Quickstart

Installation

pip install zvec

Create a Collection

A collection stores your documents. Each document contains scalar fields and vector embeddings.

Define a schema and create a collection:

Add Documents

Insert documents with scalar fields and vector embeddings:

Optimize a Collection

Optimize a collection to improve performance:

collection.optimize()

Retrieve a Document by ID

Fetch a document directly by its id:

Search with Vectors

Basic Similarity Search

Use query() to find documents most similar to a given vector embedding:

Results are ranked by similarity score.

Filtered Similarity Search

Combine vector search with conditional filters:

Only matching documents are considered during search.

Inspect a Collection

View the collection's schema:

print(collection.schema)

View the collection's statistics:

print(collection.stats)

Delete a Document

Delete a document by its ID:

collection.delete(ids="book_1")

Delete documents by filter condition:

collection.delete_by_filter(filter="publish_year < 1900")

✨ You're all set to store, retrieve, and search vector data with Zvec!

💙 Thank you for your interest in Zvec! We hope you enjoy exploring what Zvec can do!