Data Characteristics
Peptide drug registration documents typically include modules such as pharmaceutical research (e.g., synthesis process, quality standards, stability), pharmacology and toxicology research, and clinical research. Data sources are diverse, including laboratory records, analytical reports, clinical trial reports, and literature. These documents often exist in PDF, DOCX, and XLSX formats, with some data embedded as images or scanned copies. The update frequency is high, especially during research and development and clinical trial phases, as data is continuously generated and revised with experimental progress. Document structures are complex, often containing multi-level directories, cross-references, and numerous specialized terms, chemical structures, and charts. Fields involve amino acid sequences, molecular weight, purity, batch number, production process parameters, pharmacokinetic parameters, and adverse event reports. Units include milligrams, micrograms, moles, percentages, hours, and days, often accompanied by specific testing method standards.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The complex data characteristics of peptide drug registration documents impose specific requirements on tool calling and plugins. First, multi-format documents like PDFs and DOCXs, along with embedded charts and chemical structures, require robust document parsing capabilities. The tool must accurately extract text content and identify key data within charts. Parsing hundreds of pages of PDF files can lead to out-of-memory errors or parsing timeouts. This necessitates tools capable of handling large files, with mechanisms for resume-on-failure or chunked processing. Second, data is dispersed across different documents, with extensive specialized terminology and cross-references. Plugins need to efficiently extract and link information to ensure the completeness and logical consistency of the submission documents. An example is extracting purity data from pharmaceutical research reports and comparing it with limits in quality standards. Finally, the strictness required for registration documents demands accuracy and traceability from tool calling results. Any data deviation can impact the submission process. Therefore, detailed call logs and error handling mechanisms are essential for quickly identifying and correcting issues.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Longer parsing time is needed for hundreds of pages of peptide drug submission documents to avoid timeouts. |
Chunk size (Segment Length) | 800–1200 characters | Balances the integrity of peptide sequences, experimental data, and method descriptions, reducing semantic fragmentation. |
maxContext | 8192 | Ensures coverage of complex related information and context within submission documents. |
Similarity threshold (Similarity Threshold) | 0.85 | Improves the precision of recalling specialized peptide drug terminology and key data. |
Rerank result count (Rerank Return Count) | Top 5 | Selects the most relevant core information based on a high recall rate. |
Common Pitfalls
- When calling document parsing tools, large files (e.g., hundreds of pages of PDF) fail to process. Logs show insufficient memory or parsing timeouts because the default parsing timeout is too short or memory limits are too low.
- When extracting specific fields from submission documents, field values are empty or inaccurate. This may be because the document parsing tool failed to correctly identify peptide sequences, chemical structures, or tabular data.
- The tool call logs lack critical call records or error messages, making it impossible to trace problems. This may be due to improper log level settings or insufficient log storage space.
Validation Steps
- Upload a typical submission PDF file containing peptide sequences, experimental data, and charts. Check if the parsing results are complete and accurate, especially the extraction of chart and tabular data.
- Set a query condition, such as "purity standard for peptide X," and use tool calls to retrieve relevant data. Compare the retrieved data with the original document to verify data recall accuracy.
- Simulate large file parsing and complex information extraction scenarios. Check if the tool call logs fully record the calling process, parameters, and any potential errors or warning messages.
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.