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
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.
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.
from langsmith import Client
examples = [
{
"inputs": {"question": "What is the largest mammal?"},
"outputs": {"answer": "The blue whale"},
"metadata": {"source": "Wikipedia"},
},
{
"inputs": {"question": "What do mammals and birds have in common?"},
"outputs": {"answer": "They are both warm-blooded"},
"metadata": {"source": "Wikipedia"},
},
{
"inputs": {"question": "What are reptiles known for?"},
"outputs": {"answer": "Having scales"},
"metadata": {"source": "Wikipedia"},
},
{
"inputs": {"question": "What's the main characteristic of amphibians?"},
"outputs": {"answer": "They live both in water and on land"},
"metadata": {"source": "Wikipedia"},
},
]
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
client.create_examples(
dataset_id=dataset.id,
examples=examples
)
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:
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
examples = [{"inputs": run.inputs, "outputs": run.outputs} for run in runs]
# Use the bulk create_examples method
client.create_examples(
dataset_id=dataset.id,
examples=examples
)
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.
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"
)
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.
Initialize the client before running the below code snippets.
from langsmith import Client
client = Client()
Query all datasets
datasets = client.list_datasets()
List datasets by name
If you want to search by the exact name, you can do the following:
datasets = client.list_datasets(dataset_name="My Test Dataset 1")
datasets = client.list_datasets(dataset_name_contains="some substring")
List datasets by type
You can filter datasets by type. Below is an example querying for chat datasets.
datasets = client.list_datasets(data_type="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.
Initialize the client before running the below code snippets.
from langsmith import Client
client = Client()
List all examples for a dataset
You can filter by dataset ID:
examples = client.list_examples(dataset_id="c9ace0d8-a82c-4b6c-13d2-83401d68e9ab")
examples = client.list_examples(dataset_name="My Test Dataset")
List examples by id
You can also list multiple examples all by ID.
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)
{"foo": "bar", "baz": "qux"}, both {foo: bar} and {baz: qux} would match, as would {foo: bar, baz: qux}.
examples = client.list_examples(dataset_name=dataset_name, 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.
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.
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.
examples = client.list_examples(
dataset_name=dataset_name,
filter='and(not(has(metadata, \'{"foo": "bar"}\')), exists(metadata, "tenant_id"))'
)
Update examples
Update single example
You can programmatically update examples from LangSmith using the update_example/updateExample method in the Python and TypeScript SDKs. Below is an example.
client.update_example(
example_id=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.
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
)
