Merge branch 'main' into fix/chore-fix

.github/workflows/build-push.yml

@@ -125,7 +125,7 @@ jobs:
         with:
           images: ${{ env[matrix.image_name_env] }}
           tags: |
-            type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }}
+            type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') && !contains(github.ref, '-') }}
             type=ref,event=branch
             type=sha,enable=true,priority=100,prefix=,suffix=,format=long
             type=raw,value=${{ github.ref_name }},enable=${{ startsWith(github.ref, 'refs/tags/') }}

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

@@ -231,7 +231,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             except Exception as e:
                 logger.error(e)
                 break
-        yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
+        if tts_publisher:
+            yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
     def _process_stream_response(
         self,

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

@@ -212,7 +212,8 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             except Exception as e:
                 logger.error(e)
                 break
-        yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
+        if tts_publisher:
+            yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
     def _process_stream_response(
         self,

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -248,7 +248,8 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
             else:
                 start_listener_time = time.time()
                 yield MessageAudioStreamResponse(audio=audio.audio, task_id=task_id)
-        yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
+        if publisher:
+            yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
     def _process_stream_response(
         self, publisher: AppGeneratorTTSPublisher, trace_manager: Optional[TraceQueueManager] = None

api/core/model_runtime/callbacks/base_callback.py

@@ -1,3 +1,4 @@
+from abc import ABC, abstractmethod
 from typing import Optional
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
@@ -13,7 +14,7 @@ _TEXT_COLOR_MAPPING = {
 }
 
 
-class Callback:
+class Callback(ABC):
     """
     Base class for callbacks.
     Only for LLM.
@@ -21,6 +22,7 @@ class Callback:
 
     raise_error: bool = False
 
+    @abstractmethod
     def on_before_invoke(
         self,
         llm_instance: AIModel,
@@ -48,6 +50,7 @@ class Callback:
         """
         raise NotImplementedError()
 
+    @abstractmethod
     def on_new_chunk(
         self,
         llm_instance: AIModel,
@@ -77,6 +80,7 @@ class Callback:
         """
         raise NotImplementedError()
 
+    @abstractmethod
     def on_after_invoke(
         self,
         llm_instance: AIModel,
@@ -106,6 +110,7 @@ class Callback:
         """
         raise NotImplementedError()
 
+    @abstractmethod
     def on_invoke_error(
         self,
         llm_instance: AIModel,

