Data Characteristics
GMP-compliant R&D documents typically include detailed experimental records, batch production reports, quality control standard operating procedures (SOPs), deviation reports, change control documents, and training records. These documents are often in PDF, Word, or scanned image formats, with varying degrees of structural consistency. Data update frequency is relatively low, primarily occurring during new product development, manufacturing process changes, or regulatory updates. Documents contain extensive specialized terminology, abbreviations, charts, and data tables, involving chemical structures, biological indicators, and units of measurement such as mg/mL, ppm, and °C. Critical information may be scattered across text paragraphs, tables, or appendices. Document formats can also vary slightly across different batches or product lines.
Constraints Imposed on Model Integration and Configuration
The specialized nature and high compliance requirements of GMP documents demand high accuracy from structural analysis models to prevent hallucinations. The abundance of specialized terminology and abbreviations requires robust semantic understanding. This necessitates enhancing models with domain-specific dictionaries or knowledge graphs. The mix of text, tables, and charts in documents challenges the robustness of file parsers, especially since OCR quality for scanned documents directly impacts subsequent processing. Low update frequency means long accumulation cycles for model training data, requiring few-shot or transfer learning capabilities. Subtle differences in document formats require models to possess generalization capabilities to adapt to various templates. Accurate identification and association of units of measurement are crucial for entity recognition and relation extraction modules, ensuring extracted data is directly usable for subsequent analysis or system integration.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
modelId | deepseek-chat or glm-4 | Prioritize closed-source models that perform well in Chinese contexts due to their superior semantic understanding, which better handles specialized terminology. |
maxContext | 8192 token | GMP documents are often lengthy, requiring a larger context window to capture complete information and prevent loss of critical details. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness with recall efficiency. This avoids excessive segmentation that can break context while reducing the processing cost per segment. |
Recall count (Number of Recall Items) | Top 5 entries (top 5) | Given the rigor of GMP documents, increasing the number of recall items improves critical information coverage and reduces omissions. |
Similarity threshold (Similarity Threshold) | 0.85 | A high threshold ensures recalled content is highly relevant to the query, reducing interference from irrelevant information and improving accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides ample file parsing time for large PDFs or scanned documents, preventing processing failures due to timeouts. |
Common Mistakes
- Error: The model fails to correctly identify key fields such as batch numbers or production dates in documents. Analysis: The model was not sufficiently annotated or pre-trained for the specific formats and field patterns of GMP documents, leading to insufficient generalization capability.
- Error: After parsing, some documents show extensive garbled text or missing content. Analysis: The OCR engine's recognition accuracy for scanned documents is low, or the file parser's ability to handle complex tables and embedded charts within text is insufficient, resulting in low-quality raw text input.
- Error: After model integration, certain specific queries result in redundant or irrelevant "thought processes." Analysis: The
System Promptdoes not sufficiently constrain the model's output behavior, or thetemperatureparameter for the "thought process" is set too high, causing the model to introduce too much unnecessary information into the reasoning chain.
Validation Steps
- Select typical GMP documents from different product lines and eras. Perform batch structural analysis and check the extraction accuracy of key fields.
- Randomly sample parsed results. Manually compare them against the original documents to verify the completeness, correctness, and unit of measurement matching for extracted information.
- Simulate actual query scenarios. Conduct question-answering tests on the parsed knowledge base to evaluate the model's accuracy and professionalism when answering GMP-related questions, determining its alignment with business requirements.
The values given are common starting points and should be measured 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.