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226 | class Clarifai(LLM):
"""Clarifai LLM.
Examples:
`pip install llama-index-llms-clarifai`
```python
from llama_index.llms.clarifai import Clarifai
llm = Clarifai(
user_id="clarifai",
app_id="ml",
model_name="llama2-7b-alternative-4k",
model_url=(
"https://clarifai.com/clarifai/ml/models/llama2-7b-alternative-4k"
)
response = llm.complete("Hello World!")
print(response)
```
"""
model_url: Optional[str] = Field(
description=f"Full URL of the model. e.g. `{EXAMPLE_URL}`"
)
model_version_id: Optional[str] = Field(description="Model Version ID.")
app_id: Optional[str] = Field(description="Clarifai application ID of the model.")
user_id: Optional[str] = Field(description="Clarifai user ID of the model.")
pat: Optional[str] = Field(
description="Personal Access Tokens(PAT) to validate requests."
)
_model: Any = PrivateAttr()
_is_chat_model: bool = PrivateAttr()
def __init__(
self,
model_name: Optional[str] = None,
model_url: Optional[str] = None,
model_version_id: Optional[str] = "",
app_id: Optional[str] = None,
user_id: Optional[str] = None,
pat: Optional[str] = None,
temperature: float = 0.1,
max_tokens: int = 512,
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,
):
if pat is None and os.environ.get("CLARIFAI_PAT") is not None:
pat = os.environ.get("CLARIFAI_PAT")
if not pat and os.environ.get("CLARIFAI_PAT") is None:
raise ValueError(
"Set `CLARIFAI_PAT` as env variable or pass `pat` as constructor argument"
)
if model_url is not None and model_name is not None:
raise ValueError("You can only specify one of model_url or model_name.")
if model_url is None and model_name is None:
raise ValueError("You must specify one of model_url or model_name.")
if model_name is not None:
if app_id is None or user_id is None:
raise ValueError(
f"Missing one app ID or user ID of the model: {app_id=}, {user_id=}"
)
else:
self._model = Model(
user_id=user_id,
app_id=app_id,
model_id=model_name,
model_version={"id": model_version_id},
pat=pat,
)
if model_url is not None:
self._model = Model(model_url, pat=pat)
model_name = self._model.id
self._is_chat_model = False
if "chat" in self._model.app_id or "chat" in self._model.id:
self._is_chat_model = True
additional_kwargs = additional_kwargs or {}
super().__init__(
temperature=temperature,
max_tokens=max_tokens,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
model_name=model_name,
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:
return "ClarifaiLLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_tokens,
model_name=self._model,
is_chat_model=self._is_chat_model,
)
# TODO: When the Clarifai python SDK supports inference params, add here.
def chat(
self,
messages: Sequence[ChatMessage],
inference_params: Optional[Dict] = {},
**kwargs: Any,
) -> ChatResponse:
"""Chat endpoint for LLM."""
prompt = "".join([str(m) for m in messages])
try:
response = (
self._model.predict_by_bytes(
input_bytes=prompt.encode(encoding="UTF-8"),
input_type="text",
inference_params=inference_params,
)
.outputs[0]
.data.text.raw
)
except Exception as e:
raise Exception(f"Prediction failed: {e}")
return ChatResponse(message=ChatMessage(content=response))
def complete(
self,
prompt: str,
formatted: bool = False,
inference_params: Optional[Dict] = {},
**kwargs: Any,
) -> CompletionResponse:
"""Completion endpoint for LLM."""
try:
response = (
self._model.predict_by_bytes(
input_bytes=prompt.encode(encoding="utf-8"),
input_type="text",
inference_params=inference_params,
)
.outputs[0]
.data.text.raw
)
except Exception as e:
raise Exception(f"Prediction failed: {e}")
return CompletionResponse(text=response)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise NotImplementedError(
"Clarifai does not currently support streaming completion."
)
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise NotImplementedError(
"Clarifai does not currently support streaming completion."
)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
raise NotImplementedError("Currently not supported.")
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
return self.complete(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError("Currently not supported.")
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError("Clarifai does not currently support this function.")
|