Data Characteristics
Tender listing pharmacovigilance data primarily originates from provincial and municipal drug procurement platforms, official medical insurance bureau websites, and relevant industry association documents. This data updates frequently, typically aligning with medical device procurement cycles, including quarterly or annual adjustments. Urgent events trigger temporary releases. Document structures are mainly PDF, Word, and Excel formats. PDF documents often contain extensive unstructured or semi-structured text, such as tender announcements, evaluation rules, and adverse reaction monitoring requirements. Excel spreadsheets typically present structured information like drug catalogs, prices, and manufacturers. Specific fields and units require precise identification of generic drug names, dosages, specifications, manufacturers, registration numbers, winning bid prices, adverse reaction reporting requirements, and penalty clauses.
Constraints on Model Integration and Configuration
The heterogeneous nature of tender listing data, particularly the large volume of unstructured PDF documents, presents challenges for document parsing and information extraction during model integration. High-frequency updates demand an efficient incremental update mechanism for the knowledge base to ensure the model reasons with the latest data. Specific pharmacovigilance requirements within tender documents, such as adverse reaction reporting deadlines, reporting channels, and penalty details, are often embedded in lengthy text descriptions. This requires the model to have strong long-text understanding and key information extraction capabilities. The mix of structured and unstructured data means a single model or configuration cannot handle all scenarios. This necessitates flexible model orchestration and multimodal processing strategies. The need for high-accuracy identification of drug registration numbers and batch numbers also sets a higher standard for the model's entity recognition and data validation capabilities.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 tokens | Tender listing documents are often lengthy, requiring a larger context window to carry complete information. |
Chunk size | 500 characters | Balances semantic completeness with model processing efficiency, preventing truncation of critical information. |
Recall count | 10 items | Improves the accuracy and coverage of retrieving relevant tender documents from the knowledge base. |
Similarity threshold | Calibrate by actual measurement | Textual differences across various tender document types are significant; adjust based on actual data. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Allows sufficient processing time for potentially long parsing of large PDF files. |
Rerank result count | 5 items | Reranks initial recall results to prioritize the most relevant outcomes for a query. |
Common Pitfalls
- Model output lacks critical drug registration numbers or winning bid prices. This occurs when the document parsing stage incompletely extracts information from tables or complex PDF layouts, leading to incomplete model input.
- After a knowledge base update, the model still answers based on old tender information. This manifests as the model outputting outdated policies, typically because the knowledge base's incremental update mechanism was not correctly configured or triggered, failing to synchronize the latest listing data.
- When querying specific tender documents about adverse reaction reporting procedures, the model outputs "no relevant information found." This happens when the
maxContextparameter is set too low, preventing the model from acquiring complete contextual information when processing lengthy policy documents.
Verification Steps
- Select recently updated tender listing documents, upload them to the knowledge base, and await parsing completion. Verify that the knowledge base contains all key structured and unstructured information from the document.
- For a specific drug in the document, ask questions about its adverse reaction reporting requirements, winning bid price, or manufacturer. Verify the model can accurately cite and answer.
- Simulate changes in tender policies published at different times. Test whether the model can provide accurate responses based on the latest policies after a knowledge base update, and discard outdated information.
- Randomly select a batch of key fields from tender documents (e.g., registration number, dosage form, specifications). Query to verify the model can accurately identify and extract this information.
Note: The values provided are common starting points. Measure them against your own samples to determine the optimal configuration.
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.