Tool Calling and Plugins for Pharmaceutical E-commerce

Data Characteristics

Pharmaceutical e-commerce regulations and SOP documents typically exist as PDFs, Word files, or online knowledge bases. Data sources include regulatory documents from national and local drug administrations, as well as internal quality management system documents, operating procedures, and emergency plans. Updates are driven by policy adjustments and internal management needs. Regulatory documents may be revised annually; enterprise SOPs update periodically based on business development and feedback. Document structures are rigorous, often including chapters, sections, clause numbers, attachments, charts, and terminology definitions. Common fields include regulation name, publication date, effective date, revision number, scope, responsible department, operating steps, risk control points, and record requirements.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The update frequency and structured nature of pharmaceutical e-commerce regulation documents require tool calling and plugins to have flexible file format parsing capabilities during data ingestion. They must also recognize internal document hierarchies. The timeliness of regulatory documents means plugins must prioritize the latest versions during queries and support version rollback. Specific operating steps and risk control points in SOPs require tool calling to precisely match key instructions in the text. They must also identify numerical values, units, or specific fields, such as drug storage temperature ranges or expiration date formats. If regulations involve external system operations, plugins must accurately call corresponding API interfaces and process their return results. Examples include querying drug inventory or verifying order status. API call parameters and return data structures for these tasks are often strictly defined.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`3000 tokens`	Regulation documents are content-dense, requiring a longer context window to understand complex clauses.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Parsing large PDF or Word documents can be time-consuming; this prevents parsing interruptions.
`Recall count`	`Top 8 entries`	Ensures coverage of highly relevant regulatory clauses and operating procedures.
`Similarity threshold`	`0.78–0.85`	Balances recall precision and coverage, preventing interference from irrelevant clauses.
`API_RESPONSE_TIMEOUT_MS`	`5000 ms`	External system API response times are typically within seconds; this ensures timely feedback.
`ENABLE_STREAMING_OUTPUT`	`True`	Enhances user experience by progressively presenting regulation interpretations or API call feedback.

Common Pitfalls

After calling an external API, the conversation content and API return results do not match. Logs show the API call succeeded, but the output is empty. This can happen if the API's return data structure does not match the predefined parsing template, leading to key field extraction failure.
During streaming output, external links open in the current page instead of a new tab. This indicates the frontend rendering logic does not correctly handle the target="_blank" attribute, or the API-returned link format does not meet frontend expectations.
When querying specific regulatory clauses, the system returns an outdated version. This usually occurs if the knowledge base index is not updated promptly, or if the version control logic is not applied during the recall phase.

Verification Steps

Upload the latest regulatory documents and SOPs. Perform parsing operations. Verify in logs that PARSE_FILE_TIMEOUT_SECONDS completes within the expected time. Check if the document's chapter structure and key fields are correctly extracted into the knowledge base.
Simulate user questions, such as "drug GSP storage temperature requirements." Observe if the system returns the correct temperature range and relevant responsible departments. Evaluate the effect of Recall count and Similarity threshold.
Configure a plugin that calls an external drug inventory query API. Input a simulated drug batch number. Verify that the API is called correctly and that the returned inventory quantity and expiration date match the actual API response.

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