What the data for this category looks like
Daily data for the spinning and weaving category comes from raw fiber inspection, ERP, spinning and loom data, evenness testers, fabric inspectors, and order specifications. Data updates proceed with each batch of raw materials, each shift, and each piece of fabric. Daily documents include cotton blending or batching lists, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection reports, and defect records. Data fields include fiber indicators, spinning twist, weaving structure parameters, defect types, machine numbers, and more. Units include count, meter, hour, and others. This type of data is typically stored classified by production batch, machine, and inspection link.
What constraints these characteristics impose on multi-turn dialogue and prompt words
The category’s data has high update frequency and is split by production link. Multi-turn dialogue must strictly control the context scope to avoid introducing irrelevant data across batches. Multi-source data contains a large number of professional terms and subdivided fields. Prompt words must clearly distinguish the call rules for different documents to prevent confusion between cotton blending lists and defect record content. Equipment headcount is large and defects can spread continuously. When dialogue needs to trace back historical inspection records, historical data for different scenarios must be managed through grouping to avoid context overload that affects response accuracy. At the same time, single-batch document length is limited but updates are frequent. Dialogue rounds and total context length must be restricted to ensure that the knowledge base content called each time matches the current question.
How to set the configuration
This configuration is based on FastGPT v4.17.0. Specific settings are as follows:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Divide into 3 groups based on inspection work orders, defect records, and process parameters, and bind each group to the corresponding knowledge base | The category’s data is split by production link. Grouped variables can prevent confusion of historical data across different scenarios |
messages | Retain the last 10 rounds of dialogue, truncate historical content exceeding 8000 characters | Single-batch data updates frequently. Overly long context will introduce irrelevant historical records and affect the accuracy of current responses |
| Maximum Context | 6000–10000 characters | Single-batch documents for this category are mostly within 1000 characters. Controlling the total length of 10 rounds of dialogue within this range can avoid overload |
| Question Optimization | Enabled, add preconditions based on equipment model and defect type | This category has many equipment models and subdivided defect types. Optimization can accurately recall corresponding knowledge base content |
| Lexicon Binding | Bind dedicated lexicons for spinning twist, fabric surface defects, and machine numbers | This category has a large number of professional terms. Lexicons can unify term expressions and avoid ambiguity |
Three Common Misconfigurations
- Phenomenon: The response content of the dialogue overlaps the results of the previous round. Cause: No grouping binding is configured for
variables, and historical variable values are brought into the current context during multi-turn dialogue. - Phenomenon: Input guidance and lexicon are configured, but expected guidance questions are not displayed on the dialogue interface. Cause: The active trigger switch for input guidance is not enabled, or the lexicon is not associated with the current application’s knowledge base scope.
- Phenomenon: The uploaded xlsx knowledge base file cannot reproduce its content. Cause: No chunk parsing rules are configured for xlsx, or table fields and corresponding text are not correctly extracted during knowledge base import.
How to Verify Correct Configuration
- Initiate a test dialogue containing cotton blending list fiber indicators. View the context log through
chatIdto check that the response only calls thevariablesdata of the corresponding group, with no irrelevant historical content remaining. - Trigger the input guidance function to confirm that preset questions classified by equipment model and defect type are displayed on the interface.
- Upload a test xlsx file, initiate a request to reproduce the file content, and confirm that the response contains the fields and corresponding values in the table.
- Initiate a simulated inspection dialogue with 10 consecutive rounds, check that the context does not exceed the set maximum length, and that each round of response only associates the current round’s question.
The values given are common starting points and should be measured against the reader's own samples.