Data Characteristics in Bioequivalence
Bioequivalence (BE) study quality documents include research protocols, ethics approvals, informed consent forms, case report forms (CRFs), analytical method validation reports, biological sample analysis reports, statistical analysis reports, and summary reports. Clinical trial institutions, bioanalytical laboratories, and CRO companies are the primary data sources. Data updates typically align with study progress and occur periodically. Document structures are rigorous, adhering to regulatory guidelines such as ICH E6 GCP, NMPA, and FDA. Fields include drug concentration, time points, dosage, subject ID, and batch number. Units involve ng/mL, μg/mL, h (hours), and min (minutes). The data volume is large, and formats are diverse, often including charts and statistical data.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The rigor and multi-source nature of BE documents require robust multi-format parsing capabilities in the data ingestion phase of the workflow, supporting formats like PDF, Word, and Excel. The periodic nature of data updates necessitates fine-grained management of incremental update mechanisms for knowledge bases to prevent redundant processing and version conflicts. The presence of numerous specialized fields and units in documents requires the workflow to accurately identify and maintain semantic integrity during knowledge extraction, for example, distinguishing subtle differences in drug concentration units. Furthermore, compliance requirements mandate that the workflow includes strict audit and version control nodes, ensuring traceability for every update. The data validation step within the workflow is particularly critical, requiring verification of key field completeness and logical consistency, such as matching subject IDs with sample batch numbers.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures sufficient parsing time for large PDFs or scanned documents. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic integrity and recall efficiency, adapting to report content density. |
Recall count (Recall Count) | Top 8 entries | Ensures sufficient context coverage for complex queries. |
Similarity threshold (Similarity Threshold) | 0.78 | Filters out low-relevance content, improving retrieval accuracy. |
Rerank result count (Rerank Return Count) | Top 5 entries | Further refines results, focusing on the most relevant information. |
maxContext | Calibrate by actual measurement (Calibrated by actual measurement) | Prevents context overflow, balancing response speed and accuracy. |
Common Pitfalls
- A database connection plugin in the workflow executes much slower than expected. This may be due to insufficient database connection pool configuration, leading to new connection establishment for every request.
- The AI model cannot be selected in a specific classification module. This manifests as an empty dropdown list or a
model_not_founderror. The cause is typically insufficient API Key permissions or incorrect model configuration within the FastGPT platform. - Application token transmission fails during chat, preventing access to specific data sources. Logs show an
unauthorizederror. This occurs because global variables are incorrectly configured or not referenced in the workflow.
Verification Steps
- Upload a BE study summary report containing various formats (PDF, Word, Excel). Check if the workflow correctly parses it and generates knowledge base segments.
- Execute a query involving a biological sample analysis report. Verify that the recalled results include key drug concentration data and corresponding units, and compare them with the original document.
- Simulate a knowledge base update operation. Observe the update logs to confirm that the incremental update mechanism functions as expected, without duplicate segments or version conflict warnings.
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.