2,
+                ),
+                ParameterRule(
+                    name=DefaultParameterName.TOP_P.value,
+                    label=I18nObject(en_US="Top P"),
+                    type=ParameterType.FLOAT,
+                    default=float(credentials.get("top_p", 1)),
+                    min=0,
+                    max=1,
+                    precision=2,
+                ),
+                ParameterRule(
+                    name=DefaultParameterName.TOP_K.value,
+                    label=I18nObject(en_US="Top K"),
+                    type=ParameterType.INT,
+                    default=int(credentials.get("top_k", 50)),
+                    min=-2147483647,
+                    max=2147483647,
+                    precision=0,
+                ),
+                ParameterRule(
+                    name=DefaultParameterName.MAX_TOKENS.value,
+                    label=I18nObject(en_US="Max Tokens"),
+                    type=ParameterType.INT,
+                    default=512,
+                    min=1,
+                    max=int(credentials.get("max_tokens_to_sample", 4096)),
+                ),
+            ],
+            pricing=PriceConfig(
+                input=Decimal(credentials.get("input_price", 0)),
+                output=Decimal(credentials.get("output_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
+            ),
+        )
+
+        if credentials["mode"] == "chat":
+            entity.model_properties[ModelPropertyKey.MODE] = LLMMode.CHAT.value
+        elif credentials["mode"] == "completion":
+            entity.model_properties[ModelPropertyKey.MODE] = LLMMode.COMPLETION.value
+        else:
+            raise ValueError(f"Unknown completion type {credentials['completion_type']}")
+
+        return entity

api/core/model_runtime/model_providers/vessl_ai/vessl_ai.py

@@ -0,0 +1,10 @@
+import logging
+
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class VesslAIProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        pass

api/core/model_runtime/model_providers/vessl_ai/vessl_ai.yaml

@@ -0,0 +1,56 @@
+provider: vessl_ai
+label:
+  en_US: vessl_ai
+icon_small:
+  en_US: icon_s_en.svg
+icon_large:
+  en_US: icon_l_en.png
+background: "#F1EFED"
+help:
+  title:
+    en_US: How to deploy VESSL AI LLM Model Endpoint
+  url:
+    en_US: https://docs.vessl.ai/guides/get-started/llama3-deployment
+supported_model_types:
+  - llm
+configurate_methods:
+  - customizable-model
+model_credential_schema:
+  model:
+    label:
+      en_US: Model Name
+    placeholder:
+      en_US: Enter your model name
+  credential_form_schemas:
+    - variable: endpoint_url
+      label:
+        en_US: endpoint url
+      type: text-input
+      required: true
+      placeholder:
+        en_US: Enter the url of your endpoint url
+    - variable: api_key
+      required: true
+      label:
+        en_US: API Key
+      type: secret-input
+      placeholder:
+        en_US: Enter your VESSL AI secret key
+    - variable: mode
+      show_on:
+        - variable: __model_type
+          value: llm
+      label:
+        en_US: Completion mode
+      type: select
+      required: false
+      default: chat
+      placeholder:
+        en_US: Select completion mode
+      options:
+        - value: completion
+          label:
+            en_US: Completion
+        - value: chat
+          label:
+            en_US: Chat

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

@@ -115,6 +115,7 @@ class _CommonWenxin:
         "ernie-character-8k-0321": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k",
         "ernie-4.0-turbo-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k",
         "ernie-4.0-turbo-8k-preview": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k-preview",
+        "ernie-4.0-turbo-128k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-128k",
         "yi_34b_chat": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/yi_34b_chat",
         "embedding-v1": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/embedding-v1",
         "bge-large-en": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/bge_large_en",

api/core/model_runtime/model_providers/wenxin/llm/ernie-4.0-turbo-128k.yaml

@@ -0,0 +1,40 @@
+model: ernie-4.0-turbo-128k
+label:
+  en_US: Ernie-4.0-turbo-128K
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0.1
+    max: 1.0
+    default: 0.8
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 2
+    max: 4096
+  - name: presence_penalty
+    use_template: presence_penalty
+    default: 1.0
+    min: 1.0
+    max: 2.0
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: response_format
+    use_template: response_format
+  - name: disable_search
+    label:
+      zh_Hans: 禁用搜索
+      en_US: Disable Search
+    type: boolean
+    help:
+      zh_Hans: 禁用模型自行进行外部搜索。
+      en_US: Disable the model to perform external search.
+    required: false

api/core/rag/datasource/retrieval_service.py

@@ -34,6 +34,8 @@ class RetrievalService:
         reranking_mode: Optional[str] = "reranking_model",
         weights: Optional[dict] = None,
     ):
+        if not query:
+            return []
         dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
         if not dataset:
             return []

api/core/rag/datasource/vdb/baidu/baidu_vector.py

@@ -3,11 +3,13 @@ import time
 import uuid
 from typing import Any
 
+import numpy as np
 from pydantic import BaseModel, model_validator
 from pymochow import MochowClient
 from pymochow.auth.bce_credentials import BceCredentials
 from pymochow.configuration import Configuration
-from pymochow.model.enum import FieldType, IndexState, IndexType, MetricType, TableState
+from pymochow.exception import ServerError
+from pymochow.model.enum import FieldType, IndexState, IndexType, MetricType, ServerErrCode, TableState
 from pymochow.model.schema import Field, HNSWParams, Schema, VectorIndex
 from pymochow.model.table import AnnSearch, HNSWSearchParams, Partition, Row
 
@@ -116,6 +118,7 @@ class BaiduVector(BaseVector):
         self._db.table(self._collection_name).delete(filter=f"{key} = '{value}'")
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        query_vector = [float(val) if isinstance(val, np.float64) else val for val in query_vector]
         anns = AnnSearch(
             vector_field=self.field_vector,
             vector_floats=query_vector,
@@ -149,7 +152,13 @@ class BaiduVector(BaseVector):
         return docs
 
     def delete(self) -> None:
-        self._db.drop_table(table_name=self._collection_name)
+        try:
+            self._db.drop_table(table_name=self._collection_name)
+        except ServerError as e:
+            if e.code == ServerErrCode.TABLE_NOT_EXIST:
+                pass
+            else:
+                raise
 
     def _init_client(self, config) -> MochowClient:
         config = Configuration(credentials=BceCredentials(config.account, config.api_key), endpoint=config.endpoint)
@@ -166,7 +175,14 @@ class BaiduVector(BaseVector):
         if exists:
             return self._client.database(self._client_config.database)
         else:
-            return self._client.create_database(database_name=self._client_config.database)
+            try:
+                self._client.create_database(database_name=self._client_config.database)
+            except ServerError as e:
+                if e.code == ServerErrCode.DB_ALREADY_EXIST:
+                    pass
+                else:
+                    raise
+            return
 
     def _table_existed(self) -> bool:
         tables = self._db.list_table()
@@ -175,7 +191,7 @@ class BaiduVector(BaseVector):
     def _create_table(self, dimension: int) -> None:
         # Try to grab distributed lock and create table
         lock_name = "vector_indexing_lock_{}".format(self._collection_name)
-        with redis_client.lock(lock_name, timeout=20):
+        with redis_client.lock(lock_name, timeout=60):
             table_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(table_exist_cache_key):
                 return
@@ -238,15 +254,14 @@ class BaiduVector(BaseVector):
                 description="Table for Dify",
             )
 
+            # Wait for table created
+            while True:
+                time.sleep(1)
+                table = self._db.describe_table(self._collection_name)
+                if table.state == TableState.NORMAL:
+                    break
             redis_client.set(table_exist_cache_key, 1, ex=3600)
 
