Supercharge Your Readwise Library with Local, Semantic Search

Hacker News

Announcing readwise-vector-db: Supercharge Your Readwise Library with Local, Semantic Search Hey everyone! After months of tinkering, I’m excited to share readwise-vector-db—an open source project that transforms your Readwise highlights into a blazing-fast, self-hosted semantic search engine. Why? I wanted a way to instantly search my entire reading history—books, articles, PDFs, everything—using natural language, not just keywords. Now, with nightly syncs, vector search API, Prometheus metrics, and a streaming MCP server for LLM clients, it’s possible. Key features:• Full-text, semantic search of your Readwise library (local, private, fast)• Nightly sync with Readwise—no manual exports• REST API for easy integration with your tools and workflows• Prometheus metrics for monitoring• Streaming MCP server for LLM-powered apps It’s Python-based, open source (MIT), and easy to run with Docker or locally. If you want to own your reading data, build custom workflows, or experiment with local LLMs, give it a try. Would love feedback, questions, and ideas for next steps!

Visit product View on Hacker NewsJun 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, apps, dock · Missing: mac, agents, macos

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps, month, way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

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

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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