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Ragctl – document ingestion CLI for RAG (OCR, chunking, Qdrant)

Hacker News

Ragctl – document ingestion CLI for RAG (OCR, chunking, Qdrant)

Hi HN — sharing ragctl, an open-source CLI for the most failure-prone part of RAG pipelines: document ingestion, OCR, parsing/cleaning, and chunking. Vector DB setup is fairly standardized now, but getting high-quality, consistent text + metadata into it still takes a lot of brittle glue code. ragctl aims to make that “pre-vector” step repeatable: turn messy documents into retrieval-ready chunks in a few commands. Features • Multi-format input: PDF, DOCX, HTML, images • OCR for scanned/image-based docs • Semantic chunking (LangChain) • Batch runs with retries + error handling • Output: direct ingestion into Qdrant (for now) Looking for feedback • DX: is the CLI intuitive? • Performance / edge cases: weird PDFs, mixed layouts, tables • Roadmap: which connectors (S3, Slack, Notion) or vector stores should be next? Repo: https://github.com/datallmhub/ragstudio Happy to answer questions about the architecture and chunking approach.

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, code, open · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
52%52% 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 · Strong signals: intuitive · Missing: plus, platform, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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