wehub-resource-sync
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
import json
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import os
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import time
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from typing import Any
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from bfcl_eval.model_handler.api_inference.openai_completion import (
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OpenAICompletionsHandler,
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)
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from bfcl_eval.constants.enums import ModelStyle
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from bfcl_eval.model_handler.utils import (
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combine_consecutive_user_prompts,
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retry_with_backoff,
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system_prompt_pre_processing_chat_model,
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)
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from openai import OpenAI, RateLimitError
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from overrides import override
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class LingAPIHandler(OpenAICompletionsHandler):
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def __init__(
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self,
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model_name,
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temperature,
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registry_name,
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is_fc_model,
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**kwargs,
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) -> None:
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super().__init__(model_name, temperature, registry_name, is_fc_model, **kwargs)
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self.model_style = ModelStyle.OPENAI_COMPLETIONS
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api_url = "https://bailingchat.alipay.com"
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self.client = OpenAI(base_url=api_url, api_key=os.getenv("LING_API_KEY"))
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@retry_with_backoff(error_type=[RateLimitError, json.JSONDecodeError])
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def generate_with_backoff(self, **kwargs):
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start_time = time.time()
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api_response = self.client.chat.completions.create(**kwargs)
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end_time = time.time()
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return api_response, end_time - start_time
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@override
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def _query_prompting(self, inference_data: dict):
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"""
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Call the model API in prompting mode to get the response.
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Return the response object that can be used to feed into the decode method.
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"""
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message: list[dict] = inference_data["message"]
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inference_data["inference_input_log"] = {"message": repr(message)}
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if "Ling/ling-lite-v1.5" in self.model_name:
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api_name = "Ling-lite-1.5-250604"
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else:
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raise ValueError(
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f"Model name {self.model_name} not yet supported in this method"
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)
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return self.generate_with_backoff(
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model=api_name,
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messages=message,
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)
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@override
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def _pre_query_processing_prompting(self, test_entry: dict) -> dict:
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functions: list = test_entry["function"]
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test_entry_id: str = test_entry["id"]
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test_entry["question"][0] = system_prompt_pre_processing_chat_model(
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test_entry["question"][0], functions, test_entry_id
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)
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for round_idx in range(len(test_entry["question"])):
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test_entry["question"][round_idx] = combine_consecutive_user_prompts(
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test_entry["question"][round_idx]
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)
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return {"message": []}
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@override
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def _parse_query_response_prompting(self, api_response: Any) -> dict:
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response_data = super()._parse_query_response_prompting(api_response)
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self._add_reasoning_content_if_available_prompting(api_response, response_data)
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return response_data
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