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Vectordb

Vectordb is an open-source vector database that stores records and supports similarity search and retrieval for embedding-based applications, including RAG and LLM memory workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Vectordb — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Vectordb when an application needs a database component for embedding storage and retrieval, and you want to connect to a Docker-hosted backend or evaluate the experimental local Python bindings.

Who it is for

  • Developers building embedding-based search or retrieval systems
  • Teams adding a vector store to a RAG or LLM memory application
  • Operators who want to run a database backend in Docker

Documented uses

  • Store records and organize them in databases, tables and fields.
  • Search stored content using the documented natural-language query flow.
  • Use vector similarity, metadata filtering or dense-and-sparse hybrid search in retrieval workflows.
  • Connect an application through a Python, JavaScript or Ruby client, or the REST interface listed in the README.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. The README documents a Docker backend and an experimental Python-library route.

  • The documented quick start uses Docker for the backend and Python for client interaction.
  • The README lists vector search, metadata filtering, hybrid search, built-in embedding support and framework integrations.
  • The experimental Python-library approach requires building bindings locally and is presented separately from the Docker quick start.
  • The project core is written in C++.

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

Start the Docker backend and query text records with Python

Create a database table, insert sample text records and issue a natural-language query using the documented Docker and Python-client flow.

Before you start

  • Docker
  • Python and pip
  • The pyepsilla Python client
  • A host data directory mounted at /data

Steps

  1. Pull the epsilla/vectordb image and run it with port 8888 published and /data mounted as persistent storage.

  2. Install the client with pip install pyepsilla.

  3. Connect to localhost on port 8888, load and select a database, then create a table with an integer primary key and string document field.

  4. Insert text records and call query with a natural-language query_text and a result limit.

Expected outcome

A running Docker-backed database with inserted text records and a query response containing matching records.

Guide limitations

  • The sample demonstrates text-field querying; it does not provide a complete application integration or production deployment procedure.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
VectordbStore records and organize them in databases, tables and fields.Docker, Self-hosted, ServerThe project is open source under the Apache-2.0 license. The README documents a Docker backend and an experimental Python-library route.
MettisIndex text documents through an HTTP endpoint.Linux, Self-hosted, Server
Evidence and transparency

Sources & review

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