Data Characteristics for This Category
Regulatory affairs data comes from diverse sources. These include public documents like regulations, technical guidelines, review reports, and product registration approvals published by drug administration authorities. Internal enterprise data, such as R&D data, clinical trial reports, quality standards, and manufacturing process documents, also contribute. Data update frequencies vary. Regulations may be revised or new versions released annually. Review reports are generated dynamically with product approval progress.
Document structures primarily consist of PDF scans, Word documents, and Excel spreadsheets. Some data exists in structured database formats. Fields and units are highly specialized. Examples include mg/tablet for content determination in pharmaceutical research, mg/kg for dosage in toxicology research, and P-value for statistical indicators in clinical trial reports.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
Annual updates to regulatory documents require the knowledge base to support periodic full or incremental updates. This necessitates planning for data synchronization and version management mechanisms.
The large volume of unstructured PDF scans and Word documents demands high-performance document parsing (OCR) and text extraction capabilities. This directly impacts the efficiency and accuracy of knowledge base construction.
The specialized terminology and units dictate the choice of tokenizer and embedding models. These models must accurately identify and understand concepts such as ICH Guidelines and bioequivalence.
The inclusion of structured data requires the knowledge base to support multi-source heterogeneous data fusion. This may also necessitate customized retrieval strategies.
During upgrades, the complexity of parallel validation between new and old data increases. This requires detailed regression testing processes to prevent retrieval failures due to changes in data models or indexing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Regulatory affairs documents often contain numerous images and charts, leading to large individual file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large files and documents with complex layouts takes considerable time, requiring sufficient processing duration. |
Segment Length | 800–1200 characters | Regulatory provisions and technical guidelines typically contain long logical paragraphs. Maintaining paragraph integrity aids semantic understanding. |
Recall Count | Top 10 | Ensures coverage of multiple relevant regulatory provisions and review points for complex queries. |
Similarity Threshold | Calibrate by measurement | For similarity calculations involving specialized terminology, adjust based on actual data to balance recall and precision. |
Rerank Return Count | Top 5 | After an initial recall of many items, reranking focuses on the most critical ones, improving the accuracy of the final answer. |
Three Common Mistakes
- After a knowledge base update, queries for specific regulatory clauses yield no results. This happens when the incremental synchronization strategy is not configured correctly, preventing new regulations from being indexed.
- Uploading large PDF scans results in a
404 Not Founderror. This likely occurs becausenginxor theAPI Gatewaylayer does not have adjusted file size limits, causing the file to be rejected before reaching FastGPT. - The model exhibits "hallucinations" or comprehension errors when answering questions involving pharmaceutical terminology. This results from insufficient fine-tuning for regulatory affairs terminology or selecting an unsuitable embedding model.
How to Verify Correct Configuration
- Upload a batch of typical regulatory documents and review reports. Check if file parsing progress and final text segmentation meet expectations.
- Construct multiple queries based on core regulatory provisions and product technical requirements. Verify the accuracy and completeness of the recall results.
- Simulate daily consultation scenarios. Ask questions containing specialized terms and abbreviations. Evaluate the model's professionalism and fluency in its responses.
- Check the knowledge base management interface. Confirm that all expected regulatory files and internal documents are indexed according to the planned version.
Note: The values provided are common starting points. Measure them 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.