What Data for This Category Looks Like
The data subjects in foundry (sand casting, precision casting, die casting) scenarios originate from the process and quality departments of foundries, die casting workshops, and equipment component suppliers. Daily documents include casting process cards, batching lists, heat records, mold histories, first-piece inspection records, material test reports, and similar materials. Data sources cover ERP material master data, melting furnace thermometers, spectrometers, MES heat records, mold management systems, and customer technical drawings. Updates are conducted per heat, per batch, or daily. Core fields include alloy batching ratios, melting temperatures, pouring parameters, defect types, and corresponding heat and mold identification numbers.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Words
Data for this category is tied to core business fields via production batches. Multi-turn dialogue must accurately link process and equipment records for a single heat, to prevent cross-batch context confusion. Data sources span multiple system types, so prompt words must clearly specify the target document types for retrieval, to avoid including irrelevant data. Update frequency is relatively high in real time, so dialogue must support rapid recall of the latest heat and mold data. Additionally, defect tracing requires linking multi-dimensional records, so multi-turn dialogue must support step-by-step parameter queries to gradually narrow the scope of defect troubleshooting.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the current heat/batch | The context for single-heat inspection and maintenance must be tied to the corresponding production batch to avoid cross-batch data confusion |
messages | Retain the first 15–20 turns of dialogue context | Questions related to foundry inspection mostly revolve around single-heat parameters and defects; excessive context will interfere with the accuracy of the current dialogue |
variables | Preset heat number and mold number variables | Quickly associate melting records and mold history data for the corresponding heat |
| Maximum context length | 8000–12000 characters | Must accommodate multiple document contents such as process cards and heat records for a single heat |
| Single recall limit | Top 6 entries | Foundry-related document fields are concentrated; too many recall results will cause prompt word overload |
| Dialogue interruption handling | Calibrate based on actual testing | Must adapt to the real-time data push rhythm of equipment such as melting furnaces |
This configuration is compatible with FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: When the AI generates a Markdown table, the content is truncated, with a prompt such as
...[hide 38432 char]displayed at the end. Cause: The maximum context length is not set high enough, causing the table content to exceed the preset output limit. - Phenomenon: An
insufficient_quota current group upstream load is saturatederror is returned during a dialogue. Cause: A reasonable upper limit for single-dialogue content is not configured, causing the request to exceed the cluster load threshold. - Phenomenon: After the client closes the SSE connection, the dialogue record is not written to the database. Cause: The persistent storage configuration for dialogue context is not enabled, and the context write operation is not completed when the request is interrupted.
How to Verify Proper Configuration
- Initiate a test dialogue that includes a specified heat number and mold number, and check whether the preset variables automatically populate the corresponding business fields.
- Initiate a query with multi-step progressive parameters, and check whether
chatIdis linked to the historical dialogue context of the same batch. - Simulate an SSE interruption scenario, and check whether the dialogue record is successfully written to the database.
- Initiate a maintenance suggestion query that uses a table format, and check whether the output content is complete and not truncated.
The values provided are common starting points and should be evaluated against the reader's own samples.