Dingo MCP Server

import json import os from dingo.model import Model from dingo.model.llm.base_openai import BaseOpenAI from dingo.model.modelres import ModelRes from dingo.model.prompt.prompt_text_quality import PromptTextQualityV2 from dingo.model.response.response_class import ResponseScoreTypeNameReason from dingo.utils import log from dingo.utils.exception import ConvertJsonError OPENAI_MODEL = 'deepseek-chat' OPENAI_URL = 'https://api.deepseek.com/v1' OPENAI_KEY = os.getenv("OPENAI_KEY") @Model.llm_register('LlmTextQualityRegister') class LlmTextQualityRegister(BaseOpenAI): prompt = PromptTextQualityV2 @classmethod def process_response(cls, response: str) -> ModelRes: log.debug(response) if response.startswith('```json'): response = response[7:] if response.startswith('```'): response = response[3:] if response.endswith('```'): response = response[:-3] try: response_json = json.loads(response) except json.JSONDecodeError: raise ConvertJsonError(f'Convert to JSON format failed: {response}') response_model = ResponseScoreTypeNameReason(**response_json) result = ModelRes() # error_status if response_model.score == 1: result.reason = [response_model.reason] result.name = "Flawless" else: result.error_status = True result.type = response_model.type result.name = response_model.name result.reason = [response_model.reason] return result if __name__ == '__main__': from dingo.config import InputArgs from dingo.exec import Executor input_data = { "input_path": "../../test/data/test_local_jsonl.jsonl", "dataset": { "source": "local", "format": "jsonl", "field": { "content": "content", } }, "executor": { "prompt_list": ["PromptTextQualityV2"], "result_save": { "bad": True, "good": True } }, "evaluator": { "llm_config": { "LlmTextQualityRegister": { "model": OPENAI_MODEL, "key": OPENAI_KEY, "api_url": OPENAI_URL, } } } } input_args = InputArgs(**input_data) executor = Executor.exec_map["local"](input_args) result = executor.execute() print(result)