sqlite-muninn 0.1.0a1

HNSW vector search + graph traversal + Node2Vec for SQLite

sqlite-muninn

Odin's mythic raven of Memory.

A zero-dependency C extension for SQLite to add an advanced collection of knowledge graph primitives like Vector Similarity Search, HNSW Indexes, Graph database, Community Detection, Node2Vec capabilities.

Documentation | GitHub

graph LR
    subgraph Muninn Extension
        direction TB
        HNSW["HNSW Virtual Table<br/><i>hnsw_index</i>"]
        GTVF["Graph TVFs<br/><i>bfs, dfs, shortest_path,<br/>components, pagerank</i>"]
        CENT["Centrality TVFs<br/><i>degree, betweenness,<br/>closeness</i>"]
        COMM["Community Detection<br/><i>graph_leiden</i>"]
        N2V["Node2Vec Training<br/><i>node2vec_train()</i>"]
    end

    ET["Any Edge Table<br/><i>src, dst columns</i>"] --> GTVF
    ET --> CENT
    ET --> COMM
    ET --> N2V
    N2V -->|embeddings| HNSW
    APP["Application<br/><i>embeddings from<br/>any source</i>"] -->|INSERT| HNSW
    HNSW -->|KNN search| APP

Features

• HNSW Vector Index — O(log N) approximate nearest neighbor search with incremental insert/delete
• Graph Traversal — BFS, DFS, shortest path, connected components, PageRank on any edge table
• Centrality Measures — Degree, betweenness (Brandes), and closeness centrality with weighted/temporal support
• Community Detection — Leiden algorithm for discovering graph communities with modularity scoring
• Node2Vec — Learn structural node embeddings from graph topology, store in HNSW for similarity search
• Zero dependencies — Pure C11, compiles to a single .dylib/.so/.dll
• SIMD accelerated — ARM NEON and x86 SSE distance functions

Build

Requires SQLite development headers and a C11 compiler.

# macOS (Homebrew SQLite recommended)
brew install sqlite
make all

# Linux
sudo apt-get install libsqlite3-dev
make all

# Run tests
make test        # C unit tests
make test-python # Python integration tests
make test-all    # Both

Quick Start

.load ./muninn

-- Create an HNSW vector index
CREATE VIRTUAL TABLE my_vectors USING hnsw_index(
    dimensions=384, metric='cosine', m=16, ef_construction=200
);

-- Insert vectors
INSERT INTO my_vectors (rowid, vector) VALUES (1, ?);  -- 384-dim float32 blob

-- KNN search
SELECT rowid, distance FROM my_vectors
WHERE vector MATCH ?query AND k = 10 AND ef_search = 64;

-- Graph traversal on any edge table
SELECT node, depth FROM graph_bfs
WHERE edge_table = 'friendships' AND src_col = 'user_a'
  AND dst_col = 'user_b' AND start_node = 'alice' AND max_depth = 3
  AND direction = 'both';

-- Connected components
SELECT node, component_id, component_size FROM graph_components
WHERE edge_table = 'friendships' AND src_col = 'user_a' AND dst_col = 'user_b';

-- PageRank
SELECT node, rank FROM graph_pagerank
WHERE edge_table = 'citations' AND src_col = 'citing' AND dst_col = 'cited'
  AND damping = 0.85 AND iterations = 20;

-- Betweenness centrality (find bridge nodes)
SELECT node, centrality FROM graph_betweenness
WHERE edge_table = 'friendships' AND src_col = 'user_a' AND dst_col = 'user_b'
  AND direction = 'both'
ORDER BY centrality DESC LIMIT 10;

-- Community detection (Leiden algorithm)
SELECT node, community_id, modularity FROM graph_leiden
WHERE edge_table = 'friendships' AND src_col = 'user_a' AND dst_col = 'user_b';

-- Learn structural embeddings from graph topology
SELECT node2vec_train(
    'friendships', 'user_a', 'user_b', 'my_vectors',
    64, 1.0, 1.0, 10, 80, 5, 5, 0.025, 5
);

Examples

Self-contained examples in the examples/ directory:

