Tool Calling and Plugins for Academic Promotion Registration

Data Characteristics

Data for academic promotion registration and declaration document preparation comes primarily from clinical trial reports, drug monographs, research papers, industry guidelines, and regulatory documents. This data is mostly unstructured, such as PDFs and Word documents. Some data may exist in structured databases. Data update frequency varies. Clinical data and research papers might update quarterly or annually. Regulatory regulations and guidelines update more frequently, often monthly or irregularly. Document structures are complex, containing many specialized terms, figures, and tables. Key fields include drug mechanism of action, indications, dosage and administration, adverse reactions, pharmacological and toxicological data, and clinical efficacy and safety data. Units commonly used are milligrams (mg) and micrograms (μg) for dosage, moles per liter (mol/L) and percentage (%) for concentration, and hours (h), days (d), and years (y) for time.

Constraints on Tool Calling and Plugins

The complex data characteristics of academic promotion documents impose specific requirements on tool calling and plugins. The prevalence of unstructured documents demands robust document parsing and text extraction capabilities to ensure information completeness. Frequent regulatory updates require tools to quickly integrate external data sources and perform real-time or near real-time synchronization to avoid information lag. Accurate identification of specialized terms and units is critical, as any error could lead to non-compliance in declaration documents. This requires tools to handle specific biomedical vocabularies and unit conversion rules. Furthermore, due to large data volumes and multiple sources, tools need efficient data integration and comparison capabilities to identify potential conflicts or omissions, ensuring the rigor of the final declaration documents.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`4096 tokens`	Accommodates complex document structures, ensures context completeness, and prevents loss of critical information.
`Chunk size` (Segment Length)	`500-800 characters` (characters)	Balances semantic integrity with model processing efficiency, reducing errors in long text parsing.
`Recall count` (Recall Count)	`10-15 entries` (items)	Increases recall rate for relevant information, covering associated content from different sources.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Precisely matches specialized terms and regulatory clauses, reducing interference from irrelevant information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Handles parsing time for large PDF and Word documents, preventing failures due to timeouts.
`tool_call_retries`	`3 times` (times)	Addresses occasional network fluctuations or transient failures of external APIs or services, improving success rate.

Common Mistakes

When calling external regulatory query tools, the returned regulatory clauses are empty. This happens when the tool fails to correctly identify specific abbreviations for drug generic names or indications in the query parameters.
Data comparison errors occur when integrating data from different clinical trial reports due to inconsistent dosage units. This happens when the tool fails to automatically identify and convert measurement units used in different documents.
After uploading large PDF documents using a file parsing plugin, the system displays "parsing timeout." This happens when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, not covering the time required for document parsing.

Configuration Verification

Select a typical declaration document containing various specialized terms, figures, and complex layouts. Upload it and observe if the document parsing plugin can fully extract all text content and key fields.
Perform a simulated regulatory query. Ensure the tool accurately identifies query conditions and returns the expected regulatory clauses. Verify the timeliness and accuracy of the returned content.
Select two related clinical data tables from different sources. Use the tool to compare the data. Check if it can identify and report dosage unit discrepancies or data inconsistencies.
In a test environment, attempt to call an external tool and artificially create a transient network failure. Observe if the tool_call_retries configuration triggers the retry mechanism and ultimately completes the call successfully.

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.