Python Guide¶

Coming in v0.3.1

Python bindings (alopex-py) are currently in development and will be released in v0.3.1. This guide provides a preview of the upcoming API.

Overview¶

alopex-py provides Python bindings for Alopex DB via PyO3, enabling Python developers to use the full power of Alopex DB's embedded database engine.

Features¶

Database API: Full access to embedded database operations
SQL API: Execute SQL queries with parameter binding
Vector/HNSW API: High-performance vector similarity search
NumPy Integration: Zero-copy array operations
Type Stubs: Full IDE support with .pyi files

Installation¶

# Coming soon to PyPI
pip install alopex

Quick Start¶

Basic Database Operations¶

import alopex

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

# Execute SQL
db.execute_sql("""
    CREATE TABLE users (
        id INTEGER PRIMARY KEY,
        name TEXT NOT NULL,
        email TEXT
    )
""")

# Insert data with parameters
db.execute_sql(
    "INSERT INTO users (id, name, email) VALUES (?, ?, ?)",
    [1, "Alice", "alice@example.com"]
)

# Query data
results = db.execute_sql("SELECT * FROM users WHERE id = ?", [1])
for row in results:
    print(f"User: {row['name']} ({row['email']})")

Vector Search with HNSW¶

import alopex
import numpy as np

db = alopex.Database.open("./vector_data")

# Create table with vector column
db.execute_sql("""
    CREATE TABLE documents (
        id INTEGER PRIMARY KEY,
        content TEXT,
        embedding VECTOR(384)
    )
""")

# Insert documents with embeddings
embedding = np.random.rand(384).astype(np.float32)
db.execute_sql(
    "INSERT INTO documents (id, content, embedding) VALUES (?, ?, ?)",
    [1, "Hello world", embedding]
)

# Create HNSW index
db.create_hnsw_index(
    table="documents",
    column="embedding",
    metric="cosine",
    m=16,
    ef_construction=200
)

# Search similar vectors
query = np.random.rand(384).astype(np.float32)
results = db.search_hnsw("documents", query, k=10)

for doc_id, score in results:
    print(f"Document {doc_id}: score = {score:.4f}")

Hybrid SQL + Vector Search¶

import alopex
import numpy as np

db = alopex.Database.open("./hybrid_data")

# Hybrid search with SQL filtering
query_embedding = np.random.rand(384).astype(np.float32)

results = db.execute_sql("""
    SELECT id, content, vector_similarity(embedding, ?) AS score
    FROM documents
    WHERE score > 0.8
    ORDER BY score DESC
    LIMIT 5
""", [query_embedding])

for row in results:
    print(f"{row['id']}: {row['content']} (score: {row['score']:.4f})")

Transaction Support¶

import alopex

db = alopex.Database.open("./tx_data")

# Explicit transaction
tx = db.begin()
try:
    tx.execute_sql("INSERT INTO users (id, name) VALUES (?, ?)", [1, "Alice"])
    tx.execute_sql("INSERT INTO users (id, name) VALUES (?, ?)", [2, "Bob"])
    tx.commit()
except Exception as e:
    tx.rollback()
    raise

NumPy Integration¶

alopex-py provides zero-copy integration with NumPy arrays:

import alopex
import numpy as np

db = alopex.Database.open("./numpy_data")

# Direct NumPy array support
embeddings = np.random.rand(100, 384).astype(np.float32)

# Batch insert with NumPy arrays
for i, embedding in enumerate(embeddings):
    db.execute_sql(
        "INSERT INTO documents (id, embedding) VALUES (?, ?)",
        [i, embedding]  # NumPy array passed directly
    )

# Query returns NumPy-compatible data
results = db.execute_sql("SELECT embedding FROM documents WHERE id = ?", [0])
embedding = np.array(results[0]['embedding'])

Configuration Options¶

import alopex

# Configure database options
config = alopex.Config(
    path="./my_data",
    memory_limit=512 * 1024 * 1024,  # 512MB
    wal_sync=True,
    compression=True
)

db = alopex.Database.open_with_config(config)

In-Memory Mode¶

import alopex

# Create in-memory database for testing
db = alopex.Database.open_in_memory()

db.execute_sql("CREATE TABLE temp (id INTEGER PRIMARY KEY, value TEXT)")
db.execute_sql("INSERT INTO temp VALUES (1, 'temporary')")

# Data is lost when db is closed

API Reference¶

Database¶

Method	Description
`open(path)`	Open or create a database at the given path
`open_in_memory()`	Create an in-memory database
`open_with_config(config)`	Open with custom configuration
`execute_sql(sql, params)`	Execute SQL query with parameters
`begin()`	Start a new transaction
`create_hnsw_index(...)`	Create HNSW index on vector column
`search_hnsw(table, query, k)`	Search for k nearest neighbors
`close()`	Close the database connection

Transaction¶

Method	Description
`execute_sql(sql, params)`	Execute SQL within transaction
`commit()`	Commit the transaction
`rollback()`	Rollback the transaction

Config¶

Option	Type	Default	Description
`path`	str	-	Database file path
`memory_limit`	int	256MB	Maximum memory usage
`wal_sync`	bool	True	Sync WAL on commit
`compression`	bool	True	Enable compression

Development Roadmap¶

Version	Phase	Features
v0.1.0	Phase 1	Database/Transaction basic API
v0.1.1	Phase 1	Vector/HNSW API
v0.1.2	Phase 1	NumPy integration, GIL release
v0.2.0	Phase 2	DataFrame API (via alopex-dataframe)
v0.3.0	Phase 3	Client API (Server connection)