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(feat) cohere_chat provider
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ishaan-jaff
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204
litellm/llms/cohere_chat.py
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204
litellm/llms/cohere_chat.py
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import os, types
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import json
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from enum import Enum
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import requests
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import time, traceback
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from typing import Callable, Optional
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from litellm.utils import ModelResponse, Choices, Message, Usage
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import litellm
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import httpx
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class CohereError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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self.request = httpx.Request(method="POST", url="https://api.cohere.ai/v1/chat")
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self.response = httpx.Response(status_code=status_code, request=self.request)
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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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class CohereChatConfig:
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"""
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Configuration class for Cohere's API interface.
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Args:
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preamble (str, optional): When specified, the default Cohere preamble will be replaced with the provided one.
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chat_history (List[Dict[str, str]], optional): A list of previous messages between the user and the model.
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generation_id (str, optional): Unique identifier for the generated reply.
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response_id (str, optional): Unique identifier for the response.
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conversation_id (str, optional): An alternative to chat_history, creates or resumes a persisted conversation.
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prompt_truncation (str, optional): Dictates how the prompt will be constructed. Options: 'AUTO', 'AUTO_PRESERVE_ORDER', 'OFF'.
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connectors (List[Dict[str, str]], optional): List of connectors (e.g., web-search) to enrich the model's reply.
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search_queries_only (bool, optional): When true, the response will only contain a list of generated search queries.
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documents (List[Dict[str, str]], optional): A list of relevant documents that the model can cite.
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temperature (float, optional): A non-negative float that tunes the degree of randomness in generation.
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max_tokens (int, optional): The maximum number of tokens the model will generate as part of the response.
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k (int, optional): Ensures only the top k most likely tokens are considered for generation at each step.
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p (float, optional): Ensures that only the most likely tokens, with total probability mass of p, are considered for generation.
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frequency_penalty (float, optional): Used to reduce repetitiveness of generated tokens.
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presence_penalty (float, optional): Used to reduce repetitiveness of generated tokens.
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tools (List[Dict[str, str]], optional): A list of available tools (functions) that the model may suggest invoking.
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tool_results (List[Dict[str, Any]], optional): A list of results from invoking tools.
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"""
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preamble: Optional[str] = None
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chat_history: Optional[list] = None
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generation_id: Optional[str] = None
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response_id: Optional[str] = None
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conversation_id: Optional[str] = None
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prompt_truncation: Optional[str] = None
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connectors: Optional[list] = None
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search_queries_only: Optional[bool] = None
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documents: Optional[list] = None
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temperature: Optional[int] = None
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max_tokens: Optional[int] = None
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k: Optional[int] = None
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p: Optional[int] = None
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frequency_penalty: Optional[int] = None
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presence_penalty: Optional[int] = None
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tools: Optional[list] = None
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tool_results: Optional[list] = None
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def __init__(
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self,
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preamble: Optional[str] = None,
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chat_history: Optional[list] = None,
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generation_id: Optional[str] = None,
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response_id: Optional[str] = None,
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conversation_id: Optional[str] = None,
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prompt_truncation: Optional[str] = None,
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connectors: Optional[list] = None,
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search_queries_only: Optional[bool] = None,
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documents: Optional[list] = None,
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temperature: Optional[int] = None,
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max_tokens: Optional[int] = None,
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k: Optional[int] = None,
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p: Optional[int] = None,
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frequency_penalty: Optional[int] = None,
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presence_penalty: Optional[int] = None,
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tools: Optional[list] = None,
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tool_results: Optional[list] = None,
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) -> None:
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locals_ = locals()
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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 validate_environment(api_key):
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headers = {
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"accept": "application/json",
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"content-type": "application/json",
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}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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return headers
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def completion(
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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: ModelResponse,
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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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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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):
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headers = validate_environment(api_key)
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completion_url = api_base
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model = model
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prompt = " ".join(message["content"] for message in messages)
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## Load Config
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config = litellm.CohereConfig.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) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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data = {
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"model": model,
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"message": prompt,
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**optional_params,
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}
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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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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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},
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)
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## COMPLETION CALL
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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=optional_params["stream"] if "stream" in optional_params else False,
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)
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## error handling for cohere calls
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if response.status_code != 200:
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raise CohereError(message=response.text, status_code=response.status_code)
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if "stream" in optional_params and optional_params["stream"] == True:
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return response.iter_lines()
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else:
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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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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completion_response = response.json()
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try:
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model_response.choices[0].message.content = completion_response["text"] # type: ignore
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except Exception as e:
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raise CohereError(message=response.text, status_code=response.status_code)
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## CALCULATING USAGE - use cohere `billed_units` for returning usage
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billed_units = completion_response.get("meta", {}).get("billed_units", {})
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prompt_tokens = billed_units.get("input_tokens", 0)
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completion_tokens = billed_units.get("output_tokens", 0)
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model_response["created"] = int(time.time())
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model_response["model"] = model
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usage = Usage(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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)
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model_response.usage = usage
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return model_response
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@ -54,6 +54,7 @@ from .llms import (
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ollama_chat,
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cloudflare,
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cohere,
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cohere_chat,
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petals,
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oobabooga,
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openrouter,
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@ -1218,6 +1219,46 @@ def completion(
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)
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return response
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response = model_response
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elif custom_llm_provider == "cohere_chat":
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cohere_key = (
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api_key
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or litellm.cohere_key
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or get_secret("COHERE_API_KEY")
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or get_secret("CO_API_KEY")
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or litellm.api_key
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)
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api_base = (
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api_base
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or litellm.api_base
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or get_secret("COHERE_API_BASE")
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or "https://api.cohere.ai/v1/chat"
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)
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model_response = cohere_chat.completion(
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model=model,
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messages=messages,
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api_base=api_base,
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model_response=model_response,
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print_verbose=print_verbose,
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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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encoding=encoding,
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api_key=cohere_key,
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logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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# don't try to access stream object,
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response = CustomStreamWrapper(
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model_response,
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model,
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custom_llm_provider="cohere_chat",
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logging_obj=logging,
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)
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return response
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response = model_response
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elif custom_llm_provider == "maritalk":
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maritalk_key = (
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api_key
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