Introduction

RAG and agent pipelines increasingly rely on vector DBs for retrieval and LangGraph-style frameworks for flow control. Together they support complex, stateful AI apps without building everything from scratch.

Vector DB Landscape

Managed vs. self-hosted Pinecone, Weaviate, pgvector, and open-source options each fit different scale and latency needs. We summarize when to choose which and how to keep vendor lock-in low.

Embeddings and indexing Model choice, chunking, and indexing strategies directly affect recall and cost. We share patterns that work for docs, code, and mixed content.

LangGraph and Orchestration

Graphs over linear chains Cycles, conditionals, and human-in-the-loop nodes are easier to express in a graph. We walk through a small LangGraph example and when to adopt it vs. a simpler pipeline.

Production concerns Persistence, versioning, and monitoring for graph-based flows so your RAG and agents stay debuggable at scale.