Merge branch 'main' into feat/node-execution-retry

.github/workflows/api-tests.yml

@@ -50,6 +50,9 @@ jobs:
       - name: Run ModelRuntime
         run: poetry run -C api bash dev/pytest/pytest_model_runtime.sh
 
+      - name: Run dify config tests
+        run: poetry run -C api python dev/pytest/pytest_config_tests.py
+
       - name: Run Tool
         run: poetry run -C api bash dev/pytest/pytest_tools.sh
 

api/.ruff.toml

@@ -70,7 +70,6 @@ ignore = [
     "SIM113", # eumerate-for-loop
     "SIM117", # multiple-with-statements
     "SIM210", # if-expr-with-true-false
-    "SIM300", # yoda-conditions,
 ]
 
 [lint.per-file-ignores]

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.14.0",
+        default="0.14.1",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/admin.py

@@ -31,7 +31,7 @@ def admin_required(view):
         if auth_scheme != "bearer":
             raise Unauthorized("Invalid Authorization header format. Expected 'Bearer <api-key>' format.")
 
-        if dify_config.ADMIN_API_KEY != auth_token:
+        if auth_token != dify_config.ADMIN_API_KEY:
             raise Unauthorized("API key is invalid.")
 
         return view(*args, **kwargs)

api/controllers/console/explore/recommended_app.py

@@ -13,6 +13,7 @@ app_fields = {
     "name": fields.String,
     "mode": fields.String,
     "icon": fields.String,
+    "icon_type": fields.String,
     "icon_url": AppIconUrlField,
     "icon_background": fields.String,
 }

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

@@ -819,6 +819,82 @@ LLM_BASE_MODELS = [
             ),
         ),
     ),
+    AzureBaseModel(
+        base_model_name="gpt-4o-2024-11-20",
+        entity=AIModelEntity(
+            model="fake-deployment-name",
+            label=I18nObject(
+                en_US="fake-deployment-name-label",
+            ),
+            model_type=ModelType.LLM,
+            features=[
+                ModelFeature.AGENT_THOUGHT,
+                ModelFeature.VISION,
+                ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
+            ],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                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="presence_penalty",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name="frequency_penalty",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=16384),
+                ParameterRule(
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
+                    required=False,
+                    precision=2,
+                    min=0,
+                    max=1,
+                ),
+                ParameterRule(
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
+                    help=I18nObject(
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
+                    ),
+                    required=False,
+                    options=["text", "json_object", "json_schema"],
+                ),
+                ParameterRule(
+                    name="json_schema",
+                    label=I18nObject(en_US="JSON Schema"),
+                    type="text",
+                    help=I18nObject(
+                        zh_Hans="设置返回的json schema，llm将按照它返回",
+                        en_US="Set a response json schema will ensure LLM to adhere it.",
+                    ),
+                    required=False,
+                ),
+            ],
+            pricing=PriceConfig(
+                input=5.00,
+                output=15.00,
+                unit=0.000001,
+                currency="USD",
+            ),
+        ),
+    ),
     AzureBaseModel(
         base_model_name="gpt-4-turbo",
         entity=AIModelEntity(

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

@@ -171,6 +171,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: gpt-4o-2024-11-20
+          value: gpt-4o-2024-11-20
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: gpt-4-turbo
           value: gpt-4-turbo

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

@@ -92,7 +92,10 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
                 average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embedding = (average / np.linalg.norm(average)).tolist()
+            if np.isnan(embedding).any():
+                raise ValueError("Normalized embedding is nan please try again")
+            embeddings[i] = embedding
 
         # calc usage
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)

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

@@ -1,11 +1,19 @@
+from collections.abc import Mapping
+
 import boto3
 from botocore.config import Config
 
+from core.model_runtime.errors.invoke import InvokeBadRequestError
+
+
+def get_bedrock_client(service_name: str, credentials: Mapping[str, str]):
+    region_name = credentials.get("aws_region")
+    if not region_name:
+        raise InvokeBadRequestError("aws_region is required")
+    client_config = Config(region_name=region_name)
+    aws_access_key_id = credentials.get("aws_access_key_id")
+    aws_secret_access_key = credentials.get("aws_secret_access_key")
 
