Data Characteristics
Peptide drug regulations and SOP documents are typically in PDF, Word, or scanned image formats. They cover the entire lifecycle, from R&D, clinical trials, and production to quality control and post-market surveillance. These documents have a relatively low update frequency, usually revised annually or when major regulations or process changes occur. Structurally, they often feature strict hierarchical divisions, such as "General Provisions—Specific Provisions—Appendices," and contain numerous tables, diagrams, and cross-referenced clauses. Common fields include batch number, serial number, molecular weight, purity, activity unit, dosage, administration route, and storage conditions. Units involve Daltons, milligrams, micrograms, Celsius, and pH values, often accompanied by specific testing methods and standards.
Constraints Imposed by These Characteristics on Workflow Orchestration
The low update frequency of peptide drug regulation documents means that index rebuilding or knowledge base updates in workflow orchestration do not need to be frequent. The focus is on incremental updates and version management. The strict hierarchy and cross-references in the documents require that text segmentation in the workflow maintains logical integrity, avoiding truncation of critical information. It also needs to effectively parse reference relationships to ensure question-answering accuracy. The presence of many specific fields and units means that the AI Q&A component in the workflow needs strong entity recognition capabilities and the ability to understand the contextual meaning of these specialized terms. Additionally, since documents may include scanned images, the accuracy of image OCR directly impacts subsequent text processing and Q&A effectiveness.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500-800 characters | Balances document logical integrity with recall efficiency, preventing long paragraphs from diluting key information. |
Recall count | 8-12 entries | Covers related information that might be scattered across different clauses in peptide drug regulations. |
Similarity threshold | Calibrate by measurement | Balances recall rate and accuracy based on specific datasets and model performance. |
Rerank result count | 3-5 entries | Focuses on the most relevant regulatory clauses, reducing the model's burden of processing irrelevant information. |
maxContext | 3000-4000 token | Ensures sufficient peptide drug regulation details can be included within a limited context window. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses potentially long parsing times for large PDF or Word documents. |
Three Common Pitfalls
- Workflow execution returns answers containing extensive citation information, preventing direct presentation of the final conclusion. This happens when the AI Q&A component's output configuration is not set to return only the final answer, or when citation markers are not removed in post-processing.
- Code component print logs are not viewable during workflow execution, making debugging difficult. This occurs when the code component's runtime environment log output is not integrated with the workflow's overall logging system, or when log levels are not configured to display detailed information.
- Specific peptide drug names or batch numbers are incorrectly identified or understood in Q&A. This happens when the knowledge base lacks entity recognition or synonym mapping rules for these specialized terms, or when relevant cases are insufficient in the model's training data.
How to Verify Correct Configuration
- Ask questions about specific SOP clauses for peptide drugs. Check if the answer accurately cites the original text and provides a correct explanation. Verify that the cited clause numbers match the original document.
- Simulate questions about key parameters in peptide drug production processes (e.g., temperature, pH value, purity requirements). Check if the answer extracts and presents the correct values and units, and identifies their source.
- Upload regulation documents containing scanned images and ask questions about information within tables or diagrams. Verify the accuracy of OCR recognition and subsequent Q&A.
- During workflow testing, observe the input and output of each component to confirm that data flow meets expectations, especially text segmentation and entity recognition results.
Note: 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.