# Prometheux

## Docs

- [Banking Supervision](https://docs.prometheux.ai/examples/finance/banking-supervision.md): Company control structures and close-link detection for regulatory analysis using Vadalog.
- [Card Clearing Migration](https://docs.prometheux.ai/examples/finance/card-clearing-migration.md): Migrating mainframe COBOL card clearing pipelines to Prometheux using Vadalog.
- [Non-Performing Loans](https://docs.prometheux.ai/examples/finance/non-performing-loans.md): Cascading default propagation across interconnected financial entities using Vadalog.
- [Examples Overview](https://docs.prometheux.ai/examples/overview.md): Practical Vadalog programs demonstrating common patterns and real-world use cases.
- [Graph Analytics](https://docs.prometheux.ai/examples/patterns/graph-analytics.md): Transitive closure, degree centrality, and community detection using pure Vadalog rules.
- [Recursion](https://docs.prometheux.ai/examples/patterns/recursion.md): Base and recursive cases in Vadalog, with a worked transitive closure example.
- [State Machine](https://docs.prometheux.ai/examples/patterns/state-machine.md): Modeling event-driven state transitions with recursive Vadalog rules.
- [Prometheux Cloud](https://docs.prometheux.ai/get-started/installation/cloud.md): The fastest way to start with Prometheux — nothing to install.
- [Installing PX on Databricks & Connectors](https://docs.prometheux.ai/get-started/installation/databricks/connectors.md): Connect Prometheux to Databricks via JDBC and install the engine JAR on clusters.
- [Databricks Native App](https://docs.prometheux.ai/get-started/installation/databricks/native-app.md): For teams already on Databricks — deploy Prometheux as a native application in your workspace.
- [Databricks](https://docs.prometheux.ai/get-started/installation/databricks/overview.md): Prometheux integrates with Databricks as a native application or by installing the engine directly on your clusters.
- [Cluster](https://docs.prometheux.ai/get-started/installation/on-premises/cluster.md): For teams running Prometheux on self-managed Yarn or Kubernetes clusters.
- [On (Cloud) Premises](https://docs.prometheux.ai/get-started/installation/on-premises/overview.md): Deploy Prometheux on infrastructure you control, from a single VM to a multi-node cluster.
- [Single Node](https://docs.prometheux.ai/get-started/installation/on-premises/single-node.md): Deploy Prometheux on a single VM in AWS, Azure, GCP, or your own data centre using Docker Compose.
- [Installation](https://docs.prometheux.ai/get-started/installation/overview.md): Compare Prometheux deployment options and choose the one that fits your environment.
- [Snowflake Native App](https://docs.prometheux.ai/get-started/installation/snowflake.md): For teams already on Snowflake — install directly from the Marketplace with full data sovereignty.
- [Platform Overview](https://docs.prometheux.ai/get-started/platform-overview.md): A high-level overview of the Prometheux ontology-native data processing platform and what you can do with it.
- [Quickstart](https://docs.prometheux.ai/get-started/quickstart.md): A hands-on, click-by-click tour of the Prometheux app — from a fresh account to a running project and your first dashboard.
- [Your First Project](https://docs.prometheux.ai/get-started/your-first-project.md): A hands-on walkthrough that builds your first executable ontology in Vadalog, from facts to a recursive rule.
- [AI Agent](https://docs.prometheux.ai/integrations/api-endpoints/agent.md): A streaming conversational interface that autonomously plans and executes data workflows.
- [Authentication](https://docs.prometheux.ai/integrations/api-endpoints/authentication.md): Obtain, list, and revoke the API tokens used to authenticate REST requests to the Prometheux platform.
- [Concepts](https://docs.prometheux.ai/integrations/api-endpoints/concepts.md): Define, run, and read results from concepts — the Vadalog, SQL, and Python programs at the core of a Prometheux project.
- [Context Layer](https://docs.prometheux.ai/integrations/api-endpoints/context-layer.md): Store, search, and ingest context notes that ground the AI agent's knowledge base, scoped globally or per-project.
- [Dashboards](https://docs.prometheux.ai/integrations/api-endpoints/dashboards.md): Create, update, retrieve, and delete user-defined dashboards within a project.
- [Data Sources](https://docs.prometheux.ai/integrations/api-endpoints/data.md): Connect databases and files, list and refresh sources, preview data, and manage the on-disk file store.
- [Knowledge Graph](https://docs.prometheux.ai/integrations/api-endpoints/knowledge-graph.md): Build, visualize, and run analytics over knowledge graphs derived from concept output predicates.
