如何在界面中过滤实验

models = {
    "openai-gpt-5.5": ChatOpenAI(model="gpt-5.5", temperature=0),
    "openai-gpt-5.4-mini": ChatOpenAI(model="gpt-5.4-mini", temperature=0),
    "anthropic-claude-sonnet-4-6": ChatAnthropic(temperature=0, model_name="claude-sonnet-4-6")
}

prompts = {
    "singleminded": "始终使用 banana 一词回答问题。",
    "fruitminded": "始终在回答中讨论水果。",
    "basic": "你是一个聊天机器人。"
}

def answer_evaluator(run, example) -> dict:
    llm = ChatOpenAI(model="gpt-5.5", temperature=0)
    answer_grader = hub.pull("langchain-ai/rag-answer-vs-reference") | llm
    score = answer_grader.invoke(
        {
            "question": example.inputs["question"],
            "correct_answer": example.outputs["answer"],
            "student_answer": run.outputs,
        }
    )
    return {"key": "correctness", "score": score["Score"]}

dataset_name = "可过滤数据集"

for model_type, model in models.items():
    for prompt_type, prompt in prompts.items():
        def predict(example):
            return model.invoke(
                [("system", prompt), ("user", example["question"])]
            )

        model_provider = model_type.split("-")[0]
        model_name = model_type[len(model_provider) + 1:]

        evaluate(
            predict,
            data=dataset_name,
            evaluators=[answer_evaluator],
            # 在此处添加元数据！！
            metadata={
                "model_provider": model_provider,
                "model_name": model_name,
                "prompt_id": prompt_type
            }
        )