What data for this category looks like
Daily documents for this category include master production documents, batch production records, batch packaging records, cleaning records, inspection records, deviation/CAPA/change documents, and batch release review forms. Data comes from approved prescription processes, material batch information, MES systems, electronic batch records, equipment and environmental monitoring data, LIMS, and quality management systems. Data updates trigger with each batch production, execution of key process steps, and progress of monitoring plans. Fields include process parameter ranges, material batch numbers, test results, deviation descriptions, and more. No unified fixed units exist; values adjust based on corresponding document fields.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Data for this category updates dynamically with each batch production. Multi-turn dialogue must use chatId to bind exclusive context for the current batch, to avoid mixing data across batches. Document fields have no unified units and include sensitive information such as process parameters and material batch numbers. Prompts must explicitly specify extraction of fields corresponding to the current batch, to prevent invalid or non-compliant output. Since error consequences directly relate to batch release and regulatory compliance, dialogue processes must retain historical interaction records to ensure traceability during review. Complex summary and review tasks require step-by-step disassembly via multi-turn dialogue, to avoid exceeding model processing limits in a single request.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Predefine exclusive fields per current production batch, example: {"batchNo": "P2024051201", "materialLot": "M2024042803"} | Data for this category must bind to the current batch to ensure Q&A points to correct production records |
| Multi-turn dialogue context retention duration | 7200 seconds | The process execution cycle for a single batch typically spans several hours, so interaction records from this period must be retained |
| Maximum number of documents returned per request | Top 3 entries | Documents for this category contain extensive professional parameters; too many entries will exceed model processing limits and cause confusion |
messages | Append system prompts and user/assistant messages by interaction turn, do not truncate valid historical context | Process Q&A relies on historical information from prior reviews to ensure logical consistency in responses |
| Maximum token count per single request | Calibrated via actual testing, no more than 8000 tokens | Documents for this category have substantial content; context length must be controlled to maintain model processing efficiency |
| FastGPT version | v4.17.0 | This version optimizes stability of multi-turn dialogue context binding and variable substitution |
Three Common Misconfigurations
- Symptom: After a rerank model is configured, the external network can ping the model, but reranked sorting results do not appear in the conversation details. Cause: The rerank configuration is not bound to the retrieval link for this process Q&A, or the system prompt does not specify using rerank results to generate responses.
- Symptom: After the
variablesparameter is passed, prompt placeholders are not replaced, and returned content is unrelated to the target batch. Cause: The placeholder format is not correctly declared in the prompt, or thevariablesparameter is not bound to the conversation request during API calls, leading to ineffective variables. - Symptom: After user input, the text content extraction node does not return expected fields, and the process stalls after triggering a specified response. Cause: No backtracking guidance mechanism for multi-turn dialogue is configured, and users are not prompted to supplement missing batch information or process parameters, leading to stalled process progression.
How to Verify Correct Configuration
- A test request containing current production batch information is sent using FastGPT v4.17.0. Returned content is verified to accurately link to process parameters and records for the corresponding batch.
- The conversation details page is reviewed to confirm the
messagesparameter fully retains historical interaction system prompts and user messages, with no accidental truncation. - Preset
variablesparameters are passed, and prompt placeholders are verified to be correctly replaced with corresponding field values. - A text extraction failure scenario is simulated. The process is confirmed to trigger a specified response and prompt users to supplement necessary information.
The values provided here are common starting points, and should be evaluated against the reader's own samples.