What This Category of Data Looks Like
The data for this category primarily comes from data asset inventories, database metadata, business system descriptions, and regulatory documents. Four types of documents are regularly produced: data catalogs, classification and grading rules, important data identification tables, and review minutes. Data is updated quarterly or whenever the corresponding business system changes. Document structures include fields such as asset number, data classification, security level, sensitive field identification, and review conclusion. There is no unified fixed unit. Matching management requirements must be done based on the specific asset type.
What Constraints These Characteristics Impose on Context and Token Processing
The data for this category comes from scattered sources, and document lengths vary significantly. Token consumption fluctuates widely across different document types. This directly affects the total token usage of context stitching. The quarterly update frequency means context does not need frequent refreshes. The latest document fragments must be reloaded after each rule or asset list update. The data coverage includes all full business data that is not personal information. The recall accuracy of sensitive fields and grading rules is directly linked to compliance risks. Context recall must prioritize matching assets with high security levels. A single review minute or regulatory document may contain lengthy compliance requirements. If the token limit for a single quoted entry is not set, overall context overflow will occur. This triggers model errors or truncated outputs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to total token consumption fluctuations across multiple document types for this category, covers core content such as data catalogs, grading rules, and review minutes, and meets the context processing limit requirements of FastGPT v4.17.0 |
quoteMaxToken | 2000–3000 token | Limits token usage for a single quoted document, preventing core compliance requirements from being truncated in long documents such as review minutes or regulatory excerpts |
| Single recall limit | Top 8 entries | Balances recall accuracy and token consumption, adapts to the number of core asset entries for this category, and avoids excessive redundant content occupying context space |
| Context refresh cycle | Every 7 days or immediately after a business system change | Matches the rhythm of quarterly data updates or system changes, balancing data timeliness and token usage efficiency |
| Recall sorting rule | Sort by security level in descending order | Prioritizes including high security level data assets in the context, reducing compliance risks of missing important data management |
Three Common Misconfigurations
- Phenomenon: Significant differences in token consumption between the online version and self-hosted version, with unexpected token usage occurring in the self-hosted version. Cause: The
maxContextandquoteMaxTokenconfigurations were not adjusted for this category’s multiple document types, resulting in redundant historical data or non-core documents being included in the context. - Phenomenon: Search results cannot be combined with the current session context, and output content does not link to existing classification and grading rules or review minutes. Cause: Context-associated recall logic was not enabled, or the recall sorting rule did not prioritize matching the current session’s business scenario.
- Phenomenon: Context recall results include a large amount of low-security-level non-core data, leading to incomplete identification of core sensitive fields. Cause: The recall rule sorted by security level in descending order was not set, or the single recall limit was set too high, introducing redundant content that occupies context space.
How to Verify Correct Configuration
- Log in to the application configuration panel of FastGPT v4.17.0, check the values of context-related configuration items, and confirm they match the preset plan.
- Upload a document containing high-security-level data, trigger a context recall test, verify that the recall results are sorted to prioritize high-security-level entries, and adjust the sorting rule until it meets business compliance requirements.
- Launch a simulated compliance review session, observe the token consumption prompts during the session, confirm that consumption does not exceed the preset context limit, and adjust relevant configuration items until they meet the token usage rules of the business scenario.
- Trigger a context refresh operation after data asset updates, verify whether newly uploaded review minutes or data catalogs are included in the recall scope, and confirm that the configured refresh cycle takes effect.
The values provided are common starting points and should be evaluated against your own specific 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.