What this category’s data looks like
The internal office and employee service data for general contracting of building construction projects mainly comes from project construction logs, labor team attendance ledgers, material purchase lists, construction plan documents, on-site photos and CAD drawings. Data updates follow project node milestones: construction logs and attendance records are updated weekly, while material ledgers and progress reports are updated monthly. Document types include long-text plans, structured tables, images and vector drawings. Fields include project number, building number, construction date, team name, material specification and other relevant fields. Units involved include professional engineering units such as square meters, cubic meters, man-days, tons and others.
What constraints these characteristics impose on the vector models and indexes link
Long-text construction plans and logs for building construction projects require coherent contextual association, so chunking and indexing steps must avoid splitting professional terms. The presence of on-site photos and CAD drawings requires support for image vector indexing. Structured ledger data requires precise field-level retrieval. The update rhythm following project nodes requires that incremental indexing can be triggered flexibly. The existence of professional engineering terms requires that the vector model has semantic adaptation capabilities for the construction industry, to avoid semantic deviation from general-purpose models.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long-text structure of building construction plans and logs, avoiding semantic breaks in professional terms caused by overloaded single chunks |
| Overlap Length | 150–200 characters | Retains contextual information for professional terms across chunks, such as coherent engineering expressions like "post-pouring belt" and "prestressed tensioning" |
| Reranking | Enabled, use a reranking model adapted for construction specialties | Filters irrelevant recall results to meet precise retrieval needs in construction scenarios |
maxToken | 4096 | Adapts to vector storage and recall for long documents, aligns with the default parameter range of FastGPT v4.17.0 |
| Single Search Limit | Top 10 results | Covers common multi-document associated retrieval scenarios in projects |
defaultToken | Calibrated based on actual testing | Adapts to resource constraints of locally deployed servers, avoiding memory overflow or call timeouts |
Three Common Misconfigurations
- After local deployment, creating a new knowledge base shows no image indexing model option. The cause is that the community edition does not preinstall image indexing dependency packages by default; corresponding component packages must be installed manually.
- An error occurs when calling the Embedding model via OneAPI. The cause is that the access key corresponding to
defaultTokenis not configured, or the OneAPI forwarding link does not correctly align with the model call format. - After enabling reranking, no valid results appear in online recall tests. The cause is that the input length of the reranking model exceeds the
maxTokenlimit, or the reranking trigger switch is not checked in the knowledge base recall settings.
How to Confirm the Configuration Is Complete
- Enter the settings page of the target knowledge base, confirm that the chunk length and overlap length parameters match the values in the configuration table.
- Upload a PDF file of building construction drawings, wait for indexing to complete, then check if image vector index entries are generated.
- Initiate a retrieval test targeting construction log content, verify whether the recall results include matching professional engineering expressions.
- Check the FastGPT system logs to confirm that there are no timeout or format error reports for Embedding model calls.
The values provided here 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.