What Data in This Category Looks Like
Internal office data for new energy development mainly comes from project approval documents, equipment operation and maintenance logs, employee training manuals, bidding and procurement ledgers, and on-site inspection records. Update rhythms vary widely: project documents are updated in stages alongside development phases, operation and maintenance logs are generated daily or in real time, and employee manuals and institutional documents are revised quarterly or annually. Most documents include fields such as project number, equipment model, inspection duration, and fault level, with units including megawatts, hours, units, yuan and other standard engineering and management units.
What Constraints Do These Characteristics Impose on the Vector Models and Indexes Link
The multi-source heterogeneous data characteristics of new energy development create multiple constraints for the vector models and indexes workflow. Semi-structured operation and maintenance logs and plain-text project documents coexist, requiring vector models to support both standardized text and unformatted on-site records. Differences in data update rhythms require differentiated logic for full and incremental indexing, to avoid repeated indexing or missed real-time on-site data updates. Multi-field engineering documents need targeted vectorization weights configured for core business fields to improve retrieval accuracy. The presence of long documents also requires a chunking strategy aligned with the paragraph structure of engineering documents, to prevent semantic fragmentation.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of new energy project documents, avoid semantic breakage in individual chunks |
| Chunk Overlap Length | 100–150 characters | Preserve cross-chunk context connections, match the long sentence structure of engineering documents |
maxToken | 8192 token | Match the input limit of mainstream vector models, cover the content of long operation and maintenance logs and project documents |
defaultToken | Calibrated via actual testing | Adapt to default token settings for different vector models, cover input requirements for multi-source data |
| Similarity Threshold | 0.72–0.78 | Distinguish semantic similarity for engineering documents, filter low-match redundant results |
| Single Retrieval Upper Limit | Top 10 entries | Align with internal office scenario retrieval needs, avoid returning excessive non-core data |
Three Common Misconfigurations
- Phenomenon: A
400 Bad Requesterror is returned when starting the vector service, with a prompt that input length exceeds the limit. Cause: ThemaxTokenparameter is not adjusted for new energy long documents, causing a single chunk of text to exceed the token limit supported by the model. - Phenomenon: The indexing process takes an extended period, with tasks remaining in a running state for a long time. Cause: Full and incremental indexing logic is not differentiated, and full repeated indexing is performed on real-time updated operation and maintenance logs.
- Phenomenon: When a general text vector model is used to process multi-modal inspection data, retrieval results do not associate with image description information. Cause: A vector model adapted to multi-modal data is not used, preventing cross-modal information from being correctly vectorized.
How to Confirm Configuration Is Complete
- View the running logs of the vector service, confirm there are no error messages about
maxTokenexceeding limits or parameter errors. - Run a test retrieval, enter keywords related to equipment faults, and verify that returned results include business fields associated with new energy engineering.
- Check the docker-compose configuration file, confirm the port for the vector indexing service is correctly mapped and accessible for external calls.
- View the indexing task progress panel, confirm the incremental index only updates newly added operation and maintenance logs or documents, and does not repeatedly process historical data.
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.