
MongoDB-RAG
A semantic search pipeline that combines MongoDB Atlas Vector Search with VoyageAI embeddings and reranking for high-precision RAG retrieval.
User input is embedded using voyage-3.5, searched against a MongoDB Atlas vector index, and reranked with rerank-2.5 to surface the most relevant documents. Results are formatted via a template node for downstream LLM use.
Suitable for knowledge bases, document Q&A, and AI agent memory retrieval.