Data Characteristics
Batch record review data primarily originates from pharmaceutical manufacturing batch production records, batch inspection records, deviation investigation reports, change control documents, and related Standard Operating Procedures (SOPs) and quality standards. These documents are typically in PDF format. Some highly digitized enterprises may use structured XML or JSON data. Document update frequency is influenced by production batches, process changes, and regulatory revisions, usually involving incremental updates weekly or even daily. Document structure is rigorous, including fixed fields such as product name, batch number, production date, expiration date, operators, equipment numbers, critical process parameters, and inspection results. Units involve mass (kg, mg), volume (L, mL), time (min, h), temperature (℃), and pressure (Pa), with strict precision requirements.
Deployment and Upgrade Constraints from Data Characteristics
The highly structured nature and strict update frequency of batch record data require deployment solutions to support efficient document parsing and rapid incremental indexing. Tables and images embedded in PDF documents challenge text extraction accuracy, affecting vectorization quality. Diverse units and precision requirements necessitate that vector databases can recognize and match different expressions during retrieval. Production environments demand high system stability; upgrades must ensure business continuity and provide rollback mechanisms. Data sensitivity also dictates that the deployment environment meets strict data security and access control regulations, such as keeping data within the internal network. Documents contain numerous technical terms and abbreviations, requiring pre-trained models with specialized knowledge in biomedicine.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Ensures large batch record documents, potentially containing numerous charts and attachments, can be uploaded without issues. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents file processing failures due to parsing timeouts for complex PDF documents that require more time. |
Chunk size | 800–1200 characters | Balances contextual completeness and retrieval efficiency, accommodating long descriptions and table content in batch records. |
Similarity threshold | 0.8–0.85 | Batch record review demands high accuracy; a higher threshold reduces irrelevant results and improves recall precision. |
Rerank result count | Top 5 entries | Focuses on the most relevant information, reducing the review burden on engineers and improving audit efficiency. |
Model Version | 4.8.20 | Uses the latest stable version, which includes recent model optimizations and bug fixes, enhancing the ability to process complex batch records. |
Common Pitfalls
- After deployment, some batch record documents are not parsed correctly or have missing content. This often occurs when PDF documents contain scanned images or non-standard fonts, causing text extraction tools to fail.
- After a system upgrade, the accuracy or consistency of previous question-answering results decreases. This may stem from differences in how new and old model versions interpret specific terms or units in batch records, or incomplete vector database index rebuilding.
- In the test environment, using the
deepseekmodel results in channel test errors. This typically indicates insufficient GPU memory in the locally deployed OLLAMA environment, or incomplete model file downloads or incorrect configuration paths.
Validation Steps
- Upload typical batch record documents (including tables, images, multilingual content) and verify complete parsing and correct segmentation.
- Formulate questions based on critical information in batch records (e.g., batch number, production date, key parameter values). Test the system's responses against the original text for consistency and check the accuracy of cited passages.
- Compare question-answering results for the same batch records before and after an upgrade. Ensure no significant decline in accuracy or recall for core information queries. Conduct small-scale user tests to assess user satisfaction.
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.