Data Characteristics
Peptide drug registration application data originates from regulatory guidelines, technical review specifications, public review reports of approved drugs, internal R&D and production records, clinical trial data, and relevant regulations. This information is updated infrequently, typically changing with policy adjustments or new drug review standards. Documents are primarily unstructured text, such as PDF guidelines, Word reports, and scanned certificates. They contain specialized terminology, chemical structure descriptions, dosage units (e.g., mg/kg, IU), purity indicators (%), batch information, and stability data. Data formats are diverse, including text, tables, and chromatograms.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The unstructured text nature of peptide drug data requires robust text parsing capabilities to accurately extract key information. Low update frequency means significant initial investment in data cleaning and annotation during knowledge base construction, with lower ongoing maintenance costs. Diverse specialized terminology, chemical structure descriptions, and measurement units demand advanced tokenizers and embedding models capable of effectively recognizing and understanding these domain-specific entities. Tables and chromatograms within documents are difficult to cover with text-only retrieval, necessitating multimodal information processing strategies. Furthermore, precise recall of batch and stability data requires knowledge chunking that balances contextual completeness with information density, preventing critical data from being fragmented.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Peptide drug documents often contain lengthy technical descriptions and experimental results. Longer segments help maintain contextual integrity and reduce information fragmentation. |
Chunk Overlap Rate | 100–200 characters | Appropriate overlap ensures critical information is not lost at segment boundaries, improving recall accuracy, especially for cross-paragraph specialized terminology. |
Recall count | 8–12 entries | Peptide drug application data is broad. Increasing the number of recalled items can cover more potentially relevant knowledge points, enhancing retrieval comprehensiveness. |
Similarity threshold | 0.75–0.85 | The domain is highly specialized. Setting a higher similarity threshold helps filter out irrelevant general information, focusing on highly relevant technical details. |
Rerank result count | Top 5 entries | After initial recall, re-ranking algorithms select the most relevant items, reducing user reading burden and improving information acquisition efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | For large PDF or Word documents, extending the parsing timeout ensures complex documents are fully processed, preventing parsing failures due to timeouts. |
Common Mistakes
- Knowledge base file details show "Invalid dataset file key": This typically results from knowledge base storage backend configuration issues or corrupted file metadata. Check storage paths and permission settings.
- Knowledge base search time significantly increases: Possible causes include insufficient computing resources for the embedding model, improper index rebuilding, or inadequate database query optimization. Investigate system resource utilization and index status.
- Retrieval results contain a large amount of irrelevant information: This might be due to an overly coarse segmentation strategy or a similarity threshold set too low, failing to effectively distinguish the specific professional context of peptide drugs.
Verification Steps
- Upload a typical application document containing peptide structures, dosage units, and batch information. Verify that the system correctly parses it and generates knowledge chunks.
- Ask key technical questions about peptide drugs, such as "What are the stability study indicators for a certain peptide?" Check if the recall results include relevant regulatory documents or experimental data.
- Simulate user questions about specific application stages, such as "What content should be included in a preclinical toxicology study report?" Use retrieval results to confirm if corresponding guidelines or case studies can be located.
- Monitor knowledge base retrieval time. Ensure it fluctuates within an acceptable range and compare it against baseline performance to validate optimization effectiveness.
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.