Data Characteristics in this Category
CMC research data originates from pharmaceutical research reports, quality standards, manufacturing process documents, stability study reports, and regulatory submission documents. These documents are primarily in PDF format. Some data might be embedded in scanned images or pictures. Data updates closely align with drug development stages, from pre-clinical research to post-market changes, with continuous data generation and revision. Documents have complex structures, including numerous tables, charts, flowcharts, and specialized terminology. Examples include "batch number," "main component content," "impurity profile," "dissolution rate," and "stability data." Documents strictly adhere to unit systems from pharmacopoeias or international guidelines like ICH. Field naming is highly standardized, but subtle differences may exist across pharmaceutical companies or products.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complex structure and specialized nature of CMC research documents demand high-quality document parsing. Embedded tables and charts require precise identification and extraction; otherwise, critical quantitative information is lost. Accurate text recognition from scanned images directly impacts subsequent knowledge base quality. Frequent data updates and revisions necessitate effective version control within the knowledge base to prevent interference from outdated data. Highly standardized fields, units, and specialized terminology require the parser to understand domain-specific vocabulary. This ensures semantic completeness during chunking. For example, a paragraph about "stability data" must not be split into fragments containing only "stability" or "data." It must retain its holistic meaning for effective matching during pre-screening.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | CMC documents often contain many images and charts, resulting in large file sizes. A sufficient upload limit is necessary. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing complex PDF documents, especially with OCR, can be time-consuming. This prevents parsing timeouts. |
segment_length | 800–1200 characters | Each chunk should contain sufficient contextual information. This range avoids overly long chunks that might dilute semantic focus. |
overlap_rate | 0.15–0.2 | Moderate overlap helps maintain semantic continuity at chunk boundaries, improving recall. |
chunk_strategy | By Title and Paragraph Chunking，And Prioritize Table Parsing | CMC documents are highly structured. Prioritize preserving the integrity of titles and paragraphs, and ensure table data is correctly identified. |
ocr_engine_config | High-precision mode，vials Supports Multiple Languages（Chinese/English） | This improves recognition accuracy for text in scanned images and pictures. It also handles mixed Chinese and English specialized terminology. |
Common Pitfalls
- PDF file processing errors showing
{"detail":"错误信息: ..."}: This typically indicates an incorrectly installed or configured underlying PDF parser (e.g., Marker), or a corrupted file causing parsing failure. - Table data is not effectively recalled or is missing from knowledge base query results: This suggests the document parser failed to correctly identify and extract table structures from the PDF. As a result, table content was ignored or incorrectly split during chunking.
- Answers cannot be traced back to the original document, or traceability information is inaccurate: This might be due to a chunking granularity that is too large or too small. Information needed for an answer could be spread across multiple chunks, or chunks might lack sufficient metadata to link to the original document.
How to Verify Configuration
- Upload a batch of typical CMC research report PDFs. Check if the knowledge base correctly displays the document's page count and basic structural information.
- Randomly select key tables from the documents. Use the knowledge base's preview function to verify if table content is fully and correctly parsed and chunked.
- Perform knowledge base searches for specialized terminology, batch numbers, or critical quantitative data from the documents. Check if the recalled chunks are complete and semantically accurate. Confirm that traceability information points to the correct document and page number.
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.