Example	Demonstrates
Semantic Search	HNSW index, KNN queries, point lookup, delete
Movie Recommendations	Vector similarity for content-based recommendations
Social Network	Graph TVFs on a social graph (BFS, components, PageRank)
Research Papers	Citation graph analysis with Node2Vec embeddings
Transit Routes	Shortest path and graph traversal on route networks

make all
python examples/semantic_search/example.py

API Reference

HNSW Virtual Table (hnsw_index)

CREATE VIRTUAL TABLE name USING hnsw_index(
    dimensions=N,            -- vector dimensionality (required)
    metric='l2',             -- 'l2' | 'cosine' | 'inner_product'
    m=16,                    -- max connections per node per layer
    ef_construction=200      -- beam width during index construction
);

Columns:

Column	Type	Hidden	Description
rowid	INTEGER	Yes	User-assigned ID for joining with application tables
vector	BLOB	No	float32[dim] — input for INSERT, MATCH constraint for search
distance	REAL	No	Computed distance (output only, during search)
k	INTEGER	Yes	Top-k parameter (search constraint)
ef_search	INTEGER	Yes	Search beam width (search constraint)

Operations:

-- Insert
INSERT INTO t (rowid, vector) VALUES (42, ?blob);

-- KNN search
SELECT rowid, distance FROM t WHERE vector MATCH ?query AND k = 10;

-- Point lookup
SELECT vector FROM t WHERE rowid = 42;

-- Delete (with automatic neighbor reconnection)
DELETE FROM t WHERE rowid = 42;

-- Drop (removes index and all shadow tables)
DROP TABLE t;

Shadow tables (auto-managed):

• {name}_config — HNSW parameters
• {name}_nodes — stored vectors and level assignments
• {name}_edges — the proximity graph (usable by graph TVFs)

Graph Table-Valued Functions

All graph TVFs work on any existing SQLite table with source/target columns. Table and column names are validated against SQL injection.

graph_bfs / graph_dfs

Breadth-first or depth-first traversal from a start node.

SELECT node, depth, parent FROM graph_bfs
WHERE edge_table = 'edges'
  AND src_col = 'src'
  AND dst_col = 'dst'
  AND start_node = 'node-42'
  AND max_depth = 5
  AND direction = 'forward';   -- 'forward' | 'reverse' | 'both'

Output Column	Type	Description
node	TEXT	Node identifier
depth	INTEGER	Hop distance from start
parent	TEXT	Parent node in traversal tree (NULL for start)

graph_shortest_path

Unweighted (BFS) or weighted (Dijkstra) shortest path.

-- Unweighted
SELECT node, distance, path_order FROM graph_shortest_path
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst'
  AND start_node = 'A' AND end_node = 'Z' AND weight_col IS NULL;

-- Weighted (Dijkstra)
SELECT node, distance, path_order FROM graph_shortest_path
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst'
  AND start_node = 'A' AND end_node = 'Z' AND weight_col = 'weight';

Output Column	Type	Description
node	TEXT	Node on the path
distance	REAL	Cumulative distance from start
path_order	INTEGER	Position in path (0-indexed)

graph_components

Connected components via Union-Find with path compression.

SELECT node, component_id, component_size FROM graph_components
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst';

Output Column	Type	Description
node	TEXT	Node identifier
component_id	INTEGER	Component index (0-based)
component_size	INTEGER	Number of nodes in this component

graph_pagerank

Iterative power method PageRank with configurable damping and iterations.

SELECT node, rank FROM graph_pagerank
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst'
  AND damping = 0.85        -- optional, default 0.85
  AND iterations = 20;      -- optional, default 20

Output Column	Type	Description
node	TEXT	Node identifier
rank	REAL	PageRank score (sums to ~1.0)

graph_degree

Degree centrality for all nodes.

SELECT node, in_degree, out_degree, degree, centrality FROM graph_degree
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst';

Output Column	Type	Description
node	TEXT	Node identifier
in_degree	REAL	Count (or weighted sum) of incoming edges
out_degree	REAL	Count (or weighted sum) of outgoing edges
degree	REAL	Total degree (in + out)
centrality	REAL	Normalized degree centrality

Optional constraints: weight_col, direction, normalized, timestamp_col, time_start, time_end.

graph_betweenness

Betweenness centrality via Brandes' O(VE) algorithm.

