What Data for This Category Looks Like
It typically covers internal office scenarios for regulatory departments including market supervision, housing and urban-rural development, and ecological environment. Daily documents include inspection plans, inspection target lists, inspection item checklists, on-site inspection records, rectification notices, and inspection result public announcements. Data is primarily sourced from the random inspection and public disclosure regulatory platform, enterprise credit information public disclosure system, industry regulatory systems, and law enforcement officer directory. Update frequency is monthly. The content span of a single document varies widely, with both structured list entries and unstructured on-site records and public announcement text. Fields include the unified social credit code of inspection targets, law enforcement officer number, and inspection item details, among others.
Constraints on Vector Models and Indexing
The monthly update frequency requires indexes to support incremental synchronization, to avoid resource consumption from full reconstruction. The coexistence of multiple document types requires a chunking strategy compatible with both structured and unstructured content, to preserve semantic integrity of inspection items and target information. Data from multiple sources requires unified field mapping rules, to prevent index format discrepancies between identical document types exported from different systems. Regulatory scenarios also have high recall accuracy requirements, to avoid law enforcement compliance risks caused by missed indexing or duplicate indexing.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average content length of daily documents for this category (inspection records, rectification notices), avoids breaking semantic coherence by splitting chunks too small |
| Overlap Length | 100–200 characters | Retains contextual connections across chunks, ensures key content such as inspection target names and law enforcement officer information is not split |
defaultToken | 8192 | Adapts to the input length limit of mainstream vector models, covers the complete content of a single inspection plan |
maxToken | 16384 | Supports long text processing during batch indexing, meets the full document indexing needs of monthly updates |
| Similarity Threshold | 0.75–0.85 | Filters low-match related content, avoids repeated matching of historical records for the same inspection target |
| Single Search Limit | Top 10 entries | Balances recall efficiency and accuracy, adapts to daily office needs of law enforcement officers quickly matching inspection tasks |
Three Common Misconfigurations
- Phenomenon: Duplicate inspection target entries appear after index merging. Cause: No global deduplication rule is set for the law enforcement officer directory and inspection target list, causing the same entry to be indexed multiple times.
- Phenomenon: A 401 error is returned when calling the vector model. Cause: The access key for the vector model is not configured correctly, or the custom channel is not bound to the vector model type correctly.
- Phenomenon: The latest inspection result public announcement content is not recalled after triggering retrieval. Cause: The incremental indexing task is not configured according to the monthly update frequency, causing newly generated public announcement documents to not be synchronized into the index library.
How to Verify Proper Configuration
- Import a single inspection target list document, check whether duplicate entries exist in the index list, to verify that the deduplication configuration takes effect.
- Initiate a search for "2024 Q1 Inspection Plan", confirm that the returned results include the correct chunked content of the corresponding document.
- Test the vector model call, confirm that normal embedding results are returned, with no 401-level errors.
- Trigger the monthly incremental indexing task, check the FastGPT v4.17.0 task log to confirm that newly generated rectification notices and public announcement documents have been successfully indexed.
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.