Skip to content
Houssam
EnglishFrançais
← All work

Retrieval · pgvector

Document RAG Pipeline

A retrieval-augmented generation pipeline that answers questions from a document collection using vector search in PostgreSQL.

query → nearest chunks → answer
Illustration
ChunksVector spaceAnswer
Illustration of documents split into chunks, embedded as vectors, and the nearest chunks retrieved to ground an answer.

The problem

Language models don't know your documents. Answers need to be grounded in the right passages, retrieved quickly and reliably.

The approach

  1. 01Documents are split into chunks suited to retrieval.
  2. 02Each chunk is embedded and stored with pgvector in PostgreSQL.
  3. 03A query retrieves the most similar chunks.
  4. 04An LLM writes the answer from the retrieved context.

Architecture

How Document RAG Pipeline fits together.

Each step, in order. Select a node to see what it is responsible for.

  1. The source collection the system answers from.

Stack

  • PostgreSQL
  • pgvector
  • Embeddings
  • LLMs

Next project

Clinical Training Simulator

A clinical training application where students work through cases, guided by a state machine and evaluated with AI feedback and scoring.