What Data Looks Like for This Category
The core data for this category comes from CAD/CAM software, order BOM systems, sheet metal inventory ledgers, laser cutting machine and bending machine device logs, and workshop MES systems. Daily produced documents include 3D part unfolding drawings, CNC cutting programs, bending process cards, nesting diagrams, first-piece inspection records, and process flow cards. Data updates are triggered by order delivery, daily production scheduling, and each process change. Fields include bending compensation coefficient, hole position tolerance value, sheet thickness and material specification, with units mostly millimeters, kilograms, and batch numbers.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data for this category is scattered across multiple systems and updated frequently with orders and process changes. This requires multi-turn dialogue to associate exclusive documents and parameters for the current batch. The multi-variety, small-batch business model requires prompts to adapt to bending compensation and nesting rules for different workpiece models. Generic templates cannot be used. Documents are mostly drawings and process cards, so dialogue must accurately pinpoint specific issues such as hole position misalignment and bending error. Therefore, context must be limited to the process flow of the current order to avoid mixing expired data. At the same time, the dialogue process must clearly trace cutting programs, bending machine parameters, and other device logs to ensure the accuracy of analysis basis.
Configuration Settings
| Configuration Item | Suggested Approach | Rationale |
|---|---|---|
chatId | Bind to the unique number of the current production batch | Ensure conversation history only associates with cutting programs, bending process cards and device logs for the corresponding batch, avoid cross-batch data confusion, and comply with FastGPT v4.17.0 session management rules |
messages | Retain the first 20 conversation messages of the current order | Match the short order cycle characteristic of this category, prevent context overflow with irrelevant history, and optimize conversation response speed |
variables | Pass in workpiece model, sheet thickness, bending compensation coefficient | Adapt to the multi-variety, small-batch business model, allowing prompts to accurately call exclusive documents for the corresponding order |
| Single recall limit | Top 6 relevant documents | This category has concentrated document types; excessive recall will interfere with accurate analysis of bending errors and hole position misalignment |
| Context timeout | 300 seconds | Workshop workers usually complete traceability questions within 5 minutes; timeout can release invalid session resources |
Three Common Misconfigurations
- Symptom: Conversations created via API cannot be viewed in page history. Cause: The
chatIdparameter was not correctly bound in the request, causing the session to not associate with the current user's session pool. - Symptom: The Human field is null in API response record previews. Cause: The user's original question content was not correctly passed in the
messagesparameter, causing the conversation chain to lack a user input node. - Symptom: Conversation histories from different operators are mutually visible. Cause: An independent
chatIdwas not bound for each session, causing sessions from different users to be grouped into the same context pool.
How to Verify Proper Configuration
- Initiate a simulated traceability question, check whether the returned conversation history only includes relevant documents and question records for the current batch.
- Call the API to create a session, pass the specified
chatIdandvariablesparameters, confirm that the page can load the complete history of the corresponding session. - Check that the
messagesparameter only retains conversation content for the current order, and does not mix historical data from other batches. - Adjust the single recall limit, verify that the number of returned relevant documents matches the preset range.
The values provided here are common starting points and should be measured against the reader's own samples.