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299 | @dataclass(init=False)
class CohereModel(Model):
"""A model that uses the Cohere API.
Internally, this uses the [Cohere Python client](
https://github.com/cohere-ai/cohere-python) to interact with the API.
Apart from `__init__`, all methods are private or match those of the base class.
"""
client: AsyncClientV2 = field(repr=False)
_model_name: CohereModelName = field(repr=False)
_provider: Provider[AsyncClientV2] = field(repr=False)
def __init__(
self,
model_name: CohereModelName,
*,
provider: Literal['cohere'] | Provider[AsyncClientV2] = 'cohere',
profile: ModelProfileSpec | None = None,
settings: ModelSettings | None = None,
):
"""Initialize an Cohere model.
Args:
model_name: The name of the Cohere model to use. List of model names
available [here](https://docs.cohere.com/docs/models#command).
provider: The provider to use for authentication and API access. Can be either the string
'cohere' or an instance of `Provider[AsyncClientV2]`. If not provided, a new provider will be
created using the other parameters.
profile: The model profile to use. Defaults to a profile picked by the provider based on the model name.
settings: Model-specific settings that will be used as defaults for this model.
"""
self._model_name = model_name
if isinstance(provider, str):
provider = infer_provider(provider)
self._provider = provider
self.client = provider.client
super().__init__(settings=settings, profile=profile or provider.model_profile)
@property
def base_url(self) -> str:
client_wrapper = self.client._client_wrapper # type: ignore
return str(client_wrapper.get_base_url())
@property
def model_name(self) -> CohereModelName:
"""The model name."""
return self._model_name
@property
def system(self) -> str:
"""The model provider."""
return self._provider.name
async def request(
self,
messages: list[ModelMessage],
model_settings: ModelSettings | None,
model_request_parameters: ModelRequestParameters,
) -> ModelResponse:
check_allow_model_requests()
response = await self._chat(messages, cast(CohereModelSettings, model_settings or {}), model_request_parameters)
model_response = self._process_response(response)
return model_response
async def _chat(
self,
messages: list[ModelMessage],
model_settings: CohereModelSettings,
model_request_parameters: ModelRequestParameters,
) -> V2ChatResponse:
tools = self._get_tools(model_request_parameters)
if model_request_parameters.builtin_tools:
raise UserError('Cohere does not support built-in tools')
cohere_messages = self._map_messages(messages)
try:
return await self.client.chat(
model=self._model_name,
messages=cohere_messages,
tools=tools or OMIT,
max_tokens=model_settings.get('max_tokens', OMIT),
stop_sequences=model_settings.get('stop_sequences', OMIT),
temperature=model_settings.get('temperature', OMIT),
p=model_settings.get('top_p', OMIT),
seed=model_settings.get('seed', OMIT),
presence_penalty=model_settings.get('presence_penalty', OMIT),
frequency_penalty=model_settings.get('frequency_penalty', OMIT),
)
except ApiError as e:
if (status_code := e.status_code) and status_code >= 400:
raise ModelHTTPError(status_code=status_code, model_name=self.model_name, body=e.body) from e
raise # pragma: lax no cover
def _process_response(self, response: V2ChatResponse) -> ModelResponse:
"""Process a non-streamed response, and prepare a message to return."""
parts: list[ModelResponsePart] = []
if response.message.content is not None and len(response.message.content) > 0:
# While Cohere's API returns a list, it only does that for future proofing
# and currently only one item is being returned.
choice = response.message.content[0]
parts.extend(split_content_into_text_and_thinking(choice.text, self.profile.thinking_tags))
for c in response.message.tool_calls or []:
if c.function and c.function.name and c.function.arguments: # pragma: no branch
parts.append(
ToolCallPart(
tool_name=c.function.name,
args=c.function.arguments,
tool_call_id=c.id or _generate_tool_call_id(),
)
)
return ModelResponse(
parts=parts, usage=_map_usage(response), model_name=self._model_name, provider_name=self._provider.name
)
def _map_messages(self, messages: list[ModelMessage]) -> list[ChatMessageV2]:
"""Just maps a `pydantic_ai.Message` to a `cohere.ChatMessageV2`."""
cohere_messages: list[ChatMessageV2] = []
for message in messages:
if isinstance(message, ModelRequest):
cohere_messages.extend(self._map_user_message(message))
elif isinstance(message, ModelResponse):
texts: list[str] = []
tool_calls: list[ToolCallV2] = []
for item in message.parts:
if isinstance(item, TextPart):
texts.append(item.content)
elif isinstance(item, ThinkingPart):
# NOTE: We don't send ThinkingPart to the providers yet. If you are unsatisfied with this,
# please open an issue. The below code is the code to send thinking to the provider.
# texts.append(f'<think>\n{item.content}\n</think>')
pass
elif isinstance(item, ToolCallPart):
tool_calls.append(self._map_tool_call(item))
elif isinstance(item, BuiltinToolCallPart | BuiltinToolReturnPart): # pragma: no cover
# This is currently never returned from cohere
pass
else:
assert_never(item)
message_param = AssistantChatMessageV2(role='assistant')
if texts:
message_param.content = [TextAssistantMessageV2ContentItem(text='\n\n'.join(texts))]
if tool_calls:
message_param.tool_calls = tool_calls
cohere_messages.append(message_param)
else:
assert_never(message)
if instructions := self._get_instructions(messages):
cohere_messages.insert(0, SystemChatMessageV2(role='system', content=instructions))
return cohere_messages
def _get_tools(self, model_request_parameters: ModelRequestParameters) -> list[ToolV2]:
return [self._map_tool_definition(r) for r in model_request_parameters.tool_defs.values()]
@staticmethod
def _map_tool_call(t: ToolCallPart) -> ToolCallV2:
return ToolCallV2(
id=_guard_tool_call_id(t=t),
type='function',
function=ToolCallV2Function(
name=t.tool_name,
arguments=t.args_as_json_str(),
),
)
@staticmethod
def _map_tool_definition(f: ToolDefinition) -> ToolV2:
return ToolV2(
type='function',
function=ToolV2Function(
name=f.name,
description=f.description,
parameters=f.parameters_json_schema,
),
)
@classmethod
def _map_user_message(cls, message: ModelRequest) -> Iterable[ChatMessageV2]:
for part in message.parts:
if isinstance(part, SystemPromptPart):
yield SystemChatMessageV2(role='system', content=part.content)
elif isinstance(part, UserPromptPart):
if isinstance(part.content, str):
yield UserChatMessageV2(role='user', content=part.content)
else:
raise RuntimeError('Cohere does not yet support multi-modal inputs.')
elif isinstance(part, ToolReturnPart):
yield ToolChatMessageV2(
role='tool',
tool_call_id=_guard_tool_call_id(t=part),
content=part.model_response_str(),
)
elif isinstance(part, RetryPromptPart):
if part.tool_name is None:
yield UserChatMessageV2(role='user', content=part.model_response()) # pragma: no cover
else:
yield ToolChatMessageV2(
role='tool',
tool_call_id=_guard_tool_call_id(t=part),
content=part.model_response(),
)
else:
assert_never(part)
|