Deployment and Upgrades for CDMO Quality Documents

Data Characteristics for this Category

CDMO (Contract Development and Manufacturing Organization) quality documents originate from project development, clinical trials, and manufacturing. These include Batch Production Records (BPRs), Certificates of Analysis (COAs), stability study reports, deviation handling records, change control documents, supplier audit reports, and quality system documents (e.g., SOPs, quality manuals). Document update frequency is high, especially during development and production. Records are generated and archived in real time as experimental data, batch production progresses, and deviations are handled. Document structures are highly standardized, adhering to regulatory requirements like GMP (Good Manufacturing Practice). Fields are clear, and units are explicit (e.g., batch number, production date, expiry date, test item, specification, result, unit like mg/mL, ppm, %), ensuring traceability and compliance.

Constraints from these Characteristics on "Deployment and Upgrades"

The high update frequency and strict compliance requirements of CDMO quality documents impose specific constraints on FastGPT deployment and upgrades. Real-time document generation and rapid archiving demand that the vector database supports high-concurrency write and update operations. This avoids data lag affecting RAG recall accuracy. The standardized document structure and clear fields require precise document parsing strategies to ensure accurate extraction and indexing of key information. During deployment, consider scalable storage capacity to accommodate growing document data volumes. During upgrades, version compatibility and smooth data migration are crucial; any interruption can affect the normal operation of the quality management system. Compliance requirements also extend to data security and audit log configuration, ensuring all operations are traceable.

Configuration Strategy

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	CDMO documents often contain many images and charts, resulting in large file sizes. Reserve ample upload space.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF document parsing can be time-consuming. Prevent upload failures due to parsing timeouts.
`Chunk size`	`800–1200 characters`	Ensure complete information in a single segment. Avoid excessively long segments that reduce RAG recall precision, while maintaining semantic integrity.
`Recall count`	`Top 8 entries`	Increase recall scope to cover more potentially relevant information. Improve hit rate for complex queries.
`Similarity threshold`	`0.75`	Balance recall breadth and result precision. Avoid interference from irrelevant content.
`Rerank result count`	`Top 3 entries`	Select the most relevant few document snippets. Improve final answer quality and reduce LLM processing load.

Three Common Mistakes

Frontend page inaccessible: Often occurs when http://localhost:3000 is not correctly configured for HTTPS during deployment, leading to browser security policy blocking access.
Document upload or parsing failure: Often occurs when PARSE_FILE_TIMEOUT_SECONDS is set too low, preventing large or complex quality documents from being processed within the allotted time.
Insufficient query result relevance: Often occurs when Chunk size is improperly set, leading to key information being fragmented or context missing, which affects RAG recall effectiveness.

How to Confirm Proper Configuration

Upload a batch of typical CDMO quality documents (e.g., batch production records, inspection reports). Confirm all files are successfully parsed and indexed.
Perform queries including key fields like batch number, test item, and deviation type. Verify the relevance of returned results to expected document content and assess if the number of recalled items meets expectations.
Simulate high-concurrency document upload scenarios. Observe system resource utilization and processing latency. Ensure the deployment can handle actual business loads.
Check FastGPT's operational logs. Confirm no abnormal errors or warning messages appear, especially those related to file parsing and vector storage.

Note: The values provided are common starting points. Measure them 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.