-        # Wait for table created
-        while True:
-            time.sleep(1)
-            table = self._db.describe_table(self._collection_name)
-            if table.state == TableState.NORMAL:
-                break
-
 
 class BaiduVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> BaiduVector:

api/core/rag/datasource/vdb/oceanbase/oceanbase_vector.py

@@ -0,0 +1,209 @@
+import json
+import logging
+import math
+from typing import Any
+
+from pydantic import BaseModel, model_validator
+from pyobvector import VECTOR, ObVecClient
+from sqlalchemy import JSON, Column, String, func
+from sqlalchemy.dialects.mysql import LONGTEXT
+
+from configs import dify_config
+from core.rag.datasource.vdb.vector_base import BaseVector
+from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.embedding.embedding_base import Embeddings
+from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
+from models.dataset import Dataset
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_OCEANBASE_HNSW_BUILD_PARAM = {"M": 16, "efConstruction": 256}
+DEFAULT_OCEANBASE_HNSW_SEARCH_PARAM = {"efSearch": 64}
+OCEANBASE_SUPPORTED_VECTOR_INDEX_TYPE = "HNSW"
+DEFAULT_OCEANBASE_VECTOR_METRIC_TYPE = "l2"
+
+
+class OceanBaseVectorConfig(BaseModel):
+    host: str
+    port: int
+    user: str
+    password: str
+    database: str
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_config(cls, values: dict) -> dict:
+        if not values["host"]:
+            raise ValueError("config OCEANBASE_VECTOR_HOST is required")
+        if not values["port"]:
+            raise ValueError("config OCEANBASE_VECTOR_PORT is required")
+        if not values["user"]:
+            raise ValueError("config OCEANBASE_VECTOR_USER is required")
+        if not values["database"]:
+            raise ValueError("config OCEANBASE_VECTOR_DATABASE is required")
+        return values
+
+
+class OceanBaseVector(BaseVector):
+    def __init__(self, collection_name: str, config: OceanBaseVectorConfig):
+        super().__init__(collection_name)
+        self._config = config
+        self._hnsw_ef_search = -1
+        self._client = ObVecClient(
+            uri=f"{self._config.host}:{self._config.port}",
+            user=self._config.user,
+            password=self._config.password,
+            db_name=self._config.database,
+        )
+
+    def get_type(self) -> str:
+        return VectorType.OCEANBASE
+
+    def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        self._vec_dim = len(embeddings[0])
+        self._create_collection()
+        self.add_texts(texts, embeddings)
+
+    def _create_collection(self) -> None:
+        lock_name = "vector_indexing_lock_" + self._collection_name
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = "vector_indexing_" + self._collection_name
+            if redis_client.get(collection_exist_cache_key):
+                return
+
+            if self._client.check_table_exists(self._collection_name):
+                return
+
+            self.delete()
+
+            cols = [
+                Column("id", String(36), primary_key=True, autoincrement=False),
+                Column("vector", VECTOR(self._vec_dim)),
+                Column("text", LONGTEXT),
+                Column("metadata", JSON),
+            ]
+            vidx_params = self._client.prepare_index_params()
+            vidx_params.add_index(
+                field_name="vector",
+                index_type=OCEANBASE_SUPPORTED_VECTOR_INDEX_TYPE,
+                index_name="vector_index",
+                metric_type=DEFAULT_OCEANBASE_VECTOR_METRIC_TYPE,
+                params=DEFAULT_OCEANBASE_HNSW_BUILD_PARAM,
+            )
+
+            self._client.create_table_with_index_params(
+                table_name=self._collection_name,
+                columns=cols,
+                vidxs=vidx_params,
+            )
+            vals = []
+            params = self._client.perform_raw_text_sql("SHOW PARAMETERS LIKE '%ob_vector_memory_limit_percentage%'")
+            for row in params:
+                val = int(row[6])
+                vals.append(val)
+            if len(vals) == 0:
+                print("ob_vector_memory_limit_percentage not found in parameters.")
+                exit(1)
+            if any(val == 0 for val in vals):
+                try:
+                    self._client.perform_raw_text_sql("ALTER SYSTEM SET ob_vector_memory_limit_percentage = 30")
+                except Exception as e:
+                    raise Exception(
+                        "Failed to set ob_vector_memory_limit_percentage. "
+                        + "Maybe the database user has insufficient privilege.",
+                        e,
+                    )
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        ids = self._get_uuids(documents)
+        for id, doc, emb in zip(ids, documents, embeddings):
+            self._client.insert(
+                table_name=self._collection_name,
+                data={
+                    "id": id,
+                    "vector": emb,
+                    "text": doc.page_content,
+                    "metadata": doc.metadata,
+                },
+            )
+
+    def text_exists(self, id: str) -> bool:
+        cur = self._client.get(table_name=self._collection_name, id=id)
+        return cur.rowcount != 0
+
+    def delete_by_ids(self, ids: list[str]) -> None:
+        self._client.delete(table_name=self._collection_name, ids=ids)
+
+    def get_ids_by_metadata_field(self, key: str, value: str) -> list[str]:
+        cur = self._client.get(
+            table_name=self._collection_name,
+            where_clause=f"metadata->>'$.{key}' = '{value}'",
+            output_column_name=["id"],
+        )
+        return [row[0] for row in cur]
+
+    def delete_by_metadata_field(self, key: str, value: str) -> None:
+        ids = self.get_ids_by_metadata_field(key, value)
+        self.delete_by_ids(ids)
+
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        return []
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        ef_search = kwargs.get("ef_search", self._hnsw_ef_search)
+        if ef_search != self._hnsw_ef_search:
+            self._client.set_ob_hnsw_ef_search(ef_search)
+            self._hnsw_ef_search = ef_search
+        topk = kwargs.get("top_k", 10)
+        cur = self._client.ann_search(
+            table_name=self._collection_name,
+            vec_column_name="vector",
+            vec_data=query_vector,
+            topk=topk,
+            distance_func=func.l2_distance,
+            output_column_names=["text", "metadata"],
+            with_dist=True,
+        )
+        docs = []
+        for text, metadata, distance in cur:
+            metadata = json.loads(metadata)
+            metadata["score"] = 1 - distance / math.sqrt(2)
+            docs.append(
+                Document(
+                    page_content=text,
+                    metadata=metadata,
+                )
+            )
+        return docs
+
+    def delete(self) -> None:
+        self._client.drop_table_if_exist(self._collection_name)
+
+
+class OceanBaseVectorFactory(AbstractVectorFactory):
+    def init_vector(
+        self,
+        dataset: Dataset,
+        attributes: list,
+        embeddings: Embeddings,
+    ) -> BaseVector:
+        if dataset.index_struct_dict:
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
+            collection_name = class_prefix.lower()
+        else:
+            dataset_id = dataset.id
+            collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.OCEANBASE, collection_name))
+        return OceanBaseVector(
+            collection_name,
+            OceanBaseVectorConfig(
+                host=dify_config.OCEANBASE_VECTOR_HOST,
+                port=dify_config.OCEANBASE_VECTOR_PORT,
+                user=dify_config.OCEANBASE_VECTOR_USER,
+                password=(dify_config.OCEANBASE_VECTOR_PASSWORD or ""),
+                database=dify_config.OCEANBASE_VECTOR_DATABASE,
+            ),
+        )

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

@@ -4,6 +4,7 @@ import uuid
 import requests
 from requests.auth import HTTPDigestAuth
 
+from configs import dify_config
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from models.dataset import TidbAuthBinding
@@ -36,7 +37,7 @@ class TidbService:
         }
 
         spending_limit = {
-            "monthly": 100,
+            "monthly": dify_config.TIDB_SPEND_LIMIT,
         }
         password = str(uuid.uuid4()).replace("-", "")[:16]
         display_name = str(uuid.uuid4()).replace("-", "")[:16]
@@ -208,7 +209,7 @@ class TidbService:
             }
 
             spending_limit = {
-                "monthly": 10,
+                "monthly": dify_config.TIDB_SPEND_LIMIT,
             }
             password = str(uuid.uuid4()).replace("-", "")[:16]
             display_name = str(uuid.uuid4()).replace("-", "")

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

@@ -134,6 +134,10 @@ class Vector:
                 from core.rag.datasource.vdb.tidb_on_qdrant.tidb_on_qdrant_vector import TidbOnQdrantVectorFactory
 
                 return TidbOnQdrantVectorFactory
+            case VectorType.OCEANBASE:
+                from core.rag.datasource.vdb.oceanbase.oceanbase_vector import OceanBaseVectorFactory
+
+                return OceanBaseVectorFactory
             case _:
                 raise ValueError(f"Vector store {vector_type} is not supported.")
 

