Deployment and Upgrade for Peptide Drug Registration

Data Characteristics for This Category

Peptide drug registration documents include extensive structured and unstructured data. Data sources primarily cover pharmaceutical research (synthesis processes, quality standards, stability), pharmacological and toxicological research (in vitro/in vivo efficacy, toxicity reports), clinical research (clinical protocols, CRFs, statistical analysis reports), and manufacturing quality management files. Data update frequency is high during early-stage R&D, then stabilizes in clinical phases. However, regulatory and guideline updates may necessitate partial document adjustments. Documents are predominantly PDFs, Word files, and Excels, containing numerous chemical structures, spectra, biological activity data, and statistical charts. Field units vary, such as molar concentration nM, mass concentration mg/mL, dosage mg/kg, and biological activity units IU. These often include complex annotations and cross-references.

Constraints Imposed by These Characteristics on Deployment and Upgrade

The complexity and multimodal nature of peptide drug registration documents place specific demands on FastGPT's deployment and upgrade. First, the large volume of unstructured documents and frequent localized updates require high-concurrency processing capabilities and efficient incremental update strategies for file upload and parsing mechanisms. Second, the abundance of specialized terminology, chemical structures, and biological activity data dictates that embedding models must possess high-precision semantic understanding, especially for peptide sequence and pharmacological mechanism recognition. During deployment, allocate sufficient storage space and computing resources to handle data expansion and complex queries. During upgrades, model iteration may require re-indexing or fine-tuning the knowledge base. This ensures new models correctly process old data while supporting new data formats and parsing rules. Network bandwidth and storage I/O performance are critical for stable operation and rapid response.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1024 MB`	Individual reports (e.g., toxicology reports) in submission documents can be large.
`maxContext`	`4000 characters`	Ensures capture of complete context for peptide structures, experimental conditions, and results.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files and OCR recognition can be time-consuming.
`Chunk size`	`500–800 characters`	Balances semantic completeness and recall efficiency, adapting to peptide sequence and experimental data density.
`Recall count`	`Top 10 entries`	Increases relevant information coverage, addressing multi-dimensional queries unique to peptide drugs.
`Similarity threshold`	`0.78–0.85`	Balances accuracy and recall, avoiding over-generalization that leads to imprecise results.

Three Common Mistakes

Query results do not reflect the latest content after a knowledge base update. Old information is still recalled. This may be due to incomplete index rebuilding or unrefreshed cache.
File parsing fails or stalls when uploading large PDF reports, displaying 504 Gateway Timeout. This often occurs when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, causing the file to exceed the processing time limit.
System memory usage remains consistently high, especially during extensive document parsing or complex queries, leading to slow responses. This may be due to insufficient docker container resource limits or excessive memory consumption by loaded embedding models.

How to Verify Configuration

Upload a PDF file containing the latest batch of peptide quality standard updates. Query relevant fields to confirm the system accurately extracts and displays the updated data.
Select a Word document covering peptide synthesis process details and key quality attributes. Perform segmented queries. Check if segmentation maintains the integrity of critical information and if recall results include all relevant process parameters.
Check docker logs output via the FastGPT administration interface. Confirm no timeout or out-of-memory error logs occur during peak file upload and query operations.

The values given 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.