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377 | class DashScope(CustomLLM):
"""DashScope LLM.
Examples:
`pip install llama-index-llms-dashscope`
```python
from llama_index.llms.dashscope import DashScope, DashScopeGenerationModels
dashscope_llm = DashScope(model_name=DashScopeGenerationModels.QWEN_MAX)
response = llm.complete("What is the meaning of life?")
print(response.text)
```
"""
model_name: str = Field(
default=DashScopeGenerationModels.QWEN_MAX,
description="The DashScope model to use.",
)
max_tokens: Optional[int] = Field(
description="The maximum number of tokens to generate.",
default=DEFAULT_NUM_OUTPUTS,
gt=0,
)
incremental_output: Optional[bool] = Field(
description="Control stream output, If False, the subsequent \
output will include the content that has been \
output previously.",
default=True,
)
enable_search: Optional[bool] = Field(
description="The model has a built-in Internet search service. \
This parameter controls whether the model refers to \
the Internet search results when generating text.",
default=False,
)
stop: Optional[Any] = Field(
description="str, list of str or token_id, list of token id. It will automatically \
stop when the generated content is about to contain the specified string \
or token_ids, and the generated content does not contain \
the specified content.",
default=None,
)
temperature: Optional[float] = Field(
description="The temperature to use during generation.",
default=DEFAULT_TEMPERATURE,
ge=0.0,
le=2.0,
)
top_k: Optional[int] = Field(
description="Sample counter when generate.", default=None
)
top_p: Optional[float] = Field(
description="Sample probability threshold when generate."
)
seed: Optional[int] = Field(
description="Random seed when generate.", default=1234, ge=0
)
repetition_penalty: Optional[float] = Field(
description="Penalty for repeated words in generated text; \
1.0 is no penalty, values greater than 1 discourage \
repetition.",
default=None,
)
api_key: str = Field(
default=None, description="The DashScope API key.", exclude=True
)
def __init__(
self,
model_name: Optional[str] = DashScopeGenerationModels.QWEN_MAX,
max_tokens: Optional[int] = DEFAULT_NUM_OUTPUTS,
incremental_output: Optional[int] = True,
enable_search: Optional[bool] = False,
stop: Optional[Any] = None,
temperature: Optional[float] = DEFAULT_TEMPERATURE,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
seed: Optional[int] = 1234,
api_key: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
**kwargs: Any,
):
super().__init__(
model_name=model_name,
max_tokens=max_tokens,
incremental_output=incremental_output,
enable_search=enable_search,
stop=stop,
temperature=temperature,
top_k=top_k,
top_p=top_p,
seed=seed,
api_key=api_key,
callback_manager=callback_manager,
kwargs=kwargs,
)
@classmethod
def class_name(cls) -> str:
return "DashScope_LLM"
@property
def metadata(self) -> LLMMetadata:
DASHSCOPE_MODEL_META[self.model_name]["num_output"] = (
self.max_tokens or DASHSCOPE_MODEL_META[self.model_name]["num_output"]
)
return LLMMetadata(
model_name=self.model_name, **DASHSCOPE_MODEL_META[self.model_name]
)
def _get_default_parameters(self) -> Dict:
params: Dict[Any, Any] = {}
if self.max_tokens is not None:
params["max_tokens"] = self.max_tokens
params["incremental_output"] = self.incremental_output
params["enable_search"] = self.enable_search
if self.stop is not None:
params["stop"] = self.stop
if self.temperature is not None:
params["temperature"] = self.temperature
if self.top_k is not None:
params["top_k"] = self.top_k
if self.top_p is not None:
params["top_p"] = self.top_p
if self.seed is not None:
params["seed"] = self.seed
return params
def _get_input_parameters(
self, prompt: str, **kwargs: Any
) -> Tuple[ChatMessage, Dict]:
parameters = self._get_default_parameters()
parameters.update(kwargs)
parameters["stream"] = False
# we only use message response
parameters["result_format"] = "message"
message = ChatMessage(
role=MessageRole.USER.value,
content=prompt,
)
return message, parameters
@llm_completion_callback()
def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
message, parameters = self._get_input_parameters(prompt=prompt, **kwargs)
parameters.pop("incremental_output", None)
parameters.pop("stream", None)
messages = chat_message_to_dashscope_messages([message])
response = call_with_messages(
model=self.model_name,
messages=messages,
api_key=self.api_key,
parameters=parameters,
)
return dashscope_response_to_completion_response(response)
@llm_completion_callback()
async def acomplete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
message, parameters = self._get_input_parameters(prompt=prompt, **kwargs)
parameters.pop("incremental_output", None)
parameters.pop("stream", None)
messages = chat_message_to_dashscope_messages([message])
response = await acall_with_messages(
model=self.model_name,
messages=messages,
api_key=self.api_key,
parameters=parameters,
)
return dashscope_response_to_completion_response(response)
@llm_completion_callback()
def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
message, parameters = self._get_input_parameters(prompt=prompt, kwargs=kwargs)
parameters["incremental_output"] = True
parameters["stream"] = True
responses = call_with_messages(
model=self.model_name,
messages=chat_message_to_dashscope_messages([message]),
api_key=self.api_key,
parameters=parameters,
)
def gen() -> CompletionResponseGen:
content = ""
for response in responses:
if response.status_code == HTTPStatus.OK:
top_choice = response.output.choices[0]
incremental_output = top_choice["message"]["content"]
if not incremental_output:
incremental_output = ""
content += incremental_output
yield CompletionResponse(
text=content, delta=incremental_output, raw=response
)
else:
yield CompletionResponse(text="", raw=response)
return
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
parameters = self._get_default_parameters()
parameters.update({**kwargs})
parameters.pop("stream", None)
parameters.pop("incremental_output", None)
parameters["result_format"] = "message" # only use message format.
response = call_with_messages(
model=self.model_name,
messages=chat_message_to_dashscope_messages(messages),
api_key=self.api_key,
parameters=parameters,
)
return dashscope_response_to_chat_response(response)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
parameters = self._get_default_parameters()
parameters.update({**kwargs})
parameters.pop("stream", None)
parameters.pop("incremental_output", None)
parameters["result_format"] = "message" # only use message format.
response = await acall_with_messages(
model=self.model_name,
messages=chat_message_to_dashscope_messages(messages),
api_key=self.api_key,
parameters=parameters,
)
return dashscope_response_to_chat_response(response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
parameters = self._get_default_parameters()
parameters.update({**kwargs})
parameters["stream"] = True
parameters["incremental_output"] = True
parameters["result_format"] = "message" # only use message format.
response = call_with_messages(
model=self.model_name,
messages=chat_message_to_dashscope_messages(messages),
api_key=self.api_key,
parameters=parameters,
)
def gen() -> ChatResponseGen:
content = ""
for r in response:
if r.status_code == HTTPStatus.OK:
top_choice = r.output.choices[0]
incremental_output = top_choice["message"]["content"]
role = top_choice["message"]["role"]
content += incremental_output
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=incremental_output,
raw=r,
)
else:
yield ChatResponse(message=ChatMessage(), raw=response)
return
return gen()
|