Manage datasets programmatically

You can use the Python and TypeScript SDK to manage datasets programmatically. This includes creating, updating, and deleting datasets, as well as adding examples to them.

Create a dataset from list of values

The most flexible way to make a dataset using the client is by creating examples from a list of inputs and optional outputs. Below is an example.

Note that you can add arbitrary metadata to each example, such as a note or a source. The metadata is stored as a dictionary.

Bulk example creation

If you have many examples to create, consider using the create_examples/createExamples method to create multiple examples in a single request. If creating a single example, you can use the create_example/createExample method.

Python

from langsmith import Client

example_inputs = [
  ("What is the largest mammal?", "The blue whale"),
  ("What do mammals and birds have in common?", "They are both warm-blooded"),
  ("What are reptiles known for?", "Having scales"),
  ("What's the main characteristic of amphibians?", "They live both in water and on land"),
]

client = Client()
dataset_name = "Elementary Animal Questions"

# Storing inputs in a dataset lets us
# run chains and LLMs over a shared set of examples.
dataset = client.create_dataset(
    dataset_name=dataset_name, description="Questions and answers about animal phylogenetics.",
)

# Prepare inputs, outputs, and metadata for bulk creation
inputs = [{"question": input_prompt} for input_prompt, _ in example_inputs]
outputs = [{"answer": output_answer} for _, output_answer in example_inputs]
metadata = [{"source": "Wikipedia"} for _ in example_inputs]

client.create_examples(
    inputs=inputs,
    outputs=outputs,
    metadata=metadata,
    dataset_id=dataset.id,
)

TypeScript

import { Client } from "langsmith";

const client = new Client();

const exampleInputs: [string, string][] = [
  ["What is the largest mammal?", "The blue whale"],
  ["What do mammals and birds have in common?", "They are both warm-blooded"],
  ["What are reptiles known for?", "Having scales"],
  [
    "What's the main characteristic of amphibians?",
    "They live both in water and on land",
  ],
];

const datasetName = "Elementary Animal Questions";

// Storing inputs in a dataset lets us
// run chains and LLMs over a shared set of examples.
const dataset = await client.createDataset(datasetName, {
  description: "Questions and answers about animal phylogenetics",
});

// Prepare inputs, outputs, and metadata for bulk creation
const inputs = exampleInputs.map(([inputPrompt]) => ({ question: inputPrompt }));
const outputs = exampleInputs.map(([, outputAnswer]) => ({ answer: outputAnswer }));
const metadata = exampleInputs.map(() => ({ source: "Wikipedia" }));

// Use the bulk createExamples method
await client.createExamples({
  inputs,
  outputs,
  metadata,
  datasetId: dataset.id,
});

Create a dataset from traces

To create datasets from the runs (spans) of your traces, you can use the same approach. For many more examples of how to fetch and filter runs, see the export traces guide. Below is an example:

Python

from langsmith import Client

client = Client()
dataset_name = "Example Dataset"

# Filter runs to add to the dataset
runs = client.list_runs(
    project_name="my_project",
    is_root=True,
    error=False,
)

dataset = client.create_dataset(dataset_name, description="An example dataset")

# Prepare inputs and outputs for bulk creation
inputs = [run.inputs for run in runs]
outputs = [run.outputs for run in runs]

# Use the bulk create_examples method
client.create_examples(
    inputs=inputs,
    outputs=outputs,
    dataset_id=dataset.id,
)

TypeScript

import { Client, Run } from "langsmith";
const client = new Client();

const datasetName = "Example Dataset";
// Filter runs to add to the dataset
const runs: Run[] = [];
for await (const run of client.listRuns({
  projectName: "my_project",
  isRoot: 1,
  error: false,
})) {
  runs.push(run);
}

const dataset = await client.createDataset(datasetName, {
  description: "An example dataset",
  dataType: "kv",
});

// Prepare inputs and outputs for bulk creation
const inputs = runs.map(run => run.inputs);
const outputs = runs.map(run => run.outputs ?? {});

// Use the bulk createExamples method
await client.createExamples({
  inputs,
  outputs,
  datasetId: dataset.id,
});

Create a dataset from a CSV file

In this section, we will demonstrate how you can create a dataset by uploading a CSV file.

