What the Data for This Category Looks Like
Daily data for the spinning and weaving category includes documents such as cotton blending or batching lists, process on-machine sheets, equipment operation records, yarn inspection reports, grey fabric inspection results, defect records, and others. Data sources include raw fiber inspection reports, ERP systems, real-time collected data from spinning machines and looms, evenness testers, output results from fabric inspection machines, and order specification documents. Data update frequency is per batch of raw materials, per shift, per piece of fabric. Some equipment data is generated in real time. Field formats vary across different documents. Core fields include fiber grade, spinning twist, piece number, defect type and size, and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Configuration
The data characteristics of this category impose three core constraints on multi-turn dialogue and prompt configuration. First, data is updated frequently and linked to batches, shifts, or piece numbers. Multi-turn dialogue must limit the time and batch scope of context to avoid using expired or cross-batch data. Second, diverse document types have large differences in field formats. Prompts must adapt to field prefixes of different documents to ensure accurate variable substitution. Third, real-time equipment data and offline inspection records coexist. Multi-turn dialogue must support associated queries across data sources to ensure correct matching logic between process parameters and inspection results.
How to Set the Configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Single retrieval limit | Top 5-8 entries | Adapts to the single-batch or single-shift scale of spinning and weaving inspection data, avoids excessive redundant content interfering with dialogue |
| Context window length | 8000-12000 token | Supports multi-turn dialogue recall of historical information such as process parameters and piece numbers, matches the associated logic of production processes |
variables variable substitution rules | Match document field prefixes | Adapts to the structured field formats of cotton blending lists and on-machine sheets, ensures accurate matching of variable substitution to business data |
| Session ID isolation | Bind unique chatId per session | Avoids confusion of conversation contexts across different production batches, ensures accuracy of multi-turn dialogue |
| Multi-turn message retention | Retain the most recent 10 rounds of messages | Adapts to consecutive questions in multiple steps of the production process, covers the complete link from batching to inspection |
| Session expiration time | 3600 seconds | Matches the production cycle of one shift, avoids confusion of conversation contexts across shifts |
Three Common Misconfigurations
- Phenomenon: A 503 error is prompted when uploading documents on the dialogue page, but the backend log shows that the file was uploaded successfully. Cause: The token number configured for the context window exceeds the interface bearing threshold, causing request timeout.
- Phenomenon: When using a strict question-and-answer template with the knowledge base, retrieving an image address prompts no answer, while other templates can reply normally. Cause: The prompt does not configure adaptation rules for image content analysis, and only supports matching verification of text fields.
- Phenomenon: When sending two questions consecutively at short intervals, the subsequent request needs to wait for the previous generation to complete. Cause: Concurrent session isolation for
chatIdis not configured correctly, causing request queuing and blocking.
How to Verify the Configuration Is Correct
- Upload a single cotton blending or batching list, initiate a multi-turn dialogue to ask about the fiber ratio, and verify that the reply content matches the specific field values in the document.
- Send 3 consecutive questions related to process parameters, and verify that the conversation context correctly retains historical questions' piece number, shift, and other information.
- Upload a defect record document, trigger retrieval and verify that the returned results include defect type and size data corresponding to the piece number.
- Test sending two questions from different production batches at short intervals, and verify that the conversation context does not mix up batch information.
The values provided here are common starting points and should be measured against the reader's own samples.