Document Parsing and Chunking for Indication-Based

Data Characteristics

Indication data originates from drug inserts, clinical guidelines, regulatory approval documents, and clinical research reports. Update frequency depends on drug launches, clinical trial progress, and policy changes, typically quarterly or annually. Documents are semi-structured, containing numerous tables (e.g., dosage tables, adverse event rates) and free-text descriptions. Fields and units are highly specialized, including dosage (mg/kg, g/dose), frequency (times/day, q.d.), treatment duration (days, weeks), specific medical terminology, and disease classification codes. Data commonly exists in PDF, Word, or plain text formats, with PDF being dominant.

Constraints on Document Parsing and Chunking

The semi-structured nature of indication documents challenges parsing. Table data requires accurate identification and conversion into a queryable structure, preventing misalignment or data loss. This directly impacts subsequent Q&A accuracy. Specialized medical terms and disease codes require chunking to maintain term integrity, avoiding truncation that could impair semantic understanding. Data update frequency necessitates regular document parsing and chunking to ensure information timeliness. Furthermore, sensitive medical information in documents requires localized parsing to ensure data security and privacy compliance, mitigating the risk of content leakage.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`800–1200 characters`	Balances semantic completeness and retrieval efficiency, preventing information loss or redundancy from chunks that are too long or too short.
`Overlap Length`	`100–200 characters`	Ensures contextual continuity, preventing semantic breaks at chunk boundaries.
`Separators`	`\n\n`, `。`, `;`	Prioritizes splitting by natural paragraphs and sentence structures, preserving the integrity of semantic units.
`PDF Parsing Mode`	`Text First`	Indication documents have high text density; this ensures accurate text extraction.
`Table Parsing Strategy`	`Structured Extraction`	Indication documents contain extensive critical table data, requiring precise identification of table boundaries and cell content.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the time required for parsing large or complex documents, preventing timeout failures.

Common Pitfalls

Table content in uploaded PDF files appears misaligned or missing. This occurs because default parsers have limited support for complex table structures and fail to correctly identify table boundaries.
After document parsing, some specialized medical terms are incorrectly truncated. This manifests as incomplete terms in Q&A results. The cause is an excessively short chunk length or inappropriate separator selection, failing to maintain the integrity of semantic units.
File upload results in a long parsing delay or timeout error. This happens when the file size is too large or the content is too complex, exceeding the threshold set by the PARSE_FILE_TIMEOUT_SECONDS parameter.

Verification Steps

Select an indication PDF document containing complex tables and specialized terminology. Parse it and check if table content in the parsed text chunks is complete and correctly aligned.
Randomly select parsed text chunks. Verify for truncated medical terms or critical information, confirming reasonable chunk boundaries.
Upload multiple indication documents of varying sizes and complexities. Observe parsing times to confirm completion within the PARSE_FILE_TIMEOUT_SECONDS limit.
Use FastGPT's knowledge base preview function to check if chunk content meets expectations, especially the presentation of tables and key fields.

Note: The values provided are common starting points. Measure performance against specific samples to determine optimal settings.

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.