Document Parsing and Chunking for Solid Tumor Quality

Data Characteristics

Quality documents in the solid tumor domain originate primarily from drug clinical trial reports, registration submission materials, post-market surveillance reports, and related regulatory guidelines. These documents are typically updated in sync with clinical trial progress, drug review and approval cycles, and regulatory revisions. For example, clinical trial reports might update annually, while drug package inserts or quality standards may remain relatively stable after approval but are revised during significant changes. Document structures are often highly standardized, adhering to ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) or other national drug regulatory agency format requirements. They contain numerous charts, tables, specialized terminology, and abbreviations. Fields and units involve dosage, concentration, efficacy indicators (e.g., tumor response rate, progression-free survival), and toxicity grades. Units are precise, down to micrograms, milligrams, millimeters, and percentages, demanding extremely high accuracy.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The standardized structure and specialized terminology of solid tumor quality documents place high demands on document parsing. The abundance of tables and charts requires enhanced parsing capabilities for non-text content to avoid losing critical data. High update frequency and version control necessitate that document parsing effectively identifies and handles differences between versions, ensuring knowledge base timeliness. The dense specialized terminology and abbreviations require the tokenizer to have a professional medical vocabulary to prevent mis-segmentation that leads to semantic deviations. Precise fields and units require chunking to maintain data integrity, avoiding separation of critical values from their units during splitting, which would affect subsequent question-answering accuracy. Furthermore, documents are often lengthy, challenging the robustness of chunking strategies to ensure effective indexing of long documents.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	`512 characters`	Balances context completeness with recall efficiency, adapting to specialized terminology density.
`overlap_size`	`64 characters`	Ensures contextual continuity at chunk boundaries, reducing semantic fragmentation.
`parsing_strategy`	`by title and paragraph`	Leverages the structured nature of documents, prioritizing the integrity of logical units.
`max_file_size_mb`	`100 MB`	Covers the file size of most clinical trial reports and submission materials, preventing upload failures.
`timeout_seconds`	`600 seconds`	Accommodates the complex parsing requirements of large PDF documents, preventing timeouts.
`table_parsing_mode`	`structured extraction`	Ensures table data is accurately identified and converted into a queryable format.

Common Mistakes

Uploading large PDF documents results in a request error because max_file_size_mb is configured too low, causing the file to be rejected before transfer or parsing.
Incomplete dosage or efficacy data appears in question-answering results, such as numbers without units. This happens when chunk_size is too small or overlap_size is insufficient, leading to critical information being split across different chunks.
Imported documents cannot be opened in results, showing path errors or file not found. This is typically due to improper file storage path configuration or permission issues, preventing FastGPT from correctly accessing parsed files.

How to Verify Configuration

Select a solid tumor clinical trial report containing numerous tables and specialized terminology. Upload it and check the parsed text preview to confirm that table content is correctly extracted and critical data and units are complete.
Ask questions about specific efficacy indicators or adverse event descriptions within that report. Observe whether the recalled document snippets contain complete and relevant contextual information to determine if chunk_size and overlap_size are appropriate.
Attempt to upload a large file near the max_file_size_mb limit. Confirm that the upload and parsing process completes successfully without timeouts or request errors.

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.