Deployment and Upgrade for Supplier Audit Quality Documents

Data Characteristics

Supplier audit quality documents in the biopharmaceutical sector include quality system files, production records, inspection reports, change records, and audit reports. These documents are typically in PDF, DOCX, or XLSX formats. Some are scanned images. Update frequency depends on supplier re-qualification cycles, product changes, regulatory updates, and ad-hoc audits. Documents are structurally complex. They contain specialized terminology, regulatory clauses, manufacturing process details, and experimental data. Key fields include supplier name, product batch number, production date, expiration date, inspection items, results, standard limits, deviation records, and Corrective and Preventive Actions (CAPA). Units include mass (mg, g, kg), volume (mL, L), concentration (%), time (hours, days), and temperature (°C). Specific measurement standards and symbols are common.

Constraints from Data Characteristics on Deployment and Upgrade

The complexity and diversity of supplier audit documents require enhanced file parsing capabilities during FastGPT deployment. The prevalence of scanned documents necessitates OCR integration or configuration for text extraction. Specialized terminology and regulatory clauses in documents challenge model comprehension and knowledge base construction. This requires more refined text segmentation strategies and embedding model selection. The uncertain update frequency makes incremental knowledge base updates critical to avoid frequent full rebuilds. The specificity of fields and units impacts information extraction accuracy, potentially requiring customized entity recognition rules. Compliance requirements for audit documents impose stricter constraints on data storage security, access control, and traceability. The deployment environment needs sufficient computing resources to support large-scale document processing and high-concurrency queries.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Audit reports and supporting documents can contain many images and charts, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large file parsing and OCR processes are time-consuming. This prevents parsing failures due to timeouts.
`embeddingModel`	`text-embedding-ada-002` or higher	Biopharmaceutical documents contain specialized vocabulary. A high-precision embedding model improves recall accuracy.
`Chunk size`	`800–1200 characters`	Ensures each segment contains enough contextual information for the model to understand regulations and process details.
`Recall count`	`Top 8 entries`	Complex queries may require more contextual information, increasing relevant information coverage.
`similarityThreshold`	`0.78`	Ensures semantic relevance of recalled content, avoiding interference from irrelevant information.

Common Pitfalls

No model display after deployment: This might be due to incorrect BASE_URL or API_KEY configuration in config.json or docker-compose.yml, preventing proper connection to the model service.
Parsing failure after document upload, with logs indicating OCR errors or unsupported file formats: This usually means the OCR service is not correctly configured or enabled, or an unsupported scanned document type was uploaded.
Poor relevance or "hallucinations" in Q&A results, where the model fails to correctly reference document content: This occurs when Chunk size is too short or the embeddingModel is improperly chosen, leading to overly fine-grained knowledge base segmentation or semantic comprehension deviations.

Verification Steps

Upload a typical PDF audit report (including scanned and text content). Confirm successful file parsing and visible text segmentation in the knowledge base.
Ask key questions about the report. Verify the model accurately references the original text and provides reasonable answers, especially for regulatory clauses and data details.
Simulate high-concurrency requests. Observe system response times and resource utilization. Ensure stable system performance under actual usage scenarios. Determine concurrent processing capabilities through stress testing.

Note: 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.