Data Characteristics for This Category
Batch record review data originates primarily from Manufacturing Execution Systems (MES) and Quality Management Systems (QMS). This data exists as structured and semi-structured documents, such as batch production records, batch inspection records, deviation investigation reports, and change control documents. Documents are typically in PDF or Word format. They contain extensive tabular data, text descriptions, and signature information. Update frequency aligns with batch production cycles, typically generated and archived after batch production concludes. Fields include batch number, product code, production date, expiration date, critical process parameters, inspection results, and operator signatures. Units strictly adhere to pharmacopoeia and internal standards, such as milligrams, liters, Celsius, and hours.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The diverse sources of batch record review data require the HTTP interface to support multi-source data integration. It must acquire data from various systems like MES and QMS. The semi-structured nature of documents means that data extraction requires sophisticated processing for tables and nested structures, in addition to regular text parsing. This increases data preprocessing complexity. Strict data timeliness (generated immediately after batch production) demands real-time interface capabilities to prevent review delays caused by data lag. Standardized fields and unified units require the interface to strictly validate data formats and units during data transmission and parsing. This ensures data consistency and prevents review judgment errors due to data inaccuracies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 32000 token | Batch record documents are complex; a longer context is needed for analysis. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF documents takes time; this prevents timeout. |
Chunk size | 800–1200 characters | Balances semantic completeness and model processing capability, suitable for mixed tables and text. |
Recall count | Top 15 entries | Batch record review requires comprehensive consideration of multi-dimensional information; this increases recall coverage. |
Similarity threshold | 0.78 | Ensures recalled policy clauses are highly relevant to batch record content. |
Rerank result count | 5 entries | Refines the final output, focusing on the most relevant key policy clauses. |
Three Common Mistakes
- An HTTP request returns a 500 error code, and logs show "file parsing failed." This occurs because the uploaded batch record file format is incompatible or its internal structure is too complex for the parser to handle correctly.
- The model's audit suggestions lack validation results for critical parameters. This happens when specific tabular fields in the batch record are not correctly identified or extracted during data extraction, leading to incomplete information for the model.
- In the debug preview interface, the model response time is excessively long or unresponsive. This might be due to configuration issues with the locally deployed LLM service connecting to OneAPI, such as an incorrectly set
API_KEYor an unopen network port.
How to Confirm Correct Setup
- Upload a typical batch record PDF file. Check if the knowledge base successfully generates chunks with tabular and text content, and verify the accuracy of key field extraction.
- Use FastGPT's debug interface to ask specific questions from the batch record. Observe if the model accurately cites relevant policy clauses and provides reasonable answers.
- When integrating external systems, simulate a complete batch record review process. Check if HTTP interface data transmission is smooth, if response times are within acceptable limits, and if the returned data structure matches expectations.
The values provided are common starting points and should be measured against the reader's 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.