Tool Calling and Plugins for Pharmaceutical E-commerce

Data Characteristics in this Category

Pharmacovigilance data on pharmaceutical e-commerce platforms primarily originates from user-submitted adverse drug reaction (ADR) reports, consultation records, product reviews, and sales data. This data updates frequently, especially user-submitted ADR reports and product reviews, which can be generated in real-time. Document structures are diverse, including unstructured text descriptions (e.g., free-text symptoms, medication details), semi-structured form data (e.g., drug name, dosage, occurrence time, outcome fields in ADR reports), and structured transaction records. Fields may include drug generic name, brand name, batch number, manufacturer, anonymized patient basic information, symptom description, signs, diagnosis, medication history, and concomitant medications. Units for drug dosage may include milligrams (mg), grams (g), milliliters (ml), and units (U), while time units are primarily dates and timestamps.

Constraints Imposed by these Characteristics on Tool Calling and Plugins

High-frequency data updates require tool calling to have real-time or near real-time triggering mechanisms. This ensures the pharmacovigilance system can respond promptly to new adverse event reports. Diverse document structures necessitate greater flexibility during data preprocessing. This means handling both entity extraction from free text and parsing structured fields. For example, identifying drug names and potential adverse reactions from user consultations requires calling a Named Entity Recognition (NER) tool. Standardizing drug dosage and time units is crucial; different users or systems may use varying expressions. This requires plugins to perform unit conversion and normalization during data ingestion. Examples include converting "twice daily" into a specific dosing frequency or recognizing "100mg" as a standard dosage. Additionally, fields containing sensitive patient information require anonymization through tools to ensure compliance.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`8000`	Accommodates detailed adverse reaction descriptions submitted by users, retaining sufficient context.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Allows users to upload images or detailed report attachments, balancing storage and transmission efficiency.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles OCR and text extraction for large PDF or image attachments, preventing timeouts.
`Chunk size`	`500 characters`	Adapts to the length of symptom descriptions in adverse reaction reports, maintaining semantic integrity.
`Recall count`	`Top 10 entries`	Increases the probability of retrieving relevant drug instructions or historical cases from the knowledge base.
`Similarity threshold`	`0.75`	Ensures retrieved drug information is highly relevant to user-described symptoms, reducing false positives.

Three Common Pitfalls

An inaccessible document path is provided when calling an API, resulting in an API 404 error or file read failure. This occurs when the local file path in the API calling environment does not match the actual server-accessible path, or file permissions are configured incorrectly.
Inaccurate drug dosage or frequency information is returned after tool invocation, resulting in incorrect values or missing units. This happens when frontend input data is not standardized, or the backend parsing tool fails to correctly identify diverse dosage expressions (e.g., "twice daily" versus "bid").
Failure to connect to an external database tool, resulting in Connection refused or Timeout errors. This occurs when database connection parameters (e.g., host, port, username, password) are configured incorrectly, or firewall policies block communication between the FastGPT service and the database.

How to Confirm Correct Configuration

Simulate submitting reports with different drug dosages and frequencies to verify the tool can correctly identify and standardize this information.
Upload a drug instruction manual containing images or in PDF format. Check if its text content can be fully and accurately extracted and used for subsequent question answering.
Invoke a tool that requires external database access. Confirm the tool can successfully connect and execute predefined query operations, such as querying contraindication information for specific drugs.
Submit a fictitious adverse reaction report containing sensitive information. Verify the anonymization plugin can correctly identify and process relevant fields, such as patient names and ID numbers.

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.