Cardiovascular Policy Tool Calling and Plugins

Data Characteristics in This Category

Cardiovascular disease policies and Standard Operating Procedure (SOP) documents typically originate from national health commissions, various medical institutions, and professional societies. These include guidelines, consensus statements, and internal regulations. Document updates are relatively infrequent; national guidelines might update every few years, while hospital SOPs may be revised annually. Documents are primarily in PDF, Word, or structured text formats. Content covers disease diagnostic criteria, treatment pathways, medication protocols, and surgical procedures. Fields often include medical terminology, drug names, dosage units (e.g., mg, ml/h), time units (e.g., min, h), and laboratory indicator ranges. The data is highly specialized, requiring precise understanding of terminology and contextual relationships.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The specialized and structured nature of cardiovascular policy documents presents specific requirements for tool calling and plugins. For example, extracting critical information like drug dosages and treatment times requires tools with high-precision entity recognition capabilities to differentiate units across various drugs and administration methods. The document update cycle dictates the knowledge base refresh frequency; plugins need to synchronize newly published cardiovascular guidelines periodically or on demand. Complex surgical SOPs often contain decision trees or branching logic, requiring tool calls to understand and execute multi-step, conditional processes, such as dynamically selecting the next action based on patient status. Furthermore, managing synonyms and abbreviations for medical terminology is crucial for successful tool calling, ensuring accurate matches even with non-standard input terms.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Paragraphs in cardiovascular policy documents typically contain coherent medical information. Overly short chunks risk losing context, while overly long ones introduce irrelevant information.
`Recall count` (Recall Count)	`8–12 entries`	Ensures coverage of multiple relevant policies or SOP clauses for complex questions, improving information comprehensiveness.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Balances recall precision and recall rate, avoiding interference from irrelevant medical terms while not missing key information.
`Rerank result count` (Reranked Return Count)	`3–5 entries`	After reranking, focuses on the most relevant core clauses, enhancing the accuracy of the final answer.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Provides sufficient parsing time when processing large PDF or Word format guideline documents, preventing timeouts.
`tool_code_timeout`	`60 seconds`	Most plugin executions are short. This provides some redundancy to handle external API response delays, preventing task interruption due to network fluctuations.

Three Common Pitfalls

After calling an external tool, the chat interface does not display the expected chart, and logs show Unsupported media type. This indicates a mismatch between the chart data format returned by the tool and the frontend rendering capabilities. Chart data needs conversion to a FastGPT-supported image URL or specific JSON format.
When the model attempts to call a plugin, logs show Your model may not support tool_call SyntaxError. This typically means the selected model version does not support Tool Calling, or the corresponding capability is not enabled in the model configuration.
When executing a multi-step tool flow, the task fails midway, and logs show API rate limit exceeded. This might be due to rate limits on external medical databases or services, without effective rate limiting or retry mechanisms implemented for plugin requests.

How to Confirm Correct Configuration

For complex queries regarding cardiovascular disease diagnostic criteria or treatment pathways, verify that tool calling accurately identifies and executes the correct plugin, returning timely and authoritative policy clauses.
Verify that the plugin correctly extracts and applies unit information when processing queries containing critical numerical values such as drug dosages and laboratory indicators, avoiding misinterpretation of values.
Simulate a policy update scenario to verify that the knowledge base's scheduled synchronization mechanism is effective and how the plugin handles differences between old and new policy versions.
Check tool calling logs to ensure all external API calls return a 200 OK status code, and the response data structure is as expected, without abnormal errors or timeouts.

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.