What data for this category looks like
Typically, data for this scenario comes from the unified budget management system, departmental staffing systems, prior-year final account data, project repositories, and budget preparation notices issued by the Ministry of Finance. Daily generated documents include departmental budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approval documents. Data is updated on an annual cycle. Most documents contain fields such as expense calculations, project codes, and performance indicators. Units of measurement include ten thousand yuan, persons, and project cycles. The field structures of different documents vary significantly.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
Annual data updates require the knowledge base to perform full synchronization on a regular basis. The volume of data synchronized in a single operation is large, so batch recall logic must be adapted to improve synchronization efficiency. Documents contain specialized financial fields and terminology, so retrieval must preserve the semantic accuracy of fields to avoid generic recall of unrelated content. Multi-type mixed documents have large variations in the location of key information, so chunking strategies must be optimized to adapt to the structural characteristics of different documents. Most data sources are internal business systems, so dedicated data source connection permissions must be configured to ensure compliance and security of retrieved data.
How to configure this setup
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the semantic meaning of specialized financial terminology to avoid recalling unrelated non-budget documents |
limit | Top 10 entries | Controls the number of recall results per query, adapting to the multi-project parallel review scenario of departmental budget preparation |
searchMode | Hybrid recall | Combines keyword and semantic retrieval to cover the retrieval needs of different document types |
usingReRank | Enabled | Re-ranks recall results by relevance, improving the accuracy of performance target forms and project application forms |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands professional query terms related to budgets, adapting to policy terminology in Ministry of Finance notices |
collectionIds | Bound by business domain | Only recalls datasets related to budget preparation, preventing data from other financial modules from being included |
Three common configuration errors
- An
invalid configuration parameter nameerror is returned during retrieval. This occurs when an unspecified English parameter name is used, such as incorrectly configuring a parameter item not included in the recommended list. - Knowledge base search responses are slow. This happens when a reasonable
limitvalue is not set. Recalling too many documents in a single operation causes excessive vector database computing load, which cannot adapt to the retrieval scenario of annual batch data. - Scanned PDF documents cannot retrieve valid content. This occurs when image text extraction capability is not enabled. Only editable text is chunked, so specialized budget preparation terminology in scanned content cannot be recognized.
How to confirm the configuration is properly set
- Submit a test query containing budget-related specialized terminology, check the relevance of returned results, and confirm that the
similarityandsearchModeconfigurations are active. - View retrieval logs to confirm that only datasets corresponding to the bound
collectionIdsare called, and no cross-domain data is retrieved. - Upload a scanned PDF test document, check whether budget-related content can be extracted and retrieved, and verify the effectiveness of the text extraction configuration.
- Simulate concurrent queries for multiple questions, check system response status, and confirm the rationality of concurrency control and the
limitconfiguration.
The values provided are common starting points and should be evaluated against samples relevant to the specific deployment.
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.