EmbedDB Python SDK

Python bindings for EmbedDB — the embedded vector database.

Installation

pip install embeddb

Quick Start

import embeddb

# Open or create a database
db = embeddb.Database("data.embeddb")

# Create a collection
col = db.create_collection("docs", dimension=384, distance=embeddb.DistanceMetric.COSINE)

# Insert vectors
col.insert({"id": "doc1", "vector": [0.1] * 384, "metadata": {"title": "Hello"}})
col.insert({"id": "doc2", "vector": [0.2] * 384, "metadata": {"title": "World"}})

# Search
results = col.search(vector=[0.15] * 384, top_k=10)
for hit in results:
    print(f"{hit.id}: {hit.score:.4f}")

API

Database

• Database(path) — Open or create a database
• db.create_collection(name, dimension, distance) — Create a collection
• db.get_collection(name) — Get an existing collection
• db.close() — Close the database

Collection

• col.insert(doc) — Insert a document {"id": str, "vector": [float], "metadata": dict}
• col.search(vector, top_k=10) — Search for nearest neighbors

Distance Metrics

• DistanceMetric.COSINE — Cosine distance
• DistanceMetric.EUCLIDEAN — Euclidean (L2) distance
• DistanceMetric.DOT_PRODUCT — Dot product similarity