Alopex DB¶

CLI Defaults¶

On a TTY, the CLI launches the TUI by default; use --batch or --output to force batch output. In the results TUI, press a to open the admin console when available. The status bar emphasizes Connection/Focus/Action and Ops so you can see the current target and primary actions at a glance. The admin console uses a rainfrog-style layout (left resource tree, right detail input, right-bottom status/preview) with h/l focus switching.

Current Status¶

cargo add alopex-embedded alopex-sql alopex-server

The Problem¶

Modern AI applications require multiple database technologies—creating complexity, inconsistency, and operational overhead.

Key Features¶

Any Scale, One Engine¶

Start small, scale infinitely—without changing your data model or application code.

Mode	Use Case	Architecture
WASM Viewer	Browser Data Exploration	Read-only viewer with IndexedDB caching
Embedded	Mobile Apps, Local RAG, Edge Devices	Single Binary / Library (like SQLite)
Single-Node	Microservices, Dev/Test Environments	Standalone Server (HTTP/gRPC)
Replicated	High Availability, Read-heavy Workloads	Primary-Replica with automatic failover
Distributed	Large-Scale Production	Multi-Raft Cluster (Range Sharding)

Learn more about deployment modes

SQL + Vector in Action¶

-- Create a table with vector column
CREATE TABLE knowledge_chunks (
    id INTEGER PRIMARY KEY,
    content TEXT,
    embedding VECTOR(1536)
);

-- Hybrid Search: SQL Filter + Vector Similarity
SELECT id, content,
       vector_similarity(embedding, ?) AS score
FROM knowledge_chunks
ORDER BY score DESC
LIMIT 5;

-- Create HNSW index for fast similarity search
CREATE INDEX idx_embedding ON knowledge_chunks
USING HNSW (embedding)
WITH (m = 16, ef_construction = 200);

-- Search with HNSW acceleration
SELECT id, content
FROM knowledge_chunks
ORDER BY vector_similarity(embedding, ?) DESC
LIMIT 10;

use alopex_embedded::Database;

let db = Database::open("./my_data")?;

// Execute SQL
let results = db.execute_sql(
    "SELECT * FROM docs WHERE vector_similarity(embedding, ?) > 0.8",
    &[query_vector]
)?;

// HNSW search
let similar = db.search_hnsw("docs", &query_vector, 10)?;

View SQL + Vector guide

Roadmap¶

gantt
    title Alopex DB Development Timeline
    dateFormat  YYYY-MM
    axisFormat  %Y-%m

    section Foundation
    v0.1-v0.2 Core          :done, 2025-01, 2025-10
    v0.3 SQL + HNSW         :done, 2025-10, 2025-12

    section Python & Server
    v0.3.3 Python Wrapper   :done, 2025-12, 2025-12
    v0.4 Server + DataFrame :done, 2026-01, 2026-01

    section Production
    v0.5 Durability         :2026-04, 2026-06
    v0.6 WASM Viewer        :2026-06, 2026-08
    v0.7 Cluster-aware      :2026-08, 2026-10
    v1.0 GA                 :milestone, 2027-03, 0d

What's Complete¶

Version	Features	Status
v0.1-v0.2	Embedded KV, WAL, MVCC, Vector (Flat), Columnar	Complete
v0.3	SQL Frontend, HNSW Index, Embedded Integration	crates.io Published
v0.3.3	Python Wrapper (alopex-py), CLI	PyPI Published
v0.4.0	Server Mode, DataFrame API, Async/Stream	crates.io Published
Chirps v0.5	Gossip, SWIM, Membership, Raft Consensus API	Complete

What's Next¶

View detailed roadmap

Python Support (Available Now)¶

import alopex

# Open database
db = alopex.Database.open("./my_data")

# Execute SQL
results = db.execute_sql(
    "SELECT * FROM docs WHERE category = ?",
    ["science"]
)

# Vector search
similar = db.search_hnsw("docs", query_embedding, k=10)

import alopex

# Polars-compatible DataFrame API
df = alopex.read_parquet("data.parquet")

result = (
    df.lazy()
    .filter(alopex.col("score") > 0.5)
    .select(["id", "content", "embedding"])
    .collect()
)

Python Guide

Chirps — Cluster Foundation¶

Alopex Chirps is the control plane for distributed Alopex DB clusters.

Learn about Chirps architecture

Join the Pack¶

Alopex DB is open-source under the Apache 2.0 License.

We welcome contributions from engineers passionate about Rust, Distributed Systems, and Vector Search.

Contributing Guide GitHub Discussions