Data Characteristics
Product usage smart customer service in the biomedical field primarily uses data from product manuals, operating instructions, FAQs, clinical trial report summaries, and internal technical support documents. This data exists in both structured (e.g., drug components, dosages in databases) and unstructured forms (e.g., detailed PDF manuals, Word document operating guides). Data updates are relatively stable, with major updates occurring when new products launch or existing products iterate. Routine updates involve supplementing FAQs or revising instructions. Documents are rigorously structured, containing many specialized terms, dosage units (e.g., mg/kg, IU), time units (e.g., hours, days), and specific operational step sequences. Data may also include non-textual information like charts and flowcharts, which require special handling.
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The rigor and specialized nature of product usage data require smart customer service to have high-precision information extraction and comprehension capabilities during deployment. The complex structure and specialized vocabulary in unstructured documents necessitate more detailed segmentation strategies and word embedding model optimization during knowledge base construction. This prevents the loss of critical information or semantic deviations. Although data update frequency is not high, each update often involves core product information. Therefore, the upgrade process must support incremental updates and version rollback to ensure a smooth transition between new and old knowledge. The precision of dosage units and operational steps dictates that the RAG (Retrieval-Augmented Generation) system must adhere strictly to the original text when recalling and generating answers, preventing "hallucinations" or inaccurate expressions. The ability to recognize charts and flowcharts also places higher demands on front-end display and back-end processing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 300–500 characters | Balances semantic integrity with recall efficiency, avoiding dilution of key information in long paragraphs. |
Recall count (Number of Retrieved Items) | Top 5 | Ensures coverage of relevant information while controlling context length and reducing model processing load. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Guarantees high relevance of retrieved results, reduces noise interference, and calibrated through actual measurements. |
maxContext | 8000 Tokens | Accommodates the specialized nature and detail of biomedical documents, providing sufficient context for complex Q&A. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing of large PDF manuals or multi-image documents, preventing processing failures due to timeouts. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Allows uploading detailed product manuals or clinical trial reports containing many charts. |
Common Mistakes
- After uploading knowledge base documents, some specialized terms or dosage units are not accurately recognized during retrieval, leading to incomplete or incorrect answers. This often results from inappropriate tokenization strategies or word embedding models not optimized for the biomedical domain.
- After a system upgrade, existing product usage questions receive many "hallucinated" answers or outdated information. This may occur if index rebuilding is incomplete during new and old knowledge base merging or version switching, leading to data conflicts or unrefreshed caches.
- In containerized deployment environments like Sealos Cloud, container startup fails or image pulling times out. This is often due to network environment restrictions, improper image source configuration, or insufficient container resource allocation (e.g., memory, CPU) to meet model loading requirements.
Verification Steps
- Select 10 product usage questions containing specialized terms, dosage units, or complex operational steps. Simulate user queries and check the accuracy, completeness, and professionalism of the smart customer service's answers. Verify consistency with original documents.
- Upload a new version of a product manual. Verify that the system successfully identifies and updates the knowledge base content. Then, ask questions related to the updated content to confirm that the latest information is returned.
- Monitor the success rate and processing time of knowledge base document parsing tasks via system logs or monitoring panels. Ensure that
PARSE_FILE_TIMEOUT_SECONDSand other configurations meet actual needs, with no significant timeout errors.
The values given 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.