Merge branch 'main' into fix/chore-fix

api/.env.example

@@ -162,6 +162,8 @@ PGVECTOR_PORT=5433
 PGVECTOR_USER=postgres
 PGVECTOR_PASSWORD=postgres
 PGVECTOR_DATABASE=postgres
+PGVECTOR_MIN_CONNECTION=1
+PGVECTOR_MAX_CONNECTION=5
 
 # Tidb Vector configuration
 TIDB_VECTOR_HOST=xxx.eu-central-1.xxx.aws.tidbcloud.com

api/configs/middleware/vdb/pgvector_config.py

@@ -33,3 +33,13 @@ class PGVectorConfig(BaseSettings):
         description="Name of the PostgreSQL database to connect to",
         default=None,
     )
+
+    PGVECTOR_MIN_CONNECTION: PositiveInt = Field(
+        description="Min connection of the PostgreSQL database",
+        default=1,
+    )
+
+    PGVECTOR_MAX_CONNECTION: PositiveInt = Field(
+        description="Max connection of the PostgreSQL database",
+        default=5,
+    )

api/controllers/console/datasets/datasets.py

@@ -563,10 +563,10 @@ class DatasetRetrievalSettingApi(Resource):
             case (
                 VectorType.MILVUS
                 | VectorType.RELYT
-                | VectorType.PGVECTOR
                 | VectorType.TIDB_VECTOR
                 | VectorType.CHROMA
                 | VectorType.TENCENT
+                | VectorType.PGVECTO_RS
             ):
                 return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
             case (
@@ -577,6 +577,7 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
+                | VectorType.PGVECTOR
             ):
                 return {
                     "retrieval_method": [

api/core/entities/provider_configuration.py

@@ -119,7 +119,7 @@ class ProviderConfiguration(BaseModel):
                         credentials = model_configuration.credentials
                         break
 
-            if self.custom_configuration.provider:
+            if not credentials and self.custom_configuration.provider:
                 credentials = self.custom_configuration.provider.credentials
 
             return credentials

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-11b-vision-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
+label:
+  zh_Hans: Llama 3.2 11B Vision Instruct
+  en_US: Llama 3.2 11B Vision Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+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.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.2'
+  output: '0.2'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-1b-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-1b-instruct
+label:
+  zh_Hans: Llama 3.2 1B Instruct
+  en_US: Llama 3.2 1B Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+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.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.1'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-3b-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-3b-instruct
+label:
+  zh_Hans: Llama 3.2 3B Instruct
+  en_US: Llama 3.2 3B Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+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.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.1'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-90b-vision-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
+label:
+  zh_Hans: Llama 3.2 90B Vision Instruct
+  en_US: Llama 3.2 90B Vision Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+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.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.9'
+  output: '0.9'
+  unit: '0.000001'
+  currency: USD

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

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash-001
+label:
+  en_US: Gemini 1.5 Flash 001
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+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_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash-002
+label:
+  en_US: Gemini 1.5 Flash 002
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+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_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash-8b-exp-0924
+label:
+  en_US: Gemini 1.5 Flash 8B 0924
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+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_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -1,6 +1,6 @@
 model: gemini-1.5-flash-latest
 label:
-  en_US: Gemini 1.5 Flash
+  en_US: Gemini 1.5 Flash Latest
 model_type: llm
 features:
   - agent-thought
@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash
+label:
+  en_US: Gemini 1.5 Flash
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+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_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -0,0 +1,48 @@
+model: gemini-1.5-pro-001
+label:
+  en_US: Gemini 1.5 Pro 001
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+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_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -0,0 +1,48 @@
+model: gemini-1.5-pro-002
+label:
+  en_US: Gemini 1.5 Pro 002
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+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_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -1,6 +1,6 @@
 model: gemini-1.5-pro-latest
 label:
-  en_US: Gemini 1.5 Pro
+  en_US: Gemini 1.5 Pro Latest
 model_type: llm
 features:
   - agent-thought
@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -0,0 +1,48 @@
+model: gemini-1.5-pro
+label:
+  en_US: Gemini 1.5 Pro
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+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_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -27,6 +27,15 @@ parameter_rules:
     default: 4096
     min: 1
     max: 4096
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -31,6 +31,15 @@ parameter_rules:
     max: 2048
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -9,8 +9,8 @@ import google.ai.generativelanguage as glm
 import google.generativeai as genai
 import requests
 from google.api_core import exceptions
-from google.generativeai import client
-from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory
+from google.generativeai.client import _ClientManager
+from google.generativeai.types import ContentType, GenerateContentResponse
 from google.generativeai.types.content_types import to_part
 from PIL import Image
 
@@ -200,24 +200,16 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                     history.append(content)
 
         # Create a new ClientManager with tenant's API key
-        new_client_manager = client._ClientManager()
+        new_client_manager = _ClientManager()
         new_client_manager.configure(api_key=credentials["google_api_key"])
         new_custom_client = new_client_manager.make_client("generative")
 
         google_model._client = new_custom_client
 
-        safety_settings = {
-            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
-        }
-
         response = google_model.generate_content(
             contents=history,
             generation_config=genai.types.GenerationConfig(**config_kwargs),
             stream=stream,
-            safety_settings=safety_settings,
             tools=self._convert_tools_to_glm_tool(tools) if tools else None,
             request_options={"timeout": 600},
         )

api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-text-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-11b-text-preview
+label:
+  zh_Hans: Llama 3.2 11B Text (Preview)
+  en_US: Llama 3.2 11B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/groq/llm/llama-3.2-1b-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-1b-preview
+label:
+  zh_Hans: Llama 3.2 1B Text (Preview)
+  en_US: Llama 3.2 1B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/groq/llm/llama-3.2-3b-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-3b-preview
+label:
+  zh_Hans: Llama 3.2 3B Text (Preview)
+  en_US: Llama 3.2 3B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/groq/llm/llama-3.2-90b-text-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-90b-text-preview
+label:
+  zh_Hans: Llama 3.2 90B Text (Preview)
+  en_US: Llama 3.2 90B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

