mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 19:24:27 +00:00
feat - add fim codestral api
This commit is contained in:
Ishaan Jaff
parent
2a5eabcceb
commit
87386b5cc3
7 changed files with 549 additions and 132 deletions
461
litellm/llms/text_completion_codestral.py
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461
litellm/llms/text_completion_codestral.py
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@ -0,0 +1,461 @@
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# What is this?
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## Controller file for TextCompletionCodestral Integration - https://codestral.com/
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from functools import partial
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import os, types
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import traceback
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import json
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from enum import Enum
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import requests, copy # type: ignore
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import time
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from typing import Callable, Optional, List, Literal, Union
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from litellm.utils import (
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TextCompletionResponse,
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Usage,
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CustomStreamWrapper,
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Message,
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Choices,
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)
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from litellm.litellm_core_utils.core_helpers import map_finish_reason
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import litellm
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from .prompt_templates.factory import prompt_factory, custom_prompt
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
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from .base import BaseLLM
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import httpx # type: ignore
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class TextCompletionCodestralError(Exception):
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def __init__(
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self,
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status_code,
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message,
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request: Optional[httpx.Request] = None,
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response: Optional[httpx.Response] = None,
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):
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self.status_code = status_code
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self.message = message
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if request is not None:
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self.request = request
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else:
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self.request = httpx.Request(
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method="POST",
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url="https://docs.codestral.com/user-guide/inference/rest_api",
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)
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if response is not None:
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self.response = response
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else:
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self.response = httpx.Response(
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status_code=status_code, request=self.request
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)
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super().__init__(
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self.message
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) # Call the base class constructor with the parameters it needs
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async def make_call(
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client: AsyncHTTPHandler,
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api_base: str,
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headers: dict,
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data: str,
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model: str,
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messages: list,
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logging_obj,
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):
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response = await client.post(api_base, headers=headers, data=data, stream=True)
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if response.status_code != 200:
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raise TextCompletionCodestralError(
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status_code=response.status_code, message=response.text
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)
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completion_stream = response.aiter_lines()
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# LOGGING
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logging_obj.post_call(
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input=messages,
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api_key="",
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original_response=completion_stream, # Pass the completion stream for logging
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additional_args={"complete_input_dict": data},
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)
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return completion_stream
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class MistralTextCompletionConfig:
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"""
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Reference: https://docs.mistral.ai/api/#operation/createFIMCompletion
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"""
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suffix: Optional[str] = None
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temperature: Optional[int] = None
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top_p: Optional[float] = None
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max_tokens: Optional[int] = None
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min_tokens: Optional[int] = None
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stream: Optional[bool] = None
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random_seed: Optional[int] = None
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stop: Optional[str] = None
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def __init__(
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self,
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suffix: Optional[str] = None,
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temperature: Optional[int] = None,
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top_p: Optional[float] = None,
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max_tokens: Optional[int] = None,
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min_tokens: Optional[int] = None,
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stream: Optional[bool] = None,
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random_seed: Optional[int] = None,
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stop: Optional[str] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(self):
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return [
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"suffix",
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"temperature",
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"top_p",
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"max_tokens",
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"stream",
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"seed",
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"stop",
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]
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def map_openai_params(self, non_default_params: dict, optional_params: dict):
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for param, value in non_default_params.items():
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if param == "suffix":
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optional_params["suffix"] = value
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if param == "temperature":
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optional_params["temperature"] = value
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if param == "top_p":
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optional_params["top_p"] = value
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if param == "max_tokens":
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optional_params["max_tokens"] = value
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if param == "stream" and value == True:
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optional_params["stream"] = value
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if param == "stop":
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optional_params["stop"] = value
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if param == "seed":
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optional_params["random_seed"] = value
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if param == "min_tokens":
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optional_params["min_tokens"] = value
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return optional_params
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class CodestralTextCompletion(BaseLLM):
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def __init__(self) -> None:
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super().__init__()
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def _validate_environment(
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self,
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api_key: Optional[str],
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user_headers: dict,
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) -> dict:
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if api_key is None:
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raise ValueError(
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"Missing CODESTRAL_API_Key - Please add CODESTRAL_API_Key to your environment variables"
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)
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headers = {
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"content-type": "application/json",
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"Authorization": "Bearer {}".format(api_key),
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}
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if user_headers is not None and isinstance(user_headers, dict):
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headers = {**headers, **user_headers}
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return headers
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def output_parser(self, generated_text: str):
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"""
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Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens.
