Workflow Orchestration for Rehabilitation Equipment

Data Characteristics

Rehabilitation equipment procedures and SOP documents typically originate from hospital management, equipment manufacturers' operation manuals, and national or local medical device regulations. These documents have a low update frequency, primarily changing with policy adjustments, equipment upgrades, or internal process optimizations. Document formats are mainly PDF, Word, or plain text. Content includes equipment models, functions, operating steps, maintenance, troubleshooting, safety regulations, and consumable lists. Fields include device serial number, calibration date, indications, contraindications, and operator qualifications. Units involve physical quantities like hours, times, millimeters, volts, and date formats like year/month/day.

Constraints Imposed by These Characteristics on Workflow Orchestration

The low update frequency of rehabilitation equipment procedure documents means a potentially large initial data ingestion for knowledge base construction, but subsequent incremental update pressure is minimal. The diverse document formats and complex structures require the workflow's data preprocessing to effectively parse PDF and Word documents and accurately extract key information from tables and image captions. Fields contain numerous professional terms and units of measurement, demanding the model in the workflow precisely understand context to avoid ambiguity leading to biased answers. For example, calibration date is time-sensitive information that needs to be identified in the workflow to determine the validity of a procedure. Additionally, different equipment models may have similar operating steps. Workflow orchestration needs to design logic to distinguish SOPs for specific equipment to prevent confusion.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`800 tokens`	Context length for a single rehabilitation equipment SOP query, improving answer accuracy.
`Recall count` (Recall Count)	`5 entries`	Ensures coverage of multiple procedural regulations while balancing response speed.
`Similarity threshold` (Similarity Threshold)	`0.78`	Filters out irrelevant content, improving retrieval precision.
`Chunk size` (Segment Length)	`400 characters`	Adapts to SOP document paragraph length, reducing information loss.
`Rerank result count` (Reranked Return Count)	`2 entries`	Selects the most relevant results, avoiding information overload.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Prevents parsing timeouts when processing large PDF or Word documents.

Common Pitfalls

During chat, the expected device operation procedure is not returned, and logs show a large amount of irrelevant content in the thought process. This often happens when the model, even with the thought output switch off, is still influenced by internal logic and fails to fully suppress redundant information from intermediate steps.
The workflow canvas experiences significant lag and delayed response when the number of nodes increases. This may be due to an older FastGPT version, for example, in 4.6.5, workflow rendering and event handling mechanisms were not fully optimized.
The workflow outputs correctly in the preview page but shows no response in the actual chat page. This could be due to incorrect trigger conditions or output node configuration for the workflow, preventing the chat entry point from correctly calling the workflow, or the workflow output not being bound to the chat output.

Validation Steps

Upload multiple operation manuals and maintenance procedures for a specific rehabilitation device. Simulate user queries to check if the answers accurately cite key information like device model and operating steps from the documents, and verify the cited page numbers or paragraphs.
On the workflow canvas, run each node step-by-step. Check if the parsing and extraction of fields during data flow meet expectations, especially for fields containing units and dates.
Use queries of varying complexity to test the system's performance when handling ambiguous words or professional terms. Evaluate if the relevance score of the answers remains stable within a reasonable range, and observe if the recall count matches the configuration.
After deployment, monitor workflow execution time via system logs to confirm that parameters like PARSE_FILE_TIMEOUT_SECONDS are sufficient to handle actual file processing loads.

Note: The values provided 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.