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

@@ -21,3 +21,4 @@ class VectorType(str, Enum):
     VIKINGDB = "vikingdb"
     UPSTASH = "upstash"
     TIDB_ON_QDRANT = "tidb_on_qdrant"
+    OCEANBASE = "oceanbase"

api/core/rag/rerank/rerank_model.py

@@ -27,18 +27,17 @@ class RerankModelRunner(BaseRerankRunner):
         :return:
         """
         docs = []
-        doc_id = []
+        doc_id = set()
         unique_documents = []
-        dify_documents = [item for item in documents if item.provider == "dify"]
-        external_documents = [item for item in documents if item.provider == "external"]
-        for document in dify_documents:
-            if document.metadata["doc_id"] not in doc_id:
-                doc_id.append(document.metadata["doc_id"])
+        for document in documents:
+            if document.provider == "dify" and document.metadata["doc_id"] not in doc_id:
+                doc_id.add(document.metadata["doc_id"])
                 docs.append(document.page_content)
                 unique_documents.append(document)
-        for document in external_documents:
-            docs.append(document.page_content)
-            unique_documents.append(document)
+            elif document.provider == "external":
+                if document not in unique_documents:
+                    docs.append(document.page_content)
+                    unique_documents.append(document)
 
         documents = unique_documents
 

api/core/rag/rerank/rerank_type.py

@@ -1,6 +1,6 @@
 from enum import Enum
 
 
-class RerankMode(Enum):
+class RerankMode(str, Enum):
     RERANKING_MODEL = "reranking_model"
     WEIGHTED_SCORE = "weighted_score"

api/core/rag/retrieval/dataset_retrieval.py

@@ -22,6 +22,7 @@ from core.rag.datasource.keyword.jieba.jieba_keyword_table_handler import JiebaK
 from core.rag.datasource.retrieval_service import RetrievalService
 from core.rag.entities.context_entities import DocumentContext
 from core.rag.models.document import Document
+from core.rag.rerank.rerank_type import RerankMode
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.rag.retrieval.router.multi_dataset_function_call_router import FunctionCallMultiDatasetRouter
 from core.rag.retrieval.router.multi_dataset_react_route import ReactMultiDatasetRouter
@@ -361,10 +362,39 @@ class DatasetRetrieval:
         reranking_enable: bool = True,
         message_id: Optional[str] = None,
     ):
+        if not available_datasets:
+            return []
         threads = []
         all_documents = []
         dataset_ids = [dataset.id for dataset in available_datasets]
-        index_type = None
+        index_type_check = all(
+            item.indexing_technique == available_datasets[0].indexing_technique for item in available_datasets
+        )
+        if not index_type_check and (not reranking_enable or reranking_mode != RerankMode.RERANKING_MODEL):
+            raise ValueError(
+                "The configured knowledge base list have different indexing technique, please set reranking model."
+            )
+        index_type = available_datasets[0].indexing_technique
+        if index_type == "high_quality":
+            embedding_model_check = all(
+                item.embedding_model == available_datasets[0].embedding_model for item in available_datasets
+            )
+            embedding_model_provider_check = all(
+                item.embedding_model_provider == available_datasets[0].embedding_model_provider
+                for item in available_datasets
+            )
+            if (
+                reranking_enable
+                and reranking_mode == "weighted_score"
+                and (not embedding_model_check or not embedding_model_provider_check)
+            ):
+                raise ValueError(
+                    "The configured knowledge base list have different embedding model, please set reranking model."
+                )
+            if reranking_enable and reranking_mode == RerankMode.WEIGHTED_SCORE:
+                weights["vector_setting"]["embedding_provider_name"] = available_datasets[0].embedding_model_provider
+                weights["vector_setting"]["embedding_model_name"] = available_datasets[0].embedding_model
+
         for dataset in available_datasets:
             index_type = dataset.indexing_technique
             retrieval_thread = threading.Thread(

api/core/tools/provider/builtin/chart/tools/bar.py

@@ -33,7 +33,9 @@ class BarChartTool(BuiltinTool):
         if axis:
             axis = [label[:10] + "..." if len(label) > 10 else label for label in axis]
             ax.set_xticklabels(axis, rotation=45, ha="right")
-            ax.bar(axis, data)
+            # ensure all labels, including duplicates, are correctly displayed
+            ax.bar(range(len(data)), data)
+            ax.set_xticks(range(len(data)))
         else:
             ax.bar(range(len(data)), data)
 

api/core/tools/provider/builtin/comfyui/tools/comfyui_client.py

@@ -1,5 +1,3 @@
-import base64
-import io
 import json
 import random
 import uuid
@@ -8,7 +6,7 @@ import httpx
 from websocket import WebSocket
 from yarl import URL
 
-from core.file.file_manager import _get_encoded_string
+from core.file.file_manager import download
 from core.file.models import File
 
 
@@ -29,8 +27,7 @@ class ComfyUiClient:
         return response.content
 
     def upload_image(self, image_file: File) -> dict:
-        image_content = base64.b64decode(_get_encoded_string(image_file))
-        file = io.BytesIO(image_content)
+        file = download(image_file)
         files = {"image": (image_file.filename, file, image_file.mime_type), "overwrite": "true"}
         res = httpx.post(str(self.base_url / "upload/image"), files=files)
         return res.json()
@@ -47,12 +44,7 @@ class ComfyUiClient:
         ws.connect(ws_address)
         return ws, client_id
 
-    def set_prompt(
-        self, origin_prompt: dict, positive_prompt: str, negative_prompt: str = "", image_name: str = ""
-    ) -> dict:
-        """
-        find the first KSampler, then can find the prompt node through it.
-        """
+    def set_prompt_by_ksampler(self, origin_prompt: dict, positive_prompt: str, negative_prompt: str = "") -> dict:
         prompt = origin_prompt.copy()
         id_to_class_type = {id: details["class_type"] for id, details in prompt.items()}
         k_sampler = [key for key, value in id_to_class_type.items() if value == "KSampler"][0]
@@ -64,9 +56,20 @@ class ComfyUiClient:
             negative_input_id = prompt.get(k_sampler)["inputs"]["negative"][0]
             prompt.get(negative_input_id)["inputs"]["text"] = negative_prompt
 
-        if image_name != "":
-            image_loader = [key for key, value in id_to_class_type.items() if value == "LoadImage"][0]
-            prompt.get(image_loader)["inputs"]["image"] = image_name
+        return prompt
+
+    def set_prompt_images_by_ids(self, origin_prompt: dict, image_names: list[str], image_ids: list[str]) -> dict:
+        prompt = origin_prompt.copy()
+        for index, image_node_id in enumerate(image_ids):
+            prompt[image_node_id]["inputs"]["image"] = image_names[index]
+        return prompt
+
+    def set_prompt_images_by_default(self, origin_prompt: dict, image_names: list[str]) -> dict:
+        prompt = origin_prompt.copy()
+        id_to_class_type = {id: details["class_type"] for id, details in prompt.items()}
+        load_image_nodes = [key for key, value in id_to_class_type.items() if value == "LoadImage"]
+        for load_image, image_name in zip(load_image_nodes, image_names):
+            prompt.get(load_image)["inputs"]["image"] = image_name
         return prompt
 
     def track_progress(self, prompt: dict, ws: WebSocket, prompt_id: str):

api/core/tools/provider/builtin/comfyui/tools/comfyui_workflow.py

@@ -1,7 +1,9 @@
 import json
 from typing import Any
 
+from core.file import FileType
 from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.errors import ToolParameterValidationError
 from core.tools.provider.builtin.comfyui.tools.comfyui_client import ComfyUiClient
 from core.tools.tool.builtin_tool import BuiltinTool
 
@@ -10,19 +12,46 @@ class ComfyUIWorkflowTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
         comfyui = ComfyUiClient(self.runtime.credentials["base_url"])
 
-        positive_prompt = tool_parameters.get("positive_prompt")
-        negative_prompt = tool_parameters.get("negative_prompt")
+        positive_prompt = tool_parameters.get("positive_prompt", "")
+        negative_prompt = tool_parameters.get("negative_prompt", "")
+        images = tool_parameters.get("images") or []
         workflow = tool_parameters.get("workflow_json")
-        image_name = ""
-        if image := tool_parameters.get("image"):
+        image_names = []
+        for image in images:
+            if image.type != FileType.IMAGE:
+                continue
             image_name = comfyui.upload_image(image).get("name")
+            image_names.append(image_name)
+
+        set_prompt_with_ksampler = True
+        if "{{positive_prompt}}" in workflow:
+            set_prompt_with_ksampler = False
+            workflow = workflow.replace("{{positive_prompt}}", positive_prompt)
+            workflow = workflow.replace("{{negative_prompt}}", negative_prompt)
 
         try:
-            origin_prompt = json.loads(workflow)
+            prompt = json.loads(workflow)
         except:
             return self.create_text_message("the Workflow JSON is not correct")
 
