Data Characteristics for This Category
Infection control regulation data primarily originates from various internal institutional documents. These include regulations, operational guidelines, technical manuals, and emergency plans. Documents are typically in PDF, Word, or internal knowledge base page formats. Content covers hand hygiene protocols, disinfection and isolation techniques, occupational exposure procedures, and multi-drug resistant organism infection prevention strategies.
Update frequency aligns with national policy adjustments, industry standard changes, and internal hospital management requirements. Comprehensive revisions may occur annually, or temporary additions may be made in response to emergent events. Document structures are often chapter-based, including tables of contents, main text, and appendices. They contain a high volume of specialized terminology, medical abbreviations, and dosage units (e.g., g/L, ppm).
Constraints Imposed by These Characteristics on Deployment and Upgrades
Infection control regulation documents often lack high structural consistency. Tables, images, and special formatting within PDFs and Word documents can hinder text extraction, affecting FastGPT's ability to accurately identify knowledge points. Frequent policy updates and regulation revisions require FastGPT's knowledge base to support efficient content updates and version management, ensuring query result timeliness.
Unique medical terminology and abbreviations, such as VAP (ventilator-associated pneumonia) and SSI (surgical site infection), necessitate FastGPT maintaining semantic accuracy during vectorization to avoid recall errors due to vocabulary misunderstandings. Furthermore, cross-references and complex logical relationships between regulations demand advanced contextual understanding and reasoning capabilities from FastGPT.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Infection control documents are often large, containing images and complex formats. This ensures complete file uploads. |
Chunk size (Chunk Length) | 800 characters (characters) | Balances contextual completeness with information density per chunk, preventing excessive length that leads to redundancy or excessive brevity that loses critical context. |
Recall count (Recall Count) | Top 5 entries (top 5) | Ensures sufficient relevant regulation snippets are recalled, addressing cross-references between regulations. |
Similarity threshold (Similarity Threshold) | 0.75 | The medical field demands high accuracy. Raising the threshold reduces irrelevant results. |
Rerank result count (Rerank Return Count) | Top 3 entries (top 3) | After reranking, selecting the few most relevant items improves the precision of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing large files can be time-consuming. This provides sufficient time to prevent parsing interruptions. |
Three Common Mistakes
- When uploading large PDF documents, an "API request failed: 404 - Resource not found" error appears. This typically indicates
UPLOAD_FILE_MAX_SIZEis set too low, causing the server to reject the file. - After a knowledge base update, query results still show old content. This suggests the knowledge base index was not rebuilt promptly or the cache was not cleared, leading to queries hitting outdated data.
- During deployment, a Docker build error
ERROR: failed to solve: archive/tar: unknown file modoccurs. This usually points to file permission issues within the Dockerfile or build context, preventing access to necessary files during the build process.
How to Verify Correct Configuration
- Upload a standard infection control regulation PDF file containing tables and images. Confirm the file is successfully parsed and segmented into multiple knowledge blocks.
- Ask questions related to a recently updated regulation. Verify that FastGPT's answer references the latest version of the content.
- Use query statements containing medical terminology and abbreviations. Verify FastGPT accurately understands and recalls the corresponding regulation clauses. Check if the
similarityvalues of the recall results meet expectations.
The values provided are common starting points. Measure them against specific samples to determine optimal settings.
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.