Knowledge Configuration

Create and manage knowledge bases, upload documents, and generate summaries. Knowledge bases are critical information sources that let agents securely use your private data.

🔧 Create a Knowledge Base

1. Click Create Knowledge Base at the top of the left-side list
2. In the creation panel, fill in the following fields:

Field	Description
Name	Required. Must be unique and can only contain Chinese characters or lowercase letters. Spaces, slashes, and other special characters are not allowed. The system automatically checks for duplicates as you type
Embedding Model	Choose the vector model used to embed your documents. Models fall into two groups: Text Embedding (embedding) and Multimodal Embedding (multi_embedding). Selecting a multimodal model automatically enables vectorization of non-text content such as images. See Embedding Model Types
User Groups	Select which user groups can access this knowledge base (multi-select)
In-Group Permission	Controls what group members can do: Edit — upload and delete files; Read Only — view and search only; Private — only the creator can access
Preserve Source File	When enabled, uploaded files are kept in the system for later re-processing or download. When disabled, only the vectorized data is stored
Storage Quota	Optional. Set a storage cap for the knowledge base (switchable between GB and MB). The system raises a warning when storage usage approaches the quota

3. After configuring the fields above, select files to upload in the upload area below (or skip and upload later)
4. Once files are uploaded, the knowledge base is created and processing begins automatically

📁 Upload Files

Upload Files

1. Select a knowledge base from the list
2. Click the upload area to pick files (multi-select supported) or drag them in directly
3. Nexent automatically parses files, extracts text, and vectorizes the content
4. Track the processing status in the document list

File Size Limit

Maximum upload size per file is 20 MB. Files exceeding this limit cannot be uploaded.

Document Processing Status

Uploaded files go through multiple stages. There are 6 distinct statuses:

Status	Description
Waiting	File uploaded and queued for processing
Parsing	System is extracting text content from the file
Ingesting	Text is being vectorized and written into the vector database
Ready	Processing complete — the document is available for retrieval
Parse Failed	An error occurred during parsing. Hover over the status icon to see the detailed error reason and troubleshooting suggestions
Ingest Failed	An error occurred during vectorization. Hover over the status icon to see the detailed error reason and troubleshooting suggestions

💡 Hover over the status icon to view real-time progress (e.g., "23/50 chunks processed") and error details for failed documents.

Supported File Formats

Nexent supports multiple file formats, including:

• Text: .txt, .md, .json
• PDF: .pdf
• Word: .docx
• PowerPoint: .pptx
• Excel: .xlsx
• EPUB: .epub
• Data files: .csv
• Web content: .html, .xml

📊 Knowledge Base Summary

Give every knowledge base a clear summary so agents can pick the right source during retrieval.

Generate and Edit Summaries Manually

1. Click Details to the right of the knowledge base name to open the overview page
2. In the overview page, choose an LLM model and click Auto Summary to generate a description
3. Edit the generated text to improve accuracy
4. Click Save to store your changes

Scheduled Auto-Summary

In addition to manual triggers, you can configure scheduled auto-summary to let the system periodically regenerate the knowledge base summary in the background:

Frequency	Description
1 Hour	High-frequency updates — suitable for knowledge bases with frequent content changes
3 Hours	Medium-high frequency updates
6 Hours	Medium frequency updates
1 Day	Once a day — suitable for most scenarios
1 Week	Once a week — suitable for mostly static knowledge bases

Select the frequency in the summary section of the overview page. The system intelligently checks whether the knowledge base has received document updates since the last run; if no new documents have been added or changed, the generation is skipped to save resources.

📂 Chunk Management

After upload, each document is split into multiple Chunks. Each chunk contains a segment of text and has a corresponding vector index entry. You can perform fine-grained management on chunks.

Viewing Chunks

1. Click a knowledge base name to open the document list
2. Click the Chunk Details tab at the top
3. All documents in the knowledge base are listed at the top of the page. Click any document to view all of its chunks
4. Chunks are displayed as cards showing the chunk content and the source file name

Searching Chunks

Use the search box on the Chunk Details page to search for chunks. The system performs a hybrid search combining keyword matching and semantic matching, and returns results ranked by overall relevance.

Manually Managing Chunks

Action	Description
Create	Add a custom chunk manually on the Chunk Details page
Edit	Click a chunk card to enter edit mode and modify its text content
Delete	Remove unwanted chunks
Download	Export chunk content as a download

Model Compatibility Restriction

Chunk edit and create operations depend on embedding model consistency. If the model configuration has changed, these operations may be automatically disabled to prevent incompatible vector data from being written.

🧩 Embedding Model Types

Embedding models in the system are divided into two categories:

• Text Embedding models (embedding): For vectorizing pure text documents (e.g., BGE, M3E)
• Multimodal Embedding models (multi_embedding): Can process both text and image content (e.g., DashScope, Jina)

The embedding model selected when a knowledge base is created remains bound to that knowledge base for its entire lifecycle. Choose the appropriate model type based on your document content when creating a knowledge base.

🔧 Using Knowledge Bases

Nexent supports binding knowledge bases to agents. When creating an agent, enable the appropriate knowledge retrieval tool in the tool configuration panel and select the associated knowledge bases.

For Nexent-native knowledge bases, enable the knowledge_base_search tool:

Inside the tool configuration modal, you will see the list of bound knowledge bases with search and multi-select support. Each knowledge base displays its embedding model name so you can confirm compatibility before binding.

🔍 Knowledge Base Management

View Knowledge Bases

1. Knowledge Base List

   • The left column lists every created knowledge base
   • Supports searching by name and filtering by knowledge source or embedding model
   • Each knowledge base card shows the following information:

   Info	Description
   Name	The name set at creation time
   Document Count	Total number of uploaded documents
   Chunk Count	Total number of document chunks after splitting
   Source	Knowledge base origin (Nexent native / external source)
   Created At	Date when the knowledge base was created
   Embedding Model	Name of the bound embedding model
   Multimodal Badge	Shown if multimodal support is enabled
   User Group Tags	Names of the user groups this knowledge base is visible to
   Permission Icon	Your current access level for this knowledge base (hover over the icon to see details)
   No Source File Badge	Shown when "Preserve Source File" is disabled

2. Knowledge Base Details

   • Click a knowledge base name to view all documents
   • Click Details to open the overview page for viewing and editing the summary

Click Edit to manage the knowledge base name, visible user groups, and in-group permissions

Edit Knowledge Bases

1. Delete Knowledge Base

   • Click Delete to the right of the knowledge base row
   • Confirm the deletion (irreversible)

2. Delete or Add Files

   • Inside the document list, click Delete to remove a document
   • Use the upload area below the document list to add new files

🚀 Next Steps

After completing knowledge base configuration, we recommend you continue with:

1. Agent Development – Create and configure agents
2. Start Chat – Interact with your agent

Need help? Check the FAQ or open a thread in GitHub Discussions.