What data for this category looks like
Data for this category originates from formula management systems, raw material batch ledgers, internal mixer collection data, Mooney viscometers, vulcanizer operation logs, and laboratory performance test reports. Data update frequency aligns with each raw material truck delivery, each batch of compound production, and each molded product forming cycle. Daily documents include formula sheets, weighing records, mixing curves, compound quick test reports, vulcanization process cards, mold usage records, and finished product performance reports. Fields include batch number, feeding weight, mixing temperature, vulcanization duration, compound hardness, and others, with corresponding units of measurement attached.
Constraints for multi-turn dialogue and prompt engineering
This category has high-frequency, fine-grained data updates and diverse document types, which creates multiple constraints for multi-turn dialogue and prompt configuration. First, data updates occur per single batch and single mold forming cycle. Multi-turn dialogue binds the current session’s production batch via chatId to prevent mixing data across batches. Second, documents contain content in varying formats such as mixing curves and vulcanization process cards. Prompts clearly specify the scope and priority of extracted fields to avoid field confusion. Third, a single batch links to a large number of documents. Retrieval and context length are limited to avoid exceeding model processing limits. Fourth, different documents use inconsistent units of measurement for fields. Prompts enforce inclusion of standard units of measurement in outputs to ensure report readability.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Session context window | Retain the most recent 8 rounds of dialogue + batch data bound to chatId | Data updates occur frequently, requiring precise binding of exclusive ledgers for the current production session |
| Document chunking rules | Split knowledge base documents by "batch number" and hierarchical headings | Documents use batch numbers as core identifiers, and hierarchical headings separate content by type such as formula, mixing, and vulcanization |
| Retrieval match limit | Top 6 matching results | The number of documents associated with a single batch is moderate, preventing context overload from excessive content |
variables binding settings | Bind production batch variables for the current session | Automatic association of formula, process, and test ledger data for the corresponding batch is required |
| Maximum output length | 10000 token | Reports integrate multiple data types including mixing curves, vulcanization parameters, and performance tests, requiring sufficient output space |
| Prompt template | Fixed extraction of specified fields and sorting by production flow | Diverse document fields require a unified output format to generate structured production reports |
Three Common Errors
- Issue: The AI dialogue module returns a 422 status code during calls. Cause: The conversation history format for the
messagesparameter is not properly organized, leading to incoming context data that does not meet interface specifications. - Issue: Generated production reports mix production data from different batches. Cause: Knowledge base documents are not split using "batch number" as the core identifier, leading to incorrect association of ledger data across batches.
- Issue: New dialogue popup style cannot be modified after private deployment. Cause: The file path of the corresponding popup component in the front-end code is not located for custom modification.
How to Verify Successful Configuration
- Enter the keyword for a specified production batch number, and confirm that retrieved documents only contain content for that batch.
- Initiate a multi-turn dialogue, and verify that the bound
chatIdconsistently associates with production data for the current batch. - After generating a production report, confirm that extracted fields fully match the preset document fields and include standard units of measurement.
- Test modifying configuration items in FastGPT v4.17.0, and confirm that interface feedback aligns with expected adjustments.
The values provided are common starting points and should be measured against the reader's own samples.