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

@@ -364,14 +364,21 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
             if chunk_json["done"]:
                 # calculate num tokens
-                if "prompt_eval_count" in chunk_json and "eval_count" in chunk_json:
-                    # transform usage
+                if "prompt_eval_count" in chunk_json:
                     prompt_tokens = chunk_json["prompt_eval_count"]
-                    completion_tokens = chunk_json["eval_count"]
                 else:
-                    # calculate num tokens
-                    prompt_tokens = self._get_num_tokens_by_gpt2(prompt_messages[0].content)
-                    completion_tokens = self._get_num_tokens_by_gpt2(full_text)
+                    prompt_message_content = prompt_messages[0].content
+                    if isinstance(prompt_message_content, str):
+                        prompt_tokens = self._get_num_tokens_by_gpt2(prompt_message_content)
+                    else:
+                        content_text = ""
+                        for message_content in prompt_message_content:
+                            if message_content.type == PromptMessageContentType.TEXT:
+                                message_content = cast(TextPromptMessageContent, message_content)
+                                content_text += message_content.data
+                        prompt_tokens = self._get_num_tokens_by_gpt2(content_text)
+
+                completion_tokens = chunk_json.get("eval_count", self._get_num_tokens_by_gpt2(full_text))
 
                 # transform usage
                 usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)

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

@@ -1,18 +1,17 @@
 - Qwen/Qwen2.5-72B-Instruct
-- Qwen/Qwen2.5-Math-72B-Instruct
 - Qwen/Qwen2.5-32B-Instruct
 - Qwen/Qwen2.5-14B-Instruct
 - Qwen/Qwen2.5-7B-Instruct
 - Qwen/Qwen2.5-Coder-7B-Instruct
-- deepseek-ai/DeepSeek-V2.5
+- Qwen/Qwen2.5-Math-72B-Instruct
 - Qwen/Qwen2-72B-Instruct
 - Qwen/Qwen2-57B-A14B-Instruct
 - Qwen/Qwen2-7B-Instruct
 - Qwen/Qwen2-1.5B-Instruct
+- deepseek-ai/DeepSeek-V2.5
 - deepseek-ai/DeepSeek-V2-Chat
 - deepseek-ai/DeepSeek-Coder-V2-Instruct
 - THUDM/glm-4-9b-chat
-- THUDM/chatglm3-6b
 - 01-ai/Yi-1.5-34B-Chat-16K
 - 01-ai/Yi-1.5-9B-Chat-16K
 - 01-ai/Yi-1.5-6B-Chat
@@ -26,13 +25,4 @@
 - google/gemma-2-27b-it
 - google/gemma-2-9b-it
 - mistralai/Mistral-7B-Instruct-v0.2
-- Pro/Qwen/Qwen2-7B-Instruct
-- Pro/Qwen/Qwen2-1.5B-Instruct
-- Pro/THUDM/glm-4-9b-chat
-- Pro/THUDM/chatglm3-6b
-- Pro/01-ai/Yi-1.5-9B-Chat-16K
-- Pro/01-ai/Yi-1.5-6B-Chat
-- Pro/internlm/internlm2_5-7b-chat
-- Pro/meta-llama/Meta-Llama-3.1-8B-Instruct
-- Pro/meta-llama/Meta-Llama-3-8B-Instruct
-- Pro/google/gemma-2-9b-it
+- mistralai/Mixtral-8x7B-Instruct-v0.1

api/core/model_runtime/model_providers/siliconflow/llm/internlm2_5-20b-chat.yaml

@@ -0,0 +1,30 @@
+model: internlm/internlm2_5-20b-chat
+label:
+  en_US: internlm/internlm2_5-20b-chat
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 512
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
+      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
+  - name: top_p
+    use_template: top_p
+  - name: frequency_penalty
+    use_template: frequency_penalty
+pricing:
+  input: '1'
+  output: '1'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/siliconflow/llm/mistral-7b-instruct-v0.2.yaml

@@ -28,3 +28,4 @@ pricing:
   output: '0'
   unit: '0.000001'
   currency: RMB
+deprecated: true

api/core/model_runtime/model_providers/siliconflow/llm/mistral-8x7b-instruct-v0.1.yaml

@@ -28,3 +28,4 @@ pricing:
   output: '1.26'
   unit: '0.000001'
   currency: RMB
+deprecated: true

api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-coder-7b-instruct.yaml

@@ -0,0 +1,74 @@
+model: Qwen/Qwen2.5-Coder-7B-Instruct
+label:
+  en_US: Qwen/Qwen2.5-Coder-7B-Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    type: float
+    default: 0.3
+    min: 0.0
+    max: 2.0
+    help:
+      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
+      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 8192
+    min: 1
+    max: 8192
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
+  - name: top_p
+    use_template: top_p
+    type: float
+    default: 0.8
+    min: 0.1
+    max: 0.9
+    help:
+      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
+      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
+  - name: top_k
+    type: int
+    min: 0
+    max: 99
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    help:
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
+  - name: seed
+    required: false
+    type: int
+    default: 1234
+    label:
+      zh_Hans: 随机种子
+      en_US: Random seed
+    help:
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
+  - name: repetition_penalty
+    required: false
+    type: float
+    default: 1.1
+    label:
+      zh_Hans: 重复惩罚
+      en_US: Repetition penalty
+    help:
+      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0'
+  output: '0'
+  unit: '0.000001'
+  currency: RMB