- [Projects](https://docs.prometheux.ai/integrations/api-endpoints/projects.md): Create, load, copy, import/export, and snapshot Prometheux projects — the top-level containers for concepts, data sources, and ontologies.
- [Schedules](https://docs.prometheux.ai/integrations/api-endpoints/schedules.md): Create and manage evaluation policies that run concepts automatically on a cron schedule or whenever upstream data changes.
- [Vadalingo](https://docs.prometheux.ai/integrations/api-endpoints/vadalingo.md): Translate natural language, SQL, RDF, and OWL ontologies into Vadalog programs.
- [Vadalog](https://docs.prometheux.ai/integrations/api-endpoints/vadalog.md): Analyze programs, parse and build bind annotations, and evaluate Vadalog logic against the platform engine.
- [Chat API](https://docs.prometheux.ai/integrations/chat-api.md): AI-powered chat API for Vadalog code assistance and documentation queries.
- [Config](https://docs.prometheux.ai/integrations/config.md): SDK configuration and LLM setup for the Prometheux platform.
- [Connect ChatGPT](https://docs.prometheux.ai/integrations/mcp/chatgpt.md): Connect ChatGPT to Prometheux via the Remote MCP Server using Developer Mode connectors.
- [Connect Claude](https://docs.prometheux.ai/integrations/mcp/claude.md): Connect Claude Desktop or Web to Prometheux using the Remote MCP Server.
- [Connect Claude Code](https://docs.prometheux.ai/integrations/mcp/claude-code.md): Connect Claude Code (Anthropic's terminal-based coding agent) to Prometheux via the MCP server.
- [Connect Cursor](https://docs.prometheux.ai/integrations/mcp/cursor.md): Connect Cursor to Prometheux using the local MCP server.
- [Connect Genie Code](https://docs.prometheux.ai/integrations/mcp/databricks-genie.md): Connect Databricks Genie Code to Prometheux using the Remote MCP Server.
- [Local MCP Server](https://docs.prometheux.ai/integrations/mcp/local.md): Connect Claude Desktop, Claude Code, Cursor, and other MCP clients to Prometheux using the local MCP server.
- [AI Agent Integration via MCP](https://docs.prometheux.ai/integrations/mcp/overview.md): Connect AI agents to Prometheux using the Model Context Protocol (MCP).
- [Remote MCP Server](https://docs.prometheux.ai/integrations/mcp/remote.md): A cloud-hosted MCP server that enables any MCP-compatible AI agent to interact with Prometheux.
- [Connect Snowflake Cortex](https://docs.prometheux.ai/integrations/mcp/snowflake-cortex.md): Connect Snowflake Cortex to Prometheux using the Remote MCP Server.
- [PX APIs](https://docs.prometheux.ai/integrations/overview.md): Overview of the ways to interact with the Prometheux platform programmatically — Python SDK, REST API, and MCP integration.
- [Python SDK](https://docs.prometheux.ai/integrations/python-sdk.md): A high-level Python library for seamless integration with the Prometheux platform.
- [REST API](https://docs.prometheux.ai/integrations/rest-api.md): Conventions for the Prometheux platform REST API — base URL, authentication, the response envelope, scoping, pagination, and error handling.
- [Getting Started](https://docs.prometheux.ai/introduction.md): Prometheux is an ontology-native data processing engine that allows you to define executable ontologies for querying and processing data across multiple systems.
- [AI Assistant](https://docs.prometheux.ai/platform/ai-assistant.md): The conversational AI agent that works across your whole Prometheux workspace — it can create projects, connect and generate data, write and run concepts, build ontologies and dashboards, ingest documents, and remember context.
- [Compute](https://docs.prometheux.ai/platform/compute.md): How Prometheux runs reasoning workloads across Databricks, self-managed clusters, and local execution.
- [Concepts & Lineage](https://docs.prometheux.ai/platform/concepts-and-lineage.md): Understand concepts, knowledge graphs, and how Prometheux tracks data lineage and provenance through the chase graph.
- [Dashboards](https://docs.prometheux.ai/platform/dashboards.md): Build and manage dashboards to visualize the results of your concepts within a project.
- [Data Connections](https://docs.prometheux.ai/platform/data-connections.md): Connect files, databases, warehouses, and APIs to Prometheux so your concepts can read from and write to them.
- [Ontology Graph](https://docs.prometheux.ai/platform/ontology-graph.md): Model how the entities in your business relate to each other and connect to your data.
- [Projects](https://docs.prometheux.ai/platform/projects.md): Organize your work in Prometheux with projects: create, share, template, and move them across workspaces.
