What the data for this category looks like
The data used for internal office and employee services in infrastructure construction comes primarily from project construction logs, equipment operation and maintenance records, internal employee handbooks, bidding disclosure documents, team task sheets, and similar materials. Data updates follow three rhythms:
- Construction logs and task sheets update daily
- Project documents and disclosure documents update alongside project phases
- Employee handbooks and system files update quarterly or annually
Document structures include both long and short text. Fields include project number, construction section number, equipment serial number, date, responsible team, and more. Engineering units such as meters, cubic meters, and work shifts are included with documents.
What constraints these characteristics impose on vector models and indexing
Reference only; verify against the target FastGPT release and deployment documentation.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long sentence structure of infrastructure professional documents, retains complete semantic units |
| Overlap Length | 100–150 characters | Prevents semantic breaks in construction processes or professional terms after long documents are chunked |
maxToken | 4096 token | Covers the maximum context processing requirement after chunking long documents |
| Similarity Threshold | 0.72–0.78, calibrated via actual testing | Distinguishes matching accuracy between professional terms and irrelevant content |
| Single Search Limit | Top 8 results | Adapts to the usage scenario where internal office queries for infrastructure focus on specific projects |
defaultToken | 2048 token | Matches the conventional context length of daily office queries |
Three common errors
- Phenomenon: The knowledge base shows "rebuilding in progress" continuously even with a small content volume, and switching indexes produces no response. Cause: The automatic rebuild toggle is not disabled, and the new index collection has not been set as the active state.
- Phenomenon: Parameter errors are returned when adding indexes in batches, and some metadata fields are empty. Cause: Metadata parameters for batch requests have not been configured using the dedicated fields for infrastructure documents, such as project number and construction section number.
- Phenomenon: Index rebuild takes longer than the preset duration, and tasks become stuck with no progress updates. Cause: Batch data has not been split into smaller batches, and full rebuild operations do not account for the long text volume of infrastructure documents.
How to confirm the configuration is properly set
- Upload a typical infrastructure construction log document. Verify that chunking results retain complete professional sentences with no obvious semantic breaks.
- Initiate a query that includes engineering professional terms. Confirm that the matching logic of returned results aligns with the preset similarity rules.
- Manually trigger an index rebuild. Check that the task execution rhythm matches expected batch processing.
- Attempt to switch index collections. Confirm that the operation completes normally, and subsequent queries target the new index collection.
The values listed here are common starting points, and should be evaluated against the applicable 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.