Data Characteristics for This Category
Rehabilitation equipment registration data originates from various sources, including medical device regulations, national and industry standards, clinical trial reports, product technical requirements, instruction manuals, label samples, risk analysis reports, manufacturing process flows, and quality management system documents. These documents are typically in PDF, Word, or Excel formats, with some data stored in structured databases. Data update frequencies vary: regulatory documents are often revised annually, standards have longer update cycles, while product technical parameters and clinical data may update dynamically with R&D progress or batch production. Document structures are complex, containing extensive specialized terminology, diagrams, and tables. Fields are numerous and highly interconnected; for example, technical parameters include power, frequency, treatment modes, dimensions, and weight, with units requiring strict adherence to international units or specific industry norms.
Constraints Imposed by Data Characteristics on Deployment and Upgrades
The unique characteristics of rehabilitation equipment registration data impose specific requirements on deployment and upgrades. The update frequency of regulations and standards necessitates regular incremental updates or re-indexing of the knowledge base to ensure compliance. Diverse and heterogeneous document formats mean the deployment solution must support parsing and content extraction from multiple file types, effectively handling embedded diagram information. The abundance of specialized terminology and complex field interdependencies requires FastGPT's text embedding model and retrieval capabilities to accurately understand industry semantics, preventing misinterpretations due to ambiguous terms or missing context. Furthermore, the mix of structured and unstructured information in the data requires consideration of how to effectively integrate RAG (Retrieval-Augmented Generation) with traditional structured data queries to provide comprehensive answers. Strict requirements for units and numerical values necessitate validation during model fine-tuning or post-processing to prevent the generation of incorrect data.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large clinical trial reports and detailed technical documents, ensuring unhindered file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time to process diagrams and extract table content from complex PDF files. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness with retrieval efficiency, preventing loss of information in long paragraphs or insufficient context in short ones. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures retrieval covers a sufficient number of relevant regulatory provisions, technical parameters, and clinical evidence. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves retrieval precision for specialized terminology and regulatory provisions, reducing interference from irrelevant information. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | Further refines the most relevant content from the initial recall, improving the quality and relevance of generated answers. |
Three Common Pitfalls
- Calling the API returns
bad_response_status_code, typically indicating a network configuration issue preventing FastGPT from accessing external resources or hindering internal service communication. - Failing to pull DockerHub images during Docker Compose deployment suggests network environment restrictions on DockerHub access, requiring proxy configuration or the use of a domestic mirror.
- Using an older FastGPT version (e.g., below
v4.8.10) with nested knowledge base assistants in workflows returns empty values during API calls. This is usually due to version compatibility issues or workflow configurations not matching the new API interface.
How to Verify Correct Configuration
- Upload a PDF file containing complex diagrams and tables from rehabilitation equipment technical requirements. Verify that the file content is fully parsed and successfully segmented into the knowledge base.
- Query a specific regulatory provision or technical parameter using natural language. Check that FastGPT's recalled text snippets are accurate and highly relevant.
- Simulate a complete registration document query process, including questions about product functions, clinical indications, and risk control. Evaluate the accuracy and completeness of the answers, and verify that the units and numerical values cited in the answers are correct.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal configurations.
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.