What this category of data includes
This category of data comes from regulatory standards, online environmental monitoring equipment, gas detectors, personnel qualification files, equipment isolation lists, and supervision platforms. Daily documents include risk identification records, work tickets for hot work, confined space work, lifting work and other operations, environmental monitoring records, hazardous waste ledgers, emission monitoring data, and accident and hidden danger rectification reports. Data updates are triggered upon completion of individual operations, real-time updates of monitoring requirements, or periodic inspection updates. Document fields include operation type, gas detection value, personnel qualification number, and isolated equipment number, with corresponding units including ppm, cubic meters, hours, and other units.
Constraints imposed on vector models and indexes by these characteristics
This category of data is updated upon completion of individual operations, real-time updates of monitoring requirements, or periodic inspection updates. Document structures vary widely, containing both structured fields and semi-structured text. This imposes constraints on the real-time performance and adaptability of indexes. Data related to safety, environmental protection and operation permits requires strict control of recall result relevance to avoid irrelevant content interfering with risk judgment. Data such as hazardous waste and emission monitoring has clear unit fields, so vector indexes must retain field associations to prevent semantic confusion. Frequently updated work tickets and monitoring data require indexes to support incremental updates, avoiding resource occupation and query delays caused by full reindexing.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the semantic integrity of texts such as work tickets and monitoring records, avoid semantic fragmentation caused by too short length, and avoid increasing vector computing burden caused by too long length |
| Overlap Length | 50–100 characters | Retain cross-chunk context association, prevent work steps and monitoring sequences from being split by chunking |
| Similarity Threshold | 0.75–0.85 | Filter low-relevance results, avoid irrelevant work tickets or monitoring data interfering with safety judgments, and adapt to the high-confidence requirements of this category |
| Single Search Limit | Top 8 entries | Balance query speed and recall coverage, ensure that core risk-related documents are recalled first |
maxToken | 4000 token | Control the length of single-round context, avoid exceeding the model's processing limit, and adapt to the long-text verification requirements of safety Q&A |
defaultToken | 512 token | Adapt to short-text vectorization of structured fields, ensure semantic consistency of fields such as personnel qualifications and equipment numbers |
Three common configuration errors
- Phenomenon: A large number of irrelevant historical work tickets are returned in query results, with no valid risk identification content. Cause: No reasonable similarity threshold is set, or the reranking function is not enabled, resulting in low-relevance content being recalled first.
- Phenomenon: Disk usage grows rapidly after deployment, far exceeding the total size of original files. Cause: Expired work tickets, monitoring records and other data are not cleaned up, and the incremental update strategy of vector indexes is not configured, resulting in repeated storage of chunked text and embedding vectors.
- Phenomenon: Timeout error occurs during the first query, returning status code 504. Cause: Chunk length is set too large, or single search limit is too high, resulting in vector computing and context splicing exceeding the model's processing limit, causing timeout.
How to verify correct configuration
- View the vector index update log to confirm that newly added work tickets, monitoring records and other data can complete incremental indexing automatically, without full reconstruction.
- Input typical safety questions, check the relevance of returned results, and adjust the similarity threshold and single search limit to a range that meets business requirements.
- Check disk usage statistics to confirm that the occupancy ratio of embedding vectors and chunked files meets expectations, with no redundant data from repeated storage.
- Verify the index model channel configuration path of FastGPT v4.17.0 version to ensure that the specified vector model can be called normally.
The values provided are common starting points and should be evaluated against the reader's own samples.