What data for this category looks like
Data comes from dyeing and finishing mills and fabric enterprises, spanning dyeing, setting, laboratory, and quality departments. Included content covers dyeing formulas, chemical feeding logs, dye vat operation curves, sample approval sheets, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color samples, formula repositories, dyestuff batch records, dye vat control systems, colorimeters, setting machine parameters, and environmental monitoring data. Updates occur per vat, per batch, and per shift. Each individual record includes fields such as process parameters, batch information, test results, and emission metrics. Units include temperature, concentration, batch numbers, and test values.
Constraints imposed on vector models and indexes by this category's characteristics
This category has multi-source heterogeneous data structures. It includes curve files exported from instruments, scanned copies of manually filled paper ledgers, and structured test values. This requires vector models and indexes to support mixed-format content parsing and vectorization. The high update frequency requires indexes to adapt to incremental write logic, avoiding performance loss from full reindexing. Each individual record contains dense fields like process parameters and batch numbers. This requires index retrieval logic to balance process parameter matching and semantic relevance. The traceability requirement for sewage ledgers requires indexes to retain complete batch association fields, supporting precise filtering by production cycle.
How to set configurations
These configurations are based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the field density of single ledgers in this category, avoid including too much cross-batch associated information in a single chunk |
| Overlap Length | 100–150 characters | Retain process connection information between adjacent chunks, avoid context breaks for dye vat curves and formula adjustments |
| Similarity Threshold | 0.75–0.85 | Match the similarity between process parameters and historical records, balance retrieval accuracy and coverage |
| Single Search Limit | Top 10 entries | Limit the number of returned results, adapt to batch query scenarios for production ledgers |
maxToken | 8000 | Adapt to vectorization processing of long documents, meet the input requirements for complete sections of dye vat curves |
| Maximum Context | 4000–6000 characters | Control the total length of retrieved content, avoid exceeding model processing limits |
Three common misconfigurations
- No options appear in the index model dropdown menu in the interface. The cause is that the local deployment or remote call permission for the corresponding embedding model is not enabled in the system configuration, causing the optional list to fail to load.
- Key parameter segments of dye vat curves are missing from retrieval results. The cause is that the chunk length is set too small, splitting complete heating curves into multiple unrelated chunks and causing semantic breaks.
- Batch association errors appear in generated reports. The cause is that the index configuration for filtering by production batch is not enabled, causing retrieval results to include production ledger data from non-current orders.
How to confirm correct configuration
- Upload a single dye vat curve document, check if the chunked content retains complete heating, feeding, and insulation phase information.
- Submit a query containing specific dye vat parameters, verify that the number of retrieval results matches the single search limit setting.
- Import historical sewage ledger data, verify that the index supports precise filtering by production shift and batch number.
- Check that the
maxTokenconfiguration matches the maximum length of uploaded documents, avoid content truncation prompts.
The values listed here are common starting points and should be evaluated against the user's own samples.