259 lines
8.5 KiB
Python
259 lines
8.5 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.constants import DEFAULT_TEMPERATURE
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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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ChatResponseAsyncGen,
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ChatResponseGen,
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CompletionResponse,
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CompletionResponseAsyncGen,
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CompletionResponseGen,
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LLMMetadata,
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MessageRole,
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)
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from llama_index.llms.anthropic_utils import (
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anthropic_modelname_to_contextsize,
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messages_to_anthropic_prompt,
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)
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from llama_index.llms.base import (
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llm_chat_callback,
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llm_completion_callback,
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)
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from llama_index.llms.generic_utils import (
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achat_to_completion_decorator,
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astream_chat_to_completion_decorator,
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chat_to_completion_decorator,
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stream_chat_to_completion_decorator,
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)
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from llama_index.llms.llm import LLM
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from llama_index.types import BaseOutputParser, PydanticProgramMode
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DEFAULT_ANTHROPIC_MODEL = "claude-2"
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DEFAULT_ANTHROPIC_MAX_TOKENS = 512
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class Anthropic(LLM):
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model: str = Field(
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default=DEFAULT_ANTHROPIC_MODEL, description="The anthropic model to use."
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)
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temperature: float = Field(
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default=DEFAULT_TEMPERATURE,
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description="The temperature to use for sampling.",
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gte=0.0,
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lte=1.0,
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)
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max_tokens: int = Field(
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default=DEFAULT_ANTHROPIC_MAX_TOKENS,
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description="The maximum number of tokens to generate.",
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gt=0,
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)
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base_url: Optional[str] = Field(default=None, description="The base URL to use.")
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timeout: Optional[float] = Field(
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default=None, description="The timeout to use in seconds.", gte=0
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)
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max_retries: int = Field(
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default=10, description="The maximum number of API retries.", gte=0
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)
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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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_client: Any = PrivateAttr()
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_aclient: Any = PrivateAttr()
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def __init__(
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self,
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model: str = DEFAULT_ANTHROPIC_MODEL,
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temperature: float = DEFAULT_TEMPERATURE,
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max_tokens: int = DEFAULT_ANTHROPIC_MAX_TOKENS,
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base_url: Optional[str] = None,
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timeout: Optional[float] = None,
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max_retries: int = 10,
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api_key: Optional[str] = None,
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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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try:
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import anthropic
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except ImportError as e:
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raise ImportError(
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"You must install the `anthropic` package to use Anthropic."
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"Please `pip install anthropic`"
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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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self._client = anthropic.Anthropic(
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api_key=api_key, base_url=base_url, timeout=timeout, max_retries=max_retries
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)
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self._aclient = anthropic.AsyncAnthropic(
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api_key=api_key, base_url=base_url, timeout=timeout, max_retries=max_retries
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)
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super().__init__(
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temperature=temperature,
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max_tokens=max_tokens,
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additional_kwargs=additional_kwargs,
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base_url=base_url,
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timeout=timeout,
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max_retries=max_retries,
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model=model,
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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(cls) -> str:
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return "Anthropic_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=anthropic_modelname_to_contextsize(self.model),
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num_output=self.max_tokens,
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is_chat_model=True,
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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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"temperature": self.temperature,
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"max_tokens_to_sample": self.max_tokens,
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}
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return {
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**base_kwargs,
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**self.additional_kwargs,
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}
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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_chat_callback()
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
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prompt = messages_to_anthropic_prompt(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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response = self._client.completions.create(
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prompt=prompt, stream=False, **all_kwargs
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)
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return ChatResponse(
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message=ChatMessage(
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role=MessageRole.ASSISTANT, content=response.completion
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),
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raw=dict(response),
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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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complete_fn = chat_to_completion_decorator(self.chat)
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return complete_fn(prompt, **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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prompt = messages_to_anthropic_prompt(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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response = self._client.completions.create(
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prompt=prompt, stream=True, **all_kwargs
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)
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def gen() -> ChatResponseGen:
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content = ""
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role = MessageRole.ASSISTANT
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for r in response:
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content_delta = r.completion
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content += content_delta
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yield ChatResponse(
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message=ChatMessage(role=role, content=content),
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delta=content_delta,
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raw=r,
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)
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return gen()
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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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stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
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return stream_complete_fn(prompt, **kwargs)
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@llm_chat_callback()
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async def achat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponse:
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prompt = messages_to_anthropic_prompt(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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response = await self._aclient.completions.create(
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prompt=prompt, stream=False, **all_kwargs
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)
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return ChatResponse(
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message=ChatMessage(
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role=MessageRole.ASSISTANT, content=response.completion
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),
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raw=dict(response),
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)
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@llm_completion_callback()
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async def acomplete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponse:
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acomplete_fn = achat_to_completion_decorator(self.achat)
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return await acomplete_fn(prompt, **kwargs)
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@llm_chat_callback()
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async def astream_chat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponseAsyncGen:
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prompt = messages_to_anthropic_prompt(messages)
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all_kwargs = self._get_all_kwargs(**kwargs)
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response = await self._aclient.completions.create(
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prompt=prompt, stream=True, **all_kwargs
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)
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async def gen() -> ChatResponseAsyncGen:
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content = ""
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role = MessageRole.ASSISTANT
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async for r in response:
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content_delta = r.completion
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content += content_delta
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yield ChatResponse(
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message=ChatMessage(role=role, content=content),
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delta=content_delta,
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raw=r,
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)
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return gen()
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@llm_completion_callback()
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async def astream_complete(
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self, prompt: str, formatted: bool = False, **kwargs: Any
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) -> CompletionResponseAsyncGen:
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astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
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return await astream_complete_fn(prompt, **kwargs)
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