This document applies to FastGPT v4.17.0.
What this category of data looks like
This category of data primarily originates from data asset inventories, database metadata, business system descriptions, and regulatory documents. Its update cycle is quarterly, or when the corresponding business system changes. Daily produced documents include data catalogs, classification and grading rules, important data identification tables, and review minutes. Document structures focus on field mappings, rule entries, and asset lists. Involved fields include asset ID, classification level, sensitive identification, and associated business systems. There are no fixed unified units, and values are adjusted based on specific business scenarios.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
Data sources are scattered and linked to multiple types of system documents. Multi-turn dialogue must support cross-document context association, and avoid repeated inquiries about already mentioned asset information. Update frequency is not fixed, so prompt engineering must adapt to dynamically changing classification and grading rules, and avoid calling outdated regulations or rule content. Daily document structures are complex. Multi-turn dialogue must accurately identify field associations, such as the correspondence between asset ID and classification level, to prevent matching errors. Fields have no unified units across different business scenarios. Prompt engineering must support flexible adaptation to field formats to meet the query needs of different users.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | Previous 20 turns of dialogue history | This category of data requires association with multiple rounds of asset inventory and rule change records; retaining sufficient context avoids information gaps |
| Question Optimization | Enabled | Daily documents include rule entries and review minutes; optimizing questions allows precise matching of rule details |
| Single Search Limit | Top 8 retrieval results | Data catalogs and identification tables have many entries; this avoids interference from redundant information |
variables | Configured as asset ID and classification level fields | Daily documents include field mapping relationships; passing variables enables precise matching of corresponding data |
chatId | Generate a unique identifier for each conversation | Used to associate asset query records across multi-turn dialogue, avoiding context confusion |
messages | Pass complete historical dialogue context | This category of data requires association with multiple rounds of asset query records; complete context improves matching accuracy |
| Knowledge Base Update Frequency | Sync quarterly | The update cycle for this category of data is quarterly or when the business system changes; sync frequency matches data update rhythm |
Three common misconfigurations
- Empty results or no associated question prompts after starting a conversation. Cause: The
variablesparameter for the corresponding classification and grading rule base is not bound, causing the prompt to fail to match category-specific data. - Parsing failure error when uploading category-specific documents after API integration. Cause: Parsing rules for category-specific documents are not configured; document formats such as data catalogs and classification grading rules differ from general documents.
- Inconsistent results between platform testing and API calls for the same question. Cause: The API call does not carry the complete
messagesparameter and does not pass historical dialogue context, leading to deviations in context matching.
How to confirm configurations are correct
- View dialogue history records to confirm that
chatIdcorrectly associates context for each turn of dialogue. - Test API calls to check if returned results include bound classification and grading rule base content.
- Upload category-specific documents to verify that parsing results match structures such as data catalogs and rule entries.
- Compare results from platform testing and API calls to confirm that the
messagesparameter is passed completely.
The values provided here are common starting points, and should be evaluated against applicable 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.