Data Characteristics
Rehabilitation device R&D documents come from various sources. These include design specifications, test reports, clinical trial data, user manual drafts, and regulatory compliance files. Update frequency depends on the R&D stage. Updates can occur multiple times weekly during conceptual design, monthly during clinical validation, and annually after market release. Document structures vary. Some reports strictly follow national or industry standards (e.g., YY/T 0287, ISO 13485). Others are semi-structured or unstructured, like handwritten engineer notes or meeting minutes. Fields often include biomechanical parameters (e.g., torque, pressure, angle), material science indicators (e.g., Young's modulus, fatigue life), and clinical outcome scales (e.g., FIM score, Berg Balance Scale). The International System of Units (SI) is primary, but non-standard industry or regional units also appear.
Constraints from "Deployment and Upgrade"
The characteristics of rehabilitation device R&D documents impose specific requirements on FastGPT deployment and upgrades. Diverse and complex document sources necessitate flexible document import interfaces during deployment. These interfaces must support automatic recognition and parsing of various file formats. OCR capabilities require strengthening for scanned manuscripts or embedded image text. High update frequency demands an incremental update mechanism for the knowledge base. This avoids full rebuilds, reduces resource waste, and minimizes downtime, ensuring R&D personnel access the latest information promptly. Complex fields and unit systems require precise entity recognition and unit conversion capabilities in the knowledge base to prevent confusion during Q&A. For example, torque units may convert between N·m and kgf·cm; the system must correctly identify and process these. Parsing regulatory compliance files requires the system to identify and link specific regulatory clauses with R&D details. This requires pre-configuring relevant regulatory knowledge graphs or glossaries during deployment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Rehabilitation device design documents often contain numerous charts and embedded data, leading to large file sizes. |
Chunk size (Segment Length) | 800 characters (characters) | Balances complete semantics for semi-structured documents with retrieval efficiency for long texts, preventing excessive truncation. |
Recall count (Recall Count) | Top 8 entries (top 8 entries) | R&D questions often require multi-faceted information; increasing recall improves information comprehensiveness. |
Similarity threshold (Similarity Threshold) | 0.78 | R&D document content is highly specialized; increasing the threshold reduces interference from irrelevant results and ensures accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing large files and OCR recognition take a long time; this provides sufficient time to prevent timeout interruptions. |
maxContext | 3000 Tokens | Accommodates complex design specifications or clinical reports, providing a sufficiently long context for the model to understand. |
Common Pitfalls
- After a knowledge base update, Q&A results for specific domain-specific terminology are inaccurate. This occurs because the incremental update strategy fails to effectively synchronize glossaries or domain dictionaries. This leads to the model lagging in understanding newly introduced or revised professional vocabulary.
- Uploading large PDF test reports or design drawings results in prolonged parsing stagnation or errors. The symptoms are
file parsing failedorHTTP 504 Gateway Timeout. This usually happens because thePARSE_FILE_TIMEOUT_SECONDSparameter is set too low. It does not provide enough parsing time for documents containing many images and complex layouts. - After upgrading FastGPT, some historically imported document content cannot be correctly retrieved or used for Q&A. This might occur because the new version optimizes or adjusts the structured parsing algorithm. However, it does not perform compatibility processing or re-indexing for old data, leading to a mismatch between the index and content.
Verification
- Upload a typical rehabilitation device design specification file (e.g., a PDF with charts, multi-level headings, and technical parameters). Check if the parsing result is complete and if fields and units are correctly identified.
- Perform knowledge base Q&A on a test report containing newly introduced or revised professional terminology. Verify the model's understanding and accuracy in answering questions related to these terms.
- Simulate an incremental update of a large knowledge base. Observe if the update process is smooth. Check if system response speed significantly decreases after the update. This assesses the effectiveness of
PARSE_FILE_TIMEOUT_SECONDSand the incremental update strategy. - Query specific biomechanical parameters or clinical scale data. Evaluate if the knowledge base accurately extracts and displays relevant values and their units. Cross-check the accuracy of unit conversions.
Note: The values provided are common starting points. Measure them against your own 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.