vowdb 0.1.5

A high-performance synchronous vector database for similarity search

VowDB 🔥

Blazing-fast vector database for text similarity. Powered by Faiss & Sentence Transformers.

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Install 💻

Grab it with pip:

pip install vowdb

Requires Python 3.8+, faiss-cpu, sentence-transformers, numpy, psutil.

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Setup 🚀

Kick it off:

from vowdb import VowDB
db = VowDB(model_name="all-MiniLM-L6-v2", file_path="vectors.faiss")

Ready in ~1.2s.

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Add Data 📝

Drop in texts:

# One
db.insert("Hello World", metadata={"greeting": "yes"})
# ~0.57s

# Many
texts = ["Hii", "I Am Rushikesh Sunil Kotkar!", "News article"]
metadatas = [{"greeting": "yes"}, {"category": "introduction"}, {"greeting": "no"}]
db.insert_batch(texts, metadatas)

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Search 🔍

Find stuff with = style filters. Check these dope examples.

1. AND Power

db.find("hello", top_k=3, filter_query="category=news && score>0.9 && greeting=yes")

Result:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}

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2. Text Vibe

db.find("hello", top_k=3)

Results:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}
text=Hii, dist=0.48, meta={category=greeting, score=0.6, greeting=yes}

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3. Exact Match

db.find("Hello World", top_k=3, filter_query="text=Hello World")

Result:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}

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4. Prefix Hunt

db.find("Hel", top_k=3, filter_query="text=Hel")*

Results:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}
text=Hii, dist=0.48, meta={category=greeting, score=0.6, greeting=yes}

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5. Category Snap

db.find("news", top_k=3, filter_query="category=news")

Results:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}
text=News article, dist=0.50, meta={category=news, score=0.7, greeting=no}

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6. OR Flex

db.find("news", top_k=3, filter_query="category=news || category=greeting")

Results:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}
text=Hii, dist=0.48, meta={category=greeting, score=0.6, greeting=yes}

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7. Multi-Filter

db.find("news", top_k=3, filter_query="category=news && score>0.7 && greeting=yes")

Result:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}

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8. Score Range

db.find("news", top_k=3, filter_query="score=0.6-0.8")

Results:
text=Hii, dist=0.48, meta={category=greeting, score=0.6, greeting=yes}
text=News article, dist=0.50, meta={category=news, score=0.7, greeting=no}

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9. Skip Some

db.find("news", top_k=3, filter_query="text!=Hii && category!=introduction")

Results:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}
text=News article, dist=0.50, meta={category=news, score=0.7, greeting=no}

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10. Name Drop

db.find("Rushikesh", top_k=3)

Result:
text=I Am Rushikesh Sunil Kotkar!, dist=0.20, meta={category=introduction, score=0.9}

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11. Text or Score

db.find("Hel", top_k=3, filter_query="text=Hel || score>=0.9")*

Results:
text=Hello World, dist=0.45, meta={category=news, score=0.95, greeting=yes}
text=I Am Rushikesh Sunil Kotkar!, dist=0.20, meta={category=introduction, score=0.9}

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Save/Load 💾

Keep or get data:

db.save()
db.load()

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Info 🌟

Version: Author: Rushikesh Sunil Kotkar
License: MIT
GitHub: https://github.com/rushikeshkotkar04/vowdb
PyPI: Fast text similarity with slick = queries.

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Contribute 🤝

Got ideas? Issues? PRs?
Hit up: https://github.com/rushikeshkotkar04/vowdb