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Decision brief
VectordB is a minimalist Python-based vector database that focuses on providing essential functionality in the domain of embedding similarity and vector search. It is open-source under the Apache 2.0 license.
Good fit when
- Use VectordB when you are working with simple to moderately complex tasks involving embedding similarities or neural searches where minimal setup and lightweight operation are favored.
- Suitable for developers preferring a streamlined solution without additional features, emphasizing quick setup and ease of integration into existing Python projects.
Avoid when
- Avoid using VectordB if your application requires advanced functionalities beyond basic embedding similarity and vector search, as it does not come with extensive feature sets.
- Not recommended for scenarios where heavy customization or a large number of integrations are required. Other platforms might offer more robust support in these cases.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (900d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install vectordbSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
jina-ai/vectordb is a dedicated Python-based vector database designed for seamless integration into applications requiring embedding similarity searches and neural search functionalities. It supports operations relevant to sentence embeddings and vectorized data storage and retrieval.
Capability facts
- Languages
- python
Source: github.language · Aug 22, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 22, 2026)
```python from docarray import BaseDocSource link
Tags
README
Install
pip install vectordb
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Getting started with vectordb locally
- Kick things off by defining a Document schema with the DocArray dataclass syntax:
from docarray import BaseDoc
from docarray.typing import NdArray
class ToyDoc(BaseDoc):
text: str = ''
embedding: NdArray[128]
- Opt for a pre-built database (like
InMemoryExactNNVectorDBorHNSWVectorDB), and apply the schema:
from docarray import DocList
import numpy as np
from vectordb import InMemoryExactNNVectorDB, HNSWVectorDB
---
## Getting started with `vectordb` as a service
`vectordb` is designed to be easily served as a service, supporting `gRPC`, `HTTP`, and `Websocket` communication protocols.
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## Hosting `vectordb` on Jina AI Cloud
You can seamlessly deploy your `vectordb` instance to Jina AI Cloud, which ensures access to your database from any location.
Start by embedding your database instance or class into a Python file:
```python
For agents
This page has a .md twin and JSON over the API.