What data looks like for this category
Data for this category primarily comes from construction project approval management systems, construction drawing review systems, enterprise qualification databases, and public credit platforms. Daily processed documents include construction permit application files, construction drawing review qualification certificates, quality and safety supervision procedures, completion record forms, and other full-process approval materials. Data is updated daily, covering the full process from project approval to completion filing. Each document includes structured fields such as approval items, participating construction unit information, material status, and approval nodes, plus text content from attached scanned documents.
Constraints on vector models and indexing
Multi-source data format differences require indexing to support embedding of mixed text and structured fields. Daily update frequency requires configuring incremental indexing mechanisms to avoid time overhead from full reindexing. Differences in document structure—such as tabular content in construction permit applications and paragraph-style descriptions in completion record forms—require flexible chunking rules. Correlations between core business fields like participating unit qualifications and approval nodes require retaining metadata associations during indexing to avoid losing business context during retrieval.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the paragraph length of construction approval documents, avoids truncation of key approval information |
| Overlap Length | 100–200 characters | Preserves coherent business information such as approval nodes and unit names across chunks |
defaultToken | 8192 | Matches the long text embedding requirements of construction documents, adapts to the model limit of FastGPT v4.17.0 |
maxToken | 16384 | Supports complete embedding of long documents such as ultra-length completion record forms |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Distinguishes the relevance of construction approval files, avoids false matches or missed recalls |
| Single Search Limit | Top 10 results | Matches the quantity requirement for core reference materials during approval review |
Three Common Mistakes
- Phenomenon: Indexing tasks stay in the "indexing" state for long periods with no progress updates. Cause: No incremental indexing trigger rules are configured, and adaptation to the daily update frequency of construction approval data is missing. This leads to excessive time consumption for full indexing.
- Phenomenon: No matching results are returned after retrieval with a custom indexing configuration. Cause: Core fields such as participating unit qualifications and approval nodes in construction permit applications are not mapped separately. Vector embedding only covers ordinary text content, and business metadata is not associated.
- Phenomenon: A large number of irrelevant file materials appear in retrieval results. Cause: The similarity threshold is set too low, and calibration is not performed using exclusive business relevance rules for construction approval files. This leads to recall of low-match results.
How to Confirm Configurations Are Correct
- Check the FastGPT v4.17.0 indexing logs to confirm that daily incremental synchronization tasks execute normally at the daily update frequency, with no failed errors.
- Manually upload a construction permit application document, and verify that the retrieval function can recall relevant file materials such as construction drawing review qualification certificates and quality and safety supervision procedures for the same project.
- Check the vector embedding metadata fields to confirm that they include construction approval-specific business information such as approval items, participating units, and approval nodes.
- Adjust the similarity threshold, and verify that the match degree of retrieval results meets the screening requirements of business review.
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.