What Data for This Category Looks Like
Data for this category comes from engineering construction project approval management systems, construction drawing review systems, enterprise qualification databases, and credit platforms. The update frequency is daily. Daily documents include construction permit application materials, construction drawing review qualification certificates, quality and safety supervision procedure documents, completion record forms, and similar items. The structure centers on approval nodes. It includes participating construction unit qualifications, personnel information, approval process nodes, and compliance requirement fields. There are no unified fixed units. Fields vary based on the approval stage.
Constraints on Vector Models and Indexing Workflows
Differences in data format across multiple source systems require the indexing workflow to support unified vector mapping for cross-source data. This avoids vector space drift between documents from different systems. The daily update requirement means indexing configurations must support incremental update modes. This reduces resource consumption from full reindexing. Document structure changes with approval nodes, so chunk length must adapt to the content density of different documents. This avoids semantic breaks from overly fine-grained chunking, or loss of critical compliance clauses from overly coarse chunking. The specialized nature of fields such as participating construction unit qualifications requires vector models to adapt to semantic encoding of construction industry terminology.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category’s documents contain specialized compliance clauses. Chunk length is set to match the semantic integrity of clauses, to avoid splitting critical approval requirements |
| Overlap Length | 100–150 characters | Cross-chunk approval process correlation information needs to retain context, to avoid semantic gaps |
| Similarity Threshold | 0.72–0.80 | Policy clauses in this category have strong specialized content. The threshold filters low-relevance search results to ensure answer compliance |
| Single Search Limit | Top 8 results | Policy basis for engineering approval is usually concentrated in a small number of core documents. Too many results will exceed the context window |
| maxToken | 8000 token | FastGPT v4.17.0’s default context window adapts to the multi-document splicing needs of this category, to ensure complete reference of approval clauses |
| defaultToken | 500 token | Reasonable length for vector display and context splicing of single search results, to avoid redundancy |
Three Common Configuration Errors
- Phenomenon: Background indexing logs show some source documents failed to load, and the number of search results does not match the configured single search limit. Cause: Cross-system data indexing mapping rules were not correctly associated, leading to documents from some data sources not being included in the index scope.
- Phenomenon: Background logs show matching policy files were retrieved, but the large language model states no relevant basis was found. Cause:
defaultTokenormaxTokenwas not correctly configured, leading to retrieved text fragments not being fully passed to the large language model context, or incorrect context splicing order. - Phenomenon: Similarity distribution of vector search results is scattered, making it impossible to locate core approval clauses. Cause: Chunk length was set too short, splitting complete compliance clauses into multiple semantically disjoint fragments, leading to vector encoding losing critical information.
How to Confirm Correct Configuration
- Upload a single construction permit application document, run a policy Q&A test, and confirm the number of retrieved chunks matches the configured chunk length settings.
- Adjust the similarity threshold, verify that search result relevance changes as expected with threshold adjustments, with no large number of low-match results included.
- Check indexing task logs to confirm daily incremental update tasks are running normally, with no abnormal triggers for full reindexing.
- Test Q&A that includes data from multiple system sources, verify that search results from cross-source documents are correctly recalled.
The values provided are common starting points, and should be evaluated against the user’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.