Data Characteristics for this Category
siRNA nucleic acid drug regulatory documents primarily originate from guidelines and technical review requirements published by regulatory bodies (e.g., FDA, NMPA), as well as internal Standard Operating Procedures (SOPs) and Quality Management System documents. These documents have a low update frequency, typically revised quarterly or annually. Document structures are mainly PDF or Word formats, containing extensive unstructured text, tables, charts, and flowcharts. Fields and units may include biochemical units like micromolar (µM), nanomolar (nM), and percentage (%) for experimental parameters. For administrative approval processes, common fields like dates, numbers, and responsible parties are prevalent. Documents have complex citation relationships; for instance, one SOP might cite multiple guidelines, or multiple SOPs could collectively form a subset of a quality management system.
Constraints Imposed by These Characteristics on Workflow Orchestration
The low update frequency of siRNA nucleic acid drug regulatory documents means that knowledge bases do not require frequent full updates. Incremental update strategies are more suitable. The abundance of unstructured text and charts requires the workflow's document parsing stage to support various file formats and include Optical Character Recognition (OCR) capabilities. Inter-document citation relationships necessitate multi-document associative query capabilities in the workflow to overcome the limitations of single-document retrieval. The mixture of biochemical units and administrative fields demands high accuracy in entity recognition and information extraction, requiring optimization for specific terminology. Furthermore, multi-level user selection scenarios in approval processes, such as continuous questioning based on problem branches, require workflow orchestration to support complex conditional judgments and multi-turn dialogue management. This ensures users receive regulatory information tailored to their specific business scenarios.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | siRNA documents often have long paragraphs with complex descriptions. Longer segments retain more context. |
Recall count (Recall Count) | Top 5 entries (Top 5) | Regulatory Q&A requires high accuracy. Increasing the recall count moderately improves relevance coverage. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Regulatory text demands high semantic similarity. A higher threshold filters for more precise results. |
Rerank result count (Rerank Return Count) | 3 entries (3 items) | After reranking, returning a small number of high-quality results reduces the model's processing burden. |
Max Context Window | 4000 token | Ensures sufficient capacity for multiple recall results and multi-turn dialogue history, maintaining conversational coherence. |
Document Parsing Timeout | 600 seconds (600 seconds) | PDF documents with numerous charts and complex tables take longer to parse. This provides ample time. |
Three Common Pitfalls
- Symptom: The workflow abruptly terminates during a multi-turn conversation or provides answers inconsistent with previous dialogue. Reason: The
Max Context Windowsetting is too small, causing the model to lose early dialogue history and fail to maintain conversational coherence. - Symptom: User questions involve specific experimental parameters (e.g., concentration units), but the AI answer does not cite relevant data or cites irrelevant content. Reason: Incomplete extraction of tabular data containing biochemical units during the document parsing stage, or the entity recognition model was not trained for specific terminology.
- Symptom: After deploying the interface, external systems calling it find that some global variables are not passed as expected. Reason: The workflow's global variable configuration is not correctly mapped to the external interface's input parameters, or parameter names do not match during external calls.
How to Verify Correct Configuration
- Select an siRNA regulatory PDF document with complex tables and charts. Upload it and check the parsed text content to ensure text information from tables and charts is accurately extracted.
- Design a sequence of test questions with multi-level conditional judgments and branches. Simulate realistic user questioning paths to verify if the workflow correctly guides the conversation and provides appropriate responses.
- For queries involving specific biochemical units and administrative fields, check if the AI's answer accurately cites specific numerical values and field information from the document, and cross-reference with the original document.
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.