What This Type of Data Looks Like
Data comes from safety production supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems, with a daily update frequency. Commonly processed documents include hazard inspection lists, hazard governance ledgers, rectification notices, review records, and major hazard suspension and supervision orders. A single document typically contains fields such as specific hazard descriptions, rectification responsible entities, rectification time limits, and review results. Processing entities include emergency management departments, industry competent departments, safety management departments of production and business entities, and park management committees.
Constraints on Vector Models and Indexes From These Data Characteristics
Daily updated data requires indexes to support incremental synchronization, avoiding performance loss caused by full reconstruction. Differences in the structure of multiple types of documents require vector models to adapt to mixed input of unstructured text and semi-structured fields. Differences in data formats from multiple sources require a unified text preprocessing process to ensure consistency in vector extraction. Hazard descriptions dense with professional terminology require vector models to have semantic adaptation capabilities for the safety production field. Requirements for cross-document associated ledger summaries require indexes to support associated retrieval logic between multiple documents.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text length of hazard inspection documents, balancing semantic integrity and vector extraction accuracy |
| Overlap Length | 100–200 characters | Prevents semantic fragmentation after chunking, ensuring contextual association of cross-chunk content |
maxToken | 8192 token | Adapts to the input upper limit of mainstream vector models, meeting vector extraction requirements for long documents |
| Similarity Threshold | 0.75–0.85 | Balances recall rate and false recall rate of hazard-related content, adapting to the strictness requirements of safety production scenarios |
| Single Search Limit | Top 10 entries | Ensures relevant coverage of retrieval results, avoiding redundant information interference with ledger summary |
defaultToken | Calibrated via actual testing | Adapts to the default parameters of the currently used vector model, ensuring stability of basic calls |
Three Common Misconfigurations
- Phenomenon: Vectorization call logs show that only a single text chunk is passed per call, and the vectorization service returns a batch processing error prompt. Cause: Batch processing configuration is not enabled, only the default single-chunk call logic is used, and the array receiving requirements of the vectorization service are not met.
- Phenomenon: Retrieval results only contain chunked content from a single document, and cross-document ledger associated summary cannot be completed. Cause: Cross-document index association rules are not configured, only single-document chunks are indexed independently, and no association relationship between documents is established.
- Phenomenon: A parameter error occurs when calling the Alibaba multimodal-embedding-v1 model, and model loading cannot be completed. Cause: The API address and key information of the corresponding model are not filled in the model configuration page, and model adaptation registration is not completed.
How to Confirm Configurations Are Correctly Set
- View vector model call logs to confirm that the number of text chunks passed per call meets the batch processing configuration requirements, and verify that the batch processing logic is effective.
- Perform an associated retrieval test for a single hazard, and check whether the returned results include chunked content from associated documents such as rectification notices and review records.
- Check the vector model configuration page to confirm that the parameters of the corresponding model have been filled in, and that call requests can be initiated normally.
- Simulate the retrieval process for ledger summary, verify that the relevance of the returned results matches the strictness requirements of the business scenario, and adjust related configurations until expectations are met.
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.