Data Characteristics for This Category
Cardiovascular product data originates from clinical trial reports, drug inserts, medical device registration certificates, academic papers, and patient education materials. Data update frequencies vary; new drug launches, device iterations, and clinical guideline revisions drive data updates. Document structures include standardized PDF formats for drug inserts and registration certificates, containing structured fields like indications, dosage, contraindications, and adverse reactions. Clinical trial reports may contain complex text, charts, and statistical data. Fields and units involve dosage (milligrams, milliliters), treatment cycles (days, weeks), and biomarker indicators (mmol/L, ng/mL). Unit accuracy is critical for product consultations.
Constraints Imposed by These Characteristics on "Tool Calling and Plugins"
The complexity of cardiovascular product data sources requires tool calling to handle various document formats, especially PDF parsing. Asynchronous data updates necessitate periodic or on-demand data synchronization and index rebuilding to ensure consultation content timeliness. Standardized insert and registration certificate structures facilitate specific information extraction; structured extraction tools can improve accuracy. However, the semi-structured or unstructured nature of clinical trial reports and academic papers demands higher natural language understanding and information extraction capabilities, requiring more complex text processing plugins. Accurate identification and conversion of numerical information like dosage and units are key to consultation quality, requiring specialized numerical processing and unit conversion tools.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
PDF_PARSER_MODE | Structured Text Extraction | Prioritize extracting tables and paragraphs from standardized PDFs to improve information extraction accuracy. |
MAX_CHUNK_SIZE | 500 characters | Accommodate concise descriptions in cardiovascular product inserts, preventing information truncation. |
EMBEDDING_MODEL | text-embedding-ada-002 | Balances accuracy and cost, suitable for the biomedical field with specialized terminology. |
RERANK_THRESHOLD | 0.75 | Ensures effective filtering of the most relevant results for cardiovascular product consultations after recalling many related documents. |
TOOL_TIMEOUT_SECONDS | 60 seconds | Most external API response times fall within this range, preventing consultation interruptions due to prolonged waiting. |
MAX_RETRIES | 3 times | Addresses occasional transient failures of external services, improving tool calling success rates. |
Common Pitfalls
- Tool call failures show
HTTP 401 Unauthorizedin logs. This indicates incorrect or expiredAPI_KEYorAUTH_TOKENauthentication credentials for the external API. - Consultation results contain dosage or unit errors, such as misidentifying "milligrams" as "micrograms." This occurs when the text parser fails to correctly identify and convert numerical values across different unit systems.
- Workflows do not execute as expected; specific information is not extracted or aggregated. This happens when the
Function Callingtool description is inaccurate, or the model misunderstands tool parameters.
Verification of Configuration
- Conduct multiple rounds of consultation on typical cardiovascular product inserts, checking the accuracy of key information returned (e.g., indications, dosage, adverse reactions).
- Simulate external API anomalies and observe if the retry mechanism for tool call failures executes according to the
MAX_RETRIESconfiguration, and check error logs. - Use queries containing specific units of measurement (e.g., milligrams, mmol/L) to verify the system's ability to correctly identify, process, and return values with the correct units.
- Add
printorlognodes to the workflow to output parameters before tool calls and results after calls, comparing them against expected behavior.
The values provided are common starting points and should be measured against specific 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.