First, ensure your CSV file is properly formatted with columns that represent your input and output keys. These keys will be utilized to map your data properly during the upload. You can specify an optional name and description for your dataset. Otherwise, the file name will be used as the dataset name and no description will be provided.

Python

from langsmith import Client
import os

client = Client()

csv_file = 'path/to/your/csvfile.csv'
input_keys = ['column1', 'column2'] # replace with your input column names
output_keys = ['output1', 'output2'] # replace with your output column names

dataset = client.upload_csv(
    csv_file=csv_file,
    input_keys=input_keys,
    output_keys=output_keys,
    name="My CSV Dataset",
    description="Dataset created from a CSV file"
    data_type="kv"
)

TypeScript

import { Client } from "langsmith";

const client = new Client();

const csvFile = 'path/to/your/csvfile.csv';
const inputKeys = ['column1', 'column2']; // replace with your input column names
const outputKeys = ['output1', 'output2']; // replace with your output column names

const dataset = await client.uploadCsv({
    csvFile: csvFile,
    fileName: "My CSV Dataset",
    inputKeys: inputKeys,
    outputKeys: outputKeys,
    description: "Dataset created from a CSV file",
    dataType: "kv"
});

Create a dataset from pandas DataFrame (Python only)

The python client offers an additional convenience method to upload a dataset from a pandas dataframe.

from langsmith import Client
import os
import pandas as pd

client = Client()

df = pd.read_parquet('path/to/your/myfile.parquet')
input_keys = ['column1', 'column2'] # replace with your input column names
output_keys = ['output1', 'output2'] # replace with your output column names

dataset = client.upload_dataframe(
    df=df,
    input_keys=input_keys,
    output_keys=output_keys,
    name="My Parquet Dataset",
    description="Dataset created from a parquet file",
    data_type="kv" # The default
)

Fetch datasets

You can programmatically fetch datasets from LangSmith using the list_datasets/listDatasets method in the Python and TypeScript SDKs. Below are some common calls.

Prerequisites

Initialize the client before running the below code snippets.

Python

from langsmith import Client

client = Client()

TypeScript

import { Client } from "langsmith";

const client = new Client();

Query all datasets

Python

datasets = client.list_datasets()

TypeScript

const datasets = await client.listDatasets();

List datasets by name

If you want to search by the exact name, you can do the following:

Python

datasets = client.list_datasets(dataset_name="My Test Dataset 1")

TypeScript

const datasets = await client.listDatasets({datasetName: "My Test Dataset 1"});

If you want to do a case-invariant substring search, try the following:

Python

datasets = client.list_datasets(dataset_name_contains="some substring")

TypeScript

const datasets = await client.listDatasets({datasetNameContains: "some substring"});

List datasets by type

You can filter datasets by type. Below is an example querying for chat datasets.

Python

datasets = client.list_datasets(data_type="chat")

TypeScript

const datasets = await client.listDatasets({dataType: "chat"});

Fetch examples

You can programmatically fetch examples from LangSmith using the list_examples/listExamples method in the Python and TypeScript SDKs. Below are some common calls.

Prerequisites

Initialize the client before running the below code snippets.

Python

from langsmith import Client

client = Client()

TypeScript

import { Client } from "langsmith";

const client = new Client();

List all examples for a dataset

You can filter by dataset ID:

Python

examples = client.list_examples(dataset_id="c9ace0d8-a82c-4b6c-13d2-83401d68e9ab")

TypeScript

const examples = await client.listExamples({datasetId: "c9ace0d8-a82c-4b6c-13d2-83401d68e9ab"});

Or you can filter by dataset name (this must exactly match the dataset name you want to query)

Python

examples = client.list_examples(dataset_name="My Test Dataset")

TypeScript

const examples = await client.listExamples({datasetName: "My test Dataset"});

List examples by id

You can also list multiple examples all by ID.

