What This Category of Data Looks Like
Engineering consulting supervision data comes from on-site logs, sub-project acceptance records, supervision plan details, change visa forms, quality rectification notices, and other materials created during project execution. Data updates occur irregularly as projects progress. Document volume per project grows as engineering work advances. Document structures include fields such as project number, supervision section, acceptance node, rectification requirements, and rectification time limit. These involve engineering-specific units such as construction quantity, time cycle, and number of documents.
Constraints Imposed by These Characteristics on Vector Models and Indexing
Supervision documents are isolated by project and contain specialized engineering terminology. Indexes must support spatial isolation of vector storage by project dimension to avoid cross-project retrieval interference. Data updates occur irregularly as projects progress. Teams use incremental indexing logic to reduce resource consumption from full index reconstruction. Single documents contain mixed content: long text rectification plans and short text rectification time limits. Flexible chunking strategies adapt to text fragments of different lengths. Document fields include engineering-specific units and identification fields. Indexes retain metadata for subsequent precise filtering.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to mixed content in supervision documents, which includes both long text rectification plans and short text acceptance conclusions. Avoids losing semantic association due to overly fragmented chunks, or reduced vector accuracy due to overly long chunks. |
| Overlap Length | 50–80 characters | Bridges the semantic boundary between adjacent chunks, retains key information such as rectification requirements and acceptance nodes across chunks. |
| Similarity Threshold | 0.72–0.85 | Filters low-match results, avoids accidental recall of non-project supervision documents, and adapts to semantic matching accuracy of engineering terms. |
| Single Search Limit | Top 8 entries | Matches the conventional recall volume of single-project supervision documents, balances retrieval efficiency and result coverage. |
maxToken | 4096 | Adapts to the token length limit after chunking engineering documents, avoids exceeding the input limit of the vector model. |
| Question Optimization | Enabled | Completes professional terms for supervision consultation questions, improves the accuracy of retrieval matching. |
Three Common Misconfigurations
- Index progress shows no update for over 30 minutes in the interface. Cause: No metadata filtering configured by project dimension. The vector database performs retrieval on full data, resulting in excessive computation. Or chunk length is set too long, exceeding the token input limit of the FastGPT vector model.
- No retrieval results after custom index configuration. Cause: Metadata fields such as project number and supervision section of supervision documents were not retained during chunking. This prevents retrieval from isolating by project, leading to recall of irrelevant documents.
- Unable to load existing data after connecting to a custom vector database. Cause: Fields were not aligned with the vector storage format required by FastGPT. Exclusive metadata fields of engineering documents were not retained, preventing matching of retrieval conditions during vector recall.
How to Confirm Proper Configuration
- Upload a single supervision document, check the chunking preview interface. Confirm that the chunk length matches the preset value, and the overlapping part retains key acceptance node or rectification requirement information.
- Submit a supervision-related consultation question, check the retrieval result list. Confirm that all recalled documents belong to the target project, and the similarity of results aligns with the expected adjustment direction.
- Check the storage logs of the vector database. Confirm that incremental indexing only synchronizes newly added supervision documents, and no full index reconstruction operation is triggered.
- Test the connection to the custom vector database, upload existing engineering data. Confirm that fields are correctly recognized by FastGPT and can be used for retrieval filtering.
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.