import os
from dingo.config import InputArgs
from dingo.exec import Executor
if __name__ == '__main__':
# S3 配置信息
# 可以从环境变量中获取，或者直接设置
S3_ACCESS_KEY = os.getenv("S3_ACCESS_KEY", "your_access_key")
S3_SECRET_KEY = os.getenv("S3_SECRET_KEY", "your_secret_key")
S3_ENDPOINT_URL = os.getenv("S3_ENDPOINT_URL", "https://s3.amazonaws.com")
S3_BUCKET = os.getenv("S3_BUCKET", "your_bucket_name") # qa-huawei
OPENAI_MODEL = os.getenv("OPENAI_MODEL", "deepseek-chat")
OPENAI_URL = os.getenv("OPENAI_BASE_URL", "https://api.deepseek.com/v1")
OPENAI_KEY = os.getenv("OPENAI_API_KEY", "")
llm_config = {
"model": OPENAI_MODEL,
"key": OPENAI_KEY,
"api_url": OPENAI_URL,
}
input_data = {
# 数据文件路径
"input_path": "dingo/test_local_jsonl.jsonl", # 单个文件路径
# 或者 "input_path": "path/to/your/data/", # 目录路径（以 / 结尾会读取目录下所有文件）
# 数据集配置
"dataset": {
"source": "s3", # 使用 S3 数据源
"format": "jsonl", # 支持 "jsonl" 或 "plaintext"
# S3 连接配置
"s3_config": {
"s3_ak": S3_ACCESS_KEY,
"s3_sk": S3_SECRET_KEY,
"s3_endpoint_url": S3_ENDPOINT_URL,
"s3_bucket": S3_BUCKET,
"s3_addressing_style": "path", # 可选值: "path" 或 "virtual"
}
},
# 执行器配置
"executor": {
"max_workers": 10,
"batch_size": 10,
"result_save": {
"good": True,
"bad": True,
"all_labels": True
}
},
"evaluator": [
{
"fields": {"content": "content"},
"evals": [
{"name": "LLMTextQualityV4", "config": llm_config}
]
}
]
}
# 创建 InputArgs 实例
input_args = InputArgs(**input_data)
# 创建执行器
executor = Executor.exec_map["local"](input_args)
# 执行评估
result = executor.execute()
# 打印结果
print(result)
