Tool Calling and Plugins for DTP Pharmacy Regulations

Data Characteristics

DTP pharmacy regulations and SOP data originate from internal document management systems. These systems include Word, PDF, and scanned documents. The documents cover detailed operating procedures and management systems for drug procurement, storage, dispensing, sales, patient services, and quality control. Data update frequency depends on policy changes, drug batch updates, and internal process optimizations. Local revisions may occur monthly or quarterly, with major annual updates. Document structure is rigorous, often including chapter titles, clause numbers, appendices, and charts. Fields and units are industry-specific. Examples include drug batch numbers, expiration dates, storage conditions (temperature, humidity), dosage units (mg, g, ml), and de-identified patient information.

Constraints on Tool Calling and Plugins

The highly structured and specialized nature of DTP pharmacy regulation data imposes specific requirements on tool calling and plugins. First, diverse document formats require FastGPT's file parsing plugin to handle multiple formats robustly. OCR accuracy for scanned documents is critical. Second, the update frequency creates a need for version management. Tool calls must specify or identify particular versions of regulation documents to avoid referencing outdated information. Third, accurate recognition of specialized fields and units directly impacts the reliability of question-answering results. For example, extracting and comparing critical information like drug dosages and storage temperatures requires unit consistency to prevent misinterpretation. Finally, when patient services and drug safety are involved, the accuracy and traceability of tool calling results are paramount. Plugins must provide the original source of results and support dual verification mechanisms for critical information.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`4096`	Covers the length of most DTP pharmacy SOP sections, ensuring contextual completeness.
`Chunk size`	`800–1200 characters`	Balances recall accuracy and processing efficiency, avoiding information redundancy or loss of key information in long paragraphs.
`Recall count`	`Top 5 entries`	Focuses on the most relevant regulatory clauses, reducing the model's burden of processing irrelevant information.
`Similarity threshold`	`0.75`	Ensures recalled regulatory items are highly relevant to the user's question, reducing false recalls.
`Rerank result count`	`3`	Selects the most core regulations or SOP clauses, improving answer accuracy.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Most DTP pharmacy regulation files are large; this ensures the file parsing process does not time out.

Common Pitfalls

Tool calling returns a null value, and the log shows an HTTP 500 error. This happens when external service interface authentication fails or request parameters are incorrectly formatted, failing to pass the API Key or JSON structure correctly.
Quoted regulatory clauses in the answer do not match the actual regulations. This occurs when the document version in the knowledge base is not updated in time, or the file parsing plugin fails to recognize version numbers in the document, leading to the recall of old content.
Unit errors appear in answers regarding drug dosage or storage conditions. This happens when text parsing or entity recognition plugins fail to correctly handle unit conversion or recognition for specialized fields, for example, misreading milligrams as grams.

Verification

Upload the latest version of DTP pharmacy regulation documents. Check that the segmented content in the knowledge base is complete and free of garbled characters. Verify document version information.
Ask questions about critical information, such as specific drug dosages and storage temperatures. Verify that the values and units quoted in the answer match the original text.
Simulate questions about complex regulatory issues like procurement processes and quality control. Check if tool calling triggers successfully and compare the returned results with the expected regulatory clauses.

Note: 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.