api/core/model_runtime/docs/en_US/customizable_model_scale_out.md

@@ -0,0 +1,310 @@
+## Custom Integration of Pre-defined Models
+
+### Introduction
+
+After completing the vendors integration, the next step is to connect the vendor's models. To illustrate the entire connection process, we will use Xinference as an example to demonstrate a complete vendor integration.
+
+It is important to note that for custom models, each model connection requires a complete vendor credential.
+
+Unlike pre-defined models, a custom vendor integration always includes the following two parameters, which do not need to be defined in the vendor YAML file.
+
+![](images/index/image-3.png)
+
+As mentioned earlier, vendors do not need to implement validate_provider_credential. The runtime will automatically call the corresponding model layer's validate_credentials to validate the credentials based on the model type and name selected by the user.
+
+### Writing the Vendor YAML
+
+First, we need to identify the types of models supported by the vendor we are integrating.
+
+Currently supported model types are as follows:
+
+- `llm` Text Generation Models
+
+- `text_embedding` Text Embedding Models
+
+- `rerank` Rerank Models
+
+- `speech2text` Speech-to-Text
+
+- `tts` Text-to-Speech
+
+- `moderation` Moderation
+
+Xinference supports LLM, Text Embedding, and Rerank. So we will start by writing xinference.yaml.
+
+```yaml
+provider: xinference #Define the vendor identifier
+label: # Vendor display name, supports both en_US (English) and zh_Hans (Simplified Chinese). If zh_Hans is not set, it will use en_US by default.
+  en_US: Xorbits Inference
+icon_small: # Small icon, refer to other vendors' icons stored in the _assets directory within the vendor implementation directory; follows the same language policy as the label
+  en_US: icon_s_en.svg
+icon_large: # Large icon
+  en_US: icon_l_en.svg
+help: # Help information
+  title:
+    en_US: How to deploy Xinference
+    zh_Hans: 如何部署 Xinference
+  url:
+    en_US: https://github.com/xorbitsai/inference
+supported_model_types: # Supported model types. Xinference supports LLM, Text Embedding, and Rerank
+- llm
+- text-embedding
+- rerank
+configurate_methods: # Since Xinference is a locally deployed vendor with no predefined models, users need to deploy whatever models they need according to Xinference documentation. Thus, it only supports custom models.
+- customizable-model
+provider_credential_schema:
+  credential_form_schemas:
+```
+
+
+Then, we need to determine what credentials are required to define a model in Xinference.
+
+- Since it supports three different types of models, we need to specify the model_type to denote the model type. Here is how we can define it:
+
+```yaml
+provider_credential_schema:
+  credential_form_schemas:
+  - variable: model_type
+    type: select
+    label:
+      en_US: Model type
+      zh_Hans: 模型类型
+    required: true
+    options:
+    - value: text-generation
+      label:
+        en_US: Language Model
+        zh_Hans: 语言模型
+    - value: embeddings
+      label:
+        en_US: Text Embedding
+    - value: reranking
+      label:
+        en_US: Rerank
+```
+
+- Next, each model has its own model_name, so we need to define that here:
+
+```yaml
+  - variable: model_name
+    type: text-input
+    label:
+      en_US: Model name
+      zh_Hans: 模型名称
+    required: true
+    placeholder:
+      zh_Hans: 填写模型名称
+      en_US: Input model name
+```
+
+- Specify the Xinference local deployment address:
+
+```yaml
+  - variable: server_url
+    label:
+      zh_Hans: 服务器URL
+      en_US: Server url
+    type: text-input
+    required: true
+    placeholder:
+      zh_Hans: 在此输入Xinference的服务器地址，如 https://example.com/xxx
+      en_US: Enter the url of your Xinference, for example https://example.com/xxx
+```
+
+- Each model has a unique model_uid, so we also need to define that here:
+
+```yaml
+  - variable: model_uid
+    label:
+      zh_Hans: 模型UID
+      en_US: Model uid
+    type: text-input
+    required: true
+    placeholder:
+      zh_Hans: 在此输入您的Model UID
+      en_US: Enter the model uid
+```
+
+Now, we have completed the basic definition of the vendor.
+
+### Writing the Model Code
+
+Next, let's take the `llm` type as an example and write `xinference.llm.llm.py`.
+
+In `llm.py`, create a Xinference LLM class, we name it `XinferenceAILargeLanguageModel` (this can be arbitrary), inheriting from the `__base.large_language_model.LargeLanguageModel` base class, and implement the following methods:
+
+- LLM Invocation
+
+Implement the core method for LLM invocation, supporting both stream and synchronous responses.
+
+```python
+def _invoke(self, model: str, credentials: dict,
+            prompt_messages: list[PromptMessage], model_parameters: dict,
+            tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+            stream: bool = True, user: Optional[str] = None) \
+        -> Union[LLMResult, Generator]:
+    """
+    Invoke large language model
+    
+    :param model: model name
+	:param credentials: model credentials
+	:param prompt_messages: prompt messages
+	:param model_parameters: model parameters
+	:param tools: tools for tool usage
+	:param stop: stop words
+	:param stream: is the response a stream
+	:param user: unique user id
+	:return: full response or stream response chunk generator result
+	"""
+```
+
+When implementing, ensure to use two functions to return data separately for synchronous and stream responses. This is important because Python treats functions containing the `yield` keyword as generator functions, mandating them to return `Generator` types. Here’s an example (note that the example uses simplified parameters; in real implementation, use the parameter list as defined above):
+
+```python
+def _invoke(self, stream: bool, **kwargs) \
+        -> Union[LLMResult, Generator]:
+    if stream:
+          return self._handle_stream_response(**kwargs)
+    return self._handle_sync_response(**kwargs)
+
+def _handle_stream_response(self, **kwargs) -> Generator:
+    for chunk in response:
+          yield chunk
+def _handle_sync_response(self, **kwargs) -> LLMResult:
+    return LLMResult(**response)
+```
+
+- Pre-compute Input Tokens
+
+If the model does not provide an interface for pre-computing tokens, you can return 0 directly.
+
+```python
+def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],tools: Optional[list[PromptMessageTool]] = None) -> int:
+  """
+  Get number of tokens for given prompt messages
+
+  :param model: model name
+  :param credentials: model credentials
+  :param prompt_messages: prompt messages
+  :param tools: tools for tool usage
+  :return: token count
+  """
+```
+
+
+Sometimes, you might not want to return 0 directly. In such cases, you can use `self._get_num_tokens_by_gpt2(text: str)` to get pre-computed tokens. This method is provided by the `AIModel` base class, and it uses GPT2's Tokenizer for calculation. However, it should be noted that this is only a substitute and may not be fully accurate.
+
+- Model Credentials Validation
+
+Similar to vendor credentials validation, this method validates individual model credentials.
+
+```python
+def validate_credentials(self, model: str, credentials: dict) -> None:
+    """
+    Validate model credentials
+    
+    :param model: model name
+	:param credentials: model credentials
+	:return: None
+	"""
+```
+
+- Model Parameter Schema
+
+Unlike custom types, since the YAML file does not define which parameters a model supports, we need to dynamically generate the model parameter schema.
+
+For instance, Xinference supports `max_tokens`, `temperature`, and `top_p` parameters.
+
+However, some vendors may support different parameters for different models. For example, the `OpenLLM` vendor supports `top_k`, but not all models provided by this vendor support `top_k`. Let's say model A supports `top_k` but model B does not. In such cases, we need to dynamically generate the model parameter schema, as illustrated below:
+
+```python
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+            used to define customizable model schema
+        """
+        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='max_tokens', type=ParameterType.INT,
+                use_template='max_tokens',
+                min=1,
+                default=512,
+                label=I18nObject(
+                    zh_Hans='最大生成长度', en_US='Max Tokens'
+                )
+            )
+        ]
+
+        # if model is A, add top_k to rules
+        if model == 'A':
+            rules.append(
+                ParameterRule(
+                    name='top_k', type=ParameterType.INT,
+                    use_template='top_k',
+                    min=1,
+                    default=50,
+                    label=I18nObject(
+                        zh_Hans='Top K', en_US='Top K'
+                    )
+                )
+            )
+
+        """
+            some NOT IMPORTANT code here
+        """
+
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(
+                en_US=model
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=model_type,
+            model_properties={ 
+                ModelPropertyKey.MODE:  ModelType.LLM,
+            },
+            parameter_rules=rules
+        )
+
+        return entity
+```
+
+- Exception Error Mapping
+
+When a model invocation error occurs, it should be mapped to the runtime's specified `InvokeError` type, enabling Dify to handle different errors appropriately.
+
+Runtime Errors:
+
+- `InvokeConnectionError` Connection error during invocation
+- `InvokeServerUnavailableError` Service provider unavailable
+- `InvokeRateLimitError` Rate limit reached
+- `InvokeAuthorizationError` Authorization failure
+- `InvokeBadRequestError` Invalid request parameters
+
+```python
+  @property
+  def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+      """
+      Map model invoke error to unified error
+      The key is the error type thrown to the caller
+      The value is the error type thrown by the model,
+      which needs to be converted into a unified error type for the caller.
+  
+      :return: Invoke error mapping
+      """
+```
+
+For interface method details, see: [Interfaces](./interfaces.md). For specific implementations, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py).

