Vectice Python API cheatsheet

This API guide provides a quick overview of Vectice's simple Python API calls to help you get started auto-documenting your datasets, models, and notes.

Be sure to create your API key first!

Install Vectice

Start by installing the Vectice library.

# Install Vectice latest package
pip install vectice

# Install a specific version of the Vectice package
pip install vectice==<version number>

Connect to Vectice

Get started by connecting to the Vectice API and starting an iteration.

#import and connect to Vectice
import vectice
connect = vectice.connect(
    api_token = 'your-api-key',        # Paste your api key
    host = 'https://app.vectice.com',  # Paste your host
)

# Connect to your project phase using your phase ID
phase = connect.phase("PHA-XXX") #You can fetch the relevant phase ID from your chosen Vectice project in the app.

#Create an iteration
iteration = phase.create_or_get_current_iteration()

#import and connect to Vectice
import vectice
connect = vectice.connect(config="your-api-config.json") #Enter your API key JSON file

# Connect to your project phase using your phase ID
phase = connect.phase("PHA-XXX") #You can fetch the relevant phase ID from your chosen Vectice project in the app.

#Create an iteration
iteration = phase.create_or_get_current_iteration()

Auto-document your assets

Auto-document your notes, datasets, and models directly to Vectice.

Auto-document NOTES

# Auto-document your first comments or notes
iteration.log("this is a comment")

Auto-document DATASETS

For instructions on using these resources, refer to the Vectice API Reference guide's Resources section.

from vectice import FileResource, Dataset

# Auto-document your first dataset from a local file
file_resource = FileResource(paths="my/file/path", dataframe=your_df)
clean_dataset = Dataset.clean(resource=file_resource, name="your_dataset_name")
iteration.log(clean_dataset)

from vectice import BigQueryResource, Dataset

# Auto-document your first dataset from BigQuery
bq_resource = BigQueryResource(paths="your-bq-table", dataframes=your_df)
clean_dataset = Dataset.clean(resource=bq_resource, name="your_dataset_name")
iteration.log(clean_dataset)

from vectice import S3Resource, Dataset

# Auto-document your first dataset from S3
s3_resource = S3Resource(uris="s3://.../<file_path_inside_bucket>", dataframes=your_df)
clean_dataset = Dataset.clean(resource=s3_resource, name="your_dataset_name")
iteration.log(clean_dataset)

from vectice import GCSResource, Dataset

# Auto-document your first dataset from GCS
gcs_resource = GCSResource(uris="gs://.../<file_path_inside_bucket>", dataframes=your_df)
clean_dataset = Dataset.clean(gcs_resource, name, attachments)
iteration.log(clean_dataset)

from vectice import DatabricksTableResource, Dataset

# Auto-document your first dataset from Databricks
db_resource = DatabricksTableResource(paths="my-table", dataframes=your_df)
clean_dataset = Dataset.clean(resource=gcs_resource, name="your_dataset_name")
iteration.log(clean_dataset)

from vectice import SnowflakeResource, Dataset

# Auto-document your first dataset from Snowflake

connection_parameters = {
    ...
}
new_session = Session.builder.configs(connection_parameters).create()

sf_resource = SnowflakeResource(
    dataframes=your_df
    snowflake_client=new_session,
    paths="SNOWFLAKE_SAMPLE_DATA.TPCH_SF10.PART",
)
clean_dataset = Dataset.clean(resource=sf_resource, name="your_dataset_name")
iteration.log(clean_dataset)

from vectice import SparkTableResource, Dataset

# Auto-document your first dataset from SparkTable
st_resource = SparkTableResource(
    dataframes=your_df
    spark_client=spark,
    paths="my_table",
)
clean_dataset = Dataset.clean(resource=st_resource, name="your_dataset_name")
iteration.log(clean_dataset)

Auto-document MODELS

from vectice import Model

# Auto-document your first model
model = Model(metrics, properties, attachments, predictor)
iteration.log(model)

import mlflow
from mlflow.client import MlflowClient
from vectice import Model

# Auto-document your first model
model = Model.mlflow(
        run_id="your-mlflow-run-id",
        client=MlflowClient() #also work with client=mlflow
    )
iteration.log(model)

Close your iteration

Once you are done logging your assets for an iteration, mark it complete.

# Completes and closes the current iteration once you are happy with it
iteration.complete()

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