What this category's data looks like
Data for this category comes from the preparation, pulping, papermaking, quality control, and energy departments of pulp and paper enterprises. It includes documents such as pulp mixing sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, sheet break and product change records. Data sources include wood pulp and waste paper batches, DCS/QCS systems, paper machine online measurement equipment, laboratories, energy and water treatment systems. Update frequencies cover three levels: real-time, shift-based, and per-roll. Each single record contains fields like feeding parameters, operating values, and detection indicators. Some fields have clear physical units.
Constraints for vector models and indexing
The update frequencies for this category’s data cover real-time, shift-based, and per-roll levels. This requires indexes to support a combination of incremental and batch ingestion tasks. Multi-source documents include fields with different physical units. When splitting text chunks, retain contextual connections within the same business scenario, and avoid splitting records that cross business logic. Production records for a single paper roll are continuous time-series data. Indexes must establish associated dimensions by production batch to prevent mixing production data from different batches. Additionally, single records have a large number of fields. When building vector models, prioritize retaining core fields that affect production quality, to avoid redundant features interfering with matching accuracy.
Configuration settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the complete business logic of a single production record, avoids splitting critical feeding or operating parameters |
| Overlap Length | 100–150 characters | Retains temporal connections across chunks, prevents contextual breaks in continuous production records |
| Similarity Threshold | 0.75–0.85 | Matches core indicators of production ledgers, filters low-correlation historical production records |
| Single Retrieval Limit | Top 20 entries | Covers typical production data volume for a single shift, avoids excessive recall causing context overflow |
defaultToken | 4096 | Adapts to the overall text length of production ledgers, ensures complete loading of core information |
maxToken | 8192 | Supports context requirements for batch report generation, accommodates aggregated data from multiple batches |
Three common mistakes
- A 400 status code parameter error is returned when adding indexes in batch, and ingestion cannot be completed. The cause is failure to split batch tasks by production batch, merging cross-shift records for submission, which exceeds the valid range of a single ingestion.
- Unable to filter data by paper roll batch after storing all production documents in the same index library. The cause is failure to configure batch dimension metadata fields for the index, making it impossible to distinguish records from different production units during retrieval.
- Empty embedding vector fields appear when managing the vector library through a third-party system. The cause is failure to synchronize the standardized vector format generated by FastGPT, directly modifying or adding fields which breaks the association relationship of the vector index.
How to verify correct configuration
- Submit an ingestion task for a single pulp mixing sheet, check if the index generation log contains correct pulp feeding parameter fields.
- Initiate a retrieval request for production ledger summary, confirm that the number of recall results matches the configured single retrieval limit.
- View the vector library metadata, confirm that each record is associated with the corresponding production batch number.
- Test a batch ingestion task, check that index creation for all records is completed per batch with no error returns.
The values provided here are common starting points, and should be evaluated against the reader's own samples.