sqlite-vec-client

A lightweight Python client around sqlite-vec that lets you store texts, JSON metadata, and float32 embeddings in SQLite and run fast similarity search.

Features

• Simple API: One class, SQLiteVecClient, for CRUD and search.
• Vector index via sqlite-vec: Uses a vec0 virtual table under the hood.
• Automatic sync: Triggers keep the base table and vector index aligned.
• Typed results: Clear return types for results and searches.
• Filtering helpers: Fetch by rowid, text, or metadata.
• Pagination & sorting: List records with limit, offset, and order.
• Bulk operations: Efficient update_many(), get_all() generator, and transaction support.

Requirements

• Python 3.9+
• SQLite version 3.41 or higher
• The sqlite-vec extension

Installation

Install from PyPI:

pip install sqlite-vec-client

Or:

git clone https://github.com/atasoglu/sqlite-vec-client
cd sqlite-vec-client
pip install .

Quick start

from sqlite_vec_client import SQLiteVecClient

# Initialize a client bound to a specific table in a database file
client = SQLiteVecClient(table="documents", db_path="./example.db")

# Create schema (base table + vec index); choose embedding dimension and distance
client.create_table(dim=384, distance="cosine")

# Add some texts with embeddings (one embedding per text)
texts = ["hello world", "lorem ipsum", "vector databases"]
embs = [
    [0.1, 0.2, 0.3, *([0.0] * 381)],
    [0.05, 0.04, 0.03, *([0.0] * 381)],
    [0.2, 0.1, 0.05, *([0.0] * 381)],
]
rowids = client.add(texts=texts, embeddings=embs)

# Similarity search returns (rowid, text, distance)
query_emb = [0.1, 0.2, 0.3, *([0.0] * 381)]
hits = client.similarity_search(embedding=query_emb, top_k=3)

# Fetch full rows (rowid, text, metadata, embedding)
rows = client.get_many(rowids)

client.close()

Export/Import

Export and import data in JSON or CSV formats for backups, migrations, and data sharing:

# Export to JSON (includes embeddings)
count = client.export_to_json("backup.jsonl")

# Export to CSV (human-readable, optional embeddings)
count = client.export_to_csv("data.csv", include_embeddings=False)

# Export filtered data
count = client.export_to_json(
    "important.jsonl",
    filters={"priority": "high"}
)

# Import from JSON
count = client.import_from_json("backup.jsonl")

# Import from CSV
count = client.import_from_csv("data.csv")

# Backup and restore workflow
client.export_to_json("backup.jsonl")
# ... data loss ...
client.import_from_json("backup.jsonl")

See examples/export_import_example.py for more examples.

Metadata Filtering

Efficiently filter records by metadata fields using SQLite's JSON functions:

# Filter by single field
results = client.filter_by_metadata({"category": "python"})

# Filter by multiple fields
results = client.filter_by_metadata({"category": "python", "year": 2024})

# Nested JSON paths
results = client.filter_by_metadata({"author.name": "Alice"})

# Count matching records
count = client.count_by_metadata({"category": "python"})

# Combined similarity search + metadata filtering
hits = client.similarity_search_with_filter(
    embedding=query_vector,
    filters={"category": "python"},
    top_k=5
)

# Pagination
results = client.filter_by_metadata(
    {"category": "python"},
    limit=10,
    offset=0
)

See examples/metadata_filtering.py and examples/advanced_metadata_queries.py for more examples.

Bulk Operations

The client provides optimized methods for bulk operations:

# Bulk update multiple records
updates = [
    (rowid1, "new text", {"key": "value"}, None),
    (rowid2, None, {"updated": True}, new_embedding),
]
count = client.update_many(updates)

# Memory-efficient iteration over all records
for rowid, text, metadata, embedding in client.get_all(batch_size=100):
    process(text)

# Atomic transactions
with client.transaction():
    client.add(texts, embeddings)
    client.update_many(updates)
    client.delete_many(old_ids)

See examples/batch_operations.py for more examples.

How it works

SQLiteVecClient stores data in {table} and mirrors embeddings in {table}_vec (a vec0 virtual table). SQLite triggers keep both in sync when rows are inserted, updated, or deleted. Embeddings are serialized as packed float32 bytes for compact storage.

Logging

The library includes built-in logging support using Python's standard logging module. By default, logging is set to WARNING level.

Configure log level via environment variable:

export SQLITE_VEC_CLIENT_LOG_LEVEL=DEBUG  # Linux/macOS
set SQLITE_VEC_CLIENT_LOG_LEVEL=DEBUG     # Windows

Or programmatically:

import logging
from sqlite_vec_client import get_logger

logger = get_logger()
logger.setLevel(logging.DEBUG)  # DEBUG, INFO, WARNING, ERROR, CRITICAL

Available log levels:

• DEBUG: Detailed information for diagnosing issues
• INFO: General informational messages about operations
• WARNING: Warning messages (default)
• ERROR: Error messages
• CRITICAL: Critical error messages

See examples/logging_example.py for a complete example.

Testing

The project has comprehensive test coverage (91%+) with 75 tests covering:

• Unit tests for utilities and validation
• Integration tests for all client operations
• Security tests for SQL injection prevention
• Edge cases and error handling

See TESTING.md for detailed testing documentation.

Development

Setup

Install development dependencies:

pip install -r requirements-dev.txt
pre-commit install

Testing

The project uses pytest with comprehensive test coverage (89%+).

Run all tests:

pytest

Run with verbose output:

pytest -v

Run specific test categories:

pytest -m unit          # Unit tests only
pytest -m integration   # Integration tests only

Run with coverage report:

pytest --cov=sqlite_vec_client --cov-report=html

Run specific test file:

pytest tests/test_client.py
pytest tests/test_validation.py
pytest tests/test_security.py
pytest tests/test_utils.py

Code Quality

Format code:

ruff format .

Lint code:

ruff check .

Type checking:

mypy sqlite_vec_client/

Run all quality checks:

ruff check . && ruff format . && mypy sqlite_vec_client/ && pytest

Benchmarks

Run benchmarks:

python -m benchmarks

Configure benchmarks: Edit benchmarks/config.yaml to customize:

• Dataset sizes (default: 100, 1000, 10000, 50000)
• Embedding dimension (default: 384)
• Distance metric (default: cosine)
• Database modes (file, memory)
• Similarity search iterations and top-k values

Documentation

• CONTRIBUTING.md - Contribution guidelines
• CHANGELOG.md - Version history
• TESTING.md - Testing documentation
• Examples - Usage examples
  • basic_usage.py - Basic CRUD operations
  • metadata_filtering.py - Metadata filtering and queries
  • advanced_metadata_queries.py - Advanced metadata filtering with nested paths
  • export_import_example.py - Export/import data in JSON and CSV formats
  • transaction_example.py - Transaction management with all CRUD operations
  • batch_operations.py - Bulk operations
  • logging_example.py - Logging configuration
• Benchmarks - Performance benchmarks

Contributing

Contributions are very welcome! See CONTRIBUTING.md for guidelines.

License

MIT - See LICENSE for details.