llama-index

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from typing import Any, Callable, Dict, Optional, Sequence
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from llama_index.legacy.callbacks import CallbackManager
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from llama_index.legacy.constants import DEFAULT_NUM_OUTPUTS, DEFAULT_TEMPERATURE
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from llama_index.legacy.core.llms.types import ChatMessage, LLMMetadata
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from llama_index.legacy.llms.everlyai_utils import everlyai_modelname_to_contextsize
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from llama_index.legacy.llms.generic_utils import get_from_param_or_env
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from llama_index.legacy.llms.openai import OpenAI
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from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode
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EVERLYAI_API_BASE = "https://everlyai.xyz/hosted"
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DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf"
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class EverlyAI(OpenAI):
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    def __init__(
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        self,
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        model: str = DEFAULT_MODEL,
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        temperature: float = DEFAULT_TEMPERATURE,
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        max_tokens: int = DEFAULT_NUM_OUTPUTS,
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        additional_kwargs: Optional[Dict[str, Any]] = None,
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        max_retries: int = 10,
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        api_key: Optional[str] = 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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        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, "EverlyAI_API_KEY")
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        super().__init__(
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            model=model,
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            temperature=temperature,
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            max_tokens=max_tokens,
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            api_base=EVERLYAI_API_BASE,
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            api_key=api_key,
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            additional_kwargs=additional_kwargs,
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            max_retries=max_retries,
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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 "EverlyAI_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=everlyai_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 _is_chat_model(self) -> bool:
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        return True
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