Workflow Orchestration for Medical Information (MI) Response

Data Characteristics for This Category

Medical Information (MI) response traceability data primarily originates from pharmaceutical companies' internal MI service platforms or CRM systems. This data records the detailed process of each medical query. This includes the query initiation time, inquirer's identity (e.g., doctor, pharmacist), query content (typically on drug indications, contraindications, dosage, adverse reactions), MI specialist's response, referenced medical literature or internal knowledge bases, response time, response channel (phone, email, online platform), and the final response outcome and satisfaction feedback. Data updates occur continuously, aligning with business activity, with new records generated daily. The document structure is semi-structured or structured, with rich fields such as query_id, inquirer_id, query_text, response_text, reference_docs, and response_timestamp. Some fields may contain long text descriptions.

Constraints Imposed by These Characteristics on "Workflow Orchestration"

The highly structured and time-series nature of response traceability data places specific demands on response workflow orchestration. First, the workflow must accurately capture and parse structured fields from various sources (e.g., CRM, internal knowledge bases), such as query_text and reference_docs. This requires flexible field mapping capabilities in data source connectors. Second, given the large volume and continuous updates of response traceability data, the vectorization and indexing modules in the workflow need to support incremental updates. This avoids resource consumption from full rebuilds. The long text nature of response content and references requires efficient text processing tools for segmentation, summarization, and keyword extraction. This improves recall efficiency and accuracy. Additionally, due to the strict requirements of MI responses, the workflow must trace specific reference_doc_id values when generating replies and explicitly state them in the response. This imposes traceability constraints on response generation and post-processing modules.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large medical literature PDF file uploads.
`maxContext`	`2000 characters`	Ensures context completeness for complex medical queries in MI responses.
`Chunk size` (Segment Length)	`500 characters`	Balances textual semantic integrity with vector retrieval efficiency.
`Recall count` (Recall Count)	`10 items`	Increases coverage of relevant medical information recall, reducing omissions.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures medical relevance and accuracy of recalled content.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the time required for parsing large medical documents, preventing timeouts.

Common Misconfigurations

When passing variable values via application links, attempting to directly modify workflow global variables through URL parameters. Workflow global variables typically require explicit input mapping, and URL parameters do not bind directly by default.
In workflows with multiple AI model calls, expecting each model to maintain chat history independently. However, improper session ID management can lead to chat history confusion or overwriting.
File upload errors after local deployment. This can result from incorrect file storage path configuration or the CUSTOM_READ_FILE_URL environment variable, leading to inaccessible or unparseable files.

Verification of Configuration

Use workflow debugging mode to observe the output of each node. Check if key fields like query_text and response_text are parsed and passed correctly.
Execute a simulated MI response query. Verify if the generated response accurately cites reference_doc_id and confirm the relevance of the cited literature to the query.
Upload medical literature of different sizes and formats. Check if the file parsing node successfully processes them and observe if the PARSE_FILE_TIMEOUT_SECONDS setting is appropriate.
Set specific query conditions in the workflow. Verify if the number of recalled reference_docs matches the expected Recall count (Recall Count) and check the quality of recall under the Similarity threshold (Similarity Threshold).

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.