Data Characteristics
Batch record review involves quality management documents such as batch production records, batch inspection records, deviation records, and change records. These documents are typically PDF scans, electronic PDFs, or structured Excel files. Content includes production process parameters, material batch information, inspection results, equipment operation data, personnel operation records, and anomaly descriptions. Update frequency aligns with production batches; a complete set of records is generated after each batch. Document structure is highly standardized, adhering to GMP guidelines, with fixed chapters, tables, and signature pages. Fields include batch number, production date, expiration date, operator, equipment ID, key process parameters (e.g., temperature, pressure, time), inspection items, results, units (e.g., ℃, MPa, min, g/L), deviation number, root cause analysis, and corrective and preventive actions.
Constraints from Data Characteristics on Vector Models and Indexing
The highly standardized and structured nature of batch record documents requires vector models to effectively identify and preserve semantic associations between different fields during chunking and indexing. This prevents loss of critical context, such as batch numbers linked to production parameters, due to over-chunking. The update frequency, synchronized with production batches, makes incremental indexing and localized updates common. This necessitates efficient document version management and index update mechanisms. The presence of scanned documents requires high tolerance for OCR recognition quality and the ability to handle character errors introduced by OCR that affect vector representations. Batch records contain extensive domain-specific terminology and units of measurement. Vector models must accurately understand these specialized terms and be sensitive to unit differences during similarity calculations, for example, distinguishing "temperature 25℃" from "humidity 25%."
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Batch record document paragraphs are relatively complete; this length balances semantic context and recall efficiency. |
Chunk Overlap Length (Chunk Overlap Length) | 100–200 characters | Ensures that critical information associations across paragraphs are not severed, especially in tables or lists. |
maxContext | 4000 characters | Satisfies the contextual needs of common logical blocks within batch records for a single query. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall and precision, reducing the probability of irrelevant batch records being recalled while ensuring key information is retrievable. |
Recall count (Recall Count) | 5–8 items | Batch record review typically requires quickly locating a small number of the most relevant document segments, avoiding information overload. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time for OCR recognition and text extraction of large scanned batch records. |
Common Pitfalls
- After document upload, the knowledge base shows duplicate indexes or an abnormal increase in paragraph count. This typically occurs when the system fails to correctly identify version differences during document updates, leading to old versions or partial content being indexed repeatedly.
- After upgrading FastGPT, some batch record query results are inaccurate or cannot be recalled. This might be due to changes in the model version or indexing mechanism, where existing indexes are incompatible with the new system. Re-indexing documents is necessary to adapt to the new version.
- After uploading Excel-format batch records, vectorization results are poor, and table content cannot be precisely located during queries. This often happens because the Excel file content format is not standardized, or the system is not optimized for table structure parsing. This leads to the loss of semantic associations between cells during vectorization.
Validation Steps
- Select a typical batch record document, upload it, and observe if the number and content of chunks in the knowledge base meet expectations. Check if key fields and data are correctly extracted.
- Simulate audit scenario queries to verify if relevant batch record segments are accurately recalled. Check the completeness and contextual relevance of the recalled content.
- Test queries containing units of measurement and specialized terminology. Confirm that the model can distinguish between similar but different terms and verify the accuracy of unit recognition.
- Upload test batch records in different formats (PDF scans, electronic PDFs, Excel). Check the indexing time and success rate for each document type.
The values provided are common starting points and should be measured against specific 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.