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Vectordb — kagisearch

VectorDB is a lightweight Python library for local text retrieval that chunks content, uses embeddings, and searches vectors to return relevant passages.

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Independent OpenViST project profile, based on linked sources.

Vectordb — kagisearch — OpenViST project profile in RAG & Knowledge Systems.
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Understand the project

Practical overview

When it fits

Use VectorDB when a Python application needs an embedding-based retrieval component for text it can provide to the library, especially when the documented local workflow and adjustable chunking or embedding options fit the task.

Who it is for

  • Python developers adding semantic search to a document collection.
  • AI application builders who need a local text-retrieval component.
  • Developers experimenting with retrieval-based filtering of feeds or other text.

Documented uses

  • Save text and optional metadata in a searchable memory.
  • Retrieve the most relevant text chunks for a query.
  • Tune chunking, embedding-model selection, result count and multi-query result handling.
Before you choose

Requirements & limits

  • Installation uses the pip package name vectordb2, while examples import Memory from vectordb.
  • The default chunking strategy is sliding window; paragraph chunking is also documented.
  • Embedding choices include named presets and a custom Hugging Face model.
  • A memory-file path can be used for persistence; the quick example otherwise operates in memory.
  • The repository specifies an MIT license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install VectorDB and retrieve matching text

Set up the documented in-memory retrieval example with a small text collection.

Before you start

  • Python environment with pip

Steps

  1. Install the package with the command below.

    Documented command
    pip install vectordb2

  2. Import Memory from vectordb and create a Memory instance.

  3. Save text strings, optionally alongside metadata such as source URLs.

  4. Call memory.search(query, top_n=1) to retrieve the closest matching chunk.

Expected outcome

The example returns matching text chunks with associated metadata and a vector distance.

Guide limitations

  • The documented quick example loads data into memory; persistence requires providing a memory-file path.
  • The example is repository guidance, not a verified execution result.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
kagisearch/vectordb · Official homepage title · retained in its source language
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