An AI-native analytical database built for agents, not just applications.
Vector search · full-text ranking · graph traversal — one SQL engine, one process.

Paro is a multi-model analytical database written in Rust. It unifies relational, vector, full-text, and graph workloads inside a single columnar engine — no sidecar services, no cross-system glue code. An agent can walk a social graph, rank documents by keyword relevance, and re-rank by embedding similarity in one SQL statement, against one process.

Warning

Paro is currently in beta. The project is still under active development and testing. Expect rough edges, incomplete coverage, and breaking changes across SQL features, performance characteristics, and on-disk behavior. It is best used for evaluation, prototyping, and early feedback today — not production-critical workloads yet.

Features

• Multi-model — structured and semi-structured data as first-class citizens in one engine.
• Columnar storage — column-oriented layout optimized for scan-heavy analytical workloads.
• SIMD execution — vectorized compute kernels exploiting hardware-level parallelism for distance functions, expression evaluation, and more.
• SQL-first — PostgreSQL wire protocol and a familiar SQL dialect; psql works well today, while broader driver and ORM compatibility is still being validated.
• Vector search — pgvector-compatible operators (<->, <+>, <=>, <#>) with HNSW indexing.
• Full-text search — GIN indexes, to_tsvector, plainto_tsquery, ts_rank, and BM25 ranking.
• Graph queries — SQL/PGQ: CREATE PROPERTY GRAPH, GRAPH_TABLE, multi-hop path traversal.

Quick Start

Clone the repository and enter the project directory, then:

make run

From another terminal, connect with psql:

psql -h 127.0.0.1 -p 6432 -d postgres

make run compiles and starts parod on 127.0.0.1:6432 by default.

Requires Rust ≥ 1.85. psql ≥ 14 is recommended; older versions may work but are not regularly tested.

Override the listen address with PARO_HOST / PARO_PORT if needed, for example make run PARO_HOST=0.0.0.0. Authentication is not implemented yet, so do not expose Paro to untrusted networks.

Query Example

An agent is researching "retrieval-augmented generation for autonomous agents." The query below walks Alice's collaboration graph up to two hops, pre-filters papers by semantic similarity, then ranks the results with a hybrid score that blends vector proximity and full-text relevance — graph traversal, vector search, and full-text ranking in one statement.

WITH network AS (
    SELECT * FROM GRAPH_TABLE(collab_graph
        MATCH (me:Researcher WHERE me.name = 'Alice')
              -[:CollaboratesWith]->{1,2}(peer:Researcher)
        COLUMNS (peer.id AS author_id, peer.name AS author_name)
    )
),
candidates AS (
    SELECT
        id,
        title,
        author_id,
        abstract,
        1.0 / (1.0 + (embedding <-> '[0.91, 0.10, 0.80, 0.22]')) AS vec_score
    FROM papers
    ORDER BY embedding <-> '[0.91, 0.10, 0.80, 0.22]'
    LIMIT 20
)
SELECT
    c.title,
    n.author_name,
    c.vec_score
      + ts_rank(
            to_tsvector('simple', c.abstract),
            plainto_tsquery('simple', 'retrieval augmented generation agents')
        ) AS score
FROM network n
JOIN candidates c ON c.author_id = n.author_id
WHERE to_tsvector('simple', c.abstract)
   @@ plainto_tsquery('simple', 'retrieval augmented generation agents')
ORDER BY score DESC
LIMIT 10;

One engine, one query — three retrieval models working together.

To run the full example locally, load regress/cases/example/quickstart.sql:

\i regress/cases/example/quickstart.sql

Roadmap

1. Performance and stability — hardening the storage engine, query optimizer, and execution pipeline for production-grade reliability.
2. Explore AI-native database capabilities — deeper integration of model inference, embedding generation, and agent-friendly query patterns directly inside the engine.
3. Ecosystem integration — potential Supabase compatibility layer and/or a standalone database agent for autonomous data workflows.

Contributing

Contributions are welcome. Please open an issue to discuss significant changes before starting work.

Before submitting a pull request:

1. Run the static checks — make static
2. Run the full local CI pipeline — make ci-local
3. Consider using an AI assistant to review your changes before submitting

License

Paro is licensed under the Apache License 2.0.