Deployment and Upgrades for CRO Quality Documents

Data Characteristics

Contract Research Organizations (CROs) generate extensive quality documentation during biomedical research and development. Examples include clinical trial protocols, investigator brochures, informed consent forms, ethics committee approvals, Standard Operating Procedures (SOPs), audit reports, and deviation reports. These documents are typically in PDF, Word, or Excel formats and are frequently updated, especially during different phases of clinical trials or when regulatory requirements change. Document structures are often rigorous and standardized, adhering to GxP regulations (e.g., GCP, GLP, GMP). Fields and units are highly specialized, such as dosage units (mg/kg), time points (T+X hours), statistical indicators (P-value, confidence interval), and critical information like drug names, batch numbers, and subject IDs. Some documents may include scanned images or handwritten annotations, increasing parsing complexity.

Deployment and Upgrade Constraints

The specialized and standardized nature of CRO quality documents requires specific attention during deployment to the model's ability to understand professional terminology and acronyms, and its accuracy in parsing complex tables, charts, and scanned images. High document update frequency means the system must support efficient incremental updates and version management, avoiding redundant parsing and data duplication. Strict compliance requirements mandate that the system meets audit needs for data storage, access control, and logging, ensuring data security and traceability. Additionally, potential sensitive information, such as subject data, demands robust data anonymization and permission management. These characteristics dictate that system upgrades must prioritize testing the model's stability and accuracy when processing new document formats and content, and verifying the continued effectiveness of permission configurations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	CRO documents, especially clinical trial reports, can contain many images and extensive data, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDFs, OCR processing, or large Word documents require significant parsing time. This prevents parsing interruptions.
`Chunk Length`	`800–1200 characters`	This length retains sufficient context to understand professional terminology and logical relationships, preventing critical information from being truncated.
`Recall Count`	`Top 10`	This ensures enough relevant, high-quality document segments are retrieved, improving answer accuracy.
`Similarity Threshold`	`0.75–0.85`	This balances recall and precision, avoiding the retrieval of irrelevant content while not missing critical information.
`Rerank Return Count`	`Top 5`	After reranking, the most relevant document segments are prioritized for the model, enhancing the quality of generated answers.

Common Pitfalls

Symptom: During bulk document uploads, some documents fail to parse, showing "parsing failed" or remaining unresponsive for an extended period. Reason: The PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, causing complex or large documents to exceed the preset time limit and be terminated by the system.
Symptom: During model testing, models other than the language model (e.g., Embedding model) report errors such as Message field is required or 400 Bad Request. Reason: The API Key is configured incorrectly or not loaded properly, preventing the model service from authenticating requests or recognizing request parameters.
Symptom: The model fails to answer professional questions based on parsed document content, providing generic or incorrect responses. Reason: The Chunk Length is too short or the Recall Count is too low, resulting in insufficient contextual information for the model to understand the specialized details and complex logic within CRO documents.

Verification Steps

Upload various types (PDF, Word, Excel) and sizes of CRO quality documents. Observe if all parsing statuses are successful and verify that the extracted text content is complete and accurate, especially for tables and figure captions.
Ask FastGPT professional questions within the CRO domain. Verify if the model accurately cites professional terminology, data, and conclusions from uploaded documents, and if the accuracy and relevance of the answers meet expected thresholds.
Check system logs or monitoring interfaces to confirm a significant reduction in PARSE_FILE_TIMEOUT_SECONDS-related timeout errors. Verify that all model API request response status codes are 200.
Randomly select key paragraphs from parsed documents. Use FastGPT's "Q&A Test" feature to observe if the recalled document segments include these key paragraphs and if the recall order is logical.

The values provided are common starting points and should be measured against the reader's 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.