Knowledge Management Permissions (Query Metadata) Overview

Feature Description

When implementing an AI dialogue system, detailed control over the available scope of "Knowledge Base / AI Assistant / Chat Platform" is usually needed due to different user permissions and usage requirements.

In MaiAgent, you can use Query Metadata attached to different conversation/identity levels to determine "what content this person/conversation can reference".

What is Query Metadata?

Query Metadata is a set of dynamic conditions that limit the query scope, specifying what "Knowledge Base, FAQ, documents matching tag conditions" and other data content users can query under a certain chat platform.

It doesn't replace roles or contacts, but rather makes these identities "work conditionally", implementing conversation-level least privilege control.

Roles/Contacts/Conversations are containers, Query Metadata is the actual condition settings that control visible scope

Learn more through these articles:

• Contact Integration Steps

• Differences between Roles and Contacts

Permission Level Concept

Before service construction, Agent confirms all available knowledge bases through Query Metadata at different levels, with permission levels referenced in the following order:

AI Assistant > Chat Platform > User (Message/Contact/Role) > query_metadata > Query Permissions

You can specify permissions at each level using either graphical interface or JSON format

Reference these documents for operation:

1. Graphical Interface Operation Guide (Contact, Internal Q&A Settings)

2. JSON Interface Configuration Guide (Contact, Web Chat Embedding Settings)

• Contact/Role are identity containers

• Message corresponds to controlling which knowledge bases to use during internal conversations through filtering

• query_metadata is the "filter condition set" actually executed during conversations

Document Filter Condition Decision Levels

Document Filter Condition Level Diagram

The open filtering logic is:

Through layer-by-layer transmission, Query Metadata becomes the actual decision basis for AI response logic

Practical Application Scenarios

Identity	Input Conditions (query_metadata)	Response Result
Visitor	Knowledge Base: General File Documents: None Tags: Visitor FAQ: 1, 2	Get documents with Visitor tag from General knowledge base and FAQ 1, FAQ 2
Regular Member	Knowledge Base: General File Documents: A, B, C Tags: None FAQ: None	Get documents A, B, C from General knowledge base and all FAQs
Customer Service	Knowledge Base: Employee File Documents: None Tags: CS FAQ: None	Get documents tagged as CS from Employee knowledge base and all FAQs
Internal Employee	Knowledge Base: Employee File Documents: A, B Tags: None FAQ: None	Get documents A, B from Employee knowledge base and all FAQs
Administrator	Knowledge Base: Employee, General File Documents: None Tags: None FAQ: None	Get all document content and FAQs from Employee and General knowledge bases

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Summary: The Value of query_metadata for Enterprises

🎯 Multi-dimensional Identity Cross-Control (Role + Region + Product Line)

🎯 Real-time Query Control: No need to copy assistants, just change conditions to adapt to different scenarios

🎯 Flexible Large Knowledge Base Management: Tags and knowledge bases can be split and authorized according to scenarios

It is recommended to incorporate query_metadata into the core product architecture, allowing enterprises to achieve maximum authorization flexibility with minimum settings, ensuring knowledge security while improving conversation experience and operational efficiency.