What This Category of Data Looks Like
Data for this category comes from milk source testing, MES systems, homogenization and heat treatment equipment, online temperature and flow monitoring, CIP cleaning systems, and LIMS laboratory management systems. Data updates are tied to each milk tank and each batch. The synchronization frequency for some real-time monitoring parameters can reach the second level. Daily documents include raw milk acceptance records, standardized ingredient lists, pasteurization or UHT heat treatment records, CIP cleaning records, filling records, sample retention and microbial test reports. Fields cover batch numbers, physical and chemical indicator values, temperature and humidity parameters, test results, and similar items. Corresponding units include degrees Celsius, kilograms, total bacterial count units, and similar units.
Constraints on Multi-turn Dialogue and Prompt Engineering
Data for this category is updated in real time by milk tank and batch, and includes sensitive physical and chemical indicators. This requires multi-turn dialogue to bind the chatId corresponding to the target batch to avoid mixing data across batches. Daily documents have many fields with exclusive units, so multi-turn interaction needs to gradually guide users to clarify batch numbers and indicator types to reduce parameter ambiguity. At the same time, tender quotations must be based on real batch testing and production data. Prompts must restrict the model to only call valid batch content within the knowledge base, and must not generate unverified values or conclusions, to prevent tender errors caused by incorrect data. Additionally, data sources cover multiple equipment systems, so the retrieval scope must be limited to milk source, production, and testing records associated with the current project, to avoid interference from irrelevant data.
Configuration Settings
This document is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the raw milk batch number of the current tender project | Ensure multi-turn dialogue context only associates with production and testing data for the corresponding batch, avoiding interference from cross-batch information |
messages | Append batch context by conversation turn | Ensure each round of dialogue carries the latest batch parameters, maintaining context consistency |
| Single search limit | Top 6 entries | Match the typical number of data entries required for a single tender document, avoiding redundant information overload |
| Maximum context length | 800–1200 characters | Adapt to the average length of production and testing documents for a single batch, ensuring complete dialogue context without exceeding model processing thresholds |
variables | Preset the milk source batch number and testing type for the current project | Allow the model to automatically bind specified parameters during retrieval and generation, reducing repeated user input |
| Timeout period | 600 seconds | Prevent dialogue from being interrupted due to waiting for data timeout |
| Knowledge base storage limit | 1000 MB | Adapt to all production and testing documents for a single batch |
| Reranking | Enabled | Sort retrieved batch data by test time, ensuring the latest raw milk acceptance and heat treatment data is prioritized for calling |
Three Common Misconfigurations
- Phenomenon: Knowledge base results returned by retrieval include paragraph IDs, such as internal numbers of raw milk batches, which are automatically written into tender documents. Cause: The prompt does not explicitly require filtering metadata identifiers from the knowledge base, causing the model to capture non-business field content.
- Phenomenon: Supporting images for raw milk test reports cannot be displayed in the dialogue interface. Cause: The knowledge base has not been configured with supported image storage and reference rules, causing the model to fail to correctly call visual content.
- Phenomenon: Custom deployed models have inconsistent configurations between the dialogue and workspace. Cause: Project-specific batch parameters are not passed via
variables, leading to deviations in model invocation context across different scenarios.
How to Verify Correct Configuration
- A query including the specified raw milk batch number is initiated, and returned retrieval results are checked to confirm they only include production and testing data for that batch.
- A query containing professional terminology is entered, and the optimized query in the dialogue interface is verified to conform to the business logic of dairy processing.
- Multi-turn dialogue is tested, and the batch number bound to
chatIdis verified to remain consistent throughout the entire dialogue process, with no cross-batch data calls occurring. - The configuration of the custom model is checked, and model parameters for the dialogue and workspace are confirmed to be synchronized, with no missing preset variables.
The values provided are common starting points and should be measured against the reader's own samples.