What data looks like for this category
This category's data mainly comes from the fiscal bureau performance management department, budget units, third-party evaluation institutions, and competent authorities. Daily documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Data sources cover the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. In the data processing stage, collecting project output effect supporting materials, verifying the correspondence between fund expenditures and performance targets, and drafting issues and rectification suggestions are the core time-consuming steps.
Constraints imposed by these characteristics on vector models and indexing
The annual update frequency means indexes do not require frequent incremental synchronization, making a full rebuild update strategy suitable. Multiple types of business documents require indexes to support multi-format parsing, and need to adapt to mixed content of structured fields and text in performance target forms and self-assessment forms. Data from multiple systems has format differences, requiring field standardization mapping before indexing. Verifying the correspondence between funds and performance targets is a core business requirement, requiring vector retrieval to accurately match relevant content and avoid retrieving irrelevant documents. At the same time, the consequences of errors are directly linked to budgets and accountability, so the credibility of retrieval results must be ensured.
Configuration settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of performance evaluation reports, avoid including too many irrelevant contents in a single chunk |
| Overlap Length | 100–150 characters | Retain performance indicator association information across chunks, prevent key content from being truncated |
| maxToken | 4096 token | Adapt to the context length requirements of party building learning Q&A, cover the core content of a single performance evaluation |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Ensure the relevance of retrieved documents to performance targets and fund expenditures meets business verification requirements |
| Single Search Limit | Top 8 entries | Balance retrieval efficiency and information completeness, cover core supporting materials and evaluation content |
| defaultToken | 2048 token | Serve as the default context length, adapt to information needs of most performance-related Q&A. |
Three common mistakes
- Phenomenon: A large amount of irrelevant general party building content is mixed in retrieval results, making it impossible to accurately match performance-related documents. Cause: No dedicated index was created for the fiscal expenditure performance evaluation scenario, and a general party building knowledge base was utilized, leading to retrieval scope exceeding business needs.
- Phenomenon: Structured table parsing fails during index construction, and indicator fields in performance target forms are not extracted. Cause: The structured field extraction switch for multi-format parsing was not enabled, only plain text content was chunked, and key associated information in tables was lost.
- Phenomenon: Retrieval requests time out, and the interface returns status code 504. Cause: The
maxTokenand single search limit configurations were not adjusted, and the volume of documents processed in a single batch exceeded the default processing limit of FastGPT v4.17.0.
How to confirm configurations are properly set
- Upload a single typical performance evaluation report and party building learning materials, perform a retrieval test, check the proportion of content directly related to performance targets in the retrieval results, and adjust the similarity threshold to the range that meets business verification requirements.
- View index construction logs, confirm that multi-format documents such as PDF, PPT, and Word are correctly parsed, structured fields have been extracted and added to vector storage, and there are no parsing failure error records.
- Simulate the annual data update process, perform a full index rebuild, check that the rebuild time matches expectations, and confirm that the update process has no freezes or interruptions.
- Call the retrieval interface, verify that the number of returned results conforms to the single search limit configuration, and no irrelevant content beyond the business scope is retrieved.
The configuration values listed above are common starting points. They should be evaluated against the reader's own samples and use cases.
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.