Data API

The Data API allows you to connect, manage, and query external data sources within your workspaces.

Connect Data Source

Connect an external database to a workspace.

Python SDK
REST API

import prometheux_chain as px

# Create a database configuration
database_config = px.Database(
    database_type='postgresql',
    username='user',
    password='password',
    host='localhost',
    port=5432,
    database_name='mydb',
    tables=['users', 'orders']
)

# Connect the source
source_data = px.connect_sources(
    database_payload=database_config,
    compute_row_count=True
)

Function Signature

def connect_sources(
    database_payload: Database = None,
    compute_row_count=False
)

Parameters

Parameter	Type	Required	Description
`database_payload`	Database	Yes	The database connection configuration
`compute_row_count`	bool	No	Whether to compute row counts for tables. Defaults to False

ReturnsThe connection response data containing source information.

HTTP Request

POST /api/v1/data/connect

Parameters

Parameter	Type	Required	Description
scope	string	No	Data source scope (default: “user”)
database	object	Yes	Database connection configuration
computeRowCount	boolean	No	Compute row counts for tables (default: false)

The database object should contain:

Field	Type	Required	Description
databaseType	string	Yes	Database type (e.g., “postgresql”, “mysql”, “csv”)
host	string	Yes	Database host
port	integer	No	Database port
databaseName	string	Yes	Database name
username	string	Yes	Database username
password	string	Yes	Database password
tables	array	No	List of specific tables to include
schema	string	No	Database schema
options	object	No	Additional database-specific options

cURL Example

curl -X POST "https://api.prometheux.ai/jarvispy/my-org/my-user/api/v1/data/connect" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d '{
    "scope": "user",
    "database": {
      "databaseType": "postgresql",
      "host": "db.example.com",
      "port": 5432,
      "databaseName": "customers",
      "username": "user",
      "password": "password"
    },
    "computeRowCount": true
  }'

Response

{
  "data": {
    "connectionStatus": true,
    "sources": [
      {
        "id": "source_123",
        "name": "customers",
        "type": "table",
        "row_count": 1500
      }
    ],
    "errorMessage": null
  },
  "message": "Database connection successful",
  "status": "success"
}

List Data Sources

List all connected data sources in a workspace.

Python SDK
REST API

import prometheux_chain as px

# List all sources in the workspace
sources = px.list_sources()
print(f"Available sources: {sources}")

Function Signature

def list_sources()

ReturnsA list of data source information dictionaries.

HTTP Request

GET /api/v1/data/list?scope=user

Parameters

Parameter	Type	Required	Description
scope	string	No	Data source scope (default: “user”)

cURL Example

curl -X GET "https://api.prometheux.ai/jarvispy/my-org/my-user/api/v1/data/list?scope=user" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN"

Cleanup Data Sources

Remove data sources from a workspace.

Python SDK
REST API

import prometheux_chain as px

# Clean up all sources
px.cleanup_sources()

# Clean up specific sources
px.cleanup_sources(source_ids=['source1', 'source2'])

Function Signature

def cleanup_sources(source_ids=None)

Parameters

Parameter	Type	Required	Description
`source_ids`	list	No	List of specific source IDs to clean up. If None, cleans up all sources

HTTP Request

POST /api/v1/data/cleanup

Parameters

Parameter	Type	Required	Description
scope	string	No	Data source scope (default: “user”)
source_ids	array	No	Specific source IDs to delete (if not provided, deletes all)

cURL Example

curl -X POST "https://api.prometheux.ai/jarvispy/my-org/my-user/api/v1/data/cleanup" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d '{
    "scope": "user",
    "source_ids": ["source_123", "source_456"]
  }'

Infer Schema

Infer schema from a database configuration.

Python SDK
REST API

import prometheux_chain as px

db = px.Database(
    database_type='postgresql',
    username='user',
    password='password',
    host='localhost',
    port=5432,
    database_name='mydb',
)

schema_result = px.infer_schema(database=db, add_bind=True, add_model=False)

Function Signature

def infer_schema(database: Database, add_bind=True, add_model=False)

Parameters

Parameter	Type	Required	Description
`database`	Database or dict	Yes	The database connection configuration
`add_bind`	bool	No	Whether to add bind metadata. Defaults to True
`add_model`	bool	No	Whether to add model metadata. Defaults to False

ReturnsThe schema inference result from the API.

