Data Characteristics for This Category
Cardiovascular product and reagent data originate from various sources. These include clinical trial reports, drug inserts, medical device registration certificates, academic papers, and various guidelines and consensuses. Data update frequencies vary. Drug inserts and registration certificates have longer revision cycles, potentially updating quarterly or annually. Clinical trial data and academic papers may have new additions weekly or even daily. Document structures typically contain a large amount of structured data (e.g., ingredients, dosage, indications, adverse reactions, production batches, expiration dates, storage conditions) and unstructured text (e.g., mechanism of action descriptions, clinical study result analyses, precautions). Fields and units have specific characteristics. For example, dosage units involve milligrams (mg), micrograms (µg), international units (IU). Blood pressure measurement units are millimeters of mercury (mmHg), and heart rate units are beats per minute (bpm). These often come with specific reference ranges. Reagent data may include batch number, production date, expiration date, detection principle, detection range, sensitivity, and specificity.
Constraints from These Characteristics on Workflow Orchestration
The diverse sources and varying update frequencies of cardiovascular product data require workflows to flexibly adapt different crawling strategies during data ingestion. The mixed structured and unstructured document structure necessitates information extraction that combines regular expressions, Named Entity Recognition (NER), and semantic understanding models. For example, extracting specific indicator values and units from clinical trial reports, or identifying drug interactions from inserts. The unique fields and units in the cardiovascular domain place high demands on data parsing and validation modules within the workflow. This requires pre-configured rich unit conversion rules and medical terminology dictionaries to avoid information errors due to unit mismatches or misinterpretations of terminology. Furthermore, the timeliness of sensitive information like product batches and expiration dates requires the workflow to have mechanisms for regular data refreshing and handling expired information, ensuring the accuracy and safety of consultation results.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 2000 characters | Ensures coverage of key information when processing cardiovascular product inserts, while preventing overly long contexts from leading to model comprehension deviations or reduced inference efficiency. |
retrievalThreshold | 0.75 | Improves retrieval matching accuracy for specialized terminology and complex descriptions in the cardiovascular domain, reducing interference from irrelevant information. |
recallCount | 5 items | Limits the number of recalled items while ensuring information richness, reducing model processing load, and improving response speed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing needs of large clinical trial reports or PDF product inserts, providing ample parsing time. |
Chunk size | 800 characters | Balances the integrity of cardiovascular text paragraphs with model processing efficiency, preventing key information from being truncated. |
Rerank result count | 3 items | After initial retrieval, further filters out cardiovascular product information most relevant to the user query using a reranking model. |
Three Common Mistakes
- Symptom: Variables passed in the API are not effective in the workflow, causing the model to return generic content. Reason: The variable placeholder in the prompt does not match the parameter name passed in the API, or the variable is not correctly bound to the workflow's input node.
- Symptom: "Environment variable not defined" error during workflow debugging. Reason: Key environment variables like
OPENAI_BASE_URLor other dependent services are not correctly configured or loaded in the FastGPT deployment environment. - Symptom: After the text content extraction step, cardiovascular product names or dosage information cannot be extracted correctly, triggering a "specified reply." Reason: The preset text extraction rules (e.g., regular expressions or keyword lists) do not cover the diverse writing styles of cardiovascular product names or specific numerical unit combinations.
How to Confirm Correct Configuration
- Simulate user input to verify whether the workflow can accurately identify cardiovascular product names, dosages, indications, and other key information, and output relevant product consultation results.
- Check workflow logs to confirm that all stages, including data ingestion, text parsing, and vector retrieval, have no abnormal errors, and that key parameters (e.g.,
retrievalCount) meet expectations. - Use FastGPT's debugging tools to progressively trace the workflow execution path, ensuring correct data flow and variable binding at each node, especially for handling fields specific to the cardiovascular domain.
Note: 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.