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Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763
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"""
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chat_template_tokens = [
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"<|assistant|>",
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"<|system|>",
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"<|user|>",
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"<s>",
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"</s>",
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]
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for token in chat_template_tokens:
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if generated_text.strip().startswith(token):
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generated_text = generated_text.replace(token, "", 1)
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if generated_text.endswith(token):
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generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
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return generated_text
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def process_text_completion_response(
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self,
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model: str,
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response: Union[requests.Response, httpx.Response],
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model_response: TextCompletionResponse,
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stream: bool,
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logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,
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optional_params: dict,
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api_key: str,
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data: Union[dict, str],
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messages: list,
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print_verbose,
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encoding,
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) -> TextCompletionResponse:
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## LOGGING
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logging_obj.post_call(
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input=messages,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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if response.status_code != 200:
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raise TextCompletionCodestralError(
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message=str(response.text),
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status_code=response.status_code,
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)
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try:
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completion_response = response.json()
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except:
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raise TextCompletionCodestralError(message=response.text, status_code=422)
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_response = litellm.TextCompletionResponse(**completion_response)
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return _response
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def completion(
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self,
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model: str,
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messages: list,
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api_base: str,
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custom_prompt_dict: dict,
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model_response: TextCompletionResponse,
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print_verbose: Callable,
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encoding,
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api_key: str,
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logging_obj,
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optional_params: dict,
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timeout: Union[float, httpx.Timeout],
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acompletion=None,
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litellm_params=None,
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logger_fn=None,
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headers: dict = {},
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) -> Union[TextCompletionResponse, CustomStreamWrapper]:
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headers = self._validate_environment(api_key, headers)
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completion_url = api_base or "https://codestral.mistral.ai/v1/fim/completions"
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if model in custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[model]
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prompt = custom_prompt(
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages,
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)
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else:
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prompt = prompt_factory(model=model, messages=messages)
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## Load Config
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config = litellm.MistralTextCompletionConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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stream = optional_params.pop("stream", False)
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data = {
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"prompt": prompt,
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**optional_params,
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}
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input_text = prompt
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## LOGGING
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logging_obj.pre_call(
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input=input_text,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"headers": headers,
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"api_base": completion_url,
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"acompletion": acompletion,
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},
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)
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## COMPLETION CALL
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if acompletion is True:
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### ASYNC STREAMING
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if stream is True:
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return self.async_streaming(
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model=model,
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messages=messages,
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data=data,
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api_base=completion_url,
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model_response=model_response,
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print_verbose=print_verbose,
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encoding=encoding,
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api_key=api_key,
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logging_obj=logging_obj,
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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headers=headers,
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timeout=timeout,
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) # type: ignore
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else:
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### ASYNC COMPLETION
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return self.async_completion(
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model=model,
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messages=messages,
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data=data,
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api_base=completion_url,
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model_response=model_response,
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print_verbose=print_verbose,
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encoding=encoding,
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api_key=api_key,
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logging_obj=logging_obj,
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optional_params=optional_params,
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stream=False,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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headers=headers,
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timeout=timeout,
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) # type: ignore
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### SYNC STREAMING
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if stream is True:
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response = requests.post(
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completion_url,
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headers=headers,
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data=json.dumps(data),
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stream=stream,
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)
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_response = CustomStreamWrapper(
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response.iter_lines(),
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model,
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custom_llm_provider="codestral",
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logging_obj=logging_obj,
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)
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return _response
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### SYNC COMPLETION
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else:
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response = requests.post(
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url=completion_url,
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headers=headers,
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data=json.dumps(data),
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)
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return self.process_text_completion_response(
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model=model,
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response=response,
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model_response=model_response,
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stream=optional_params.get("stream", False),
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logging_obj=logging_obj, # type: ignore
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optional_params=optional_params,
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api_key=api_key,
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data=data,
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messages=messages,
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print_verbose=print_verbose,
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encoding=encoding,
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)
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async def async_completion(
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self,
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model: str,
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messages: list,
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api_base: str,
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model_response: TextCompletionResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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stream,
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data: dict,
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optional_params: dict,
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timeout: Union[float, httpx.Timeout],
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litellm_params=None,
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logger_fn=None,
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headers={},
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) -> TextCompletionResponse:
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async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=timeout))
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try:
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response = await async_handler.post(
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api_base, headers=headers, data=json.dumps(data)
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)
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except httpx.HTTPStatusError as e:
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raise TextCompletionCodestralError(
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status_code=e.response.status_code,
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message="HTTPStatusError - {}".format(e.response.text),
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)
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except Exception as e:
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raise TextCompletionCodestralError(
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status_code=500, message="{}\n{}".format(str(e), traceback.format_exc())
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)
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return self.process_text_completion_response(
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model=model,
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response=response,
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model_response=model_response,
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stream=stream,
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logging_obj=logging_obj,
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api_key=api_key,
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data=data,
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messages=messages,
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print_verbose=print_verbose,
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optional_params=optional_params,
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encoding=encoding,
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)
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async def async_streaming(
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self,
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model: str,
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messages: list,
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api_base: str,
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model_response: TextCompletionResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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data: dict,
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timeout: Union[float, httpx.Timeout],
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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headers={},
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) -> CustomStreamWrapper:
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data["stream"] = True
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streamwrapper = CustomStreamWrapper(
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completion_stream=None,
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make_call=partial(
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make_call,
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api_base=api_base,
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headers=headers,
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data=json.dumps(data),
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model=model,
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messages=messages,
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logging_obj=logging_obj,
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),
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model=model,
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custom_llm_provider="codestral",
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logging_obj=logging_obj,
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)
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return streamwrapper
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def embedding(self, *args, **kwargs):
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pass
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