Vpuna AI Search – A semantic search platform

Hacker News

Dear HN Community, I am a long time fan and first-time contributor. I just launched a developer focused semantic search platform and wanted to share it with the community. The idea is simple: upload structured or unstructured documents, select the fields you want to index and tag as metadata, and instantly get a clean search API you can use in your own app. Here is what it currently supports: - Manage your own tenants and projects - Upload .json and .txt files (support for .pdf, .docx, .xlsx, .yml, etc. coming soon) - Expose 3 APIs: search, upload document (embeddings), and delete document - Manage your own API keys - Uses CPU based sentence-transformers/all-MiniLM-L6-v2 for embeddings ( support for other local and online models are coming soon) LLM summarization and Model Context Protocol (MCP) support are on the roadmap Why I built it: In my consulting work, I kept seeing client wanting to move beyond basic keyword search and integrate semantic search with optional summarization. Most existing tools are either too expensive, too restrictive, or require custom layers (like custom Python servers for pre processing queries and embeddings). I wanted something API first, developer friendly, and easy to self host or use out of the box. This is the first release, and I would love your feedback. Would you use this? What is missing for your use case? Here is the README with all the links https://github.com/vpuna/vpuna-ai-search Thank you for your time.

Visit product View on Hacker NewsJun 20, 2025

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

73%73% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created

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

AppSumoStrong fit for a featured deal · Strong signals: platform, host, friendly · Missing: plus, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

23%23% 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

13%13% 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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