Data Overview for This Category
This category covers documents produced by housing and urban-rural development quality and safety supervision stations, construction contractors, supervision firms, and project owners. Daily generated documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality issue handling forms, and completion acceptance supervision reports. Data comes from the engineering quality and safety supervision system, on-site construction personnel real-name management platform, supervision logs, and test reports. The update frequency is once per week. Core data is generated around the on-site construction supervision workflow, including on-site inspection records, rectification closure information, and acceptance-related materials. No additional statistical quantitative fields are included.
Constraints on Vector Models and Indexing
Multi-source heterogeneous data sources require the indexing workflow to support cross-system format adaptation, to prevent embedding deviations caused by inconsistent fields. The fixed weekly update cycle means incremental indexing logic must be prioritized, to reduce resource consumption from full reindexing. Documents are built around the on-site construction supervision workflow, with strongly linked content such as on-site inspections and rectification closures. Chunking must preserve complete business context, to avoid breaking the association between rectification notices and their corresponding inspection records during splitting. Additionally, no additional statistical quantitative fields are present, so precise retrieval must rely on text semantic matching. The configuration requirements for maxToken must also be accommodated to support spliced content from multiple documents.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average character count per document in this category, retains complete semantics of a single supervision record or rectification notice |
| Overlap Length | 100–150 characters | Connects context between adjacent chunks, avoids breaking process associations related to hidden danger rectification when splitting |
| Similarity Threshold | 0.72–0.78 | Matches the semantic similarity characteristics of documents in this category, filters low-relevance non-supervision content |
| Single Recall Limit | Top 10 entries | Covers the number of supervision-related documents required for a single round of party building learning Q&A, avoids interference from redundant results |
maxToken | 4096 token | Adapts to the context length requirements of party building Q&A, accommodates spliced content from multiple supervision documents |
defaultToken | 2048 token | Serves as the input length limit for the default embedding model, matches the average semantic unit length of documents in this category |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Configuration Errors
- Indexing runs extremely slowly. The symptom is that background indexing task logs show execution time exceeding 30 minutes, and task status remains in processing for an extended period. The cause is that incremental indexing logic is not configured, and full scans of all data from multi-source systems are executed for every update.
- No matching model appears in the text understanding model dropdown when creating a knowledge base. The symptom is that the ollama qwen2.5 language model and bge-m3 indexing model have been added to the model channels, but the dropdown list for the corresponding link is empty. The cause is that the embedding model is not bound to the vector index configuration item, and only the channel configuration for the language model has been completed.
- Commercial edition indexing enhancement and automatic image indexing do not perform as expected. The symptom is that after uploading a scanned copy of a hidden danger rectification notice, the extracted text has missing or incorrect content. The cause is that the OCR enhancement switch for engineering document images is not enabled, or the association logic between image metadata and text chunking is not configured.
How to Verify Correct Configuration
- Manually upload one standard supervision inspection record, trigger manual indexing, and check the chunking log in the indexing task. Confirm that the chunk character count falls within the 800–1200 character range.
- Input a party building-related quality and safety supervision question, check the number of documents returned in the search results, and confirm it matches the configured single recall limit.
- Go to the model configuration page, check the dropdown lists for vector models and language models. Confirm that the added ollama qwen2.5 and bge-m3 models appear in the options for their respective links.
- Wait one week to trigger the incremental indexing task, check the indexing logs, and confirm that only newly added test reports and supervision log data are updated, and no full reindexing is performed.
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.