What the data for this category looks like
Data for this category comes primarily from software asset management systems, procurement systems, endpoint scanning tools, and vendor authorization documents. Daily output includes license ledgers, software usage lists, procurement contracts, and compliance audit reports. Updates to this data occur monthly or quarterly. Core fields include authorized license quantity, vendor authorization validity period, endpoint installation quantity, and compliance check status. Units include units, copies, and calendar days. Most data uses structured ledger formats, with accompanying unstructured materials such as scanned vendor authorization documents and contract text.
What constraints these characteristics impose on context and token processing
Low-frequency data updated monthly or quarterly requires the context window to adapt to the total length of batch historical ledgers and single audit reports. Mixed data from multiple sources, including structured fields and unstructured text, requires weighted token allocation to prevent unstructured scanned documents from occupying excessive context resources. The long text nature of compliance audit reports requires limiting per-segment token caps to avoid exceeding model context thresholds. The token proportion of numeric fields such as authorized quantity and installed endpoint count must be controlled to ensure alignment with semantic associations in the context. Heterogeneous data across systems requires unified token encoding rules to avoid context bias caused by field confusion.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the batch text length of monthly compliance audit reports, covering the total token volume of multiple license ledgers and contracts |
quoteMaxToken | 2000–3000 token | Limits the token proportion of a single referenced unstructured authorization file or scanned document to avoid context overflow |
| Chunk Length | 1500–2000 characters | Matches the length of single records in license ledgers, balancing semantic completeness and token utilization after chunking |
| Overlap Length | 100–200 characters | Retains contextual associations for key fields such as authorization validity period and endpoint count across chunks |
| Single Recall Limit | Top 8 entries | Adapts to the monthly updated license data volume, avoiding excessive historical records from occupying context |
Three common misconfigurations
- Token limit exceeded errors trigger when uploading vendor authorization scanned documents. Some small-sized files upload normally. This happens because no reasonable token quota is allocated to extracted text from unstructured image files. Large-sized scanned documents produce extracted text that exceeds the per-segment token limit.
- After setting the maximum context window, key authorized quantity fields are missing from compliance audit report recall results. The chunk length is set too small. Cross-chunk field association information is lost during splitting.
- After configuring the maximum quote token count, numeric fields in license ledgers are not correctly associated with the query context. Token allocation is not differentiated between structured fields and unstructured text. The token proportion of numeric fields becomes overly restricted.
How to confirm configurations are applied correctly
- Import a single monthly license ledger. Check the actual occupied token count of the context window. Confirm it does not exceed the set
maxContextvalue. - Upload vendor authorization files of different sizes. Check whether token limit exceeded errors trigger. Verify that the
quoteMaxTokenconfiguration takes effect. - Split a long-text compliance audit report. Check whether overlapping content after chunking retains key fields such as authorization validity period and installed endpoint count.
- Trigger a compliance audit query. Check whether the number of recalled historical records matches the set single recall limit.
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 2026-10-19.