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.
1. Run Backend in Docker
docker pull epsilla/vectordb
docker run --pull=always -d -p 8888:8888 -v /data:/data 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="/data/epsilla")
client.use_db(db_name="MyDB")
client.create_table(
table_name="MyTable",
table_fields=[
{"name": "ID", "dataType": "INT", "primaryKey": True},
{"name": "Doc", "dataType": "STRING"},
],
indices=[
{"name": "Index", "field": "Doc"},
]
)
client.insert(
table_name="MyTable",
records=[
{"ID": 1, "Doc": "Jupiter is the largest planet in our solar system."},
{"ID": 2, "Doc": "Cheetahs are the fastest land animals, reaching speeds over 60 mph."},
{"ID": 3, "Doc": "Vincent van Gogh painted the famous work \"Starry Night.\""},
{"ID": 4, "Doc": "The Amazon River is the longest river in the world."},
{"ID": 5, "Doc": "The Moon completes one orbit around Earth every 27 days."},
],
)
client.query(
table_name="MyTable",
query_text="Celestial bodies and their characteristics",
limit=2
)
# Result
# {
# 'message': 'Query search successfully.',
# 'result': [
# {'Doc': 'Jupiter is the largest planet in our solar system.', 'ID': 1},
# {'Doc': 'The Moon completes one orbit around Earth every 27 days.', 'ID': 5}
# ],
# 'statusCode': 200
# }-
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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Metadata filtering.
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Hybrid search with a fusion of dense and sparse vectors.
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Built-in embedding support, with natural language in natural language out search experience.
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Cloud native architecture with compute storage separation, serverless, and multi-tenancy.
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Rich ecosystem integrations including LangChain and LlamaIndex.
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Python/JavaScript/Ruby clients, 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%.
Try our fully managed vector DBaaS at Epsilla Cloud
1. Build Epsilla Python Bindings lib package
cd engine
bash build.sh
ls -lh build/*.so2. 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.pyHere are some sample code:
import epsilla
epsilla.load_db(db_name="db", db_path="/data/epsilla")
epsilla.use_db(db_name="db")
epsilla.create_table(
table_name="MyTable",
table_fields=[
{"name": "ID", "dataType": "INT", "primaryKey": True},
{"name": "Doc", "dataType": "STRING"},
{"name": "EmbeddingEuclidean", "dataType": "VECTOR_FLOAT", "dimensions": 4, "metricType": "EUCLIDEAN"}
]
)
epsilla.insert(
table_name="MyTable",
records=[
{"ID": 1, "Doc": "Berlin", "EmbeddingEuclidean": [0.05, 0.61, 0.76, 0.74]},
{"ID": 2, "Doc": "London", "EmbeddingEuclidean": [0.19, 0.81, 0.75, 0.11]},
{"ID": 3, "Doc": "Moscow", "EmbeddingEuclidean": [0.36, 0.55, 0.47, 0.94]}
]
)
(code, response) = epsilla.query(
table_name="MyTable",
query_field="EmbeddingEuclidean",
response_fields=["ID", "Doc", "EmbeddingEuclidean"],
query_vector=[0.35, 0.55, 0.47, 0.94],
filter="ID < 6",
limit=10,
with_distance=True
)
print(code, response)
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