- [Settings](https://docs.prometheux.ai/platform/settings.md): Manage your Prometheux configuration: authentication token, LLM provider and model selection, and usage limits.
- [Learning Resources](https://docs.prometheux.ai/resources/community.md): Tutorials, learning paths, and resources for getting the most out of Prometheux.
- [FAQ](https://docs.prometheux.ai/resources/faq.md): Frequently asked questions about Prometheux and Vadalog.
- [Annotations](https://docs.prometheux.ai/vadalog/annotations.md): Annotations for configuring data sources, outputs, schemas, parameters, and post-processing in Vadalog.
- [Chase & Provenance](https://docs.prometheux.ai/vadalog/chase-provenance.md): Materializing the chase graph for full explanations of logical derivations in Vadalog.
- [Cypher Integration](https://docs.prometheux.ai/vadalog/cypher-integration.md): Embedding inline Cypher queries within Vadalog rules for pattern matching, multi-hop traversals, and graph analytics over any data source — files, databases, vector stores, and Neo4j.
- [Data Sources](https://docs.prometheux.ai/vadalog/data-sources.md): Connecting Vadalog to CSV, Parquet, Iceberg, Excel, JSON, COBOL, PostgreSQL, Neo4j, DynamoDB, Qdrant, S3, HDFS, REST APIs, and many more data sources.
- [Engine API (Low-Level)](https://docs.prometheux.ai/vadalog/engine-api.md): The low-level REST API exposed directly by the Vadalog engine for evaluating programs and managing configuration.
- [Explanations](https://docs.prometheux.ai/vadalog/explanations.md): Generating natural language and JSON explanations for derived facts using @explain in Vadalog.
- [Aggregations](https://docs.prometheux.ai/vadalog/expressions/aggregations.md): Monotonic aggregations for computing sums, counts, min/max, and medians in Vadalog.
- [AI Functions](https://docs.prometheux.ai/vadalog/expressions/ai-functions.md): Embeddings and LLM integration in Vadalog for semantic similarity and AI-powered data processing.
- [Collections](https://docs.prometheux.ai/vadalog/expressions/collections.md): Working with arrays, lists, maps, and sets in Vadalog.
- [Conditions & Assignments](https://docs.prometheux.ai/vadalog/expressions/conditions-assignments.md): Constraining values with conditions and generating new values with assignments in Vadalog.
- [Negation](https://docs.prometheux.ai/vadalog/expressions/negation.md): Negating truth values in Vadalog and safe usage patterns.
- [Operators](https://docs.prometheux.ai/vadalog/expressions/operators.md): Comparison, arithmetic, boolean, logical, string, and set operators in Vadalog.
- [Expressions](https://docs.prometheux.ai/vadalog/expressions/overview.md): An overview of expressions in Vadalog: operators, conditions, assignments, and functions.
- [Recursion](https://docs.prometheux.ai/vadalog/expressions/recursion.md): Recursive rules and graph traversal in Vadalog.
- [Specialized Functions](https://docs.prometheux.ai/vadalog/expressions/specialized-functions.md): Math, hash, date/time, type casting, interval, null-handling, utility, anonymization, and Kalman filter functions in Vadalog.
- [Grammar Reference](https://docs.prometheux.ai/vadalog/grammar.md): Comprehensive reference for the Vadalog grammar, covering all syntax rules, operators, and language constructs.
- [Graph Analytics](https://docs.prometheux.ai/vadalog/graph-analytics.md): Built-in graph algorithms in Vadalog including transitive closure, shortest paths, connected components, and centrality measures.
- [Language Primitives](https://docs.prometheux.ai/vadalog/language-primitives.md): Core Vadalog concepts: rules, facts, data types, variables, and labelled nulls.
- [What is Vadalog?](https://docs.prometheux.ai/vadalog/overview.md): An introduction to Vadalog, the declarative logic programming language powering Prometheux.
- [Rules](https://docs.prometheux.ai/vadalog/rules.md): Linear rules, join rules, facts, and constants in Vadalog.
- [SQL Integration](https://docs.prometheux.ai/vadalog/sql-integration.md): Embedding native SQL queries within Vadalog rules for cross-source joins, aggregations, and data transformations.
- [Thinking in Vadalog](https://docs.prometheux.ai/vadalog/thinking-in-vadalog.md): A conceptual guide to thinking declaratively and recursively when modelling business logic in Vadalog.

## Optional

- [Community (Coming Soon)](https://docs.prometheux.ai/community/resources)