What data for this category looks like
Data for this category comes from manufacturing enterprise MES systems, online inspection equipment, laboratory systems, process parameter libraries, customer standards, and historical defect databases. Data update cadence is per production batch, continuous collection per shift, or real-time updates with process adjustments. Daily documents include control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product review records, release orders, and quality monthly reports. Fields cover inspection batch numbers, defect codes, release thresholds, process parameter values, and associated units of measurement.
What constraints do these characteristics impose on the multi-turn dialogue and prompt words link
Multi-source and real-time updated data requires multi-turn dialogue to retain contextual information such as batch numbers and inspection items, to avoid repeated questions. Diverse document structures require prompt words to explicitly specify extracted fields and units, to ensure Q&A results align with business formats. Frequently updated processes and standards require prompt words to automatically adapt to the latest thresholds, without manual adjustments. Multiple types of fields require the dialogue flow to verify unit consistency, to prevent release judgment errors caused by unit mistakes. FastGPT v4.17.0’s context management function can adapt to these constraints, but targeted parameter configuration is required.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 6000–10000 characters | Supports complete text content of multiple inspection reports and SPC reports |
| Number of retained conversation history per single session | First 8 entries | Focuses on the context of the current inspection batch, avoiding interference from redundant information |
variables binding | Bind batch number and release threshold variables | Automatically associates business data, simplifying user question input |
| Context recall upper limit | First 6 entries | Matches the most relevant process standards and historical defect data |
| Question optimization switch | Enabled | Converts colloquial inspection questions into standard technical terminology |
| Maximum response token count | 2000 token | Covers complete analysis and judgment content for nonconforming product reviews |
Three common mistakes
- Phenomenon: Multi-turn dialogue only responds to the first question, subsequent questions cannot associate previous batch or inspection item information, and no obvious error is displayed on the interface. Cause: The context transfer rules for
messagesare not correctly configured, causing the conversation history to not be retained by the system. - Phenomenon: After calling the model, the returned result does not match the configured local model, and the deployed local large language model cannot be called normally. Cause: The proxy configuration parameters do not lock the target model, or environment variable conflicts cause the model to be replaced.
- Phenomenon: After inputting JSON-formatted inspection records or SPC reports, the conversation return result is truncated or prompts character limit exceeded. Cause: The maximum context length setting is not adapted to the character scale of JSON text, exceeding the preset limit.
How to confirm the configuration is complete
- Initiate a question that includes a specific batch number and inspection item, then initiate a follow-up supplementary question associated with that batch, confirm that the answer can provide corresponding results based on the previous context.
- View the application’s model configuration list, confirm that only the target deployed model type is displayed, with no other unrelated model options.
- Upload a JSON-formatted inspection record file, confirm that the conversation can fully parse the text and return relevant process standards and historical defect references.
- Test multiple consecutive rounds of questions, confirm that each answer does not deviate from the business scope of the current inspection batch.
The values provided here are common starting points and should be measured against the reader's own samples.