api/core/model_runtime/docs/en_US/predefined_model_scale_out.md

@@ -0,0 +1,173 @@
+## Predefined Model Integration
+
+After completing the vendor integration, the next step is to integrate the models from the vendor.
+
+First, we need to determine the type of model to be integrated and create the corresponding model type `module` under the respective vendor's directory.
+
+Currently supported model types are:
+
+- `llm` Text Generation Model
+- `text_embedding` Text Embedding Model
+- `rerank` Rerank Model
+- `speech2text` Speech-to-Text
+- `tts` Text-to-Speech
+- `moderation` Moderation
+
+Continuing with `Anthropic` as an example, `Anthropic` only supports LLM, so create a `module` named `llm` under `model_providers.anthropic`.
+
+For predefined models, we first need to create a YAML file named after the model under the `llm` `module`, such as `claude-2.1.yaml`.
+
+### Prepare Model YAML
+
+```yaml
+model: claude-2.1  # Model identifier
+# Display name of the model, which can be set to en_US English or zh_Hans Chinese. If zh_Hans is not set, it will default to en_US.
+# This can also be omitted, in which case the model identifier will be used as the label
+label:
+  en_US: claude-2.1
+model_type: llm  # Model type, claude-2.1 is an LLM
+features:  # Supported features, agent-thought supports Agent reasoning, vision supports image understanding
+- agent-thought
+model_properties:  # Model properties
+  mode: chat  # LLM mode, complete for text completion models, chat for conversation models
+  context_size: 200000  # Maximum context size
+parameter_rules:  # Parameter rules for the model call; only LLM requires this
+- name: temperature  # Parameter variable name
+  # Five default configuration templates are provided: temperature/top_p/max_tokens/presence_penalty/frequency_penalty
+  # The template variable name can be set directly in use_template, which will use the default configuration in entities.defaults.PARAMETER_RULE_TEMPLATE
+  # Additional configuration parameters will override the default configuration if set
+  use_template: temperature
+- name: top_p
+  use_template: top_p
+- name: top_k
+  label:  # Display name of the parameter
+    zh_Hans: 取样数量
+    en_US: Top k
+  type: int  # Parameter type, supports float/int/string/boolean
+  help:  # Help information, describing the parameter's function
+    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  # Whether the parameter is mandatory; can be omitted
+- name: max_tokens_to_sample
+  use_template: max_tokens
+  default: 4096  # Default value of the parameter
+  min: 1  # Minimum value of the parameter, applicable to float/int only
+  max: 4096  # Maximum value of the parameter, applicable to float/int only
+pricing:  # Pricing information
+  input: '8.00'  # Input unit price, i.e., prompt price
+  output: '24.00'  # Output unit price, i.e., response content price
+  unit: '0.000001'  # Price unit, meaning the above prices are per 100K
+  currency: USD  # Price currency
+```
+
+It is recommended to prepare all model configurations before starting the implementation of the model code.
+
+You can also refer to the YAML configuration information under the corresponding model type directories of other vendors in the `model_providers` directory. For the complete YAML rules, refer to: [Schema](schema.md#aimodelentity).
+
+### Implement the Model Call Code
+
+Next, create a Python file named `llm.py` under the `llm` `module` to write the implementation code.
+
+Create an Anthropic LLM class named `AnthropicLargeLanguageModel` (or any other name), inheriting from the `__base.large_language_model.LargeLanguageModel` base class, and implement the following methods:
+
+- LLM Call
+
+Implement the core method for calling the LLM, supporting both streaming and synchronous responses.
+
+```python
+  def _invoke(self, model: str, credentials: dict,
+              prompt_messages: list[PromptMessage], model_parameters: dict,
+              tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+              stream: bool = True, user: Optional[str] = None) \
+          -> Union[LLMResult, Generator]:
+      """
+      Invoke large language model
+  
+      :param model: model name
+      :param credentials: model credentials
+      :param prompt_messages: prompt messages
+      :param model_parameters: model parameters
+      :param tools: tools for tool calling
+      :param stop: stop words
+      :param stream: is stream response
+      :param user: unique user id
+      :return: full response or stream response chunk generator result
+      """
+```
+
+Ensure to use two functions for returning data, one for synchronous returns and the other for streaming returns, because Python identifies functions containing the `yield` keyword as generator functions, fixing the return type to `Generator`. Thus, synchronous and streaming returns need to be implemented separately, as shown below (note that the example uses simplified parameters, for actual implementation follow the above parameter list):
+
+```python
+  def _invoke(self, stream: bool, **kwargs) \
+          -> Union[LLMResult, Generator]:
+      if stream:
+            return self._handle_stream_response(**kwargs)
+      return self._handle_sync_response(**kwargs)
+
+  def _handle_stream_response(self, **kwargs) -> Generator:
+      for chunk in response:
+            yield chunk
+  def _handle_sync_response(self, **kwargs) -> LLMResult:
+      return LLMResult(**response)
+```
+
+- Pre-compute Input Tokens
+
+If the model does not provide an interface to precompute tokens, return 0 directly.
+
+```python
+  def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                     tools: Optional[list[PromptMessageTool]] = None) -> int:
+      """
+      Get number of tokens for given prompt messages
+
+      :param model: model name
+      :param credentials: model credentials
+      :param prompt_messages: prompt messages
+      :param tools: tools for tool calling
+      :return:
+      """
+```
+
+- Validate Model Credentials
+
+Similar to vendor credential validation, but specific to a single model.
+
+```python
+  def validate_credentials(self, model: str, credentials: dict) -> None:
+      """
+      Validate model credentials
+  
+      :param model: model name
+      :param credentials: model credentials
+      :return:
+      """
+```
+
+- Map Invoke Errors
+
+When a model call fails, map it to a specific `InvokeError` type as required by Runtime, allowing Dify to handle different errors accordingly.
+
+Runtime Errors:
+
+- `InvokeConnectionError` Connection error
+
+- `InvokeServerUnavailableError` Service provider unavailable
+- `InvokeRateLimitError` Rate limit reached
+- `InvokeAuthorizationError` Authorization failed
+- `InvokeBadRequestError` Parameter error
+
+```python
+  @property
+  def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+      """
+      Map model invoke error to unified error
+      The key is the error type thrown to the caller
+      The value is the error type thrown by the model,
+      which needs to be converted into a unified error type for the caller.
+  
+      :return: Invoke error mapping
+      """
+```
+
+For interface method explanations, see: [Interfaces](./interfaces.md). For detailed implementation, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py).

api/core/model_runtime/docs/en_US/provider_scale_out.md

@@ -58,7 +58,7 @@ provider_credential_schema:  # Provider credential rules, as Anthropic only supp
       en_US: Enter your API URL
 ```
 
-You can also refer to the YAML configuration information under other provider directories in `model_providers`. The complete YAML rules are available at: [Schema](schema.md#Provider).
+You can also refer to the YAML configuration information under other provider directories in `model_providers`. The complete YAML rules are available at: [Schema](schema.md#provider).
 
 ### Implementing Provider Code
 

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

@@ -117,7 +117,7 @@ model_credential_schema:
       en_US: Enter your API Base
 ```
 
-也可以参考  `model_providers` 目录下其他供应商目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#Provider)。
+也可以参考  `model_providers` 目录下其他供应商目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#provider)。
 
