What data looks like for this use case
The data for this use case primarily comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information public disclosure system, and departmental supervision databases. The update frequency is daily. Daily processed documents include on-site inspection records, interrogation transcripts, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, and service receipts. Individual case data includes unified case numbers, law enforcement subject information, records of illegal facts, discretionary application basis, final penalty decisions, and various document attachments. Most fields are linked to law enforcement process nodes, and there is no unified cross-department standard format.
What constraints do these characteristics impose on the vector model and indexing workflow
Multi-source mixed data sources require vector models to adapt to text transcriptions in different formats, including colloquial records from law enforcement recorders and structured document text. The daily batch update feature requires indexes to support incremental construction to avoid excessive system resource usage from full reindexing. Case data is linked to multiple process documents, so indexes must be grouped and associated by case number to avoid semantic interference across cases. The semantic importance of different documents varies significantly; core information in penalty decisions and interrogation transcripts carries higher weight than service receipts, so targeted adjustments to chunking and retrieval rules are needed.
How to set configurations
Core configuration items for the vector model and indexing workflow include maxToken, defaultToken, and others. Below are specific configuration recommendations for this use case:
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| maxToken | 8192 token | Adapts to the input limit of mainstream embedding models, covers the standard text length of a single penalty decision |
| Chunk Length | 800–1200 characters | Matches the single-page text length of on-site inspection records and interrogation transcripts, avoids semantic fragmentation that impacts retrieval accuracy |
| Overlap Length | 100–200 characters | Retains cross-chunk law enforcement process association information, ensuring semantic integrity of the evidence chain |
| Similarity Threshold | Calibrated based on actual testing | Adjust based on semantic matching accuracy for law enforcement scenarios, to meet precise retrieval needs for illegal facts and penalty basis |
| Single Search Limit | Top 10 results | Matches the typical review volume during internal office legal review processes, avoids excessive redundant results interfering with review efficiency |
| defaultToken | 4096 token | Serves as the basic unit for default chunking, balancing semantic integrity and retrieval response speed |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common mistakes
- Phenomenon: After replacing the embedding model, the platform interface shows that the knowledge base index status has not updated, and manually triggering a rebuild has no response. Cause: The batch index rebuild function provided by the platform is not used, and documents are still being reimported individually, resulting in unsynchronized index updates.
- Phenomenon: When configuring an embedding model other than ada 002, an undefined model error pop-up appears. Cause: The API access permission for the corresponding model is not configured on the platform, or the model name format does not comply with platform verification rules.
- Phenomenon: Semantic retrieval scores are high but the results do not hit core illegal facts. Cause: Chunking rules are not adjusted for transcript-style documents, resulting in loss of key law enforcement details after chunking, leading to deviations in semantic matching.
How to confirm configurations are correctly set
- Upload a single complete penalty decision, check that the chunked text retains complete illegal facts and penalty basis, with no obvious semantic fragmentation.
- After replacing the embedding model, trigger a batch index rebuild operation, confirm that the index progress bar updates normally and there are no error logs.
- Enter typical illegal fact keywords, check whether the search results are sorted in line with business logic, with core cases displayed first.
- View the platform index logs, confirm that there are no model call failures, chunking exceptions, or permission error prompts.
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.