-def get_bedrock_client(service_name, credentials=None):
-    client_config = Config(region_name=credentials["aws_region"])
-    aws_access_key_id = credentials["aws_access_key_id"]
-    aws_secret_access_key = credentials["aws_secret_access_key"]
     if aws_access_key_id and aws_secret_access_key:
         # use aksk to call bedrock
         client = boto3.client(

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

@@ -62,7 +62,10 @@ class BedrockRerankModel(RerankModel):
                 }
             )
         modelId = model
-        region = credentials["aws_region"]
+        region = credentials.get("aws_region")
+        # region is a required field
+        if not region:
+            raise InvokeBadRequestError("aws_region is required in credentials")
         model_package_arn = f"arn:aws:bedrock:{region}::foundation-model/{modelId}"
         rerankingConfiguration = {
             "type": "BEDROCK_RERANKING_MODEL",

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

@@ -88,7 +88,10 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
                 average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embedding = (average / np.linalg.norm(average)).tolist()
+            if np.isnan(embedding).any():
+                raise ValueError("Normalized embedding is nan please try again")
+            embeddings[i] = embedding
 
         # calc usage
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)

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

@@ -1,4 +1,5 @@
 - gemini-2.0-flash-exp
+- gemini-2.0-flash-thinking-exp-1219
 - gemini-1.5-pro
 - gemini-1.5-pro-latest
 - gemini-1.5-pro-001

api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-thinking-exp-1219.yaml

@@ -0,0 +1,39 @@
+model: gemini-2.0-flash-thinking-exp-1219
+label:
+  en_US: Gemini 2.0 Flash Thinking Exp 1219
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -97,7 +97,10 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
                 average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embedding = (average / np.linalg.norm(average)).tolist()
+            if np.isnan(embedding).any():
+                raise ValueError("Normalized embedding is nan please try again")
+            embeddings[i] = embedding
 
         # calc usage
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)

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

@@ -119,7 +119,7 @@ class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
                 embeddings.append(result[0].get("embedding"))
 
             return [list(map(float, e)) for e in embeddings]
-        elif "texts" == text_input_key:
+        elif text_input_key == "texts":
             result = client.run(
                 replicate_model_version,
                 input={

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

@@ -18,7 +18,7 @@ class SiliconflowProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(model="deepseek-ai/DeepSeek-V2-Chat", credentials=credentials)
+            model_instance.validate_credentials(model="deepseek-ai/DeepSeek-V2.5", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:

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

@@ -100,7 +100,10 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
                 average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embedding = (average / np.linalg.norm(average)).tolist()
+            if np.isnan(embedding).any():
+                raise ValueError("Normalized embedding is nan please try again")
+            embeddings[i] = embedding
 
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
 

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

@@ -40,6 +40,10 @@ configs: dict[str, ModelConfig] = {
         properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
         features=[ModelFeature.TOOL_CALL],
     ),
+    "Doubao-pro-256k": ModelConfig(
+        properties=ModelProperties(context_size=262144, max_tokens=4096, mode=LLMMode.CHAT),
+        features=[],
+    ),
     "Doubao-pro-128k": ModelConfig(
         properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
         features=[ModelFeature.TOOL_CALL],

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

@@ -12,6 +12,7 @@ class ModelConfig(BaseModel):
 
 ModelConfigs = {
     "Doubao-embedding": ModelConfig(properties=ModelProperties(context_size=4096, max_chunks=32)),
+    "Doubao-embedding-large": ModelConfig(properties=ModelProperties(context_size=4096, max_chunks=32)),
 }
 
 
@@ -21,7 +22,7 @@ def get_model_config(credentials: dict) -> ModelConfig:
     if not model_configs:
         return ModelConfig(
             properties=ModelProperties(
-                context_size=int(credentials.get("context_size", 0)),
+                context_size=int(credentials.get("context_size", 4096)),
                 max_chunks=int(credentials.get("max_chunks", 1)),
             )
         )

