This document is written for FastGPT v4.17.0.
What this category of data looks like
Data for this category comes from material grade standards, customer technical requirements, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic testing equipment, and workshop MES systems. Daily generated documents include heat treatment process cards, furnace loading layout diagrams, real-time furnace temperature curves, hardness and metallographic test reports, equipment inspection forms, and exception handling sheets.
Data is updated per furnace load and per batch. Single documents contain fields such as furnace temperature range, holding duration, hardness value, and metallographic structure rating. Most units are degrees Celsius, hours, HV grades, and similar units.
What constraints do these characteristics impose on the "model access and configuration" link
The high-frequency, small-batch update feature of this category requires the model access link to adapt to real-time data pulling logic, to avoid audit delays.
Mixed input of multiple types of heterogeneous documents — including furnace temperature curve charts, process text, and test forms — requires enabling multi-modal parsing capabilities in the configuration to adapt to data sources of different formats.
Standardization requirements for professional fields require matching embedding rules of corresponding industry knowledge bases, to ensure the accuracy of semantic retrieval.
The limited data volume per furnace load but high update frequency requires limiting the context scale loaded in a single session, to prevent excessive resource usage.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | enabled | Adapt to parsing requirements for image-based documents such as furnace temperature curves and furnace loading diagrams |
maxContext | 8000–12000 token | Match the total text length of multiple documents per furnace load, to avoid context overflow |
quoteMaxToken | 1500–2000 token | Limit the text length of retrieved references, to ensure audit content focuses on key parameters |
temperature | 0.1–0.3 | Reduce randomness of generated results, to ensure consistency of professional audit conclusions |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed proxy address | Adapt to internal enterprise API call links, to ensure compliance of data transmission |
| Workflow/API tool selection | auto | Allow the model to independently choose whether to call tools to complete multi-step audit processes |
Three common errors
- Phenomenon: Connection failure occurs when calling external tools, with a 400 status code returned. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, resulting in an unavailable API link. - Phenomenon: The model cannot parse furnace temperature curve images and outputs empty fields. Cause: The
visionconfiguration item is not enabled, and multi-modal parsing capability is not activated. - Phenomenon: The model only retrieves knowledge base content and cannot call online tools to supplement the latest parameters. Cause: Tool calling rules are not properly configured, limiting the model's ability to obtain external information.
How to confirm the configuration is properly set
- Upload a single furnace temperature curve image, and check whether the model can correctly identify the temperature range and change trend.
- Import multiple process cards and test reports from the same furnace load, and check whether the context loads normally without obvious truncation.
- Submit a professional audit request, and check whether the generated results match the preset professional parameter range without obvious random deviations.
- Trigger the tool calling process, and check whether external interfaces can be normally invoked and corresponding processing results returned.
The values provided here are common starting points and should be evaluated against relevant in-house samples.