Data Characteristics
Data for batch record review systems originate from pharmaceutical quality management system documents. These include Standard Operating Procedures (SOPs), Batch Production Records (BPRs), Batch Packaging Records (BPLs), deviation records, change control records, and regulatory guidelines. Documents exist as PDFs, Word files, or scanned images, with varying degrees of structural organization. SOP documents typically have strict version control and low update frequency, with revisions every 1-3 years. Batch records are generated frequently, used once, and archived. Data fields include production process parameters, quality control indicators, operator signatures, and timestamps. Units include temperature (℃), pressure (kPa), time (min), and concentration (%), often with decimal precision, requiring high compliance.
Constraints on Citation and Traceability
Document characteristics of batch record review systems impose specific requirements on citation and traceability. The low update frequency and strict version control of SOP documents necessitate precise version management and citation support in the knowledge base. The high-frequency generation and archival nature of batch records demand rapid indexing of large volumes of historical records and unique batch file identification for each query. Production process parameters and quality control indicators within documents require accurate semantic understanding and unit recognition; incorrect units or numerical citations can lead to serious compliance issues. Furthermore, as some batch records may be scanned images, OCR accuracy directly impacts subsequent text analysis and citation. FastGPT must prioritize text extraction accuracy, fine-grained version control, and parsing capabilities for structured and semi-structured information when processing this data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Ensures sufficient context in each knowledge segment, prevents truncation of critical steps in batch records, and controls segment length for efficient recall. |
Recall count (Recall Count) | Top 8 entries (top 8) | Batch record review demands high information completeness. Increasing recall count covers more relevant clauses and batch information, reducing omissions. |
Similarity threshold (Similarity Threshold) | 0.75 | Guarantees matching between recall results and batch record review regulations. A lower threshold may introduce irrelevant content; a higher threshold may miss subtle regulatory differences. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | Selects the most relevant items through reranking, enhancing final answer precision while maintaining recall breadth. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Batch record files can be large, containing numerous charts and scanned images. Extending parsing time ensures complete file processing and prevents data loss due to timeouts. |
EMBEDDING_MODEL | text-embedding-ada-002 | Suitable for specialized terminology and long text semantic understanding in the biomedical field, improving embedding vector quality and thus similarity calculation accuracy. |
Common Pitfalls
- Failure to cite specific SOP version numbers or batch numbers in answers results in incomplete traceability information. This often occurs when the knowledge base fails to correctly identify and associate document version information or batch identifiers during chunking or metadata extraction.
- Inability to cite English documents from the knowledge base after a Chinese query, or vice versa. This indicates limitations in the model's multilingual processing or cross-language retrieval, potentially requiring embedding model adjustments or the introduction of multilingual processing modules.
- Incorrect citation or missing units for numerical fields (e.g., temperature, pressure) in batch records. This often stems from OCR errors or text parsers failing to correctly identify and extract numerical values with units, leading to data semantic loss.
Verification Steps
- Query with a batch record review document containing a specific batch number and SOP version number. Verify that the answer accurately cites the corresponding batch number and SOP version number.
- Query with a batch record or SOP document containing mixed Chinese and English content. Verify that the model accurately cites English content for Chinese queries and Chinese content for English queries.
- Randomly select multiple batch records and ask questions about specific production parameters (e.g., sterilization temperature, filling volume). Verify that the cited numerical values in the answer match the source document and that units are correct.
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.