What This Category of Data Looks Like
Data sources include raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Daily documents cover forging process specifications, cutting lists, heating furnace logs, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data updates per forging piece, production batch, or furnace charge. Document fields include forging number, furnace batch number, pressure parameters, mold wear amount, detected defect type, and more. Units include temperature, pressure, millimeters, hours, and other standard industrial units.
Constraints on Multi-turn Dialogue and Prompt Engineering
Per-batch or per-furnace-charge data updates require multi-turn dialogue to bind a session identifier for the corresponding batch. This prevents cross-batch data mixing. Multiple professional documents and multi-field parameters require prompts to limit retrieval scope to equipment inspection and maintenance content. This blocks irrelevant information interference. Frequently updated data such as mold life and heating records requires the dialogue flow to call real-time latest data. Expired records must not be used. When handling defects like folds and cracks, guide users to gradually provide parameters and defect details. Multi-turn dialogue must properly control context length. This ensures professional information is complete without redundancy.
Configuration Settings
The following configurations are based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Generate a unique identifier per user session | Isolate historical conversations between different users, avoid mixing session data, and support multi-user usage scenarios |
messages | Retain the most recent 20 rounds of conversation content | Forging equipment inspection and maintenance involves multi-step parameter verification. Retaining sufficient rounds allows review of historical operation records, while avoiding context overload |
variables | Pre-set forging furnace batch number and equipment model variables | Quickly associate heating furnace records and mold life data for the corresponding batch, improving Q&A accuracy |
| Maximum context length | 8000–12000 characters | Forging-related documents contain multiple sets of parameters and testing data. Sufficient length can carry complete inspection records and maintenance process information |
| Question optimization | Calibrate based on actual testing | Forging has many professional terms. Optimization improves the model's understanding accuracy of professional questions |
Three Common Mistakes
- Phenomenon: When calling the API to fetch conversation data, the Human content in the returned
messagesfield is empty. Cause: User input content was not correctly populated in themessagesparameter of the API request, resulting in missing user-side input in the conversation record. - Phenomenon: When multiple users use the application, different users can view each other's historical conversation records. Cause: An independent
chatIdwas not generated for each user, and session data was not isolated using user identifiers. - Phenomenon: The mold life data called during the conversation does not match the current forging batch. Cause: No batch matching constraint was added to the prompt, or the furnace batch number parameter for the current session was not bound via
variables.
How to Verify Correct Configuration
- Send two API conversation requests from different users. Check that each returned conversation data only contains its own conversation content. Confirm the
chatIdbinding logic works correctly. - Enter a professional question about forging equipment inspection into the application. Check if the reply associates the
variablesparameter content preset for the current session. Verify that variable binding is active. - Initiate multiple consecutive conversation rounds. Check that the number of historical rounds retained in the
messagesparameter matches the preset quantity. Ensure no excessive context truncation or redundant loading occurs. - Simulate an API request and fully populate the conversation content between the user and the assistant. Confirm the returned
messagesfield contains all valid conversation records with no empty fields.
The values given are common starting points and should be measured against the reader's own samples.