Data Characteristics in This Category
Biomedical tender listing data originates primarily from provincial and municipal pharmaceutical centralized procurement platforms, official medical insurance bureau websites, and third-party data service providers. This data updates frequently, usually monthly or quarterly. It includes key information such as product catalogs, prices, winning bidders, and listing status. Documents are often structured or semi-structured, like Excel spreadsheets, PDF announcements, or web pages. Common fields include "Generic Name," "Trade Name," "Manufacturer," "Dosage Form," "Specification," "Packaging," "Listed Price," "Listing Batch," "Medical Insurance Payment Standard," and "Procurement Area." The "Listed Price" field may involve various pricing strategies and units, such as "CNY/box," "CNY/vial (tax incl.)," or "CNY/minimum preparation unit." Data is characterized by its timeliness and regional variations; listing rules and pricing systems can differ across provinces.
Constraints Imposed by These Features on Tool Calling and Plugins
The timeliness of tender listing data requires tools to respond quickly to data source changes, for example, by regularly fetching the latest announcements via API. The regional nature of the data means tool design must accommodate multiple regional data sources, avoiding hardcoding parsing logic for specific provinces. The diversity of document structures (Excel, PDF, web pages) demands robust parsing tools, as a single parser may not cover all scenarios. For instance, tables in PDF announcements might require OCR recognition followed by structured extraction. The complexity of field units, such as converting "CNY/box" to "CNY/minimum preparation unit," necessitates standardization during post-tool-calling data processing to prevent data comparison errors. Additionally, given the large volume of historical tender listing data, incremental updates and version management for the knowledge base are important considerations.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 1500 tokens | Ensures the model can process the full context of typical listing announcements, preventing information truncation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses potentially long processing times for large PDF announcements or complex web page parsing. |
Recall count (Recall Count) | Top 10 entries (Top 10) | Tender listings involve numerous products; increasing the recall count improves coverage of relevant information. |
Similarity threshold (Similarity Threshold) | 0.75 | Accurately matches product information, reducing false recalls due to similar names. |
Rerank result count (Rerank Return Count) | Top 5 entries (Top 5) | Further filters recalled information to the most relevant few items, improving model processing efficiency. |
tool_call_max_retries | 3 times (3 times) | Handles external API calls that might fail due to network fluctuations or temporary service unavailability. |
Three Common Mistakes
- External API calls for data return
HTTP 502 Bad Gatewayerrors. This can occur if the API service is overloaded or undergoing temporary maintenance, and no retry mechanism is configured. - After parsing a PDF announcement, specific fields like "Listed Price" are empty. This happens because PDF formats vary, and existing parsers may lack sufficient recognition capabilities for certain layouts or scanned documents.
- Knowledge base query results show inconsistent price units for the same product across different batches. This is due to a lack of standardization in the data source, leading to unit confusion upon direct import.
How to Confirm Correct Configuration
- Select multiple tender listing announcements from different provinces and in various formats (Excel, PDF, web pages). Parse them using the tool calling process. Check if key fields like "Listed Price" and "Manufacturer" are extracted accurately.
- Compare historical data with the latest data. Confirm that the knowledge base's incremental update mechanism functions correctly and that new product listing information is accurately indexed.
- Simulate a user query such as "What is the latest listed price for XX drug in XX province?" Verify if the model can use tool calling to retrieve and correctly interpret the latest data, providing a value with units.
The values provided 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.