 #### 实现供应商代码
 

api/core/model_runtime/model_providers/_position.yaml

@@ -40,3 +40,4 @@
 - fireworks
 - mixedbread
 - nomic
+- voyage

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

@@ -1,238 +0,0 @@
-import json
-import logging
-import time
-from typing import Optional
-
-import boto3
-from botocore.config import Config
-from botocore.exceptions import (
-    ClientError,
-    EndpointConnectionError,
-    NoRegionError,
-    ServiceNotInRegionError,
-    UnknownServiceError,
-)
-
-from core.embedding.embedding_constant import EmbeddingInputType
-from core.model_runtime.entities.model_entities import PriceType
-from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
-from core.model_runtime.errors.invoke import (
-    InvokeAuthorizationError,
-    InvokeBadRequestError,
-    InvokeConnectionError,
-    InvokeError,
-    InvokeRateLimitError,
-    InvokeServerUnavailableError,
-)
-from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-
-logger = logging.getLogger(__name__)
-
-
-class BedrockTextEmbeddingModel(TextEmbeddingModel):
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        texts: list[str],
-        user: Optional[str] = None,
-        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
-    ) -> TextEmbeddingResult:
-        """
-        Invoke text embedding model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param texts: texts to embed
-        :param user: unique user id
-        :param input_type: input type
-        :return: embeddings result
-        """
-        client_config = Config(region_name=credentials["aws_region"])
-
-        bedrock_runtime = boto3.client(
-            service_name="bedrock-runtime",
-            config=client_config,
-            aws_access_key_id=credentials.get("aws_access_key_id"),
-            aws_secret_access_key=credentials.get("aws_secret_access_key"),
-        )
-
-        embeddings = []
-        token_usage = 0
-
-        model_prefix = model.split(".")[0]
-
-        if model_prefix == "amazon":
-            for text in texts:
-                body = {
-                    "inputText": text,
-                }
-                response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
-                embeddings.extend([response_body.get("embedding")])
-                token_usage += response_body.get("inputTextTokenCount")
-            logger.warning(f"Total Tokens: {token_usage}")
-            result = TextEmbeddingResult(
-                model=model,
-                embeddings=embeddings,
-                usage=self._calc_response_usage(model=model, credentials=credentials, tokens=token_usage),
-            )
-            return result
-
-        if model_prefix == "cohere":
-            input_type = "search_document" if len(texts) > 1 else "search_query"
-            for text in texts:
-                body = {
-                    "texts": [text],
-                    "input_type": input_type,
-                }
-                response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
-                embeddings.extend(response_body.get("embeddings"))
-                token_usage += len(text)
-            result = TextEmbeddingResult(
-                model=model,
-                embeddings=embeddings,
-                usage=self._calc_response_usage(model=model, credentials=credentials, tokens=token_usage),
-            )
-            return result
-
-        # others
-        raise ValueError(f"Got unknown model prefix {model_prefix} when handling block response")
-
-    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
-        """
-        Get number of tokens for given prompt messages
-
-        :param model: model name
-        :param credentials: model credentials
-        :param texts: texts to embed
-        :return:
-        """
-        num_tokens = 0
-        for text in texts:
-            num_tokens += self._get_num_tokens_by_gpt2(text)
-        return num_tokens
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-
-    @property
-    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
-        """
-        Map model invoke error to unified error
-        The key is the ermd = genai.GenerativeModel(model) error type thrown to the caller
-        The value is the md = genai.GenerativeModel(model) error type thrown by the model,
-        which needs to be converted into a unified error type for the caller.
-
-        :return: Invoke emd = genai.GenerativeModel(model) error mapping
-        """
-        return {
-            InvokeConnectionError: [],
-            InvokeServerUnavailableError: [],
-            InvokeRateLimitError: [],
-            InvokeAuthorizationError: [],
-            InvokeBadRequestError: [],
-        }
-
-    def _create_payload(
-        self,
-        model_prefix: str,
-        texts: list[str],
-        model_parameters: dict,
-        stop: Optional[list[str]] = None,
-        stream: bool = True,
-    ):
-        """
-        Create payload for bedrock api call depending on model provider
-        """
-        payload = {}
-
-        if model_prefix == "amazon":
-            payload["inputText"] = texts
-
-    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
-        """
-        Calculate response usage
-
-        :param model: model name
-        :param credentials: model credentials
-        :param tokens: input tokens
-        :return: usage
-        """
-        # get input price info
-        input_price_info = self.get_price(
-            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
-        )
-
-        # transform usage
-        usage = EmbeddingUsage(
-            tokens=tokens,
-            total_tokens=tokens,
-            unit_price=input_price_info.unit_price,
-            price_unit=input_price_info.unit,
-            total_price=input_price_info.total_amount,
-            currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at,
-        )
-
-        return usage
-
-    def _map_client_to_invoke_error(self, error_code: str, error_msg: str) -> type[InvokeError]:
-        """
-        Map client error to invoke error
-
-        :param error_code: error code
-        :param error_msg: error message
-        :return: invoke error
-        """
-
-        if error_code == "AccessDeniedException":
-            return InvokeAuthorizationError(error_msg)
-        elif error_code in {"ResourceNotFoundException", "ValidationException"}:
-            return InvokeBadRequestError(error_msg)
-        elif error_code in {"ThrottlingException", "ServiceQuotaExceededException"}:
-            return InvokeRateLimitError(error_msg)
-        elif error_code in {
-            "ModelTimeoutException",
-            "ModelErrorException",
-            "InternalServerException",
-            "ModelNotReadyException",
-        }:
-            return InvokeServerUnavailableError(error_msg)
-        elif error_code == "ModelStreamErrorException":
-            return InvokeConnectionError(error_msg)
-
-        return InvokeError(error_msg)
-
-    def _invoke_bedrock_embedding(
-        self,
-        model: str,
-        bedrock_runtime,
-        body: dict,
-    ):
-        accept = "application/json"
-        content_type = "application/json"
-        try:
-            response = bedrock_runtime.invoke_model(
-                body=json.dumps(body), modelId=model, accept=accept, contentType=content_type
-            )
-            response_body = json.loads(response.get("body").read().decode("utf-8"))
-            return response_body
-        except ClientError as ex:
-            error_code = ex.response["Error"]["Code"]
-            full_error_msg = f"{error_code}: {ex.response['Error']['Message']}"
-            raise self._map_client_to_invoke_error(error_code, full_error_msg)
-
-        except (EndpointConnectionError, NoRegionError, ServiceNotInRegionError) as ex:
-            raise InvokeConnectionError(str(ex))
-
-        except UnknownServiceError as ex:
-            raise InvokeServerUnavailableError(str(ex))
-
-        except Exception as ex:
-            raise InvokeError(str(ex))

