What the data for this category looks like
Daily documents for welded structural component manufacturing include welding procedure qualification records, welding procedure specifications, filler metal issue records, welder qualification records, weld inspection reports, and repair orders. Data sources include design drawings, material quality certificates, welding equipment parameters, filler metal storage rooms, non-destructive testing systems, and personnel qualification ledgers. Data updates trigger per weld seam, per batch, or per shift. Single-batch data includes fields such as weld number, base material grade, filler metal batch number, welder certification number, non-destructive testing results, and other relevant fields. Data volume changes dynamically based on the number of welding nodes.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data sources for welded structural component manufacturing are scattered, with high update frequency. Multi-turn dialogue must gradually retrieve field information from different systems to avoid context confusion. Rigid document requirements mandate that prompt engineering verify welder qualification and procedure qualification compliance, and cannot omit required fields. Single-batch data updates dynamically with each node. Dialogue context must bind the current batch’s unique identifier to ensure the latest data is called. Abnormal report judgment requires combining non-destructive testing results. Prompt engineering must guide the output of traceable repair plans, and avoid vague statements.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique ID of the current weld batch | Achieve context isolation for single-batch welding data, avoid confusion across batches |
messages | Retain the last 30 historical conversation messages | Cover the full process dialogue logic from process preparation to abnormal repair for a single batch |
variables | Bind fields such as base material grade, filler metal batch number, welder certification number, non-destructive testing results | Meet the rigid verification requirements of welded structural component manufacturing, ensure the prompt has referenceable business data |
| Conversation file expiration time | 7 days, adjustable based on batch delivery cycle | Align with the default FastGPT CHATFILEEXPIRE_TIME setting, adapt to delivery cycles of most small and medium batches |
| Token calculation rule | Count based on actual total input and output tokens | Match the platform's native call log statistics logic, facilitate subsequent operation and maintenance verification |
| Format verification switch | Enable mandatory output format verification | Avoid unclosed reference garbled code issues, standardize the format of output content |
Three Common Misconfigurations
- Phenomenon: The large language model returns an empty response. Cause: The welding data fields corresponding to
variablesare not bound. The prompt lacks referenceable business context, so valid content cannot be generated. - Phenomenon: Unclosed reference garbled code appears at the end of the output content, with subsequent automatic correction to standard quotation marks. Cause: The closing rules for output format are not clearly specified in the prompt, and
messageshistorical messages retain content with non-standard formats. - Phenomenon: Token statistics do not match actual calls, or historical data cannot be retrieved after the conversation file expires. Cause: The correct token calculation rule is not configured, or the conversation file expiration time setting is not adjusted, failing to meet traceability requirements for long-cycle batches.
How to Verify Successful Configuration
- Initiate a test conversation using FastGPT v4.17.0. Check whether
chatIdis bound to the unique ID of the current weld batch, to ensure single-batch data is not confused with other batches. - Verify whether
variablesinclude required fields such as base material grade, filler metal batch number, welder certification number. Confirm by retrieving corresponding field values through test conversations. - Trigger the abnormal report drafting workflow. Check whether the output content has no garbled code, no empty responses, and meets preset format requirements.
- View platform logs to confirm that token statistics match the actual total input and output, and comply with the configured token calculation rules.
- Adjust the conversation file expiration time to 14 days. Confirm that historical conversation files can be retrieved normally, with no expiration errors.
The values provided are common starting points and should be measured against the reader's own samples.