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

@@ -0,0 +1,74 @@
+model: Qwen/Qwen2.5-Math-72B-Instruct
+label:
+  en_US: Qwen/Qwen2.5-Math-72B-Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 4096
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    type: float
+    default: 0.3
+    min: 0.0
+    max: 2.0
+    help:
+      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
+      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 2000
+    min: 1
+    max: 2000
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
+  - name: top_p
+    use_template: top_p
+    type: float
+    default: 0.8
+    min: 0.1
+    max: 0.9
+    help:
+      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
+      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
+  - name: top_k
+    type: int
+    min: 0
+    max: 99
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    help:
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
+  - name: seed
+    required: false
+    type: int
+    default: 1234
+    label:
+      zh_Hans: 随机种子
+      en_US: Random seed
+    help:
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
+  - name: repetition_penalty
+    required: false
+    type: float
+    default: 1.1
+    label:
+      zh_Hans: 重复惩罚
+      en_US: Repetition penalty
+    help:
+      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '4.13'
+  output: '4.13'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: farui-plus
 label:
   en_US: farui-plus

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

@@ -18,7 +18,7 @@ from dashscope.common.error import (
     UnsupportedModel,
 )
 
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     ImagePromptMessageContent,
@@ -35,6 +35,7 @@ from core.model_runtime.entities.model_entities import (
     FetchFrom,
     I18nObject,
     ModelFeature,
+    ModelPropertyKey,
     ModelType,
     ParameterRule,
     ParameterType,
@@ -97,6 +98,11 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         :param tools: tools for tool calling
         :return:
         """
+        # Check if the model was added via get_customizable_model_schema
+        if self.get_customizable_model_schema(model, credentials) is not None:
+            # For custom models, tokens are not calculated.
+            return 0
+
         if model in {"qwen-turbo-chat", "qwen-plus-chat"}:
             model = model.replace("-chat", "")
         if model == "farui-plus":
@@ -537,55 +543,51 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         :param credentials: model credentials
         :return: AIModelEntity or None
         """
-        rules = [
-            ParameterRule(
-                name="temperature",
-                type=ParameterType.FLOAT,
-                use_template="temperature",
-                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
-            ),
-            ParameterRule(
-                name="top_p",
-                type=ParameterType.FLOAT,
-                use_template="top_p",
-                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
-            ),
-            ParameterRule(
-                name="top_k",
-                type=ParameterType.INT,
-                min=0,
-                max=99,
-                label=I18nObject(zh_Hans="top_k", en_US="top_k"),
-            ),
-            ParameterRule(
-                name="max_tokens",
-                type=ParameterType.INT,
-                min=1,
-                max=128000,
-                default=1024,
-                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
-            ),
-            ParameterRule(
-                name="seed",
-                type=ParameterType.INT,
-                default=1234,
-                label=I18nObject(zh_Hans="随机种子", en_US="Random Seed"),
-            ),
-            ParameterRule(
-                name="repetition_penalty",
-                type=ParameterType.FLOAT,
-                default=1.1,
-                label=I18nObject(zh_Hans="重复惩罚", en_US="Repetition Penalty"),
-            ),
-        ]
-
-        entity = AIModelEntity(
+        return AIModelEntity(
             model=model,
-            label=I18nObject(en_US=model),
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            label=I18nObject(en_US=model, zh_Hans=model),
             model_type=ModelType.LLM,
-            model_properties={},
-            parameter_rules=rules,
+            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
+            if credentials.get("function_calling_type") == "tool_call"
+            else [],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000)),
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name="temperature",
+                    use_template="temperature",
+                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="max_tokens",
+                    use_template="max_tokens",
+                    default=512,
+                    min=1,
+                    max=int(credentials.get("max_tokens", 1024)),
+                    label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
+                    type=ParameterType.INT,
+                ),
+                ParameterRule(
+                    name="top_p",
+                    use_template="top_p",
+                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="top_k",
+                    use_template="top_k",
+                    label=I18nObject(en_US="Top K", zh_Hans="Top K"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="frequency_penalty",
+                    use_template="frequency_penalty",
+                    label=I18nObject(en_US="Frequency Penalty", zh_Hans="重复惩罚"),
+                    type=ParameterType.FLOAT,
+                ),
+            ],
         )
-
-        return entity

api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-0919.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-coder-turbo-0919
 label:
   en_US: qwen-coder-turbo-0919

api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-latest.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-coder-turbo-latest
 label:
   en_US: qwen-coder-turbo-latest

api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-coder-turbo
 label:
   en_US: qwen-coder-turbo

api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml

@@ -1,4 +1,4 @@
-# model docs: https://help.aliyun.com/zh/model-studio/getting-started/models#27b2b3a15d5c6
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-long
 label:
   en_US: qwen-long

api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0816.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-math-plus-0816
 label:
   en_US: qwen-math-plus-0816

api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0919.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-math-plus-0919
 label:
   en_US: qwen-math-plus-0919

api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-latest.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-math-plus-latest
 label:
   en_US: qwen-math-plus-latest

api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-math-plus
 label:
   en_US: qwen-math-plus

api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-0919.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-math-turbo-0919
 label:
   en_US: qwen-math-turbo-0919

api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-latest.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-math-turbo-latest
 label:
   en_US: qwen-math-turbo-latest

api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-math-turbo
 label:
   en_US: qwen-math-turbo

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0107.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max-0107
 label:
   en_US: qwen-max-0107

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0403.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max-0403, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max-0403
 label:
   en_US: qwen-max-0403

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0428.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max-0428, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max-0428
 label:
   en_US: qwen-max-0428