-        prompt = comfyui.set_prompt(origin_prompt, positive_prompt, negative_prompt, image_name)
+        if set_prompt_with_ksampler:
+            try:
+                prompt = comfyui.set_prompt_by_ksampler(prompt, positive_prompt, negative_prompt)
+            except:
+                raise ToolParameterValidationError(
+                    "Failed set prompt with KSampler, try replace prompt to {{positive_prompt}} in your workflow json"
+                )
+
+        if image_names:
+            if image_ids := tool_parameters.get("image_ids"):
+                image_ids = image_ids.split(",")
+                try:
+                    prompt = comfyui.set_prompt_images_by_ids(prompt, image_names, image_ids)
+                except:
+                    raise ToolParameterValidationError("the Image Node ID List not match your upload image files.")
+            else:
+                prompt = comfyui.set_prompt_images_by_default(prompt, image_names)
+
         images = comfyui.generate_image_by_prompt(prompt)
         result = []
         for img in images:

api/core/tools/provider/builtin/comfyui/tools/comfyui_workflow.yaml

@@ -24,12 +24,12 @@ parameters:
       zh_Hans: 负面提示词
     llm_description: Negative prompt, you should describe the image you don't want to generate as a list of words as possible as detailed, the prompt must be written in English.
     form: llm
-  - name: image
-    type: file
+  - name: images
+    type: files
     label:
-      en_US: Input Image
+      en_US: Input Images
       zh_Hans: 输入的图片
-    llm_description: The input image, used to transfer to the comfyui workflow to generate another image.
+    llm_description: The input images, used to transfer to the comfyui workflow to generate another image.
     form: llm
   - name: workflow_json
     type: string
@@ -40,3 +40,15 @@ parameters:
       en_US: exported from ComfyUI workflow
       zh_Hans: 从ComfyUI的工作流中导出
     form: form
+  - name: image_ids
+    type: string
+    label:
+      en_US: Image Node ID List
+      zh_Hans: 图片节点ID列表
+    placeholder:
+      en_US: Use commas to separate multiple node ID
+      zh_Hans: 多个节点ID时使用半角逗号分隔
+    human_description:
+      en_US: When the workflow has multiple image nodes, enter the ID list of these nodes, and the images will be passed to ComfyUI in the order of the list.
+      zh_Hans: 当工作流有多个图片节点时，输入这些节点的ID列表，图片将按列表顺序传给ComfyUI
+    form: form

api/core/tools/tool_manager.py

@@ -3,7 +3,7 @@ import logging
 import mimetypes
 from collections.abc import Generator
 from os import listdir, path
-from threading import Lock
+from threading import Lock, Thread
 from typing import Any, Optional, Union
 
 from configs import dify_config
@@ -647,4 +647,5 @@ class ToolManager:
             raise ValueError(f"provider type {provider_type} not found")
 
 
-ToolManager.load_builtin_providers_cache()
+# preload builtin tool providers
+Thread(target=ToolManager.load_builtin_providers_cache, name="pre_load_builtin_providers_cache", daemon=True).start()

api/core/workflow/nodes/answer/answer_stream_generate_router.py

@@ -153,6 +153,7 @@ class AnswerStreamGeneratorRouter:
                 NodeType.IF_ELSE,
                 NodeType.QUESTION_CLASSIFIER,
                 NodeType.ITERATION,
+                NodeType.CONVERSATION_VARIABLE_ASSIGNER,
             }:
                 answer_dependencies[answer_node_id].append(source_node_id)
             else:

api/core/workflow/nodes/document_extractor/node.py

@@ -5,6 +5,7 @@ import json
 import docx
 import pandas as pd
 import pypdfium2
+import yaml
 from unstructured.partition.email import partition_email
 from unstructured.partition.epub import partition_epub
 from unstructured.partition.msg import partition_msg
@@ -101,6 +102,8 @@ def _extract_text_by_mime_type(*, file_content: bytes, mime_type: str) -> str:
             return _extract_text_from_msg(file_content)
         case "application/json":
             return _extract_text_from_json(file_content)
+        case "application/x-yaml" | "text/yaml":
+            return _extract_text_from_yaml(file_content)
         case _:
             raise UnsupportedFileTypeError(f"Unsupported MIME type: {mime_type}")
 
@@ -112,6 +115,8 @@ def _extract_text_by_file_extension(*, file_content: bytes, file_extension: str)
             return _extract_text_from_plain_text(file_content)
         case ".json":
             return _extract_text_from_json(file_content)
+        case ".yaml" | ".yml":
+            return _extract_text_from_yaml(file_content)
         case ".pdf":
             return _extract_text_from_pdf(file_content)
         case ".doc" | ".docx":
@@ -149,6 +154,15 @@ def _extract_text_from_json(file_content: bytes) -> str:
         raise TextExtractionError(f"Failed to decode or parse JSON file: {e}") from e
 
 
+def _extract_text_from_yaml(file_content: bytes) -> str:
+    """Extract the content from yaml file"""
+    try:
+        yaml_data = yaml.safe_load_all(file_content.decode("utf-8"))
+        return yaml.dump_all(yaml_data, allow_unicode=True, sort_keys=False)
+    except (UnicodeDecodeError, yaml.YAMLError) as e:
+        raise TextExtractionError(f"Failed to decode or parse YAML file: {e}") from e
+
+
 def _extract_text_from_pdf(file_content: bytes) -> str:
     try:
         pdf_file = io.BytesIO(file_content)

api/core/workflow/nodes/http_request/entities.py

@@ -94,7 +94,7 @@ class Response:
     @property
     def is_file(self):
         content_type = self.content_type
-        content_disposition = self.response.headers.get("Content-Disposition", "")
+        content_disposition = self.response.headers.get("content-disposition", "")
 
         return "attachment" in content_disposition or (
             not any(non_file in content_type for non_file in NON_FILE_CONTENT_TYPES)
@@ -103,7 +103,7 @@ class Response:
 
     @property
     def content_type(self) -> str:
-        return self.headers.get("Content-Type", "")
+        return self.headers.get("content-type", "")
 
     @property
     def text(self) -> str:

api/core/workflow/nodes/http_request/node.py

@@ -142,10 +142,11 @@ class HttpRequestNode(BaseNode[HttpRequestNodeData]):
         Extract files from response
         """
         files = []
+        is_file = response.is_file
         content_type = response.content_type
         content = response.content
 
-        if content_type:
+        if is_file and content_type:
             # extract filename from url
             filename = path.basename(url)
             # extract extension if possible

api/core/workflow/nodes/llm/node.py

@@ -327,7 +327,7 @@ class LLMNode(BaseNode[LLMNodeData]):
             if variable is None:
                 raise ValueError(f"Variable {variable_selector.variable} not found")
             if isinstance(variable, NoneSegment):
-                continue
+                inputs[variable_selector.variable] = ""
             inputs[variable_selector.variable] = variable.to_object()
 
         memory = node_data.memory
@@ -349,13 +349,11 @@ class LLMNode(BaseNode[LLMNodeData]):
         variable = self.graph_runtime_state.variable_pool.get(selector)
         if variable is None:
             return []
-        if isinstance(variable, FileSegment):
+        elif isinstance(variable, FileSegment):
             return [variable.value]
-        if isinstance(variable, ArrayFileSegment):
+        elif isinstance(variable, ArrayFileSegment):
             return variable.value
-        # FIXME: Temporary fix for empty array,
-        # all variables added to variable pool should be a Segment instance.
-        if isinstance(variable, ArrayAnySegment) and len(variable.value) == 0:
+        elif isinstance(variable, NoneSegment | ArrayAnySegment):
             return []
         raise ValueError(f"Invalid variable type: {type(variable)}")
 

api/factories/variable_factory.py

@@ -10,6 +10,7 @@ from core.variables import (
     ArrayNumberVariable,
     ArrayObjectSegment,
     ArrayObjectVariable,
+    ArraySegment,
     ArrayStringSegment,
     ArrayStringVariable,
     FileSegment,
@@ -79,7 +80,7 @@ def build_segment(value: Any, /) -> Segment:
     if isinstance(value, list):
         items = [build_segment(item) for item in value]
         types = {item.value_type for item in items}
-        if len(types) != 1:
+        if len(types) != 1 or all(isinstance(item, ArraySegment) for item in items):
             return ArrayAnySegment(value=value)
         match types.pop():
             case SegmentType.STRING:

api/fields/conversation_fields.py

@@ -121,6 +121,7 @@ conversation_fields = {
     "from_account_name": fields.String,
     "read_at": TimestampField,
     "created_at": TimestampField,
+    "updated_at": TimestampField,
     "annotation": fields.Nested(annotation_fields, allow_null=True),
     "model_config": fields.Nested(simple_model_config_fields),
     "user_feedback_stats": fields.Nested(feedback_stat_fields),
@@ -182,6 +183,7 @@ conversation_detail_fields = {
     "from_end_user_id": fields.String,
     "from_account_id": fields.String,
     "created_at": TimestampField,
+    "updated_at": TimestampField,
     "annotated": fields.Boolean,
     "introduction": fields.String,
     "model_config": fields.Nested(model_config_fields),
@@ -197,6 +199,7 @@ simple_conversation_fields = {
     "status": fields.String,
     "introduction": fields.String,
     "created_at": TimestampField,
+    "updated_at": TimestampField,
 }
 
 conversation_infinite_scroll_pagination_fields = {

api/models/model.py

@@ -396,7 +396,7 @@ class AppModelConfig(db.Model):
             "file_upload": self.file_upload_dict,
         }
 
