Data Characteristics
Drug safety data in tender procurement primarily originates from provincial and municipal drug procurement platforms, internal healthcare institution systems, and pharmaceutical company reports. Data update frequency is irregular, influenced by policy changes, new drug launches, and batch updates. Updates can occur several times a month or once per quarter, lacking a unified release schedule. Document structures include both structured tables (e.g., Excel, CSV) and unstructured text (e.g., PDF tender notices, procurement documents). Structured data typically contains fields such as drug name, generic name, manufacturer, batch number, specifications, tender price, purchasing unit, medication quantity, and adverse reaction reporting batch. Unstructured text may include detailed descriptions of adverse event incidents, handling measures, and expert opinions. These texts often contain medical terminology, abbreviations, and non-standardized descriptions. Field and unit standardization is low; for example, dosage units may mix milligrams (mg) and grams (g), and frequency descriptions may range from "twice daily" to "BID."
Constraints Imposed by These Characteristics on Workflow Orchestration
Irregular data update frequency requires flexible workflow triggers. These triggers must support manual uploads, API-driven or scheduled periodic fetching, and accommodate heterogeneous data sources. The coexistence of structured and unstructured data necessitates integrating multiple data preprocessing modules into the workflow. Examples include OCR recognition and text extraction for PDF files, and data cleaning and standardization for tabular data. Non-standardized fields and units demand more robust entity recognition and information extraction modules within the workflow, requiring enhanced semantic understanding capabilities and custom dictionary configurations. Furthermore, the sensitive nature of tender procurement data (involving commercial enterprise information and patient privacy) mandates integrating strict data anonymization and access control mechanisms into the workflow. File sizes may exceed large language model context limits, requiring the workflow to perform intelligent segmentation or summary generation during data input to avoid 400 errors.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large PDF and Excel file uploads while preventing excessive system resource consumption. |
maxContext | 8000 tokens | Adapts to potentially lengthy descriptions in tender procurement documents, ensuring critical information is not truncated. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time for complex PDF structures or large Excel spreadsheets. |
Chunk size | 1000 characters | Optimizes large language model processing efficiency for unstructured text while maintaining semantic integrity. |
Recall count | Top 10 entries | Ensures retrieval of enough relevant adverse reaction cases or drug instructions from the knowledge base to aid judgment. |
Similarity threshold | 0.75 | Balances recall accuracy and completeness, avoiding interference from too many irrelevant results while not missing potential associations. |
Common Pitfalls
400errors during file upload, with system messages like "request body too large" or "Payload Too Large." This occurs when the uploaded file size exceeds theUPLOAD_FILE_MAX_SIZElimit, or the data volume of a single request surpasses the default limits of the server or gateway.- AI model inaccurately identifies drug batch numbers or dosage units in adverse reaction reports, leading to missing or incorrect critical information. This happens when the entity recognition model in the workflow has not been adequately trained or configured with custom dictionaries for the specific format and terminology of tender procurement data.
- AI cannot link specific drug batch adverse reactions to their corresponding tender procurement information. This indicates insufficient knowledge base update frequency in the workflow, failing to synchronize the latest tender procurement data in time, or loss of critical batch identifiers during data cleaning.
Verification Steps
- Upload a tender procurement PDF file exceeding
100MBwith complex tables and text. Verify successful parsing and knowledge base index creation. - Select multiple adverse reaction reports with different dosage units (e.g., mg, g, IU) and batch number formats. Test if the AI model accurately extracts this information and compare it against a predefined standardized format.
- Simulate a new tender procurement data release. Use the workflow's automatic fetching or manual import function to verify that the knowledge base updates within the specified time and that new tender information is retrievable through queries.
- For a text containing a lengthy adverse reaction description, test if the AI model's summary generation or information extraction results include all critical medical events and handling measures. Human review can confirm completeness.
The values given are common starting points and should be measured against the reader's 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.