[Unlocking the Power of LangSmith Chat Datasets: Fine-Tune Your Model for Enhanced Performance]

    # 引言
    在现代人工智能应用中，高质量的聊天模型往往需要大量的训练数据来提升其表现。LangSmith提供了一种简便的方法来使用聊天数据集对模型进行微调，从而更符合具体应用场景的需求。在这篇文章中，我们将介绍如何利用LangSmith的工具加载聊天数据集，并对模型进行微调。
    
    # 主要内容
    
    ## 1. 创建数据集
    微调模型的第一步是准备合适的数据集。在这里，我们将通过LangSmith的API创建并加载一个聊天数据集。
    
    ```python
    import os
    import uuid
    from langsmith.client import Client
    
    # 使用API代理服务提高访问稳定性
    os.environ["LANGCHAIN_API_KEY"] = "YOUR API KEY"
    
    client = Client()
    
    import requests
    
    url = "https://raw.githubusercontent.com/langchain-ai/langchain/master/docs/docs/integrations/chat_loaders/example_data/langsmith_chat_dataset.json"
    response = requests.get(url)
    response.raise_for_status()
    data = response.json()
    
    uid = uuid.uuid4().hex[:6]
    dataset_name = f"Extraction Fine-tuning Dataset {uid}"
    ds = client.create_dataset(dataset_name=dataset_name, data_type="chat")
    
    _ = client.create_examples(
    inputs=[e["inputs"] for e in data],
    outputs=[e["outputs"] for e in data],
    dataset_id=ds.id,
    )
    
    
    markdown
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-07-13/HyLBsuMY6zpkcx1fwGDrjE5ZFNT8.png)

    from langchain_community.chat_loaders.langsmith import LangSmithDatasetChatLoader
    from langchain_community.adapters.openai import convert_messages_for_finetuning
    
    loader = LangSmithDatasetChatLoader(dataset_name=dataset_name)
    
    chat_sessions = loader.lazy_load()
    
    training_data = convert_messages_for_finetuning(chat_sessions)
    
    
    python

    import json
    import time
    from io import BytesIO
    import openai
    
    my_file = BytesIO()
    for dialog in training_data:
    my_file.write((json.dumps({"messages": dialog}) + "\n").encode("utf-8"))
    
    my_file.seek(0)
    training_file = openai.files.create(file=my_file, purpose="fine-tune")
    
    job = openai.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="gpt-3.5-turbo",
    )
    
    # 等待微调完成
    status = openai.fine_tuning.jobs.retrieve(job.id).status
    start_time = time.time()
    while status != "succeeded":
    print(f"Status=[{status}]... {time.time() - start_time:.2f}s", end="\r", flush=True)
    time.sleep(5)
    status = openai.fine_tuning.jobs.retrieve(job.id).status
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-07-13/nJQwtmkTDCisXe3NgYZH9h6Koaj1.png)

    from langchain_openai import ChatOpenAI
    
    # 获取微调模型ID
    job = openai.fine_tuning.jobs.retrieve(job.id)
    model_id = job.fine_tuned_model
    
    model = ChatOpenAI(
    model=model_id,
    temperature=1,
    )
    
    # 使用微调后的模型
    model.invoke("There were three ravens sat on a tree.")
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-07-13/z45Z9kmnUQlTLuav3R8XGpHfiO6t.png)

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