What Data for This Category Looks Like
This category of data originates from approved formulation processes, material batch ledgers, MES electronic batch records, equipment and environmental monitoring data, LIMS test results, and QMS quality management records. Daily documents cover master production documents, batch production records, batch packaging records, cleaning records, test records, deviation/CAPA/change documents, and batch release approval forms. Data is updated per production batch, key process steps, and fixed monitoring plans. Fields include material batch numbers, process parameter values, test results, deviation descriptions and approval statuses, with no additional non-standard statistical units.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Data for this category is updated per individual production batch and key process nodes. Multi-turn dialogue must bind chatId to associate exclusive context for a single batch, to avoid mixing data across batches. Daily documents include multiple types of compliance process documents. Prompts must limit retrieval scope to the current batch’s associated records to ensure outputs comply with GMP requirements. Structured fields include material batch numbers, process parameters, deviation approval statuses, and others. Multi-turn dialogue must pass batch identifiers, abnormal events, and other context in the order of messages to ensure coherent dialogue logic. Data is updated at a high frequency, so dynamic loading of the latest monitoring and test records for the current batch must be configured to avoid using expired data.
Configuration Settings
This configuration is adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current production batch | Associate exclusive data for a single batch to avoid cross-batch confusion |
messages | Pass batch numbers and abnormal event descriptions in order per dialogue turn, retain up to 20 turns of dialogue context | Match the single-batch traceability logic of pharmaceutical batch records, ensure coherent context |
variables | Preset fixed variables such as current batch material batch numbers and production workshops | Quickly locate document data for the corresponding batch, reduce retrieval scope |
| Single retrieval reference limit | Top 8 retrieval results | Balance information completeness for compliance review and response efficiency |
| Maximum context length | 8000–12000 characters | Accommodate the complete content of multiple records per batch, avoid truncating key compliance information |
| Streaming output toggle | Enable as needed | Adapt to usage scenarios for real-time viewing of traceability results, align with the operational habits of quality personnel |
Three Common Misconfigurations
- Symptom: After switching AI models, the dialogue fails to load current batch history. Cause:
chatIdwas not bound to the new model’s session, breaking context association. - Symptom: After configuring an API key, unauthorized prompts persist and retrieval results are empty. Cause: The current batch’s permission scope was not bound via
variables, so the system cannot verify access permissions. - Symptom: After increasing the single retrieval reference limit, the large language model does not reference any retrieved content. Cause: The context length exceeded the preset threshold, and the system automatically truncated the associated retrieval result information, preventing the large language model from accessing reference basis.
How to Confirm Successful Configuration
- Initiate a query that includes the current batch number, confirm whether the dialogue context automatically associates records for that batch.
- Adjust the single retrieval reference limit, verify that the large language model’s response correctly references retrieved document fragments.
- Enable the streaming output toggle, test whether content is output in expected segments during the dialogue process.
- Switch between different AI models, confirm that the session history bound to
chatIdremains complete.
The values provided here are common starting points and should be measured against the reader's own samples.