Tool Calling and Plugins for Intent Recognition in Private

Data Characteristics for This Category

In biopharmaceutical private domain consultation conversion, intent recognition data primarily comes from user text conversations in private channels (e.g., WeChat Official Accounts, WeChat Work, in-app consultations), form submissions, and some structured questionnaire data. This data is typically unstructured text, supplemented by a few structured fields like disease type, medication history, and age range. The update frequency is high, synchronizing with real-time user consultations, exhibiting streaming data characteristics. Document structures are usually dialogue logs, including timestamps, user IDs, and message content. Fields are diverse and contain many domain-specific terms, such as drug names, disease diagnoses, treatment plans, and patient-described symptoms and needs. Units are often described in Chinese, lacking unified standardization.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The highly unstructured and domain-specific nature of intent recognition data places specific requirements on tool calling and plugin configuration. First, real-time and high-concurrency conversational data streams demand that tool calls have fast response and high throughput capabilities to avoid recognition delays that impact user experience. Second, dialogue content contains a large number of biopharmaceutical professional terms and colloquial patient expressions. Plugins need to handle complex natural language understanding, perform entity recognition, and extract relationships to accurately determine user intent. Additionally, due to dispersed and inconsistent data sources, tool calls require flexible data preprocessing capabilities to unify data from different sources before feeding it into the model. Finally, intent recognition results often need to be integrated with internal business systems (e.g., CRM, scheduling systems) for subsequent consultation allocation or information recording. Therefore, plugins need to support rich API integration capabilities.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for This Value
`max_tokens`	`200`	Intent recognition focuses on key user intent; longer text output may introduce noise.
`temperature`	`0.3`	Reduces model generation randomness, ensuring accuracy and stability of intent recognition results.
`top_p`	`0.7`	Limits the sampling range, reducing the appearance of irrelevant words and improving recognition accuracy.
`tool_call_timeout_seconds`	`60 seconds`	Accounts for external tool interface response times and data processing volume, allowing sufficient time.
`retrieval_limit`	`5`	Recalling too many similar intents can increase confusion; a small number of precise recalls is more effective.
`similarity_threshold`	`0.85`	Biopharmaceutical intent differentiation is high, requiring a high similarity to accurately match.

Three Common Mistakes

Tool calls return HTTP 500 errors or Connection refused. This happens when the external service address is misconfigured or the service is not running, preventing FastGPT from establishing a connection.
Intent recognition results are empty or incorrect. The agent cannot provide effective responses to user intent. This is usually due to insufficient intent training data or the model's misunderstanding of biopharmaceutical domain-specific terms.
After uploading a file, such as an xlsx file, AI conversation does not work. This occurs because the file type is not explicitly supported in the tool configuration or the corresponding file parsing plugin is missing, preventing the file content from being extracted and understood.

How to Verify Configuration

Test with typical user consultation texts. Verify if the agent accurately identifies intent and triggers the expected tool calls (e.g., drug inquiry, appointment scheduling).
Check tool call logs. Confirm that external API calls are successful and return data in the correct format, without connection timeouts or parsing failure error codes.
Use queries containing specific biopharmaceutical professional vocabulary. Test the robustness of intent recognition. Verify if recognition results are highly consistent with expected intent.
Upload intent description files in different formats (e.g., .txt, .pdf, .json). Ensure that file parsing plugins can correctly extract text content and use it for intent recognition.

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.