Data Characteristics in This Category
Data for regulations and Standard Operating Procedures (SOPs) in pharmaceutical e-commerce primarily comes from regulatory documents issued by national and provincial drug administrations, self-regulatory norms from industry associations, and internal quality management system documents, operating procedures, and emergency plans. These documents are updated frequently, especially during policy adjustments. Document formats are mainly PDF, Word, and Excel. Content is rigorously structured, including numerous clauses, detailed rules, flowcharts, and tables. Fields and units have industry-specific characteristics, such as drug batch numbers, production dates, expiration dates, storage conditions (temperature unit: ℃, humidity unit: %RH), and delivery times (unit: hours). This information is critical for compliance-related question answering.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
Pharmaceutical e-commerce regulation documents have extremely high compliance requirements. Data accuracy and timeliness are core concerns. Therefore, during deployment, ensure complete document import, without omitting any regulatory clauses or operational details. Due to frequent policy updates, the upgrade mechanism must support rapid, seamless replacement of old document versions and supplementation of new regulations. It must also differentiate and mark version differences to avoid confusion. Specific fields and units within documents, such as drug batch numbers, temperature, and humidity, require the vectorization model to accurately recognize and understand their semantics. This directly impacts the precision of question-answering recall. Additionally, embedded flowcharts and complex tables in documents require robust parsing capabilities to ensure information is not incorrectly extracted or lost.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Accommodates large regulatory compilations or comprehensive enterprise management system documents. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Ensures sufficient time for parsing complex PDF or Word documents. |
Chunk size | 600–800 characters | Balances semantic completeness with recall efficiency, suitable for regulatory clause lengths. |
Recall count | Top 8 entries | Ensures coverage of multi-faceted regulatory clauses, improving answer comprehensiveness. |
Similarity threshold | 0.75 | Improves the precision of recall results, avoiding interference from irrelevant clauses. |
Rerank result count | Top 3 entries | Filters for the most relevant few clauses, reducing the model's processing load. |
Three Common Mistakes
- During Docker Compose deployment, containers fail to start or some services are unavailable. This manifests as
docker psshowing abnormal status for some containers or inaccessible ports. The cause is insufficient memory or CPU allocation, leading to resource exhaustion, especially when processing large volumes of documents. - When uploading large policy files or SOP documents, the interface displays a "timeout of 60000ms exceeded" error. This is because the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, preventing document parsing from completing within the specified time. - Question-answering results show errors or missing information related to specific fields like drug batch numbers or storage conditions. This manifests as the answer being unable to accurately cite values or units from the original text. The cause is that these specific data formats were not correctly recognized or extracted during document parsing.
How to Verify Correct Configuration
- Upload a PDF document containing complex tables and flowcharts. Check if the document's segments in the knowledge base are complete and semantically coherent, paying special attention to whether table content is correctly parsed.
- Import a Word document with specific fields such as drug batch numbers, production dates, and storage temperatures. Verify through questioning whether the model can accurately recall and cite these fields and their units.
- Simulate a policy update by uploading a new version of a regulatory document. Verify if the system can identify version differences and provide accurate answers, while ensuring older content is no longer preferentially recalled.
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.