Data Characteristics for this Category
Bioequivalence (BE) study data primarily originates from clinical trial reports, pharmacokinetic (PK) data, bioanalytical reports, and related regulatory documents. This data typically exists as structured tables (e.g., CSV, Excel), PDF report documents (containing numerous charts, statistical data, and textual descriptions), and semi-structured electronic data capture (EDC) system export files. Data update frequency is closely tied to new drug development and generic drug application progress. Batch updates usually occur after trial phases are completed or application materials are updated. Core fields include subject ID, dosing regimen, sampling time points, plasma drug concentration, pharmacokinetic parameters (e.g., AUC, Cmax, Tmax), and statistical analysis results (e.g., geometric mean ratios, 90% confidence intervals). For units, concentration often uses ng/mL or μg/mL, and time uses hours or minutes. Document structures are complex, frequently including multiple chapters, appendices, and cross-references.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The complexity and diversity of bioequivalence data impose specific requirements on FastGPT's deployment and upgrade processes. The large number of PDF reports and charts necessitates enhanced document parsing capabilities, especially for identifying and extracting non-textual content. The irregular nature of data updates requires the knowledge base to have flexible data import and version management mechanisms. This ensures rapid integration of new information and index reconstruction after each data update. The precision of pharmacokinetic parameters and statistical results demands effective differentiation between numerical data and descriptive text during vector embedding and retrieval, preventing incorrect answers due to semantic understanding deviations. Furthermore, standardization of fields and units places higher demands on knowledge base content cleaning and structured processing to ensure query accuracy. During upgrades, compatibility with original data formats and parsing logic must be ensured. A rollback mechanism should also be available to address potential data parsing issues.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | BE reports often include numerous charts and high-resolution images, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF documents is time-consuming; sufficient time is needed to prevent parsing failures. |
Chunk size (Segment Length) | 800–1200 characters | Ensures individual segments contain enough context to understand pharmacokinetic data and statistical conclusions. |
Recall count (Recall Count) | Top 8 entries (Top 8) | Improves recall rate, covering more relevant pharmacokinetic parameters and experimental results. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures retrieved document segments are highly relevant to the query content, avoiding irrelevant information. |
Rerank result count (Rerank Return Count) | Top 3 entries (Top 3) | Further optimizes retrieval results, focusing on the most core pharmacokinetic and biostatistical data. |
Three Common Mistakes
- After mirror deployment, configured models fail to load. The symptom is a 500 error during conversation. The cause is typically a mismatch between the model name in the configuration file and the actual deployed model service name, or the model service not starting correctly.
- The SaaS version frequently experiences timeouts during peak hours. The symptom is excessively long request response times or direct timeout errors. The cause is likely that the concurrent request volume exceeds current resource capacity, requiring scaling up or load balancing optimization.
- After importing PDF documents containing complex tables, some critical data fields are empty. The cause is usually inaccurate recognition of table structures or specific fonts by the document parser, leading to incomplete data extraction.
How to Confirm Correct Configuration
- Upload and parse a PDF report containing a pharmacokinetic parameter table. Check if the parsed segment content completely includes key numerical values and units.
- For a report with known bioequivalence conclusions, query relevant pharmacokinetic parameters (e.g., geometric mean ratios for Cmax, AUC). Cross-reference FastGPT's results with the original report and check its cited document sources.
- Simulate high-concurrency query scenarios. Check if system response time is stable, without request timeouts or service interruptions. Thresholds should be set based on business needs and user experience standards.
- Search the knowledge base for specific subject IDs or batch numbers. Confirm that relevant clinical trial data and reports are accurately recalled. Verify that the similarity scores of the recalled documents are within the expected range.
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.