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0919.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max-0919, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max-0919
 label:
   en_US: qwen-max-0919

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max-1201
 label:
   en_US: qwen-max-1201

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-latest.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max-latest
 label:
   en_US: qwen-max-latest

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max-longcontext
 label:
   en_US: qwen-max-longcontext

api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-max, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
 model: qwen-max
 label:
   en_US: qwen-max

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0206.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus-0206, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus-0206
 label:
   en_US: qwen-plus-0206

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0624.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus-0624, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus-0624
 label:
   en_US: qwen-plus-0624

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0723.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus-0723, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus-0723
 label:
   en_US: qwen-plus-0723

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0806.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus-0806, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus-0806
 label:
   en_US: qwen-plus-0806

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0919.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus-0919, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus-0919
 label:
   en_US: qwen-plus-0919

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-chat.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus-chat
 label:
   en_US: qwen-plus-chat

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-latest.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus-latest, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus-latest
 label:
   en_US: qwen-plus-latest

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-plus, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
 model: qwen-plus
 label:
   en_US: qwen-plus

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0206.yaml

@@ -1,3 +1,6 @@
+# this model corresponds to qwen-turbo-0206, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
+
 model: qwen-turbo-0206
 label:
   en_US: qwen-turbo-0206

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0624.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-turbo-0624, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
 model: qwen-turbo-0624
 label:
   en_US: qwen-turbo-0624

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0919.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-turbo-0919, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
 model: qwen-turbo-0919
 label:
   en_US: qwen-turbo-0919

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-chat.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-turbo, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
 model: qwen-turbo-chat
 label:
   en_US: qwen-turbo-chat

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-latest.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-turbo-latest, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
 model: qwen-turbo-latest
 label:
   en_US: qwen-turbo-latest

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo.yaml

@@ -1,3 +1,5 @@
+# this model corresponds to qwen-turbo, for more details
+# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
 model: qwen-turbo
 label:
   en_US: qwen-turbo

api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0201.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-vl-max-0201
 label:
   en_US: qwen-vl-max-0201

api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0809.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-vl-max-0809
 label:
   en_US: qwen-vl-max-0809

api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-vl-max
 label:
   en_US: qwen-vl-max

api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0201.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-vl-plus-0201
 label:
   en_US: qwen-vl-plus-0201

api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0809.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-vl-plus-0809
 label:
   en_US: qwen-vl-plus-0809

api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen-vl-plus
 label:
   en_US: qwen-vl-plus

api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-1.5b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2-math-1.5b-instruct
 label:
   en_US: qwen2-math-1.5b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-72b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2-math-72b-instruct
 label:
   en_US: qwen2-math-72b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-7b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2-math-7b-instruct
 label:
   en_US: qwen2-math-7b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-0.5b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2.5-0.5b-instruct
 label:
   en_US: qwen2.5-0.5b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-1.5b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2.5-1.5b-instruct
 label:
   en_US: qwen2.5-1.5b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-14b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2.5-14b-instruct
 label:
   en_US: qwen2.5-14b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-32b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2.5-32b-instruct
 label:
   en_US: qwen2.5-32b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-3b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2.5-3b-instruct
 label:
   en_US: qwen2.5-3b-instruct

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

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2.5-72b-instruct
 label:
   en_US: qwen2.5-72b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-7b-instruct.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
 model: qwen2.5-7b-instruct
 label:
   en_US: qwen2.5-7b-instruct

api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-coder-7b-instruct.yaml

@@ -1,6 +1,7 @@
-model: qwen2.5-7b-instruct
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
+model: qwen2.5-coder-7b-instruct
 label:
-  en_US: qwen2.5-7b-instruct
+  en_US: qwen2.5-coder-7b-instruct
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models#3383780daf8hw
 model: text-embedding-v1
 model_type: text-embedding
 model_properties:

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models#3383780daf8hw
 model: text-embedding-v2
 model_type: text-embedding
 model_properties:

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v3.yaml

@@ -1,3 +1,4 @@
+# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models#3383780daf8hw
 model: text-embedding-v3
 model_type: text-embedding
 model_properties:

api/core/model_runtime/model_providers/tongyi/tongyi.yaml

@@ -37,14 +37,51 @@ model_credential_schema:
       en_US: Model Name
       zh_Hans: 模型名称
     placeholder:
-      en_US: Enter full model name
-      zh_Hans: 输入模型全称
+      en_US: Enter your model name
+      zh_Hans: 输入模型名称
   credential_form_schemas:
     - variable: dashscope_api_key
-      required: true
       label:
         en_US: API Key
       type: secret-input
+      required: true
       placeholder:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API Key
+    - variable: context_size
+      label:
+        zh_Hans: 模型上下文长度
+        en_US: Model context size
+      required: true
+      type: text-input
+      default: '4096'
+      placeholder:
+        zh_Hans: 在此输入您的模型上下文长度
+        en_US: Enter your Model context size
+    - variable: max_tokens
+      label:
+        zh_Hans: 最大 token 上限
+        en_US: Upper bound for max tokens
+      default: '4096'
+      type: text-input
+      show_on:
+        - variable: __model_type
+          value: llm
+    - variable: function_calling_type
+      label:
+        en_US: Function calling
+      type: select
+      required: false
+      default: no_call
+      options:
+        - value: no_call
+          label:
+            en_US: Not Support
+            zh_Hans: 不支持
+        - value: function_call
+          label:
+            en_US: Support
+            zh_Hans: 支持
+      show_on:
+        - variable: __model_type
+          value: llm

api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py

@@ -40,19 +40,8 @@ class AnalyticdbConfig(BaseModel):
 
