Data Characteristics of This Category
Supplier audit quality documents include audit reports, Corrective and Preventive Action (CAPA) records, supplier qualification certificates, production process control documents, and quality agreements. Data sources typically include supplier submissions, on-site audit collection, laboratory test reports, and internal enterprise quality management systems. Update frequency depends on the audit cycle and CAPA closure status. Updates are usually quarterly or annually, but CAPA-related documents may undergo multiple revisions within weeks. Document structure is complex, containing extensive unstructured text, tabular data, images, and attachments. Beyond general document properties, fields include supplier code, audit date, finding ID, defect description, risk level, rectification plan, and completion date. Units may include dates, percentages, quantities, and batch numbers. Industry-specific abbreviations are common.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The complexity and unstructured nature of supplier audit documents impose specific requirements on workflow orchestration. First, diverse document sources and varying update frequencies demand flexible workflow triggers. For example, workflows should activate based on file upload events or scheduled scans of specific directories. Second, documents contain numerous critical fields and tabular data. Traditional text segmentation methods can lead to semantic loss. Workflows require integrated table parsing and entity extraction capabilities during preprocessing to ensure effective identification and indexing of key information. Third, audit finding risk levels and rectification plans are crucial inputs for downstream AI conversations or decisions. Workflows must accurately extract this information and pass it as context variables. Finally, documents often contain industry-specific terminology and abbreviations. Workflows need language models or knowledge bases capable of effectively processing these to avoid inaccuracies caused by misunderstandings.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_TRIGGER_PATH | /audits/suppliers/ | Audit documents are typically archived by supplier and date. Specify a particular directory to monitor new file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Audit reports can contain extensive content and complex tables, requiring longer parsing times. |
CHUNK_SIZE | 800–1200 characters | Balances contextual completeness and retrieval efficiency, accommodating documents with mixed long text and tables. |
OVERLAP_SIZE | 100 characters | Ensures sufficient overlap between segments, improving semantic coherence across paragraphs and preventing information loss. |
EXTRACT_ENTITY_TYPES | Supplier Code, Audit Date, Risk Level | Core information for supplier audits, facilitating subsequent AI Q&A and data analysis. |
MAX_RETRIES | 3 times | Increases workflow robustness against network fluctuations or transient API failures by adding a retry mechanism. |
Three Common Pitfalls
- Workflow execution halts midway, with logs showing "API call failed, error code: 500." This typically occurs when calling a third-party API within the workflow, and the data format does not match API expectations or authentication information has expired.
- AI conversation results lack specific details about audit findings, even when the document contains them. This can happen if the document parsing stage fails to correctly identify and extract critical fields from tables, preventing this information from being effectively indexed or passed as context.
- Global variables set at the beginning of the process are null in subsequent steps. This can occur when attempting to retrieve variables via an external request before the process starts, but the request fails or the returned data structure does not match expectations, leading to incorrect assignment.
How to Verify Correct Configuration
- Upload a typical supplier audit report. Check the file parsing logs to confirm
PARSE_FILE_STATUSshowsSUCCESSand parsing time is withinPARSE_FILE_TIMEOUT_SECONDS. - Use the knowledge base retrieval function. Input a key finding description or supplier name from the audit report. Verify that relevant document snippets are recalled and that the recalled snippets contain field values defined in
EXTRACT_ENTITY_TYPES. - Run a workflow that includes an AI conversation. Simulate asking, "What high-risk findings are in the latest audit report for a specific supplier?" Observe whether the AI's response accurately references the audit date, risk level, and specific rectification measures. This assesses the effectiveness of entity extraction and context passing.
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.