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Barq Db

Barq Db is a Rust-based retrieval system for AI applications, combining dense vector search, BM25 text retrieval, asynchronous ingestion and segment-based storage behind a gRPC-first API.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Barq Db — 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 Barq Db when an AI application needs a retrieval backend that coordinates ingestion, indexing and hybrid vector-plus-keyword querying.

Who it is for

  • Developers building retrieval-augmented generation systems.
  • Teams adding semantic search or recommendation retrieval to AI applications.
  • Engineers who need a self-hosted retrieval service with asynchronous ingestion and hybrid search.

Documented uses

  • Retrieve relevant records using dense vectors and BM25 keyword search.
  • Ingest and index documents through queued, batched asynchronous pipelines.
  • Serve retrieval queries through gRPC, with REST maintained for compatibility.
  • Use Python, TypeScript, Go or Rust SDKs to integrate the system.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. The README documents Docker Compose and source-based startup.

  • The primary API is gRPC; REST is maintained for compatibility.
  • Memory controls include mmap-backed cold storage, configurable limits and eviction.
  • Single-node writes use NodeLocal durability; replicated multi-node writes use per-shard Raft quorum commit.
  • The current Raft engine is in-memory; durable term/log persistence and real inter-node transport are described as future work.
  • The project calls for continued real-world validation, particularly for large-scale and distributed use.

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 Barq Db server locally

Launch the server using one of the repository's documented methods.

Before you start

  • Use Docker Compose, or run from source with Cargo.

Steps

  1. From the repository, run docker-compose up -d to start with Docker.

  2. Alternatively, run the command below to start from source.

    Documented command
    cargo run --bin barq-server

Expected outcome

The README lists HTTP at localhost:8080 and gRPC at localhost:50051.

Guide limitations

  • These steps describe server startup, not a verified execution test.
  • The README notes that real-world validation is still needed, especially for large-scale and distributed scenarios.
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
Barq DbRetrieve relevant records using dense vectors and BM25 keyword search.Self-hosted, ServerThe repository is public and identifies the project as MIT-licensed. The README documents Docker Compose and source-based startup.
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
15 October 2026
Original website title
YASSERRMD/barq-db · Official homepage title · retained in its source language
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