Data Characteristics
Vendor audit data for clinical trial pre-screening primarily originates from audit reports, qualification certificates, quality management system documents, past project collaboration evaluations, on-site inspection records, and various technical documents submitted by vendors. These documents come in diverse formats, including scanned PDF audit reports, Word or Excel quality system files, PNG or JPG certificate images, and structured data like exported vendor basic information databases. Data update frequencies vary; qualification certificates might update annually, audit reports generate after audit completion, and quality management system documents revise based on internal process changes or regulatory requirements. Document structures are complex; for example, audit reports typically include executive summaries, findings, recommendations, and responses, often containing tables and charts. Fields and units involve compliance levels, defect classifications, risk scores, certification validity periods, equipment calibration dates, and personnel training records, with both numerical and textual descriptions present.
Constraints Imposed by Data Characteristics on Workflow Orchestration
The diversity and complexity of vendor audit data impose specific requirements on workflow orchestration. PDF scans and images require OCR for text extraction, increasing the computational load and potential recognition errors in the preprocessing stage. Extracting structured information from Word and Excel documents demands more refined parsing strategies to differentiate between body text, tables, and lists. The uncertainty in data update frequency necessitates workflow support for incremental updates and version management, preventing redundant processing and data duplication. Complex document structures, especially in audit reports, dictate that knowledge base construction considers semantic chunking to ensure contextual completeness, such as associating audit findings with corresponding vendor responses. The mix of fields and units means that after information extraction, standardization is necessary. This includes unifying different date formats or mapping textual descriptions to predefined classification systems to support subsequent automated evaluation and decision-making.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
File Type Whitelist | pdf, docx, xlsx, png, jpg | Covers common audit document and qualification certificate formats, ensuring files can be uploaded and processed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the parsing requirements of large audit reports and complex structured documents, preventing parsing failures due to timeouts. |
Chunk size | 800–1200 characters | Balances contextual completeness and model input limitations, ensuring each segment contains sufficient semantic information. |
Recall count | Top 5-8 entries | In audit scenarios, cross-validation from multiple dimensions is often required; increasing recall count aids comprehensiveness. |
Similarity threshold | Calibrate by measurement | Based on the sensitivity of specific audit questions, determine a threshold through testing that recalls relevant information without introducing excessive noise. |
Rerank result count | Top 3 entries | Further optimizes results based on initial recall, focusing on the most relevant and critical information. |
Common Pitfalls
- Knowledge base query results are empty or incomplete. The model cannot answer questions strongly related to uploaded files. This occurs because the document parsing node fails to correctly identify and extract all critical information, especially text within tables or images.
- Workflow execution times out or errors occur. Errors appear when using variables with database connection tools. This happens because variable types or formats do not match database expectations, or SQL query statements have injection risks, leading to execution failure.
- The model cannot dynamically switch knowledge bases during dialogue based on user selection. The chatbot cannot accurately respond to questions for specific audit stages. This is due to the workflow orchestration lacking dynamic knowledge base selection logic or unclear knowledge base routing rules.
Verification of Configuration
- Upload representative vendor audit reports and qualification certificates. Verify that the document parsing node can completely and accurately extract all text content, especially information within tables and scanned documents.
- Design database query scenarios that include variables. Execute the workflow. Confirm that the database connection tool correctly handles variables and returns expected results.
- Conduct simulated audit inquiries in the dialogue interface. Attempt to guide the conversation to different vendors or audit topics. Verify that the workflow dynamically selects and queries the appropriate knowledge base based on the dialogue context.
The values provided are common starting points and should be measured against specific 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.