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364 | class Cohere(LLM):
"""Cohere LLM.
例子:
`pip install llama-index-llms-cohere`
```python
from llama_index.llms.cohere import Cohere
llm = Cohere(model="command", api_key=api_key)
resp = llm.complete("Paul Graham is ")
print(resp)
```"""
model: str = Field(description="The cohere model to use.")
temperature: float = Field(
description="The temperature to use for sampling.", default=None
)
max_retries: int = Field(
default=10, description="The maximum number of API retries."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Cohere API."
)
max_tokens: int = Field(description="The maximum number of tokens to generate.")
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
def __init__(
self,
model: str = "command-r",
temperature: Optional[float] = None,
max_tokens: Optional[int] = 8192,
timeout: Optional[float] = None,
max_retries: int = 10,
api_key: Optional[str] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
self._client = cohere.Client(api_key, client_name="llama_index")
self._aclient = cohere.AsyncClient(api_key, client_name="llama_index")
super().__init__(
temperature=temperature,
additional_kwargs=additional_kwargs,
timeout=timeout,
max_retries=max_retries,
model=model,
callback_manager=callback_manager,
max_tokens=max_tokens,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
"""获取类名。"""
return "Cohere_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=cohere_modelname_to_contextsize(self.model),
num_output=self.max_tokens,
is_chat_model=True,
model_name=self.model,
system_role=MessageRole.CHATBOT,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"model": self.model,
"temperature": self.temperature,
}
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = messages[-1].content
remaining, documents = remove_documents_from_messages(messages[:-1])
history = messages_to_cohere_history(remaining)
all_kwargs = self._get_all_kwargs(**kwargs)
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
documents=documents,
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
raw=response.__dict__,
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
return CompletionResponse(
text=response.generations[0].text,
raw=response.__dict__,
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = messages[-1].content
remaining, documents = remove_documents_from_messages(messages[:-1])
history = messages_to_cohere_history(remaining)
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
documents=documents,
**all_kwargs,
)
def gen() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for r in response:
if "text" in r.__dict__:
content_delta = r.text
else:
content_delta = ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=r.__dict__,
)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
response = completion_with_retry(
client=self._client,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
def gen() -> CompletionResponseGen:
content = ""
for r in response:
content_delta = r.text
content += content_delta
yield CompletionResponse(
text=content, delta=content_delta, raw=r._asdict()
)
return gen()
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
return ChatResponse(
message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
raw=response.__dict__,
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
all_kwargs = self._get_all_kwargs(**kwargs)
if "stream" in all_kwargs:
warnings.warn(
"Parameter `stream` is not supported by the `chat` method."
"Use the `stream_chat` method instead"
)
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
return CompletionResponse(
text=response.generations[0].text,
raw=response.__dict__,
)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
history = messages_to_cohere_history(messages[:-1])
prompt = messages[-1].content
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
if all_kwargs["model"] not in CHAT_MODELS:
raise ValueError(f"{all_kwargs['model']} not supported for chat")
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=True,
message=prompt,
chat_history=history,
**all_kwargs,
)
async def gen() -> ChatResponseAsyncGen:
content = ""
role = MessageRole.ASSISTANT
async for r in response:
if "text" in r.__dict__:
content_delta = r.text
else:
content_delta = ""
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=r.__dict__,
)
return gen()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
all_kwargs = self._get_all_kwargs(**kwargs)
all_kwargs["stream"] = True
response = await acompletion_with_retry(
aclient=self._aclient,
max_retries=self.max_retries,
chat=False,
prompt=prompt,
**all_kwargs,
)
async def gen() -> CompletionResponseAsyncGen:
content = ""
async for r in response:
content_delta = r.text
content += content_delta
yield CompletionResponse(
text=content, delta=content_delta, raw=r._asdict()
)
return gen()
|