Data Characteristics for This Category
Vendor audit data for clinical trial pre-screening comes from diverse sources. These include vendor qualification documents, quality management system documents, historical audit reports, Corrective and Preventive Action (CAPA) records, and compliance certifications. Most of these documents are unstructured data, such as PDF certificates, Word audit reports, and scanned Excel production records. Update frequency varies: qualification documents typically update annually or biennially, audit reports generate according to audit cycles, and CAPA records update dynamically. Audit reports usually have a fixed structure, including executive summaries, audit scopes, findings, recommendations, and conclusions. Common fields and units include vendor name, registration number, production license number, quality system certification standards (e.g., ISO 13485), defect severity, and completion date. Naming conventions and formats for these fields can differ across vendors or regulatory systems. Some fields may contain free-text descriptions.
Constraints on Deployment and Upgrade Due to These Characteristics
The unstructured nature of vendor audit data requires FastGPT to have robust document parsing capabilities configured during deployment to accurately extract key information. Diverse document formats increase pre-processing complexity, necessitating support for multiple file type parsers. The relatively low data update frequency means the knowledge base does not require high-frequency incremental indexing during daily operation. However, annual or biennial large-scale batch updates need stable data import mechanisms. The fixed chapter structure of audit reports provides structural clues for information extraction, improving RAG recall precision. Inconsistent fields and units, where the same concept uses different terminology or units across documents, challenges semantic understanding and entity recognition. This requires the model to have generalization and alias recognition capabilities. Furthermore, historical audit reports may contain sensitive information, so the deployment environment must meet data security and compliance requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Audit reports and qualification documents may contain many scanned images or pictures, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing complex PDFs and scanned documents takes a long time. This prevents parsing failures due to timeouts. |
Chunk size | 800–1200 characters | Ensures each segment contains sufficient contextual information to understand audit findings or regulatory terms, while avoiding information overload from excessive length. |
Recall count | 10 entries | Audit queries often require cross-referencing multi-dimensional information. Increasing recall count improves information coverage. |
Similarity threshold | 0.75 | Vendor audit documents use specialized and rigorous terminology. A higher threshold helps precisely match relevant regulations or historical defect records. |
Rerank result count | 5 entries | Filters out the most relevant core information for the query, reducing the burden of manual screening for engineers and improving pre-screening efficiency. |
Three Common Mistakes
- Deleting a knowledge base folder with many documents results in a
timeout of 60000ms exceedederror. This occurs because deletion involves synchronous cleanup of the database and file system. The default timeout is insufficient when the number of documents is too large. - The public cloud version experiences
502 Bad Gatewayerrors during peak hours. This often happens when backend service resources are insufficient to handle sudden high-concurrency requests, leading to service crashes or slow responses. - When initially setting up a local development deployment, some key fields are empty after document upload and parsing. This often indicates that the file pre-processing component or OCR service is not correctly configured to accurately identify and extract text information from scanned documents.
How to Verify Correct Configuration
- Upload typical vendor qualification documents in different formats (e.g., scanned PDFs, Word audit reports). Check if the parsed knowledge base segments are complete and free of garbled characters.
- Query specific regulatory clauses or historical defect descriptions. Verify that RAG recall results accurately include relevant document snippets and compare them with expected outcomes.
- Simulate high-concurrency query scenarios. Observe system response times and resource utilization to ensure service stability under expected load.
Note: The values provided are common starting points. Measure them against your own samples to determine optimal settings.
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.