prometheux-chain 0.3.0

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

definition = """
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, definition=definition)

Run the concept to generate results

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

Fetch the produced facts

results = px.fetch_results(project_id=project_id, output_predicate="location")

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Domains

The SDK exposes a flat, function-based API (import prometheux_chain as px) covering the user-facing JarvisPy backend:

• Projects — save_project, list_projects, load_project, copy_project, export_project / import_project, export_workspace / import_workspace, list_templates, import_template, create_project_from_context, create_snapshot, list_snapshots, restore_snapshot, delete_snapshot.
• Data sources — connect_sources, list_sources, infer_schema, list_sheets, list_demo_sources, refresh_sources, preview_datasource, all_pairs_join, cleanup_sources.
• Files (disk/) — upload_file, list_files, make_directory, delete_files, move_file, download_file.
• Concepts — save_concept, rename_concept, run_concept, run_concept_stream, list_concepts, reorder_concepts, fetch_results, search_results, llm_analysis, download_concept, generate_concept_description, get_concept_description, get_execution_status, get_execution_statuses, cleanup_concepts.
• Knowledge graphs — visualize_concept_lineage, build_graph, list_graph_functions, run_graph_analytics.
• Ontology — save_ontology, load_ontology, update_concept_ontology_type, add_to_lineage, describe_ontology, import_owl.
• Knowledge / context layer — list_context_notes, create_context_note, create_context_notes_from_file, get_context_note, update_context_note, delete_context_note, search_context_notes, auto_seed, interview_template, submit_interview, onboarding_status, project_text.
• Agent — agent_chat (streaming), agent_reset.
• Sharing — create_share, revoke_share, update_share_role, list_shares, list_inbox, accept_share, leave_share, sync_inbox.
• Dashboards — list_all_dashboards, list_dashboards, get_dashboard, save_dashboard, delete_dashboard.
• Schedules — create_policy, list_policies, get_policy, update_policy, delete_policy, trigger_policy, get_run_history.
• Alerts — get_alert_history, reprocess_alert.
• Chat history — list_sessions, get_session, rename_session, delete_session.
• Compute — check_compute_availability.
• Users / account — save_user_config, load_user_config, get_role, list_llm_models, get_usage_status, get_login_activity.
• Auth / tokens — issue_token, list_tokens, revoke_token, revoke_specific_token, revoke_all_tokens.
• Vadalog authoring — analyze_program, build_bind, parse_binds, evaluate_program.
• Vadalingo translation — translate_nl_to_vadalog, translate_sql_to_vadalog, translate_rdf_to_vadalog, translate_owl_to_vadalog.

Optional configuration

Some recipient-side sharing flows require a Supabase token. Set it via the SUPABASE_TOKEN environment variable (or px.config) and it is attached automatically as the X-Supabase-Token header when present.

Streaming

A few endpoints stream results instead of returning a single response. These return Python generators you iterate over.

Chat with the Vadalog AI agent (NDJSON stream)

for event in px.agent_chat(project_id=project_id, message="What does this project do?"):
    if event.get("type") == "content":
        print(event["data"]["chunk"], end="")

Run a concept with live status updates (WebSocket stream)

WebSocket streaming requires the websocket-client dependency (installed automatically with the SDK).

for event in px.run_concept_stream(project_id=project_id, concept_name="location"):
    print(event.get("event"), event.get("data"))
    # iteration ends after the terminal "complete" or "error" event

Auto-seed the context layer from connected data sources (NDJSON stream)

for event in px.auto_seed(scope="project", scope_id=project_id):
    print(event)

Tokens

Issue and manage API tokens programmatically:

token = px.issue_token(name="ci-bot", expires_in_minutes=60)
print(token["token"])   # the raw JWT — shown only once
px.list_tokens()
px.revoke_specific_token(token["jti"])

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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