Data Characteristics for This Category
Cardiovascular intervention product data originates primarily from medical device manufacturers' product manuals, clinical trial reports, technical white papers, operating instructions, and various professional journal articles. These documents update frequently, especially with new product launches, technological iterations, or regulatory policy changes. Document structures are complex, often containing numerous charts, images, and professional reports in PDF format. Data fields include device model, specifications, material composition, indications, contraindications, operating procedures, complications, and follow-up data. These often involve specialized medical terminology and units, such as catheter "F" (French) size, stent "mm" (millimeter) diameter and length, and chemical names of various biocompatible materials.
Constraints on "Deployment and Upgrades" Due to These Characteristics
The high update frequency and complex document structures of cardiovascular intervention product data require the deployed FastGPT system to have efficient data ingestion and parsing capabilities. Specifically, professional reports in PDF format, with embedded charts and complex layouts, challenge text extraction and structuring. The specificity of professional terminology and measurement units dictates that the model must achieve precise matching during knowledge recall and generation to avoid misleading information due to semantic misinterpretation. Additionally, the system must process a large volume of image and tabular data. Effectively utilizing this non-textual information within the RAG process is a key deployment consideration. Proxy configurations in an intranet deployment environment, especially when involving username and password authentication, demand high network connection stability and security. This directly impacts the acquisition of external knowledge sources and online model updates.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Cardiovascular intervention product manuals and clinical reports often contain high-resolution images and numerous charts, resulting in large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF file parsing takes longer; a longer timeout prevents interruptions. |
Chunk size | 800 characters | Ensures the completeness of medical terminology and context, preventing critical information from being truncated. |
Recall count | Top 8 entries | Increasing the number of recalled items improves the likelihood of retrieving professional terminology and relevant details. |
Similarity threshold | 0.75 | For highly specialized texts requiring precise terminology, a higher similarity threshold ensures recall quality. |
PROXY_HTTP | http://user:[email protected]:8080 | Full proxy string configuration is required for intranet deployments when the proxy needs username and password authentication. |
Three Common Pitfalls
- A
Cannot read properties of undefined (reading 'incl')error after local deployment typically indicates failed front-end resource loading or version mismatch. - The model confusing professional terminology or using incorrect units when answering cardiovascular intervention questions results from a lack of domain-specific vocabulary in the knowledge base or improper segmentation strategies, leading to incorrect splitting of professional terms or loss of context.
- A network connection timeout when the system attempts to update models or synchronize external knowledge often means incorrect HTTP proxy configuration, such as wrong proxy address, port, or authentication information.
How to Verify Correct Configuration
- Upload a cardiovascular intervention product PDF manual containing complex charts and professional terminology. Observe if the file parses successfully and if content segmentation is reasonable.
- Test with domain-specific queries, such as "What are the material components of coronary stents?" Check if the answer accurately cites professional terminology and data from the knowledge base, and confirm no basic errors occur.
- Attempt an online knowledge base synchronization or model update operation. Confirm the system can access external network resources normally via the configured HTTP proxy, without timeout or authentication failure messages.
- Check FastGPT backend logs for any critical errors related to file parsing, knowledge recall, or network connectivity.
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.