What this category of data looks like
Data for this category comes from purchase orders, drawings and specification requirements, supplier quality certificates, inspection equipment-collected data, historical quality records, and supplier portals. Daily documents include inspection specifications, sampling plans, incoming material reports, supplier audit reports, abnormality contact sheets, 8D reports, and concession acceptance approval documents. New records are created for each batch of incoming materials. Overall supplier evaluations take place monthly or quarterly. Special audits occur on an irregular basis. Fields include incoming material batch number, sampling quantity, non-conformity type, supplier code, inspection date, and more. Units include pieces, batches, days, and more.
Constraints on multi-turn dialogue and prompt engineering
This category of data updates by batch and cycle, and covers multiple types of business documents. Multi-turn dialogue must bind exclusive context for the corresponding batch or cycle to avoid cross-batch historical data confusion. Call rules for different documents must be clearly defined in the prompt. Prioritize calling the current batch’s inspection specifications to match current inspection task requirements. Dialogue must retain full history of supplier contact and abnormality handling to ensure coherent audit logic. Context retention range must tie tightly to the business scenario, focusing only on content related to the current incoming material batch’s inspection and supplier quality. Data fields include exclusive identifiers such as batch and code. The prompt must accurately call associated data for the corresponding field to prevent generalized responses.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind incoming material batch number + current inspection task ID | Associate historical inspection and supplier communication records for the corresponding batch, avoid cross-batch context confusion |
messages | Retain the first 6 dialogue context entries | Cover the entire communication and abnormality determination process for a single inspection, while controlling API call load |
variables | Bind supplier code, incoming material batch number, sampling plan version | Allow the prompt to accurately call exclusive business data for this category, prevent generalized responses |
| Maximum context length | 1200–1800 token | Adapt to the context requirements of multi-document splicing for this category, avoid exceeding model window limits |
| Context binding rule | Associate by current inspection task ID | Distinguish inspection sessions for different incoming material batches, prevent context reuse across sessions |
Three Common Misconfigurations
- Setting the number of context entries to 0 in the workflow. This prevents the AI from calling historical incoming material inspection data. The root cause is configuring the
messagesparameter to 0, which fails to retain necessary session context. - Configuring 6 chat history entries in the plugin node, but failing to achieve multi-turn dialogue continuity in the workflow. The root cause is not binding
chatIdto the current inspection task, so session context does not pass across workflow nodes. - Receiving empty results when calling the application interface. The root cause is not passing key parameters such as supplier code and incoming material batch number via
variables, so the prompt cannot match exclusive business documents for this category.
How to Verify Correct Configuration
- Launch a multi-turn dialogue for incoming material inspection. Check if returned results include the current batch’s sampling plan and historical abnormality records.
- Check the
messagesparameter in workflow nodes. Confirm it binds the historical content of the corresponding session, with no null values or incorrect truncation. - Verify that
variablespasses exclusive fields such as supplier code and incoming material batch number, with no omissions. - Test inspection sessions for different batches. Confirm that context does not mix across batches.
The values provided are common starting points and should be measured against the reader's own samples.