Deployment and Upgrade for Attenuated and Inactivated

Data Characteristics for This Category

Quality documentation for attenuated and inactivated vaccines primarily includes batch production records, inspection reports, stability study data, deviation and change records, validation documents, and supplier qualification files. These documents are mainly in PDF, Word, and Excel formats. Some batch records and inspection data may exist as scanned images. Data updates are frequent, especially after production batch releases and inspection results are issued, with relevant documents archived promptly. Document structures typically follow GMP guidelines, containing specific titles, sections, and tables, such as manufacturing process flows, critical control point parameters, inspection methods, and result judgment criteria. Common fields include batch number, production date, expiration date, inspection item, result, unit (e.g., IU/mL, TCID50/mL, pH value, OD value), equipment number, and operator signature.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The data characteristics of attenuated and inactivated vaccine quality documentation impose specific constraints on deployment and upgrade. The presence of numerous scanned images requires the system to have efficient OCR capabilities to ensure text content searchability. High-frequency data updates, particularly for batch-related documents, necessitate a deployment solution that supports incremental updates and version management to avoid duplicate ingestion and data redundancy. Document structures are standardized, but field types are diverse and include unique units. This requires accurate identification and parsing of this information during data ingestion, for example, treating batch numbers and inspection results as searchable metadata. Concurrently, the need for traceability of historical documents means that the upgrade process must ensure data migration completeness and consistency, preventing data loss or format incompatibility due due to version iterations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	A single batch record or inspection report may contain numerous charts and attachments, resulting in large file sizes.
`maxContext`	`800–1200 characters`	Quality document paragraphs are long; sufficient context is needed to understand specialized terminology and related information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	OCR processing of scanned images is time-consuming, requiring a longer parsing timeout.
`Chunk size`	`500 characters`	Ensures each segment contains a complete or relatively complete technical description or inspection result.
`Recall count`	`10 entries`	Increases the recall rate of relevant information, covering different batches or inspection items.
`Similarity threshold`	Calibrate based on actual measurements	Balances recall precision and noise based on actual query performance, ensuring relevant regulations or standards are recalled.

Common Pitfalls

The aiproxy_pg container fails to start in an offline environment. Symptoms include container logs showing database connection failures or insufficient permissions. This is typically due to offline environment network configuration or volume mounting issues, preventing aiproxy_pg from initializing correctly or connecting to data storage.
After a version upgrade, the number of global variables available through / input in prompts decreases. Symptoms include global variables that were available in the old version being absent from the new version interface. This may be because the definition or storage structure of global variables changed during version iteration, and data migration or compatibility handling was not performed correctly.
After document upload, some batch number or inspection result fields are empty. Symptoms include inability to retrieve specific batch data during queries. This is because of insufficient OCR recognition rates or inaccurate regular expression matching during the document parsing phase, failing to correctly extract fields in specific formats.

Verification Steps

Upload a batch of production records containing scanned images. Check if text content within the scanned images can be retrieved using keywords, and verify the accuracy of extracted metadata such as batch numbers and production dates.
Simulate an incremental update by uploading a revised inspection report for the same batch. Verify if the system correctly identifies it as an update and retains historical versions for traceability.
For critical control point parameters or inspection items, use query statements containing specialized terminology and units. Verify if the returned results include relevant document snippets and check the effect of the Similarity threshold.

The values provided are common starting points and should be measured against your 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.