Data Profile for This Category
The primary data for this category comes from units including petition bureaus and departmental petition offices. Daily documents include petition registration forms, acceptance notices, transfer and assignment letters, handling opinions, review and reexamination applications, resolution plans, and more. Data sources cover the national petition information system, local petition systems, the 12345 citizen hotline, grassroots governance platforms, and historical petition archives. The update frequency is daily. Document structures include fields such as appeal content, handling process nodes, responsible unit information, and policy basis. There is no unified fixed format. Some historical archives are unstructured content converted from scanned documents to text.
Constraints for Vector Models and Indexing
Batch data updated daily requires indexes to support incremental triggering, to avoid blockages caused by full repeated loading. Multi-type, unstructured documents need flexible chunking rules, to prevent splitting key handling processes or policy explanations. Data from multiple sources must first be unified in format, otherwise semantic deviation will occur during vector embedding. Long text content in historical archives requires sufficient token capacity, to avoid truncating core handling opinions. At the same time, the accuracy of index results directly affects the identification and handling of petition matters. Matching thresholds must be strictly controlled to reduce the risk of repeated petitions or petitions submitted outside the proper hierarchical channel.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of petition-related documents, avoids splitting key handling process nodes |
| Overlap Length | 100–200 characters | Retains cross-chunk context, ensures coherence of policy explanations and responsible unit information |
| Similarity Threshold | 0.75–0.85 | Distinguishes valid appeals from invalid duplicate content, reduces misjudgment risk |
| Single Search Limit | Top 10 entries | Matches the single review scope for petition matter handling, avoids interference from irrelevant data |
defaultToken | 8192 | Adapts to the total token limit of petition documents, meets the indexing requirements for complete handling opinions |
maxToken | 16384 | Handles batch archived historical petition files, avoids truncating critical content |
Three Common Configuration Mistakes
- Stalled knowledge base indexing with residual uncompleted data Phenomenon: The interface shows stagnant indexing progress, and the log indicates the "some files not indexed" field is empty. Cause: Incremental indexing trigger rules are not configured. Full indexing of daily updated data causes blockages by repeatedly loading historical archives.
- Abnormal matching after switching vector models Phenomenon: Search results return non-petition-related irrelevant content, with similarity matching deviation exceeding 0.2. Cause: Model adaptation parameters are not adjusted for petition document specialized terminology. Direct use of a general vector model leads to semantic deviation.
- Unable to confirm indexing completion after file upload Phenomenon: No return status field appears after calling the upload API, making it impossible to determine whether data and indexing are complete. Cause: API callback configuration is not enabled, and the status verification interface for indexing completion is not bound.
How to Verify Correct Configuration
- Check the indexing logs in FastGPT v4.17.0, confirm that daily incrementally updated petition documents are properly chunked, with no truncated or missing handling opinion content.
- Simulate a search using a petition appeal keyword, verify that the similarity thresholds of returned results fall within the preset range, and that only petition-related documents are included.
- After calling the file upload API, check the returned task ID, and confirm that the indexing progress reaches 100% via the supporting status query interface.
- Compare search results before and after switching vector models, confirm that the matching accuracy of specialized terms such as review and reexamination and transfer and assignment meets expectations.
The values listed above are common starting points, and 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.