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

@@ -1,223 +0,0 @@
-import json
-import time
-from decimal import Decimal
-from typing import Optional
-from urllib.parse import urljoin
-
-import numpy as np
-import requests
-
-from core.embedding.embedding_constant import EmbeddingInputType
-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import (
-    AIModelEntity,
-    FetchFrom,
-    ModelPropertyKey,
-    ModelType,
-    PriceConfig,
-    PriceType,
-)
-from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
-
-
-class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
-    """
-    Model class for an OpenAI API-compatible text embedding model.
-    """
-
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        texts: list[str],
-        user: Optional[str] = None,
-        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
-    ) -> TextEmbeddingResult:
-        """
-        Invoke text embedding model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param texts: texts to embed
-        :param user: unique user id
-        :param input_type: input type
-        :return: embeddings result
-        """
-
-        # Prepare headers and payload for the request
-        headers = {"Content-Type": "application/json"}
-
-        api_key = credentials.get("api_key")
-        if api_key:
-            headers["Authorization"] = f"Bearer {api_key}"
-
-        if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
-            endpoint_url = "https://cloud.perfxlab.cn/v1/"
-        else:
-            endpoint_url = credentials.get("endpoint_url")
-            if not endpoint_url.endswith("/"):
-                endpoint_url += "/"
-
-        endpoint_url = urljoin(endpoint_url, "embeddings")
-
-        extra_model_kwargs = {}
-        if user:
-            extra_model_kwargs["user"] = user
-
-        extra_model_kwargs["encoding_format"] = "float"
-
-        # get model properties
-        context_size = self._get_context_size(model, credentials)
-        max_chunks = self._get_max_chunks(model, credentials)
-
-        inputs = []
-        indices = []
-        used_tokens = 0
-
-        for i, text in enumerate(texts):
-            # Here token count is only an approximation based on the GPT2 tokenizer
-            # TODO: Optimize for better token estimation and chunking
-            num_tokens = self._get_num_tokens_by_gpt2(text)
-
-            if num_tokens >= context_size:
-                cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
-                # if num tokens is larger than context length, only use the start
-                inputs.append(text[0:cutoff])
-            else:
-                inputs.append(text)
-            indices += [i]
-
-        batched_embeddings = []
-        _iter = range(0, len(inputs), max_chunks)
-
-        for i in _iter:
-            # Prepare the payload for the request
-            payload = {"input": inputs[i : i + max_chunks], "model": model, **extra_model_kwargs}
-
-            # Make the request to the OpenAI API
-            response = requests.post(endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
-
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            response_data = response.json()
-
-            # Extract embeddings and used tokens from the response
-            embeddings_batch = [data["embedding"] for data in response_data["data"]]
-            embedding_used_tokens = response_data["usage"]["total_tokens"]
-
-            used_tokens += embedding_used_tokens
-            batched_embeddings += embeddings_batch
-
-        # calc usage
-        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
-
-        return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
-
-    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
-        """
-        Approximate number of tokens for given messages using GPT2 tokenizer
-
-        :param model: model name
-        :param credentials: model credentials
-        :param texts: texts to embed
-        :return:
-        """
-        return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-        try:
-            headers = {"Content-Type": "application/json"}
-
-            api_key = credentials.get("api_key")
-
-            if api_key:
-                headers["Authorization"] = f"Bearer {api_key}"
-
-            if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
-                endpoint_url = "https://cloud.perfxlab.cn/v1/"
-            else:
-                endpoint_url = credentials.get("endpoint_url")
-                if not endpoint_url.endswith("/"):
-                    endpoint_url += "/"
-
-            endpoint_url = urljoin(endpoint_url, "embeddings")
-
-            payload = {"input": "ping", "model": model}
-
-            response = requests.post(url=endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
-
-            if response.status_code != 200:
-                raise CredentialsValidateFailedError(
-                    f"Credentials validation failed with status code {response.status_code}"
-                )
-
-            try:
-                json_result = response.json()
-            except json.JSONDecodeError as e:
-                raise CredentialsValidateFailedError("Credentials validation failed: JSON decode error")
-
-            if "model" not in json_result:
-                raise CredentialsValidateFailedError("Credentials validation failed: invalid response")
-        except CredentialsValidateFailedError:
-            raise
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))
-
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
-        """
-        generate custom model entities from credentials
-        """
-        entity = AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model),
-            model_type=ModelType.TEXT_EMBEDDING,
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
-                ModelPropertyKey.MAX_CHUNKS: 1,
-            },
-            parameter_rules=[],
-            pricing=PriceConfig(
-                input=Decimal(credentials.get("input_price", 0)),
-                unit=Decimal(credentials.get("unit", 0)),
-                currency=credentials.get("currency", "USD"),
-            ),
-        )
-
-        return entity
-
-    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
-        """
-        Calculate response usage
-
-        :param model: model name
-        :param credentials: model credentials
-        :param tokens: input tokens
-        :return: usage
-        """
-        # get input price info
-        input_price_info = self.get_price(
-            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
-        )
-
-        # transform usage
-        usage = EmbeddingUsage(
-            tokens=tokens,
-            total_tokens=tokens,
-            unit_price=input_price_info.unit_price,
-            price_unit=input_price_info.unit,
-            total_price=input_price_info.total_amount,
-            currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at,
-        )
-
-        return usage

api/core/model_runtime/model_providers/voyage/_assets/icon_l_en.svg

@@ -0,0 +1,21 @@
+<svg version="1.0" xmlns="http://www.w3.org/2000/svg" width="100.000000pt" height="19.000000pt" viewBox="0 0 300.000000 57.000000" preserveAspectRatio="xMidYMid meet"><g transform="translate(0.000000,57.000000) scale(0.100000,-0.100000)" fill="#000000" stroke="none"><path d="M2505 368 c-38 -84 -86 -188 -106 -230 l-38 -78 27 0 c24 0 30 7 55
+    75 l28 75 100 0 100 0 25 -55 c13 -31 24 -64 24 -75 0 -17 7 -20 44 -20 l43 0
+    -37 73 c-20 39 -68 143 -106 229 -38 87 -74 158 -80 158 -5 0 -41 -69 -79
+    -152z m110 -30 c22 -51 41 -95 42 -98 2 -3 -36 -6 -83 -7 -76 -1 -85 0 -81 15
+    12 40 72 182 77 182 3 0 24 -41 45 -92z"/><path d="M63 493 c19 -61 197 -438 209 -440 10 -2 147 282 216 449 2 4 -10 8
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+    -80 153 -80 68 0 110 21 149 73 32 43 30 150 -3 196 -47 66 -158 90 -233 51z
+    m133 -16 c59 -30 89 -156 54 -224 -45 -87 -162 -78 -201 16 -18 44 -18 128 1
+    164 28 55 90 73 146 44z"/><path d="M935 303 l76 -98 -7 -72 -6 -73 33 0 34 0 -3 78 -4 77 71 93 c65 85
+    68 92 46 92 -15 0 -29 -9 -36 -22 -18 -33 -90 -128 -98 -128 -6 1 -67 85 -88
+    122 -8 15 -24 23 -53 25 l-41 4 76 -98z"/><path d="M1257 230 c-82 -169 -83 -170 -57 -170 17 0 27 6 27 15 0 8 7 31 17
+    52 l17 38 79 0 78 1 16 -34 c9 -18 16 -42 16 -52 0 -17 7 -20 41 -20 22 0 39
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+    -66 -5 -34 -4 -64 -5 -67 -3 -3 3 -5 36 -5 73 l0 68 55 -6 c49 -5 55 -4 55 13
+    0 17 -6 19 -55 16 l-55 -4 0 61 0 61 64 0 c48 0 65 4 70 15 4 13 -10 15 -92
+    15 l-97 0 0 -170z"/></g></svg>

