LangSmithLoader integration | LangChain Español

This guide provides a quick overview for getting started with the LangSmithLoader document loader. For detailed documentation of all LangSmithLoader features and configurations head to the API reference.

Overview

Integration details

Class	Package	Local	Serializable	JS support
`LangSmithLoader`	`langchain-core`	❌	❌	❌

Loader features

Source	Lazy loading	Native async
`LangSmithLoader`	✅	❌

Setup

To access the LangSmith document loader you'll need to install langchain-core, create a LangSmith account and get an API key.

Credentials

import getpass
import os

if not os.environ.get("LANGSMITH_API_KEY"):
    os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

If you want to get automated best-in-class tracing, you can also turn on LangSmith tracing:

os.environ["LANGSMITH_TRACING"] = "true"

Installation

Install langchain-core:

pip install -qU langchain-core

Clone example dataset

For this example, we'll clone and load a public LangSmith dataset. Cloning creates a copy of this dataset on our personal LangSmith account. You can only load datasets that you have a personal copy of.

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)

Initialization

Now we can instantiate our document loader and load documents:

from langchain_core.document_loaders import LangSmithLoader

loader = LangSmithLoader(
    dataset_name=dataset_name,
    content_key="question",
    limit=50,
    # format_content=...,
    # ...
)

Load

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']

Lazy load

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)