-    def from_model_config_dict(self, model_config: dict):
+    def from_model_config_dict(self, model_config: Mapping[str, Any]):
         self.opening_statement = model_config.get("opening_statement")
         self.suggested_questions = (
             json.dumps(model_config["suggested_questions"]) if model_config.get("suggested_questions") else None

api/poetry.lock

@@ -7269,6 +7269,22 @@ files = [
 ed25519 = ["PyNaCl (>=1.4.0)"]
 rsa = ["cryptography"]
 
+[[package]]
+name = "pyobvector"
+version = "0.1.6"
+description = "A python SDK for OceanBase Vector Store, based on SQLAlchemy, compatible with Milvus API."
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "pyobvector-0.1.6-py3-none-any.whl", hash = "sha256:0d700e865a85b4716b9a03384189e49288cd9d5f3cef88aed4740bc82d5fd136"},
+    {file = "pyobvector-0.1.6.tar.gz", hash = "sha256:05551addcac8c596992d5e38b480c83ca3481c6cfc6f56a1a1bddfb2e6ae037e"},
+]
+
+[package.dependencies]
+numpy = ">=1.26.0,<2.0.0"
+pymysql = ">=1.1.1,<2.0.0"
+sqlalchemy = ">=2.0.32,<3.0.0"
+
 [[package]]
 name = "pyopenssl"
 version = "24.2.1"
@@ -8677,6 +8693,11 @@ files = [
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
     {file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:b0768ad641981f5d3a198430a1d31c3e044ed2e8a6f22166b4d546a5116d7908"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:178ddd0a5cb0044464fc1bfc4cca5b1833bfc7bb022d70b05db8530da4bb3dd3"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7284ade780084d94505632241bf78c44ab3b6f1e8ccab3d2af58e0e950f9c12"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:b7b0f9a0b1040830d38c39b91b3a44e1b643f4b36e36567b80b7c6bd2202a27f"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
     {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
@@ -10919,4 +10940,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "52552faf5f4823056eb48afe05349ab2f0e9a5bc42105211ccbbb54b59e27b59"
+content-hash = "ef927b98c33d704d680e08db0e5c7d9a4e05454c66fcd6a5f656a65eb08e886b"

api/pyproject.toml

@@ -247,6 +247,7 @@ pgvecto-rs = { version = "~0.2.1", extras = ['sqlalchemy'] }
 pgvector = "0.2.5"
 pymilvus = "~2.4.4"
 pymochow = "1.3.1"
+pyobvector = "~0.1.6"
 qdrant-client = "1.7.3"
 tcvectordb = "1.3.2"
 tidb-vector = "0.0.9"

api/services/app_dsl_service/__init__.py

@@ -0,0 +1,3 @@
+from .service import AppDslService
+
+__all__ = ["AppDslService"]

api/services/app_dsl_service/exc.py

@@ -0,0 +1,34 @@
+class DSLVersionNotSupportedError(ValueError):
+    """Raised when the imported DSL version is not supported by the current Dify version."""
+
+
+class InvalidYAMLFormatError(ValueError):
+    """Raised when the provided YAML format is invalid."""
+
+
+class MissingAppDataError(ValueError):
+    """Raised when the app data is missing in the provided DSL."""
+
+
+class InvalidAppModeError(ValueError):
+    """Raised when the app mode is invalid."""
+
+
+class MissingWorkflowDataError(ValueError):
+    """Raised when the workflow data is missing in the provided DSL."""
+
+
+class MissingModelConfigError(ValueError):
+    """Raised when the model config data is missing in the provided DSL."""
+
+
+class FileSizeLimitExceededError(ValueError):
+    """Raised when the file size exceeds the allowed limit."""
+
+
+class EmptyContentError(ValueError):
+    """Raised when the content fetched from the URL is empty."""
+
+
+class ContentDecodingError(ValueError):
+    """Raised when there is an error decoding the content."""

api/services/app_dsl_service/service.py

@@ -1,8 +1,11 @@
 import logging
+from collections.abc import Mapping
+from typing import Any
 
-import httpx
-import yaml  # type: ignore
+import yaml
+from packaging import version
 
+from core.helper import ssrf_proxy
 from events.app_event import app_model_config_was_updated, app_was_created
 from extensions.ext_database import db
 from factories import variable_factory
@@ -11,6 +14,18 @@ from models.model import App, AppMode, AppModelConfig
 from models.workflow import Workflow
 from services.workflow_service import WorkflowService
 
+from .exc import (
+    ContentDecodingError,
+    DSLVersionNotSupportedError,
+    EmptyContentError,
+    FileSizeLimitExceededError,
+    InvalidAppModeError,
+    InvalidYAMLFormatError,
+    MissingAppDataError,
+    MissingModelConfigError,
+    MissingWorkflowDataError,
+)
+
 logger = logging.getLogger(__name__)
 
 current_dsl_version = "0.1.2"
@@ -30,32 +45,21 @@ class AppDslService:
         :param args: request args
         :param account: Account instance
         """
-        try:
-            max_size = 10 * 1024 * 1024  # 10MB
-            timeout = httpx.Timeout(10.0)
-            with httpx.stream("GET", url.strip(), follow_redirects=True, timeout=timeout) as response:
-                response.raise_for_status()
-                total_size = 0
-                content = b""
-                for chunk in response.iter_bytes():
-                    total_size += len(chunk)
-                    if total_size > max_size:
-                        raise ValueError("File size exceeds the limit of 10MB")
-                    content += chunk
-        except httpx.HTTPStatusError as http_err:
-            raise ValueError(f"HTTP error occurred: {http_err}")
-        except httpx.RequestError as req_err:
-            raise ValueError(f"Request error occurred: {req_err}")
-        except Exception as e:
-            raise ValueError(f"Failed to fetch DSL from URL: {e}")
+        max_size = 10 * 1024 * 1024  # 10MB
+        response = ssrf_proxy.get(url.strip(), follow_redirects=True, timeout=(10, 10))
+        response.raise_for_status()
+        content = response.content
+
+        if len(content) > max_size:
+            raise FileSizeLimitExceededError("File size exceeds the limit of 10MB")
 
         if not content:
-            raise ValueError("Empty content from url")
+            raise EmptyContentError("Empty content from url")
 
         try:
             data = content.decode("utf-8")
         except UnicodeDecodeError as e:
-            raise ValueError(f"Error decoding content: {e}")
+            raise ContentDecodingError(f"Error decoding content: {e}")
 
         return cls.import_and_create_new_app(tenant_id, data, args, account)
 
@@ -71,14 +75,14 @@ class AppDslService:
         try:
             import_data = yaml.safe_load(data)
         except yaml.YAMLError:
-            raise ValueError("Invalid YAML format in data argument.")
+            raise InvalidYAMLFormatError("Invalid YAML format in data argument.")
 
         # check or repair dsl version
-        import_data = cls._check_or_fix_dsl(import_data)
+        import_data = _check_or_fix_dsl(import_data)
 
         app_data = import_data.get("app")
         if not app_data:
-            raise ValueError("Missing app in data argument")
+            raise MissingAppDataError("Missing app in data argument")
 
         # get app basic info
         name = args.get("name") or app_data.get("name")
@@ -90,11 +94,18 @@ class AppDslService:
 
         # import dsl and create app
         app_mode = AppMode.value_of(app_data.get("mode"))
+
         if app_mode in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
+            workflow_data = import_data.get("workflow")
+            if not workflow_data or not isinstance(workflow_data, dict):
+                raise MissingWorkflowDataError(
+                    "Missing workflow in data argument when app mode is advanced-chat or workflow"
+                )
+
             app = cls._import_and_create_new_workflow_based_app(
                 tenant_id=tenant_id,
                 app_mode=app_mode,
-                workflow_data=import_data.get("workflow"),
+                workflow_data=workflow_data,
                 account=account,
                 name=name,
                 description=description,
@@ -104,10 +115,16 @@ class AppDslService:
                 use_icon_as_answer_icon=use_icon_as_answer_icon,
             )
         elif app_mode in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.COMPLETION}:
+            model_config = import_data.get("model_config")
+            if not model_config or not isinstance(model_config, dict):
+                raise MissingModelConfigError(
+                    "Missing model_config in data argument when app mode is chat, agent-chat or completion"
+                )
+
             app = cls._import_and_create_new_model_config_based_app(
                 tenant_id=tenant_id,
                 app_mode=app_mode,
-                model_config_data=import_data.get("model_config"),
+                model_config_data=model_config,
                 account=account,
                 name=name,
                 description=description,
@@ -117,7 +134,7 @@ class AppDslService:
                 use_icon_as_answer_icon=use_icon_as_answer_icon,
             )
         else:
-            raise ValueError("Invalid app mode")
+            raise InvalidAppModeError("Invalid app mode")
 
         return app
 
@@ -132,26 +149,32 @@ class AppDslService:
         try:
             import_data = yaml.safe_load(data)
         except yaml.YAMLError:
-            raise ValueError("Invalid YAML format in data argument.")
+            raise InvalidYAMLFormatError("Invalid YAML format in data argument.")
 
