---
title: "Retrieval Integrations"
description: "Vector backends (pgvector, Pinecone, Weaviate, Qdrant). RAG API: semantic search, answer generation."
canonical: https://www.norcaster.com/docs/retrieval
---
<!-- Generated at build time from the rendered page by portal/scripts/generate-llms.mjs. The page is the source of truth; edit it, not this file. -->

# Retrieval Integrations

Vector stores for semantic search and RAG. Configure pgvector (built-in), Pinecone, Weaviate, or Qdrant. Query knowledge bases for chunks or full answers with citations.

## Vector backends

**pgvector** — Default, built into Postgres. No extra setup.

**Pinecone** — Cloud. Create index, add API key to config.

**Weaviate** — Local (Docker) or cloud.

**Qdrant** — Local (Docker) or cloud.

Create vector indexes in **Dashboard → Knowledge → Vector indexes**. Assign to a knowledge base.

## RAG API

**POST /retrieval/query** — Semantic search. Returns chunks only.

Body: `knowledge_base_id`, `query`, `top_k`

**POST /rag/run** — Full RAG. Retrieve + generate answer with citations.

Body: `knowledge_base_id`, `provider_model_id` (chat model), `question`

## Try RAG

**Dashboard → Knowledge** — Add a knowledge base, add sources, upload documents, run ingestion. Then use the Try RAG modal to ask questions and see answers with citations.

[Deploy a full RAG model step by step →](https://www.norcaster.com/docs/rag-deployment)