142 lines
4.7 KiB
Python
142 lines
4.7 KiB
Python
from typing import Any, Callable, Dict, Optional, Sequence
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from llama_index.bridge.pydantic import Field, PrivateAttr
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from llama_index.callbacks import CallbackManager
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from llama_index.core.llms.types import (
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ChatMessage,
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ChatResponse,
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ChatResponseGen,
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CompletionResponse,
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CompletionResponseGen,
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LLMMetadata,
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)
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from llama_index.llms.ai21_utils import ai21_model_to_context_size
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from llama_index.llms.base import llm_chat_callback, llm_completion_callback
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from llama_index.llms.custom import CustomLLM
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from llama_index.llms.generic_utils import (
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completion_to_chat_decorator,
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get_from_param_or_env,
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)
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from llama_index.types import BaseOutputParser, PydanticProgramMode
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class AI21(CustomLLM):
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"""AI21 Labs LLM."""
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model: str = Field(description="The AI21 model to use.")
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maxTokens: int = Field(description="The maximum number of tokens to generate.")
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temperature: float = Field(description="The temperature to use for sampling.")
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additional_kwargs: Dict[str, Any] = Field(
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default_factory=dict, description="Additional kwargs for the anthropic API."
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)
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_api_key = PrivateAttr()
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def __init__(
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self,
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api_key: Optional[str] = None,
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model: Optional[str] = "j2-mid",
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maxTokens: Optional[int] = 512,
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temperature: Optional[float] = 0.1,
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additional_kwargs: Optional[Dict[str, Any]] = None,
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callback_manager: Optional[CallbackManager] = None,
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system_prompt: Optional[str] = None,
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messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
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completion_to_prompt: Optional[Callable[[str], str]] = None,
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pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
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output_parser: Optional[BaseOutputParser] = None,
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) -> None:
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"""Initialize params."""
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try:
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import ai21 as _ # noqa
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except ImportError as e:
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raise ImportError(
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"You must install the `ai21` package to use AI21."
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"Please `pip install ai21`"
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) from e
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additional_kwargs = additional_kwargs or {}
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callback_manager = callback_manager or CallbackManager([])
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api_key = get_from_param_or_env("api_key", api_key, "AI21_API_KEY")
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self._api_key = api_key
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super().__init__(
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model=model,
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maxTokens=maxTokens,
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temperature=temperature,
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additional_kwargs=additional_kwargs,
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callback_manager=callback_manager,
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system_prompt=system_prompt,
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messages_to_prompt=messages_to_prompt,
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completion_to_prompt=completion_to_prompt,
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pydantic_program_mode=pydantic_program_mode,
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output_parser=output_parser,
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)
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@classmethod
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def class_name(self) -> str:
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"""Get Class Name."""
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return "AI21_LLM"
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@property
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def metadata(self) -> LLMMetadata:
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return LLMMetadata(
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context_window=ai21_model_to_context_size(self.model),
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num_output=self.maxTokens,
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model_name=self.model,
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)
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@property
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def _model_kwargs(self) -> Dict[str, Any]:
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base_kwargs = {
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"model": self.model,
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"maxTokens": self.maxTokens,
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"temperature": self.temperature,
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}
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return {**base_kwargs, **self.additional_kwargs}
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def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
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return {
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**self._model_kwargs,
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**kwargs,
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}
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@llm_completion_callback()
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def complete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponse:
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all_kwargs = self._get_all_kwargs(**kwargs)
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import ai21
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ai21.api_key = self._api_key
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response = ai21.Completion.execute(**all_kwargs, prompt=prompt)
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return CompletionResponse(
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text=response["completions"][0]["data"]["text"], raw=response.__dict__
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)
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@llm_completion_callback()
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def stream_complete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponseGen:
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raise NotImplementedError(
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"AI21 does not currently support streaming completion."
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)
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@llm_chat_callback()
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
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all_kwargs = self._get_all_kwargs(**kwargs)
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chat_fn = completion_to_chat_decorator(self.complete)
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return chat_fn(messages, **all_kwargs)
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@llm_chat_callback()
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def stream_chat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponseGen:
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raise NotImplementedError("AI21 does not Currently Support Streaming Chat.")
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