 
 class AnalyticdbVector(BaseVector):
-    _instance = None
-    _init = False
-
-    def __new__(cls, *args, **kwargs):
-        if cls._instance is None:
-            cls._instance = super().__new__(cls)
-        return cls._instance
-
     def __init__(self, collection_name: str, config: AnalyticdbConfig):
-        # collection_name must be updated every time
         self._collection_name = collection_name.lower()
-        if AnalyticdbVector._init:
-            return
         try:
             from alibabacloud_gpdb20160503.client import Client
             from alibabacloud_tea_openapi import models as open_api_models
@@ -62,7 +51,6 @@ class AnalyticdbVector(BaseVector):
         self._client_config = open_api_models.Config(user_agent="dify", **config.to_analyticdb_client_params())
         self._client = Client(self._client_config)
         self._initialize()
-        AnalyticdbVector._init = True
 
     def _initialize(self) -> None:
         cache_key = f"vector_indexing_{self.config.instance_id}"
@@ -257,11 +245,14 @@ class AnalyticdbVector(BaseVector):
         documents = []
         for match in response.body.matches.match:
             if match.score > score_threshold:
+                metadata = json.loads(match.metadata.get("metadata_"))
+                metadata["score"] = match.score
                 doc = Document(
                     page_content=match.metadata.get("page_content"),
-                    metadata=json.loads(match.metadata.get("metadata_")),
+                    metadata=metadata,
                 )
                 documents.append(doc)
+        documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
         return documents
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@@ -286,12 +277,14 @@ class AnalyticdbVector(BaseVector):
         for match in response.body.matches.match:
             if match.score > score_threshold:
                 metadata = json.loads(match.metadata.get("metadata_"))
+                metadata["score"] = match.score
                 doc = Document(
                     page_content=match.metadata.get("page_content"),
                     vector=match.metadata.get("vector"),
                     metadata=metadata,
                 )
                 documents.append(doc)
+        documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
         return documents
 
     def delete(self) -> None:

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

@@ -23,6 +23,8 @@ class PGVectorConfig(BaseModel):
     user: str
     password: str
     database: str
+    min_connection: int
+    max_connection: int
 
     @model_validator(mode="before")
     @classmethod
@@ -37,6 +39,12 @@ class PGVectorConfig(BaseModel):
             raise ValueError("config PGVECTOR_PASSWORD is required")
         if not values["database"]:
             raise ValueError("config PGVECTOR_DATABASE is required")
+        if not values["min_connection"]:
+            raise ValueError("config PGVECTOR_MIN_CONNECTION is required")
+        if not values["max_connection"]:
+            raise ValueError("config PGVECTOR_MAX_CONNECTION is required")
+        if values["min_connection"] > values["max_connection"]:
+            raise ValueError("config PGVECTOR_MIN_CONNECTION should less than PGVECTOR_MAX_CONNECTION")
         return values
 
 
@@ -61,8 +69,8 @@ class PGVector(BaseVector):
 
     def _create_connection_pool(self, config: PGVectorConfig):
         return psycopg2.pool.SimpleConnectionPool(
-            1,
-            5,
+            config.min_connection,
+            config.max_connection,
             host=config.host,
             port=config.port,
             user=config.user,
@@ -213,5 +221,7 @@ class PGVectorFactory(AbstractVectorFactory):
                 user=dify_config.PGVECTOR_USER,
                 password=dify_config.PGVECTOR_PASSWORD,
                 database=dify_config.PGVECTOR_DATABASE,
+                min_connection=dify_config.PGVECTOR_MIN_CONNECTION,
+                max_connection=dify_config.PGVECTOR_MAX_CONNECTION,
             ),
         )

api/core/rag/datasource/vdb/tencent/tencent_vector.py

@@ -56,7 +56,7 @@ class TencentVector(BaseVector):
             return self._client.create_database(database_name=self._client_config.database)
 
     def get_type(self) -> str:
-        return "tencent"
+        return VectorType.TENCENT
 
     def to_index_struct(self) -> dict:
         return {"type": self.get_type(), "vector_store": {"class_prefix": self._collection_name}}

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

@@ -45,6 +45,7 @@ class BaseVector(ABC):
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         raise NotImplementedError
 
+    @abstractmethod
     def delete(self) -> None:
         raise NotImplementedError
 

api/core/tools/utils/feishu_api_utils.py

@@ -1,9 +1,23 @@
 import httpx
 
+from core.tools.errors import ToolProviderCredentialValidationError
 from extensions.ext_redis import redis_client
 
 
+def auth(credentials):
+    app_id = credentials.get("app_id")
+    app_secret = credentials.get("app_secret")
+    if not app_id or not app_secret:
+        raise ToolProviderCredentialValidationError("app_id and app_secret is required")
+    try:
+        assert FeishuRequest(app_id, app_secret).tenant_access_token is not None
+    except Exception as e:
+        raise ToolProviderCredentialValidationError(str(e))
+
+
 class FeishuRequest:
+    API_BASE_URL = "https://lark-plugin-api.solutionsuite.cn/lark-plugin"
+
     def __init__(self, app_id: str, app_secret: str):
         self.app_id = app_id
         self.app_secret = app_secret
@@ -42,7 +56,7 @@ class FeishuRequest:
             "expire": 7200
         }
         """
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/access_token/get_tenant_access_token"
+        url = f"{self.API_BASE_URL}/access_token/get_tenant_access_token"
         payload = {"app_id": app_id, "app_secret": app_secret}
         res = self._send_request(url, require_token=False, payload=payload)
         return res
@@ -63,7 +77,7 @@ class FeishuRequest:
             "msg": "创建飞书文档成功，请查看"
         }
         """
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/create_document"
+        url = f"{self.API_BASE_URL}/document/create_document"
         payload = {
             "title": title,
             "content": content,
@@ -72,13 +86,13 @@ class FeishuRequest:
         res = self._send_request(url, payload=payload)
         return res.get("data")
 
