Data Characteristics in This Category
Quality documents in the biopharmaceutical sector primarily originate from batch records, inspection reports, equipment calibration logs, Standard Operating Procedures (SOPs), and deviation reports. These documents are typically in PDF, Word, or scanned image formats. Update frequency is relatively low, primarily occurring during regulatory changes, process optimizations, or product iterations. Document structures are highly standardized, often including fixed metadata such as numbers, versions, effective dates, revision histories, and approvers. The main content consists of structured tabular data (e.g., test results, material batch numbers), semi-structured text (e.g., operating procedures, deviation descriptions), and unstructured images (e.g., instrument chromatograms, signature pages). Fields and units strictly adhere to industry standards and regulatory requirements, such as concentration (mg/mL), purity (%), and temperature (°C), and are often accompanied by specific batch information and expiration dates.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The high standardization and structured nature of quality documents demand high accuracy in document parsing. Fixed metadata requires precise extraction to support subsequent retrieval and traceability. For embedded tabular data, the semantic relationship between rows and columns must be maintained during parsing to avoid losing critical test results or material information. Parsing image content (e.g., signatures, chromatograms) presents a challenge for OCR accuracy. Due to the low update frequency, initial parsing accuracy is crucial to minimize subsequent manual intervention. The strictness of fields and units requires chunking to preserve semantic integrity and prevent numerical values from being separated from their units by sentence breaks, which would affect RAG recall accuracy. Parsing failures or incomplete data can directly impact product quality traceability and compliance reviews, leading to severe consequences.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Quality documents often contain numerous scanned images, resulting in large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large PDF files or complex table parsing can take a long time; this prevents timeouts. |
chunk_size | 800 characters | Balances semantic completeness and recall efficiency, avoiding overly long or short chunks. |
overlap_size | 100 characters | Ensures contextual continuity, especially at table or paragraph boundaries. |
parser_strategy | auto | Prioritizes FastGPT's built-in multimodal parsing capabilities for text, tables, and images. |
ocr_engine | FastGPT_OCR_v2.1 | Improves recognition accuracy for text in scanned documents and images. |
Three Common Mistakes
- After uploading a large PDF file, the system remains unresponsive for an extended period or reports a request failure. This occurs because the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is too low, not allowing the model enough time to complete parsing complex documents. - Parts of tabular data in the knowledge base are missing or incomplete. During retrieval, this manifests as an inability to obtain complete test results. The reason is that the document parser failed to correctly identify and extract the table structure, leading to data being misidentified as plain text.
- After parsing, specific fields (e.g., batch number, expiration date) are missing from retrieval results. This can happen if the chunking strategy separates critical fields from their context, or if the parser fails to recognize these highly standardized fields.
How to Confirm Proper Configuration
- Upload a typical quality document containing complex tables and scanned images. Check the document preview in the knowledge base to confirm the completeness of table structures and image text recognition results.
- Perform a retrieval query for specific batch numbers, test items, or SOP numbers within the document. Verify that the system accurately recalls complete chunks containing this information.
- Randomly select parsed document chunks and compare them to the original document content. Verify that the chunk content is semantically coherent and avoids separation of numerical values from their units.
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.