api/core/model_runtime/model_providers/voyage/rerank/rerank-1.yaml

@@ -0,0 +1,4 @@
+model: rerank-1
+model_type: rerank
+model_properties:
+  context_size: 8000

api/core/model_runtime/model_providers/voyage/rerank/rerank-lite-1.yaml

@@ -0,0 +1,4 @@
+model: rerank-lite-1
+model_type: rerank
+model_properties:
+  context_size: 4000

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

@@ -0,0 +1,123 @@
+from typing import Optional
+
+import httpx
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+
+class VoyageRerankModel(RerankModel):
+    """
+    Model class for Voyage rerank model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
+        """
+        Invoke rerank model
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n documents to return
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=[])
+
+        base_url = credentials.get("base_url", "https://api.voyageai.com/v1")
+        base_url = base_url.removesuffix("/")
+
+        try:
+            response = httpx.post(
+                base_url + "/rerank",
+                json={"model": model, "query": query, "documents": docs, "top_k": top_n, "return_documents": True},
+                headers={"Authorization": f"Bearer {credentials.get('api_key')}", "Content-Type": "application/json"},
+            )
+            response.raise_for_status()
+            results = response.json()
+
+            rerank_documents = []
+            for result in results["data"]:
+                rerank_document = RerankDocument(
+                    index=result["index"],
+                    text=result["document"],
+                    score=result["relevance_score"],
+                )
+                if score_threshold is None or result["relevance_score"] >= score_threshold:
+                    rerank_documents.append(rerank_document)
+
+            return RerankResult(model=model, docs=rerank_documents)
+        except httpx.HTTPStatusError as e:
+            raise InvokeServerUnavailableError(str(e))
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8,
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        """
+        return {
+            InvokeConnectionError: [httpx.ConnectError],
+            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [httpx.HTTPStatusError],
+            InvokeBadRequestError: [httpx.RequestError],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+        generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.RERANK,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "8000"))},
+        )
+
+        return entity

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

@@ -0,0 +1,172 @@
+import time
+from json import JSONDecodeError, dumps
+from typing import Optional
+
+import requests
+
+from core.embedding.embedding_constant import EmbeddingInputType
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+
+
+class VoyageTextEmbeddingModel(TextEmbeddingModel):
+    """
+    Model class for Voyage text embedding model.
+    """
+
+    api_base: str = "https://api.voyageai.com/v1"
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
+    ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :param input_type: input type
+        :return: embeddings result
+        """
+        api_key = credentials["api_key"]
+        if not api_key:
+            raise CredentialsValidateFailedError("api_key is required")
+
+        base_url = credentials.get("base_url", self.api_base)
+        base_url = base_url.removesuffix("/")
+
+        url = base_url + "/embeddings"
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
+        voyage_input_type = "null"
+        if input_type is not None:
+            voyage_input_type = input_type.value
+        data = {"model": model, "input": texts, "input_type": voyage_input_type}
+
+        try:
+            response = requests.post(url, headers=headers, data=dumps(data))
+        except Exception as e:
+            raise InvokeConnectionError(str(e))
+
+        if response.status_code != 200:
+            try:
+                resp = response.json()
+                msg = resp["detail"]
+                if response.status_code == 401:
+                    raise InvokeAuthorizationError(msg)
+                elif response.status_code == 429:
+                    raise InvokeRateLimitError(msg)
+                elif response.status_code == 500:
+                    raise InvokeServerUnavailableError(msg)
+                else:
+                    raise InvokeBadRequestError(msg)
+            except JSONDecodeError as e:
+                raise InvokeServerUnavailableError(
+                    f"Failed to convert response to json: {e} with text: {response.text}"
+                )
+
+        try:
+            resp = response.json()
+            embeddings = resp["data"]
+            usage = resp["usage"]
+        except Exception as e:
+            raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
+
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"])
+
+        result = TextEmbeddingResult(
+            model=model, embeddings=[[float(data) for data in x["embedding"]] for x in embeddings], usage=usage
+        )
+
+        return result
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
+        except Exception as e:
+            raise CredentialsValidateFailedError(f"Credentials validation failed: {e}")
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        return {
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError, InvokeBadRequestError],
+        }
+
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param tokens: input tokens
+        :return: usage
+        """
+        # get input price info
+        input_price_info = self.get_price(
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
+        )
+
+        # transform usage
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at,
+        )
+
+        return usage
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+        generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.TEXT_EMBEDDING,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+        )
+
+        return entity

api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3-lite.yaml

@@ -0,0 +1,8 @@
+model: voyage-3-lite
+model_type: text-embedding
+model_properties:
+  context_size: 32000
+pricing:
+  input: '0.00002'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3.yaml

@@ -0,0 +1,8 @@
+model: voyage-3
+model_type: text-embedding
+model_properties:
+  context_size: 32000
+pricing:
+  input: '0.00006'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/voyage/voyage.py

@@ -0,0 +1,28 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class VoyageProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING)
+
+            # Use `voyage-3` model for validate,
+            # no matter what model you pass in, text completion model or chat model
+            model_instance.validate_credentials(model="voyage-3", credentials=credentials)
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise ex

api/core/model_runtime/model_providers/voyage/voyage.yaml

@@ -0,0 +1,31 @@
+provider: voyage
+label:
+  en_US: Voyage
+description:
+  en_US: Embedding and Rerank Model Supported
+icon_small:
+  en_US: icon_s_en.svg
+icon_large:
+  en_US: icon_l_en.svg
+background: "#EFFDFD"
+help:
+  title:
+    en_US: Get your API key from Voyage AI
+    zh_Hans: 从 Voyage 获取 API Key
+  url:
+    en_US: https://dash.voyageai.com/
+supported_model_types:
+  - text-embedding
+  - rerank
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: api_key
+      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

