What the data for this category looks like
Typical entities in the spinning and weaving category include cotton spinning, chemical fiber spinning, woven and knitting enterprises. Daily generated documents include cotton blending or material mixing lists, process setup sheets, equipment operation records, yarn inspection sheets, gray fabric inspection sheets, defect records, and more. Data sources cover raw fiber inspection data, ERP systems, real-time data from spinning and weaving machines, evenness tester detection data, fabric inspection machine detection data, and order specification files. Data updates occur per raw material batch, per shift, and per piece of fabric. Each document includes fields such as fiber grade, mixing ratio, twist, warp density, and defect type. Some fields use units of weight, length, or count.
Constraints on multi-turn dialogue and prompt engineering
Data sources for this category are scattered, covering inspection, ERP, equipment and other systems. Multi-turn dialogue must associate fields from different data sources to complete ledger summary. Data updates happen frequently. New data is generated per raw material batch, per shift, and per piece of fabric. Prompts must dynamically call the latest batch of document content, and cannot rely on static context. Single documents have many closely related fields. Special field parameters must be passed via variables to distinguish different types of ledgers. Additionally, defect records have continuous propagation characteristics. Multi-turn dialogue must track the defect chain associated with a batch to avoid context breaks. chatId must be used to isolate dialogue contexts for different workshops or batches, to prevent data confusion.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to production batch number, each batch corresponds to a unique chatId | Parallel production of multiple batches in spinning and weaving requires isolating dialogue contexts for different batches |
messages | Retain 25–35 recent conversation records, archive old content when exceeding this limit | Single-batch ledger dialogues have many turns; overly long contexts reduce response efficiency |
variables | Bind three core variables: fiber grade, twist, defect type | Match exclusive cotton blending, process, and inspection fields for this category, improving dialogue accuracy |
| Single retrieval limit | 6–10 entries | Single ledger documents for this category have large length; too many retrieval results exceed context capacity |
| Context refresh interval | 10–20 minutes | Matches the shift-wise update frequency of equipment operation records, ensuring dialogues use the latest data |
Three Common Configuration Errors
- Symptom: Application internal detailed records cannot be displayed in conversation details, with corresponding fields empty. Cause:
chatIdis not correctly bound to production batches, so the session context is not associated with corresponding ledger data. - Symptom: After uploading files such as cotton blending sheets or process setup sheets, the data processing step returns empty results. Cause: The length range of category documents is not adapted, leading to exceeding preset capacity during document splitting.
- Symptom: Conversation history records cannot be queried in MongoDB, with no data in the detail interface. Cause: The storage rules for
chatIdare not correctly configured, so session data is not archived to the corresponding collection by batch.
How to Verify Correct Configuration
- Launch a multi-turn dialogue targeting a single-batch production ledger, verify that bound
variablesfields can be correctly identified and associated with corresponding documents. - View session details, confirm that
chatIdmatches the current production batch number, and that historical conversation records load normally. - Upload category-exclusive documents, verify that the data processing step can normally generate retrievable text blocks.
- Launch continuous multi-turn dialogue, verify that the context refresh frequency matches the shift-wise update rhythm, and that old data does not overwrite new data.
The values provided are common starting points, and should be evaluated against the reader's own samples.