What the Data for This Category Looks Like
The data for this category primarily comes from data asset inventory results, database metadata, business system documentation, and public regulatory documents. Core regularly maintained documents include data catalogs, classification and grading rule tables, important data identification lists, and review minutes. Data updates occur on a quarterly basis or when the corresponding business system changes. Most document structures include fields such as asset number, sensitivity level, affiliated system, and compliance requirements. There are no unified fixed units. Adjustments must be made based on the attributes of specific data assets.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall?
Dispersed data sources and uneven update frequencies lead to mixed old and new content in the knowledge base. Regular synchronization of the latest classification and grading rules and data asset lists is required to maintain compliance. Document structures include multi-dimensional precise fields. Retrieval must match attributes such as asset number and sensitivity level to avoid generic recall of irrelevant content. The number of documents recalled in a single operation must be reasonably controlled. This prevents overload of compliance-related sensitive content from impacting AI judgment. Timely documents such as review minutes must also be prioritized for retrieval. This category covers data assets outside of personal information. Retrieval must distinguish the asset scope of different business systems to prevent compliance risks caused by cross-system data confusion.
Configuration Settings
The following configurations are based on FastGPT v4.17.0, and are adapted to knowledge base retrieval and recall requirements for the data security classification and grading scenario:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Most documents in this category are structured rules and lists. Low-match irrelevant data must be filtered to ensure recalled content is strongly relevant to compliance requirements |
limit | Top 8–12 entries | Data asset lists contain many entries. Excessive recall will exceed the context window, while ensuring coverage of main grading rules and asset information |
searchMode | vector+keyword | Match both structured fields (such as asset number) and text rules (such as grading standards), balancing accuracy and recall rate |
usingReRank | Enabled | Sort recalled results by compliance relevance to avoid non-core rules or old review minutes being included in final reference content |
datasetSearchUsingExtensionQuery | Enabled | Convert user technical support questions into extended queries, covering different expression methods of classification and grading rules, improving retrieval coverage |
collectionIds | Bind exclusive dataset ID | Isolate knowledge base content for this category to avoid confusion with other business knowledge bases and prevent interference from cross-category retrieval |
Three Common Misconfigurations
- The first issue is a stuck process when creating an exclusive knowledge base for this category. The Ollama log prompts to reduce the batch size. The cause is failure to adjust batch parameters for large documents of this category, such as complete data asset inventory tables. The number of documents loaded in a single operation exceeds the model's supported upper limit.
- The second issue is that the AI cannot correctly reference knowledge base content after retrieval results are passed to an HTTP workflow. The cause is failure to return structured format including document metadata, such as sensitivity level and affiliated system, only returning plain text fragments. This prevents the AI from identifying reference basis.
- The third issue is that the number of knowledge base recall results does not match the configuration, with a large number of irrelevant old review minutes appearing. The cause is failure to set the
similaritythreshold, or setting the threshold too low, leading to recall of low-match non-current business data.
How to Confirm Proper Configuration
- Upload typical documents for this category, such as classification and grading rule tables, perform retrieval tests, and confirm that matching results include content with corresponding fields.
- Check the knowledge base synchronization log to confirm that the latest quarterly updated data has completed synchronization, with no error messages.
- Adjust the
usingReRankswitch, compare the sorting of recall results before and after, and confirm that the reranking function is active. - Test retrieval across business systems to confirm that only content within the bound dataset is recalled, with no knowledge base data from other categories mixed in.
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.