Data Characteristics
Infectious disease product data comes from diverse sources. These include drug inserts, clinical trial reports, guidelines, consensus documents, and academic literature. Update frequency depends on new drug approvals, changes in drug resistance, and epidemiological data releases. Updates typically occur quarterly or annually. Data related to urgent public health events may update in real-time. Document structures are primarily semi-structured and unstructured, such as PDF drug inserts or Word document clinical study reports.
Key fields include pathogen names, drug components, indications, dosage and administration, adverse reactions, interactions, pharmacokinetic parameters (e.g., t1/2, Cmax), resistance profiles, diagnostic methods, and treatment plans. Units commonly used are milligrams (mg) and grams (g) for dosage, micrograms per milliliter (μg/mL) for concentration, and hours (h) and days (d) for time.
Constraints on Workflow Orchestration
The specialized nature and varying update frequencies of infectious disease product data demand real-time accuracy in workflow orchestration. Unstructured documents require efficient preprocessing and information extraction to ensure accurate identification of key fields. For example, workflows must extract drug sensitivity results for specific strains from clinical trial reports or identify recommended dosages for different age groups from drug inserts.
Periodic data updates mean knowledge base synchronization mechanisms require flexible configuration to adapt to different data update rhythms. Numerical data, such as pharmacokinetic parameters, require precise unit conversion within the workflow during comparisons or calculations to prevent misjudgments caused by inconsistent units.
Complex medical terminology and potential ambiguities necessitate high robustness in AI modules within the workflow. This robustness helps handle variations in specialized terms and context-dependent understanding challenges. Potential needs for multimodal data, such as processing pathology images or gene sequencing reports, also require workflow extensibility.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 800-1200 characters | Balances semantic completeness and recall efficiency. Avoids diluting key information in long texts. |
Recall Count | Top 5 | For infectious disease diagnosis and treatment, ensures coverage of critical information points. Prevents information omission. |
Similarity Threshold | 0.75 | Guarantees precision of recalled content. Reduces interference from irrelevant or low-relevance information. |
Rerank Return Count | Top 3 | Further optimizes result relevance. Prioritizes core diagnostic, treatment, or product information. |
HTTP_REQUEST_TIMEOUT | 600 seconds | Accounts for complex queries in some network searches or external API calls. Provides sufficient response time. |
PARSER_CONCURRENCY_LIMIT | 2 | Balances parsing speed and system resource consumption when processing large numbers of PDF and Word documents. |
Common Pitfalls
- Symptom: The workflow fails to correctly extract contraindication information for a specific drug from a drug insert. Reason: During document preprocessing, the OCR recognition or text parsing module lacks sufficient structural extraction capability for tables or complex layouts, leading to missing key fields.
- Symptom: After multi-node deployment, AI conversations cite outdated or incorrect drug resistance profile data. Reason: The FastGPT knowledge base data synchronization strategy does not match the actual update frequency of infectious disease data, causing cached data to not refresh in a timely manner.
- Symptom: When calling the workflow API, drug dosage units in the returned disease diagnosis suggestions are confused, e.g.,
mgis mistaken forg. Reason: The workflow does not strictly standardize units for numerical entities or lacks intermediate steps for unit conversion.
Validation Steps
- For typical infectious disease (e.g., influenza, pneumonia) products, input queries containing key diagnostic and treatment information. Verify that the AI's returned diagnostic suggestions, medication plans, and adverse reactions are accurate and complete.
- Randomly select a batch of recently updated drug inserts or clinical guidelines. Import them into the knowledge base. Execute relevant queries to verify the workflow can accurately retrieve and cite the latest information.
- Write test cases simulating user inquiries involving numerical parameters like drug dosage and concentration. Check if the AI's returned values and units are consistent and comply with medical standards.
The values provided are common starting points and should be measured against the reader's 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.