Quick Start

Get up and running with Neumann in under 5 minutes. This guide walks you through relational queries, graph operations, vector search, and the cross-engine “wow” moment.

Start the Shell

# In-memory (data lost on exit)
neumann

# With persistence (recommended)
neumann --wal-dir ./data

You will see:

Neumann v0.1.0
Type 'help' for available commands.
neumann>

1. Relational Queries

Create a table and insert some data:

CREATE TABLE people (id INT PRIMARY KEY, name TEXT, role TEXT, team TEXT);

INSERT INTO people VALUES (1, 'Alice', 'Staff Engineer', 'Platform');
INSERT INTO people VALUES (2, 'Bob', 'Engineering Manager', 'Platform');
INSERT INTO people VALUES (3, 'Carol', 'Senior Engineer', 'ML');
INSERT INTO people VALUES (4, 'Dave', 'Junior Engineer', 'Platform');

Query it:

SELECT * FROM people WHERE team = 'Platform';
SELECT name, role FROM people ORDER BY name;
SELECT team, COUNT(*) AS headcount FROM people GROUP BY team;

2. Graph Operations

Create nodes with labels and properties:

NODE CREATE person { name: 'Alice', role: 'Staff Engineer' }
NODE CREATE person { name: 'Bob', role: 'Engineering Manager' }
NODE CREATE person { name: 'Carol', role: 'Senior Engineer' }
NODE CREATE person { name: 'Dave', role: 'Junior Engineer' }

List the nodes to see their auto-generated IDs:

NODE LIST person

Create edges (replace the IDs with the actual values from NODE LIST):

EDGE CREATE 'alice-node-id' -> 'bob-node-id' : reports_to
EDGE CREATE 'dave-node-id' -> 'bob-node-id' : reports_to
EDGE CREATE 'alice-node-id' -> 'dave-node-id' : mentors

Traverse the graph:

NEIGHBORS 'bob-node-id' INCOMING : reports_to
PATH SHORTEST 'dave-node-id' TO 'bob-node-id'

Run graph algorithms:

PAGERANK

3. Vector Search

Store embeddings with string keys:

EMBED STORE 'alice' [0.9, 0.4, 0.1, 0.7, 0.6, 0.3]
EMBED STORE 'bob' [0.6, 0.2, 0.1, 0.5, 0.3, 0.2]
EMBED STORE 'carol' [0.3, 0.9, 0.1, 0.4, 0.8, 0.1]
EMBED STORE 'dave' [0.4, 0.1, 0.2, 0.5, 0.2, 0.1]

Find similar items by key or by vector:

SIMILAR 'alice' LIMIT 3
SIMILAR [0.8, 0.5, 0.1, 0.6, 0.5, 0.2] LIMIT 3 METRIC COSINE

Check what is stored:

SHOW EMBEDDINGS
COUNT EMBEDDINGS

4. The Cross-Engine Moment

This is where Neumann shines. Combine graph traversal with vector similarity in a single query:

SIMILAR 'alice' LIMIT 3 CONNECTED TO 'bob-node-id'

This finds embeddings similar to Alice’s that are also connected to Bob in the graph. No joins across separate databases needed.

Search across all engines with FIND:

FIND NODE person WHERE name = 'Alice'

Create unified entities that span relational, graph, and vector storage:

ENTITY CREATE 'project-x' { name: 'Project X', status: 'active' } EMBEDDING [0.5, 0.3, 0.7, 0.2, 0.4, 0.1]
ENTITY GET 'project-x'

5. Persistence

Save a checkpoint:

CHECKPOINT 'my-first-checkpoint'
CHECKPOINTS

If you started with --wal-dir, your data persists across restarts. You can also save and load binary snapshots:

SAVE 'backup.bin'
LOAD 'backup.bin'

Next Steps

Five-Minute Tutorial – Build a mini RAG system
Use Cases – Real-world application patterns
Query Language Reference – Full command list
Architecture Overview – How it works under the hood
Python SDK – Use Neumann from Python
TypeScript SDK – Use Neumann from TypeScript

Sample Dataset

A ready-made dataset is available in samples/knowledge-base.nql. Load it with:

neumann --wal-dir ./data

neumann> \i samples/knowledge-base.nql

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