What data for this category looks like
Typical data for this category comes from the "Double Random, One Public" regulatory platform, enterprise credit information disclosure system, industry regulatory systems, and law enforcement officer directory. Daily generated documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, inspection result announcements, and regulatory policy interpretation materials related to party building learning. Data updates occur monthly. Maintenance tasks include synchronizing and maintaining inspection item and target databases, matching law enforcement officers and generating tasks, entering inspection results and completing announcements. Document fields include unified social credit codes of inspection targets, law enforcement officer numbers, inspection item categories, rectification completion status, policy association tags, and more. Minor differences in data format exist across different sources.
What constraints do these characteristics impose on vector models and indexing
The monthly update requirement means indexes must support incremental synchronization to avoid resource consumption from full reindexing. Mixed storage of multi-source, multi-type documents requires differentiated chunking rules for different document types. For example, inspection result announcements must retain all complete announcement fields. On-site inspection records can be split by paragraph. Party building-related policy interpretation documents must be split by chapter to preserve semantic coherence. The semantic matching requirement for party building learning Q&A means the index’s recall scope must cover all documents related to regulatory policies and law enforcement procedures. The index recall logic must be adapted to party building-related semantic features. Differences in fields across documents require unified metadata mapping during index construction, to avoid missing fields or matching errors during subsequent retrieval.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to semantic integrity of long texts such as on-site inspection records, rectification notices, and party building policy interpretations, and matches the default chunking adaptation range of FastGPT v4.17.0 |
| Overlap Length | 100–150 characters | Prevents key semantics from being truncated after chunking, and ensures cross-chunk contextual coherence |
defaultToken | 8192 | Adapts to the long context requirements of party building learning Q&A, and matches the input upper limit of mainstream open-source and commercial vector models |
maxToken | 16384 | Meets indexing requirements for ultra-long documents such as complete inspection plans and inspection result announcements |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Balances precision and recall for regulatory target retrieval, and adapts to policy semantic matching requirements for party building learning Q&A |
| Single Search Limit | Top 10 results | Meets basic recall requirements for law enforcement officer matching and inspection item retrieval, and avoids returning excessive redundant data |
Three common misconfigurations
- The index model option in the interface cannot be selected normally, with a loading failure prompt. The cause is that the call key or gateway address of the index model was not configured correctly. Some users who use One API for forwarding did not correctly bind model permissions.
- Confusing the configuration of index models and text generation models. Mistakenly using Ollama text generation models as index models, resulting in insufficient semantic matching of retrieval results. The cause is failing to clearly distinguish the applicable scenarios of the two model types, and not selecting a dedicated vector indexing model as required by the documentation.
- Non-compliant inspection target extraction occurs after incremental index updates. The cause is that the incremental synchronization verification logic of the index was not enabled, and associated data from the law enforcement officer directory was not synchronized and updated, resulting in residual old data during matching.
How to confirm the configuration is complete
- Enter the FastGPT index management page, view the configured index model list, and confirm that the target model has been loaded normally and shows an available status.
- Upload a standard inspection plan document, perform a chunking test, and check that the chunked text length matches the set chunk length range.
- Input a party building-related regulatory inspection question, perform a retrieval, and check that the returned results include exclusive document types for this category, and the number of results matches the set single search limit.
- Trigger an incremental index synchronization, check that there are no error messages in the synchronization log, and that the updated documents can be retrieved normally.
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.