Model Integration and Configuration for Batch Record Review

Batch Record Data Characteristics

Batch record review data originates from paper or electronic batch production records in pharmaceutical manufacturing. Update frequency aligns with batch production cycles, typically daily or weekly. Document structure is complex, containing extensive unstructured text, semi-structured tabular data, and critical information like signatures and dates. Common fields include batch number, product name, production date, expiry date, operator, equipment ID, material batch number, process parameters (e.g., temperature, pressure, time), inspection results, deviation records, and change control. Units involve various measurement types, such as temperature (°C), pressure (kPa), time (hours/minutes), weight (kg/g), and volume (L/mL). Challenges include inconsistent record formats and difficulties in recognizing handwritten entries.

Constraints on Model Integration and Configuration

Batch record data complexity imposes specific requirements on model integration and configuration. The presence of unstructured text and semi-structured tables necessitates strong information extraction capabilities to accurately identify key fields from diverse batch record formats. Update frequency dictates data synchronization strategies, requiring support for regular bulk imports and incremental updates to ensure the model always processes the latest data. Document structure variability makes preprocessing critical, requiring resources for data cleaning, format standardization, and information structuring. Furthermore, the multiple measurement units and potential handwritten recognition challenges in batch records constrain tokenization strategies and entity recognition model selection, potentially requiring domain-specific dictionaries or OCR technology.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size`	`800–1200 characters`	Balances context completeness with model processing capacity, avoiding excessively long or short segments.
`Recall count`	`Top 5–8 entries`	Ensures retrieval of highly relevant segments while controlling model input token count.
`Similarity threshold`	`0.75–0.85`	Filters low-relevance segments, ensuring retrieval results highly match batch record review questions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides sufficient file parsing time when processing large or complex batch record files.
`maxContext`	`20000 Token`	Accommodates potentially lengthy descriptions and multi-information cross-validation needs in batch records.
`Model Temperature (temperature)`	`0.1–0.3`	Prioritizes accuracy and stability of answers, reducing model's creative freedom.

Common Pitfalls

Model calls return a 500 error code. This typically indicates that a locally deployed DeepSeek model service is not running correctly, or the API address or key configured in FastGPT is incorrect.
After parsing batch record files, some key fields (e.g., batch number, production date) are empty. This often occurs due to insufficient OCR recognition accuracy during file preprocessing or regular expression matching rules that do not cover all batch record formats.
The model generates hallucinated information, confusing irrelevant content with batch record review questions. This usually happens when Similarity threshold is set too low, leading to the retrieval of document segments with low relevance to the query.

Verification of Configuration

Upload typical batch record files. Check if key fields (e.g., batch number, production date, deviation records) are accurately extracted and displayed in the knowledge base. Verify extraction results against original documents.
Conduct model question-answering tests for common batch record questions (e.g., "Was there an out-of-scope operation for a certain batch?"). Evaluate the accuracy, completeness, and relevance of answers. Compare with manual review results to determine acceptable thresholds.
In model call logs, observe if PARSE_FILE_TIMEOUT_SECONDS causes file parsing timeouts. Adjust the parameter based on actual processing times to ensure all batch record files are processed effectively.

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.