SELECT node, centrality FROM graph_betweenness
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst'
  AND direction = 'both';

Output Column	Type	Description
node	TEXT	Node identifier
centrality	REAL	Betweenness centrality score

Optional constraints: weight_col, direction, normalized, timestamp_col, time_start, time_end.

graph_closeness

Closeness centrality with Wasserman-Faust normalization for disconnected graphs.

SELECT node, centrality FROM graph_closeness
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst'
  AND direction = 'both';

Output Column	Type	Description
node	TEXT	Node identifier
centrality	REAL	Closeness centrality score

Optional constraints: weight_col, direction, timestamp_col, time_start, time_end.

graph_leiden

Community detection via the Leiden algorithm (Traag et al., 2019).

SELECT node, community_id, modularity FROM graph_leiden
WHERE edge_table = 'edges' AND src_col = 'src' AND dst_col = 'dst';

Output Column	Type	Description
node	TEXT	Node identifier
community_id	INTEGER	Community assignment (0-based)
modularity	REAL	Global modularity score of the partition

Optional constraints: weight_col, resolution (default 1.0), timestamp_col, time_start, time_end.

node2vec_train()

Learn vector embeddings from graph structure using biased random walks (Node2Vec) and Skip-gram with Negative Sampling (SGNS).

SELECT node2vec_train(
    edge_table,       -- name of edge table
    src_col,          -- source column name
    dst_col,          -- destination column name
    output_table,     -- HNSW table to store embeddings (must exist)
    dimensions,       -- embedding size (must match HNSW table)
    p,                -- return parameter (1.0 = uniform/DeepWalk)
    q,                -- in-out parameter (1.0 = uniform/DeepWalk)
    num_walks,        -- walks per node
    walk_length,      -- max steps per walk
    window_size,      -- SGNS context window
    negative_samples, -- negative samples per positive
    learning_rate,    -- initial learning rate (decays linearly)
    epochs            -- training epochs
);
-- Returns: number of nodes embedded

p, q parameter guide:

Setting	Walk Behavior	Best For
p=1, q=1	Uniform (DeepWalk)	General structural similarity
Low p (0.25)	BFS-like, stays local	Community/cluster detection
Low q (0.5)	DFS-like, explores far	Structural role similarity

Benchmarks

The project includes a comprehensive benchmark suite comparing muninn against other SQLite extensions across real-world workloads.

Vector search benchmarks compare against sqlite-vector, sqlite-vec, and vectorlite using 3 embedding models (MiniLM, MPNet, BGE-Large) and 2 text datasets (AG News, Wealth of Nations) at scales up to 250K vectors.

Graph traversal benchmarks compare muninn TVFs against recursive CTEs and GraphQLite on synthetic graphs (Erdos-Renyi, Barabasi-Albert) at scales up to 100K nodes.

Results include interactive Plotly charts for insert throughput, search latency, recall, database size, and tipping-point analysis. See the full benchmark results on the documentation site.

make -C benchmarks help       # List all benchmark targets
make -C benchmarks analyze    # Generate charts and reports from existing results

Project Structure

src/                  C11 source (extension entry point, HNSW, graph TVFs, Node2Vec)
test/                 C unit tests (custom minimal framework)
pytests/              Python integration tests (pytest)
examples/             Self-contained usage examples
benchmarks/
  scripts/            Benchmark runners and analysis scripts
  charts/             Plotly JSON chart specs (committed for docs site)
  results/            JSONL benchmark data (generated, not committed)
docs/                 MkDocs documentation source

Documentation

Full documentation is published at neozenith.github.io/sqlite-muninn via MkDocs Material with interactive Plotly charts.

make docs-serve    # Local dev server with live reload
make docs-build    # Build static site

Research References

Feature	Paper
HNSW	Malkov & Yashunin, TPAMI 2020
MN-RU insert repair	arXiv:2407.07871, 2024
Patience early termination	SISAP 2025
Betweenness centrality	Brandes, J. Math. Sociol. 2001
Leiden community detection	Traag, Waltman & van Eck, Sci. Rep. 2019
Node2Vec	Grover & Leskovec, KDD 2016
SGNS	Mikolov et al., 2013

License

MIT. See LICENSE.