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158 | class Replicate(CustomLLM):
"""Replicate LLM.
Examples:
`pip install llama-index-llms-replicate`
```python
from llama_index.llms.replicate import Replicate
# Set up the Replicate API token
import os
os.environ["REPLICATE_API_TOKEN"] = "<your API key>"
# Initialize the Replicate class
llm = Replicate(
model="replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b"
)
# Example of calling the 'complete' method with a prompt
resp = llm.complete("Who is Paul Graham?")
print(resp)
```
"""
model: str = Field(description="The Replicate model to use.")
temperature: float = Field(
default=DEFAULT_REPLICATE_TEMP,
description="The temperature to use for sampling.",
gte=0.01,
lte=1.0,
)
image: str = Field(
default="", description="The image file for multimodal model to use. (optional)"
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
prompt_key: str = Field(
default="prompt", description="The key to use for the prompt in API calls."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Replicate API."
)
is_chat_model: bool = Field(
default=False, description="Whether the model is a chat model."
)
@classmethod
def class_name(cls) -> str:
return "Replicate_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=DEFAULT_NUM_OUTPUTS,
model_name=self.model,
is_chat_model=self.is_chat_model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs: Dict[str, Any] = {
"temperature": self.temperature,
"max_length": self.context_window,
}
if self.image != "":
try:
base_kwargs["image"] = open(self.image, "rb")
except FileNotFoundError:
raise FileNotFoundError(
"Could not load image file. Please check whether the file exists"
)
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_input_dict(self, prompt: str, **kwargs: Any) -> Dict[str, Any]:
return {self.prompt_key: prompt, **self._model_kwargs, **kwargs}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
response_gen = self.stream_complete(prompt, formatted=formatted, **kwargs)
response_list = list(response_gen)
final_response = response_list[-1]
final_response.delta = None
return final_response
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
try:
import replicate
except ImportError:
raise ImportError(
"Could not import replicate library."
"Please install replicate with `pip install replicate`"
)
if not formatted:
prompt = self.completion_to_prompt(prompt)
input_dict = self._get_input_dict(prompt, **kwargs)
response_iter = replicate.run(self.model, input=input_dict)
def gen() -> CompletionResponseGen:
text = ""
for delta in response_iter:
text += delta
yield CompletionResponse(
delta=delta,
text=text,
)
return gen()
|