Data Characteristics for This Category
Data for respiratory system products and reagents comes from diverse sources. These include clinical trial reports, drug monographs, medical journal literature, case databases, gene sequencing data, and in vitro diagnostic reagent instructions. Data update frequencies vary by source. For example, clinical trial data typically releases in batches after trials conclude, while medical journal literature updates continuously. Document structures are diverse. Drug monographs are usually structured text with fixed fields like indications, dosage, and adverse reactions. Clinical trial reports contain semi-structured content such as study design and results analysis. Gene sequencing data might exist in VCF or FASTA formats. Fields involve drug molecular structures, target information, mechanisms of action, disease staging, and biomarkers. Units commonly include milligrams (mg) and micrograms (μg) for dosage, nanomoles (nM) and micromoles (μM) for concentration, hours (h) and days (d) for time, and FPKM or TPM for gene expression levels.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The diversity and complexity of respiratory system product and reagent data impose specific requirements on FastGPT's deployment and upgrade. First, the wide range of data sources necessitates support for parsing and importing various file formats, such as PDF, DOCX, TXT, and custom parsers for genetic data. Second, the coexistence of structured and semi-structured data requires flexible knowledge base segmentation strategies. These strategies must segment by fixed chapters and intelligently identify paragraph semantic boundaries. The specialized nature of medical terminology (e.g., disease names, drug names, gene loci) demands high accuracy from embedding models for training and retrieval. This may require domain-specific vocabulary enhancement. Non-uniform data updates, such as new clinical guideline releases or drug launches, mean the knowledge base needs to support incremental updates and version management to ensure information timeliness. Additionally, varying sensitivity levels across data sources require strict access control and data isolation policies during deployment.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large clinical trial reports and gene sequencing files for single-batch uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents parsing timeouts for complex PDF documents, avoiding upload failures. |
Chunk size | 800–1200 characters | Ensures completeness of medical concepts and context, preventing truncation of critical information. |
Similarity threshold | Calibrate based on actual measurements 0.75–0.85 | Balances recall and accuracy, avoiding irrelevant information while retrieving relevant medical concepts. |
Rerank result count | Top 5 entries | Medical consultations typically require precise and focused answers, reducing interference from irrelevant information. |
MAX_TOKENS_PER_REQUEST | 4096 | Allows for longer medical literature or report segments, enabling in-depth analysis by the model. |
Three Common Pitfalls
- Knowledge base inaccessible after training: Incorrect modifications to the MongoDB configuration file
mongod.conf, such as incorrect IP binding or missing authentication parameters, prevent the FastGPT service from connecting to the database. - Poor RAG training performance with multi-column Excel files: Failure to preprocess Excel data for its specific characteristics. Direct automatic segmentation destroys the semantic correlation of each row, leading to inaccurate knowledge retrieval.
- Old version workflow import fails in new version: Differences in workflow orchestration structure or component definitions between old and new FastGPT platform versions cause parsing errors when importing
workflow.jsonfiles.
How to Verify Correct Configuration
- Upload representative respiratory system product instructions and clinical trial reports. Check for complete file parsing and expected segmentation.
- Perform multi-turn Q&A using keywords for specific diseases or drugs. Evaluate the accuracy and completeness of knowledge retrieval. Verify if core information is accessible.
- Simulate consultation scenarios for different user roles (e.g., doctors, researchers). Verify that permission configurations are effective and sensitive data is properly isolated.
Note: The values provided are common starting points. Measure them against your own samples for optimal performance.
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.