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Showing content from https://python.langchain.com/docs/integrations/document_loaders/langsmith/ below:

LangSmithLoader | 🦜️🔗 LangChain

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

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

Sign up at https://langsmith.com and generate an API key. Once you've done this set the LANGSMITH_API_KEY environment variable:

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:

Install langchain-core:

%pip install -qU langchain-core

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)

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,
    
    
)

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']

page = []
for doc in loader.lazy_load():
    page.append(doc)
    if len(page) >= 10:
        
        
        
        break
len(page)

For detailed documentation of all LangSmithLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html

• Document loader conceptual guide
• Document loader how-to guides

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