Data Characteristics for this Category
Bioequivalence study data primarily originates from clinical trial reports, pharmacokinetic data, statistical analysis reports, and relevant regulatory documents. This data updates infrequently, typically generated upon completion of study batches. Once generated, it exhibits high stability and authority. Document types are diverse, including PDF clinical study reports, Word submission templates, and Excel raw pharmacokinetic data and statistical results. Fields and units require high standardization. For example, plasma drug concentration (ng/mL), area under the curve AUC (ng·h/mL), time to maximum concentration Tmax (h), and various statistical parameters such as geometric mean ratio and 90% confidence interval must strictly adhere to ICH guidelines and national drug regulatory agency technical requirements.
Constraints Imposed by these Characteristics on "Deployment and Upgrades"
The standardized and specialized nature of bioequivalence data necessitates FastGPT's refined text parsing capabilities and specific field recognition when processing these documents. Low data update frequency, coupled with large single data volumes, means deployment requires importing a large amount of historical data at once. This demands ensuring the completeness and accuracy of data indexing, requiring high stability in index construction. Strict regulatory compliance requires that during upgrades, the model's understanding of new and old regulatory terms remains consistent, preventing knowledge recall bias due to model updates. Furthermore, diverse document formats demand higher robustness from the file preprocessing module, especially when handling complex tables and charts, to ensure no information loss. Recognizing specific fields and units also requires the model to accurately differentiate values from units, preventing confusion in context understanding.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | A single clinical trial report or integrated package can be large; this ensures complete document uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large PDF files or complex tables require longer parsing times; this prevents parsing timeouts. |
Chunk size | 800 characters | Ensures each segment contains sufficient context while preventing overly long segments from affecting recall accuracy. |
Recall count | Top 10 entries | Bioequivalence reports contain many details; increasing recall items improves the hit rate of key information. |
Similarity threshold | 0.75 | Guarantees high relevance between recall results and query intent, reducing interference from imprecise information. |
Rerank result count | 5 entries | After reranking, a small number of the most relevant pieces of information are selected, improving user reading efficiency. |
Three Common Mistakes
- After an upgrade, users find that queries for old pharmacokinetic parameters are inaccurate. This might be because the model's entity recognition rules for specific fields were subtly adjusted during the upgrade, leading to a mismatch between old index labels and the new model's recognition logic.
- In the workflow configuration interface, text input lags when there are many nodes. This can be due to high browser rendering pressure or unoptimized front-end resource loading strategies. This issue is amplified in private deployments with network latency or insufficient server performance.
- After executing the
docker compose pullupgrade command, the service fails to start and reports an error. This might be due to conflicts between new and old version dependencies or database structure changes without corresponding migration scripts, leading to service initialization failure.
How to Confirm Correct Configuration
- Upload bioequivalence study reports in various formats (PDF, Word, Excel) to verify complete file parsing and accurate extraction of key fields such as AUC, Tmax, and geometric mean ratio.
- Use query statements containing specific pharmacokinetic parameters and statistical indicators to test the knowledge base's recall accuracy. Compare results with original documents to verify the completeness and relevance of the recalled information.
- In a private deployment environment, simulate high-concurrency query scenarios. Monitor system resource utilization to confirm response times are within an acceptable range. Check logs for abnormal errors to verify deployment stability.
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.