What Typical Data for This Category Looks Like
Typical data sources include wood pulp and waste paper batch ledgers, DCS/QCS system data, paper machine online measurement records, laboratory test reports, and energy and water treatment system logs. Daily generated documents include pulp preparation sheets, beating operation records, paper machine operation logs, online quality curve data, laboratory test reports, and sheet break and product change ledgers. Data updates occur in real time, per shift, or per production roll cycle. Most document fields are process parameter readings, including beating degree, pulp ratio, web thickness, and energy consumption values. Some data is unstructured time-series curve data without a unified standard format.
What Constraints These Characteristics Impose on Vector Models and Indexing
High-frequency real-time, per-shift, and per-roll data requires indexes to support incremental updates. This avoids excessive compute resource usage from full reindexing. Mixed structured and unstructured document formats from multiple sources require vector models to align both process parameter semantics and text content. Strong cross-document process relevance exists between documents such as pulp preparation sheets and beating records, or sheet break ledgers and quality curves. This requires indexes to support cross-document associated recall, preventing missing critical information. Minor fluctuations in process parameters can affect production outcomes. Vector model matching accuracy must fit the range of process parameter differences.
How to Set Configurations
The following configurations are based on the FastGPT v4.17.0 interface logic.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the single-paragraph text length of paper machine operation records and laboratory reports, avoids semantic fragmentation across process steps |
| Overlap Length | 100–150 characters | Covers cross-paragraph associated information in pulp preparation sheets and beating records, prevents loss of process context from chunking |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Matches minor differences in pulp and paper process parameters, avoids misjudging similar documents or missing critical associations |
| Single Search Limit | Top 8 results | Covers the number of associated documents for a single production roll, balances recall accuracy and response speed |
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. |
maxToken | 16384 | Supports concatenation of multiple recalled documents during cross-review, prevents context truncation |
Three Common Misconfigurations
- A 401 status code is returned when connecting to a third-party vector model. The cause is incorrect configuration of the API key and calling address, or lack of authorization for the corresponding vector service permissions.
- Duplicate chunks are automatically deleted during index storage after custom document splitting, resulting in loss of the original chunk order. The cause is that the global deduplication configuration is enabled by default, and the custom split chunk order is not retained.
- Requests are incorrectly routed to the LLM interface when using a forwarding tool to connect to a vector model. The cause is that the channel is not explicitly designated as a vector service channel in the channel configuration.
How to Verify Proper Configuration
- Upload a single paper machine operation record document, check that the embedding task status shows success, and confirm that the embedded vector dimension matches the selected model.
- Submit a query containing process parameters such as beating degree and web thickness, check that the recall results include target category documents such as pulp preparation sheets, beating records, and sheet break ledgers, and that the quantity matches expectations.
- Submit a single production roll document for incremental update, check that the index only adds the corresponding data without triggering a full reindex.
- Review the vector model call logs, check for abnormal returns such as 401 errors, timeouts, or routing errors.
The values provided are common starting points and should be measured against the reader's own samples.