Data Characteristics for Batch Record Review
Batch record review data originates from pharmaceutical production lines. Sources include batch production records, batch inspection records, deviation investigation reports, and change control documents. These documents are typically scanned PDFs, Word documents, or structured spreadsheets. Data update frequency aligns with production batches; a new set of records is generated after each batch, with update cycles ranging from days to weeks. Document structures are complex, containing extensive specialized terminology, charts, signatures, and dates. Fields and units are highly specialized. For example, "process parameters" might include "stirring speed (rpm)" and "reaction temperature (℃)"; "material balance" involves "feed quantity (kg)" and "yield (%)". Subtle format differences can exist between batches.
Deployment and Upgrade Constraints from Data Characteristics
The highly specialized and complex nature of batch record data imposes specific requirements on FastGPT's model selection and knowledge base construction during deployment. First, the prevalence of scanned PDFs and complex tables necessitates robust OCR and document parsing capabilities for accurate information extraction. Second, the frequent appearance of specialized terminology and units requires the model to possess domain-specific knowledge in biomedicine. Without this, comprehension errors can lead to imprecise answers. Third, batch record update frequency, synchronized with production batches, demands an efficient incremental update mechanism for the knowledge base. This avoids rebuilding the entire knowledge base with each update. Finally, due to data sensitivity, local deployment is the primary choice. This places higher demands on server hardware configuration, Docker containerization stability, and resource allocation capabilities.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Batch record documents are large; large file uploads are required. |
maxContext | 3000 Tokens | Batch records are dense; a longer context window is needed for logical understanding. |
Chunk size | 800 characters | Ensures completeness of key information blocks in batch records, reducing semantic fragmentation. |
Recall count | Top 8 entries | Increases the probability of recalling relevant information fragments from complex batch records. |
Similarity threshold | 0.75 | Batch record details are precise; a higher threshold ensures recall result accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing and OCR processes are time-consuming, preventing timeouts. |
Common Pitfalls
- Model answers lack accuracy in specialized terminology, for example, confusing "batch number" with "product code." This occurs because model training data has insufficient coverage in the biomedical domain.
- After uploading large scanned PDF files, knowledge base construction fails or some content is missing. This happens due to file parsing timeouts or OCR recognition errors.
- When multiple users access concurrently, answer response speed significantly decreases or the service lags. This is due to insufficient server VRAM or CPU resources to support model inference demands.
Verification of Setup
- Upload typical batch record documents (including tables, charts, specialized terminology). Check if knowledge base segmentation is complete and if key information is accurately extracted.
- Ask questions about specific production steps, inspection results, and deviation handling within batch records. Verify the model's answer accuracy and use of specialized terminology.
- Simulate multi-user concurrent query scenarios. Observe if system response times are within an acceptable range, ensuring service stability under high load.
- Check system logs to confirm no
TimeoutorOCR Errormessages occurred during file parsing and knowledge base updates.
Note: The values provided are common starting points and should be measured 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.