What this category of data looks like
Data for this category is produced by data governance teams, security consultants, and legal compliance teams. Daily documents include data catalogs, classification and grading rules, important data identification tables, and review minutes. Data sources include data asset inventory results, database metadata, business system documentation, and public regulatory documents. Update frequency is quarterly or when the corresponding business system changes. Single document length varies widely, with core fields such as data fields, sensitivity levels, and control requirements. There is no unified fixed unit standard, and adjustments are made based on the corresponding business scenario.
What constraints these characteristics impose on the context and token workflow
Data from this category has diverse sources, including structured metadata, unstructured business documentation, and regulatory texts. Large differences in text format across sources cause formatting chaos during context concatenation. Update frequency is quarterly or when the system changes, so the knowledge base must fully sync asset data on a regular basis. The volume of text synced in a single operation is large. If the context window is not configured properly, key rules are likely to be truncated and lost. Documents include multi-dimensional fields. When recalling context, accurate matching of data fields and control requirements is required. If token allocation is unreasonable, core identification rules cannot be fully sent to the large model, affecting the accuracy of classification and grading. At the same time, error consequences involve compliance risks: missing key regulatory basis in the context will directly invalidate review results, so the integrity and relevance of recalled content must be ensured.
How to set configurations
This configuration explanation is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-source, wide-length document characteristics of this category, preventing key classification rules from being truncated |
quoteMaxToken | 2000–3000 characters | Matches the typical length of a single classification and grading rule document, ensuring complete single reference content |
| Single search limit | Top 8 entries | Covers the multi-entry characteristics of data catalogs and identification tables, preventing excessive recalled content from occupying tokens |
| Context reordering | Descending by relevance | Prioritizes recalling sensitive field identification rules matching the currently drafted document, improving context validity |
| Query optimization | Enabled | Optimizes the precision of retrieval queries for professional terms in classification and grading rules, reducing invalid context |
Three common configuration mistakes
- Phenomenon: When the maximum single reference token is set beyond the model's supported single context length, retrieval results cannot be properly sent to the large model, and the context field in the interface is empty. Cause: Reference content exceeding the token limit is automatically filtered out and not included in the final context pool.
- Phenomenon: When the maximum context is set to a value exceeding the model's total token threshold, the large model returns a format error or interrupts the response directly. Cause: The total context length exceeds the model's carrying limit, triggering truncation or error reporting mechanisms.
- Phenomenon: When query optimization is not enabled, most retrieved content is general regulatory text, and does not match the currently drafted classification and grading rules. Cause: Retrieval queries are not optimized for professional documents, leading to insufficient recall relevance.
How to confirm the configuration is complete
- Enter the context and token settings page in the application configuration, check that the configured parameters match the preset plan.
- Upload a typical classification and grading rule document, initiate a retrieval test, and confirm that the relevance and quantity of recalled results meet expectations.
- Submit a question based on the document content to the large model, confirm that the returned results include key rule fields from the uploaded document.
- Check the application logs, confirm that the context field has been loaded correctly, with no truncation or filtering prompts.
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.