What Data for This Category Looks Like
Data for this category comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Commonly processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Data is updated annually. The volume of data imported in a single batch fluctuates with the number of declared projects that year. Most document structures combine structured tables and unstructured supporting text. Fields include project identifiers, performance indicators, fund usage details, rectification items, and more. Some supporting materials are fragmented text transcribed from scanned documents.
Constraints on Vector Models and Indexing
The annual update cycle means indexes do not require frequent incremental construction. Full index rebuilding can be used to reduce operational maintenance costs. The mixed structure of structured tables and unstructured text requires a chunking strategy that preserves both table field integrity and long-text semantic coherence. Data from multiple sources has format inconsistencies. Standardized preprocessing must be completed before indexing to prevent field misalignment that reduces vector matching accuracy. Most supporting materials are fragmented, project-related voucher text. Index recall must prioritize content linked to performance indicators and fund usage. Index weights should be adjusted to prioritize core business fields.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the semantic unit length of documents such as performance evaluation reports and self-assessment forms, avoids overly fragmented or overly long chunks |
| Overlap Length | 100–150 characters | Ensures semantic coherence across chunks, prevents key performance indicators from being split across different chunks |
maxToken | 8192 | Matches the context window of mainstream vector models supported by FastGPT v4.17.0, meets the indexing length requirements for annual full dataset imports |
defaultToken | 256 | Matches the average length of short texts such as individual performance targets and rectification items, ensures semantic completeness of individual vector chunks |
| Similarity Threshold | 0.72–0.85 (calibrated via testing) | Distinguishes performance-related supporting materials from irrelevant text, reduces false recall rates |
| Maximum Search Results per Query | Top 10 | Covers the number of core supporting materials for a single declared project, balances recall efficiency and result relevance |
Three Common Misconfigurations
First Issue
Phenomenon: After a full index rebuild, the system provides no progress feedback for an extended period. The newly generated index cannot be manually switched to. Cause: Index version management and alias switching logic are not configured. The original index cannot be replaced smoothly during the rebuild. Business calls continue to point to the old index.
Second Issue
Phenomenon: The batch indexing interface returns parameter verification failures. Multiple declaration materials cannot be imported in batches. Cause: Chunk parameters and batch size are not set correctly. The batch size exceeds the system's single-processing threshold, so requests are blocked.
Third Issue
Phenomenon: When retrieving performance-related supporting materials, only fragments from a single document are returned. Other relevant materials under the same project cannot be recalled. Cause: Single-document single-index mode is used. All materials under the same project are not unified into a single index collection. Cross-document semantic associations cannot be recognized by the vector model.
How to Verify Correct Configuration
- Run an indexing test on a single performance target form. Check the number of split text chunks to confirm chunking parameters are active.
- Call the batch indexing interface, submit a small number of test documents. Check the status codes and parameter prompts in returned results to confirm batch configuration is correct.
- Enter performance indicator keywords to run a retrieval test. Check the coverage of recall results and the relevance of materials from the same project to confirm index collection configuration meets business requirements.
- View the index management interface. Confirm index version aliases have been set to allow quick switching after rebuild completion. Confirm version management configuration is active.
The values provided here are common starting points. They should be evaluated 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.