         # check or repair dsl version
-        import_data = cls._check_or_fix_dsl(import_data)
+        import_data = _check_or_fix_dsl(import_data)
 
         app_data = import_data.get("app")
         if not app_data:
-            raise ValueError("Missing app in data argument")
+            raise MissingAppDataError("Missing app in data argument")
 
         # import dsl and overwrite app
         app_mode = AppMode.value_of(app_data.get("mode"))
         if app_mode not in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
-            raise ValueError("Only support import workflow in advanced-chat or workflow app.")
+            raise InvalidAppModeError("Only support import workflow in advanced-chat or workflow app.")
 
         if app_data.get("mode") != app_model.mode:
             raise ValueError(f"App mode {app_data.get('mode')} is not matched with current app mode {app_mode.value}")
 
+        workflow_data = import_data.get("workflow")
+        if not workflow_data or not isinstance(workflow_data, dict):
+            raise MissingWorkflowDataError(
+                "Missing workflow in data argument when app mode is advanced-chat or workflow"
+            )
+
         return cls._import_and_overwrite_workflow_based_app(
             app_model=app_model,
-            workflow_data=import_data.get("workflow"),
+            workflow_data=workflow_data,
             account=account,
         )
 
@@ -186,35 +209,12 @@ class AppDslService:
 
         return yaml.dump(export_data, allow_unicode=True)
 
-    @classmethod
-    def _check_or_fix_dsl(cls, import_data: dict) -> dict:
-        """
-        Check or fix dsl
-
-        :param import_data: import data
-        """
-        if not import_data.get("version"):
-            import_data["version"] = "0.1.0"
-
-        if not import_data.get("kind") or import_data.get("kind") != "app":
-            import_data["kind"] = "app"
-
-        if import_data.get("version") != current_dsl_version:
-            # Currently only one DSL version, so no difference checks or compatibility fixes will be performed.
-            logger.warning(
-                f"DSL version {import_data.get('version')} is not compatible "
-                f"with current version {current_dsl_version}, related to "
-                f"Dify version {dsl_to_dify_version_mapping.get(current_dsl_version)}."
-            )
-
-        return import_data
-
     @classmethod
     def _import_and_create_new_workflow_based_app(
         cls,
         tenant_id: str,
         app_mode: AppMode,
-        workflow_data: dict,
+        workflow_data: Mapping[str, Any],
         account: Account,
         name: str,
         description: str,
@@ -238,7 +238,9 @@ class AppDslService:
         :param use_icon_as_answer_icon: use app icon as answer icon
         """
         if not workflow_data:
-            raise ValueError("Missing workflow in data argument when app mode is advanced-chat or workflow")
+            raise MissingWorkflowDataError(
+                "Missing workflow in data argument when app mode is advanced-chat or workflow"
+            )
 
         app = cls._create_app(
             tenant_id=tenant_id,
@@ -277,7 +279,7 @@ class AppDslService:
 
     @classmethod
     def _import_and_overwrite_workflow_based_app(
-        cls, app_model: App, workflow_data: dict, account: Account
+        cls, app_model: App, workflow_data: Mapping[str, Any], account: Account
     ) -> Workflow:
         """
         Import app dsl and overwrite workflow based app
@@ -287,7 +289,9 @@ class AppDslService:
         :param account: Account instance
         """
         if not workflow_data:
-            raise ValueError("Missing workflow in data argument when app mode is advanced-chat or workflow")
+            raise MissingWorkflowDataError(
+                "Missing workflow in data argument when app mode is advanced-chat or workflow"
+            )
 
         # fetch draft workflow by app_model
         workflow_service = WorkflowService()
@@ -323,7 +327,7 @@ class AppDslService:
         cls,
         tenant_id: str,
         app_mode: AppMode,
-        model_config_data: dict,
+        model_config_data: Mapping[str, Any],
         account: Account,
         name: str,
         description: str,
@@ -345,7 +349,9 @@ class AppDslService:
         :param icon_background: app icon background
         """
         if not model_config_data:
-            raise ValueError("Missing model_config in data argument when app mode is chat, agent-chat or completion")
+            raise MissingModelConfigError(
+                "Missing model_config in data argument when app mode is chat, agent-chat or completion"
+            )
 
         app = cls._create_app(
             tenant_id=tenant_id,
@@ -448,3 +454,34 @@ class AppDslService:
             raise ValueError("Missing app configuration, please check.")
 
         export_data["model_config"] = app_model_config.to_dict()
+
+
+def _check_or_fix_dsl(import_data: dict[str, Any]) -> Mapping[str, Any]:
+    """
+    Check or fix dsl
+
+    :param import_data: import data
+    :raises DSLVersionNotSupportedError: if the imported DSL version is newer than the current version
+    """
+    if not import_data.get("version"):
+        import_data["version"] = "0.1.0"
+
+    if not import_data.get("kind") or import_data.get("kind") != "app":
+        import_data["kind"] = "app"
+
+    imported_version = import_data.get("version")
+    if imported_version != current_dsl_version:
+        if imported_version and version.parse(imported_version) > version.parse(current_dsl_version):
+            raise DSLVersionNotSupportedError(
+                f"The imported DSL version {imported_version} is newer than "
+                f"the current supported version {current_dsl_version}. "
+                f"Please upgrade your Dify instance to import this configuration."
+            )
+        else:
+            logger.warning(
+                f"DSL version {imported_version} is older than "
+                f"the current version {current_dsl_version}. "
+                f"This may cause compatibility issues."
+            )
+
+    return import_data

api/services/dataset_service.py

@@ -736,11 +736,12 @@ class DocumentService:
                     dataset.retrieval_model = document_data.get("retrieval_model") or default_retrieval_model
 
         documents = []
-        batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999))
         if document_data.get("original_document_id"):
             document = DocumentService.update_document_with_dataset_id(dataset, document_data, account)
             documents.append(document)
+            batch = document.batch
         else:
+            batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999))
             # save process rule
             if not dataset_process_rule:
                 process_rule = document_data["process_rule"]
@@ -921,7 +922,7 @@ class DocumentService:
                 if duplicate_document_ids:
                     duplicate_document_indexing_task.delay(dataset.id, duplicate_document_ids)
 
-            return documents, batch
+        return documents, batch
 
     @staticmethod
     def check_documents_upload_quota(count: int, features: FeatureModel):

api/services/file_service.py

@@ -35,7 +35,7 @@ class FileService:
         filename = file.filename
         if not filename:
             raise FileNotExistsError
-        extension = filename.split(".")[-1]
+        extension = filename.split(".")[-1].lower()
         if len(filename) > 200:
             filename = filename.split(".")[0][:200] + "." + extension
 

api/tests/integration_tests/.env.example

@@ -84,5 +84,10 @@ VOLC_EMBEDDING_ENDPOINT_ID=
 # 360 AI Credentials
 ZHINAO_API_KEY=
 
