What Data for This Category Looks Like
Data related to equipment inspection and maintenance for active pharmaceutical ingredient and pharmaceutical intermediate manufacturing is sourced from batch production records, material balance records, reaction and purification records, cleaning records, impurity profile reports, deviation and change documents from synthesis, fermentation, purification and other departments. Data sources include registered process documents, raw material batch information, DCS systems, weighing systems, LIMS, chromatography data systems and QMS. Data is updated per production batch and each process step. Single-batch documents include fields such as batch record number, equipment ID, reaction parameters, test results and maintenance logs, with fields aligned to professional production process and quality control requirements.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Words
Data sources for this category are scattered and specialized. Multi-turn dialogue must gradually guide users to provide precise fields including batch record number and equipment ID, to avoid retrieving data from irrelevant batches or equipment. Data update frequency is high, so multi-turn dialogue must limit context scope to prevent loading expired historical batch data. Single-batch documents have complex structures, so prompt words must clearly specify the document type to retrieve, ensuring priority matching of records for the target business scenario. There are many professional terms, so prompt words need preset term mapping rules to allow equipment inspection questions to accurately match collected data from corresponding systems. Cross-step associated data requirements demand that multi-turn dialogue retains historical context, enabling the model to recall previous conversation content to associate inspection and maintenance information across multiple steps.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique chatId of the current session | In FastGPT v4.17.0, binding chatId ensures unique association between session context and logs, avoiding confusion across batch data |
messages | Retain the most recent 15-25 conversation messages | Covers conversation requirements for 3-6 process steps per single batch, calibrated based on actual testing |
variables | Preset batch record number, equipment ID and test date variables | Matches core retrieval fields for this category, quickly locating inspection and maintenance data for the corresponding batch |
| Maximum Context | 7000-9000 tokens | Adapts to retrieval requirements for complete single-batch documents, calibrated based on actual testing |
| Question Optimization | Enabled | Processes active pharmaceutical ingredient professional terms, converting colloquial inspection questions into standardized retrieval terms |
| Single Search Limit | Top 8 matching results | Avoids interference from excessive irrelevant data, focusing on records for core batches and equipment |
Three Common Misconfigurations
- Phenomenon: After deleting a conversation, the backend cannot retrieve log records for the corresponding session. Cause:
chatIdis not bound to the log archiving rule, and associated log files are cleared when the session is deleted. - Phenomenon: Prompt words cannot automatically reference historical context content from the current conversation. Cause: The context retention rule for the
messagesparameter is not configured, causing the prompt word to only obtain single-round input content. - Phenomenon: After uploading equipment inspection photos, the model returns that it cannot recognize the image content. Cause: The multi-modal support switch for the session is not enabled, or the mapping field for the image upload path is not configured in
variables.
How to Confirm Proper Configuration
- Initiate a single-round equipment inspection query, confirm the model returns inspection records for the corresponding batch, and verify
chatIdis correctly recorded in session logs. - Initiate a multi-turn conversation, gradually add equipment number and batch information, confirm the model recalls previous conversation content and associates corresponding data.
- Upload equipment maintenance photos, confirm the model recognizes equipment components in images and associates maintenance records, and verify multi-modal support configuration is active.
- Test session deletion, confirm backend log archiving rules perform as expected, and verify
chatIdbinding effectiveness.
All provided configuration values are common starting points, and should be validated against the actual samples relevant to the deployment.