Data Characteristics for This Category
Documentation for rehabilitation equipment policies and SOPs primarily originates from medical device registration certificates, product manuals, clinical trial reports, maintenance manuals, and internal quality management system documents. These documents are typically in PDF, Word, or scanned image formats. Update frequency is relatively low, usually tied to product iterations, regulatory revisions, or internal process optimizations. Document structures are rigorous, containing extensive specialized terminology, technical parameters, and operational procedures. Fields and units are highly specialized, including device models, serial numbers, calibration cycles, safety levels, power consumption units (W), frequency (Hz), and pressure (kPa). Complex charts and flowcharts are also common.
Constraints Imposed by These Characteristics on "Deployment and Upgrades"
The diverse sources and specialized nature of rehabilitation equipment documentation require FastGPT to have robust multi-format parsing capabilities during data ingestion, with high accuracy for optical character recognition (OCR) on scanned documents. The low update frequency means a comprehensive import of historical data is necessary during initial deployment. However, subsequent incremental update pressure is minimal, with the focus on ensuring timely replacement of new document versions. The complex structure and specialized terminology in documents challenge model comprehension. This necessitates a longer Chunk size (segment length) to maintain contextual coherence and may require customized embedding models to better capture domain semantics. The specificity of fields and units demands precise matching of specialized vocabulary and numerical values in user queries during retrieval, avoiding recall deviations caused by synonyms or unit differences.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Rehabilitation equipment documents often contain many images and charts, leading to large file sizes. |
Chunk size | 800–1200 characters | Ensures contextual completeness for specialized terminology and operational procedures. |
Similarity threshold | Calibrate based on actual measurements | Requires adjustment based on actual Q&A effectiveness to balance recall rate and accuracy. |
Recall count | Top 5 entries | Covers primary relevant information, avoiding interference from excessive redundant content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex structures and large PDF documents can be time-consuming. |
Rerank result count | 3 entries | Ensures the refinement and relevance of the final displayed results. |
Three Common Pitfalls
- Knowledge base query results are empty. This can happen if document parsing fails or critical information is not extracted correctly, preventing the model from matching user queries.
- Answers are vague and lack specific technical parameters or operational steps. This usually occurs when the
Chunk size(segment length) is set too short, leading to loss of contextual information. - System response time is too long, especially when uploading large PDF files. This indicates
PARSE_FILE_TIMEOUT_SECONDSis set too low, or server processing capacity is limited.
How to Verify Correct Configuration
- Upload a rehabilitation equipment manual containing complex charts and specialized terminology. Verify that it is successfully parsed and generates retrievable knowledge snippets.
- Ask questions about specific device models, calibration cycles, and other key fields within the document. Confirm that the answers accurately include this information.
- Simulate user inquiries about detailed steps in SOP processes. Confirm the system provides clear, complete, and logically structured answers, correctly citing relevant documents.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.