What the Data for This Category Looks Like
Business data for this category typically comes from the unified budget management system, departmental staffing system, prior year final accounts, project repository, and Ministry of Finance budget preparation notices. Update frequency is annual. Commonly processed documents include departmental budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approvals. Most documents include fields such as project code, budget amount, number of staff, and performance indicators. Common units are ten thousand yuan, person, and percentage point.
What Constraints These Characteristics Impose on the Knowledge Base Retrieval and Recall Link
Annual updated data sources require the knowledge base’s incremental update cycle to align with the fiscal year rhythm. This prevents use of outdated policies or data. Multi-source, heterogeneous document structures require retrieval to balance semantic matching and field rules. This ensures accurate location of budget-related content. The number of documents increases with unit hierarchy. The upper limit for single retrieval results must be strictly controlled. Otherwise, the context window will be exceeded. Retrieval accuracy is directly linked to budget review pass rates. Balance recall coverage and precision to avoid incorrectly recalling irrelevant expense entries.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.72–0.82 | Department budget data is rigorous, so balance recall coverage and result precision to avoid incorrectly recalling irrelevant expense calculation content |
limit | Top 15–20 entries | Declaration materials and file entries are numerous; too many results will exceed the context window and cause generation interruption |
usingReRank | Enabled | Filter semantically similar redundant budget entries to improve the relevance of retrieval results |
datasetSearchUsingExtensionQuery | Enabled | Adapt to professional terminology for budget preparation, use extended query terms to retrieve hidden policy basis documents |
collectionIds | Bind corresponding fiscal budget dataset IDs | Isolate business data from other categories, narrow the retrieval scope, and improve retrieval speed |
Three Common Configuration Mistakes
- Symptom: The AI generation process stops suddenly with no complete response. Cause: The
limitparameter is not configured. Too many retrieved entries exceed the context window limit, causing generation interruption. - Symptom: Retrieval response time exceeds 10 seconds, and a timeout prompt appears in the interface. Cause:
usingReRankis not enabled and the retrieval scope is not limited viacollectionIds, leading to delays from retrieval across multiple unrelated datasets. - Symptom: Retrieval results do not carry metadata such as project number and budget year, making it impossible to associate corresponding file information. Cause: Metadata synchronization is not enabled in the knowledge base configuration, and system fields are not bound to retrieval results.
How to Verify Successful Configuration
The following verification steps must be completed in FastGPT v4.17.0 or later:
- Initiate a query containing professional budget preparation terminology, and check whether the number of returned results is within the range set by the
limitparameter. - View retrieval logs to confirm that content is only recalled from the bound
collectionIdsdataset, with no business documents from other categories. - Verify that after
usingReRankis enabled, semantically similar budget entries are properly filtered, and result relevance is improved. - Check the metadata fields of retrieval results to confirm that information such as project code and budget year is displayed synchronously. Adjust metadata display rules according to business needs.
The values provided here are common starting points and should be measured against relevant samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.