Tool Calling and Plugins for Pharmaceutical E-commerce

Data Characteristics in this Category

Pharmaceutical e-commerce registration documents involve diverse data sources. These primarily include drug inserts, registration certificate attachments, production approvals, inspection reports, clinical trial data, and internal compliance documents. Data typically exists in various formats such as PDF, Word, and Excel. Scanned PDFs and image files constitute a significant portion. Data update frequencies vary; drug inserts and registration certificate attachments are relatively stable, while approvals and inspection reports may update periodically due to policy changes or product modifications. Document structures are complex, containing numerous tables, charts, and unstructured text. Field naming lacks uniform standards; for example, "production enterprise" might appear as "manufacturer" or "producer." Measurement units also vary, such as "mg/tablet," "g/bag," or "IU/ml," often mixing Chinese abbreviations and international units.

Constraints Imposed by these Characteristics on Tool Calling and Plugins

The complex data characteristics of pharmaceutical e-commerce registration documents impose specific constraints on tool calling and plugins. First, multi-format documents, especially scanned PDFs and images, require the toolchain to have robust OCR (Optical Character Recognition) capabilities for accurate text extraction, preventing information loss. Second, inconsistent field naming and measurement units make direct structured data extraction difficult, necessitating semantic understanding or custom rules for mapping and standardization. Third, data sources with varying update frequencies demand that tool calling includes version management and incremental update capabilities, ensuring processing of the latest compliance materials. Finally, the presence of complex structures like tables and charts requires more advanced information extraction tools; simple text segmentation or keyword matching cannot accurately capture intrinsic relationships, potentially leading to critical information omissions or misinterpretations. These constraints directly influence plugin selection, parameter configuration, and error handling strategies.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`6000 characters`	Accommodates long text characteristics of pharmaceutical data while balancing model processing efficiency.
`chunkOverlap`	`100 characters`	Ensures context continuity and handles semantic relationships between tables and paragraphs.
`similarityThreshold`	`0.78`	Improves recall accuracy and filters out irrelevant regulations or product information.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Addresses the time required for large PDF files and complex OCR processing.
`OCR_ENGINE_TYPE`	`PaddleOCR`	Selects an OCR engine with superior recognition performance for a high proportion of scanned documents and images.
`TOOL_ACTIVATION_MODE`	`AUTO_TRIGGER`	Pharmaceutical declaration processes are complex; automated tool calling reduces manual intervention.

Three Common Pitfalls

Tool calls return a 400 Bad Request error. This commonly occurs when parameters passed to an external tool do not conform to its API specification, such as passing a string value to a field expecting a numeric type.
Key fields like approval_number or manufacturer_name are empty in the results returned after plugin execution. This is typically due to insufficient OCR accuracy or data extraction rules failing to cover various expressions of fields within the document.
The mcp service configured in FastGPT is unresponsive, manifesting as Connection refused or Timeout. This often indicates a network configuration issue in the mcp service deployment environment or incompatibility between its startup method (e.g., npx) and FastGPT's expected communication protocol (e.g., SSE).

How to Verify Correct Configuration

Select a registration document PDF containing complex tables and scanned pages. Upload it through the system and observe the file parsing logs to confirm that OCR recognition and text extraction are complete and free of obvious errors.
Construct a query containing key information (e.g., drug name, specifications, manufacturer). Observe whether fields like registration_number and manufacturer_name are accurately extracted and populated in the results returned after tool calling.
Simulate a complete declaration document preparation process. Observe whether each plugin triggers as expected when processing multiple documents and if no 5xx server-side errors occur.

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.