LangSmithLoader
本笔记本提供了快速入门LangSmith 文档加载器的概述。有关LangSmithLoader所有功能和配置的详细文档，请前往API参考。
概述
集成详情
| 类 | 包 | 本地 | 可序列化 | JS支持 |
|---|---|---|---|---|
| LangSmithLoader | langchain-core | ❌ | ❌ | ❌ |
加载器特性
| 来源 | 懒加载 | 原生异步 |
|---|---|---|
| LangSmithLoader | ✅ | ❌ |
设置
要访问LangSmith文档加载器，您需要安装langchain-core，创建一个LangSmith账户并获取一个API密钥。
凭证
在https://langsmith.com注册并生成一个API密钥。完成此操作后，设置LANGSMITH_API_KEY环境变量：
import getpass
import os
if not os.environ.get("LANGSMITH_API_KEY"):
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
如果你想获得自动化的最佳追踪，你也可以开启LangSmith追踪：
# os.environ["LANGSMITH_TRACING"] = "true"
安装
安装 langchain-core:
%pip install -qU langchain-core
克隆示例数据集
对于这个例子，我们将克隆并加载一个公共的LangSmith数据集。克隆会在我们的个人LangSmith账户上创建该数据集的副本。您只能加载您拥有个人副本的数据集。
from langsmith import Client as LangSmithClient
ls_client = LangSmithClient()
dataset_name = "LangSmith Few Shot Datasets Notebook"
dataset_public_url = (
"https://smith.langchain.com/public/55658626-124a-4223-af45-07fb774a6212/d"
)
ls_client.clone_public_dataset(dataset_public_url)
初始化
现在我们可以实例化我们的文档加载器并加载文档：
from langchain_core.document_loaders import LangSmithLoader
loader = LangSmithLoader(
dataset_name=dataset_name,
content_key="question",
limit=50,
# format_content=...,
# ...
)
API Reference:LangSmithLoader
加载
docs = loader.load()
print(docs[0].page_content)
Show me an example using Weaviate, but customizing the vectorStoreRetriever to return the top 10 k nearest neighbors.
print(docs[0].metadata["inputs"])
{'question': 'Show me an example using Weaviate, but customizing the vectorStoreRetriever to return the top 10 k nearest neighbors. '}
print(docs[0].metadata["outputs"])
{'answer': 'To customize the Weaviate client and return the top 10 k nearest neighbors, you can utilize the `as_retriever` method with the appropriate parameters. Here\'s how you can achieve this:\n\n\`\`\`python\n# Assuming you have imported the necessary modules and classes\n\n# Create the Weaviate client\nclient = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)\n\n# Initialize the Weaviate wrapper\nweaviate = Weaviate(client, index_name, text_key)\n\n# Customize the client to return top 10 k nearest neighbors using as_retriever\ncustom_retriever = weaviate.as_retriever(\n search_type="similarity",\n search_kwargs={\n \'k\': 10 # Customize the value of k as needed\n }\n)\n\n# Now you can use the custom_retriever to perform searches\nresults = custom_retriever.search(query, ...)\n\`\`\`'}
list(docs[0].metadata.keys())
['dataset_id',
'inputs',
'outputs',
'metadata',
'id',
'created_at',
'modified_at',
'runs',
'source_run_id']
懒加载
page = []
for doc in loader.lazy_load():
page.append(doc)
if len(page) >= 10:
# do some paged operation, e.g.
# index.upsert(page)
# page = []
break
len(page)
10
API 参考
有关所有LangSmithLoader功能和配置的详细文档，请访问API参考：https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html