A specialized database that stores data as high-dimensional numerical vectors (embeddings) instead of rows and columns. Vector databases enable semantic similarity search — finding content by meaning rather than exact keywords. GenMB's Vector DB service uses pgvector (a PostgreSQL extension) with automatic text chunking and embedding generation, making it easy to add semantic search or RAG to generated apps.
An AI pattern that improves response quality by first retrieving relevant documents from a knowledge base, then passing them as context to the language model. GenMB's Vector DB service enables RAG in generated apps: upload documents, auto-chunk them, generate embeddings, and query semantically. Used in GenMB Assistants for knowledge base Q&A.
AI-powered chatbots built in GenMB that respond to messages on Telegram, Slack, and Email. Each assistant has a configurable personality, knowledge base, and set of scheduled tasks. Assistants are deployed as dedicated GKE pods and can hold multi-turn conversations, answer questions from uploaded documents, and trigger actions on a schedule. Available on Business plan.
Building both the frontend (user interface) and backend (server, database, APIs, authentication) of a web application. GenMB generates full-stack applications including UI, database schemas, CRUD operations, authentication flows, and API endpoints from a single prompt.
Put these concepts into practice. Describe your app idea and let GenMB generate the code.
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