Python

example_ids = [
 '734fc6a0-c187-4266-9721-90b7a025751a',
 'd6b4c1b9-6160-4d63-9b61-b034c585074f',
 '4d31df4e-f9c3-4a6e-8b6c-65701c2fed13',
]
examples = client.list_examples(example_ids=example_ids)

TypeScript

const exampleIds = [
  "734fc6a0-c187-4266-9721-90b7a025751a",
  "d6b4c1b9-6160-4d63-9b61-b034c585074f",
  "4d31df4e-f9c3-4a6e-8b6c-65701c2fed13",
];
const examples = await client.listExamples({exampleIds: exampleIds});

List examples by metadata

You can also filter examples by metadata. Below is an example querying for examples with a specific metadata key-value pair. Under the hood, we check to see if the example's metadata contains the key-value pair(s) you specify.

For example, if you have an example with metadata {"foo": "bar", "baz": "qux"}, both {foo: bar} and {baz: qux} would match, as would {foo: bar, baz: qux}.

Python

examples = client.list_examples(dataset_name=dataset_name, metadata={"foo": "bar"})

TypeScript

const examples = await client.listExamples({datasetName: datasetName, metadata: {foo: "bar"}});

List examples by structured filter

Similar to how you can use the structured filter query language to fetch runs, you can use it to fetch examples.

note

This is currently only available in v0.1.83 and later of the Python SDK and v0.1.35 and later of the TypeScript SDK.

Additionally, the structured filter query language is only supported for metadata fields.

You can use the has operator to fetch examples with metadata fields that contain specific key/value pairs and the exists operator to fetch examples with metadata fields that contain a specific key. Additionally, you can also chain multiple filters together using the and operator and negate a filter using the not operator.

Python

examples = client.list_examples(
                dataset_name=dataset_name,
                filter='and(not(has(metadata, \'{"foo": "bar"}\')), exists(metadata, "tenant_id"))'
            )

TypeScript

const examples = await client.listExamples({datasetName: datasetName, filter: 'and(not(has(metadata, \'{"foo": "bar"}\')), exists(metadata, "tenant_id"))'});

Update examples

You can programmatically update examples from LangSmith using the update_example/updateExample method in the Python and TypeScript SDKs. Below is an example.

Python

client.update_example(
    example_id=example.id,
    inputs={"input": "updated input"},
    outputs={"output": "updated output"},
    metadata={"foo": "bar"},
    split="train"
)

TypeScript

await client.updateExample(example.id, {
  inputs: { input: "updated input" },
  outputs: { output: "updated output" },
  metadata: { "foo": "bar" },
  split: "train",
})

Bulk update examples

You can also programmatically update multiple examples in a single request with the update_examples/updateExamples method in the Python and TypeScript SDKs. Below is an example.

Python

client.update_examples(
    example_ids=[example.id, example_2.id],
    inputs=[{"input": "updated input 1"}, {"input": "updated input 2"}],
    outputs=[
        {"output": "updated output 1"},
        {"output": "updated output 2"},
    ],
    metadata=[{"foo": "baz"}, {"foo": "qux"}],
    splits=[["training", "foo"], "training"] # Splits can be arrays or standalone strings
)

TypeScript

await client.updateExamples([
  {
    id: example.id,
    inputs: { input: "updated input 1" },
    outputs: { output: "updated output 1" },
    metadata: { foo: "baz" },
    split: ["training", "foo"] // Splits can be arrays or standalone strings
  },
  {
    id: example2.id,
    inputs: { input: "updated input 2" },
    outputs: { output: "updated output 2" },
    metadata: { foo: "qux" },
    split: "training"
  },
])