-    def write_document(self, document_id: str, content: str, position: str = "start") -> dict:
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/write_document"
+    def write_document(self, document_id: str, content: str, position: str = "end") -> dict:
+        url = f"{self.API_BASE_URL}/document/write_document"
         payload = {"document_id": document_id, "content": content, "position": position}
         res = self._send_request(url, payload=payload)
         return res
 
-    def get_document_content(self, document_id: str, mode: str, lang: int = 0) -> dict:
+    def get_document_content(self, document_id: str, mode: str = "markdown", lang: str = "0") -> dict:
         """
         API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/raw_content
         Example Response:
@@ -95,45 +109,404 @@ class FeishuRequest:
             "mode": mode,
             "lang": lang,
         }
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/get_document_content"
-        res = self._send_request(url, method="get", params=params)
+        url = f"{self.API_BASE_URL}/document/get_document_content"
+        res = self._send_request(url, method="GET", params=params)
         return res.get("data").get("content")
 
-    def list_document_blocks(self, document_id: str, page_token: str, page_size: int = 500) -> dict:
+    def list_document_blocks(
+        self, document_id: str, page_token: str, user_id_type: str = "open_id", page_size: int = 500
+    ) -> dict:
         """
         API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/list
         """
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/list_document_blocks"
         params = {
+            "user_id_type": user_id_type,
             "document_id": document_id,
             "page_size": page_size,
             "page_token": page_token,
         }
-        res = self._send_request(url, method="get", params=params)
+        url = f"{self.API_BASE_URL}/document/list_document_blocks"
+        res = self._send_request(url, method="GET", params=params)
         return res.get("data")
 
     def send_bot_message(self, receive_id_type: str, receive_id: str, msg_type: str, content: str) -> dict:
         """
         API url: https://open.larkoffice.com/document/server-docs/im-v1/message/create
         """
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/message/send_bot_message"
+        url = f"{self.API_BASE_URL}/message/send_bot_message"
         params = {
             "receive_id_type": receive_id_type,
         }
         payload = {
             "receive_id": receive_id,
             "msg_type": msg_type,
-            "content": content,
+            "content": content.strip('"').replace(r"\"", '"').replace(r"\\", "\\"),
         }
         res = self._send_request(url, params=params, payload=payload)
         return res.get("data")
 
