Ragpi – Open-Source AI Assistant for Chatting with Your Developer Docs

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I’ve been working on Ragpi, an open-source AI assistant that builds knowledge bases from docs, GitHub Issues and READMEs. It uses PostgreSQL with pgvector as a vector DB and leverages RAG to answer technical questions through an API. Ragpi also integrates with Discord and Slack, making it easy to interact with directly from those platforms. 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, Ollama, DeepSeek, or any OpenAI-compatible API - Provides a simple REST API for querying and managing sources - Integrates with Discord and Slack for easy interaction Built with: FastAPI, Celery and Postgres It’s still a work in progress, but I’d love some feedback! Repo: https://github.com/ragpi/ragpi Docs: https://docs.ragpi.io/

Visit product View on Hacker NewsMar 17, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, openai, open · Missing: mac, agents, macos

97%97% 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 · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

39%39% 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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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