What data for this category looks like
Data for this category typically comes from raw fiber inspection reports, enterprise ERP systems, real-time collected data from spinning and weaving machines, testing equipment such as evenness testers and fabric inspection machines, and order specification documents. Daily retained documents include cotton blending or ingredient lists, process setup sheets, equipment operation records, yarn inspection forms, grey fabric inspection reports, and fabric defect record sheets. Data updates occur once per batch of raw materials, once per production shift, and once per piece of fabric produced. Fields cover fiber fineness, twist, width, inspection batch number, and similar items, with corresponding testing units attached.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The multi-turn dialogue and prompt configuration for this category must adapt to the traits of high data update frequency and large single-turn data volume. The high-frequency updates for each raw material batch, shift, and fabric piece require dialogue contexts to synchronize the latest batch ingredient lists and process parameters in a timely manner, to avoid using expired data. Differences in fields across multiple data sources require prompts to clearly specify the document types and corresponding fields to call, to prevent confusion between test results collected by different equipment. Single-piece fabric defect records are continuous data; multi-turn dialogue must support filtering historical conversation content by batch or production node, to avoid redundant data interfering with current audit logic. Additionally, the large number of equipment units requires configuration that can isolate dialogue contexts by machine station, to ensure audit logic focuses on the corresponding production unit.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | 8000–12000 token | Adapts to the content volume of multiple inspection sheets and process sheets that need to be loaded for single-turn dialogue in this category |
| Number of retained historical messages | First 10 | Avoids excessive expired defect records and ingredient list data from interfering with current audit logic |
variables | Bind order specifications and ingredient list IDs for the current production batch | Ensures that exclusive data for the corresponding batch can be accurately called during multi-turn dialogue |
messages format verification | Mandatory inclusion of batch number and machine station number fields | Distinguishes test data from different production units to prevent cross-batch confusion |
| Dialogue timeout period | 600 seconds | Adapts to the time required to query multiple equipment records during the audit process for this category |
chatId isolation rule | Divide by production machine station + batch number | Isolates dialogue contexts from different machine stations to ensure audit logic focuses on the corresponding production unit |
Three common mistakes
- Phenomenon: Two linked process parameter and defect record messages cannot be sent simultaneously in a dialogue. Cause: The array structure of
messagesis not configured correctly, resulting in only one message being allowed per single-turn dialogue. - Phenomenon: The Markdown-format inspection report returned by the AI cannot be displayed normally on the connected WeChat channel. Cause: No compatibility configuration for message format adjusted for the WeChat channel, retaining some Markdown syntax not supported by WeChat.
- Phenomenon: After multiple rounds of audit dialogues, the accuracy of answers to the same question decreases, but normal performance is restored when a new dialogue is opened. Cause: The effective data range of the context window is not limited, resulting in accumulation of expired batch data and redundant defect records, which interfere with the model's current judgment logic.
How to confirm the configuration is correct
- Initiate a simulated audit dialogue, enter keywords for the ingredient list of the current production batch, and check whether the
variablescalled in the dialogue are bound to the order specifications of the corresponding batch. - View the conversation history record, confirm that only the first 10 valid messages are retained, with no expired non-current batch data.
- Send a test message containing the batch number and machine station number, and check whether the
messagesformat verification normally blocks input with missing fields. - Wait for the dialogue response, confirm that no timeout error occurs, meeting the time requirements of the audit process for this category.
The values provided are common starting points and should be measured against the reader's own samples.