This page is compatible with FastGPT v4.17.0
What this category of data looks like
Data for this category primarily comes from wood pulp and waste paper batch ledgers, DCS and QCS control systems, on-paper machine measurement equipment, laboratory testing systems, and energy and water treatment systems. Daily generated documents include pulp preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, sheet break and product change records.
Data update cadences cover three dimensions: real-time, per-shift, and per-roll. Each single record includes fields such as pulp composition, beating parameters, paper machine operation parameters, quality inspection indicators, and abnormal event identifiers. Some associated data has timestamp and batch number fields.
Constraints imposed by these data characteristics on vector models and indexing
The data characteristics of this category impose multiple constraints on the vector model and indexing workflow. Heterogeneous data sources produce structured parameters, curve-based time-series data, and text records, which requires indexes to support unified storage and retrieval of mixed-type vectors. Concurrent write requirements with multiple update cadences require the index’s incremental update mechanism to adapt to different write frequencies of real-time, per-shift, and per-roll updates. The full-process record data corresponding to a single roll of paper is relatively large, so the chunking logic needs to be adjusted in combination with model input limits to avoid exceeding vector dimension limits. At the same time, the high recall requirement for low-frequency abnormal records such as sheet breaks and product changes requires indexes to retain metadata fields such as batch numbers and timestamps for precise filtering.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph text length of paper machine operation records and laboratory reports, avoiding overly fragmented or overly long chunks |
| Overlap Length | 100–150 characters | Retains contextual connections across chunks, adapting to the continuous process descriptions in beating records |
| Similarity Threshold | 0.75–0.85 | Matches the fluctuation range of pulp quality indicators, avoiding false matches or missed detections of qualified batches |
| Single Search Limit | Top 10 entries | Covers the full-process abnormal troubleshooting requirements for a single roll of paper, controlling retrieval overhead |
defaultToken | 2000 token | Adapts to the time-series text parsing length of online quality curves, complying with the input limits of mainstream embedding models |
| Maximum Context | 4000–6000 characters | Retains complete pulp preparation and beating process information, supporting associated analysis in the audit link |
Three common misconfigurations
- The index task stays in the "indexing" state for a long time with no progress updates, and cannot enter the ready state after restarting the service. Cause: Incremental indexing rules are not configured for the real-time and multi-frequency data updates of the pulp and paper industry, and repeated full index execution occupies system resources.
- The number of index entries in the dataset increases automatically over time, with multiple sets of duplicate index entries for the same batch. Cause: The batch trigger condition for data updates is not bound, and real-time data streams are mistakenly imported at fixed full cycles, generating duplicate indexes.
- After configuring the vector model, the original language model configuration options disappear, and a complete question-and-answer audit link cannot be built. Cause: The independent configuration logic of vector models and language models is not distinguished, and the associated parameters of the language model are incorrectly overwritten during index configuration.
How to confirm correct configuration
- View the progress logs of the index task to confirm that the trigger frequency of incremental indexing matches the real-time, per-shift, and per-roll update cadences of data.
- Randomly extract record samples from different update cycles to verify that retrieval results can be associated with pulp preparation, beating, and quality inspection documents corresponding to the target batch.
- Check that the configuration options for both the vector model and the language model are not overwritten, and the associated link can be called normally.
- Import a single low-frequency abnormal record to confirm that the index can complete the loading of new entries within a reasonable time and support precise retrieval.
The values provided here are common starting points, and should be evaluated against the reader's own samples.