Data Characteristics
Supplier audit regulation documents in the biomedical sector are typically PDFs or Word files. Some supporting data may be in Excel format. These documents are highly structured. They contain detailed audit standards, processes, checklists, responsibility assignments, and penalty mechanisms. Updates are regular, usually annually or as regulations change. Documents frequently use specialized terminology, legal citations, internal codes, and units (e.g., mg/kg, ppm, °C, kPa). Excel files often record audit results, non-conformities, and corrective actions. These files can be large, contain multiple worksheets, and have complex, deeply nested headers.
Constraints from "Document Parsing and Chunking"
Structured document content requires the parser to accurately identify chapter titles, paragraphs, lists, and tables. This prevents content confusion. Specialized terminology and legal citations require high semantic understanding from the model. This ensures critical information points are not split during chunking. The update frequency means the knowledge base needs regular incremental or full refreshes. The parsing process must be efficient. Complex Excel headers and large datasets challenge default chunking strategies. Chunks that are too large reduce recall precision. Chunks that are too small lose context and increase token consumption. Therefore, fine-grained control over chunk granularity is necessary. Special handling for table data maintains data integrity and relevance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances contextual completeness and recall precision. Avoids chunks that are too long or too short. |
Overlap Length | 100–200 characters | Ensures semantic continuity at chunk boundaries. Improves relevant recall. |
maxContext | 32000 tokens | Accommodates the specialized and detailed nature of biomedical documents. Provides a sufficient context window. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Allows for large file uploads, considering audit documents may contain many images or embedded objects. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides enough time to process large PDFs or complex structured Word documents. |
Excel Table Parsing Strategy | Parse by row and associate headers | Ensures each data row carries its corresponding header information. Prevents isolated information. |
Common Pitfalls
- When parsing Excel files, the model returns "incomplete command or request": This usually happens when the default chunking strategy fails to correctly identify complex headers, leading to a lack of semantic context in data blocks.
- Table data in recall results is incomplete or unassociated: This occurs when Excel files are not effectively chunked by row and header association, causing individual data rows to lose their context.
- Long document parsing times out or some content is lost: This might be due to
PARSE_FILE_TIMEOUT_SECONDSbeing set too short, or the parser's inability to handle complex layouts (e.g., multi-column layouts, nested tables).
Verification Steps
- Upload typical audit regulation documents (PDF, Word, Excel). Check if the parsed knowledge blocks contain complete chapters, paragraphs, and table data.
- For Excel knowledge blocks, randomly select several rows of data. Verify that they carry the correct column header information and contextual associations.
- Use key regulatory terms, specialized terminology, or audit processes from the documents as queries. Observe the completeness and accuracy of the recall results to evaluate chunk quality.
- Through the FastGPT backend's knowledge base management interface, check the parsing status and the number of knowledge blocks. Ensure all uploaded documents are successfully parsed without errors.
The values provided are common starting points. Measure them 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.