Data Characteristics
Data in the infection control domain originates from clinical records, research project reports, academic papers, and regulatory documents across hospitals and disease control centers. Data updates frequently. Clinical case reports and research progress may see daily or weekly increments. Regulations are more stable, typically updated quarterly or annually.
Document structures are complex. They include structured table data (e.g., pathogen detection results, antibiotic dosages) and extensive unstructured text (e.g., case discussions, infection source analysis, clinical trial protocols). Key fields include pathogen name, resistance spectrum, infection site, intervention measures, drug name, dosage unit (mg/kg, IU), treatment duration (days), and prognosis indicators (CFR, incidence rate). Standardizing units presents a data processing challenge.
Constraints on Database and Operations
The multi-source and high-frequency updates of infection control data require databases with strong concurrent write capabilities and real-time indexing mechanisms. This handles the continuous influx of new data. The mix of unstructured text and structured data necessitates a hybrid storage model. This could involve combining document databases with relational databases, or integrating text embeddings and metadata within a single database.
Complex document structures and diverse field units demand high standards for data cleansing and normalization. Automated tools are needed for preprocessing to ensure data quality. Sensitive medical data also requires strict access control and encryption at the database level to meet compliance requirements and ensure data traceability.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 Tokens | Infection control documents often contain extensive background information. Increasing context capacity helps understand complex pathological descriptions. |
Chunk size (Chunk Length) | 500 characters (characters) | Balances semantic completeness with recall efficiency, preventing information dilution in long paragraphs. |
Recall count (Recall Count) | Top 10 entries (top 10) | Ensures coverage of sufficient relevant knowledge points, improving recall accuracy. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Infection control terminology is highly specific. Adjust based on actual data to balance recall and precision. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Processing large clinical trial reports or regulatory documents requires longer parsing times. |
DATABASE_MAX_CONNECTIONS | 50 | Handles concurrent queries, especially during knowledge base updates or peak multi-user access. |
Common Pitfalls
- Database connection failures, with error messages showing
400 Messages with role. This occurs when tool calls have incorrect database connection strings or credential configurations, leading to authentication failures or mismatched connection parameters. - After local deployment, tool calls to the database show no output, or variables passed to SQL cause errors. This typically happens when environment variables are not loaded correctly, or the syntax for variable placeholders in SQL statements does not match the database connection tool's expectations.
- Frequent timeouts when parsing large PDF documents, resulting in some content not being structured. This might be due to
PARSE_FILE_TIMEOUT_SECONDSbeing set too short for complex document parsing needs.
Verification Steps
- Execute a series of parsing tasks for infection control documents with varying data types and complexities. Verify that all documents are successfully parsed and imported into the database.
- Query complex questions related to infection control management through the FastGPT platform. Cross-reference the recall results for accuracy, completeness, and inclusion of key field information.
- Simulate high-concurrency access scenarios. Monitor database connection pool usage and response times to ensure system stability.
Note: The values provided are common starting points. Always measure 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.