Data Characteristics for This Category
Data for product and reagent consultation in the autoimmune field comes from clinical study reports, drug inserts, diagnostic kit instructions, academic papers, and regulatory guidelines. Update frequency varies by source. New drug approvals, clinical trial results, and revised diagnostic standards lead to incremental updates, typically quarterly or semi-annually. Document structures are diverse, including detailed PDF manuals, Word document clinical guidelines, and structured experimental data in databases. Key fields include specific antibody titers, cytokine levels, genetic polymorphisms, and disease activity scores (e.g., DAS28, SLEDAI). Units encompass international units (IU/mL), molar concentrations (nM), mass concentrations (ng/mL), and various scoring scales.
Constraints on "Deployment and Upgrade" from These Characteristics
The complexity of autoimmune data sources and diverse document structures require robust file parsing capabilities during FastGPT deployment. The system must efficiently parse PDF and Word documents and accurately extract key information from unstructured text. The periodicity of data updates necessitates that the upgrade process handles incremental data imports and includes version management to ensure smooth transitions between old and new knowledge. Specialized fields like disease activity scores require customized data cleaning and standardization to maintain knowledge base accuracy. The varied unit systems mean unit conversion or clear labeling is necessary during query matching and results presentation to avoid confusion. Furthermore, recognizing specialized terms for specific antibodies and cytokines demands higher requirements for model training and vocabulary expansion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical study reports and inserts are large files and require upload support. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF documents can be time-consuming; this prevents timeouts. |
Chunk size | 800 characters | Ensures complete disease descriptions, reagent usage, or experimental results are included. |
Recall count | 10 entries | Increases recall rate for relevant information, covering multi-faceted consultation needs. |
Similarity threshold | Calibrate by actual measurement | Precisely matches professional terms to avoid generalized answers. |
Rerank result count | 5 entries | Reduces redundant information while maintaining accuracy. |
Three Common Mistakes
- After uploading a large PDF document, the system remains unresponsive for an extended period or reports a file parsing failure. This occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too short, causing the system to terminate file parsing before completion. - In knowledge base query results, numerical values for antibody titers or cytokine levels are inaccurate or units are inconsistent. This happens when specific fields are not standardized for units during data import, or the model has not sufficiently learned the relationships between units.
- After a FastGPT upgrade, some older browsers cannot access the frontend interface. This is because the new version's frontend relies on updated libraries with higher browser compatibility requirements, preventing older browsers from loading new JavaScript modules.
How to Verify Correct Configuration
- Upload a complex PDF document from the autoimmune field containing intricate charts and extensive text. Check if the file uploads and parses successfully.
- For specific fields like disease activity scores and antibody titers in the knowledge base, pose targeted questions. Verify that the numerical values and units in the returned results are accurate.
- Using an older browser that could access FastGPT's frontend before the upgrade, attempt to access the upgraded frontend. Confirm that it loads and interacts correctly.
- Query the usage or adverse reactions of a specific reagent kit. Verify that the recalled document snippets fully cover the relevant information and check the relevance of the results within
Rerank result count.
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.