What Data Looks Like for This Category
Core data for pulp and paper scenarios comes from wood pulp and waste paper batches, DCS/QCS systems, on-machine measuring equipment, laboratories, energy and water treatment systems. This is business data for high-speed continuous web production. Daily documents include pulp preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, and sheet break and product change records. Fields cover pulp ratio, beating degree, grammage, moisture, thickness, and other metrics. Update frequencies cover three levels: real-time, shift-based, and per roll.
Constraints for Vector Models and Indexing
Real-time, shift-based, multi-batch update frequencies require indexes to support incremental updates and batch rebuilding. This avoids redundant full re-import operations. Mixed input of multiple document types requires vector models to adapt to both structured fields and semi-structured time-series data. maxToken must cover the length of long time-series paper machine operation records. Large per-batch data size requires index recall efficiency to match business query response requirements. It also requires retention of cross-block process association information. This prevents chunking from breaking the continuous business logic of pulp preparation and papermaking.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single documents such as pulp preparation sheets and beating records. Avoids truncating key business parameters in single chunks. |
| Overlap Length | 50–80 characters | Retains cross-block association information from beating to papermaking. Adapts to the continuous time-series characteristics of online quality curves. |
maxToken | 8192 token | Adapts to the maximum length of long time-series paper machine operation records. Prevents the model from truncating core operation data. |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
| Similarity Threshold | Calibrated based on actual testing | Adapts to the quality deviation characteristics of the pulp and paper scenario. Distinguishes normal production data from abnormal problem records. |
| Single Search Limit | Top 10 results | Balances recall efficiency and retrieval coverage. Meets business needs for quickly locating quality issues. |
Three Common Misconfigurations
Phenomenon 1
After replacing the embedding model, the previously imported knowledge base cannot be reused directly. The index must be regenerated. Cause: Vector indexes are strongly bound to the output dimensions of the embedding model. Vector features generated by different models are incompatible. The incremental index update workflow of FastGPT v4.17.0 is not configured.
Phenomenon 2
Knowledge base search results are sorted unexpectedly. They do not display in priority order of similarity. Cause: Reranking configuration is not enabled. Or the reranking model is not adapted to the quality characteristic fields of the pulp and paper scenario. This causes the sorting logic to deviate from business requirements.
Phenomenon 3
Semantic retrieval scores are high. But returned results do not match actual quality issues. The problem persists after replacing the embedding model. Cause: Chunk length is set too large. This causes key quality parameters to be truncated. Vector features cannot accurately match query intent.
How to Confirm Proper Configuration
- Import a single complete paper machine operation record document. Check that the vector generation logs of FastGPT v4.17.0 have no token truncation or model error prompts.
- Initiate a query for "find records with abnormal pulp ratio for the current batch". Verify that the chunk information of the returned results matches the business logic of the original document.
- After replacing the embedding model, trigger batch index rebuilding. Verify that the index generation progress is normal with no abnormal interruptions.
- View search result sorting. Confirm that the correlation degree with quality deviations meets business expectations. No obvious sorting confusion is present.
The values provided are common starting points, and should be evaluated against the reader's own samples.