Data Characteristics
Hematologic oncology R&D data primarily originates from clinical trial reports, pathology analysis reports, gene sequencing data, drug development logs, and related medical literature. These documents update frequently; clinical trial progress and gene sequencing results, in particular, may see incremental data weekly or even daily. Document structures often include structured fields (e.g., patient ID, diagnosis, treatment plan, efficacy assessment) and unstructured descriptions (e.g., medical history, physician's diagnostic opinions) within reports. Gene sequencing data stores in specific formats, containing extensive variant site information. Common units include micromoles (μmol) and nanomoles (nmol) for drug concentration, genomic coordinates (e.g., chr1:1000) for genetic variations, and percentages (%) for cell infiltration rates.
Constraints on Deployment and Upgrade
High-frequency data updates demand robust incremental parsing and indexing capabilities. The system must quickly identify and process newly uploaded or updated documents, avoiding redundant processing. Diverse document structures require a flexible parser, especially for extracting key structured information from unstructured text. For example, the system needs to identify and correctly link patient IDs with corresponding gene variation data. Large gene sequencing data files impact file upload size limits and parsing timeout settings. Furthermore, specialized terminology and abbreviations unique to hematologic oncology challenge the domain adaptability of tokenizers and embedding models. Models must accurately understand and represent these terms.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates large gene sequencing reports, ensuring complete data package uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time for complex pathology reports and large gene sequencing files, preventing parsing interruptions. |
Chunk size | 800–1200 characters | Balances context completeness and retrieval efficiency, suitable for high-density medical terminology. |
Similarity threshold | 0.75 | Higher threshold ensures precision in recalled content due to specialized domain terminology. |
Rerank result count | Top 5 entries | Prioritizes the most relevant few results, reducing interference from redundant information. |
maxContext | 4000 | Ensures the model receives sufficient context for answers, handling complex medical record analysis. |
Common Mistakes
- Version number not updated after upgrade: Container caching or deployment script errors cause old containers to run, preventing the new version from starting correctly.
- Model test error "Message field is required" or 401: Incorrect API Key configuration, wrong model service address, or network policies restricting FastGPT communication with the model service.
- File parsing succeeds, but the model cannot answer based on content: The embedding model or large language model is not optimized for hematologic oncology terminology, leading to semantic misunderstanding despite indexed document content.
Verification Steps
- Upload a compressed package containing multiple gene sequencing reports and clinical trial documents. Verify that all files parse successfully and generate corresponding vector indexes.
- Use a test question with hematologic oncology specific terminology. Verify the model accurately recalls relevant passages from the uploaded documents and provides correct answers.
- Simulate high-concurrency file uploads. Observe system resource usage and file parsing times to ensure stable system operation under expected load and that the parsing timeout rate meets expectations.
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.