Vector Databases, RAG Systems & Enterprise Search

Vector Databases, RAG Systems & Enterprise Search

What is RAG?

Retrieval‑Augmented Generation (RAG) couples a large language model with a vector similarity search over your proprietary documents, enabling up‑to‑date, context‑aware responses without fine‑tuning the model.

Vector Database Options

• Managed Private Cloud – Fully managed, encrypted at rest, isolated VPC.
• PostgreSQL Extensions – Open‑source vector extensions hosted within existing database clusters.
• High-Performance Vector Stores – Supports hybrid (dense + sparse) vector search.

Architecture Blueprint

1. 1Ingestion Pipeline – Extract PDFs, code repos, and DB dumps; chunk into 500‑word passages.
2. 2Embedding – Use a sentence‑transformer model (e.g., BGE‑large) to generate 768‑dimensional vectors.
3. 3Storage – Insert vectors into a private vector store inside your VPC.
4. 4Query Flow – User query → embed → similarity search → top‑k results → concatenate with prompt → LLM inference.

Enterprise Security

• Network Isolation: Vector store and LLM containers reside in the same private subnet; no internet egress.
• Access Controls: IAM policies limit who can query or update the index.
• Audit Logging: Record each query, retrieved documents, and model response for compliance.

Benefits

• Privacy: No data leaves your environment.
• Performance: Sub‑second similarity search on billions of vectors.
• Scalability: Horizontal scaling of both the vector store and inference layer.