satanson / vectordb

Epsilla is a high performance Vector Database Management System. Try out hosted Epsilla at https://cloud.epsilla.com/

A 10x faster, cheaper, and better vector database

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Epsilla is an open-source vector database. Our focus is on ensuring scalability, high performance, and cost-effectiveness of vector search. EpsillaDB bridges the gap between information retrieval and memory retention in Large Language Models.

The key features of Epsilla include:

• High performance and production-scale similarity search for embedding vectors.

• Full fledged database management system with familiar database, table, and field concepts. Vector is just another field type.

• Native Python support and REST API interface.

Epsilla's core is written in C++ and leverages the advanced academic parallel graph traversal techniques for vector indexing, achieving 10 times faster vector search than HNSW while maintaining precision levels of over 99.9%.

Quick Start with Epsilla Vector Database in Docker

1. Run Backend in Docker

docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 -v /tmp:/tmp epsilla/vectordb

2. Interact with Python Client

pip install pyepsilla

from pyepsilla import vectordb

client = vectordb.Client(host='localhost', port='8888')
client.load_db(db_name="MyDB", db_path="/tmp/epsilla")
client.use_db(db_name="MyDB")

client.create_table(
  table_name="MyTable",
  table_fields=[
    {"name": "ID", "dataType": "INT"},
    {"name": "Doc", "dataType": "STRING"},
    {"name": "Embedding", "dataType": "VECTOR_FLOAT", "dimensions": 4}
  ]
)

client.insert(
  table_name="MyTable",
  records=[
    {"ID": 1, "Doc": "Berlin", "Embedding": [0.05, 0.61, 0.76, 0.74]},
    {"ID": 2, "Doc": "London", "Embedding": [0.19, 0.81, 0.75, 0.11]},
    {"ID": 3, "Doc": "Moscow", "Embedding": [0.36, 0.55, 0.47, 0.94]},
    {"ID": 4, "Doc": "San Francisco", "Embedding": [0.18, 0.01, 0.85, 0.80]},
    {"ID": 5, "Doc": "Shanghai", "Embedding": [0.24, 0.18, 0.22, 0.44]}
  ]
)

status_code, response = client.query(
  table_name="MyTable",
  query_field="Embedding",
  query_vector=[0.35, 0.55, 0.47, 0.94],
  limit=2
)

Epsilla Cloud

Try our fully managed vector DBaaS at Epsilla Cloud

Quick Start with Epsilla Python Bindings Lib, without launching epsilla vector database

1. Build Epsilla Python Bindings lib package

cd engine
bash build.sh
ls -lh build/*.so

2. Run test with python bindings lib "epsilla.so" "libvectordb_dylib.so in the folder "build" built in the previous step

cd engine
export PYTHONPATH=./build/
export DB_PATH=/tmp/db33
python3 test/bindings/python/test.py