Data Characteristics in This Category
Telemedicine R&D document data primarily originates from clinical trial reports, drug development logs, medical imaging analysis results, genomic sequencing data reports, and remote monitoring device records. These documents update frequently, especially during clinical trials, where data may update daily or weekly. Document structures vary, including structured tabular data (e.g., patient vital signs, medication records), semi-structured medical reports (e.g., physician diagnostic opinions, pathology analyses), and unstructured text (e.g., patient self-reports, R&D team discussion records). Common fields include patient_id, drug_code, trial_phase, and device_sn. Units involve physiological indicators like mg/dL, bpm, kPa, and biomolecular units like nm, kDa.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The high update frequency of telemedicine R&D documents requires the knowledge base to support efficient incremental indexing. This ensures the timeliness of retrieval results. Diverse document structures, particularly the presence of semi-structured and unstructured text, mean simple keyword matching is insufficient for precise recall. Advanced semantic understanding and entity recognition capabilities are necessary. The specialized nature of fields and units challenges text segmentation and vectorization processes. This requires avoiding incorrect segmentation of professional terms or numerical values with units, which could impact retrieval accuracy. Additionally, due to wide-ranging data sources, documents may have interdependencies (e.g., records for the same patient across different trial phases). The recall mechanism must identify and aggregate related information to prevent fragmentation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness and segment recall efficiency. Accommodates long sentences and multi-concept expressions in medical texts. |
Recall count (Number of Retrieved Items) | 8–12 entries (items) | Ensures coverage while avoiding excessive irrelevant information. Reduces the burden on subsequent reranking and generation models. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall precision and recall rate. Prevents overly broad or overly narrow document segment retrieval. |
Rerank result count (Number of Reranked Items) | 3–5 entries (items) | Filters the most relevant and information-dense segments for the generation model after optimization by the reranking model. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accommodates parsing time for large clinical trial reports or image analysis documents. Prevents parsing failures due to timeouts. |
Embedding Model | text-embedding-ada-002 or bge-large-zh | Selects general or domain-optimized models that perform well in the medical field. Enhances semantic understanding capabilities. |
Three Common Mistakes
- Knowledge base retrieval results show too few citations, such as "Knowledge Base Citation (1 item)". This may be due to overly large document segmentation granularity or overly conservative recall parameter settings. Relevant information might be scattered across different paragraphs but not effectively recalled.
- After importing a JSON-formatted knowledge base file, the interface shows an empty knowledge base. This may be because the JSON structure does not conform to platform specifications, or it lacks the necessary
contentfield. The parser cannot identify valid text. - After enabling the Rerank model, the "Result Reranking" status remains inactive or shows an error. This may be due to an abnormal connection to the Rerank model service or incorrect configuration of authentication information like
RERANK_API_KEY.
How to Confirm Correct Configuration
- For specific telemedicine R&D queries, such as side effects of a certain drug in a specific population, check if the recall results include multiple semantically highly relevant segments from different documents.
- Import a clinical trial report with complex tables and long texts. Check if document parsing correctly identifies and segments key information paragraphs, and if it avoids a large number of invalid or duplicate segments.
- After adjusting the
Similarity threshold(Similarity Threshold) parameter, observe changes in the recall rate and precision of retrieval results. Ensure sufficient relevant information is recalled across different query scenarios, and clearly irrelevant noise is filtered out. - Test with real business queries. Verify that the knowledge base entries cited in the final answer accurately support the answer content, and that the cited document sources and content are highly consistent with the query intent.
The values provided are common starting points. Measure them 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.