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.
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.
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.
Persistent project context documents that carry across chat sessions in GenMB. Users upload design specifications, API references, product requirements, or coding conventions. The AI references these files during every generation, ensuring consistent output that respects project-specific constraints.
Put these concepts into practice. Describe your app idea and let GenMB generate the code.
Try GenMB Free