+# VESSL AI Credentials
+VESSL_AI_MODEL_NAME=
+VESSL_AI_API_KEY=
+VESSL_AI_ENDPOINT_URL=
+
 # Gitee AI Credentials
-GITEE_AI_API_KEY=
+GITEE_AI_API_KEY=

api/tests/integration_tests/model_runtime/vessl_ai/test_llm.py

@@ -0,0 +1,131 @@
+import os
+from collections.abc import Generator
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    SystemPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.vessl_ai.llm.llm import VesslAILargeLanguageModel
+
+
+def test_validate_credentials():
+    model = VesslAILargeLanguageModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model=os.environ.get("VESSL_AI_MODEL_NAME"),
+            credentials={
+                "api_key": "invalid_key",
+                "endpoint_url": os.environ.get("VESSL_AI_ENDPOINT_URL"),
+                "mode": "chat",
+            },
+        )
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model=os.environ.get("VESSL_AI_MODEL_NAME"),
+            credentials={
+                "api_key": os.environ.get("VESSL_AI_API_KEY"),
+                "endpoint_url": "http://invalid_url",
+                "mode": "chat",
+            },
+        )
+
+    model.validate_credentials(
+        model=os.environ.get("VESSL_AI_MODEL_NAME"),
+        credentials={
+            "api_key": os.environ.get("VESSL_AI_API_KEY"),
+            "endpoint_url": os.environ.get("VESSL_AI_ENDPOINT_URL"),
+            "mode": "chat",
+        },
+    )
+
+
+def test_invoke_model():
+    model = VesslAILargeLanguageModel()
+
+    response = model.invoke(
+        model=os.environ.get("VESSL_AI_MODEL_NAME"),
+        credentials={
+            "api_key": os.environ.get("VESSL_AI_API_KEY"),
+            "endpoint_url": os.environ.get("VESSL_AI_ENDPOINT_URL"),
+            "mode": "chat",
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Who are you?"),
+        ],
+        model_parameters={
+            "temperature": 1.0,
+            "top_k": 2,
+            "top_p": 0.5,
+        },
+        stop=["How"],
+        stream=False,
+        user="abc-123",
+    )
+
+    assert isinstance(response, LLMResult)
+    assert len(response.message.content) > 0
+
+
+def test_invoke_stream_model():
+    model = VesslAILargeLanguageModel()
+
+    response = model.invoke(
+        model=os.environ.get("VESSL_AI_MODEL_NAME"),
+        credentials={
+            "api_key": os.environ.get("VESSL_AI_API_KEY"),
+            "endpoint_url": os.environ.get("VESSL_AI_ENDPOINT_URL"),
+            "mode": "chat",
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Who are you?"),
+        ],
+        model_parameters={
+            "temperature": 1.0,
+            "top_k": 2,
+            "top_p": 0.5,
+        },
+        stop=["How"],
+        stream=True,
+        user="abc-123",
+    )
+
+    assert isinstance(response, Generator)
+
+    for chunk in response:
+        assert isinstance(chunk, LLMResultChunk)
+        assert isinstance(chunk.delta, LLMResultChunkDelta)
+        assert isinstance(chunk.delta.message, AssistantPromptMessage)
+
+
+def test_get_num_tokens():
+    model = VesslAILargeLanguageModel()
+
+    num_tokens = model.get_num_tokens(
+        model=os.environ.get("VESSL_AI_MODEL_NAME"),
+        credentials={
+            "api_key": os.environ.get("VESSL_AI_API_KEY"),
+            "endpoint_url": os.environ.get("VESSL_AI_ENDPOINT_URL"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+    )
+
+    assert isinstance(num_tokens, int)
+    assert num_tokens == 21

api/tests/integration_tests/vdb/oceanbase/test_oceanbase.py

@@ -0,0 +1,71 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from core.rag.datasource.vdb.oceanbase.oceanbase_vector import (
+    OceanBaseVector,
+    OceanBaseVectorConfig,
+)
+from tests.integration_tests.vdb.__mock.tcvectordb import setup_tcvectordb_mock
+from tests.integration_tests.vdb.test_vector_store import (
+    AbstractVectorTest,
+    get_example_text,
+    setup_mock_redis,
+)
+
+
+@pytest.fixture
+def oceanbase_vector():
+    return OceanBaseVector(
+        "dify_test_collection",
+        config=OceanBaseVectorConfig(
+            host="127.0.0.1",
+            port="2881",
+            user="root@test",
+            database="test",
+            password="test",
+        ),
+    )
+
+
+class OceanBaseVectorTest(AbstractVectorTest):
+    def __init__(self, vector: OceanBaseVector):
+        super().__init__()
+        self.vector = vector
+
+    def search_by_vector(self):
+        hits_by_vector = self.vector.search_by_vector(query_vector=self.example_embedding)
+        assert len(hits_by_vector) == 0
+
+    def search_by_full_text(self):
+        hits_by_full_text = self.vector.search_by_full_text(query=get_example_text())
+        assert len(hits_by_full_text) == 0
+
+    def text_exists(self):
+        exist = self.vector.text_exists(self.example_doc_id)
+        assert exist == True
+
+    def get_ids_by_metadata_field(self):
+        ids = self.vector.get_ids_by_metadata_field(key="document_id", value=self.example_doc_id)
+        assert len(ids) == 0
+
+
+@pytest.fixture
+def setup_mock_oceanbase_client():
+    with patch("core.rag.datasource.vdb.oceanbase.oceanbase_vector.ObVecClient", new_callable=MagicMock) as mock_client:
+        yield mock_client
+
+
+@pytest.fixture
+def setup_mock_oceanbase_vector(oceanbase_vector):
+    with patch.object(oceanbase_vector, "_client"):
+        yield oceanbase_vector
+
+
+def test_oceanbase_vector(
+    setup_mock_redis,
+    setup_mock_oceanbase_client,
+    setup_mock_oceanbase_vector,
+    oceanbase_vector,
+):
+    OceanBaseVectorTest(oceanbase_vector).run_all_tests()

api/tests/integration_tests/workflow/nodes/test_http.py

@@ -430,3 +430,37 @@ def test_multi_colons_parse(setup_http_mock):
     assert urlencode({"Redirect": "http://example2.com"}) in result.process_data.get("request", "")
     assert 'form-data; name="Redirect"\r\n\r\nhttp://example6.com' in result.process_data.get("request", "")
     # assert "http://example3.com" == resp.get("headers", {}).get("referer")
+
+
+def test_image_file(monkeypatch):
+    from types import SimpleNamespace
+
+    monkeypatch.setattr(
+        "core.tools.tool_file_manager.ToolFileManager.create_file_by_raw",
+        lambda *args, **kwargs: SimpleNamespace(id="1"),
+    )
+
+    node = init_http_node(
+        config={
+            "id": "1",
+            "data": {
+                "title": "http",
+                "desc": "",
+                "method": "get",
+                "url": "https://cloud.dify.ai/logo/logo-site.png",
+                "authorization": {
+                    "type": "no-auth",
+                    "config": None,
+                },
+                "params": "",
+                "headers": "",
+                "body": None,
+            },
+        }
+    )
+
+    result = node._run()
+    assert result.process_data is not None
+    assert result.outputs is not None
+    resp = result.outputs
+    assert len(resp.get("files", [])) == 1

api/tests/unit_tests/controllers/test_compare_versions.py

@@ -22,17 +22,3 @@ from controllers.console.version import _has_new_version
 )
 def test_has_new_version(latest_version, current_version, expected):
     assert _has_new_version(latest_version=latest_version, current_version=current_version) == expected
-
-
-def test_has_new_version_invalid_input():
-    with pytest.raises(ValueError):
-        _has_new_version(latest_version="1.0", current_version="1.0.0")
-
-    with pytest.raises(ValueError):
-        _has_new_version(latest_version="1.0.0", current_version="1.0")
-
-    with pytest.raises(ValueError):
-        _has_new_version(latest_version="invalid", current_version="1.0.0")
-
-    with pytest.raises(ValueError):
-        _has_new_version(latest_version="1.0.0", current_version="invalid")

