Data Characteristics
Dermatology quality documentation includes disease diagnostic standards, treatment plans, drug usage guidelines, adverse reaction monitoring reports, clinical trial data, and patient follow-up records. These documents originate from various sources: guidelines from professional societies, drug specification updates from pharmaceutical companies, regulations from national drug administrations, and internal hospital records and research reports. Data updates are frequent, especially with new drug approvals, treatment plan improvements, or adverse reaction events. Document structures are often PDF, Word, or Excel tables, containing medical terminology, dosage units, and experimental indicators. For example, treatment plans detail drug components, concentrations (e.g., mg/mL), administration routes, and frequencies. Clinical trial data may involve complex statistical parameters.
Constraints on Deployment and Upgrade
Dermatology quality documentation characteristics impose specific requirements on FastGPT deployment and upgrades. First, the medical specificity and multimodal nature of the documents (e.g., skin lesion appearances in images, procedural demonstrations in videos) demand strong semantic understanding and multimodal processing capabilities. Accurate recognition and association of image and video content directly impact knowledge base accuracy. Second, high-frequency data updates require deployment solutions that support flexible, efficient incremental update mechanisms to ensure the knowledge base remains current. Complex tables and structured data, such as drug dosage tables, require accurate extraction of key information while preserving structural integrity during parsing. Additionally, data often contains sensitive patient information or trade secrets, making deployment environment security, data isolation, and compliance critical considerations. On-premise deployment, such as running within an intranet, can effectively address these security and compliance challenges.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Dermatology documents often contain high-resolution images or embedded videos, requiring large file support. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDFs or multi-page Word documents can be time-consuming; increase timeout to prevent interruptions. |
Chunk size | 800–1200 characters | Ensures completeness of medical terminology and context, preventing truncation of key information. |
Recall count | Top 10 entries | Improves recall accuracy, covering a broader range of medical concepts, providing sufficient candidates for re-ranking. |
Similarity threshold | 0.75 | Dermatology concepts are highly distinct; a higher threshold reduces irrelevant results. |
minio_endpoint | http://minio.internal.example.com | Adapts to intranet deployment environments, ensuring accessibility of the file storage service. |
Common Pitfalls
- Observation: After uploading documents, some images or table content are not recognized or are incorrectly identified. Reason: Default models may have insufficient support for medical images or complex table structures, or document preprocessing may not fully extract multimodal information.
- Observation: After a knowledge base update, the model still references old treatment plans or drug information. Reason: Knowledge base indexes are not rebuilt promptly, or incremental update strategies are misconfigured, preventing new data from being effectively included in search results.
- Observation: After on-premise deployment, external AI model APIs cannot be connected, or specific features (e.g., video parsing) are unavailable. Reason: Firewalls, proxy settings, or network policies restrict intranet access to external services, or the FastGPT version does not support the target AI model's API interface.
Verification Steps
- Upload a dermatology guideline document containing complex tables and medical images. Verify that parsing accurately identifies and extracts table data and image descriptions.
- Upload an updated drug specification. Immediately perform question-answering tests to confirm the model provides answers based on the latest information and no longer references old content.
- Within the intranet environment, attempt to call external AI model APIs for text generation or multimodal analysis. Confirm network connectivity and authentication configurations are correct.
- Check system logs to ensure no timeout or data loss errors occurred during file parsing and knowledge base construction.
Note: 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.