This document applies to FastGPT v4.17.0.
What Data for This Category Looks Like
Data for this category comes from business systems, App permission lists, privacy policies, and user request records. Its update frequency is quarterly or per product change. Daily used documents include personal information inventory, processing rules, impact assessment reports, and user authorization texts. Document fields include personal information type, processing purpose, storage period, legal basis, and more. Different documents have varying structures and field priorities. For example, personal information inventory prioritizes field completeness, while impact assessment reports prioritize scenario relevance.
Constraints Imposed by These Characteristics on Context and Token
Multi-source compliance data requires context recall to cover different systems and document types. This avoids missing key compliance entries. Dynamic update rhythms require the context window to adapt to changes in document length. A fixed single value is not suitable. Diverse document structures require token allocation to distinguish field priorities. Core compliance content must be retained first. The sensitivity of personal information requires context screening to exclude unnecessary sensitive fields. This prevents sensitive information leaks when token overflow occurs. Parameters such as maxContext and quoteMaxToken must be set appropriately. This balances context recall and token consumption. Context update triggers must also be adjusted to match the update rhythm of compliance data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| maxContext | 12000–15000 token | Covers the total token requirements of multiple compliance documents such as personal information inventory and processing rules, and adapts to the context window of general large models |
| quoteMaxToken | 2000 token per document | Limits the token occupancy of a single compliance document fragment, avoiding context overload caused by recalling a large number of documents simultaneously |
| Context recall count | Top 5 | Balances the recall accuracy of compliance documents and token consumption, and matches the update frequency of compliance data |
| Context update trigger condition | Product change or quarterly node | Aligns with the update rhythm of compliance data, reducing invalid context refreshes and token consumption |
| Token overflow handling strategy | Retain the latest 3 core documents | Prioritizes ensuring the validity of the context of core compliance content such as the latest authorization texts and impact assessment reports |
Three Common Misconfigurations
- A prompt error occurs when initializing the root password. The cause is failure to correctly match the INITIAL_ROOT environment variable value in docker-compose.yml, resulting in inconsistent configuration.
- The application context cannot be accessed via the
/fastgptpath during private deployment. The cause is failure to correctly bind the context path in the reverse proxy configuration, leading to incorrect request routing. - Model token consumption exceeds expectations, and token overflow errors occur. The cause is failure to set
quoteMaxTokento limit the token occupancy of a single document, leading to simultaneous recall of a large number of compliance document fragments.
How to Confirm Proper Configuration
- Upload a standard personal information inventory document, and verify whether the context recall module can correctly extract core compliance fields from the document.
- Trigger a code change review process, and check whether the latest compliance processing rules and authorization texts are included in the context window.
- View the model call logs to confirm that the token consumption of a single request conforms to the preset context window limits.
- Test the private deployment access path, and confirm that the application can normally load the configuration page via the specified context path.
The values provided here 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.