Data Characteristics
Peptide drug R&D documents originate from lab notebooks, high-throughput screening reports, mass spectrometry analysis reports, NMR spectra, synthesis batch records, and preclinical study data. This data updates frequently, especially during early R&D, with new experimental results potentially appearing weekly or even daily. Document structures vary, including structured experimental data tables and extensive unstructured text descriptions. Common fields include peptide sequences (e.g., in ACGT format), molecular weight (unit Da), purity (unit %), retention time (unit min), and biological activity (unit nM or μM). Unit standardization is inconsistent; the same metric may use different units across labs or reports.
Constraints on Deployment and Upgrade
The unique nature of peptide sequences requires tokenizers to recognize and preserve complete peptide chain structures, preventing semantic loss from default tokenization strategies. High update frequency necessitates efficient incremental update mechanisms for the knowledge base and rapid processing of newly uploaded documents. Diverse document structures demand robust parsers capable of accurately extracting structured data and understanding key information in unstructured text. Inconsistent units require standardization during data preprocessing to ensure accurate retrieval and inference. These constraints collectively dictate the focus on model selection, data synchronization strategies, and parsing process configuration during deployment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates potentially large high-throughput screening or mass spectrometry data files. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances peptide sequence integrity with contextual relevance. |
maxContext | 32000 tokens | Covers the context of longer experimental records and analysis reports. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses the longer parsing times for complex structured documents. |
Recall count (Recall Count) | Top 8 entries (top 8) | Ensures sufficient initial recall of relevant experimental data and sequence information. |
Similarity threshold (Similarity Threshold) | Calibrate based on empirical testing | Adjust based on peptide sequence and experimental data retrieval effectiveness, e.g., 0.75. |
Three Common Pitfalls
- Some applications are inaccessible after deployment, with logs showing an
Invalid URLerror. This typically results from incorrect network configurations between services in thedocker-compose.ymlfile or an improperly setFASTGPT_URLenvironment variable. - After upgrading an application from a previous version, some fields, such as
tmbId, are missing in the database. This might occur if the database migration script is not fully compatible with the old data structure or if data initialization was not performed correctly during the upgrade. - After deploying a custom model, the model list in the chat interface is inconsistent with the workspace (simple application). The new model cannot be selected in the workspace. This is usually due to an unrefreshed model configuration cache or a failure to synchronize model registration information across all service modules.
Verification Steps
- Upload typical high-throughput screening reports and experimental records. Check if parsed text segments retain complete peptide sequence information and verify that key fields (e.g., molecular weight, purity) are extracted correctly.
- Use the knowledge base retrieval function to input specific peptide sequences or experimental conditions. Verify that the recalled results include relevant R&D document snippets and assess if the relevance of recalled items meets expectations.
- Simulate multi-user concurrent uploads and queries. Observe system response speed and resource utilization to confirm stable operation under high load without timeouts or service interruptions.
- Check the knowledge base incremental update function. Upload a document containing new experimental data and corrected information. Verify that the system identifies and updates corresponding knowledge snippets while preserving existing information.
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.