Knowledge (RAG)

Knowledge bases hold documents that are chunked and embedded so agents can retrieve grounded context.

How it works

Create a knowledge base.
Add documents (title + text). Each document is chunked (~1000 chars) and every chunk is embedded.
Retrieval runs a cosine-similarity search over the chunks with pgvector.

Embeddings are produced by a self-hosted embedder (bge-small-en-v1.5, 384-dim), so no external embeddings key is required.

From the CLI

cruqai knowledge create "Product docs"
cruqai knowledge add-doc "Product docs" --title "Policies" --file policies.txt
cruqai knowledge search "how long do refunds take" --k 3

From an agent

Add a tool with the built-in key knowledge_search and reference it from an agent node. At run time the agent calls knowledge_search(query), which embeds the query and returns the top matching chunks for the model to answer from.

API

POST /v1/knowledge - create a base
POST /v1/knowledge/{kb}/documents - ingest a document
POST /v1/knowledge/search - search chunks

See the API overview.

Getting Started

Concepts

Reference

Knowledge (RAG)

How it works

From the CLI

From an agent

API

​How it works

​From the CLI

​From an agent

​API

How it works

From the CLI

From an agent

API