What does data for this category look like
Data for this category comes from water source and raw material testing, formulation systems, online sugar and acidity monitoring, filling equipment, visual inspection systems, and LIMS systems. Daily documents include formulation sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection records, and finished product inspection records. Data is updated per production batch, per shift patrol, and for continuous equipment status collection. Document fields include raw material batch number, sugar and acidity values, filling liquid level parameters, sealing pass rate, batch number, and more. Units involve professional testing units such as mg/L, %, ml, seconds, and others.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
Data for this category has a high update frequency and is generated according to production batches, patrol cycles, or continuous collection. Multi-turn dialogue must bind the context of the current production batch to avoid calling outdated data. Documents include professional testing parameters and batch identifiers. Prompts must clearly limit the retrieval scope to the current batch’s formulation, water treatment, and filling records to prevent returning content from unrelated batches. At the same time, the consequences of errors are directly linked to product safety. The length of dialogue context must be strictly controlled, retaining only historical interactions and production data relevant to the current question, to avoid redundant information interfering with AI judgment.
How to Configure
This configuration is based on FastGPT v4.17.0. Specific configuration items and their bases are as follows:
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current production batch number | Ensure multi-turn dialogue only calls process and testing data for the corresponding batch, avoiding cross-batch confusion |
messages | Retain the first 8 interaction histories of the current batch | Control context length to avoid redundant information interfering with professional judgment |
| Maximum context length | 800–1200 characters | Adapt to the field length of professional documents for this category, preventing exceeding AI processing limits |
| Single retrieval limit | Top 5 matching documents | The document structure for a single batch in this category is clear, limiting the number of results ensures retrieval accuracy |
| Prompt variables | Bind raw material batch number and filling parameters from variables | Allow the AI to directly call specific current production parameters without additional retrieval |
| Context timeout | 600 seconds | Match the interaction cycle of single-batch production or patrols, preventing expired contexts from being loaded |
Three Common Misconfigurations
- Dialogue results are only displayed in the side panel and do not appear directly in the main dialog box: The cause is that the
messagesparameter is not configured to bind the interaction context of the current session. The system mounts retrieval results to the side knowledge base panel by default. - Each new question creates an independent session, and multi-turn interaction cannot be continued: The cause is that
chatIdis not bound to the current production batch number. The system automatically generates a new session identifier for each request. - AI replies enforce Markdown syntax and cannot be switched to plain text: The cause is that the prompt does not explicitly specify the output format, and rules to disable Markdown are not added to the prompt variables.
How to Verify Successful Configuration
- Initiate a process-related question about the current production batch, check if the reply appears directly in the main dialog box without redirecting to the side panel.
- Submit three consecutive questions about water treatment parameters for the same batch, verify that no new session is created and interaction history is retained normally.
- Request the AI to output formulation parameters, check that the reply uses only plain text without Markdown formatting.
- Initiate a question about a different batch, verify that the system does not return process data from other batches and only focuses on content from the current batch.
The values provided are common starting points and should be measured against the reader's own samples.