A

A tool to create and evaluate document processing pipelines for RAG

Hacker News

A tool to create and evaluate document processing pipelines for RAG

Hey HN, I built [ragbandit]( https://ragbandit.com ), a tool to help you evaluate different document processing pipelines for the retrieval stage of your RAG systems. I was a bit overwhelmed with the different ways that you can process documents to create embeddings for RAG, so I wanted to create a tool to experiment with different OCR models, refining the OCR results, different chunking methods, and different embedding models. You can: - search processed documents in the playground - evaluate the retrieval results using an llm-as-judge (not perfect, but can be a useful signal) - compare different datasets (using aggregate metrics or by side by side comparison in the playground) You can also manually inspect the results of each query, and of each intermediate document processing result. To get a better idea, check out one of the use cases: https://ragbandit.com/use-cases/optimizing-insurance-documen... To be completely fair, I haven't added that many options for the different stages of the document processing pipeline! There are tons of features that I'd like to add, but I've already spent quite a bit of time on this, so I'd really appreciate it if you could let me know if this is something that could be useful for you/you find interesting. Would you use something like this? Tech stack: Postgres (with pgvector), fastapi, [ragbandit-core]( https://github.com/MartimChaves/ragbandit-core ) (the document processing core is open source), typescript with react, celery for background tasks (and redis as the broker). It's currently a credits-based subscription with optional top-ups. You can get 1000 credits to try it out (I ask for card info for these 1000 credits as a spam filter). Thanks, Martim

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, tasks · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, open source, ide · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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