What Data Looks Like for This Category
Data sources for this category include registered process documents, raw material batch ledgers, DCS production control systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Data is updated per production batch and process step. Daily stored records include batch production records, material balance ledgers, reaction and purification process records, cleaning validation records, impurity profile reports, and deviation and change documents. Data fields include batch identifiers, feeding parameters, reaction temperature, yield values, impurity content, solvent residue levels, and more. These fields are linked to production links and quality test results.
Constraints Imposed on Workflow Orchestration
Data sources are scattered and linked to multiple systems. Workflows must support calling fields from different data sources across nodes. They must also configure variables to store core identifiers such as batch numbers passed between nodes. Data is updated per batch. Workflows must bind batch-level context to avoid mixing cross-batch data. Daily documents have complex structures, including text records and structured test data. Workflows must support extraction and association of multi-format content. Correlation analysis of impurity profiles and deviation files requires fixed step order. Workflows must support conditional trigger logic between nodes to ensure traceability processes advance according to process links.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
| Chat history context count | Top 6 entries | Quality traceability issues for API production usually revolve around 3-5 process steps per single batch. Retaining the top 6 entries covers complete conversation and core batch information, avoiding redundant context interference with analysis. |
| Context filtering for text extraction nodes | Filter by the batch number stored in variables | Must accurately link production records, test data, and deviation documents for the same batch. Cross-batch data will cause deviations in traceability results. |
| Workflow node execution sequence binding | Specify nodeId according to business processes | In FastGPT v4.17.0, nodeId must be used to precisely control node execution sequence, ensuring traceability processes for process steps advance in order. |
| Maximum context length | 800–1200 characters | Core content of process and test documents for a single batch falls within this range, balancing information completeness and processing efficiency. |
| Node processing timeout threshold | 600 seconds | Pulling data from multiple systems and analyzing impurity profiles requires relatively long processing time. This threshold covers conventional analysis processes. |
Three Common Mistakes
- Phenomenon: After the workflow runs, subsequent conversations still use the batch information from the first trigger, and cannot be updated to new batch parameters. Cause: Dynamic update logic for
variablesis not configured. Only the initial batch parameters are read fixedly, and context variables are not updated synchronously with user queries. - Phenomenon: Workflow nodes run overtime, and the system prompts a processing timeout. Cause: A reasonable timeout threshold is not set, or parallel nodes for pulling data from multiple systems are not configured, leading to serial data pulling exceeding the processing time limit.
- Phenomenon: The text extraction node returns empty results, and no valid data is extracted. Cause: Context is not filtered by batch number. The extraction range includes document content from unrelated batches, resulting in failure to match valid information for the corresponding batch.
How to Confirm Proper Configuration
- Trigger a single-batch quality traceability request. Check whether the workflow correctly reads the
variablesparameters of the corresponding batch and links to the corresponding production and test documents. - Initiate two consecutive traceability queries. Confirm that the workflow can update context variables along with the queries, and subsequent requests use the latest batch information.
- Simulate node trigger conditions. Verify that the workflow starts the analysis node only after the corresponding process step is completed, and does not run early or delayed.
- View node operation logs. Confirm that the return results of the text extraction node only contain valid data for the current batch, with no cross-batch content.
The values given are common starting points and should be measured against the reader's own samples.