2 posts tagged with "rag"

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLMs in factual, up-to-date data. Instead of fine-tuning a model on your corpus (expensive, slow, stale within weeks), you retrieve relevant context at query time and feed it to the LLM alongside the user's question.

In 2026, RAG is no longer experimental. It powers customer support bots, internal knowledge search, legal document analysis, and code assistants at thousands of companies. The architecture has stabilized around a common pattern: ingest documents, generate embeddings, store vectors, retrieve at query time. What varies is how well you operate the infrastructure underneath.

This post walks through building a production RAG pipeline on FoundryDB using PostgreSQL with pgvector, Kafka for document ingestion, and Valkey for result caching.

Every RAG system needs the same boring middle layer: watch a table for changes, call an embedding API, write vectors back, handle retries, manage batches, build indexes, schedule cron jobs, and pray nothing drifts out of sync at 3 AM. FoundryDB's managed embedding pipelines eliminate that entire layer. You configure a pipeline, and your PostgreSQL data gets auto-vectorized with an HNSW index, ready for similarity search.

No ETL scripts. No cron jobs. No model orchestration code.