What Data for This Category Looks Like
General mechanical equipment assembly data typically originates from PLM/ERP, part inspection data, assembly tool collection systems, test benches, MES, and customer technical agreements. Update frequency is once per piece of equipment or per order. Daily documents include assembly process cards, BOM lists, torque records, gap adjustment records, commissioning reports, factory inspection certificates, and packing lists. Data fields cover material codes, tightening torque values, coaxiality values, gap parameters, inspection batches, and additional relevant fields. Corresponding units include N·m, mm, %, and other standard units.
Constraints for Multi-turn Dialogue and Prompt Engineering
Data for this category is multi-sourced and updated per order or equipment unit. Multi-turn dialogue must bind to a specific order or equipment identifier to avoid mixing ledger data from different batches. Differences in fields across multi-level BOMs and multiple types of assembly records require prompts to explicitly define the scope of extracted fields. This prevents cross-document information confusion and supports the core requirement of checking multi-level BOMs and physical part availability for this category. Multi-source data retrieval increases response latency, so dialogue context length must be limited to avoid loading redundant historical data. Single-order exclusive parameters and global default parameters must be separated to prevent accidental variable overwriting during sessions, ensuring the accuracy of ledger summaries.
Configuration Settings
This page’s configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Dialogue context window | Previous 8 turns of dialogue or 3000 characters | Adapt to multi-turn confirmation needs of assembly ledgers, avoid redundant information slowing retrieval |
| Variable storage rules | Distinguish between variables and session global default variables | Separate single-order exclusive parameters from global general configurations to prevent accidental variable overwriting |
| Session ID binding | Bind the current order’s chatId | Accurately associate PLM, MES, and assembly record data for the corresponding batch |
| Retrieval data source scope | Limit to documents associated with the current order | Filter out ledger data from unrelated batches to improve retrieval efficiency |
| Number of single retrieval results | Top 6 results | Balance information completeness and retrieval latency, adapt to retrieval load of multi-source data |
| Timeout threshold | 120 seconds | Cover time required for multi-source data pulling and complex BOM checking, calibrated based on actual testing |
Three Common Misconfigurations
- Symptom: Global default variables are overwritten by single-order parameters, or variable storage is empty. Cause: Failure to distinguish between
variablesand session global variables, with single-order parameters written to the global storage area. - Symptom: Response speed slows as the number of questions increases in the same dialogue window, and knowledge base retrieval latency exceeds 1 minute. Cause: Failure to limit dialogue context window length, with redundant historical data continuously increasing retrieval load.
- Symptom: Unable to close AI dialogue content after tool invocation, or unable to directly reference ledger fields from historical dialogue. Cause: Failure to configure content filtering rules for dialogue context, or failure to enable binding association between historical dialogue and the current session.
How to Confirm Successful Configuration
- Initiate a test dialogue, input the equipment number of a specific order, confirm retrieval results only include assembly process cards, BOMs, and inspection records corresponding to the order.
- Manually set global default variables and single-order exclusive variables, initiate a multi-turn dialogue, confirm the two types of variables are not overwritten by each other.
- Submit 10 identical types of questions consecutively, observe that retrieval latency does not increase significantly, confirm the dialogue context window configuration is effective.
- Trigger a tool invocation, check whether AI dialogue content can be retained or hidden, confirm configuration items are effective.
The values provided here are common starting points, and should be evaluated against local testing samples.