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

@@ -166,6 +166,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: Doubao-pro-256k
+          value: Doubao-pro-256k
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: Llama3-8B
           value: Llama3-8B
@@ -220,6 +226,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: text-embedding
+        - label:
+            en_US: Doubao-embedding-large
+          value: Doubao-embedding-large
+          show_on:
+            - variable: __model_type
+              value: text-embedding
         - label:
             en_US: Custom
             zh_Hans: 自定义

api/core/rag/embedding/cached_embedding.py

@@ -65,6 +65,11 @@ class CacheEmbedding(Embeddings):
                     for vector in embedding_result.embeddings:
                         try:
                             normalized_embedding = (vector / np.linalg.norm(vector)).tolist()
+                            # stackoverflow best way: https://stackoverflow.com/questions/20319813/how-to-check-list-containing-nan
+                            if np.isnan(normalized_embedding).any():
+                                # for issue #11827  float values are not json compliant
+                                logger.warning(f"Normalized embedding is nan: {normalized_embedding}")
+                                continue
                             embedding_queue_embeddings.append(normalized_embedding)
                         except IntegrityError:
                             db.session.rollback()
@@ -111,6 +116,8 @@ class CacheEmbedding(Embeddings):
 
             embedding_results = embedding_result.embeddings[0]
             embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
+            if np.isnan(embedding_results).any():
+                raise ValueError("Normalized embedding is nan please try again")
         except Exception as ex:
             if dify_config.DEBUG:
                 logging.exception(f"Failed to embed query text '{text[:10]}...({len(text)} chars)'")

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

@@ -11,7 +11,10 @@ class ComfyUIProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         ws = websocket.WebSocket()
         base_url = URL(credentials.get("base_url"))
-        ws_address = f"ws://{base_url.authority}/ws?clientId=test123"
+        ws_protocol = "ws"
+        if base_url.scheme == "https":
+            ws_protocol = "wss"
+        ws_address = f"{ws_protocol}://{base_url.authority}/ws?clientId=test123"
 
         try:
             ws.connect(ws_address)

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

@@ -40,7 +40,10 @@ class ComfyUiClient:
     def open_websocket_connection(self) -> tuple[WebSocket, str]:
         client_id = str(uuid.uuid4())
         ws = WebSocket()
-        ws_address = f"ws://{self.base_url.authority}/ws?clientId={client_id}"
+        ws_protocol = "ws"
+        if self.base_url.scheme == "https":
+            ws_protocol = "wss"
+        ws_address = f"{ws_protocol}://{self.base_url.authority}/ws?clientId={client_id}"
         ws.connect(ws_address)
         return ws, client_id
 

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

@@ -1,4 +1,5 @@
 import logging
+import mimetypes
 from collections.abc import Mapping, Sequence
 from typing import Any
 
@@ -165,20 +166,24 @@ class HttpRequestNode(BaseNode[HttpRequestNodeData]):
 
     def extract_files(self, url: str, response: Response) -> list[File]:
         """
-        Extract files from response
+        Extract files from response by checking both Content-Type header and URL
         """
         files = []
         is_file = response.is_file
         content_type = response.content_type
         content = response.content
 
-        if is_file and content_type:
+        if is_file:
+            # Guess file extension from URL or Content-Type header
+            filename = url.split("?")[0].split("/")[-1] or ""
+            mime_type = content_type or mimetypes.guess_type(filename)[0] or "application/octet-stream"
+
             tool_file = ToolFileManager.create_file_by_raw(
                 user_id=self.user_id,
                 tenant_id=self.tenant_id,
                 conversation_id=None,
                 file_binary=content,
-                mimetype=content_type,
+                mimetype=mime_type,
             )
 
             mapping = {

api/tests/integration_tests/model_runtime/__mock/xinference.py

@@ -21,13 +21,13 @@ class MockXinferenceClass:
         if not re.match(r"https?:\/\/[^\s\/$.?#].[^\s]*$", self.base_url):
             raise RuntimeError("404 Not Found")
 
-        if "generate" == model_uid:
+        if model_uid == "generate":
             return RESTfulGenerateModelHandle(model_uid, base_url=self.base_url, auth_headers={})
-        if "chat" == model_uid:
+        if model_uid == "chat":
             return RESTfulChatModelHandle(model_uid, base_url=self.base_url, auth_headers={})
-        if "embedding" == model_uid:
+        if model_uid == "embedding":
             return RESTfulEmbeddingModelHandle(model_uid, base_url=self.base_url, auth_headers={})
-        if "rerank" == model_uid:
+        if model_uid == "rerank":
             return RESTfulRerankModelHandle(model_uid, base_url=self.base_url, auth_headers={})
         raise RuntimeError("404 Not Found")
 

