prometheux-chain 0.2.13

Prometheux chain is a Python SDK designed to build, evolve and deploy your new knowledge graphs.

Prometheux_chain

Description

Prometheux Chain is a Python SDK designed to help you create, evolve, and deploy knowledge graphs with ease. The SDK offers the following capabilities:

• Data Ingestion: Seamlessly integrate data from various sources, including databases and files.
• Reasoning & Knowledge Augmentation: Perform logical reasoning to derive new insights and augment your existing knowledge base.
• Explainability: Gain clear explanations of the results generated by the system.

For more information refer to the documentation

Features

• Supports a wide range of data sources.
• Built-in reasoning engine for deriving new knowledge.
• Easy-to-understand explanations for enhanced interpretability.
• Ready-to-use configurations for fast deployment.

Installation

Requirements

• Python 3.9 or higher (Python 3.13 recommended)

Install Using pip

1. Set Up a Virtual Environment (recommended):

python3 -m venv myenv
source myenv/bin/activate  # On Windows: myenv\Scripts\activate

2. Install the SDK via pip:

pip install --upgrade prometheux_chain

Usage

This guide demonstrates how to get started with the Prometheux Chain SDK. The example below outlines a typical workflow, including creating a project, defining concept logic, and running concepts to generate results.

Workflow

Import the prometheux_chain

import prometheux_chain as px
import os

Define the PMTX_TOKEN environment variable for authentication

os.environ['PMTX_TOKEN'] = 'my_pmtx_token'

Configure the backend connection using your Prometheux account

px.config.set('JARVISPY_URL', "https://platform.prometheux.ai/jarvispy/'my_organization'/'my_username'")

Create a new project

project_id = px.save_project(project_name="test_project")

Define concept logic using Vadalog syntax and save it

concept_logic = """
company("Apple", "Redwood City, CA").
company("Google", "Mountain View, CA").
company("Microsoft", "Redmond, WA").
company("Amazon", "Seattle, WA").
company("Facebook", "Menlo Park, CA").
company("Twitter", "San Francisco, CA").
company("LinkedIn", "Sunnyvale, CA").
company("Instagram", "Menlo Park, CA").

location(Location) :- company(_,Location).

@output("location").
"""
px.save_concept(project_id=project_id, concept_logic=concept_logic)

Run the concept to generate results

px.run_concept(project_id=project_id, concept_name="location")

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Access to Prometheux Backend

The Prometheux backend is required to use this SDK. To request access:

• 📧 Email: [e-mail], [e-mail], or [e-mail]
• 🌐 Website: https://www.prometheux.ai

License

BSD 3-Clause License — see LICENSE file for details.

About Prometheux

Prometheux is an ontology native data engine that processes data anywhere it lives. Define ontologies once and unlock knowledge that spans databases, warehouses, and platforms—built on the Vadalog reasoning engine.

Key capabilities:

• Connect: Query across Snowflake, Databricks, Neo4j, SQL, CSV, and more without ETL or vendor lock-in
• Think: Replace 100+ lines of PySpark/SQL with simple declarative logic. Power graph analytics without GraphDBs
• Explain: Full lineage & traceability with deterministic, repeatable results. Ground AI in structured, explainable context

Exponentially faster and simpler than traditional approaches. Learn more at prometheux.ai.

Support

For issues, questions, or access requests:

• Homepage: https://www.prometheux.ai
• PyPI: https://pypi.org/project/prometheux-chain/
• Email: [e-mail], [e-mail], or [e-mail]
• Documentation: https://docs.prometheux.ai/sdk
• Issues: GitHub Issues

Related Projects

• Prometheux MCP — MCP client for AI agents
• Vadalog Extension — JupyterLab extension for Vadalog
• Vadalog Jupyter Kernel — Jupyter kernel for Vadalog