What the data for this category looks like
Data for this category comes from commercial concrete mixing plants, project laboratories, and quality scheduling departments. Daily documents include mix proportion notices, opening identifications, production and transport orders, raw material inspection records, test block ledgers, and strength reports. Data sources cover engineering orders, sand, gravel, cement, and admixture inspections, moisture content measurements, mixing plant control systems, and laboratory test data. Update frequencies follow per-vehicle production, per-batch raw material inspections, and daily summaries. Fields include aggregate moisture content, mix proportion parameters, slump indicators, strength grades, and additional relevant metrics.
What constraints these characteristics impose on the multi-turn dialogues and prompt engineering link
Frequently updated data requires multi-turn dialogues to associate the latest raw material inspection, production record, and test block data in real time. It prevents the use of expired information. Multiple types of professional documents and scattered cross-departmental data sources require prompt engineering to distinguish the verification logic of different documents. It supports cross-module context calls. The feature of small single-data volume but frequent updates requires limiting redundant content in the dialogue context window. It also ensures that each dialogue accurately matches the business data of the corresponding batch. In addition, business processes require linking moisture content correction for batching, coordinating production and transport, and other links. This requires multi-turn dialogues to automatically call real-time data for corresponding fields.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier based on user identity | Isolates dialogue histories for different users, ensures each user can only view their own dialogue and associated data |
messages | Retain the first 20 dialogue context entries | Adapts to frequently updated per-vehicle or per-batch data, avoids context overload that slows response speeds |
variables | Bind raw material inspection and moisture content data for the current batch | Automatically calls real-time business data exclusive to ready-mixed concrete production during dialogues |
| Maximum context window | 8000–12000 token | Covers the context length required for verification across multiple documents such as mix proportions and test block records |
| Dialogue history retention period | 7 days | Matches the business cycle of daily summaries and batch traceability |
| Prompt template | Configure by document type as modules | Adapts to different cross-review scenarios such as mix proportion review and test block strength verification |
Three common mistakes
- Phenomenon: The Human field is empty in the response record preview when calling the API. Cause: User input content is not passed in the correct format in the
messagesparameter. This leads to missing user-side identifiers in the dialogue context. - Phenomenon: Different users cannot view their own exclusive historical dialogues. Cause: A unique identifier is not generated for each user based on
chatId. This leads to dialogue histories not being stored separately by user. - Phenomenon: Multi-turn dialogue verification results do not match actual production data. Cause:
variablesare not bound to real-time data such as raw material inspection and moisture content for the current batch. This leads to expired business documents being called.
How to verify a correct configuration
- Send an API dialogue request with the specified
chatId. Check whether the returned results only include historical dialogues corresponding to that identifier. - Upload the mix proportion notice and raw material inspection records for the corresponding batch. Send a verification dialogue. Check whether the prompt template automatically calls the bound real-time business data.
- View the dialogue history list. Confirm that the
chatIdvalues for different users are not associated. Users cannot view historical dialogues across accounts. - Test continuous multi-turn dialogues. Confirm that no redundant expired data appears in the context. Confirm that the response logic complies with preset review rules.
The values provided here are common starting points. They should be evaluated against the samples used in the specific implementation.