What the data for this category looks like
Data for this category comes from melting, quality control, and production departments of foundries and die casting workshops. It covers documents including casting process cards, batching lists, furnace run records, mold history records, and first article inspection records. Data sources include ERP material master data, melting furnace thermometers, spectrometers, MES furnace run records, mold management systems, and customer technical drawings. Data updates are completed per furnace run, per batch, or daily. Core fields include furnace run number, alloy batching ratio, melting temperature, pouring parameters, defect type, and inspection results, and are linked to molds, materials, and customer technical requirements.
What constraints do these characteristics impose on multi-turn dialogue and prompt words
The multi-source, heterogeneous nature of data in this category requires multi-turn dialogue to gradually align field formats across different systems, to avoid ledger summary errors caused by terminology differences. Frequently updated furnace run and batch data requires dialogue context to only retain valid records from the past 7 days, to prevent redundant information from interfering with generation logic. Structured batching and temperature data, plus unstructured defect review forms, require prompt words to adapt to structured query and semantic traceability scenarios respectively, to ensure accurate matching of ledger fields. At the same time, defect traceability needs to link furnace run, mold times, and composition data, so multi-turn dialogue must carry out parameter verification step by step according to business logic.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the first 20 rounds of dialogue context | Under FastGPT v4.17.0, single furnace run data for this category has small volume but frequent updates; limiting context avoids redundancy |
variables | Pass two core variables: furnace run number and mold number | Accurately locate corresponding ledger data and narrow the retrieval scope |
chatId | Bind the session ID for a single production batch | Distinguish dialogue contexts of different batches, ensuring summary data corresponds to the correct production unit |
| Question optimization | Calibrate based on actual measurements | There are many professional terms in casting scenarios; optimization improves term recognition accuracy |
| Single search limit | Top 8 search results | Core data for this category is concentrated in furnace run and mold-related records; a small number of results covers requirements |
| Maximum context | 1200–1500 token | Avoid overly long context causing the model to confuse production batch information |
Three common misconfigurations
- Phenomenon: Dialogue return content contains a large number of meaningless punctuation marks such as # and *. Cause: The dialogue output format is not configured as plain text, and default enabled Markdown rendering leaves residual format symbols.
- Phenomenon: Results returned after calling the dialogue interface do not include knowledge base-linked identification information. Cause: Knowledge base association log recording for dialogue context is not enabled, and the parameter for returning reference metadata is not configured.
- Phenomenon: Image links in the knowledge base cannot display normally in dialogues during local deployment. Cause: The image URL is not configured as a public address accessible to the model, or related settings for knowledge base image analysis are not enabled.
How to confirm the configuration is correct
- Initiate a ledger query dialogue for a single furnace run, verify that the returned content includes the furnace run number and mold number passed by the preset
variablesparameter. - View the dialogue log, confirm that the
messagescontext only retains valid dialogue content for the specified number of rounds, with no redundant historical information. - Test sessions for different production batches, confirm that the session bound to
chatIdonly links to ledger data for the corresponding batch, with no cross-batch confusion. - Submit a query containing defect types, verify that the returned results are linked to corresponding melting parameters and mold history records.
The values provided are common starting points and should be measured against the reader's own samples.