api/tests/integration_tests/tools/api_tool/test_api_tool.py

@@ -34,9 +34,9 @@ def test_api_tool(setup_http_mock):
     response = tool.do_http_request(tool.api_bundle.server_url, tool.api_bundle.method, headers, parameters)
 
     assert response.status_code == 200
-    assert "/p_param" == response.request.url.path
-    assert b"query_param=q_param" == response.request.url.query
-    assert "h_param" == response.request.headers.get("header_param")
-    assert "application/json" == response.request.headers.get("content-type")
-    assert "cookie_param=c_param" == response.request.headers.get("cookie")
+    assert response.request.url.path == "/p_param"
+    assert response.request.url.query == b"query_param=q_param"
+    assert response.request.headers.get("header_param") == "h_param"
+    assert response.request.headers.get("content-type") == "application/json"
+    assert response.request.headers.get("cookie") == "cookie_param=c_param"
     assert "b_param" in response.content.decode()

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

@@ -384,7 +384,7 @@ def test_mock_404(setup_http_mock):
     assert result.outputs is not None
     resp = result.outputs
 
-    assert 404 == resp.get("status_code")
+    assert resp.get("status_code") == 404
     assert "Not Found" in resp.get("body", "")
 
 

dev/pytest/pytest_config_tests.py

@@ -0,0 +1,111 @@
+import yaml  # type: ignore
+from dotenv import dotenv_values
+from pathlib import Path
+
+BASE_API_AND_DOCKER_CONFIG_SET_DIFF = {
+    "APP_MAX_EXECUTION_TIME",
+    "BATCH_UPLOAD_LIMIT",
+    "CELERY_BEAT_SCHEDULER_TIME",
+    "CODE_EXECUTION_API_KEY",
+    "HTTP_REQUEST_MAX_CONNECT_TIMEOUT",
+    "HTTP_REQUEST_MAX_READ_TIMEOUT",
+    "HTTP_REQUEST_MAX_WRITE_TIMEOUT",
+    "KEYWORD_DATA_SOURCE_TYPE",
+    "LOGIN_LOCKOUT_DURATION",
+    "LOG_FORMAT",
+    "OCI_ACCESS_KEY",
+    "OCI_BUCKET_NAME",
+    "OCI_ENDPOINT",
+    "OCI_REGION",
+    "OCI_SECRET_KEY",
+    "REDIS_DB",
+    "RESEND_API_URL",
+    "RESPECT_XFORWARD_HEADERS_ENABLED",
+    "SENTRY_DSN",
+    "SSRF_DEFAULT_CONNECT_TIME_OUT",
+    "SSRF_DEFAULT_MAX_RETRIES",
+    "SSRF_DEFAULT_READ_TIME_OUT",
+    "SSRF_DEFAULT_TIME_OUT",
+    "SSRF_DEFAULT_WRITE_TIME_OUT",
+    "UPSTASH_VECTOR_TOKEN",
+    "UPSTASH_VECTOR_URL",
+    "USING_UGC_INDEX",
+    "WEAVIATE_BATCH_SIZE",
+    "WEAVIATE_GRPC_ENABLED",
+}
+
+BASE_API_AND_DOCKER_COMPOSE_CONFIG_SET_DIFF = {
+    "BATCH_UPLOAD_LIMIT",
