Data Characteristics in This Category
Data in rehabilitation equipment pharmacovigilance primarily originates from clinical records, patient feedback, regulatory reports, and manufacturer documentation such as product manuals and repair guides. Update frequencies vary. Clinical records and patient feedback may update in real-time or daily, while regulatory reports are typically quarterly or annual. Product manuals update with new equipment versions. Document structures are diverse, including unstructured free text (e.g., patient descriptions, physician diagnoses), semi-structured tables (e.g., adverse event report forms), and structured database records. Fields and units are industry-specific. Examples include equipment model, serial number, firmware version, usage duration (hours/times), fault codes, repair dates, patient age, weight (kg), and rehabilitation indicators (e.g., ROM angle, muscle strength grade). These often involve both international standard units and custom medical measurement units.
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The diversity of rehabilitation equipment data challenges the accuracy of multi-turn conversations. Subjective patient descriptions in unstructured text require models to possess high semantic understanding, extracting key information from ambiguous statements to avoid misinterpretation. For semi-structured table data, especially adverse event reports, prompts must guide the model to identify specific fields like "adverse event type" and "occurrence date" for structured extraction. Precise information such as equipment models and firmware versions requires prompts to enforce exact matching, preventing information loss due to character discrepancies. The specificity of units, like rehabilitation indicators, demands that prompts guide the model to correctly identify and convert units during conversations, avoiding misunderstandings caused by unit confusion. Varying data update frequencies require the dialogue system to differentiate between old and new information, prioritizing the latest and most authoritative data sources.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12 | Adverse event reports for rehabilitation equipment often involve extensive context, requiring conversational coherence and support for multiple follow-up questions. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic integrity of unstructured text with field density of structured table data, improving recall accuracy. |
Recall count (Recall Count) | Top 5 | Balances recall breadth with model processing load, ensuring retrieval of the most relevant document segments. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Rehabilitation equipment terminology is highly specialized; adjustment based on actual data ensures high-relevance recall. |
Rerank result count (Reranked Return Count) | 3 | Further refines recall results, prioritizing the most matching document segments and reducing noise for the model. |
promptSuffix | Guide the model to focus on key fields such as equipment model, firmware version, and event date. | Ensures the model focuses on core information in multi-turn conversations, avoiding generic responses. |
Three Common Mistakes
- Model output includes extra spaces or case changes, leading to mismatches for precise information like equipment models or serial numbers. This occurs because prompts do not strictly constrain the model to faithfully reproduce specific strings.
- FastGPT conversations fail to invoke file parsing tools. This may be due to incorrect
API_KEYorENDPOINTconfigurations, preventing the model from accessing external tool interfaces. - Historical memory contains output from specified reply plugins, affecting subsequent conversation context. This happens when the
historyparameter is not correctly configured to ignore or filter out intermediate plugin responses.
How to Confirm Correct Configuration
- Conduct multi-turn dialogue tests to verify if the model accurately identifies and extracts different equipment models and firmware version information, cross-referencing with original data sources.
- Simulate adverse event reporting scenarios to check if the model correctly extracts key fields like event type, occurrence date, and patient symptoms from free text, comparing against expert-annotated results.
- Test equipment usage issues from different time periods to observe if the model prioritizes the latest product manuals or regulatory updates, thereby checking the data timeliness processing logic.
The values provided are common starting points and should be measured against specific samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.