What the data for this category looks like
Typical entities in this category are carbonated drink, juice, tea, functional beverage, and packaged drinking water factories. Daily generated documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data comes from water source and raw material test results, formula systems, online sugar and acidity monitors, filling equipment, visual inspection equipment, and LIMS systems. Data is updated per production batch, per shift operation, and via continuous equipment data collection. Fields include sugar content, acidity, filling liquid level, sealing pressure, product batch number, packaging material model, and similar items, with no extra redundant statistical fields.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources for this category cover raw materials and the entire production process. Multi-turn dialogue must gradually guide users to locate detailed data for a specific production batch, to avoid mixing records from unrelated batches. The volume of continuously collected equipment data is large, so dialogue context must be limited to data from the production period associated with the current question, to prevent context overflow. Fields include process parameters such as sugar content and acidity, as well as batch number and packaging material information. Prompts must clearly specify matching rules for associated fields, to avoid result deviations caused by fuzzy matching. Additionally, since this category focuses on water treatment and aseptic filling control, dialogue must prioritize linking records and test results from these links.
How to set up the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to production batch number | Ensure each dialogue is associated with a unique production batch, to avoid mixing data across batches |
messages | Only retain 3 turns of dialogue history for the current batch | Limit context length to adapt to the high data volume feature of continuous collection |
variables | Pass current product batch number and production period | Provide precise filtering conditions for prompts, narrow data retrieval scope |
| Maximum context | 800–1200 characters | Match the average length of core documents for a single batch in this category, prevent context overflow |
| Question optimization | Calibrate based on actual testing | Adapt to complex queries with multiple process parameters for this category, optimize question splitting logic |
| Single recall limit | Top 6 records | Match the number of core records for a single batch in this category, avoid redundant results |
This configuration is based on FastGPT v4.17.0.
Three common mistakes
- Phenomenon: Some dialogues are routed to incorrect process operations by the question classification module. Cause: The batch number of the current production batch is not passed as a classification filtering condition via
variables, causing the system to match irrelevant data across batches and trigger incorrect processes. - Phenomenon: Dialogues cannot be used normally, and the interface displays parameter exception prompts. Cause: The
messagesparameter does not limit context length, introducing multi-batch historical data leading to context overflow. - Phenomenon: An extra historical record appears in returned results after starting a dialogue via API. Cause:
chatIdis not correctly bound to a unique production batch, causing duplicate session creation and generating redundant history.
How to confirm the configuration is correct
- Pass the batch number of a known production batch as the
variablesparameter, initiate a quality traceability query, and verify that returned results only include document data related to that batch. - View the dialogue history panel, confirm that the
messagesparameter only retains specified turns of content for the current session, with no redundant historical data across batches. - Trigger a question classification operation, verify that routing is directed to the corresponding quality problem analysis process.
- Complete a dialogue via API, check the session list, confirm no extra session records are generated, and like/dislike operations can be properly synchronized to the system backend.
The values provided are common starting points and should be evaluated against the reader's own samples.