What this type of data looks like
Heat treatment-related data originates from material grades, customer technical requirements, temperature control instruments, furnace temperature uniformity tests, hardness testers and metallographic equipment, and MES systems. Data is updated per heat and per batch. Daily documents include heat treatment process cards, furnace loading diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling forms. Data fields include material grades, heating temperature, holding time, cooling rate, hardness values, and more, with corresponding units such as degrees Celsius, hours, HV, and others.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The high-frequency update requirement per heat means dialogue contexts must be bound to unique project and heat identifiers to prevent cross-use of data from different batches. Multiple types of professional documents require prompts to clearly distinguish usage scenarios for different documents, avoiding model confusion between process parameters and test results. The specificity of fields and professional units requires prompts to force the model to strictly follow format specifications, preventing unit errors or parameter deviations. Additionally, the serious potential consequences of errors require adding a parameter verification step in multi-turn dialogue to ensure the accuracy of core information such as heating, holding, and cooling systems, and furnace loading positions.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | A single batch of heat treatment bidding involves multiple process documents and parameters, requiring sufficient space for complete dialogue and business data |
| Single search limit | Top 5 entries | The number of heat treatment-related data entries for a single batch is limited; too many results will distract the model |
variables | Bind the current project's material grade and customer technical requirements | Pass fixed business parameters through preset variables to avoid repeated input and ensure data accuracy |
messages | Retain the first 3 rounds of core interaction content | Excessive historical dialogue will dilute key information for current quotation preparation; the first 3 rounds can cover requirement confirmation and parameter verification |
chatId | Bind the unique identifier of the current bidding project | Isolate the context of different projects via dialogue IDs to prevent data cross-use |
| Question optimization switch | Enabled | For heat treatment parameters with many professional terms, optimize question wording to improve model understanding accuracy |
Three common mistakes
- Phenomenon: No streaming output occurs during the dialogue process. The interface only displays the final complete result, and does not show generation progress word by word. Cause: Streaming response configuration is not enabled, or the model call link does not correctly enable streaming transmission.
- Phenomenon: The bid document output by the model contains extra spaces and incorrect letter capitalization, which does not match the character format of the original document. Cause: The prompt does not explicitly require strict adherence to the original document's character and format specifications, or irrelevant format data is introduced during knowledge base matching.
- Phenomenon: The file parsing tool cannot be invoked to read content from documents such as heat treatment process cards and furnace temperature curves. Cause: Parsing rules for the corresponding document types are not configured, or the file upload format exceeds the platform's supported range.
How to confirm the configuration is properly set
- In the FastGPT v4.17.0 environment, initiate a test dialogue containing single-heat heat treatment parameters, and check whether the model can accurately call the preset
variablesparameters to generate a qualified preliminary quotation. - Test the streaming output function to confirm that generated content is displayed word by word during the dialogue, with no delays or freezes.
- Import a heat treatment process card and a furnace temperature curve, and check whether the model can correctly parse the document content and match current business requirements.
- View the historical conversation record to confirm that non-business interactive plugin call content is not included in the context memory.
The values provided are common starting points and should be measured against the reader's own samples.