What this category's data looks like
This category of data originates from internal system documents and permissioned business data documents of state-owned enterprises, government agencies, and public service institutions. The data update schedule follows the system revision cycle, typically monthly or quarterly, with immediate synchronization for emergency revisions. Most documents include document numbers, issuing departments, effective dates, hierarchical main text, and attachment lists. Fields include unique document identifiers, permission level tags, release times, main text content, and attachment metadata, with individual files as the basic storage unit.
What constraints do these features place on the vector models and indexing step?
The permission layering feature of internal systems and permissioned data requires vector indexes to be bound to permission metadata, ensuring recall results only correspond to the document range accessible to users. The hierarchical structure and long text attributes of documents require retaining context hierarchy during chunking, to avoid splitting that disrupts business logic. The non-fixed update rhythm requires support for both incremental and full index modes, to adapt to immediate synchronization needs for emergency revisions. Multi-attachment scenarios require the indexing process to support parsing attachments in multiple formats, ensuring attachment content can be correctly vectorized.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Internal system documents are mostly paragraph-style long text; this range preserves complete business logic and avoids semantic fragmentation |
| Overlap Length | 100–150 characters | Compensates for semantic breaks at chunk boundaries, ensuring coherent context across chunks |
| Similarity Threshold | 0.75–0.85 | Meets the rigor requirements of internal systems, filtering low-relevance non-permissioned content |
| Single Search Limit | Top 10 entries | Controls the number of returned results, avoiding exceeding the user's accessible document range |
maxToken | 4000 token | Adapts to the long context requirements of state-owned enterprise and government system documents, ensuring recalled content can be fully passed to the large language model |
| Permission Metadata Binding | Enabled | Binds the document's permission level tags to enable permission filtering during vector recall |
Three common misconfigurations
- Issue: After customizing index configurations, recall results do not filter non-permissioned documents. Cause: The document's permission level tags were not synchronized to the vector index's metadata fields, preventing permission checks during retrieval.
- Issue: Vector generation tasks time out. Cause: Chunk length is set to more than 2000 characters, causing single-batch vector generation time to exceed system thresholds.
- Issue: After connecting an external vector database, recall results cannot be filtered by permission. Cause: Permission tags were not synchronized to the external vector database as metadata fields, preventing association of permission conditions during retrieval.
How to confirm configurations are correctly applied
- Upload a test document with clear permission tags, initiate a query using an account with corresponding permissions, and confirm that recall results only include content within that permission range.
- View the vector index metadata list, confirm that fields such as permission levels and release times of all documents have been correctly synchronized to the index.
- Adjust the chunk length parameter, upload a long document and check the chunking results, confirm that there are no semantic breaks or lost content.
- In the FastGPT v4.17.0 environment, call the vector recall interface and pass permission filtering parameters, verify that the returned results match the preset permission range.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.