api/tests/unit_tests/core/workflow/nodes/llm/test_node.py

@@ -0,0 +1,125 @@
+import pytest
+
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.file import File, FileTransferMethod, FileType
+from core.model_runtime.entities.message_entities import ImagePromptMessageContent
+from core.variables import ArrayAnySegment, ArrayFileSegment, NoneSegment
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.graph_engine import Graph, GraphInitParams, GraphRuntimeState
+from core.workflow.nodes.answer import AnswerStreamGenerateRoute
+from core.workflow.nodes.end import EndStreamParam
+from core.workflow.nodes.llm.entities import ContextConfig, LLMNodeData, ModelConfig, VisionConfig, VisionConfigOptions
+from core.workflow.nodes.llm.node import LLMNode
+from models.enums import UserFrom
+from models.workflow import WorkflowType
+
+
+class TestLLMNode:
+    @pytest.fixture
+    def llm_node(self):
+        data = LLMNodeData(
+            title="Test LLM",
+            model=ModelConfig(provider="openai", name="gpt-3.5-turbo", mode="chat", completion_params={}),
+            prompt_template=[],
+            memory=None,
+            context=ContextConfig(enabled=False),
+            vision=VisionConfig(
+                enabled=True,
+                configs=VisionConfigOptions(
+                    variable_selector=["sys", "files"],
+                    detail=ImagePromptMessageContent.DETAIL.HIGH,
+                ),
+            ),
+        )
+        variable_pool = VariablePool(
+            system_variables={},
+            user_inputs={},
+        )
+        node = LLMNode(
+            id="1",
+            config={
+                "id": "1",
+                "data": data.model_dump(),
+            },
+            graph_init_params=GraphInitParams(
+                tenant_id="1",
+                app_id="1",
+                workflow_type=WorkflowType.WORKFLOW,
+                workflow_id="1",
+                graph_config={},
+                user_id="1",
+                user_from=UserFrom.ACCOUNT,
+                invoke_from=InvokeFrom.SERVICE_API,
+                call_depth=0,
+            ),
+            graph=Graph(
+                root_node_id="1",
+                answer_stream_generate_routes=AnswerStreamGenerateRoute(
+                    answer_dependencies={},
+                    answer_generate_route={},
+                ),
+                end_stream_param=EndStreamParam(
+                    end_dependencies={},
+                    end_stream_variable_selector_mapping={},
+                ),
+            ),
+            graph_runtime_state=GraphRuntimeState(
+                variable_pool=variable_pool,
+                start_at=0,
+            ),
+        )
+        return node
+
+    def test_fetch_files_with_file_segment(self, llm_node):
+        file = File(
+            id="1",
+            tenant_id="test",
+            type=FileType.IMAGE,
+            filename="test.jpg",
+            transfer_method=FileTransferMethod.LOCAL_FILE,
+            related_id="1",
+        )
+        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], file)
+
+        result = llm_node._fetch_files(selector=["sys", "files"])
+        assert result == [file]
+
+    def test_fetch_files_with_array_file_segment(self, llm_node):
+        files = [
+            File(
+                id="1",
+                tenant_id="test",
+                type=FileType.IMAGE,
+                filename="test1.jpg",
+                transfer_method=FileTransferMethod.LOCAL_FILE,
+                related_id="1",
+            ),
+            File(
+                id="2",
+                tenant_id="test",
+                type=FileType.IMAGE,
+                filename="test2.jpg",
+                transfer_method=FileTransferMethod.LOCAL_FILE,
+                related_id="2",
+            ),
+        ]
+        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], ArrayFileSegment(value=files))
+
+        result = llm_node._fetch_files(selector=["sys", "files"])
+        assert result == files
+
+    def test_fetch_files_with_none_segment(self, llm_node):
+        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], NoneSegment())
+
+        result = llm_node._fetch_files(selector=["sys", "files"])
+        assert result == []
+
+    def test_fetch_files_with_array_any_segment(self, llm_node):
+        llm_node.graph_runtime_state.variable_pool.add(["sys", "files"], ArrayAnySegment(value=[]))
+
+        result = llm_node._fetch_files(selector=["sys", "files"])
+        assert result == []
+
+    def test_fetch_files_with_non_existent_variable(self, llm_node):
+        result = llm_node._fetch_files(selector=["sys", "files"])
+        assert result == []

api/tests/unit_tests/oss/__mock/aliyun_oss.py

@@ -0,0 +1,100 @@
+import os
+import posixpath
+from unittest.mock import MagicMock
+
+import pytest
+from _pytest.monkeypatch import MonkeyPatch
+from oss2 import Bucket
+from oss2.models import GetObjectResult, PutObjectResult
+
+from tests.unit_tests.oss.__mock.base import (
+    get_example_bucket,
+    get_example_data,
+    get_example_filename,
+    get_example_filepath,
+    get_example_folder,
+)
+
+
+class MockResponse:
+    def __init__(self, status, headers, request_id):
+        self.status = status
+        self.headers = headers
+        self.request_id = request_id
+
+
+class MockAliyunOssClass:
+    def __init__(
+        self,
+        auth,
+        endpoint,
+        bucket_name,
+        is_cname=False,
+        session=None,
+        connect_timeout=None,
+        app_name="",
+        enable_crc=True,
+        proxies=None,
+        region=None,
+        cloudbox_id=None,
+        is_path_style=False,
+        is_verify_object_strict=True,
+    ):
+        self.bucket_name = get_example_bucket()
+        self.key = posixpath.join(get_example_folder(), get_example_filename())
+        self.content = get_example_data()
+        self.filepath = get_example_filepath()
+        self.resp = MockResponse(
+            200,
+            {
+                "etag": "ee8de918d05640145b18f70f4c3aa602",
+                "x-oss-version-id": "CAEQNhiBgMDJgZCA0BYiIDc4MGZjZGI2OTBjOTRmNTE5NmU5NmFhZjhjYmY0****",
+            },
+            "request_id",
+        )
+
+    def put_object(self, key, data, headers=None, progress_callback=None):
+        assert key == self.key
+        assert data == self.content
+        return PutObjectResult(self.resp)
+
+    def get_object(self, key, byte_range=None, headers=None, progress_callback=None, process=None, params=None):
+        assert key == self.key
+
+        get_object_output = MagicMock(GetObjectResult)
+        get_object_output.read.return_value = self.content
+        return get_object_output
+
+    def get_object_to_file(
+        self, key, filename, byte_range=None, headers=None, progress_callback=None, process=None, params=None
+    ):
+        assert key == self.key
+        assert filename == self.filepath
+
+    def object_exists(self, key, headers=None):
+        assert key == self.key
+        return True
+
+    def delete_object(self, key, params=None, headers=None):
+        assert key == self.key
+        self.resp.headers["x-oss-delete-marker"] = True
+        return self.resp
+
+
+MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
+
+
+@pytest.fixture
+def setup_aliyun_oss_mock(monkeypatch: MonkeyPatch):
+    if MOCK:
+        monkeypatch.setattr(Bucket, "__init__", MockAliyunOssClass.__init__)
+        monkeypatch.setattr(Bucket, "put_object", MockAliyunOssClass.put_object)
+        monkeypatch.setattr(Bucket, "get_object", MockAliyunOssClass.get_object)
+        monkeypatch.setattr(Bucket, "get_object_to_file", MockAliyunOssClass.get_object_to_file)
+        monkeypatch.setattr(Bucket, "object_exists", MockAliyunOssClass.object_exists)
+        monkeypatch.setattr(Bucket, "delete_object", MockAliyunOssClass.delete_object)
+
+    yield
+
+    if MOCK:
+        monkeypatch.undo()

api/tests/unit_tests/oss/aliyun_oss/aliyun_oss/test_aliyun_oss.py

@@ -0,0 +1,22 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+from oss2 import Auth
+
+from extensions.storage.aliyun_oss_storage import AliyunOssStorage
+from tests.unit_tests.oss.__mock.aliyun_oss import setup_aliyun_oss_mock
+from tests.unit_tests.oss.__mock.base import (
+    BaseStorageTest,
+    get_example_bucket,
+    get_example_folder,
+)
+
+
+class TestAliyunOss(BaseStorageTest):
+    @pytest.fixture(autouse=True)
+    def setup_method(self, setup_aliyun_oss_mock):
+        """Executed before each test method."""
+        with patch.object(Auth, "__init__", return_value=None):
+            self.storage = AliyunOssStorage()
+        self.storage.bucket_name = get_example_bucket()
+        self.storage.folder = get_example_folder()

api/tests/unit_tests/services/app_dsl_service/test_app_dsl_service.py

@@ -0,0 +1,41 @@
+import pytest
+from packaging import version
+
+from services.app_dsl_service import AppDslService
+from services.app_dsl_service.exc import DSLVersionNotSupportedError
+from services.app_dsl_service.service import _check_or_fix_dsl, current_dsl_version
+
+
+class TestAppDSLService:
+    def test_check_or_fix_dsl_missing_version(self):
+        import_data = {}
+        result = _check_or_fix_dsl(import_data)
+        assert result["version"] == "0.1.0"
+        assert result["kind"] == "app"
+
+    def test_check_or_fix_dsl_missing_kind(self):
+        import_data = {"version": "0.1.0"}
+        result = _check_or_fix_dsl(import_data)
+        assert result["kind"] == "app"
+
+    def test_check_or_fix_dsl_older_version(self):
+        import_data = {"version": "0.0.9", "kind": "app"}
+        result = _check_or_fix_dsl(import_data)
+        assert result["version"] == "0.0.9"
+
+    def test_check_or_fix_dsl_current_version(self):
+        import_data = {"version": current_dsl_version, "kind": "app"}
+        result = _check_or_fix_dsl(import_data)
+        assert result["version"] == current_dsl_version
+
+    def test_check_or_fix_dsl_newer_version(self):
+        current_version = version.parse(current_dsl_version)
+        newer_version = f"{current_version.major}.{current_version.minor + 1}.0"
+        import_data = {"version": newer_version, "kind": "app"}
+        with pytest.raises(DSLVersionNotSupportedError):
+            _check_or_fix_dsl(import_data)
+
+    def test_check_or_fix_dsl_invalid_kind(self):
+        import_data = {"version": current_dsl_version, "kind": "invalid"}
+        result = _check_or_fix_dsl(import_data)
+        assert result["kind"] == "app"