Data Characteristics for This Category
WeChat Work group Q&A data in the biomedical field primarily comes from historical chat messages. This data typically includes user questions, customer service or expert answers, and group member discussions. The data structure is mostly unstructured text, ordered chronologically, and may contain non-text information like images and voice messages. The update frequency is high, almost real-time with message sending. Document structures are usually logically divided by group and time. Each message record contains fields such as sender ID, message content, and timestamp. Message content may involve drug indications, dosage, adverse reactions, disease knowledge, academic conference information, or internal notifications. It is dense with specialized terminology and often includes specific drug codes or disease classification codes.
Constraints Imposed by These Characteristics on "Forms and Interaction"
The real-time and unstructured nature of in-group Q&A data places specific demands on form and interaction design. Due to frequent data updates, the system needs to support near real-time data ingestion and indexing to ensure the timeliness of Q&A results. Unstructured text content means traditional structured query forms are not directly applicable. More flexible natural language input fields are required, combined with semantic understanding capabilities. The specialized nature of user questions requires forms to guide users in entering key information, such as drug names and symptom descriptions, to improve matching accuracy. The interactive characteristics of group chats dictate that output results must be concise and clear, avoiding lengthy replies, and may need to support multi-turn conversations to clarify questions or provide more detailed information. Recognizing specific drug codes or disease classification codes requires the form to provide corresponding prompts or auto-completion features during input.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Captures the full context of user questions while maintaining processing efficiency. |
Chunk size (Chunk Length) | 500 characters (characters) | Balances text granularity and semantic completeness for chat messages, reducing splitting loss. |
Recall count (Recall Count) | Top 10 entries (top 10) | Increases the coverage of relevant information retrieved from the knowledge base, improving answer accuracy. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances the relevance and generalization ability of recall results, preventing interference from irrelevant information. |
Rerank result count (Reranked Return Count) | Top 3 entries (top 3) | Selects the most relevant items from recall results for display, enhancing user experience. |
QUERY_TIMEOUT_SECONDS | 60 seconds (seconds) | Sets a reasonable query waiting time to prevent experience issues caused by prolonged unresponsiveness. |
Three Common Pitfalls
- Symptom: After a user submits a form, the system remains unresponsive for an extended period or returns empty results. Reason: The
QUERY_TIMEOUT_SECONDSparameter is set too low, not providing enough time for the system to process complex knowledge retrieval tasks. - Symptom: When a user enters specialized terms or drug names into the input field, the system cannot effectively recognize or match relevant knowledge. Reason: The knowledge base lacks indexing or synonym mapping for specific professional vocabulary, leading to insufficient semantic understanding.
- Symptom: The system's reply in the group chat is not entirely relevant to the key information in the user's question. Reason: The
Similarity threshold(Similarity Threshold) is set too high, making recall results overly strict and failing to cover all potentially relevant information.
How to Verify the Configuration
- Simulate typical user questions to verify if the system can return relevant and accurate answers within an acceptable timeframe.
- Randomly select a batch of historical group chat questions to test the system's understanding and information recall effectiveness for these questions.
- Check the recognition accuracy of key specialized terms and drug names to ensure they are processed correctly by the system.
- Observe the system's response speed and stability under concurrent requests to evaluate the reasonableness of parameters like
QUERY_TIMEOUT_SECONDS.
The values provided are common starting points and should be measured against your 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.