What this category of data looks like
This data typically includes business data from regulatory departments such as market supervision, housing and urban-rural development, ecological environment, cultural and tourism, and human resources and social security. Sources include the "Double Random, One Public" regulatory platform, enterprise credit information publicity system, industry regulatory systems, and law enforcement officer directory. Daily documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. Data updates occur monthly. Most data fields involve unified social credit codes of regulatory objects, law enforcement officer numbers, inspection item categories, and similar details. Some data is stored as structured tables, while other parts are unstructured text records.
What constraints these characteristics impose on the vector models and indexes link
Monthly periodic data requires adaptation to incremental indexing rules to avoid excessive resource usage from full index reconstruction. Mixed-format documents (structured tables and unstructured text) need indexes that support shard processing for multiple data types. Data with strong field associations, such as law enforcement officer directories and inspection target lists, require field-level association indexes to support precise matching. Inspection result announcements have high timeliness requirements, so index update delay must be controlled to prevent returning outdated information. Semantic matching for exclusive business fields needs targeted vector mapping rules to ensure retrieval results align with regulatory business logic.
How to configure the settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the common length of regulatory documents, preserves core semantic units such as inspection items and inspection records |
| Overlap Length | 100–200 characters | Avoids breaking cross-chunk semantic coherence, ensures retrieval integrity of associated fields |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-regulatory related content, meets the precision requirements of law enforcement inspections |
| Single Search Limit | Top 10 entries | Controls redundancy of returned results, adapts to the limited result display needs of regulatory business |
defaultToken | 1000 | Adapts to the single-chunk length of most regulatory documents, matches the parameter requirements of basic vector encoding |
maxToken | 4000 | Adapts to the context window upper limit of FastGPT v4.17.0, meets the splicing needs of multi-chunk retrieval results |
Three common misconfigurations
- Index tasks stay in training or reconstruction status for more than one day. This happens when incremental indexing rules are not configured. The large volume of full monthly update data processed via full indexing causes task blocking.
- A 60-second timeout occurs when switching knowledge base indexes. This occurs when the single search limit is set too high, or the chunk length exceeds the system processing threshold, leading to excessively long index loading time.
- A specific file remains in the indexing state and cannot be completed. This happens when the file contains garbled characters or non-standard format text. The vector model cannot complete effective encoding, causing the indexing task to get stuck.
How to confirm the configuration is properly set
- View the index configuration logs in FastGPT v4.17.0, and confirm that parameters such as chunk length and overlap length match the set values.
- Test retrieval for questions related to inspection target lists, and check whether the similarity of returned results falls within the preset threshold range.
- Trigger an incremental indexing task, and confirm that the update time conforms to the conventional processing cycle for monthly data.
- Verify whether the configurations of
defaultTokenandmaxTokenadapt to the maximum document length of the current knowledge base.
The values provided 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.