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

@@ -1,142 +0,0 @@
-import time
-from typing import Optional
-
-from core.embedding.embedding_constant import EmbeddingInputType
-from core.model_runtime.entities.model_entities import PriceType
-from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.model_runtime.model_providers.zhipuai._common import _CommonZhipuaiAI
-from core.model_runtime.model_providers.zhipuai.zhipuai_sdk._client import ZhipuAI
-
-
-class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
-    """
-    Model class for ZhipuAI text embedding model.
-    """
-
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        texts: list[str],
-        user: Optional[str] = None,
-        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
-    ) -> TextEmbeddingResult:
-        """
-        Invoke text embedding model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param texts: texts to embed
-        :param user: unique user id
-        :param input_type: input type
-        :return: embeddings result
-        """
-        credentials_kwargs = self._to_credential_kwargs(credentials)
-        client = ZhipuAI(api_key=credentials_kwargs["api_key"])
-
-        embeddings, embedding_used_tokens = self.embed_documents(model, client, texts)
-
-        return TextEmbeddingResult(
-            embeddings=embeddings,
-            usage=self._calc_response_usage(model, credentials_kwargs, embedding_used_tokens),
-            model=model,
-        )
-
-    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
-        """
-        Get number of tokens for given prompt messages
-
-        :param model: model name
-        :param credentials: model credentials
-        :param texts: texts to embed
-        :return:
-        """
-        if len(texts) == 0:
-            return 0
-
-        total_num_tokens = 0
-        for text in texts:
-            total_num_tokens += self._get_num_tokens_by_gpt2(text)
-
-        return total_num_tokens
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-        try:
-            # transform credentials to kwargs for model instance
-            credentials_kwargs = self._to_credential_kwargs(credentials)
-            client = ZhipuAI(api_key=credentials_kwargs["api_key"])
-
-            # call embedding model
-            self.embed_documents(
-                model=model,
-                client=client,
-                texts=["ping"],
-            )
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))
-
-    def embed_documents(self, model: str, client: ZhipuAI, texts: list[str]) -> tuple[list[list[float]], int]:
-        """Call out to ZhipuAI's embedding endpoint.
-
-        Args:
-            texts: The list of texts to embed.
-
-        Returns:
-            List of embeddings, one for each text.
-        """
-        embeddings = []
-        embedding_used_tokens = 0
-
-        for text in texts:
-            response = client.embeddings.create(model=model, input=text)
-            data = response.data[0]
-            embeddings.append(data.embedding)
-            embedding_used_tokens += response.usage.total_tokens
-
-        return [list(map(float, e)) for e in embeddings], embedding_used_tokens
-
-    def embed_query(self, text: str) -> list[float]:
-        """Call out to ZhipuAI's embedding endpoint.
-
-        Args:
-            text: The text to embed.
-
-        Returns:
-            Embeddings for the text.
-        """
-        return self.embed_documents([text])[0]
-
-    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
-        """
-        Calculate response usage
-
-        :param model: model name
-        :param tokens: input tokens
-        :return: usage
-        """
-        # get input price info
-        input_price_info = self.get_price(
-            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
-        )
-
-        # transform usage
-        usage = EmbeddingUsage(
-            tokens=tokens,
-            total_tokens=tokens,
-            unit_price=input_price_info.unit_price,
-            price_unit=input_price_info.unit,
-            total_price=input_price_info.total_amount,
-            currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at,
-        )
-
-        return usage

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

@@ -45,7 +45,7 @@ class Jieba(BaseKeyword):
             keyword_table_handler = JiebaKeywordTableHandler()
 
             keyword_table = self._get_dataset_keyword_table()
-            keywords_list = kwargs.get("keywords_list", None)
+            keywords_list = kwargs.get("keywords_list")
             for i in range(len(texts)):
                 text = texts[i]
                 if keywords_list:

api/events/event_handlers/create_document_index.py

@@ -14,7 +14,7 @@ from models.dataset import Document
 @document_index_created.connect
 def handle(sender, **kwargs):
     dataset_id = sender
-    document_ids = kwargs.get("document_ids", None)
+    document_ids = kwargs.get("document_ids")
     documents = []
     start_at = time.perf_counter()
     for document_id in document_ids:

api/poetry.lock

@@ -8074,29 +8074,29 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.6.5"
+version = "0.6.8"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "ruff-0.6.8-py3-none-linux_armv6l.whl", hash = "sha256:77944bca110ff0a43b768f05a529fecd0706aac7bcce36d7f1eeb4cbfca5f0f2"},
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+    {file = "ruff-0.6.8-py3-none-win_amd64.whl", hash = "sha256:ec0517dc0f37cad14a5319ba7bba6e7e339d03fbf967a6d69b0907d61be7a263"},
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+    {file = "ruff-0.6.8.tar.gz", hash = "sha256:a5bf44b1aa0adaf6d9d20f86162b34f7c593bfedabc51239953e446aefc8ce18"},
 ]
 
 [[package]]
@@ -10501,4 +10501,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "69b42bb1ff033f14e199fee8335356275099421d72bbd7037b7a991ea65cae08"
+content-hash = "c4580c22e2b220c8c80dbc3f765060a09e14874ed29b690c13a533bf0365e789"

api/pyproject.toml

@@ -123,6 +123,7 @@ FIRECRAWL_API_KEY = "fc-"
 TEI_EMBEDDING_SERVER_URL = "http://a.abc.com:11451"
 TEI_RERANK_SERVER_URL = "http://a.abc.com:11451"
 MIXEDBREAD_API_KEY = "mk-aaaaaaaaaaaaaaaaaaaa"
+VOYAGE_API_KEY = "va-aaaaaaaaaaaaaaaaaaaa"
 
