Document Data Characteristics
In the product and reagent consulting scenarios for biopharmaceutical CDMOs (Contract Development and Manufacturing Organizations), document data originates from project proposals, manufacturing batch records, quality control reports, analytical method validation reports, technology transfer documents, change control documents, and product specifications. These documents are typically in PDF, DOCX, or scanned image formats. Data update frequency is closely tied to project cycles, with new batch reports or revised documents potentially generated weekly or even daily. Document structures are complex, containing numerous tables, charts, chemical structures, and specialized terminology. Fields include batch numbers, production dates, expiry dates, test results, limits, equipment parameters, and operating procedures. Units involve milligrams, liters, moles, percentages, pH values, and temperatures (Celsius), demanding extremely high precision and consistency.
Constraints on Document Parsing and Chunking
The complex structure and high precision requirements of CDMO documents pose specific challenges for document parsing and chunking. Extensive tables and chemical structures require precise identification and extraction; conventional text chunking methods might lose critical data relationships. Frequent document updates necessitate support for incremental parsing and version management to ensure the knowledge base's timeliness. Accurate identification of specialized terminology and measurement units is fundamental to understanding document semantics and avoiding ambiguity. Documents often contain significant redundant or non-core information, requiring intelligent filtering to improve recall efficiency. Furthermore, the accuracy of Optical Character Recognition (OCR) for scanned documents directly impacts subsequent parsing quality, especially for handwritten annotations or low-quality scans. Large file sizes can lead to parsing timeouts, requiring consideration of parallel processing or task queuing mechanisms.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | CDMO documents often contain many images and charts, resulting in large file sizes, requiring ample upload space. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF files or scanned documents requiring OCR can be time-consuming; this prevents timeouts. |
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness and vector retrieval efficiency, preventing dilution from excessive length and loss of context from being too short. |
Chunk Overlap Length (Chunk Overlap Length) | 150–200 characters | Ensures continuity of context at chunk boundaries, improving the accuracy of information recall across segments. |
chunk_strategy | recursive_text combined with table_extraction | Recursive text chunking is suitable for most content, while table extraction ensures critical structured data is not corrupted. |
ocr_enabled | True | CDMO documents include many scanned images, making OCR a necessary step for text content acquisition. |
Common Pitfalls
- Table data in parsing results is garbled or missing. This occurs when table extraction is not enabled or incorrectly configured, leading to table content being treated as plain text segments.
- File parsing remains unresponsive for an extended period and eventually times out. This happens when
PARSE_FILE_TIMEOUT_SECONDSis set too short for large PDFs or low-quality scanned documents, or when a lack of effective queuing mechanisms leads to excessive concurrent processing pressure. - Retrieval fails to recall information containing specific batch numbers or test units. This is due to incorrect identification or extraction of specialized terminology and measurement units during document parsing, resulting in inaccurate vector representations.
Verification Steps
- Upload a typical large CDMO technical document (e.g., a batch record with multiple pages of tables and charts). Observe whether the parsing task completes normally without timeouts or failures.
- Randomly select several parsed documents and perform keyword searches within the knowledge base. Verify the completeness and accuracy of retrieval results, especially for table data and specialized terminology.
- Use the API interface to retrieve the raw chunked data of a parsed document. Check chunk length, overlap, and whether expected table content was identified.
- Upload a document containing handwritten annotations or low-quality scanned pages. Verify that the OCR-identified text content is clear and readable, and that critical information is free of errors.
*** 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.