Data Characteristics for This Category
Peptide drug quality document data originates from R&D, production, and quality control. This includes batch production records, inspection reports, stability study data, raw and auxiliary material release records, and equipment calibration and maintenance records. These documents are typically stored in PDF, DOCX, and XLSX formats. Data updates are frequent, especially during R&D and clinical stages, with new experimental data and analysis reports generated weekly or even daily. After commercial production, updates follow batch production and periodic review cycles. Document structures are complex, often containing charts, chemical structures, and extensive specialized terminology. Fields and units are specific, such as peptide sequence, purity (%), molecular weight (Da), isoelectric point (pI), chromatographic retention time (min), and content (mg/mL). High precision and unit consistency are critical for these values.
Constraints from These Characteristics on "Deployment and Upgrade"
The complex data characteristics of peptide drug quality documents impose specific requirements on FastGPT's deployment and upgrade. High update frequency necessitates efficient incremental indexing and version management to ensure knowledge base timeliness and accuracy. Chemical structures and specialized terminology in documents require the model to have strong semantic understanding, potentially requiring model fine-tuning or optimized embedding strategies. Diverse file formats and complex internal structures challenge the robustness of document parsers, which must accurately extract key information from text, tables, and charts. The specificity of fields and units requires precise identification and processing of this specialized data during knowledge recall and answer generation, avoiding unit confusion or numerical misinterpretation. This directly impacts the configuration of the retrieval and generation stages in the RAG process.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large batch production records or comprehensive reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF and XLSX document parsing can be time-consuming |
Chunk size | 800–1200 characters | Balances peptide sequence and experimental data context, avoids information fragmentation |
Recall count | Top 8 entries | Increases coverage of peptide-related specialized knowledge, ensures critical information is not missed |
Similarity threshold | Calibrate by measurement, initial 0.75 | Balances recall precision and recall rate, sensitive to specialized terminology |
Rerank result count | Top 5 entries | Further refines document segments most relevant to peptide drug queries |
Common Pitfalls
- Empty or inaccurate query results after deployment often occur because the document parser fails to correctly identify chemical structure images embedded in PDFs, leading to loss of relevant contextual information.
- After a system upgrade, model answers may show deviations in specialized terminology or unit errors. This can happen if the new model version's embedding vectors for peptide-specific vocabulary differ from the old version, requiring re-training or adjustment of the embedding model.
- After a knowledge base update, query results may not include the latest batch of peptide inspection data. This is typically due to improper incremental indexing configuration, failing to timely capture and process newly added inspection report files.
How to Confirm Proper Configuration
- Upload and index a typical inspection report containing key information such as peptide sequence, purity percentage, and molecular weight. Query to verify precise extraction and answering of these numerical values.
- Simulate daily query scenarios for quality control personnel, such as "What is the purity of peptide batch XXX?". Check if the system accurately recalls the corresponding batch inspection report and provides the correct answer.
- Test with documents containing complex tables and charts. Confirm the system correctly parses and understands peptide content data in tables and chart trends. Verify its understanding through questioning.
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.