Engineering blueprint · RAG · Embeddings · Vector search

RAG Architecture for Company Knowledge

Large language models are only useful for business questions when they answer from your data. Retrieval-augmented generation (RAG) grounds every answer in your own documents and shows the sources.

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RAG Architecture for Company Knowledge diagram

What the Architecture Covers

Ingestion
PDFs, docs and web pages parsed, cleaned and split into overlapping chunks with metadata.
Embeddings and vector store
Chunks embedded and stored in PostgreSQL with pgvector, filterable by source, date or team.
Retrieval
Hybrid keyword and vector search returns the top passages, then a re-ranking step keeps the most relevant.
Grounded generation
The LLM answers only from retrieved context and cites each source, so answers can be checked.
Evaluation
Test questions measure retrieval quality and answer accuracy before and after every change.
Cost and guardrails
Caching, token budgets and fallbacks keep responses fast, affordable and safe.

Tools & Stack

Python
FastAPI
OpenAI
Claude
Embeddings
pgvector
PostgreSQL

In Practice

This blueprint is part of my AI workflow automation & integration service. Tell me about your system on Upwork and I'll propose the right setup, timeline and cost.