Introduction
Vpuna AI Search is a developer-first platform that enables you to embed, index, and perform semantic search over structured and unstructured data using vector embeddings, with optional summarization powered by large language models (LLMs).
It provides a complete toolkit for teams building:
- Internal knowledge tools
- Custom search experiences
- LLM-enabled applications
- Content discovery layers
- Semantic product catalogs
The platform is built to support real-world use cases where data exists in mixed formats—like nested JSON, YAML, CSV, PDF, or DOCX—and where developers need both flexibility and control.
What Makes Vpuna Unique?
- Support for Structured & Unstructured Files: Upload files, extract and map fields, and control indexing at the field level.
- Multi-Tenant Developer Portal: Each organization can manage multiple projects and configure custom schemas, embedding models, and metadata.
- Open-Core Architecture: The core platform is self-hostable and designed for extensibility.
- API-First Design: Exposes production-ready REST APIs so you can embed search and summarization directly into your own apps.
- LLM Integration Out of the Box (Coming Soon): Built-in support for semantic search and summarization using OpenAI, Claude, or your own local models.
Core Capabilities
- Upload documents via developer console or via API
- Index and embed documents using configurable metadata fields
- Query via semantic search
- Manage tenants, projects, and usage from a dedicated developer console
- Generate LLM-based answers from indexed results (Coming soonS)
Who Is This For?
Vpuna AI Search is built for:
- Developers building LLM-powered applications
- Platform teams managing internal tools or documentation search
- Product owners launching generative AI features with minimal overhead
- Data engineers who need to unify messy document inputs under a common retrieval layer
If you're looking for a self-serve, programmable stack to embed AI into your document search pipeline, Vpuna AI Search is built for you.
Continue to the Architecture section to understand how the platform components interact.