Data Characteristics
Rehabilitation equipment quality documentation originates from various sources, including product manuals, design documents, test reports, clinical validation reports, risk management files, user manuals, and periodic equipment maintenance and calibration records. These documents typically use formats such as PDF, Word, and Excel. Data update frequency varies by document type; for example, firmware update logs, recall notices, and regulatory revisions trigger high-frequency updates, while design documents and product manual revisions have longer cycles. Document structure often adheres to medical device industry quality management system requirements, such as ISO 13485, with clear sectioning and numbering systems. Fields and units are highly specialized, with performance indicators often involving mechanics (Newtons, millimeters), electrical (Volts, Amperes), and time/frequency (seconds, Hertz), frequently accompanied by specific measurement tolerances and calibration periods.
Constraints on Deployment and Upgrades
The characteristics of rehabilitation equipment quality documentation impose specific requirements on FastGPT's deployment and upgrade processes. First, multi-source heterogeneous document formats require robust compatibility from file processing components to ensure all critical information is effectively parsed and vectorized. Second, some documents (e.g., clinical validation reports) may contain numerous charts and complex layouts, challenging the accuracy and completeness of text extraction and requiring optimized preprocessing. Varying update frequencies necessitate support for incremental updates and version management, avoiding redundant processing of unchanged data and enabling historical document version tracking. Specialized fields and units demand precise named entity recognition and relationship extraction capabilities during knowledge base construction to accurately match and understand industry terminology during queries. Furthermore, strict compliance requirements make data security and access control critical deployment considerations, ensuring sensitive quality data is not accessed without authorization.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Rehabilitation equipment documents often contain many images and charts, leading to larger file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex PDFs and scanned documents can take a long time; this prevents parsing timeouts. |
Chunk size | 800–1200 characters | Ensures each segment contains sufficient contextual information while avoiding semantic drift from excessive length. |
Recall count | Top 10 entries | Improves retrieval relevance by covering more potentially relevant quality document snippets. |
Similarity threshold | 0.75 | Balances accuracy and recall, filtering out low-relevance results while retaining critical information. |
CHUNK_OVERLAP_SIZE | 100 characters | Ensures sufficient overlap between segments, preventing critical information from being split across different segments. |
Common Pitfalls
- FastGPT fails to connect to a local MongoDB instance, with logs showing connection timeouts or authentication failures. This can be due to an incorrect IP address or port in the
MONGODB_URIconfiguration string, or MongoDB not allowing external connections. - After document parsing, text from some charts or scanned documents is missing, resulting in an incomplete knowledge base index. This occurs because default text extraction components have limited capabilities for recognizing text within images, requiring integration of OCR functionality or optimization of the preprocessing pipeline.
- The text content extraction component in a workflow fails to extract specific fields from knowledge base references, appearing as empty or inaccurate extraction results. This happens when the data structure in the knowledge base reference does not match the component's preset extraction pattern, requiring adjustment of extraction rules or use of custom functions.
Verification Steps
- Upload multiple quality documents for rehabilitation equipment in different formats (PDF, Word, Excel) and verify that files are successfully parsed and knowledge base entries are created.
- Query for specific equipment models or fault codes to verify FastGPT accurately retrieves relevant test reports, repair manuals, or risk assessment documents, and check the completeness of the retrieved content.
- Simulate an equipment upgrade process by querying for differences and compatibility notes between old and new version documents, confirming the system can distinguish and provide versioned information.
- Check log output to confirm the absence of file parsing failures, database connection exceptions, or out-of-memory errors.
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.