Settings
In the Settings tab of the Knowledge detail screen, you manage metadata (name, description, tags) and search-related options (search methods, embedding model). AI auto-generation features can be used to efficiently populate metadata, and you can understand the impact of search option changes on existing data to safely adjust settings.
General Settings
The area for managing basic Knowledge metadata. Each field can be entered manually or generated using AI.
Alias
The display name of the Knowledge. This name is used in the list screen, search results, etc.
| Item | Description |
|---|---|
| Manual Input | Enter the desired name directly in the text field |
| AI Generation | Click the Generate button to have AI automatically suggest an appropriate name based on collected document content |
Description
The description text of the Knowledge. Describe the purpose, included content, target domain, etc.
| Item | Description |
|---|---|
| Manual Input | Write a description freely in the text area |
| AI Generation | Click the Generate button to have AI analyze collected documents and automatically generate a summary description |
Tags
A tag list for categorizing the Knowledge. Used for filtering and searching on the list screen.
| Item | Description |
|---|---|
| Manual Input | Manually add or remove tags |
| AI Generation | Click the Generate button to have AI analyze document content and suggest relevant tags. Tags are merged with existing tags, and duplicates are automatically removed |
Systematically managing tags enables quick discovery even as the number of Knowledges grows. It's recommended to establish tag naming conventions (e.g., domain prefixes) within your team in advance.
Collections
Link Knowledge to collections to systematically group resources.
| Action | Description |
|---|---|
| Add Collection | Select an existing collection from the dropdown to link |
| Remove Collection | Click the remove button next to a linked collection |
| Create New Collection | Use the creation option at the bottom of the dropdown to create a new collection and link it immediately |
Generate All
A button that auto-generates Alias, Description, and Tags all at once using AI. It comprehensively analyzes collected document content to fill all three fields simultaneously. This produces more consistent results than generating each field individually.
The AI generation feature requires collected documents in the Knowledge to provide accurate results. Results may be of lower quality when there are no or few documents.
Search Options
The area for configuring Knowledge search methods and embedding models. These settings determine how data is stored during document indexing, so changes are restricted when existing data is present.
Search Methods
Select search methods for the Knowledge using checkboxes. At least one must be selected.
| Search Method | Engine | Description |
|---|---|---|
| VECTOR | Vector DB | Semantic similarity search based on embedding vectors |
| TEXT | Text search engine | BM25-based keyword full-text search |
| GRAPH | Graph DB | Entity/relationship extraction-based graph exploration |
In most cases, the VECTOR + TEXT combination is most effective. Vector search ensures semantic similarity, while Text search supplements keyword accuracy to maximize Hybrid search quality. Add GRAPH only for special cases where entity relationship exploration is needed.
Embedding Model
When the VECTOR search method is selected, specify the embedding model. Selecting a model from the dropdown displays its dimensions (vector dimensions) and provider information.
| Display Info | Description |
|---|---|
| Model Name | Unique name of the embedding model |
| Dimensions | Number of dimensions of the generated vector (higher is more precise but increases storage) |
| Provider | Model provider (e.g., local, openai) |
Search quality and performance vary depending on the embedding model. For detailed model comparisons, see the Chunking and Options document.
For Knowledges that already have indexed documents, Search Methods and Embedding Model become locked. This is because existing indexed data may not be compatible with new settings. To change search options, you must first perform a Settings Reset as described below.
Saving Setting Changes
After modifying General Settings items (Alias, Description, Tags, Collections), click the Save button to save changes. Changes will be lost if you navigate to another tab without saving.
Settings Reset
When you need to change Search Options (Search Methods, Embedding Model), all existing indexed data must be reset.
Reset Procedure
- Click the Reset button at the bottom of the Settings tab
- Confirm the reset scope in the confirmation modal
- Click Confirm to execute the reset
Reset Scope
When Reset is executed, data is deleted in the following order:
| Order | Deletion Target | Description |
|---|---|---|
| 1 | All Chunks | Indexed chunk data (Vector DB, Text DB, Graph DB) |
| 2 | All Documents | Collected document metadata and original references |
Once reset is complete, the Search Methods and Embedding Model fields are unlocked so you can set new values.
Settings reset permanently deletes all indexed data for that Knowledge. All chunks and document metadata stored in Vector DB, Text DB, and Graph DB are removed. This action cannot be undone, so only execute it when reset is truly necessary.
After reset, you must reconfigure search options and re-collect documents for indexing. You can add documents again using the same methods as before — web crawling, file upload, and manual input.
Precautions When Changing Settings
| Item | Can Be Changed | Notes |
|---|---|---|
| Alias | ✅ Anytime | No impact on existing data |
| Description | ✅ Anytime | No impact on existing data |
| Tags | ✅ Anytime | No impact on existing data |
| Collections | ✅ Anytime | No impact on existing data |
| Search Methods | ⚠️ Conditional | Locked when documents exist → Reset required |
| Embedding Model | ⚠️ Conditional | Locked when documents exist → Reset required |
Next Steps
- Chunking and Options — Detailed reference for chunking strategies, embedding models, and indexing options
- Search Test — Verify the impact of changed settings on search quality
- AI Chat — Utilize knowledge through RAG-based conversation