What This Category of Data Looks Like
Typical entities in this category include dyeing and finishing factories, as well as dyeing, setting, laboratory and quality departments of fabric enterprises. Daily generated documents include dyeing formulas, chemical dosing records, dye vat curves, sample confirmation reports, color difference reports, setting process sheets and sewage ledgers. Data sources include customer color samples, formula libraries, dye chemical batches, dye vat control systems, colorimeters, setting machines and environmental monitoring equipment. Data updates occur per vat, per batch and per shift. Document fields include specific values and batch identifiers, such as dye vat temperature, chemical dosing weight and color difference values. These fields are dynamically adjusted alongside production processes.
How These Characteristics Impact Source Citation and Traceability
This category of data centers on batch wet chemical processing. Each production vat corresponds to multiple associated documents, with very high update frequency. Citation and traceability must accurately link dye chemicals, color measurement data and process parameters to each batch. It cannot rely on general global matching logic. A single document like a dye vat curve contains many continuous values. Context length can easily exceed model limits, so the total token count for citations must be strictly controlled. At the same time, traceability must clearly point to equipment, batches and operator information for specific production links. Complete source document fields and paths must be retained, and key identifiers cannot be simplified or truncated.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 1200–1800 tokens | A single dye vat curve or color difference report can reach hundreds of characters. Setting this range avoids context overflow and ensures key parameters load completely |
responseData | First 6–10 entries | Each batch of dyeing and finishing production corresponds to multiple process and test records. Returning 6 to 10 entries covers associated data for the complete business workflow |
detail | Enabled | Traceability must accurately target specific fields like dye chemical batches, colorimeter readings and dye vat numbers. Enabling this setting displays complete source file paths and field information |
| Citation Trigger Matching Threshold | Calibrated via actual testing | Match characteristic fields of dyeing processes such as color difference values and heating curve slope to avoid recalling irrelevant historical data |
| Knowledge Base Chunking Rule | Split by Production Batch | Use each vat or batch of data as an independent chunk. This ensures citation and traceability can accurately locate single-batch production records |
Three Common Misconfigurations
- Issue: When initiating a process query, the response only includes generic model-generated vague replies, and does not include dyeing formulas or dye vat records from the local knowledge base. Cause: The dedicated formula library or dye vat record knowledge base for dyeing and finishing factories is not correctly bound, or the number of entries returned by the
responseDataconfiguration is too low to cover the associated data required for the business. - Issue: The cited source shows empty fields or missing key identifiers like dye chemical batches or dye vat numbers. Cause: The
detailsetting is not enabled, or core traceability fields are not retained during knowledge base chunking, so complete information cannot be extracted during citation. - Issue: The API call returns a context length error. Cause: The
quoteMaxTokensetting exceeds the interface limit of FastGPT v4.17.0, so the model cannot load complete citation source data.
How to Verify Correct Configuration
- Upload a single dye vat curve document, initiate a query that includes specific parameters, and check if the returned results include measured values like temperature and duration from the document.
- Initiate a traceability query, and check if each citation displays the complete source file name, storage path and batch identifier.
- Call the FastGPT API interface, and check if the returned
responseDatafield includes matching knowledge base entries with complete fields. - Simulate a batch query scenario, confirm that the interface does not experience timeouts or context overflow errors, and that the configured
quoteMaxTokenmeets interface requirements.
The values provided here are common starting points. They should be measured against the reader's own samples.