def validate_provider_credentials(self, credentials: dict) -> None:
"""
Validate provider credentials
You can choose any validate_credentials method of model type or implement validate method by yourself,
such as: get model list api
if validate failed, raise exception
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
"""
@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
"""
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:
"""
def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
"""
Get customizable model schema
:param model: model name
:param credentials: model credentials
:return: model schema
"""
def _invoke(self, model: str, credentials: dict,
texts: list[str], user: Optional[str] = None) \
-> TextEmbeddingResult:
"""
Invoke large language model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:return: embeddings 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:
"""
def _invoke(self, model: str, credentials: dict,
file: IO[bytes], user: Optional[str] = None) \
-> str:
"""
Invoke large language model
:param model: model name
:param credentials: model credentials
:param file: audio file
:param user: unique user id
:return: text for given audio file
"""
def _invoke(self, model: str, credentials: dict, content_text: str, streaming: bool, user: Optional[str] = None):
"""
Invoke large language model
:param model: model name
:param credentials: model credentials
:param content_text: text content to be translated
:param streaming: output is streaming
:param user: unique user id
:return: translated audio file
"""
def _invoke(self, model: str, credentials: dict,
text: str, user: Optional[str] = None) \
-> bool:
"""
Invoke large language model
:param model: model name
:param credentials: model credentials
:param text: text to moderate
:param user: unique user id
:return: false if text is safe, true otherwise
"""
class TextPromptMessageContent(PromptMessageContent):
"""
Model class for text prompt message content.
"""
type: PromptMessageContentType = PromptMessageContentType.TEXT
若传入图文，其中文字需要构造此实体作为 content 列表中的一部分。
ImagePromptMessageContent
class ImagePromptMessageContent(PromptMessageContent):
"""
Model class for image prompt message content.
"""
class DETAIL(Enum):
LOW = 'low'
HIGH = 'high'
type: PromptMessageContentType = PromptMessageContentType.IMAGE
detail: DETAIL = DETAIL.LOW # 分辨率
若传入图文，其中图片需要构造此实体作为 content 列表中的一部分
data 可以为 url 或者图片 base64 加密后的字符串。
PromptMessage
所有 Role 消息体的基类，仅作为参数声明用，不可初始化。
class PromptMessage(ABC, BaseModel):
"""
Model class for prompt message.
"""
role: PromptMessageRole # 消息角色
content: Optional[str | list[PromptMessageContent]] = None # 支持两种类型，字符串和内容列表，内容列表是为了满足多模态的需要，可详见 PromptMessageContent 说明。
name: Optional[str] = None # 名称，可选。
UserPromptMessage
UserMessage 消息体，代表用户消息。
class UserPromptMessage(PromptMessage):
"""
Model class for user prompt message.
"""
role: PromptMessageRole = PromptMessageRole.USER
AssistantPromptMessage
代表模型返回消息，通常用于 few-shots 或聊天历史传入。
class AssistantPromptMessage(PromptMessage):
"""
Model class for assistant prompt message.
"""
class ToolCall(BaseModel):
"""
Model class for assistant prompt message tool call.
"""
class ToolCallFunction(BaseModel):
"""
Model class for assistant prompt message tool call function.
"""
name: str # 工具名称
arguments: str # 工具参数
id: str # 工具 ID，仅在 OpenAI tool call 生效，为工具调用的唯一 ID，同一个工具可以调用多次
type: str # 默认 function
function: ToolCallFunction # 工具调用信息
role: PromptMessageRole = PromptMessageRole.ASSISTANT
tool_calls: list[ToolCall] = [] # 模型回复的工具调用结果（仅当传入 tools，并且模型认为需要调用工具时返回）
class SystemPromptMessage(PromptMessage):
"""
Model class for system prompt message.
"""
role: PromptMessageRole = PromptMessageRole.SYSTEM
ToolPromptMessage
代表工具消息，用于工具执行后将结果交给模型进行下一步计划。
class ToolPromptMessage(PromptMessage):
"""
Model class for tool prompt message.
"""
role: PromptMessageRole = PromptMessageRole.TOOL
tool_call_id: str # 工具调用 ID，若不支持 OpenAI tool call，也可传入工具名称
基类的 content 传入工具执行结果。
PromptMessageTool
class PromptMessageTool(BaseModel):
"""
Model class for prompt message tool.
"""
name: str # 工具名称
description: str # 工具描述
parameters: dict # 工具参数 dict
LLMResult
class LLMResult(BaseModel):
"""
Model class for llm result.
"""
model: str # 实际使用模型
prompt_messages: list[PromptMessage] # prompt 消息列表
message: AssistantPromptMessage # 回复消息
usage: LLMUsage # 使用的 tokens 及费用信息
system_fingerprint: Optional[str] = None # 请求指纹，可参考 OpenAI 该参数定义
LLMResultChunkDelta
流式返回中每个迭代内部 delta 实体
class LLMResultChunkDelta(BaseModel):
"""
Model class for llm result chunk delta.
"""
index: int # 序号
message: AssistantPromptMessage # 回复消息
usage: Optional[LLMUsage] = None # 使用的 tokens 及费用信息，仅最后一条返回
finish_reason: Optional[str] = None # 结束原因，仅最后一条返回
LLMResultChunk
流式返回中每个迭代实体
class LLMResultChunk(BaseModel):
"""
Model class for llm result chunk.
"""
model: str # 实际使用模型
prompt_messages: list[PromptMessage] # prompt 消息列表
system_fingerprint: Optional[str] = None # 请求指纹，可参考 OpenAI 该参数定义
delta: LLMResultChunkDelta # 每个迭代存在变化的内容
LLMUsage
class LLMUsage(ModelUsage):
"""
Model class for llm usage.
"""
prompt_tokens: int # prompt 使用 tokens
prompt_unit_price: Decimal # prompt 单价
prompt_price_unit: Decimal # prompt 价格单位，即单价基于多少 tokens
prompt_price: Decimal # prompt 费用
completion_tokens: int # 回复使用 tokens
completion_unit_price: Decimal # 回复单价
completion_price_unit: Decimal # 回复价格单位，即单价基于多少 tokens
completion_price: Decimal # 回复费用
total_tokens: int # 总使用 token 数
total_price: Decimal # 总费用
currency: str # 货币单位
latency: float # 请求耗时(s)
TextEmbeddingResult
class TextEmbeddingResult(BaseModel):
"""
Model class for text embedding result.
"""
model: str # 实际使用模型
embeddings: list[list[float]] # embedding 向量列表，对应传入的 texts 列表
usage: EmbeddingUsage # 使用信息
EmbeddingUsage
class EmbeddingUsage(ModelUsage):
"""
Model class for embedding usage.
"""
tokens: int # 使用 token 数
total_tokens: int # 总使用 token 数
unit_price: Decimal # 单价
price_unit: Decimal # 价格单位，即单价基于多少 tokens
total_price: Decimal # 总费用
currency: str # 货币单位
latency: float # 请求耗时(s)
RerankResult
class RerankResult(BaseModel):
"""
Model class for rerank result.
"""
model: str # 实际使用模型
docs: list[RerankDocument] # 重排后的分段列表
RerankDocument
class RerankDocument(BaseModel):
"""
Model class for rerank document.
"""
index: int # 原序号
text: str # 分段文本内容
score: float # 分数
