Data Characteristics in this Category
Cardiovascular data primarily originates from clinical trial reports, drug inserts, medical guidelines, academic papers, and regulatory databases. This data updates frequently, especially with new drug approvals, clinical research advancements, and treatment protocol iterations. Major updates typically occur quarterly or semi-annually. Document structures are mostly unstructured text, such as PDF clinical reports and Word document guidelines. Some structured data also exists, like patient cohort information and biomarker data in Excel spreadsheets.
Core fields include drug name, indications, contraindications, dosage and administration, adverse reactions, pharmacological actions, clinical trial results (e.g., P value, RR, HR), biomarkers (e.g., LDL-C, blood pressure mmHg), and treatment regimens. Units strictly adhere to international standards, such as mg, ml, mmol/L, mmHg.
Constraints from "Deployment and Upgrade"
High update frequency of cardiovascular product data requires the deployment system to have efficient data ingestion and knowledge base update mechanisms. The high proportion of unstructured documents challenges text parsing and chunking strategies. This necessitates more refined preprocessing to ensure RAG recall quality. For example, tables and charts in clinical trial reports require special handling to extract key numerical values.
Diverse fields and strict unit specifications require the knowledge base to accurately identify and retain this information during data cleaning and vectorization. This prevents errors due to unit confusion. The highly sensitive nature of medical information means deployment must consider data isolation and access control, especially in multi-tenant or hybrid deployment scenarios. Additionally, large and complex datasets demand fast model inference and response times. This requires attention to computing resource allocation and model selection.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Medical literature and clinical reports in cardiology often contain numerous images and charts, resulting in large file sizes. |
maxContext | 3000 Tokens | Complex medical concepts and multi-step treatment protocols require a longer context window for understanding and reasoning. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large files and complex documents take longer to parse, requiring a longer timeout to prevent parsing failures. |
Chunk size (Chunk Length) | 800–1200 characters | Medical texts are highly specialized. Longer chunks better preserve the integrity of medical concepts. |
Recall count (Recall Count) | Top 10 | This ensures enough medical evidence and relevant guidelines are recalled to support accurate product inquiries. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Cardiovascular terminology is highly specific. Dynamic adjustment based on actual recall performance ensures accuracy. |
Common Pitfalls
- Observation: Model responses show confusion in drug dosage or biomarker units, such as mistaking
mgforg. Reason: The knowledge base failed to accurately distinguish or standardize units representing the same concept across different documents during text chunking or entity recognition. - Observation: When querying specific clinical trial results, the model fails to recall relevant data or returns incomplete trial data. Reason: Table data or chart information from original clinical trial reports was not effectively extracted and vectorized. This led to missing critical structured information in the knowledge base.
- Observation: After deployment, the model cannot correctly respond to newly published medical guidelines or drug inserts. Reason: The knowledge base update mechanism is not synchronized with the data source's update frequency. This causes the knowledge base content to lag behind the latest medical advancements.
Verification Steps
- Select a recently published drug insert or clinical guideline in cardiology. Upload it to the knowledge base. Ask FastGPT questions about indications, adverse reactions, or dosage and administration. Verify the accuracy and timeliness of the answers.
- Use queries containing specific biomarker units (e.g.,
mmol/L,mg/dL). Check if the model correctly understands and cites relevant values, and verify unit correctness. - For a clinical trial report containing complex tables or charts, ask for key numerical values from the report (e.g.,
P value < 0.05,HR = 0.75). Confirm the model can accurately extract and present these from unstructured data.
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.