Data Characteristics
Telemedicine product and reagent consultation scenarios involve diverse data sources. These include product manuals, clinical trial reports, regulatory approval documents, adverse drug reaction reports, user manuals, and frequently asked questions (FAQs). Data typically exists in document formats like PDF, DOCX, and XLSX. Some data may reside in internal databases. Data update frequency varies based on product lifecycles and regulatory requirements. New product launches, product iterations, or regulatory updates trigger data revisions. Document structures are complex, containing numerous specialized terms, dosage instructions, contraindications, storage conditions, and charts. Fields and units are highly specialized, such as dosage units (mg/kg, IU), concentration units (%, mol/L), time units (h, day), and various medical abbreviations.
Constraints on Knowledge Base Retrieval and Recall
The complexity of telemedicine product data poses multiple challenges for knowledge base retrieval and recall. Specialized terms and abbreviations require the model to possess a high degree of domain understanding. Otherwise, recall results may be inaccurate or omit critical information. Numerical information like dosages and concentrations requires precise matching and contextual understanding; simple keyword matching is insufficient. Complex document structures, including numerous tables and figures, demand robust file parsing capabilities. Traditional text chunking methods may not effectively extract structured data from tables. The uncertain data update frequency necessitates an efficient incremental update mechanism for the knowledge base to ensure information timeliness. User queries often involve comparing multiple products or reagents, requiring the knowledge base to perform cross-document associative retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Telemedicine documents often contain long paragraphs of instructions and specialized content, ensuring contextual completeness. |
Chunk Overlap Rate (Chunk Overlap Rate) | 0.1 | Reduces information redundancy while maintaining contextual connections between adjacent paragraphs. |
Recall count (Recall Count) | 8–12 | Increases the diversity of recall results, covering more comprehensive relevant information to address complex queries. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall precision and recall rate, reducing interference from irrelevant documents. |
Rerank result count (Rerank Return Count) | 5 | Further optimizes the relevance of recall results, prioritizing the most matching content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDFs or XLSX files containing complex tables, preventing parsing timeouts. |
Common Pitfalls
- The model's response indicates it cannot read XLSX file content. This typically occurs because the file parser fails to correctly identify and extract structured data within tables. The model only retrieves file metadata or partial text content.
- During a conversation, the model fails to reference a specified file from the knowledge base for its answer. This manifests as answers inconsistent with file information or overly general responses. This happens because the retrieval strategy fails to effectively associate user intent with specific documents, or the file's weight in the recall results is insufficient.
- After knowledge base upload, retrieval results contain a large amount of irrelevant or low-relevance content, overwhelming useful information. This is due to an improper chunking strategy or a similarity threshold set too low, introducing excessive noise.
Verification Steps
- Perform a series of complex queries containing specialized terms, dosage units, and product models. Check if the model accurately recalls document snippets containing this information.
- Upload an XLSX file with multi-page tabular data. Ask questions about specific cells or rows within the table. Verify if the model can correctly extract and answer.
- Simulate a product update scenario. After uploading a new version of a product manual, query relevant content to ensure the model prioritizes recalling the latest version of the information.
- Manually review the recalled document snippets. Check their relevance to the query and assess whether the recall results cover all key aspects of the query intent.
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.