Data Characteristics in This Category
Metabolism and endocrinology protocols and SOP documents typically originate from national health commissions, society guidelines, internal hospital regulations, and pharmaceutical clinical trial protocols. Data update frequency is relatively stable. National policies or guidelines usually update annually, while internal SOPs may undergo quarterly or semi-annual revisions based on clinical practice or new drug launches. Document structures are primarily PDF or Word formats, containing numerous tables, charts, and flowcharts. Text content is highly specialized, often involving medical terminology, drug names, dosage units, and diagnostic criteria. Common fields include disease diagnosis codes (e.g., ICD-10), drug dosages (e.g., mg/kg), treatment cycles (e.g., days, weeks), and test indicators (e.g., HbA1c %, blood glucose mmol/L).
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The update frequency of metabolism and endocrinology protocols dictates that the knowledge base refresh mechanism must support periodic automatic updates or convenient manual bulk imports. The complex document structures, particularly nested tables and flowcharts, demand high document parsing capabilities. The parser must accurately extract key information and preserve its semantic relationships. The abundance of specialized terminology and abbreviations requires domain-specific dictionaries for entity recognition and standardization during preprocessing. Furthermore, precise dosage and unit information require the workflow to identify and correctly handle numerical and unit matching during the Q&A process, preventing misjudgments due to unit confusion. The need to trace historical SOP versions also requires the knowledge base to have version management capabilities, ensuring the workflow can access protocol texts from specific points in time.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Balances contextual completeness with information density per segment, reducing the risk of semantic loss across segments. |
Recall count (Recall Count) | 8–12 items | Covers key information, reduces omissions, and avoids overwhelming the model with redundant information. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Ensures the precision of recalled content, filtering out irrelevant or weakly related protocol clauses. |
Rerank result count (Rerank Return Count) | 3–5 items | Focuses on the most relevant content, improving model processing efficiency and answer quality. |
PARSER_MODE | SEMANTIC_SPLIT | Suitable for mixed structured and unstructured documents, maintaining semantic integrity. |
FILE_PARSE_TIMEOUT_SECONDS | 600 seconds | Handles large PDF or Word files, preventing interruptions due to excessively long parsing times. |
Three Common Mistakes
- Workflow node connections are normal, but the process stalls and does not proceed to the next step. This often occurs when the output variable format of the previous node does not match the expected input format of the subsequent node. An example is a global variable string array not being assigned in the JSON format
["value1", "value2"]. - In AI chat responses, the value of variable B is superimposed with the result of variable A. This usually happens when two consecutive AI chat nodes in the workflow are configured with the same context variable name, leading to historical chat records being repeatedly referenced or overwritten.
- After document parsing, some table content is missing or misaligned. The parsing log shows
Table parsing error. This is often due to complex table structures in the original PDF or Word document, including merged cells or image-based tables, which exceed the default parser's capabilities.
How to Confirm Correct Configuration
- Conduct multi-round Q&A tests on core protocol documents. Verify the accuracy of recall and understanding for key terms, dosages, and unit information. Ensure answers meet expectations.
- Check workflow logs. Confirm the input and output variable formats for each node. Look for
type mismatchorvariable emptywarnings. - Import a batch of SOP documents with complex tables and flowcharts. Check if segments in the knowledge base are complete and if table content is correctly extracted and retrievable. Compare the consistency between original documents and knowledge base content.
- Simulate updates of protocol documents with different frequencies. Verify if the knowledge base refresh mechanism operates as planned. Check for differences in Q&A results between old and new versions of the protocols.
The values given are common starting points and should be measured against the reader's 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.