What data this category involves
Data for this category comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. Daily processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. These cover performance-related information across the full project lifecycle. Each document includes fields such as project identifiers, fund usage details, and performance indicator completion status. Some documents are structured tables, others are unstructured text.
What constraints these characteristics impose on vector models and indexes
The annual update frequency means high-frequency incremental indexing is unnecessary. A full regular reindexing strategy can be used to reduce operational costs. The mix of multiple document types requires chunking strategies to adapt to the different length characteristics of structured tables and unstructured text. This avoids damaging semantic integrity. Data formats from multiple systems are inconsistent. Field standardization processing must be completed before index construction to ensure matching consistency during retrieval. The need to associate supporting materials with evaluation reports requires indexes to support cross-document associated recall. This matches the correspondence between performance targets and fund expenditures, improving the accuracy of business process guidance.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the typical paragraph length of unstructured documents such as performance evaluation reports and supporting materials, avoiding semantic integrity damage from chunking |
| Overlap Length | 100–200 characters | Compensates for semantic breaks caused by chunking, ensuring contextual coherence across chunks |
| Similarity Threshold | Calibrated based on actual testing | Must match the association matching accuracy of performance targets and fund expenditure data, avoiding false recalls or missed recalls |
| Single Search Limit | Top 10 entries | Balances recall efficiency and result coverage, adapting to the associated information needs of a single business process |
maxToken | 8192 | Adapts to the input length limit of mainstream vector models, ensuring contextual integrity after multiple documents are spliced |
defaultToken | Use platform default configuration | Used for scenarios where parameters are not specified, ensuring basic index functionality is available |
Three common mistakes
- Symptom: After enabling the specified vector model, filling in a custom request address and API key, and clicking test, an error occurs directly. Cause: The request interface format of the vector model is not matched correctly, or the bound API key does not have vector service permissions enabled.
- Symptom: Retrieval results cannot be associated with corresponding performance targets and fund expenditure data. Cause: The chunking strategy does not adapt to structured table content, leading to semantic splitting of performance indicator fields, or the similarity threshold is set too high, filtering valid matching entries.
- Symptom: Index construction takes far longer than expected. Cause: The index update cycle is not configured according to the annual update frequency, high-frequency incremental indexing is accidentally enabled, or the chunk length is set too small, leading to an excessive total number of chunks.
How to confirm the configuration is correct
- Execute a vector model test request, confirm that the returned result has no errors, and the returned vector dimension matches the configuration requirements.
- Select a single performance evaluation report and corresponding supporting materials, perform a retrieval test, confirm that associated document fragments can be recalled.
- Check the index construction logs, confirm that there are no error messages for format conversion failures or missing fields.
- Adjust the similarity threshold and perform retrieval, confirm that the number of recall results and matching accuracy meet business expectations.
The values given 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 2026-09-28.