Data Characteristics for This Category
High-value consumable regulation documents originate primarily from internal medical institution management regulations and relevant policies issued by national and local health commissions. Data update frequency is relatively low, typically quarterly or annually, with potential ad-hoc updates for policy changes. Document structures are predominantly unstructured text, often including Word documents, PDF files, and a small number of Excel spreadsheets. Core fields include consumable code, item name, specifications and model, manufacturer, registration certificate number, procurement price, using department, approval process, and scrap standards. Units involve monetary amounts (yuan), quantities (pieces/sets), and time (days/months/years); some consumables may involve specialized units like milliliters or grams. Documents also contain extensive process descriptions, responsible parties, and exception handling details.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The low update frequency of high-value consumable regulation data means knowledge base re-indexing does not need to be overly frequent, which can reduce system resource consumption. The predominance of unstructured text in documents requires robust text parsing and segmentation strategies to ensure the integrity of regulatory clauses. Regulation documents contain numerous process descriptions and responsible parties, necessitating that the workflow accurately identifies and links these entities to provide precise process guidance in Q&A. The diversity of fields and units, especially sensitive information like procurement price and using department, requires the workflow to perform appropriate context understanding and information extraction during Q&A to avoid misinterpretations or omissions of critical data. Additionally, regulations often include exception descriptions, requiring the workflow to possess logical judgment capabilities to handle complex queries.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Ensures the integrity of regulatory clauses, preventing truncation of key information. |
Chunk Overlap Length (Segment Overlap Length) | 100 characters (characters) | Guarantees contextual continuity and improves recall quality, especially for process descriptions. |
Recall count (Recall Count) | Top 5–8 entries (top 5–8 items) | Considering the rigor of regulation documents, increasing recall count covers more relevant clauses. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves the relevance of recall results, filtering out irrelevant regulatory provisions. |
Rerank result count (Rerank Return Count) | 3 entries (3 items) | From a high recall volume, reranking selects the most core regulatory bases. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Regulation documents are often lengthy; extending the parsing timeout avoids parsing failures. |
Three Common Mistakes
- The AI response fails to accurately cite regulatory clauses, instead summarizing or generating content. This occurs due to improper segmentation strategies, leading to the original text being fragmented too much, making it difficult for the LLM to reconstruct the complete context.
- When users ask about the approval process for specific consumables, the AI cannot provide clear steps or responsible parties. This happens because the entity recognition and relationship extraction modules in the workflow are insufficiently configured, failing to effectively identify key nodes and participants in the process.
- Uploading lengthy regulation documents results in parsing failure or excessive time consumption. This is caused by
PARSE_FILE_TIMEOUT_SECONDSbeing set too low, or the file parsing service lacking sufficient concurrency to handle large files.
How to Confirm Proper Configuration
- Upload and parse 5 typical regulation documents. Check parsing logs for errors and randomly inspect 3-5 document segments to confirm they meet expectations.
- For core processes like procurement, usage, and scrapping of high-value consumables, ask over 10 questions covering different roles. Check if the AI response accurately cites the original regulations and provides correct process guidance.
- Simulate user queries containing specific consumable names and price information. Verify if the AI response can accurately extract and present these values, and confirm the units are correct.
Note: The values provided are common starting points. It is recommended to measure them against your own samples for optimal performance.
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.