Data Characteristics
Peptide drug quality documentation includes production batch records, inspection reports, stability study data, raw and auxiliary material quality inspection reports, and validation reports. These documents typically exist as PDFs, Word files, or structured data tables (e.g., Excel or CSV). They contain extensive specialized terminology, chemical structures, chromatograms, and mass spectra. Data sources are diverse, involving R&D, manufacturing, quality control, and clinical departments. Update frequency varies by document type: batch records and inspection reports are generated in real-time with each production batch, stability data updates periodically, and validation reports update during specific changes or periodic evaluations. Document structures are rigorous, adhering to GMP/GLP guidelines. Field names like "Batch Number," "Production Date," "Test Item," "Analysis Method," "Detection Limit," "Content," "Purity," and "Impurities" are highly standardized. Units cover common mass units (mg, g), concentration units (μg/mL, mM), time units (h, d, month), and various instrumental analysis units (AU, ppm, %).
Constraints on Workflow Orchestration
The complexity of peptide drug quality documentation imposes specific requirements on workflow orchestration. First, the multimodal nature of documents (text, tables, images) demands heterogeneous data processing capabilities within the workflow to effectively parse and extract key information. For example, numerical values in chromatograms and mass spectra require specific image recognition or OCR modules. Second, strong inter-document relationships (batch records referencing raw material reports, inspection reports corresponding to stability data) necessitate multi-document correlation analysis. The workflow must build knowledge graphs or cross-reference chains to support rapid traceability during inspections. Third, frequent data updates (batch records, inspection reports) mean the workflow needs to support incremental updates and version management, ensuring the use of the latest controlled data. Additionally, strict compliance requirements mandate data integrity and traceability during data extraction, transformation, and loading (ETL) processes. All intermediate processing steps must be auditable, influencing the design of logging and error handling mechanisms.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6 | Ensures multi-turn conversations cover the context of peptide drug quality issues, such as the correlation between batch information and inspection results. |
Chunk size (Segment Length) | 800–1000 characters | Balances the integrity of long sentences and tabular information in peptide documents, preventing truncation of critical data. |
Recall count (Recall Count) | 8–10 items | Guarantees enough supporting segments are recalled from multiple relevant documents for complex queries. |
Similarity threshold (Similarity Threshold) | 0.75 | Increases matching precision for specialized terminology and standardized expressions, reducing irrelevant results. |
Rerank result count (Reranked Return Count) | 5 items | Further refines the most relevant segments from the initial recall, improving the quality of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time for parsing peptide documents (e.g., large batch record PDFs), which can be time-consuming. |
Common Pitfalls
- The workflow fails to conduct multi-turn conversations, with the model exhibiting "amnesia" in the second round of questioning. This usually occurs because the
maxContextparameter is set too low, preventing effective transmission of historical conversation records to the model. - After document parsing, critical inspection data (e.g., purity percentages) is not extracted correctly or appears as garbled text. This stems from tables and charts in PDF or image-based documents, where the OCR engine or data extraction rules are unsuitable for the complex layouts and specialized symbols unique to peptide drugs.
- When calling a plugin for text content extraction, the error log shows "
plugin_execution_failed" or returns an empty value. This typically happens because the plugin's internal regular expressions or extraction logic do not match the actual document structure. For example, it might not account for variations in peptide names or specific batch number naming conventions.
Verification Steps
- Upload a batch of typical peptide drug quality documents (e.g., batch records, inspection reports). Check if the parsed text content is complete and free of garbled characters, paying special attention to whether numerical values in tables and charts are correctly recognized and extracted.
- Design multi-turn conversation test cases. For instance, first inquire about the purity of a specific batch, then follow up with a question about the impurity content of that batch. Verify if the model can maintain contextual relevance across different turns and provide accurate answers.
- Build a test set with complex queries, such as "Query the production date, stability data, and relevant inspection report numbers for a specific batch of peptide drug." Check if the workflow can correctly link multiple documents and recall relevant information.
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.