Deployment and Upgrades for CRO R&D Document Structural

Data Characteristics

Contract Research Organizations (CROs) generate extensive documentation during biomedical R&D. This includes clinical trial protocols, investigator brochures, informed consent forms, case report forms (CRFs), and study reports. These documents are often PDFs, Word files, or scanned images. They have complex structures and contain specialized terminology, medical abbreviations, charts, and data tables. Data sources are diverse, covering sponsors, research centers, and laboratories. Update frequency depends on project progress, with multiple revisions possible during trial design, execution, and reporting. Fields include subject information, dosage, adverse events, laboratory results, and statistical analysis data. Units strictly follow international standards, such as milligrams (mg), milliliters (mL), and mmol/L.

Constraints on Deployment and Upgrades

The complex structure and specialized nature of CRO R&D documents impose specific deployment requirements for structural analysis. For example, many scanned documents require high-quality OCR for accurate text extraction. Nested tables and chart data need specialized parsing strategies to prevent information loss or incorrect associations. Frequent document revisions necessitate version management and incremental update capabilities to avoid reprocessing unchanged content. Dense specialized terminology and medical abbreviations require integrating domain-specific dictionaries or pre-trained models to improve entity recognition and relationship extraction accuracy. Strict data compliance also demands attention to data isolation, access control, and audit logs during deployment to ensure the security of sensitive R&D data.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	CRO documents, especially reports with many images or scanned pages, often have large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	OCR and structural analysis of complex PDFs or scanned documents can be time-consuming, requiring longer processing times.
`Chunk size`	`800–1200 characters`	Preserves the integrity of biomedical context, preventing key information from being split.
`Recall count`	`Top 8 entries`	Ensures sufficient relevant paragraphs are covered in complex queries, improving recall rate.
`Similarity threshold`	Calibrate based on actual measurements	Requires adjustment based on specific document content and query needs to balance recall and precision.
`Rerank result count`	`Top 5 entries`	After reranking, the top few items typically provide the most relevant answers, reducing redundancy.

Common Pitfalls

After importing data into the knowledge base, training errors occur or some fields are empty. This happens when the CSV template format of the imported file does not match the current FastGPT version requirements, or when documents contain special characters that are not correctly encoded.
After deployment, the query result reference limit cannot be adjusted, leading to incomplete content. This occurs when maxContext or other related parameters are not correctly configured or activated in the deployment configuration file.
After a system upgrade, knowledge base data exported from an older version fails to train when imported into the new version. This happens because data structures or parsing logic might differ between versions, requiring data format adjustments according to the new version's specifications.

Verification Steps

Upload a CRO R&D report containing complex tables and multiple scanned pages. Verify successful parsing and extraction of key information.
Execute queries for specific medical terms or data points within the report. Confirm that the recall results include the expected relevant paragraphs.
Check system logs to ensure no timeouts or critical error messages occurred during file upload, parsing, and embedding.
Use API tests to verify that knowledge base query response times are within acceptable limits and that the returned reference content meets expectations.

Note: The values provided are common starting points. Measure them against your own samples for optimal performance.

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.