+    "CELERY_BEAT_SCHEDULER_TIME",
+    "HTTP_REQUEST_MAX_CONNECT_TIMEOUT",
+    "HTTP_REQUEST_MAX_READ_TIMEOUT",
+    "HTTP_REQUEST_MAX_WRITE_TIMEOUT",
+    "KEYWORD_DATA_SOURCE_TYPE",
+    "LOGIN_LOCKOUT_DURATION",
+    "LOG_FORMAT",
+    "OPENDAL_FS_ROOT",
+    "OPENDAL_S3_ACCESS_KEY_ID",
+    "OPENDAL_S3_BUCKET",
+    "OPENDAL_S3_ENDPOINT",
+    "OPENDAL_S3_REGION",
+    "OPENDAL_S3_ROOT",
+    "OPENDAL_S3_SECRET_ACCESS_KEY",
+    "OPENDAL_S3_SERVER_SIDE_ENCRYPTION",
+    "PGVECTOR_MAX_CONNECTION",
+    "PGVECTOR_MIN_CONNECTION",
+    "PGVECTO_RS_DATABASE",
+    "PGVECTO_RS_HOST",
+    "PGVECTO_RS_PASSWORD",
+    "PGVECTO_RS_PORT",
+    "PGVECTO_RS_USER",
+    "RESPECT_XFORWARD_HEADERS_ENABLED",
+    "SCARF_NO_ANALYTICS",
+    "SSRF_DEFAULT_CONNECT_TIME_OUT",
+    "SSRF_DEFAULT_MAX_RETRIES",
+    "SSRF_DEFAULT_READ_TIME_OUT",
+    "SSRF_DEFAULT_TIME_OUT",
+    "SSRF_DEFAULT_WRITE_TIME_OUT",
+    "STORAGE_OPENDAL_SCHEME",
+    "SUPABASE_API_KEY",
+    "SUPABASE_BUCKET_NAME",
+    "SUPABASE_URL",
+    "USING_UGC_INDEX",
+    "VIKINGDB_CONNECTION_TIMEOUT",
+    "VIKINGDB_SOCKET_TIMEOUT",
+    "WEAVIATE_BATCH_SIZE",
+    "WEAVIATE_GRPC_ENABLED",
+}
+
+API_CONFIG_SET = set(dotenv_values(Path("api") / Path(".env.example")).keys())
+DOCKER_CONFIG_SET = set(dotenv_values(Path("docker") / Path(".env.example")).keys())
+DOCKER_COMPOSE_CONFIG_SET = set()
+
+with open(Path("docker") / Path("docker-compose.yaml")) as f:
+    DOCKER_COMPOSE_CONFIG_SET = set(yaml.safe_load(f.read())["x-shared-env"].keys())
+
+
+def test_yaml_config():
+    # python set == operator is used to compare two sets
+    DIFF_API_WITH_DOCKER = (
+        API_CONFIG_SET - DOCKER_CONFIG_SET - BASE_API_AND_DOCKER_CONFIG_SET_DIFF
+    )
+    if DIFF_API_WITH_DOCKER:
+        print(
+            f"API and Docker config sets are different with key: {DIFF_API_WITH_DOCKER}"
+        )
+        raise Exception("API and Docker config sets are different")
+    DIFF_API_WITH_DOCKER_COMPOSE = (
+        API_CONFIG_SET
+        - DOCKER_COMPOSE_CONFIG_SET
+        - BASE_API_AND_DOCKER_COMPOSE_CONFIG_SET_DIFF
+    )
+    if DIFF_API_WITH_DOCKER_COMPOSE:
+        print(
+            f"API and Docker Compose config sets are different with key: {DIFF_API_WITH_DOCKER_COMPOSE}"
+        )
+        raise Exception("API and Docker Compose config sets are different")
+    print("All tests passed!")
+
+
+if __name__ == "__main__":
+    test_yaml_config()

