Data Characteristics in This Category
Home medical device R&D document data comes from diverse sources. These include internal product design specifications, test reports, and user manual drafts. External sources include regulatory standards and clinical trial data. Document updates are relatively frequent, especially during product iterations and regulatory revisions. Document structures typically contain numerous specifications, parameters, charts, operational procedures, and safety warnings. Textual and non-textual information is often intertwined. Fields involve physiological indicators like blood pressure, blood glucose, and heart rate, along with their units. Engineering indicators include material composition and electrical parameters. Unit representation is strict and varied, such as mmHg, mmol/L, bpm, V, mA, and °C, often accompanied by tolerance ranges.
Constraints on Deployment and Upgrades
These characteristics of home medical R&D documents pose specific requirements for FastGPT deployment and upgrades. The high frequency of numerical values and units in documents is critical for accurate structured analysis. The parsing model must identify and correctly extract this information. This prevents data misinterpretation due to unit confusion. High update frequency means the model and knowledge base must support rapid iteration and incremental updates to maintain information timeliness. The ability to process charts and non-textual information determines the completeness of the knowledge base. Strict regulatory compliance requires a stable and reliable parsing process. Any parsing error could affect subsequent compliance reviews. Therefore, deployment must focus on the model's robustness in identifying specific entities. Upgrades require verifying the performance improvement of new versions in handling complex document structures and multi-unit data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Accounts for the common size of a single R&D document (including charts). |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for complex document structures and numerous charts. |
Chunk size | 800–1200 characters | Balances context completeness and recall efficiency, suitable for technical specification descriptions. |
Similarity threshold | 0.75 | Ensures recall of document segments highly relevant to physiological indicators and engineering parameters. |
Rerank result count | 5 entries | Prioritizes displaying the most relevant core parameters and key processes. |
maxContext | 8192 | Ensures the large model can process contexts containing detailed technical parameters and operating steps. |
Common Mistakes
- After importing the knowledge base, some key fields are empty. The large model failed to correctly identify specific formats of physiological or engineering parameters in the document, leading to structured extraction failure.
- The Reranker model failed to start. Logs indicated an invalid or unset
ACCESS Token. This happened because the environment variable was not correctly configured, or the credential had expired. - After a system upgrade, knowledge base content displayed in English. This was due to incorrect handling of multi-language configurations during migration, or language packs were not updated synchronously.
How to Verify Configuration
- Upload a typical R&D document containing various physiological and engineering parameters. Check if these fields are correctly extracted with units in the parsed knowledge base.
- Use the knowledge base retrieval function. Input specific model and parameter combinations. Verify accurate recall of relevant document segments and check if the ranking of recalled content is reasonable.
- Simulate product design changes or regulatory updates. Import new document versions. Observe the integration and coverage of new and old information after the knowledge base update.
- Check system logs. Confirm no abnormal errors or timeout records occurred during file parsing and knowledge base updates.
The values provided are common starting points and should be measured against the reader's 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.