Data Characteristics in This Category
Home medical device quality documentation originates from various sources. These include product manuals, registration certificates, production licenses, quality management system documents (e.g., ISO 13485 certification), risk management reports, testing reports, clinical evaluation reports from manufacturers, and user feedback or adverse event reports. Regulatory requirements and product lifecycles influence document update frequency. Updates typically occur during new product launches, regulatory changes, or significant design modifications, with routine maintenance remaining relatively stable.
Document structure is highly standardized. For example, manuals usually contain fixed sections such as product model, technical parameters, usage instructions, precautions, and maintenance. Fields and units adhere to clear international or national standards for technical parameters like voltage (V), current (A), power (W), dimensions (mm), and weight (kg).
Constraints Imposed by These Characteristics on "HTTP API and External Systems"
The characteristics of home medical quality documentation impose specific constraints on HTTP API and external system integration. First, diverse document sources and sensitive data require robust authentication and authorization mechanisms for APIs to ensure data security. Second, regulatory-driven update frequencies mean external systems must support periodic or event-triggered incremental updates, for example, by using webhooks to monitor changes in specific document libraries.
The highly structured nature of documents requires precise text and table content parsing from common formats like PDF and Word during data extraction. This identifies key fields such as product model, serial number, and production date. Standardized units for technical parameters demand consistency during data transmission to prevent information discrepancies from unit conversion errors. This requires APIs to validate data or define clear data formats before transmission.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 Tokens | Accommodates critical information from most quality documents while balancing model processing efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Quality documents often contain extensive text and charts, requiring ample time for parsing. |
Chunk size | 800–1200 characters | Adapts to document content density, ensuring each segment contains sufficient context to improve recall accuracy. |
Recall count | Top 8 entries | Balances retrieval efficiency and coverage, ensuring no critical information is missed. |
Similarity threshold | 0.75 | Balances the breadth and precision of recall, filtering out irrelevant document snippets. |
HTTP Header | Authorization: Bearer <TOKEN> | Ensures external systems authenticate via tokens when calling the API, safeguarding data security. |
Three Common Mistakes
- Calling the API returns a
Connection refusederror, preventing knowledge base content retrieval. This occurs because the external system did not correctly configure the FastGPT service network address or port, or a firewall blocked the connection. - API call responses do not reflect specific regulatory requirements or product parameters from the knowledge base. This happens when the segmentation strategy for relevant documents in the knowledge base is inappropriate, leading to critical information being truncated or split, preventing complete recall during retrieval.
- Documents uploaded via API remain in a processing state in the knowledge base for an extended period and eventually fail. This is due to the uploaded document's
Content-Typenot matching API requirements, or the document size exceeding theUPLOAD_FILE_MAX_SIZElimit.
How to Verify Configuration
- Call the knowledge base API with a home medical product model. Check if the returned result accurately mentions the model's technical parameters and usage precautions. Compare with the original knowledge base text to confirm information consistency.
- Simulate an external system updating a knowledge base document. Check the FastGPT management interface to see if the corresponding document's update timestamp matches the external system's operation time and if the content has synchronized.
- Attempt to upload a home medical quality document containing special characters or complex tables. Confirm that the document parsing status is normal and verify through retrieval that its content can be correctly indexed and recalled.
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.