Who should use ragbits-document-search?

ragbits-document-search is an open-source project built for AI developers and data scientists.

What platforms does ragbits-document-search run on?

ragbits-document-search runs on the web and the command line.

Is ragbits-document-search still maintained?

The GitHub repository shows 24 commits in the last 90 days.

What projects are similar to ragbits-document-search?

Similar projects tracked by PulseGate include ragbits, ragdata, and zettabrain-rag.

Who makes ragbits-document-search?

ragbits-document-search is developed by deepsense.ai.

When did ragbits-document-search launch?

ragbits-document-search first shipped in 2024.

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ragbits-document-search

Name: ragbits-document-search
Author: deepsense.ai

github.comInfrastructure

Document Search module for Ragbits

Open SourceMITWebCLI

Visit github.com

1.6kstars

139forks

5features

2024since

Overview

5 features

In the RAG, search & retrieval space, ragbits-document-search takes a focused approach. It focuses on enabling efficient document search and retrieval for generative AI and RAG workflows. It is built as an open-source project for AI developers and data scientists. ragbits-document-search is open source under the MIT license. ragbits-document-search is available on the web and the command line.

Behind ragbits-document-search is deepsense.ai, and it first shipped in 2024. The project is developed in the open on GitHub with 1.6k stars and 24 commits in the last 90 days. Among its 5 catalogued features are document search, RAG integration, and python module.

Document search
RAG integration
Python module
Open source
LLM support

Built with & integrations

Runs on

BrowserCLI

Trust & compliance

License

MIT

Verified signals

HTTPSOpen Source GitHub · ★ 1.6kActive maintenance

Indexing history

What PulseGate has recorded for this listing

Indexed17 Jun · 11:59 UTC
Listing verified against its public source
Source: PulseGate · Open

Frequently asked questions about ragbits-document-search

What does ragbits-document-search do?: Ragbits-document-search focuses on enabling efficient document search and retrieval for generative AI and RAG workflows. It is catalogued under RAG, search & retrieval on PulseGate.
Who should use ragbits-document-search?: ragbits-document-search is an open-source project built for AI developers and data scientists.
Does ragbits-document-search have a free plan?: Yes — ragbits-document-search is open source under the MIT license and free to use.
What platforms does ragbits-document-search run on?: ragbits-document-search runs on the web and the command line.
Is ragbits-document-search still maintained?: The GitHub repository shows 24 commits in the last 90 days.
What projects are similar to ragbits-document-search?: Similar projects tracked by PulseGate include ragbits, ragdata, and zettabrain-rag.ragbits ragdata zettabrain-rag
Who makes ragbits-document-search?: ragbits-document-search is developed by deepsense.ai.
When did ragbits-document-search launch?: ragbits-document-search first shipped in 2024.

At a glance

Platforms: CLI
Languages: English
Open source: Yes · ★ 1.6k
License: MIT
Built for: AI developers and data scientists
Model: Open source
Solves: Enabling efficient document search and retrieval for generative AI and RAG workflows.

Registered as

GitHub: deepsense-ai/db-ally
GitHub: deepsense-ai/ragbits
PyPI: ragbits-document-search

Developer

deepsense.ai

Community-driven

GitHub

Open source

View on GitHub

Stars: 1,649
Forks: 139
Open issues: 50
Last commit: 18 May 2026
Commits 90d: 24
Contributors: 45
Authorship: Community-driven
Default branch: main
Latest release: v1.6.2 · 31 Mar 2026

Index record

Indexed: 17 Jun 2026
Lifecycle: Alive
Last seen: 17 Jun 2026

Identity audit (12)

Slug: ragbits-document-search-pypi-org
Lifecycle last checked: 8 Aug 2026
Verification state: Indexed for public listing
Listing state: Listed: yes
Index status: Included in index
Latest evidence snapshot: 17 Jun 2026
Timeline basis: Indexed-at chronology. This listing's first-seen date was written by the catalog backfill, not observed here, so it is not treated as a sighting.
Name from: Written by a language model from the project's public pages.
Category from: Assigned by a language model.
Summary from: Taken from the package registry entry.
Languages from: Detected by a language model from page content.
Canonical URL: https://github.com/deepsense-ai/db-ally

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