docker-legacy/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.14.0
+    image: langgenius/dify-api:0.14.1
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -227,7 +227,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.14.0
+    image: langgenius/dify-api:0.14.1
     restart: always
     environment:
       CONSOLE_WEB_URL: ''
@@ -397,7 +397,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.14.0
+    image: langgenius/dify-web:0.14.1
     restart: always
     environment:
       # The base URL of console application api server, refers to the Console base URL of WEB service if console domain is

docker/.env.example

@@ -107,6 +107,7 @@ ACCESS_TOKEN_EXPIRE_MINUTES=60
 
 # The maximum number of active requests for the application, where 0 means unlimited, should be a non-negative integer.
 APP_MAX_ACTIVE_REQUESTS=0
+APP_MAX_EXECUTION_TIME=1200
 
 # ------------------------------
 # Container Startup Related Configuration
@@ -606,6 +607,7 @@ UPLOAD_AUDIO_FILE_SIZE_LIMIT=50
 # Sentry Configuration
 # Used for application monitoring and error log tracking.
 # ------------------------------
+SENTRY_DSN=
 
 # API Service Sentry DSN address, default is empty, when empty,
 # all monitoring information is not reported to Sentry.

docker/docker-compose-template.yaml

@@ -2,7 +2,7 @@ x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.14.0
+    image: langgenius/dify-api:0.14.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -25,7 +25,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.14.0
+    image: langgenius/dify-api:0.14.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -47,7 +47,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.14.0
+    image: langgenius/dify-web:0.14.1
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}

docker/docker-compose.yaml

@@ -18,6 +18,7 @@ x-shared-env: &shared-api-worker-env
   LOG_DATEFORMAT: ${LOG_DATEFORMAT:-"%Y-%m-%d %H:%M:%S"}
   LOG_TZ: ${LOG_TZ:-UTC}
   DEBUG: ${DEBUG:-false}
+  SENTRY_DSN: ${SENTRY_DSN:-}
   FLASK_DEBUG: ${FLASK_DEBUG:-false}
   SECRET_KEY: ${SECRET_KEY:-sk-9f73s3ljTXVcMT3Blb3ljTqtsKiGHXVcMT3BlbkFJLK7U}
   INIT_PASSWORD: ${INIT_PASSWORD:-}
@@ -28,6 +29,7 @@ x-shared-env: &shared-api-worker-env
   FILES_ACCESS_TIMEOUT: ${FILES_ACCESS_TIMEOUT:-300}
   ACCESS_TOKEN_EXPIRE_MINUTES: ${ACCESS_TOKEN_EXPIRE_MINUTES:-60}
   APP_MAX_ACTIVE_REQUESTS: ${APP_MAX_ACTIVE_REQUESTS:-0}
+  APP_MAX_EXECUTION_TIME: ${APP_MAX_EXECUTION_TIME:-1200}
   DIFY_BIND_ADDRESS: ${DIFY_BIND_ADDRESS:-0.0.0.0}
   DIFY_PORT: ${DIFY_PORT:-5001}
   SERVER_WORKER_AMOUNT: ${SERVER_WORKER_AMOUNT:-}
@@ -389,7 +391,7 @@ x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.14.0
+    image: langgenius/dify-api:0.14.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -412,7 +414,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.14.0
+    image: langgenius/dify-api:0.14.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -434,7 +436,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.14.0
+    image: langgenius/dify-web:0.14.1
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}

web/app/components/tools/add-tool-modal/empty.tsx

@@ -1,13 +1,19 @@
+'use client'
+import { useSearchParams } from 'next/navigation'
 import { useTranslation } from 'react-i18next'
-
 const Empty = () => {
   const { t } = useTranslation()
+  const searchParams = useSearchParams()
 
   return (
     <div className='flex flex-col items-center'>
       <div className="shrink-0 w-[163px] h-[149px] bg-cover bg-no-repeat bg-[url('~@/app/components/tools/add-tool-modal/empty.png')]"></div>
-      <div className='mb-1 text-[13px] font-medium text-text-primary leading-[18px]'>{t('tools.addToolModal.emptyTitle')}</div>
-      <div className='text-[13px] text-text-tertiary leading-[18px]'>{t('tools.addToolModal.emptyTip')}</div>
+      <div className='mb-1 text-[13px] font-medium text-text-primary leading-[18px]'>
+        {t(`tools.addToolModal.${searchParams.get('category') === 'workflow' ? 'emptyTitle' : 'emptyTitleCustom'}`)}
+      </div>
+      <div className='text-[13px] text-text-tertiary leading-[18px]'>
+        {t(`tools.addToolModal.${searchParams.get('category') === 'workflow' ? 'emptyTip' : 'emptyTipCustom'}`)}
+      </div>
     </div>
   )
 }

web/i18n/en-US/tools.ts

@@ -31,6 +31,8 @@ const translation = {
     manageInTools: 'Manage in Tools',
     emptyTitle: 'No workflow tool available',
     emptyTip: 'Go to "Workflow -> Publish as Tool"',
+    emptyTitleCustom: 'No custom tool available',
+    emptyTipCustom: 'Create a custom tool',
   },
   createTool: {
     title: 'Create Custom Tool',

web/i18n/zh-Hans/tools.ts

@@ -31,6 +31,8 @@ const translation = {
     manageInTools: '去工具列表管理',
     emptyTitle: '没有可用的工作流工具',
     emptyTip: '去 “工作流 -> 发布为工具” 添加',
+    emptyTitleCustom: '没有可用的自定义工具',
+    emptyTipCustom: '创建自定义工具',
   },
   createTool: {
     title: '创建自定义工具',

web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "dify-web",
-  "version": "0.14.0",
+  "version": "0.14.1",
   "private": true,
   "engines": {
     "node": ">=18.17.0"