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rag

Knowledge bases you host yourself: ingest documents, then retrieve relevant passages at query time (hybrid BM25 + semantic). Reach for this when you want control over the vector backend and chunking.

On digitorn.ai (managed)? You don't need this module.

To give an agent a knowledge base on the hosted platform, open the Knowledge manager in Studio — upload files or add a website, attach it to your agent, and it searches automatically through the built-in knowledge.search tool. No YAML, no vector store to run, nothing to grant. This page documents the self-hosted rag module — reach for it only when you run your own daemon and vector backend (Qdrant/pgvector/Elasticsearch/…).

Exact tools and parameters

catalog.describe_module rag returns the real tools and every parameter, from the live registry - the authority for what actually runs.

Tools, at a glance: create_knowledge_base, list_knowledge_bases, knowledge_base_stats, delete_knowledge_base, ingest, ingest_file, ingest_directory, query, reindex, index_stats, migrate_embeddings.

Gotchas​

  • query retrieves; it does not write the answer. The agent's own model writes the answer from what query returned.
  • Database/web sources are configured on the module and driven by the indexer as background indexing - that's config, not agent tool calls. auto_index.schedule is a cron string handled by the indexer directly.

A working app​

Compiles as-is. The brain uses a Digitorn gateway model (the default - no API key of your own needed); see the note below to use your own key or a local model instead.

yaml
app:
app_id: kb_assistant
name: Knowledge Assistant
runtime:
entry_agent: main
agents:
- id: main
system_prompt: |
Answer from the knowledge bases: query for relevant passages, then write
the answer from what you retrieved. Ingest new documents when asked.
brain:
provider: openai
backend: openai_compat
model: mimo-v2.5-free # a model the Digitorn gateway serves
config:
api_key: placeholder # ignored in gateway mode
tools:
modules:
rag: {}
capabilities:
grant:
- module: rag
tools: [query, create_knowledge_base, ingest, ingest_file, list_knowledge_bases]
  • Longer YAML / concepts: RAG