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I Built an Open-Source RAG API for Docs, GitHub Issues and READMEs

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I Built an Open-Source RAG API for Docs, GitHub Issues and READMEs

I’ve been working on Ragpi, an open-source AI assistant that builds knowledge bases from docs, GitHub Issues, and READMEs. It uses Redis Stack as a vector DB and leverages RAG to answer technical questions through an API. Some things it does: - Creates knowledge bases from documentation websites, GitHub Issues, and READMEs - Uses hybrid search (semantic + keyword) for retrieval - Uses tool calling to dynamically search and retrieve relevant information during conversations - Works with OpenAI or Ollama - Provides a simple REST API for querying and managing sources Built with: FastAPI, Redis Stack, and Celery. It’s still a work in progress, but I’d love some feedback! Repo: https://github.com/ragpi/ragpi API Reference: https://docs.ragpi.io

Visit productView on Hacker NewsJan 23, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, open · Missing: mac, agents, macos
95%95% 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.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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