Workflow Orchestration for Intent Recognition in Private

Data Characteristics for This Category

Intent recognition data for private domain consultation conversion in the biomedical field primarily originates from user conversation records, survey feedback, and behavioral tracking data within WeChat Work, WeChat Official Accounts, and CRM systems. This data updates frequently, typically in real-time or near real-time. Conversation records are mainly unstructured text, containing user questions, descriptions, emotional expressions, and focus points on specific products or diseases. Survey data is often structured or semi-structured, with fields such as medical_history, medication_status, product_of_interest, and consultation_purpose. Behavioral tracking data records user actions like browsing, clicking, and dwell time within private domain content, providing timestamps and event types. Collectively, this data outlines user intent. Field values commonly include medical terminology, drug names, or disease codes.

Constraints Imposed by These Characteristics on "Workflow Orchestration"

High-frequency real-time or near real-time data updates require workflows to respond quickly to avoid reduced timeliness in intent recognition. Unstructured conversational text demands robust natural language processing capabilities for semantic understanding and key information extraction. This dictates the selection of AI Chat nodes and the design focus of prompts within the workflow. The structured nature of survey and behavioral data allows workflows to perform efficient rule matching and intent classification through Conditional Judgment nodes. Specialized terminology and sensitive information in the biomedical field require models within the workflow to possess expert knowledge and undergo anonymization during data preprocessing to ensure compliance. The diversity of data sources means the workflow needs to integrate multiple data sources and perform effective data cleaning and format unification.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	800–1200 characters	Ensures the AI model fully understands the context of multi-turn user conversations, capturing intent details.
`temperature`	0.3–0.5	Reduces the randomness of model output, making intent recognition results more stable and focused.
`prompt`	Calibrated by actual measurement	Optimized for biomedical terminology, guiding the model to accurately identify product and disease intents.
`recall_top_k`	Top 5 items	Recalls enough relevant information from the knowledge base to assist the model in intent judgment.
`similarity_threshold`	0.75–0.85	Ensures recalled knowledge is highly relevant to user queries, avoiding the introduction of irrelevant information.
`streaming`	`true`	Optimizes user experience by allowing users to see intermediate results or preliminary intent judgments in real-time.

Three Common Mistakes

The AI Chat node in the workflow outputs too much content, leading to lengthy final responses that include internal judgment processes. This occurs because the AI Chat node displays all output by default, and the prompt does not explicitly limit the output format and content.
Intent recognition results frequently deviate, failing to accurately classify user queries. This happens when the prompt design is too generic and does not adequately incorporate specialized terminology and specific intent classification standards from the biomedical field.
Workflow execution is slow, leading to long user wait times, especially when processing large volumes of text conversations. This is due to the Model Calling node not enabling streaming or inefficient data preprocessing.

How to Confirm Proper Configuration

Select a batch of real user consultation dialogues with varying intent types and complexities. Manually label their intent categories. Run the workflow as a test set and verify if the recognition results match the manual labels.
Check workflow logs to confirm that the AI Chat node output meets expectations, without superfluous internal reasoning, displaying only the final intent judgment.
Simulate scenarios with different data volumes and update frequencies. Monitor workflow response times to ensure acceptable latency even under peak loads.
When the similarity_threshold is 0.75 or higher, review whether all recalled knowledge items are highly relevant to the user's intent, without off-topic redundant information.

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.