pylibseekdb 1.3.0.post2

Python bindings for the seekdb C client

pylibseekdb

Low-level Python bindings for the seekdb C client library.

🚀 What is OceanBase seekdb?

OceanBase seekdb is an AI-native search database that unifies relational, vector, full-text, JSON, and GIS in a single engine, enabling hybrid search and in-database AI workflows.

📖 Read the launch blog → · 📚 Docs →

✨ Why seekdb for Agents?

🔥 Streaming Write + Concurrent Search, Without the P99 Spike

Agent workloads are continuous write + millisecond-later read. seekdb's async index pipeline (Change Stream) decouples DML from index build, and its two-level HNSW (incremental + snapshot) makes newly-written vectors immediately searchable.

The write path commits and returns without waiting on index construction. The Change Stream pipeline consumes the redo log asynchronously and updates the delta HNSW. Queries hit both delta and snapshot indexes with fine-grained read locks — this is why P99 stays flat under concurrency.

🌿 Copy-on-Write Sandboxes for Agent Exploration

FORK DATABASE snapshots an entire database in seconds — no data copy. Agents experiment freely (write, query, even break tables); then MERGE TABLE commits the work back, or DROP DATABASE discards it.

🔍 Hybrid Search in a Single SQL

Vector + full-text + scalar filter pushed into one execution plan. No N+1 client-side merging, no glue code to combine results.

🐬 MySQL-Compatible, ACID, Embeddable

Built on the proven OceanBase SQL engine. Works as an embedded library, a single-node server, or in the OceanBase distributed cluster. Full ACID, real-time writes, and the entire MySQL ecosystem out of the box.

Installation

pip install pylibseekdb

Requirements

• CPython >= 3.11
• Linux x86_64 with glibc >= 2.28 (Alpine / musl not supported yet)
• macOS arm64 >= 15.5

🎬 Quick Start

pylibseekdb exposes a lightweight DB-API 2-style interface directly over the seekdb C driver. It currently starts a local seekdb runtime via open(). Native embedded-mode support will be released soon.

import pylibseekdb as seekdb

# Start a local seekdb runtime (embedded-mode support will be released soon)
seekdb.open(db_dir="./seekdb.db")

# Get a connection and a cursor
conn   = seekdb.connect(database="test", autocommit=True)
cursor = conn.cursor()

# Create a table with a vector column and an HNSW index
cursor.execute("""
    CREATE TABLE IF NOT EXISTS articles (
        id        INT PRIMARY KEY,
        title     TEXT,
        embedding VECTOR(4),
        VECTOR INDEX idx_vec (embedding)
            WITH (DISTANCE=l2, TYPE=hnsw, LIB=vsag)
    ) ORGANIZATION = HEAP
""")

# Insert a row
cursor.execute(
    "INSERT INTO articles VALUES (1, 'Hello seekdb', '[0.1, 0.2, 0.3, 0.4]')"
)

# Hybrid / vector search
cursor.execute("""
    SELECT id, title,
           l2_distance(embedding, '[0.1, 0.2, 0.3, 0.4]') AS dist
    FROM articles
    ORDER BY dist APPROXIMATE
    LIMIT 5
""")
rows = cursor.fetchall()
for row in rows:
    print(row)

cursor.close()
conn.close()

Transaction support

conn = seekdb.connect(database="test", autocommit=False)
cursor = conn.cursor()
try:
    conn.begin()
    cursor.execute("INSERT INTO articles VALUES (2, 'Second', '[0.5,0.6,0.7,0.8]')")
    conn.commit()
except seekdb.SeekdbError:
    conn.rollback()
    raise
finally:
    cursor.close()
    conn.close()

SQL — Hybrid Search

-- Create table with vector column, full-text index, and HNSW vector index
CREATE TABLE docs (
    id        INT PRIMARY KEY,
    title     TEXT,
    content   TEXT,
    embedding VECTOR(384),
    FULLTEXT INDEX idx_fts (content) WITH PARSER ik,
    VECTOR   INDEX idx_vec (embedding)
        WITH (DISTANCE=l2, TYPE=hnsw, LIB=vsag)
) ORGANIZATION = HEAP;

-- Hybrid search: vector similarity + full-text match in one query
SELECT id, title,
       l2_distance(embedding, '[0.12, 0.34, ...]') AS dist
FROM docs
WHERE MATCH(content) AGAINST('quarterly report')
ORDER BY dist APPROXIMATE
LIMIT 10;

API Reference

Module-level functions

Function	Description
open(db_dir="./seekdb.db")	Start a local seekdb runtime for the given database directory. Embedded-mode support will be released soon. Must be called before connect().
connect(database="test", autocommit=False)	Return a Connection to the given database.

Connection

Method	Description
cursor()	Return a new Cursor.
begin()	Begin a transaction.
commit()	Commit the current transaction.
rollback()	Roll back the current transaction.
close()	Disconnect and release resources.

Cursor

Method	Description
execute(sql)	Execute sql; returns the number of rows in the result set (0 for statements without a result set).
fetchone()	Return the next row as a tuple, or None.
fetchall()	Return all remaining rows as a list of tuple.
close()	Free the result set.

SeekdbError

Exception raised on driver errors. Subclass of RuntimeError.

📚 Use Cases

• 🤖 Agentic AI — streaming memory writes, millisecond-later vector retrieval, FORK DATABASE for safe exploration
• 📖 RAG & Knowledge Retrieval — hybrid search across enterprise knowledge bases
• 🔍 Semantic Search — embedding-based search for text, images, and other modalities
• 💻 AI-Assisted Coding — semantic code search with multi-project isolation
• 📱 On-Device & Edge AI — lightweight local deployments today, with embedded-mode support coming soon

🌐 Resources

• 📖 Docs
• 🐍 pyseekdb (high-level SDK)
• 🐛 Issues
• 💬 Discord
• 🏗️ seekdb-bindings (this repo)

License

Apache-2.0 — see LICENSE.