DataBridge - An open-source, modular, multi-modal RAG solution
For the past few weeks, I've been working on DataBridge, an open-source solution for easy data ingestion and querying. We support text, PDFs, images, and as of recently, we've added a video parser that can analyze and work well over frames and audio. We are also adding object tracking to improve video ingestion and context, and plan to do this for various data types. To get started, you can find the installation section in our docs at https://databridge.gitbook.io/databridge-docs/getting-starte.... There are a bunch of other useful functions and examples available there.…
What it does
In the maker’s words, at launch
For the past few weeks, I've been working on DataBridge, an open-source solution for easy data ingestion and querying. We support text, PDFs, images, and as of recently, we've added a video parser that can analyze and work well over frames and audio. We are also adding object tracking to improve video ingestion and context, and plan to do this for various data types. To get started, you can find the installation section in our docs at https://databridge.gitbook.io/databridge-docs/getting-starte.... There are a bunch of other useful functions and examples available there. Our docs aren't 100% caught up with all these new features, so if you're curious about the latest and greatest, the git repo is the source of truth. We're still shaping DataBridge (we have a skeleton and want to add the meaty parts) to best serve the LLM and RAG developer community, so I'd love your feedback about what features you're currently missing in RAG pipelines, whether specialized parsing (e.g., for medical docs, legal texts, or multimedia) is something you'd want, what your ideal RAG workflow looks like, and what some must-haves are. Thanks for checking out DataBridge, and feel free to open issues or PRs on GitHub if you have ideas, requests, or want to help shape the next set of features. If this is helpful, I'd really appreciate it if you could give it a star on GitHub! Looking forward to hearing your thoughts! Happy building!
Does the same job
all alternatives →- IBI built an offline open-source RAG system DataBridge2025 · github.com · ▲7
Hey HN! Over the past few weeks, I’ve been working on DataBridge, an open-source solution for data ingestion and querying across text, PDFs, images, and videos. In our latest update, we’ve added a fully local deployment option: - No internet required – Runs entirely offline. - Customizable Models – Supports any LLM and embedding model via Ollama (with options for any other private providers) - Extensibility – You can plug in your own models or tools easily. This local-first approach ensures better privacy, security, and flexibility, especially for teams dealing with sensitive data. You can…
- MMMulti-modal RAG with ColQwen in a single line of Code2025 · github.com · ▲5
Hi HN, we're Arnav and Adi, and we're building DataBridge - a multi-modal database built from the ground up with AI use cases in mind. We recently launched support for ColPali-style image embeddings and late-interaction retrieval. We've implemented a hamming distance version of retrieval which helps this approach scale significantly more when compared with the regular late-interaction similarity scoring. These embeddings provide a significantly better retrieval accuracy, with ColQwen achieving around an 89% average score on the ViDoRe benchmark, compared to around 67% for traditional parsing…
- BVBuild video AI agents with DataBridge2025 · youtube.com · ▲8
DataBridge is a multimodal database for building multi agent AI systems. It's open source and can run fully locally (with easy model swaps to run your favorite models). Github: https://github.com/databridge-org/databridge-core Docs: https://databridge.gitbook.io/databridge-docs
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
