Data Characteristics for This Category
Supplier audit R&D documents primarily include audit reports, non-conformance lists, Corrective and Preventive Action (CAPA) records, change control documents, supplier qualification certificates, quality agreements, and production process flow documents. These documents originate from supplier submissions, on-site audit records, and internal quality management systems. Update frequency is relatively fixed, typically following an annual audit cycle or triggered by specific events (e.g., major changes, non-conformance closure). Document structure is semi-structured, containing extensive free-text descriptions, tabular data, and embedded images. Common fields include audit date, auditor, supplier name, non-conformance ID, risk level, corrective action plan, and completion date. Units primarily involve time units, quantity units, and risk ratings.
Constraints Imposed by These Characteristics on "Reference and Traceability"
The diversity of supplier audit document sources and their semi-structured nature demand high accuracy in parsing references. Key conclusions in audit reports may span multiple paragraphs or even different documents. This requires the system to precisely locate the original information point and provide context when citing. A relatively fixed update frequency means the knowledge base needs regular incremental updates or full re-indexing to ensure timely references. The mix of tabular data and free text in documents means traditional text chunking strategies may not effectively capture critical information, impacting traceability granularity. Furthermore, semantic understanding of specific fields (e.g., riskLevel, correctionPlan) directly affects the relevance and accuracy of cited content, requiring the system to deeply understand business logic.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Balances the integrity of long paragraphs in audit reports with the semantic independence of short sentences. |
Recall count | Top 8 entries | Covers multiple information points, avoiding omission of key audit findings and corrective actions. |
Similarity threshold | 0.75 | Ensures recalled content is highly relevant to the audit query intent, filtering low-quality references. |
Rerank result count | Top 5 entries | Improves the quality and relevance of the final returned references, focusing on core information. |
maxContext | 3000 Tokens | Accommodates the complex contextual needs in audit documents, providing more comprehensive background. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles the time required to parse large audit reports or documents with complex tables. |
Three Common Mistakes
- Phenomenon: The model's output for audit conclusions references irrelevant supplier qualification documents. Reason: The knowledge base chunking strategy failed to effectively isolate information from different suppliers or audit periods, leading to confusion during similarity calculation.
- Phenomenon: When calling the conversation interface, the
detailfield in the response lacks knowledge baseidor fileidinformation. Reason: The system was not correctly configured for streaming output or asynchronous processing, causing reference metadata to be lost or not included during transmission. - Phenomenon: The model cannot provide traceability for a corrective action plan related to a non-conformance, or the traced original text does not contain the specific plan. Reason: During document parsing, the system failed to identify and extract the specific field or table row containing the corrective action plan, resulting in a lack of this information in the knowledge base.
How to Confirm Correct Configuration
- For typical audit scenarios, simulate questions and check if the model's returned references accurately point to specific paragraphs in original audit reports, CAPA records, or quality agreements.
- Check the
detailfield or log output to confirm that the knowledge baseidand fileidare returned for each conversation reference, and that theseids can locate the corresponding original document. - Randomly select quoted snippets from the model's output and manually cross-reference them with the original document to ensure consistency in content and absence of semantic deviation.
- Test audit files containing complex tables or multi-level headings to verify the system's ability to correctly parse table content and associate it with the appropriate reference source.
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.