What data looks like for this category
Data for this category originates from engineering quality and safety supervision systems, on-site real-name management platforms, supervision logs, and test reports. The update frequency is weekly. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Most data fields include project numbers, on-site locations, rectification deadlines, acceptance conclusions, and similar details. Some documents are structured forms, while others are handwritten on-site scanned documents, with no unified fixed format.
Constraints on vector models and indexes
Weekly updated data requires indexes to support incremental synchronization logic, to avoid additional time spent on full reconstruction. Mixed document types from multiple sources, including structured forms, on-site scanned documents, and plain text logs, require indexes to adapt to text splitting rules for different formats. Documents contain precise association fields such as project numbers and on-site locations, which requires indexes to support metadata filtering capabilities to narrow recall scope and improve accuracy. Hidden danger rectification documents have strong timeliness, which requires recall results to prioritize entries updated most recently.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of documents in this category such as supervision inspection records and supervision logs, avoiding overly fragmented splitting or context breaks |
| Overlap Length | 100–150 characters | Ensures contextual coherence between adjacent text blocks, covering hidden danger rectification related information across blocks |
| Similarity Threshold | 0.72–0.85 (calibrated via actual testing) | Adapts to the recall accuracy of hidden danger rectification documents, balancing accuracy and recall coverage |
| Single Search Limit | Top 10 entries | Balances the richness of recall results and system response speed, meeting conventional business query needs |
defaultToken | 1024 token | Adapts to the embedding length requirements of basic text blocks in this category |
maxToken | 8000 token | Complies with the context limit of FastGPT v4.17.0, adapting to the context length of business queries in this category |
| Metadata Filter Switch | Enabled | Uses fields such as project numbers and on-site locations to narrow recall scope, improving query accuracy for specific projects |
Three common configuration mistakes
- Issue: Some project documents cannot be recalled after index creation. Cause: The metadata filter switch is not enabled, causing confusion between documents of different projects during full recall.
- Issue: Permission errors occur when directly managing the vector database via MongoDB. Cause: Multi-user permissions for the vector database are not configured in FastGPT v4.17.0, and only the default root user permission is retained.
- Issue: Loading fails when calling a non-OpenAI vector model. Cause: The model is not confirmed to be compatible with FastGPT's embedding interface specifications, and compatibility testing is not completed in advance.
How to confirm proper configuration
- View the index creation log to confirm that documents from all sources are correctly split and stored in the database, with no format errors.
- Initiate test queries related to the business, and verify that the metadata of recall results matches the target business fields.
- Test the incremental update operation to confirm that only newly added or modified documents are synchronized to the index, with no full reconstruction triggered.
- Switch the vector model for testing, confirm that the model loads normally and the recall results align with business scenario requirements.
The values provided here 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.