What this category of data looks like
Typical data for this category comes from daily business systems of housing and urban-rural development quality and safety supervision stations, construction units, supervision units, and construction owners. It includes documents such as supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Data sources include engineering quality and safety supervision systems, on-site construction real-name management platforms, supervision logs, and test reports. Update frequency is weekly. Documents contain structured fields and unstructured descriptions. Some test reports include visual data. Core content focuses on on-site supervision, hidden danger closed-loop management, and acceptance criteria.
Constraints on vector models and indexing
This category has diverse data sources: structured forms, unstructured logs, and test reports with visual content. This requires vector models to support multimodal content embedding and retention of structured fields. The weekly update frequency requires the index system to support incremental updates. This avoids resource waste from full reindexing. Document length varies widely, from short rectification notices to complete completion acceptance supervision reports. This requires chunking and embedding logic to adapt to text of different lengths. Additionally, the review process needs to associate context across multiple documents. The index retrieval logic must match cross-document related content to avoid missing hidden danger rectification closed-loop information.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the length of single core segments of documents in this category, avoiding splitting that breaks key logic such as rectification requirements and acceptance clauses |
| Overlap Length | 100–200 characters | Ensures contextual coherence across chunks, adapting to associated descriptions of hidden danger rectification closed-loop processes |
maxToken | 8192 token | Meets the full embedding requirements for long documents such as completion acceptance supervision reports, avoiding truncation of key content |
defaultToken | Set based on actual measurement | Adapts to the input length limits of different embedding models, matching the average character count of documents in this category |
| Similarity Threshold | 0.75–0.85 | Distinguishes between different types of supervision documents, avoiding mismatching unrelated rectification records to review tasks |
| Single Search Limit | Top 10 results | Covers the weekly updated file data volume, ensuring comprehensive information during reviews |
Three common configuration errors
- When test report images uploaded to the knowledge base are updated, the index is not synchronized, and the interface still displays the old thumbnail. The cause is that the weekly incremental index trigger rule is not configured, and only full reindexing is relied on. This cannot match the weekly data update frequency of this category.
- After switching the vector model, the index construction task has no progress and cannot roll back to the original model. The background log returns a 404 status code. The cause is that the integrity of the embedding model's API address and key configuration is not verified. This results in failed routing of requests to the target service.
- Key parameter fields of test reports are missing from embedded file review results, making it impossible to verify the authenticity of materials. The cause is that the chunk length is set too large. This truncates core parameter content in test reports and prevents key information from being included in the index.
How to confirm correct configuration
- Upload a typical hidden danger rectification notice. Check the chunk preview interface to confirm that key rectification clauses are not truncated.
- Trigger an incremental index task. Wait for the task to complete, then check the index progress log to confirm that it matches the weekly updated data range.
- Configure the target embedding model and send a test request. Confirm that there are no 404 errors, and normal vector data is returned.
- Initiate a file review retrieval test. Confirm that the number of returned results matches the preset single search limit, and that the content matches the review requirements.
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.