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140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
"""OpenAIChat client."""
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import copy
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import logging
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import os
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from typing import Any, Dict, Optional
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from manifest.clients.openai import OpenAIClient
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from manifest.request import LMRequest
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logger = logging.getLogger(__name__)
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# List from https://platform.openai.com/docs/models/model-endpoint-compatibility
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OPENAICHAT_ENGINES = {"gpt-3.5-turbo", "gpt-4", "gpt-4-32k"}
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class OpenAIChatClient(OpenAIClient):
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"""OpenAI Chat client."""
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# User param -> (client param, default value)
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PARAMS = {
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"engine": ("model", "gpt-3.5-turbo"),
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"temperature": ("temperature", 1.0),
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"max_tokens": ("max_tokens", 10),
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"n": ("n", 1),
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"top_p": ("top_p", 1.0),
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"stop_sequences": ("stop", None), # OpenAI doesn't like empty lists
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"presence_penalty": ("presence_penalty", 0.0),
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"frequency_penalty": ("frequency_penalty", 0.0),
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"batch_size": ("batch_size", 1),
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}
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REQUEST_CLS = LMRequest
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NAME = "openaichat"
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IS_CHAT = True
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def connect(
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self,
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connection_str: Optional[str] = None,
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client_args: Dict[str, Any] = {},
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) -> None:
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"""
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Connect to the OpenAI server.
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connection_str is passed as default OPENAI_API_KEY if variable not set.
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Args:
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connection_str: connection string.
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client_args: client arguments.
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"""
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self.api_key = connection_str or os.environ.get("OPENAI_API_KEY")
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if self.api_key is None:
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raise ValueError(
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"OpenAI API key not set. Set OPENAI_API_KEY environment "
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"variable or pass through `client_connection`."
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)
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self.host = "https://api.openai.com/v1"
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for key in self.PARAMS:
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setattr(self, key, client_args.pop(key, self.PARAMS[key][1]))
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if getattr(self, "engine") not in OPENAICHAT_ENGINES:
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raise ValueError(
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f"Invalid engine {getattr(self, 'engine')}. "
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f"Must be {OPENAICHAT_ENGINES}."
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)
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def get_generation_url(self) -> str:
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"""Get generation URL."""
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return self.host + "/chat/completions"
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def supports_batch_inference(self) -> bool:
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"""Return whether the client supports batch inference."""
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return False
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def get_model_params(self) -> Dict:
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"""
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Get model params.
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By getting model params from the server, we can add to request
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and make sure cache keys are unique to model.
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Returns:
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model params.
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"""
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return {"model_name": self.NAME, "engine": getattr(self, "engine")}
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def preprocess_request_params(self, request: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Preprocess request params.
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Args:
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request: request params.
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Returns:
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request params.
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"""
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# Format for chat model
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request = copy.deepcopy(request)
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prompt = request.pop("prompt")
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if isinstance(prompt, str):
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messages = [{"role": "user", "content": prompt}]
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elif isinstance(prompt, list) and isinstance(prompt[0], str):
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prompt_list = prompt
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messages = [{"role": "user", "content": prompt} for prompt in prompt_list]
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elif isinstance(prompt, list) and isinstance(prompt[0], dict):
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for pmt_dict in prompt:
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if "role" not in pmt_dict or "content" not in pmt_dict:
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raise ValueError(
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"Prompt must be list of dicts with 'role' and 'content' "
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f"keys. Got {prompt}."
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)
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messages = prompt
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else:
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raise ValueError(
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"Prompt must be string, list of strings, or list of dicts."
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f"Got {prompt}"
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)
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request["messages"] = messages
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return super().preprocess_request_params(request)
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def postprocess_response(self, response: Dict, request: Dict) -> Dict[str, Any]:
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"""
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Postprocess and validate response as dict.
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Args:
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response: response
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request: request
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Return:
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response as dict
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"""
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new_choices = []
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response = copy.deepcopy(response)
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for message in response["choices"]:
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if "delta" in message:
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# This is a streaming response
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if "content" in message["delta"]:
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new_choices.append({"text": message["delta"]["content"]})
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else:
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new_choices.append({"text": message["message"]["content"]})
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response["choices"] = new_choices
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return super().postprocess_response(response, request)
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