What the data for this category looks like
Forging and die forging production and audit data is sourced from raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Update frequency follows per piece, per batch, or per furnace heat. Daily documents include forging process specifications, cutting cards, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Document fields include furnace number, heating temperature, holding time, forging batch number, defect equivalent, forging ratio, and others. Units include ℃, hours, mm, and more. The length of a single document typically ranges from hundreds to thousands of characters.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Since data is updated frequently per furnace heat or batch, dialogue context must limit turn count to avoid old data interfering with batch matching. Multi-field business documents require prompts to clearly specify core audit fields for extraction, preventing omission of key information such as furnace number and forging batch number. Multiple data sources require multi-turn dialogue to guide users to supplement missing parameters, ensuring audit completeness. The high severity of errors requires mandatory verification of key matching items in multi-turn check links, preventing batch confusion or missed defect detection.
How to set the configuration
This configuration is based on FastGPT v4.17.0 logic.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context turn limit | First 3 turns of dialogue | Forging production data updates per furnace heat or batch; old dialogue content is redundant and easily introduces batch confusion |
| Document chunk length | 800–1200 characters | Single-segment content of forging process specifications and test reports falls within this range |
| Session parameter binding | Bind chatId, messages, variables | Track session batches, dialogue history, and audit parameters entered by users |
| Single recall result count | Top 5 results | Forging-related document entries are concentrated; too many results easily interfere with audit judgment |
| Prompt variable configuration | Fill in forging batch number, furnace number, defect type | Match core business fields that need to be verified for forging audits |
| Retrieval result sorting | Sort by detection time in descending order | Prioritize the latest heating furnace records and ultrasonic testing reports |
Three Common Mistakes
- Omitting the
messagesparameter when calling the dialogue interface prevents association of retrieved forging documents. The symptom is that returned results do not reference process specifications or test reports. The cause is incorrect configuration of session context binding. - Failing to bind the
chatIdparameter prevents persistent storage of dialogue history. The symptom is that no corresponding session record entry exists in MongoDB. The cause is neglecting required configuration for session tracking. - Failing to limit core audit fields in the prompt, and not guiding verification of material furnace batches and forging batches during multi-turn dialogue, leads to batch matching errors in audit results. The cause is not designing prompts based on business fields of the forging category.
How to Verify Correct Configuration
- Initiate a test dialogue, enter a forging batch number, and check whether returned results are associated with heating furnace records and ultrasonic testing reports for the corresponding batch.
- View the session storage medium, and confirm that the dialogue history corresponding to
chatIdhas been correctly written. - Trigger a multi-turn dialogue, enter different audit parameters in sequence, and check whether
variablescorrectly transmits field values entered by users. - Debug the knowledge base retrieval link in the workflow, and confirm that the number of recalled results matches the configured single recall limit.
The values provided are common starting points and should be measured against the reader's own samples.