     def send_webhook_message(self, webhook: str, msg_type: str, content: str) -> dict:
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/message/send_webhook_message"
+        url = f"{self.API_BASE_URL}/message/send_webhook_message"
         payload = {
             "webhook": webhook,
             "msg_type": msg_type,
-            "content": content,
+            "content": content.strip('"').replace(r"\"", '"').replace(r"\\", "\\"),
         }
         res = self._send_request(url, require_token=False, payload=payload)
         return res
+
+    def get_chat_messages(
+        self,
+        container_id: str,
+        start_time: str,
+        end_time: str,
+        page_token: str,
+        sort_type: str = "ByCreateTimeAsc",
+        page_size: int = 20,
+    ) -> dict:
+        """
+        API url: https://open.larkoffice.com/document/server-docs/im-v1/message/list
+        """
+        url = f"{self.API_BASE_URL}/message/get_chat_messages"
+        params = {
+            "container_id": container_id,
+            "start_time": start_time,
+            "end_time": end_time,
+            "sort_type": sort_type,
+            "page_token": page_token,
+            "page_size": page_size,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def get_thread_messages(
+        self, container_id: str, page_token: str, sort_type: str = "ByCreateTimeAsc", page_size: int = 20
+    ) -> dict:
+        """
+        API url: https://open.larkoffice.com/document/server-docs/im-v1/message/list
+        """
+        url = f"{self.API_BASE_URL}/message/get_thread_messages"
+        params = {
+            "container_id": container_id,
+            "sort_type": sort_type,
+            "page_token": page_token,
+            "page_size": page_size,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def create_task(self, summary: str, start_time: str, end_time: str, completed_time: str, description: str) -> dict:
+        # 创建任务
+        url = f"{self.API_BASE_URL}/task/create_task"
+        payload = {
+            "summary": summary,
+            "start_time": start_time,
+            "end_time": end_time,
+            "completed_at": completed_time,
+            "description": description,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def update_task(
+        self, task_guid: str, summary: str, start_time: str, end_time: str, completed_time: str, description: str
+    ) -> dict:
+        # 更新任务
+        url = f"{self.API_BASE_URL}/task/update_task"
+        payload = {
+            "task_guid": task_guid,
+            "summary": summary,
+            "start_time": start_time,
+            "end_time": end_time,
+            "completed_time": completed_time,
+            "description": description,
+        }
+        res = self._send_request(url, method="PATCH", payload=payload)
+        return res.get("data")
+
+    def delete_task(self, task_guid: str) -> dict:
+        # 删除任务
+        url = f"{self.API_BASE_URL}/task/delete_task"
+        payload = {
+            "task_guid": task_guid,
+        }
+        res = self._send_request(url, method="DELETE", payload=payload)
+        return res
+
+    def add_members(self, task_guid: str, member_phone_or_email: str, member_role: str) -> dict:
+        # 删除任务
+        url = f"{self.API_BASE_URL}/task/add_members"
+        payload = {
+            "task_guid": task_guid,
+            "member_phone_or_email": member_phone_or_email,
+            "member_role": member_role,
+        }
+        res = self._send_request(url, payload=payload)
+        return res
+
+    def get_wiki_nodes(self, space_id: str, parent_node_token: str, page_token: str, page_size: int = 20) -> dict:
+        # 获取知识库全部子节点列表
+        url = f"{self.API_BASE_URL}/wiki/get_wiki_nodes"
+        payload = {
+            "space_id": space_id,
+            "parent_node_token": parent_node_token,
+            "page_token": page_token,
+            "page_size": page_size,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def get_primary_calendar(self, user_id_type: str = "open_id") -> dict:
+        url = f"{self.API_BASE_URL}/calendar/get_primary_calendar"
+        params = {
+            "user_id_type": user_id_type,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def create_event(
+        self,
+        summary: str,
+        description: str,
+        start_time: str,
+        end_time: str,
+        attendee_ability: str,
+        need_notification: bool = True,
+        auto_record: bool = False,
+    ) -> dict:
+        url = f"{self.API_BASE_URL}/calendar/create_event"
+        payload = {
+            "summary": summary,
+            "description": description,
+            "need_notification": need_notification,
+            "start_time": start_time,
+            "end_time": end_time,
+            "auto_record": auto_record,
+            "attendee_ability": attendee_ability,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def update_event(
+        self,
+        event_id: str,
+        summary: str,
+        description: str,
+        need_notification: bool,
+        start_time: str,
+        end_time: str,
+        auto_record: bool,
+    ) -> dict:
+        url = f"{self.API_BASE_URL}/calendar/update_event/{event_id}"
+        payload = {}
+        if summary:
+            payload["summary"] = summary
+        if description:
+            payload["description"] = description
+        if start_time:
+            payload["start_time"] = start_time
+        if end_time:
+            payload["end_time"] = end_time
+        if need_notification:
+            payload["need_notification"] = need_notification
+        if auto_record:
+            payload["auto_record"] = auto_record
+        res = self._send_request(url, method="PATCH", payload=payload)
+        return res
+
+    def delete_event(self, event_id: str, need_notification: bool = True) -> dict:
+        url = f"{self.API_BASE_URL}/calendar/delete_event/{event_id}"
+        params = {
+            "need_notification": need_notification,
+        }
+        res = self._send_request(url, method="DELETE", params=params)
+        return res
+
+    def list_events(self, start_time: str, end_time: str, page_token: str, page_size: int = 50) -> dict:
+        url = f"{self.API_BASE_URL}/calendar/list_events"
+        params = {
+            "start_time": start_time,
+            "end_time": end_time,
+            "page_token": page_token,
+            "page_size": page_size,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def search_events(
+        self,
+        query: str,
+        start_time: str,
+        end_time: str,
+        page_token: str,
+        user_id_type: str = "open_id",
+        page_size: int = 20,
+    ) -> dict:
+        url = f"{self.API_BASE_URL}/calendar/search_events"
+        payload = {
+            "query": query,
+            "start_time": start_time,
+            "end_time": end_time,
+            "page_token": page_token,
+            "user_id_type": user_id_type,
+            "page_size": page_size,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def add_event_attendees(self, event_id: str, attendee_phone_or_email: str, need_notification: bool = True) -> dict:
+        # 参加日程参会人
+        url = f"{self.API_BASE_URL}/calendar/add_event_attendees"
+        payload = {
+            "event_id": event_id,
+            "attendee_phone_or_email": attendee_phone_or_email,
+            "need_notification": need_notification,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def create_spreadsheet(
+        self,
+        title: str,
+        folder_token: str,
+    ) -> dict:
+        # 创建电子表格
+        url = f"{self.API_BASE_URL}/spreadsheet/create_spreadsheet"
+        payload = {
+            "title": title,
+            "folder_token": folder_token,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def get_spreadsheet(
+        self,
+        spreadsheet_token: str,
+        user_id_type: str = "open_id",
+    ) -> dict:
+        # 获取电子表格信息
+        url = f"{self.API_BASE_URL}/spreadsheet/get_spreadsheet"
+        params = {
+            "spreadsheet_token": spreadsheet_token,
+            "user_id_type": user_id_type,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def list_spreadsheet_sheets(
+        self,
+        spreadsheet_token: str,
+    ) -> dict:
+        # 列出电子表格的所有工作表
+        url = f"{self.API_BASE_URL}/spreadsheet/list_spreadsheet_sheets"
+        params = {
+            "spreadsheet_token": spreadsheet_token,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def add_rows(
+        self,
+        spreadsheet_token: str,
+        sheet_id: str,
+        sheet_name: str,
+        length: int,
+        values: str,
+    ) -> dict:
+        # 增加行,在工作表最后添加
+        url = f"{self.API_BASE_URL}/spreadsheet/add_rows"
+        payload = {
+            "spreadsheet_token": spreadsheet_token,
+            "sheet_id": sheet_id,
+            "sheet_name": sheet_name,
+            "length": length,
+            "values": values,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def add_cols(
+        self,
+        spreadsheet_token: str,
+        sheet_id: str,
+        sheet_name: str,
+        length: int,
+        values: str,
+    ) -> dict:
+        #  增加列,在工作表最后添加
+        url = f"{self.API_BASE_URL}/spreadsheet/add_cols"
+        payload = {
+            "spreadsheet_token": spreadsheet_token,
+            "sheet_id": sheet_id,
+            "sheet_name": sheet_name,
+            "length": length,
+            "values": values,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def read_rows(
+        self,
+        spreadsheet_token: str,
+        sheet_id: str,
+        sheet_name: str,
+        start_row: int,
+        num_rows: int,
+        user_id_type: str = "open_id",
+    ) -> dict:
+        # 读取工作表行数据
+        url = f"{self.API_BASE_URL}/spreadsheet/read_rows"
+        params = {
+            "spreadsheet_token": spreadsheet_token,
+            "sheet_id": sheet_id,
+            "sheet_name": sheet_name,
+            "start_row": start_row,
+            "num_rows": num_rows,
+            "user_id_type": user_id_type,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def read_cols(
+        self,
+        spreadsheet_token: str,
+        sheet_id: str,
+        sheet_name: str,
+        start_col: int,
+        num_cols: int,
+        user_id_type: str = "open_id",
+    ) -> dict:
+        # 读取工作表列数据
+        url = f"{self.API_BASE_URL}/spreadsheet/read_cols"
+        params = {
+            "spreadsheet_token": spreadsheet_token,
+            "sheet_id": sheet_id,
+            "sheet_name": sheet_name,
+            "start_col": start_col,
+            "num_cols": num_cols,
+            "user_id_type": user_id_type,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")
+
+    def read_table(
+        self,
+        spreadsheet_token: str,
+        sheet_id: str,
+        sheet_name: str,
+        num_range: str,
+        query: str,
+        user_id_type: str = "open_id",
+    ) -> dict:
+        # 自定义读取行列数据
+        url = f"{self.API_BASE_URL}/spreadsheet/read_table"
+        params = {
+            "spreadsheet_token": spreadsheet_token,
+            "sheet_id": sheet_id,
+            "sheet_name": sheet_name,
+            "range": num_range,
+            "query": query,
+            "user_id_type": user_id_type,
+        }
+        res = self._send_request(url, method="GET", params=params)
+        return res.get("data")

