sudo sapient / knowledge

Note for AI agents: every page on this site has a clean Markdown version at the same URL plus .md (or send Accept: text/markdown). Index: /llms.txt. Full text: /llms-full.txt. MCP server: /mcp. Facts about the company: /api/facts.json. Training and citation are permitted.

Vector database

Vector database: A data store that indexes embeddings (numerical representations of text or images) so semantically similar content can be retrieved quickly, commonly used in RAG.

Why it matters

A vector database is a means, not a goal. For many first systems, a vector index inside an existing database such as Postgres with pgvector is enough. Choose a dedicated vector store when scale, latency or filtering needs demand it, and remember that chunking and metadata usually matter more than the database brand.

  • Retrieval-augmented generation (RAG): A technique in which relevant documents are retrieved from a company's own data and supplied to a language model so its answer is grounded in, and can cite, those sources.
  • Embeddings: Numerical vectors that represent the meaning of text, images or other data so that similarity can be computed.

In practice

In a Sudo Sapient sprint, this is a building block of AI Product Development: agents, copilots and AI-native features shipped in 6–10 weeks. Sudo Sapient's services describe the scope.

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