Data Characteristics for This Category
Follow-up reminder data primarily originates from user behavior records, consultation content, medical knowledge bases, and sales staff feedback within biopharmaceutical private domain consultation scenarios. This data exists as unstructured text (e.g., consultation dialogue logs, user questions), semi-structured data (e.g., user profile tags, product interests, consultation history), and structured data (e.g., drug names, disease diagnoses, follow-up plans). Data updates frequently, often in real-time or near real-time during active consultation periods. Document structure for consultation records is typically time-series dialogue, including user questions, AI or human responses, and user feedback. Fields may include userID, consultationID, timestamp, messageContent, productMentioned, and followUpStatus. Medical knowledge bases store information in standardized text format, usually medical guidelines, drug instructions, and clinical research reports.
Constraints Imposed by Data Characteristics on "Deployment and Upgrade"
Follow-up reminders require high data timeliness and accuracy. This necessitates configuring efficient data ingestion and processing pipelines during deployment. High-frequency data streams demand incremental update mechanisms for the knowledge base, avoiding resource waste and latency from full rebuilds. The prevalence of unstructured text makes text segmentation, embedding, and indexing strategies critical, requiring optimization for specialized biopharmaceutical terminology and long texts. Semi-structured data requires flexible metadata management capabilities to support precise retrieval based on tags and fields. Resource allocation during deployment must account for query response speed under high concurrency, especially to ensure timely reminders during peak hours. During upgrades, version compatibility and smooth data migration are key. Historical consultation records and knowledge bases must remain seamlessly accessible and usable after an upgrade, and the new version's understanding of specialized terminology must be validated.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large medical literature or batch files of historical consultation records. |
maxContext | 2000 characters | Ensures a single retrieval covers longer consultation contexts and medical explanations, aiding in understanding complex conditions. |
Chunk size (Segment Length) | 300–500 characters | Balances semantic completeness with retrieval granularity, preventing excessive fragmentation of medical concepts. |
Similarity threshold (Similarity Threshold) | 0.75 | Increases the relevance of retrieval results, reducing the recommendation of inaccurate medical information. |
Recall count (Number of Retrieved Items) | Top 8 | Provides sufficient candidate information for the model's comprehensive judgment while controlling inference costs. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient parsing time when processing large PDF medical reports or drug instructions. |
Common Pitfalls
- Follow-up reminders fail to trigger promptly or provide inaccurate content after deployment. This occurs due to data synchronization delays or incomplete knowledge base index updates, preventing the AI model from accessing the latest or most comprehensive consultation context.
- Keyword recall rates for some historical consultation records significantly decrease after a version upgrade. This happens if the new version's tokenizer or embedding model is incompatible with older data, requiring reprocessing or adaptation of historical data.
- When processing medical terms or drug names, the AI frequently responds with "cannot understand" or "missing information." This indicates a lack of definitions for corresponding professional vocabulary or relevant documents in the knowledge base, or insufficient training of the embedding model on specialized corpora.
Verification of Configuration
- Perform end-to-end tests by simulating user consultations. Observe if follow-up reminders trigger within the expected time according to the defined logic and check if the reminder content is highly relevant to the consultation topic.
- Randomly select historical consultation records and perform keyword recall tests via API. Compare the retrieved results with expected medical knowledge points or product information to ensure consistency. Verify that the similarity threshold and number of retrieved items are set appropriately.
- Upload documents containing complex medical terminology and long texts to the knowledge base. Observe the knowledge base processing progress and segmentation effects. Check if parameters like
PARSE_FILE_TIMEOUT_SECONDSare sufficient to handle typical files.
The values provided are common starting points. Measure performance against your own 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.