Overview

Introduction

A knowledge base is the foundation of RAG retrieval. You can add product manuals, internal policies, business terminology, FAQs, and other documents to a knowledge base. NocoBase converts these documents into searchable segments and vector data. When an AI employee answers a question, it can retrieve related content from the knowledge base first, then send that content to the model as context.

For most scenarios, a Local knowledge base is enough. Only consider Readonly or External knowledge bases when documents and vector data are already maintained by an external system.

Open knowledge base management

Open the AI employees plugin configuration page and click the Knowledge base tab to view the knowledge base list. Each card shows the knowledge base name, document count, character count, number of AI employee references, and enabled status.

Click a knowledge base card to open its detail page:

Documents is used to upload documents, run vectorization, and open segment management
Hit tests is used to test which segments can be matched by a query
Settings is used to adjust basic information, vector store, and default segmentation parameters

Create a knowledge base

Click Add new in the upper-right corner to create a knowledge base. The menu shows three types: Local, Readonly, and External.

The three knowledge base types have different capability boundaries:

Type	Use case
Local	Documents, segment files, and vector data are managed by NocoBase. It supports uploading documents in the UI, and also supports creating, updating, deleting, and retrieving knowledge base documents through workflow nodes
Readonly	Documents and vector data are maintained by an external system. NocoBase cannot maintain this data in the UI or workflows, and only uses the knowledge base as a RAG retrieval source. Currently, PGVector is the only supported vector database
External	Documents, vector data, and retrieval logic are all handled by an external system. NocoBase cannot directly maintain documents or vector data. Developers need to provide a plugin and implement the retrieval logic in that plugin, such as connecting to a vector database not yet supported by NocoBase or calling a third-party retrieval API. For development details, see External Knowledge Base plugin

Local knowledge bases are recommended by default. Only consider Readonly or External when documents and vector data are already maintained outside NocoBase and NocoBase only needs to read retrieval results.

A Local knowledge base usually needs the following information:

Key: unique identifier of the knowledge base. It cannot be changed after creation
Name: knowledge base name
File storage: where original documents and segment files are stored
Vector store: vector store used to generate and retrieve vectors
Description: knowledge base description
Split document, Chunk size, and Chunk overlap: default segmentation parameters used after document upload
Enabled: whether the knowledge base is enabled

Prerequisites

Before creating a Local knowledge base, prepare two dependencies: file storage for original documents and segment files, see File storage; and vector storage for generating and retrieving vectors, see Vector database and Vector store.

Next steps

I want to...	Where to go
Upload files, view vectorization status, and rerun vectorization	Document management
View, edit, disable, or delete document segments	Segment management
Test which segments a question can match	Hit tests
Modify knowledge base information and default segmentation settings	Settings

At this point, the knowledge base is ready to be connected to RAG. You can enable knowledge base retrieval in AI employee settings. See RAG retrieval for details.

#Overview

#Introduction

#Open knowledge base management

#Create a knowledge base

#Next steps

Overview

Introduction

Open knowledge base management

Create a knowledge base

Next steps