Tool Calling and Plugins for Pharmacoeconomics Regulations

Data Characteristics

Pharmacoeconomics regulation data primarily comes from policy documents, guidelines, expert consensuses published by national healthcare security administrations, health commissions, and provincial/municipal drug procurement platforms. It also includes pharmacoeconomics evaluation reports submitted by pharmaceutical companies. These documents are often PDFs, containing extensive tables, charts, and complex text. Updates are infrequent, typically quarterly or annually. Document structures often feature nested headings, lists, and citations. Fields include specialized terms such as generic drug names, indications, treatment plans, costs, efficacy, utility, and ICER (Incremental Cost-Effectiveness Ratio). Units vary, including CNY, USD, QALY (Quality-Adjusted Life Year), and LY (Life Year), and can differ across reports.

Constraints on Tool Calling and Plugins

The complexity of pharmacoeconomics documents imposes specific requirements on tool calling and plugins. The presence of tables and charts in PDFs demands robust document parsing capabilities to accurately extract unstructured data into structured, queryable information. The low update frequency allows for stable, long-term indexing in the knowledge base, but requires effective version difference identification during updates. Diverse fields and units, especially core metrics like ICER, necessitate standardization or mapping during tool calls to ensure calculation and comparison accuracy. Furthermore, regulation Q&A often involves cross-referencing multiple documents, requiring advanced knowledge base retrieval strategies and context management to prevent incomplete answers due to insufficient information from a single file.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Pharmacoeconomics reports are often large, containing charts and attachments.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF parsing takes time; this prevents parsing failures due to timeouts.
`Chunk size`	`800–1200 characters`	Ensures each segment contains sufficient context while maintaining retrieval efficiency.
`Recall count`	`Top 8 entries`	Regulation Q&A often requires multi-perspective information support, increasing retrieval coverage.
`Similarity threshold`	`0.78`	Distinguishes subtle differences in specialized terminology, improving relevance.
`Rerank result count`	`Top 5 entries`	Refines the final context provided to the model, focusing on core information.

Common Pitfalls

Table content missing or misaligned after document parsing: This occurs because PDF parsers lack sufficient support for complex table structures, leading to failed structured information extraction.
Incorrect ICER values or units in answers: This happens when original data units in the knowledge base are not standardized, or the model fails to perform unit conversions during citation.
Incomplete answers to questions about specific policy provisions: This results from insufficient Recall count (retrieval count) or Chunk size (segment length), failing to provide adequate contextual information to the model.

Verification Steps

Upload a pharmacoeconomics report PDF containing complex tables and charts. Verify that the parsed text content is complete and structurally correct.
Ask questions about key pharmacoeconomics indicators (e.g., ICER values) from the report. Cross-check if the numerical values and units in the answer match the original text.
Pose questions about regulatory clauses that involve cross-referencing multiple chapters or documents within the report. Evaluate the comprehensiveness and logical coherence of the answers.

The values provided are common starting points. Measure them 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.