Workflow Orchestration for Real-World Research Regulations

Data Characteristics

Real-World Research (RWR) regulatory data originates from guidelines and regulations issued by supervisory bodies, and from Standard Operating Procedures (SOPs) developed by healthcare institutions and research centers. These documents are typically in PDF or Word format, with varying degrees of structural complexity. Update frequency is low, usually yearly or every few years, coinciding with policy changes or industry consensus shifts. Documents contain extensive specialized terminology and acronyms, covering research design, data collection, ethical review, and statistical analysis. Fields and units may include text descriptions, dates, version numbers, and section numbers. Numerical data or standard units are rare. For example, an SOP might specify a "data de-identification process" without providing specific de-identification rates.

Constraints on Workflow Orchestration

The low update frequency and high specialization of RWR regulatory data mean that real-time retrieval is less critical. However, high accuracy and deep contextual understanding are essential. Document structures are complex, often including multi-level headings, figures, and appendices. This requires document parsers to accurately identify section boundaries and semantic blocks. The non-standardized nature of fields and units limits the effectiveness of strict field-matching retrieval. Semantic understanding and vector search are more critical. Infrequent data updates mean full index rebuilds are not frequently triggered; focus should be on incremental updates and version management. Error handling must distinguish between document absence due to parsing failure (e.g., PARSE_FILE_TIMEOUT_SECONDS) and low relevance results from inadequate retrieval strategies.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`800–1200 characters`	RWR regulatory documents have long logical paragraphs; this ensures contextual completeness.
`Chunk Overlap Length` (Chunk Overlap)	`100 characters`	Ensures semantic continuity between paragraphs, preventing critical information from being split.
`maxContext`	`4096`	Covers the typical context needs for regulatory Q&A, balancing inference costs.
`Recall count` (Retrieval Count)	`Top 5`	Regulatory Q&A demands high accuracy; the top results are most relevant.
`Similarity threshold` (Similarity Threshold)	`Calibrated by measurement`	Determined through testing with a small set of Q&A pairs to ensure high-precision retrieval.
`Rerank result count` (Reranked Return Count)	`3`	Further refines retrieval results, providing concise core answers.

Common Pitfalls

Irrelevant or outdated information appears in Q&A results. This occurs when document parsing fails to correctly identify version information, leading to the retrieval of old regulations.
Frequent timeouts or empty results during workflow execution. This happens when PARSE_FILE_TIMEOUT_SECONDS is set too low, preventing large PDF documents from being parsed in time.
The system cannot provide answers with specific operational steps after a user query. This is caused by excessively small document chunks, which split complete operational steps across different segments, breaking semantic coherence.

Validation

For core regulatory documents, verify that parsed chunks maintain the original document's logical structure and section integrity.
Randomly select 20 real-world RWR-related questions. Examine the distribution of the similarity metric for retrieval results and manually assess their relevance to the questions.
Simulate user queries. Check if workflow response times are within acceptable limits. Observe logs for Key is error or other anomalous information.
Compare new and old versions of regulatory documents. Verify that updated indexes correctly retrieve the latest content and exclude interference from old versions.

Note: The values provided are common starting points. They should be measured and adjusted 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.