What Data This Category Includes
Data sources for this category include asset management systems, procurement systems, financial fixed asset modules, and terminal management platforms. Four standardized documents are routinely generated: asset ledgers, inventory sheets, loan and return forms, and scrap application forms. Data updates follow monthly, quarterly, or annual cycles. Document fields include asset number, device name, storage location, user, purchase date, asset status, and other standard business fields for enterprise IT asset management. No custom special units are used.
Constraints Related to Context and Token
Data updates occur infrequently, so frequent knowledge base incremental synchronization is unnecessary. The time range for context recall can be appropriately extended. Multiple standardized documents have significant field differences. Ensure recalled context only includes asset-related fields tied to the current review, to avoid irrelevant information consuming token quotas. Core processes such as account-physical reconciliation rely on historical inventory data. Sufficient context length is required to cover the full business chain, preventing review errors caused by truncated key information. Asset ownership and user fields serve as core review criteria. Restrict recall results to only valid content for these fields, reducing unnecessary token consumption.
Configuration Settings
The following configurations are based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Max Context | 8000–12000 token | Covers core field content of multiple asset ledgers and inventory sheets, avoiding truncation of key review information |
| Max Token per Reference | 2000 token | Matches the average length of a single asset document, preventing a single reference from occupying too much context quota |
| Search Result Limit | Top 8 entries | Limits the number of recalled historical inventory records, balancing context completeness and token consumption |
| Context Recall Time Range | Past 24 months | Adapts to annual/quarterly asset inventory update cycles, covering valid data across complete business cycles |
| Reranking Switch | Enabled | Prioritizes recalling context strongly related to the current review’s asset number and user, improving information accuracy |
Three Common Misconfigurations
- Issue: Offline deployed tokenizer service reports errors when requesting external network resources, with logs showing failure to retrieve the cl100k.tiktoken file. Cause: Local tokenizer file path is not configured, and the system attempts to pull external network resources by default.
- Issue: Setting the single search limit too high causes context to fail sending to the large model. Cause: Excessive recalled results lead to total token consumption exceeding the configured threshold, and the system automatically filters context content.
- Issue: Configured max context length exceeds the support limit of the bound large model, resulting in missing content or errors in large model responses. Cause: The context window limit of the large model is not matched, and excess content is automatically discarded by the system.
How to Verify Successful Configuration
- Enter the FastGPT knowledge base configuration interface, check context and token related setting items, and confirm parameter values match preset rules.
- Submit an asset inventory-related review request, and confirm that the recalled content in the context preview area before the large model’s reply includes core fields such as the current review’s asset number and user.
- Check system operation logs to confirm there are no error records for token consumption exceeding limits, context truncation, or external resource request failures.
- Adjust the value of a single configuration item, and verify whether the number of context recalls and total token consumption change with the configuration.
The values provided are common starting points and should be evaluated 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.