wehub-resource-sync
118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
import os
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from typing import Any
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from bfcl_eval.model_handler.api_inference.openai_completion import OpenAICompletionsHandler
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from bfcl_eval.constants.enums import ModelStyle
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from openai import OpenAI
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from overrides import override
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import time
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class NanbeigeAPIHandler(OpenAICompletionsHandler):
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"""
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This is the OpenAI-compatible API handler with streaming enabled.
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"""
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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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self.client = OpenAI(
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base_url="https://nanbeige.zhipin.com/api/gpt/open/chat/openai/v1",
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api_key=os.getenv("NBG_API_KEY"),
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)
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#### FC methods ####
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@override
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def _query_FC(self, inference_data: dict):
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message: list[dict] = inference_data["message"]
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tools = inference_data["tools"]
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inference_data["inference_input_log"] = {"message": repr(message), "tools": tools}
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return self.generate_with_backoff(
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messages=inference_data["message"],
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model=self.model_name,
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tools=tools,
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timeout=72000,
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)
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@override
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def _parse_query_response_FC(self, api_response: Any) -> dict:
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tool_info = []
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reasoning_content = api_response.choices[0].message.reasoning_content
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answer_content = api_response.choices[0].message.content
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if api_response.choices[0].message.tool_calls:
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tool_calls = api_response.choices[0].message.tool_calls
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for tool_call in tool_calls:
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tool_info.append({})
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tool_info[-1]["id"] = tool_info[-1].get("id", "") + tool_call.id
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tool_info[-1]["name"] = (
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tool_info[-1].get("name", "") + tool_call.function.name
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)
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tool_info[-1]["arguments"] = (
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tool_info[-1].get("arguments", "") + tool_call.function.arguments
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)
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tool_call_ids = []
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for item in tool_info:
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tool_call_ids.append(item["id"])
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if len(tool_info) > 0:
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# Build tool_calls structure required by OpenAI-compatible API
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tool_calls_for_history = []
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for item in tool_info:
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tool_calls_for_history.append(
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{
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"id": item["id"],
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"type": "function",
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"function": {
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"name": item["name"],
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"arguments": item["arguments"],
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},
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}
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)
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model_response = [{item["name"]: item["arguments"]} for item in tool_info]
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model_response_message_for_chat_history = {
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"role": "assistant",
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"content": None,
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"tool_calls": tool_calls_for_history,
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}
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# Attach reasoning content so that it can be passed to the next turn
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if reasoning_content:
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model_response_message_for_chat_history["reasoning_content"] = (
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reasoning_content
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)
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else:
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model_response = answer_content
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model_response_message_for_chat_history = {
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"role": "assistant",
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"content": answer_content,
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}
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# Attach reasoning content so that it can be passed to the next turn
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if reasoning_content:
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model_response_message_for_chat_history["reasoning_content"] = (
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reasoning_content
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)
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response_data = {
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"model_responses": model_response,
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"model_responses_message_for_chat_history": model_response_message_for_chat_history,
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"reasoning_content": reasoning_content,
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"tool_call_ids": tool_call_ids,
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"input_token": api_response.usage.prompt_tokens,
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"output_token": api_response.usage.completion_tokens,
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}
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if not reasoning_content:
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del response_data["reasoning_content"]
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return response_data
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