Introduction

Neumann is a unified tensor-based runtime that stores relational data, graph relationships, and vector embeddings in a single system. Instead of stitching together a SQL database, a graph store, and a vector index, Neumann gives you all three behind one query language.

Choose Your Path

What Makes Neumann Different

One system, three engines. Store a table, connect entities in a graph, and search by vector similarity without moving data between systems.

-- Relational
CREATE TABLE documents (id INT PRIMARY KEY, title TEXT, author TEXT);
INSERT INTO documents VALUES (1, 'Intro to ML', 'Alice');

-- Graph
NODE CREATE topic { name: 'machine-learning' }
ENTITY CONNECT 'doc-1' -> 'topic-ml' : covers

-- Vector
EMBED STORE 'doc-1' [0.1, 0.2, 0.3, 0.4]

-- Cross-engine: find similar documents connected to a topic
SIMILAR 'doc-1' LIMIT 5 CONNECTED TO 'topic-ml'

Encrypted vault. Store secrets with AES-256-GCM encryption and graph-based access control.

LLM cache. Cache LLM responses with exact and semantic matching to reduce API costs.

Built-in consensus. Raft-based distributed consensus with 2PC transactions for multi-node deployments.

Architecture

                    +-------------------+
                    |   neumann_shell   |    Interactive CLI
                    |   neumann_server  |    gRPC server
                    +-------------------+
                             |
                    +-------------------+
                    |   query_router    |    Unified query execution
                    +-------------------+
                             |
        +----------+---------+---------+----------+
        |          |         |         |          |
   relational   graph    vector    tensor_    tensor_
   _engine     _engine  _engine   _vault     _cache
        |          |         |         |          |
        +----------+---------+---------+----------+
                             |
                    +-------------------+
                    |   tensor_store    |    Core storage (HNSW, sharded B-trees)
                    +-------------------+

Additional subsystems: tensor_blob (S3-style blob storage), tensor_chain (blockchain with Raft), tensor_checkpoint (snapshots), tensor_compress (tensor train decomposition).

Getting Started

• Installation – Install Neumann
• Quick Start – Your first queries
• Five-Minute Tutorial – Build a mini RAG system
• Use Cases – Real-world applications
• Building from Source – Compile from source

Reference

• Query Language – Full command reference
• Data Types – Scalar, vector, and sparse types
• Functions – Aggregates, distance metrics, operators
• API Reference – Rustdoc output