Definition
Semantic Search
Semantic search finds results by meaning rather than exact keywords, using vector embeddings that place similar concepts near each other in mathematical space. A query for 'login broken' can surface an issue titled 'users can't authenticate' even with no shared words. It powers more relevant search and is the retrieval layer behind many AI features.
Key takeaways
- Semantic search finds results by meaning rather than exact keywords, using vector embeddings that place similar concepts near each other.
- A query for 'login broken' can surface an issue titled 'users can't authenticate' even with no shared words.
- Every searchable item is embedded once into a vector index; at query time the search text is embedded and the nearest vectors are returned.
- It is also the retrieval half of retrieval-augmented generation (RAG) — the layer that grounds an AI answer in relevant context before it responds.
Traditional keyword search matches the literal words you type. It fails when people describe the same idea differently — 'sign-in fails' versus 'authentication error.' Semantic search solves this by converting text into embeddings: high-dimensional vectors where distance reflects meaning. Two phrases about the same concept land close together, so a query retrieves what it means, not just what it spells.
Under the hood, every searchable item is embedded once and stored in a vector index. At query time the search text is embedded the same way, and the index returns the nearest vectors. Specialized index structures make this fast even across millions of items, so meaning-based retrieval stays interactive rather than scanning everything.
Semantic search is also the retrieval half of retrieval-augmented generation: before an AI answers, it semantically pulls the most relevant context to ground its response. The same machinery that makes human search smarter makes AI answers accurate.
Planoda indexes issues and documents as vector embeddings with HNSW indexes in PostgreSQL, so search and AI features retrieve by meaning, fast and tenant-scoped.
Related terms
- MCP ServerAn MCP server implements the Model Context Protocol, an open standard that lets AI assistants connect to external tools and data through a uniform interface. Rather than building a bespoke integration per assistant, a tool exposes one MCP server describing its available actions and resources, and any MCP-capable AI can discover and use them safely.
- Row-Level Security (RLS)Row-level security (RLS) is a database feature that restricts which rows a query can read or modify based on the current user or context. Instead of relying solely on application code to filter data, the database itself enforces access policies on every query — a strong defense for multi-tenant systems where one workspace's data must never leak to another.
- WebhookA webhook is an automated HTTP request a system sends to a URL you provide whenever a specified event occurs — an issue created, a status changed, a comment added. Instead of repeatedly polling an API for changes, your service receives a real-time push. Webhooks are the backbone of integrations, letting tools react to each other instantly.
- Issue TrackerAn issue tracker is the system of record for a team's work — every bug, feature, and task captured as a structured issue with a state, assignee, priority, and history. It replaces scattered emails and spreadsheets with one searchable, accountable source of truth that the whole team plans, executes, and reports against.