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. It is the Hippocampus for AI.
The key features of Epsilla include:
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High performance and production-scale similarity search for embedding vectors.
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Full fledged database management system with familiar database, table, and field concepts. Vector is just another field type.
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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%.
1. Run Backend in Docker
docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 -v /tmp:/tmp epsilla/vectordb2. Interact with Python Client
pip install pyepsillafrom 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
)