Data Characteristics
Cardiovascular data comes from diverse sources. These include clinical trial reports, drug inserts, medical guidelines, academic papers, patient education materials, and regulatory documents. This data updates frequently, especially with new drug approvals, clinical research advancements, and guideline revisions. Document structures typically contain precise technical terms, dosage instructions, indications, contraindications, adverse reactions, and pharmacological mechanisms. Fields and units are highly standardized. For example, blood pressure uses mmHg, heart rate uses bpm, drug dosage uses mg or IU, and lipid levels use mmol/L or mg/dL. Documents often include complex medical abbreviations and multi-level classification information.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The high update frequency of cardiovascular data requires the knowledge base to support efficient incremental updates and version management. This ensures timely and accurate retrieval results. Precise technical terms and abbreviations make exact matching and semantic understanding critical. This avoids incorrect recall due to lexical ambiguity. Complex document structures and multi-level information necessitate advanced segmentation strategies. These strategies ensure that each knowledge chunk contains a complete semantic unit without introducing excessive irrelevant information. Standardized fields and units require normalization during data preprocessing. Recall must support queries based on numerical ranges or unit conversions. Retrieving sensitive information, such as adverse reactions and contraindications, demands higher recall precision and ranking priority to prevent misleading information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters (characters) | Adapts to the semantic completeness of paragraphs in cardiovascular documents. Avoids fragmentation or redundancy from chunks that are too long or too short. |
Chunk Overlap Length (Chunk Overlap Length) | 50–100 characters (characters) | Ensures contextual continuity. Retains necessary connecting information at chunk boundaries to facilitate semantic understanding. |
Recall count (Number of Retrieved Chunks) | 8–12 entries (chunks) | Balances coverage with the processing efficiency of the backend LLM. Prevents retrieval of too much irrelevant content. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Cardiovascular terminology requires high precision. Adjust through testing to distinguish between highly similar but semantically different content. |
Rerank result count (Number of Reranked Chunks) | 3–5 entries (chunks) | Optimizes the final results presented to the user. Prioritizes the most relevant and high-quality knowledge chunks to improve accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses long parsing times for large clinical trial reports or medical guidelines. Prevents parsing timeouts. |
Common Pitfalls
- Observation: When users ask about specific drug dosages, retrieved knowledge chunks show inconsistent units or incorrect numerical values. Reason: Numerical fields and their units were not standardized and extracted during data preprocessing.
- Observation: After a knowledge base update, newly published guideline content is not retrievable, or search results still show outdated information. Reason: The knowledge base's incremental update mechanism was not configured correctly or not triggered, causing the index to not synchronize with the latest data in time.
- Observation: When querying contraindications for cardiovascular diseases, retrieval results contain many descriptions that are not contraindications, leading to inaccurate information. Reason: The chunking strategy failed to effectively isolate sensitive information, or the retrieval model's understanding of negative semantics was insufficient.
How to Confirm Correct Configuration
- Use a set of test questions, including new drug information and guideline revisions, to verify if the knowledge base retrieves the latest relevant knowledge chunks.
- For queries containing technical terms and abbreviations, check if retrieval results accurately match or semantically understand these terms.
- Use different query types (e.g., dosage, indications, adverse reactions) to evaluate the completeness and relevance of retrieved knowledge chunks.
- Monitor knowledge base log outputs to confirm that file parsing and index construction processes do not show
TimeoutorFailedstatus codes.
Note: 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.