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.