> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LangSmithLoader integration

> Integrate with the LangSmithLoader document loader using LangChain Python.

This guide provides a quick overview for getting started with the `LangSmithLoader` [document loader](/oss/python/integrations/document_loaders). For detailed documentation of all `LangSmithLoader` features and configurations head to the [API reference](https://reference.langchain.com/python/langchain-core/document_loaders/langsmith/LangSmithLoader).

## Overview

### Integration details

| Class             | Package                                                                   | Local | Serializable | JS support |
| :---------------- | :------------------------------------------------------------------------ | :---: | :----------: | :--------: |
| `LangSmithLoader` | [`langchain-core`](https://reference.langchain.com/python/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](https://langsmith.com) account and get an API key.

### Credentials

Sign up at [langsmith.com](https://langsmith.com) and generate an API key. Once you've done this set the LANGSMITH\_API\_KEY environment variable:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_TRACING"] = "true"
```

### Installation

Install `langchain-core`:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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.

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_core.document_loaders import LangSmithLoader

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

## Load

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
docs = loader.load()
print(docs[0].page_content)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
Show me an example using Weaviate, but customizing the vectorStoreRetriever to return the top 10 k nearest neighbors.
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
print(docs[0].metadata["inputs"])
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
{'question': 'Show me an example using Weaviate, but customizing the vectorStoreRetriever to return the top 10 k nearest neighbors. '}
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
print(docs[0].metadata["outputs"])
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
{'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\`\`\`'}
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
list(docs[0].metadata.keys())
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
['dataset_id',
 'inputs',
 'outputs',
 'metadata',
 'id',
 'created_at',
 'modified_at',
 'runs',
 'source_run_id']
```

## Lazy load

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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)
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
10
```

***

***

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