What Data for This Category Looks Like
Infrastructure construction business process data primarily comes from official review documents across the full project lifecycle: project initiation, construction, and acceptance. These documents include construction permit applications, progress review forms, site visa records, acceptance inspection specifications, and more. Data update timing follows project milestones. New or updated documents are added when a single review submission is completed. Each document includes standard fields: project number, section name, review date, approval node, required material list, filling format requirements, and others. Units use standard engineering units such as square meters, cubic meters, working days, and ten thousand yuan.
Constraints Imposed on Vector Models and Indexes
The multi-field structure of infrastructure business documents requires vector indexes to associate metadata fields. This prevents confusion between review content from different sections during retrieval. Irregular milestone-based updates require indexes to support incremental synchronization. This reduces resource consumption from full index rebuilding. Document length varies widely: review forms as short as hundreds of characters, acceptance specifications as long as tens of thousands of characters. This demands adaptable chunking strategies. The presence of specialized engineering units requires vector models to recognize engineering terminology and unit associations. This improves retrieval accuracy. The unique project identifier field must be used as an index filter condition. This narrows retrieval scope.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the span of infrastructure documents, which range from short review forms to long specifications. Avoids splitting approval logic across single chunks |
| Overlap Length | 100–150 characters | Retains associated approval node information between adjacent chunks. Prevents loss of context during cross-chunk retrieval |
| Default Token Count | 2000 token | Matches the average length of metadata plus main text for a single infrastructure document. Reduces loss of metadata after chunking |
| Maximum Token Count | 8000 token | Covers the longest acceptance specification documents. Avoids truncation of critical approval requirements |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Filters low-match non-target section documents. Adapts to semantic differences in engineering terminology |
| Single Search Limit | Top 10 entries | Controls the scale of retrieval results. Avoids introducing irrelevant cross-section data |
Three Common Mistakes
- Incomplete approval workflow information after chunking Phenomenon: Retrieved business process guidance lacks continuous node descriptions Cause: Chunk length set too large or too small. This splits or truncates content for a single approval node, preventing the formation of a complete process logic
- Local embedding model call timeout Phenomenon: Embedding model calls return 504 timeout status code Cause: The
maxTokenparameter is not adjusted for long infrastructure documents. Ultra-long acceptance specification documents trigger model overload, leading to timeout - Token count statistics do not match actual consumption Phenomenon: Application token consumption statistics differ from token counts returned by the model Cause: Metadata fields for engineering documents are not included in the
defaultTokencounting scope. This causes token consumption for some content to be omitted from statistics
How to Confirm Proper Configuration
- Enter keywords for a specific project number during retrieval. Check if retrieved results only include business documents for that project. Confirm the filter rule is active
- Upload the single longest acceptance specification document. Check if all approval node descriptions are fully retained in chunked content. Confirm chunking parameters adapt to document length
- View the application’s token consumption logs. Check if statistics match token counts returned by the model. Confirm the counting scope covers both metadata and main text
- Call a locally deployed embedding model. Upload an ultra-long document. Check if timeout errors are triggered. Confirm the
maxTokenparameter is set appropriately
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.