 [tool.poetry]
 name = "dify-api"
@@ -286,4 +287,4 @@ optional = true
 
 [tool.poetry.group.lint.dependencies]
 dotenv-linter = "~0.5.0"
-ruff = "~0.6.5"
+ruff = "~0.6.8"

api/tests/integration_tests/model_runtime/voyage/test_provider.py

@@ -0,0 +1,25 @@
+import os
+from unittest.mock import Mock, patch
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.voyage.voyage import VoyageProvider
+
+
+def test_validate_provider_credentials():
+    provider = VoyageProvider()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        provider.validate_provider_credentials(credentials={"api_key": "hahahaha"})
+    with patch("requests.post") as mock_post:
+        mock_response = Mock()
+        mock_response.json.return_value = {
+            "object": "list",
+            "data": [{"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 0}],
+            "model": "voyage-3",
+            "usage": {"total_tokens": 1},
+        }
+        mock_response.status_code = 200
+        mock_post.return_value = mock_response
+        provider.validate_provider_credentials(credentials={"api_key": os.environ.get("VOYAGE_API_KEY")})

api/tests/integration_tests/model_runtime/voyage/test_rerank.py

@@ -0,0 +1,92 @@
+import os
+from unittest.mock import Mock, patch
+
+import pytest
+
+from core.model_runtime.entities.rerank_entities import RerankResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.voyage.rerank.rerank import VoyageRerankModel
+
+
+def test_validate_credentials():
+    model = VoyageRerankModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="rerank-lite-1",
+            credentials={"api_key": "invalid_key"},
+        )
+    with patch("httpx.post") as mock_post:
+        mock_response = Mock()
+        mock_response.json.return_value = {
+            "object": "list",
+            "data": [
+                {
+                    "relevance_score": 0.546875,
+                    "index": 0,
+                    "document": "Carson City is the capital city of the American state of Nevada. At the 2010 United "
+                    "States Census, Carson City had a population of 55,274.",
+                },
+                {
+                    "relevance_score": 0.4765625,
+                    "index": 1,
+                    "document": "The Commonwealth of the Northern Mariana Islands is a group of islands in the "
+                    "Pacific Ocean that are a political division controlled by the United States. Its "
+                    "capital is Saipan.",
+                },
+            ],
+            "model": "rerank-lite-1",
+            "usage": {"total_tokens": 96},
+        }
+        mock_response.status_code = 200
+        mock_post.return_value = mock_response
+        model.validate_credentials(
+            model="rerank-lite-1",
+            credentials={
+                "api_key": os.environ.get("VOYAGE_API_KEY"),
+            },
+        )
+
+
+def test_invoke_model():
+    model = VoyageRerankModel()
+    with patch("httpx.post") as mock_post:
+        mock_response = Mock()
+        mock_response.json.return_value = {
+            "object": "list",
+            "data": [
+                {
+                    "relevance_score": 0.84375,
+                    "index": 0,
+                    "document": "Kasumi is a girl name of Japanese origin meaning mist.",
+                },
+                {
+                    "relevance_score": 0.4765625,
+                    "index": 1,
+                    "document": "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music and she "
+                    "leads a team named PopiParty.",
+                },
+            ],
+            "model": "rerank-lite-1",
+            "usage": {"total_tokens": 59},
+        }
+        mock_response.status_code = 200
+        mock_post.return_value = mock_response
+        result = model.invoke(
+            model="rerank-lite-1",
+            credentials={
+                "api_key": os.environ.get("VOYAGE_API_KEY"),
+            },
+            query="Who is Kasumi?",
+            docs=[
+                "Kasumi is a girl name of Japanese origin meaning mist.",
+                "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music and she leads a team named "
+                "PopiParty.",
+            ],
+            score_threshold=0.5,
+        )
+
+        assert isinstance(result, RerankResult)
+        assert len(result.docs) == 1
+        assert result.docs[0].index == 0
+        assert result.docs[0].score >= 0.5

api/tests/integration_tests/model_runtime/voyage/test_text_embedding.py

@@ -0,0 +1,70 @@
+import os
+from unittest.mock import Mock, patch
+
+import pytest
+
+from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.voyage.text_embedding.text_embedding import VoyageTextEmbeddingModel
+
+
+def test_validate_credentials():
+    model = VoyageTextEmbeddingModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(model="voyage-3", credentials={"api_key": "invalid_key"})
+    with patch("requests.post") as mock_post:
+        mock_response = Mock()
+        mock_response.json.return_value = {
+            "object": "list",
+            "data": [{"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 0}],
+            "model": "voyage-3",
+            "usage": {"total_tokens": 1},
+        }
+        mock_response.status_code = 200
+        mock_post.return_value = mock_response
+        model.validate_credentials(model="voyage-3", credentials={"api_key": os.environ.get("VOYAGE_API_KEY")})
+
+
+def test_invoke_model():
+    model = VoyageTextEmbeddingModel()
+
+    with patch("requests.post") as mock_post:
+        mock_response = Mock()
+        mock_response.json.return_value = {
+            "object": "list",
+            "data": [
+                {"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 0},
+                {"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 1},
+            ],
+            "model": "voyage-3",
+            "usage": {"total_tokens": 2},
+        }
+        mock_response.status_code = 200
+        mock_post.return_value = mock_response
+        result = model.invoke(
+            model="voyage-3",
+            credentials={
+                "api_key": os.environ.get("VOYAGE_API_KEY"),
+            },
+            texts=["hello", "world"],
+            user="abc-123",
+        )
+
+        assert isinstance(result, TextEmbeddingResult)
+        assert len(result.embeddings) == 2
+        assert result.usage.total_tokens == 2
+
+
+def test_get_num_tokens():
+    model = VoyageTextEmbeddingModel()
+
+    num_tokens = model.get_num_tokens(
+        model="voyage-3",
+        credentials={
+            "api_key": os.environ.get("VOYAGE_API_KEY"),
+        },
+        texts=["ping"],
+    )
+
+    assert num_tokens == 1

api/tests/integration_tests/tools/__mock/http.py

@@ -17,7 +17,7 @@ class MockedHttp:
         request = httpx.Request(
             method, url, params=kwargs.get("params"), headers=kwargs.get("headers"), cookies=kwargs.get("cookies")
         )
-        data = kwargs.get("data", None)
+        data = kwargs.get("data")
         resp = json.dumps(data).encode("utf-8") if data else b"OK"
         response = httpx.Response(
             status_code=200,

api/tests/integration_tests/workflow/nodes/__mock/http.py

@@ -22,8 +22,8 @@ class MockedHttp:
             return response
 
         # get data, files
-        data = kwargs.get("data", None)
-        files = kwargs.get("files", None)
+        data = kwargs.get("data")
+        files = kwargs.get("files")
         if data is not None:
             resp = dumps(data).encode("utf-8")
         elif files is not None:

dev/pytest/pytest_model_runtime.sh

@@ -9,4 +9,5 @@ pytest api/tests/integration_tests/model_runtime/anthropic \
   api/tests/integration_tests/model_runtime/upstage \
   api/tests/integration_tests/model_runtime/fireworks \
   api/tests/integration_tests/model_runtime/nomic \
-  api/tests/integration_tests/model_runtime/mixedbread
+  api/tests/integration_tests/model_runtime/mixedbread \
+  api/tests/integration_tests/model_runtime/voyage