Deploying and Upgrading for Stability Studies

Data Characteristics

Stability study data comes from long-term sample observation, accelerated tests, and intermediate test reports during drug research and development. Data updates are infrequent, typically quarterly or annually. However, batch updates can lead to periods of intensive data updates. Documents are primarily PDF reports, containing many tables, graphs, and text descriptions. Key fields include batch number, production date, expiration date, storage conditions, test items, test results (e.g., content, dissolution, impurities), test methods, and judgment criteria. Units include concentration (mg/mL), percentage (%), and time (months, years). Numerical precision is critical, often accompanied by upper and lower limit descriptions.

Constraints on Deployment and Upgrade

Stability study data updates are infrequent, but a single update can involve large data volumes. Documents are often unstructured PDF reports. This requires robust data import during initial deployment and an effective incremental update strategy. The presence of numerous tables and graphs necessitates strong file parsing capabilities to accurately extract critical data. Fields like batch number and expiration date have strict relationships and time sensitivity. The knowledge base must identify and maintain these relationships to avoid confusion in responses. High-precision numerical values and range identification challenge the model's understanding and answer generation accuracy, requiring more refined text segmentation and vectorization strategies.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Stability study reports often contain multi-page charts, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF reports is time-consuming; this prevents parsing interruptions.
`Chunk size`	`800–1200 characters`	Ensures complete tables or paragraphs in stability reports are not excessively split.
`Similarity threshold`	`0.8`	Stability data requires high precision; increasing the threshold reduces irrelevant recalls.
`Recall count`	`Top 10 entries`	Ensures coverage of relevant information across different batches or test items.
`maxContext`	`4000`	Stability reports have strong contextual relevance, requiring a longer context window.

Common Pitfalls

File upload fails after deployment with a 413 Request Entity Too Large error. This occurs when nginx or the FastGPT container's UPLOAD_FILE_MAX_SIZE environment variable is not configured correctly, causing the uploaded file size to exceed the limit.
When querying stability data for a specific batch, the answer mixes information from multiple batches. This happens when the document processing stage lacks an effective segmentation strategy, leading to data from different batches being vectorized into the same segment.
The model provides inaccurate or missing answers when asked about the specific numerical range of a test item. This results from incomplete table data extraction during PDF parsing or the vectorization model failing to effectively capture the relationship between values, units, and upper/lower limits.

Verification Steps

Upload a stability study report PDF containing multi-page tables and graphs. Verify that the file parses successfully and its complete text content is visible in the knowledge base.
Ask about the content change trend for a specific batch of a particular drug under accelerated testing. Check if the answer accurately cites the batch number, test item, and numerical values from the report.
Inquire about the acceptance criteria or judgment basis for a specific test item. Confirm that the model extracts the corresponding text description from the document and validates its accuracy against thresholds.

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.