# Knowledge Management

The character system supports two knowledge modes:

#### Classic Mode (Default)[​](https://elizaos.github.io/eliza/docs/core/characterfile#classic-mode-default) <a href="#classic-mode-default" id="classic-mode-default"></a>

* Direct string knowledge added to character's context
* No chunking or semantic search
* Enabled by default (`settings.ragKnowledge: false`)
* Only processes string knowledge entries
* Simpler but less sophisticated

#### RAG Mode[​](https://elizaos.github.io/eliza/docs/core/characterfile#rag-mode) <a href="#rag-mode" id="rag-mode"></a>

* Advanced knowledge processing with semantic search
* Chunks content and uses embeddings
* Must be explicitly enabled (`settings.ragKnowledge: true`)
* Supports three knowledge types:
  1. Direct string knowledge
  2. Single file references: `{ "path": "path/to/file.md", "shared": false }`
  3. Directory references: `{ "directory": "knowledge/dir", "shared": false }`
* Supported file types: .md, .txt, .pdf
* Optional `shared` flag for knowledge reuse across characters

#### Knowledge Path Configuration[​](https://elizaos.github.io/eliza/docs/core/characterfile#knowledge-path-configuration) <a href="#knowledge-path-configuration" id="knowledge-path-configuration"></a>

* Knowledge files are relative to the `characters/knowledge` directory
* Paths should not contain `../` (sanitized for security)
* Both shared and private knowledge supported
* Files automatically reloaded if content changes

**Knowledge Tips**

* Focus on relevant information
* Organize in digestible chunks
* Update regularly to maintain relevance

Use the provided tools to convert documents into knowledge:


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.tars.pro/sona-framework/core-concepts/knowledge-management.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.