Data Characteristics for This Category
Batch records document all operations, inspections, and deviation handling for each product batch in biopharmaceutical manufacturing, from raw materials to finished goods. Data sources typically include paper or electronic forms from production lines, LIMS system export files, and raw data reports from instruments. Update frequency aligns with batch production cycles, usually daily or per batch. Document structure is highly standardized, adhering to GMP (Good Manufacturing Practice) requirements. Batch records include fixed templates, section divisions, signatures, dates, material batch numbers, equipment IDs, process parameters (e.g., temperature, pressure, time), and inspection results (e.g., content, purity, pH). Field values are often numerical, enumerative, or datetime, frequently accompanied by specific units (e.g., mg/mL, ℃, psi, min) and tolerance ranges.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The high standardization and timeliness of batch record data impose specific requirements on deployment and upgrade solutions. First, a fixed document structure means the parsing model must accurately identify specific fields. Model training data should sufficiently cover batch record samples from different batches and product types. Second, diverse data sources (scanned paper, electronic tables, system exports) require the deployment solution to support preprocessing for various file formats, such as OCR and table parsing. The update frequency of batch records dictates real-time needs for data ingestion and indexing. System upgrades must ensure zero or minimal downtime to avoid disrupting the continuity of production data. Additionally, batch records contain extensive numerical data and units. Structured parsing must retain this information and support subsequent validation and comparison, requiring parsing results to accurately map to a predefined structured schema.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | Batch record files may contain many images or scanned documents; reserve sufficient size to prevent upload failures. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | OCR and complex table parsing can be time-consuming; prevent parsing interruptions due to timeouts. |
maxContext | 4000 characters | Individual batch record documents are long; a larger context window is needed to capture complete information. |
Chunk size (Segment Length) | 500–800 characters | Ensure each segment can contain one or more complete process steps or inspection items for better understanding. |
Recall count (Recall Count) | Top 8 entries (Top 8 items) | Batch record queries often involve comparing multiple related parameters; increasing the recall count improves relevance. |
Similarity threshold (Similarity Threshold) | 0.75 | Batch record field names may have subtle differences; slightly lowering the threshold can improve matching accuracy. |
Three Common Pitfalls
- The parsing model fails to accurately identify specific process parameters or inspection results in batch records. This manifests as critical fields in the structured output being empty or having incorrect values. This is typically due to insufficient coverage of various expression forms for specific fields in the model's training data or inadequate OCR accuracy.
- A locally deployed FastGPT cannot connect to a locally deployed DeepSeek model. Common symptoms include FastGPT reporting connection errors or model loading failures in the interface. This often stems from network configuration issues, such as a firewall blocking the FastGPT container from accessing the DeepSeek service port, or the DeepSeek service not being correctly bound to an externally accessible IP address.
- Uploading large batch record files results in slow system response or direct errors, with logs showing out-of-memory issues or file processing timeouts. This occurs because file preprocessing (e.g., OCR) consumes significant computing resources, and server resources are insufficient, or the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too short.
How to Verify Correct Configuration
- Upload and parse batch record documents from different batches and product types. Check if the values of critical fields (e.g., batch number, production date, process parameters, inspection results) in the structured output are complete and accurate. Compare them with the original documents to confirm if parsing accuracy meets expectations.
- Simulate high-concurrency upload and parsing operations. Observe if system resource usage (CPU, memory, disk I/O) remains within acceptable limits. Check the average response time for parsing tasks to ensure system stability meets production requirements.
- Call the parsing function via the API interface. Check if the returned HTTP status code is 200. Validate if the returned JSON structured data conforms to the predefined Schema definition to confirm correct interface functionality.
Note: The values provided are common starting points. Measure them against your own samples for optimal results.
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.