Data Characteristics for This Category
In private domain consultation conversion within the biomedical sector, follow-up reminder data primarily originates from CRM systems, user behavior logs, and manual entries. This data updates frequently, typically incrementally on an hourly or daily basis. Document structures are predominantly structured data, supplemented by unstructured communication records. Structured data includes fields such as user ID, consulted product, consultation time, follow-up status, next follow-up time, and follow-up owner. Unstructured data comprises chat logs, transcribed phone recordings, and email content. Time-related fields are precise to the minute. Status fields use enumerated values, such as "to be contacted," "contacted," and "converted."
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
High-frequency data updates require the knowledge base to synchronize the latest information quickly. This ensures the accuracy of follow-up statuses and plans referenced in multi-turn conversations. The mixture of structured and unstructured data means retrieval must balance keyword matching and semantic understanding. Multi-turn conversations need to accurately identify user intent, such as querying a specific follow-up record, updating a follow-up status, or scheduling a new follow-up task. Prompt design must account for different response variations based on follow-up status and guide users to provide critical information to complete tasks. Dialogue output speed is crucial for user experience, especially in scenarios requiring immediate feedback on follow-up progress.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Balances multi-turn conversation coherence and model processing efficiency |
Recall Count | Top 5 | Balances recall breadth and relevance, reducing unnecessary information interference |
Similarity Threshold | Calibrated by actual measurement | Ensures high relevance of recall results to user queries, avoiding misleading information |
Rerank Return Count | Top 3 | Further optimizes the ranking of recall results, improving dialogue accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large files (e.g., 100,000-character documents or large Excel datasets) |
Segment Length | 500 characters | Ensures semantic completeness of knowledge segments for better model understanding |
Common Pitfalls
- Failure to correctly identify user intent in multi-turn conversations, leading to responses that do not match actual needs. This occurs when prompts provide insufficient guidance for intent recognition or the knowledge base does not cover relevant intents.
- Slow AI dialogue output speed, resulting in response delays when users query follow-up records. This happens due to decreased retrieval efficiency from large knowledge base data volumes or long model inference times.
- System errors or incorrect parsing of file content after uploading through the dialogue interface. This is caused by
UPLOAD_FILE_MAX_SIZEbeing set too low, or file encoding/format incompatibility.
Verification of Configuration
- Simulate user conversations in various follow-up scenarios. Observe the fluidity and accuracy of multi-turn dialogues, paying close attention to the recognition and citation of key information (e.g., user ID, product name).
- Upload follow-up record files of different sizes and formats (e.g., DOCX, XLSX). Check if the system successfully parses them and if they are retrievable in the knowledge base.
- Evaluate AI response speed by querying user cases with specific follow-up statuses. Ensure responses are provided within an acceptable timeframe.
- Verify that follow-up reminder-related document segments in the knowledge base are semantically complete, without misinterpretations.
Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.
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.