bx-ai-rag

Ground AI answers with your own documents using retrieval-augmented generation to load, chunk, embed, and query a document corpus. This fetches relevant context from a vector memory to deliver grounded results without model retraining.

Building a knowledge base assistant involves ingesting documents via aiDocuments, generating embeddings with aiEmbed, and storing them in a vector memory. You then wire an agent to fetch relevant context and answer user questions from your knowledge base.

Document chunking and embedding for retrieval-augmented generation works by loading files into scalable context chunks with aiDocuments, converting them to vector embeddings with aiEmbed, and storing them in a vector memory provider for fast retrieval.

You can use vector memory providers to store embeddings for a customer support bot. The system stores embeddings generated by aiEmbed in a vector DB, allowing an agent workflow to quickly retrieve relevant context and answer support questions.

You need a vector DB or vector memory provider to keep AI answers current without model retraining. It stores your document embeddings, allowing the agent to retrieve updated context dynamically rather than relying on static model weights.

A RAG agent returns irrelevant context when document chunking is poorly configured or embeddings are mismatched. Ensure aiDocuments properly chunks files for scalable context and aiEmbed generates accurate vectors stored in the vector memory.