Data Characteristics in This Category
Gene therapy AAV (adeno-associated virus) product documentation typically covers the entire lifecycle, from research and development to production and clinical application. Data sources are diverse, including research papers, patent literature, clinical trial reports, production batch records, quality control reports, regulatory submission materials (such as IND/BLA applications), and product manuals. These documents have varying update frequencies; research papers and clinical data may update frequently, while regulatory submission materials are generated intensively at specific stages. Document structures are complex, often containing numerous figures, chemical structures, sequence information, experimental data tables, and specialized terminology. Fields and units are highly specialized, for example, viral titer (vg/mL), gene expression levels (copy numbers), cell transfection efficiency (%), purity (%), host cell protein residue (ng/mg), and often involve specific nomenclature and abbreviations.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complexity of gene therapy AAV product documentation places specific demands on document parsing and chunking. First, diverse document types and structures require flexible parsing strategies to ensure effective extraction of information from text, tables, and figure descriptions. Second, data sources with varying update frequencies necessitate incremental update and version management capabilities to avoid redundant processing and outdated information. The presence of specialized terminology and units of measurement can challenge conventional tokenization and semantic understanding models, requiring support from more specialized vocabularies or pre-trained models. Additionally, non-textual content common in documents, such as sequence information and structural formulas, requires specialized handling, for example, conversion into searchable descriptive text. All these factors influence FastGPT's chunking granularity choices and metadata extraction strategies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Regulatory and clinical reports for gene therapy AAV often contain many images and figures, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF documents can be time-consuming; increasing the timeout prevents parsing interruptions. |
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness and retrieval efficiency. AAV product descriptions often involve complex mechanisms and experimental details. |
Overlap Length | 150 characters | Ensures contextual continuity, especially when describing continuous information like gene sequences or experimental procedures. |
Enable Table Parsing | Yes | Clinical data and quality control reports contain significant critical tabular data that needs structured extraction. |
Enable Image OCR | Yes | Figures in documents often contain critical text descriptions; OCR converts them into searchable text. |
Three Common Mistakes
- After uploading a large PDF document, the system displays a "request failed" message, and logs show
504 Gateway Timeout. This typically occurs when thePARSE_FILE_TIMEOUT_SECONDSconfiguration is too low, not allowing enough time for the model to process complex documents. - After parsing, the knowledge base provides incomplete or incorrect numerical information when answering questions about viral titer or purity. This may be because tabular data in the document was not correctly identified and extracted, leading to the loss of key quantitative information.
- After importing a batch of updated clinical trial data files, the knowledge base content does not update as expected, and query results still show old data. This happens due to a lack of version management or incremental update mechanisms, preventing the system from identifying differences between old and new documents and performing effective replacements.
How to Verify Configuration
- Upload a gene therapy AAV product manual containing complex tables and figures. Check whether the parsing results fully retain the table content and text descriptions within the figures.
- Select paragraphs from the document that contain specialized terminology and specific units of measurement. Use the retrieval function to verify whether these keywords are accurately recalled and evaluate the contextual completeness of the recalled content.
- For a clinical trial report with clear version iterations, upload both the old and new versions. After the knowledge base updates, check whether query results reflect the information from the latest version.
Note: The values provided are common starting points. They 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.