What Data for This Category Looks Like
Production data for batch fine chemical synthesis comes from formula and process libraries, raw material batch ledgers, reactor DCS systems, weighing systems, LIMS laboratory information management systems, and warehouse systems. Daily generated documents include batch production instructions, feeding verification slips, reaction process records, cleaning records, test reports, and deviation and change orders. Data updates trigger once per production batch or key process step. Individual documents include fields such as batch number, feeding amount, reaction temperature, and residence time. Supported units include kilograms, degrees Celsius, hours, and others.
Constraints on Citation Sources and Traceability
The multi-variety, multi-step batch switching production model for this category requires citation traceability to accurately bind full-link data for a single batch. This prevents mixing of formulas and feeding data across batches. Frequent batch switching and cleaning requirements mean traceability data must include cleaning records. These records verify cross-contamination prevention measures. The confidential formula attribute limits citation scope to authorized data sources for the current review batch. It must not disclose process parameters from other batches. Cross-review across multiple links requires the traceability system to associate corresponding fields across multiple documents, including feeding, reaction, and testing records.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | Single process documents for batch fine chemical synthesis are relatively long, and need to cover complete content of feeding, reaction, cleaning and other links |
responseData | Only return data associated with the current batch | This category manages production by batch, so irrelevant data from non-current batches must be filtered to ensure accuracy of cross-review |
detail | Enable full-field traceability display | Need to display category-specific production fields such as batch number, feeding amount, reaction temperature, and cleaning records |
| Data source binding rule | Match and associate by batch number | Match raw material batch, DCS, LIMS and other system data for the current review batch to ensure complete traceability link |
| Single recall limit | Top 6 entries | Cover core production documents for the current batch, avoid redundant data interfering with the review process |
Three Common Misconfigurations
- Phenomenon: An error occurs when viewing knowledge base citations in chat responses, with the prompt "No valid data source matched". Cause: Real-time systems such as reactor DCS and LIMS have not been bound to FastGPT's knowledge base at the batch level.
- Phenomenon: Returned citation content only includes a single feeding slip, and cannot cover the entire process link. Cause: The value set for
quoteMaxTokenis too low, resulting in truncation of cross-document content across multiple links. - Phenomenon: Citation content includes formula data from other batches, violating confidentiality requirements. Cause: No batch number-based filtering matching rule has been set, resulting in recall of documents from non-current review batches.
How to Verify Correct Configuration
- Log in to the technical document cross-review scenario configuration page of FastGPT v4.17.0, confirm that the value of
quoteMaxTokenfalls within the800–1200 tokenrange. - Submit a review request for a known batch, check that the returned citation data only includes documents such as feeding verification slips and reaction process records for that batch, with no cross-batch content.
- View the traceability fields in the returned results, confirm that they include category-specific production parameters such as batch number, raw material batch, reactor number, and cleaning time.
- Trigger a review request for a deviation change order, confirm that the citation content only associates deviation and change records for the current batch, with no irrelevant data from other batches.
The values provided are common starting points and should be measured against the reader's own samples.