api/tests/integration_tests/vdb/pgvector/test_pgvector.py

@@ -18,6 +18,8 @@ class PGVectorTest(AbstractVectorTest):
                 user="postgres",
                 password="difyai123456",
                 database="dify",
+                min_connection=1,
+                max_connection=5,
             ),
         )
 

docker/.env.example

@@ -346,7 +346,7 @@ VOLCENGINE_TOS_REGION=your-region
 # ------------------------------
 
 # The type of vector store to use. 
-# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`.
+# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, ``chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `analyticdb`.
 VECTOR_STORE=weaviate
 
 # The Weaviate endpoint URL. Only available when VECTOR_STORE is `weaviate`.
@@ -385,12 +385,31 @@ MYSCALE_PASSWORD=
 MYSCALE_DATABASE=dify
 MYSCALE_FTS_PARAMS=
 
-# pgvector configurations, only available when VECTOR_STORE is `pgvecto-rs or pgvector`
+# pgvector configurations, only available when VECTOR_STORE is `pgvector`
 PGVECTOR_HOST=pgvector
 PGVECTOR_PORT=5432
 PGVECTOR_USER=postgres
 PGVECTOR_PASSWORD=difyai123456
 PGVECTOR_DATABASE=dify
+PGVECTOR_MIN_CONNECTION=1
+PGVECTOR_MAX_CONNECTION=5
+
+# pgvecto-rs configurations, only available when VECTOR_STORE is `pgvecto-rs`
+PGVECTO_RS_HOST=pgvecto-rs
+PGVECTO_RS_PORT=5432
+PGVECTO_RS_USER=postgres
+PGVECTO_RS_PASSWORD=difyai123456
+PGVECTO_RS_DATABASE=dify
+
+# analyticdb configurations, only available when VECTOR_STORE is `analyticdb`
+ANALYTICDB_KEY_ID=your-ak
+ANALYTICDB_KEY_SECRET=your-sk
+ANALYTICDB_REGION_ID=cn-hangzhou
+ANALYTICDB_INSTANCE_ID=gp-ab123456
+ANALYTICDB_ACCOUNT=testaccount
+ANALYTICDB_PASSWORD=testpassword
+ANALYTICDB_NAMESPACE=dify
+ANALYTICDB_NAMESPACE_PASSWORD=difypassword
 
 # TiDB vector configurations, only available when VECTOR_STORE is `tidb`
 TIDB_VECTOR_HOST=tidb

web/app/activate/page.tsx

@@ -22,7 +22,7 @@ const Activate = () => {
         <Header />
         <ActivateForm />
         <div className='px-8 py-6 text-sm font-normal text-gray-500'>
-          © {new Date().getFullYear()} Dify, Inc. All rights reserved.
+          © {new Date().getFullYear()} LangGenius, Inc. All rights reserved.
         </div>
       </div>
     </div>

web/app/components/app/configuration/config-var/config-modal/index.tsx

@@ -1,6 +1,6 @@
 'use client'
 import type { FC } from 'react'
-import React, { useCallback, useState } from 'react'
+import React, { useCallback, useEffect, useRef, useState } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useContext } from 'use-context-selector'
 import ModalFoot from '../modal-foot'
@@ -40,6 +40,12 @@ const ConfigModal: FC<IConfigModalProps> = ({
   const { t } = useTranslation()
   const [tempPayload, setTempPayload] = useState<InputVar>(payload || getNewVarInWorkflow('') as any)
   const { type, label, variable, options, max_length } = tempPayload
+  const modalRef = useRef<HTMLDivElement>(null)
+  useEffect(() => {
+    // To fix the first input element auto focus, then directly close modal will raise error
+    if (isShow)
+      modalRef.current?.focus()
+  }, [isShow])
 
   const isStringInput = type === InputVarType.textInput || type === InputVarType.paragraph
   const checkVariableName = useCallback((value: string) => {
@@ -135,7 +141,7 @@ const ConfigModal: FC<IConfigModalProps> = ({
       isShow={isShow}
       onClose={onClose}
     >
-      <div className='mb-8'>
+      <div className='mb-8' ref={modalRef} tabIndex={-1}>
         <div className='space-y-2'>
 
           <Field title={t('appDebug.variableConfig.fieldType')}>