What Data for This Category Looks Like
Data sources for this category include purchase orders, drawings and specifications, supplier quality certificates, inspection equipment data, historical quality data, and supplier portals. A corresponding record is generated for each batch of incoming materials when data is updated. Supplier overall evaluations are completed monthly or quarterly. Special audits are conducted irregularly. Daily documents cover inspection specifications, sampling plans, incoming material reports, supplier audit reports, abnormal contact sheets, 8D reports, and concession acceptance approvals. Fields include batch number, supplier code, inspection items, non-conformity description, rectification status, and more. Core units are batch, item, and day.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The characteristics of scattered data updated per batch require multi-turn dialogue to bind chatId to associate exclusive context for the current batch, to avoid mixing up cross-batch inspection records. Documents have diverse types and clear fields, so prompts must be limited to calling only documents related to incoming material inspection and supplier quality, and must not introduce irrelevant production process inspection rules. The high-frequency and irregular update rhythm requires dialogue context to support dynamic loading of the latest supplier evaluation and rectification data, and must not rely on static caching. Quality traceability requires a complete link, so multi-turn dialogue must retain every inspection and abnormal contact dialogue node, to ensure that all links can be traced for subsequent analysis.
Configuration Settings
The following configurations are based on FastGPT v4.17.0, and are adapted for the incoming material inspection and supplier quality business scenario.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current incoming material batch | Ensure each batch's dialogue context is independent, avoiding cross-batch data mixing |
messages | Retain the first 10 rounds of dialogue content, calibrated via actual testing | Cover the complete link from inspection initiation, abnormality reporting to rectification follow-up, avoiding context loss |
variables | Inject batch number, supplier code, inspection specification version | Enable prompts to accurately call exclusive data and documents for the corresponding batch |
| Maximum context length | 8000–12000 characters | Accommodate complete content of multiple incoming material reports and 8D reports, avoiding truncation of critical information |
| Single dialogue round limit | 15 rounds | Cover the complete process from initial inspection to root cause analysis and rectification verification, avoiding accumulation of invalid dialogue |
| Automatic prompt variable filling | Associate with current batch's inspection data | Reduce manual input errors, ensure fields called by prompts match the current incoming material batch |
Three Common Mistakes
- Symptom: The top title of the dialogue page is accidentally modified and cannot be restored. Cause:
chatIdis not bound to the current batch identifier, causing dialogue context to associate with incorrect page configuration parameters. - Symptom: AI responses exceed the limited business scope, introducing irrelevant inspection rules or content. Cause: The prompt does not clearly limit calling only exclusive documents for incoming material inspection and supplier quality, and does not bind exclusive fields in
variables. - Symptom: Dialogue output context and AI response content are mixed, making it impossible to correctly trace abnormal links. Cause: Historical dialogue context in
messagesand current interaction content are not distinguished, leading to incorrect context data being called during subsequent retrieval.
How to Verify Proper Configuration
- Initiate a simulated incoming material inspection dialogue, check whether
chatIdis bound to the preset batch identifier, and ensure context does not mix with other batches. - Enter a query containing a non-conformity description, check whether the AI response only calls documents related to incoming material inspection and supplier quality, and does not introduce irrelevant content.
- View the output of dialogue nodes, confirm that
messagesand AI response content are correctly separated, and can be used separately for context storage and result display. - Test the multi-turn dialogue round limit, confirm that new interactions automatically stop after reaching the set value, avoiding accumulation of invalid content.
The values provided are common starting points and should be measured against the reader's own samples.