What this category of data looks like
Data for the pulp and paper category comes mainly from daily documents of preparation, pulping, paper making, quality and energy departments. These documents include pulp preparation sheets, beating records, paper machine operation records, online quality curves, laboratory reports, and sheet break and product change records. Data sources cover wood pulp and waste paper batches, DCS/QCS systems, paper machine online measuring equipment, laboratories, energy and water treatment systems. Update frequencies range from real-time, per shift, to per roll. Most documents include fields such as pulp ratio, operating parameters, and quality indicators. Some documents have professional numerical units, such as grammage, moisture percentage, and thickness in millimeters.
Constraints on vector models and indexing from these characteristics
First, data sources are scattered, and update rhythms vary widely. Real-time paper machine online data requires high-frequency incremental indexing. Documents generated per shift or per roll require batch import into indexes. This means the system must support mixed update modes. Second, documents contain mixed text descriptions and numerical parameters. Vector models must adapt to mixed-modal feature extraction to avoid bias from single-text features. Third, sheet break and product change records generated during high-speed continuous production have strong correlations with process parameters. Indexes must support cross-document associated recall to match cross-document process-related content. Fourth, there are many professional process parameter fields. Index chunking must retain full context to avoid splitting that disrupts process logic.
Configuration Settings
These configurations are written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the context length of single process parameters in paper machine operation records and pulp preparation sheets, avoiding splitting that disrupts process logic |
| Overlap Length | 100–150 characters | Retains cross-chunk process-related information, such as the front and rear connection of beating parameters |
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 | 0.72–0.80 range | Distinguishes the similarity of process parameters, avoiding false recall of unmatched sheet break records |
| Single Search Limit | Top 8 entries | Matches the number of associated documents commonly used in pulp and paper equipment inspection, covering key process data for a single shift |
maxToken | 16384 | Supports batch import of quality reports and product change records for multiple rolls of paper, meeting full context recall requirements |
Three Common Errors
- Phenomenon: An interface error occurs when configuring an indexing model via One API, with a 401 status code returned. Cause: API key binding rules for general question answering and indexing tasks are not distinguished, so key permissions do not cover the indexing vector generation link.
- Phenomenon: After importing a single pulp preparation sheet, retrieval results do not match relevant process content. Cause: Chunk length is set too large, and a single chunk contains excessive irrelevant information, causing vector features to fail to accurately match retrieval requests.
- Phenomenon: Index update tasks time out when batch importing quality curves for multiple rolls of paper. Cause: Reasonable batch import batches are not configured, so the volume of data processed in a single batch exceeds the system's carrying capacity.
How to Verify Correct Configuration
- Upload a single pulp preparation sheet document, trigger index construction, and confirm the index progress status in the FastGPT interface is completed.
- Enter a query such as "methods for adjusting beating parameters", and verify that retrieved results include relevant content from pulp preparation sheets and beating records, and the number of results matches the single search limit setting.
- Test calling One API to generate vectors, confirm the returned vector dimensions match the configured model parameters, and no error messages are present.
- Adjust the similarity threshold, observe changes in retrieval result relevance, and confirm the threshold setting meets the recall requirements of the business scenario.
The values provided here are common starting points and should be evaluated against the reader's own samples.