Tool Calling and Plugins for Tender Bidding and

Data Characteristics

Tender bidding and pharmacovigilance data originates from provincial drug procurement platforms, official medical insurance bureau websites, and industry association announcements. Update frequencies vary; some provinces update weekly, others monthly or quarterly. Document formats are diverse, commonly PDFs, Excel spreadsheets, or web pages, with low structural consistency. PDFs often contain extensive unstructured text, including approval numbers, manufacturers, drug names, dosages, specifications, and winning bids, alongside irrelevant information. Excel spreadsheets are more structured, but field names can be inconsistent or heterogeneous (e.g., "生产商" and "Manufacturer" both meaning "manufacturer"). Units for drug specifications frequently include milligrams (mg), grams (g), milliliters (ml), and units (U), and may appear in abbreviated forms.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

Dispersed data sources and uneven update frequencies require tool calling strategies to aggregate data from multiple sources and flexibly configure different fetching cycles. The coexistence of unstructured and semi-structured documents necessitates significant effort in the data preprocessing stage for information extraction and structural transformation. For example, identifying and extracting generic drug names and approval numbers from PDFs relies on advanced text parsing and named entity recognition. Inconsistent field names and heterogeneous units challenge the data mapping and standardization capabilities of tool calling, requiring rich synonym dictionaries and unit conversion rules to ensure data consistency. Additionally, potentially large data volumes and sensitive drug information demand high concurrency and security from tool calling. Complex elements like images and tables within documents also increase parsing difficulty, requiring plugin support for various formats.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`FETCH_INTERVAL_HOURS`	`24 hours`	Most tender bidding platforms update daily or post new announcements, ensuring timely capture of changes.
`PARSE_TIMEOUT_SECONDS`	`300 seconds`	Provides sufficient parsing time for large PDF documents or complex web content, preventing timeouts.
`TEXT_CHUNK_SIZE`	`800 characters`	Balances semantic integrity and retrieval efficiency, avoiding excessive splitting that leads to context loss.
`EMBEDDING_MODEL`	`text-embedding-ada-002`	A widely used and effective embedding model suitable for multi-domain text understanding.
`MAX_CONCURRENT_REQUESTS`	`10`	Balances request pressure on data sources with system resource consumption, preventing IP bans.
`FALLBACK_MODEL`	`gpt-3.5-turbo`	Ensures basic service availability when the primary model call fails or responds slowly.

Common Pitfalls

External API calls return 500 or 502 status codes due to target service instability or rate limiting. This requires implementing retry mechanisms and exponential backoff strategies.
Model-generated summaries or key information contain errors or omissions in drug specifications or approval numbers. This occurs because complex tables or text within images were not correctly identified and extracted during the original document parsing stage.
Scheduled tasks fail to trigger for extended periods or terminate unexpectedly, with no clear error messages in system logs. This can be caused by improper scheduler configuration or intermittent failure of dependent services (e.g., database connections).

Verification Steps

Select at least three tender bidding announcements from different sources and formats (PDF, Excel, web page). Manually verify that key information (drug name, approval number, winning bid) extracted by the tool calling matches the original text, and confirm correct field mapping.
Simulate a complete data fetching and processing workflow. Check system logs to ensure no errors due to timeouts or parsing failures, and verify that the time taken for each stage is within expected ranges.
For the configured FETCH_INTERVAL_HOURS, observe whether the system initiates data update tasks on schedule at the start of the next fetching cycle and successfully processes any new announcements.
Verify that when an external API call fails, the FALLBACK_MODEL or retry mechanism is correctly triggered, and corresponding failure logs are recorded.

The values provided are common starting points and should be measured against your 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.