Data Characteristics
R&D document data in the health management sector comes from various sources. These include clinical trial reports, gene sequencing data, wearable device monitoring data, user health questionnaires, and physical examination reports. Data updates are frequent; for example, wearable device data can upload in real-time, and clinical trial reports release with phase-based results. Document structures commonly include PDF reports, Word research proposals, and structured data in CSV or JSON formats. Document content contains extensive specialized terminology, medical indicators, and units, such as blood pressure (mmHg), blood sugar (mmol/L), and heart rate (bpm). Complex tables, charts, and formulas are also common. Field naming can be inconsistent; for instance, "weight" might appear as "Weight" or "Body Mass."
Constraints on Deployment and Upgrade
High-frequency data updates require deployment solutions to support incremental updates and real-time synchronization. This avoids full parsing with every update, reducing resource consumption and processing time. Diverse document formats necessitate robust file parsing capabilities, requiring automatic recognition and processing of multiple formats like PDF, Word, CSV, and JSON. Specialized terminology, medical indicators, and inconsistent field naming demand that the model accurately identify and standardize entities during structural analysis to prevent information loss or misinterpretation. Accuracy of parsing results is critical; any incorrect identification of metrics can impact subsequent health assessments or R&D decisions, directly influencing model selection and the complexity of post-processing logic. The deployment environment must provide sufficient computational resources to handle complex document parsing and high-concurrency data updates.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Health management reports often contain numerous charts and complex text, making parsing time-consuming. Increasing the timeout prevents task interruptions. |
Chunk size (Segment Length) | 800–1200 characters | Ensures each segment contains a complete medical concept or experimental result, balancing context and recall efficiency. |
Recall count (Recall Count) | Top 10 | Increases the recall rate of relevant information, covering multi-dimensional data points in clinical trials. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures recalled document segments are highly relevant to the query intent, reducing noise interference. |
Rerank result count (Rerank Return Count) | Top 5 | In health management scenarios, precision is paramount; reranking further prioritizes critical information. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large clinical trial reports or gene sequencing data files that include extensive images and tables. |
Common Pitfalls
- Knowledge base queries return empty results after deployment, even if data exists in the knowledge base. This occurs when the large language model does not correctly receive or process the context information passed from the knowledge base.
- Data writing takes an extended time to complete after a service restart or upgrade. This can be due to a massive data volume or inefficient write operations caused by improper database connection configurations.
- Rerank model configuration fails after a version upgrade. This typically happens because the new version has specific requirements for model parameters or environmental dependencies, and these adjustments were not made according to the upgrade guide.
Verification Steps
- Upload health management R&D documents in various formats (PDF, Word, CSV) to confirm successful parsing and knowledge base entry generation.
- Query specific medical indicators within the documents to verify that recall results include correct segments and to evaluate the effectiveness of the
Similarity threshold(Similarity Threshold). - Simulate high-concurrency data uploads and queries via API to observe system response times and resource utilization, ensuring stable operation under expected load.
- Review log output to confirm the absence of error messages related to file parsing, data writing, or model invocation.
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.