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132 lines
3.9 KiB
Python
132 lines
3.9 KiB
Python
"""OpenAI client."""
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import logging
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import os
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from typing import Any, Dict, List, Optional
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import tiktoken
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from manifest.clients.client import Client
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from manifest.request import LMRequest
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logger = logging.getLogger(__name__)
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OPENAI_ENGINES = {
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"text-davinci-003",
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"text-davinci-002",
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"text-davinci-001",
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"davinci",
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"curie",
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"ada",
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"babbage",
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"text-curie-001",
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"text-babbage-001",
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"text-ada-001",
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"code-davinci-002",
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"code-cushman-001",
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}
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class OpenAIClient(Client):
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"""OpenAI client."""
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# User param -> (client param, default value)
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PARAMS = {
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"engine": ("model", "text-davinci-003"),
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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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"top_k": ("best_of", 1),
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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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}
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REQUEST_CLS = LMRequest
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NAME = "openai"
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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 = os.environ.get("OPENAI_API_KEY", connection_str)
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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 OPENAI_ENGINES:
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raise ValueError(
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f"Invalid engine {getattr(self, 'engine')}. Must be {OPENAI_ENGINES}."
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)
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def close(self) -> None:
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"""Close the client."""
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pass
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def get_generation_url(self) -> str:
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"""Get generation URL."""
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return self.host + "/completions"
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def get_generation_header(self) -> Dict[str, str]:
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"""
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Get generation header.
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Returns:
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header.
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"""
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return {"Authorization": f"Bearer {self.api_key}"}
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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 True
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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": "openai", "engine": getattr(self, "engine")}
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def split_usage(self, request: Dict, choices: List[str]) -> List[Dict[str, int]]:
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"""Split usage into list of usages for each prompt."""
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try:
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encoding = tiktoken.encoding_for_model(getattr(self, "engine"))
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except Exception:
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return []
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prompt = request["prompt"]
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# If n > 1 and prompt is a string, we need to split it into a list
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if isinstance(prompt, str):
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prompts = [prompt] * len(choices)
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else:
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prompts = prompt
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assert len(prompts) == len(choices)
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usages = []
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for pmt, chc in zip(prompts, choices):
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pmt_tokens = len(encoding.encode(pmt))
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chc_tokens = len(encoding.encode(chc["text"])) # type: ignore
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usage = {
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"prompt_tokens": pmt_tokens,
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"completion_tokens": chc_tokens,
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"total_tokens": pmt_tokens + chc_tokens,
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}
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usages.append(usage)
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return usages
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