Data Characteristics for This Category
Tender listing pharmacovigilance data primarily originates from provincial or national drug centralized procurement platforms, healthcare security administration websites, and various notices issued by drug regulatory authorities. This data typically exists in structured or semi-structured document formats, such as PDF tender announcements, winning bid notifications, drug catalog adjustment files, and Excel or CSV format listing price lists, or adverse reaction monitoring report templates. Data update frequencies vary; drug catalog adjustments might occur quarterly or annually, while adverse reaction monitoring report requirements are usually ongoing or event-triggered. Document structures often include fields like generic drug name, dosage form, specification, manufacturer, and winning bid price in tender announcements. Adverse reaction reports focus on drug name, batch number, adverse event description, reporting time, and reporter information. Field units are typically CNY, milligrams, or tablets/injections. Adverse reaction event descriptions are unstructured text.
Constraints Imposed by These Characteristics on "Reference and Traceability"
The dispersed and heterogeneous nature of tender listing data presents challenges for consistent reference sourcing. Accurate extraction of tabular data from PDF documents is critical, directly impacting traceability precision. While Excel and CSV files are structured, issues like inconsistent field naming and data entry errors are common, requiring cleaning and standardization during data preprocessing. Unstructured text in adverse reaction reports demands advanced entity recognition and key information extraction capabilities for referencing. Inconsistent data update frequencies mean the knowledge base needs incremental update and version management capabilities to ensure timely references. Furthermore, given drug compliance and risk considerations, tracing back to the original file path or specific page location is crucial for pharmacovigilance decision support, ensuring the authority and verifiability of references.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4000 tokens | Ensures sufficient capacity for key information in tender announcements or adverse reaction reports, preventing truncation of important context. |
similarityThreshold | 0.75 | Balances recall accuracy and quantity, reducing interference from irrelevant information and improving reference quality. |
retrievalTopK | 5 | Prioritizes the 5 most relevant references, reducing model processing load and improving response speed. |
chunkSize | 800 characters | Balances semantic completeness and retrieval granularity, avoiding long paragraphs diluting key information and preventing overly short chunks from lacking context. |
overlapSize | 100 characters | Ensures sufficient contextual overlap between segments, improving continuity for cross-paragraph information retrieval. |
parseTimeoutSeconds | 600 seconds | Accommodates the parsing time for large PDF or complex Excel files, preventing processing failures due to timeouts. |
Three Common Pitfalls
- Garbled or malformed reference content: Typically due to encoding issues in the original PDF document, incomplete font embedding, or improper handling of special characters by the parser.
- Traceability links pointing to incorrect locations or being inaccessible: This occurs when the knowledge base indexing fails to correctly extract internal document links, or when the original file storage location changes, invalidating the links.
- Model answers referencing irrelevant tender information: This might stem from a
similarityThresholdset too low, leading to the retrieval of semantically similar knowledge blocks that do not align with the current query intent.
How to Verify Configuration
- Randomly select multiple tender listing files from different sources (PDF, Excel, CSV) and verify that their parsed text content is complete and free of garbling.
- Ask questions about specific adverse reaction events. Check if the knowledge points referenced in the model's answer accurately correspond to the specific original report files and page numbers, and attempt to open these traceability links.
- Construct queries containing ambiguity or highly similar keywords. Observe the
retrievalTopKknowledge blocks referenced by the model, evaluate if their relevance ranking meets expectations, and adjustsimilarityThresholdbased on actual business feedback.
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.