HTTP Request

POST /api/v1/data/infer-schema

Parameters

Parameter	Type	Required	Description
database	object	Yes	Database connection configuration
addBind	boolean	No	Add bind metadata (default: true)
addModel	boolean	No	Add model metadata (default: false)

cURL Example

curl -X POST "https://api.prometheux.ai/jarvispy/my-org/my-user/api/v1/data/infer-schema" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d '{
    "database": {
      "databaseType": "postgresql",
      "host": "db.example.com",
      "port": 5432,
      "databaseName": "customers",
      "username": "user",
      "password": "password"
    },
    "addBind": true,
    "addModel": false
  }'

Database Class

The Python SDK provides a Database class for structured database configuration.

from prometheux_chain.data.database import Database

db = Database(
    database_type='postgresql',
    username='user',
    password='password',
    host='localhost',
    port=5432,
    database_name='mydb',
    tables=['users', 'orders'],
    schema='public'
)

# Convert to dictionary for API call
db_config = db.to_dict()

Constructor Parameters

Parameter	Type	Required	Description
`database_type`	str	Yes	Database type (e.g., ‘postgresql’, ‘mysql’, ‘csv’, ‘snowflake’)
`username`	str	No	Database username
`password`	str	No	Database password
`host`	str	No	Database host address
`port`	int	No	Database port number
`database_name`	str	No	Name of the database
`tables`	list	No	List of specific tables to include
`schema`	str	No	Database schema name
`catalog`	str	No	Database catalog name
`query`	str	No	Custom SQL query
`options`	dict	No	Additional database-specific options
`selected_columns`	list	No	Specific columns to include
`ignore_columns`	list	No	Columns to exclude
`ignore_tables`	list	No	Tables to exclude
`url`	str	No	Direct connection URL

Methods

to_dict(): Converts the Database object to a dictionary format suitable for API calls
from_dict(data): Class method to create a Database object from a dictionary

Complete Workflow Example

Python SDK
REST API

import prometheux_chain as px
import os

# Set up authentication and configuration
os.environ['PMTX_TOKEN'] = 'my_pmtx_token'
px.config.set('JARVISPY_URL', "https://api.prometheux.ai/jarvispy/my-org/my-user")

# Create a database configuration using the Database class
database = px.Database(
    database_type='postgresql',
    username='myuser',
    password='mypassword',
    host='localhost',
    port=5432,
    database_name='my_database',
    tables=['customers', 'orders'],
    schema='public'
)

# Connect the database source
source_data = px.connect_sources(database_payload=database)

# List all sources
sources = px.list_sources()
print(f"Connected sources: {sources}")

# Clean up when done
px.cleanup_sources()

# Connect a database
curl -X POST "https://api.prometheux.ai/jarvispy/my-org/my-user/api/v1/data/connect" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d '{
    "scope": "user",
    "database": {
      "databaseType": "postgresql",
      "host": "localhost",
      "port": 5432,
      "databaseName": "my_database",
      "username": "myuser",
      "password": "mypassword"
    },
    "computeRowCount": true
  }'

# List data sources
curl -X GET "https://api.prometheux.ai/jarvispy/my-org/my-user/api/v1/data/list?scope=user" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN"

# Cleanup data sources
curl -X POST "https://api.prometheux.ai/jarvispy/my-org/my-user/api/v1/data/cleanup" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d '{"scope": "user"}'

Introduction

Get Started

Platform

Integrations

Examples

Vadalog Reference

Resources

Data API

Data API

Connect Data Source

HTTP Request

Parameters

cURL Example

Response

List Data Sources

HTTP Request

Parameters

cURL Example

Cleanup Data Sources

HTTP Request

Parameters

cURL Example

Infer Schema

HTTP Request

Parameters

cURL Example

Database Class

Constructor Parameters

Methods

Complete Workflow Example

Introduction

Get Started

Platform

Integrations

Examples

Vadalog Reference

Resources

​Data API

​Connect Data Source

​HTTP Request

​Parameters

​cURL Example

​Response

​List Data Sources

​HTTP Request

​Parameters

​cURL Example

​Cleanup Data Sources

​HTTP Request

​Parameters

​cURL Example

​Infer Schema

​HTTP Request

​Parameters

​cURL Example

​Database Class

​Constructor Parameters

​Methods

​Complete Workflow Example

Data API

Connect Data Source

HTTP Request

Parameters

cURL Example

Response

List Data Sources

HTTP Request

Parameters

cURL Example

Cleanup Data Sources

HTTP Request

Parameters

cURL Example

Infer Schema

HTTP Request

Parameters

cURL Example

Database Class

Constructor Parameters

Methods

Complete Workflow Example