Document Parsing and Chunking for siRNA Nucleic Acid Drug

Data Characteristics

siRNA nucleic acid drug quality documents originate from research and development experimental reports, manufacturing batch records, and regulatory submission materials. These documents are updated frequently, especially during clinical trials and manufacturing process optimization. Document structures are complex, often containing numerous charts, chemical structures, sequence information, and experimental data. For example, stability study reports include detection data at multiple time points, and purity analysis reports involve High-Performance Liquid Chromatography (HPLC) chromatograms. Common fields include Batch Number, Test Item, Test Method, Result, Unit (e.g., nM, %, OD, ng/mL), and QbD (Quality by Design) related parameters. The language in these documents is highly specialized, frequently mixing English abbreviations with Chinese descriptions.

Constraints on Document Parsing and Chunking

The complexity of siRNA nucleic acid drug quality documents imposes multiple constraints on document parsing and chunking. First, charts and chemical structures require specialized image recognition or text extraction techniques; traditional text-based chunking methods are ineffective. Second, the extensive use of specialized terminology and abbreviations necessitates that chunking preserves term integrity to prevent semantic loss from word breaks. For example, 2'-O-methyl modification or GalNAc conjugate structures should be recognized as a single entity. Additionally, batch records and experimental data often appear in tables; the internal data relationships within these tables must be maintained during chunking to avoid context fragmentation. High update frequency requires efficient incremental parsing and updating mechanisms to ensure the knowledge base's timeliness.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`800–1200 characters`	Balances the length of siRNA terminology with contextual completeness, preventing semantic fragmentation from overly short chunks.
`Overlap Length`	`150–250 characters`	Retains sufficient contextual information, ensuring semantic coherence between adjacent chunks, particularly in tables or sequence descriptions.
`File Type Whitelist`	`['.pdf', '.docx', '.xlsx', '.json']`	Covers the primary file formats for siRNA quality documents, including reports, data sheets, and structured data.
`Enable Smart Table Parsing`	`True`	Effectively extracts table data from Excel and PDF, converting it into structured text and maintaining data relationships.
`OCR Text Recognition Threshold`	`0.85`	Ensures high accuracy for specialized terminology and data extracted from scanned documents or images.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the parsing time for large reports or documents containing complex charts, preventing parsing failures due to timeouts.

Common Pitfalls

After document upload, some chunk vectorizations show errors. This typically manifests as the vectorization service returning HTTP 500 or empty embedding results. The cause is text segments in the document that are too long or contain special characters, exceeding the input length limit for the vector model's single-pass processing.
During multi-turn conversations, queries for specific batches return incomplete experimental data or incorrect units. This manifests as missing key fields in the DB node query result JSON array, or an empty unit field. The cause is that Excel or PDF table parsing failed to correctly identify the correspondence between headers and data rows, or incorrectly merged data from different columns.
After updating quality documents, queries still return old information, leading to data inconsistency. This manifests as knowledge base recall content not matching the latest document content, and the update timestamp field not being updated. The cause is that the document parsing system did not correctly trigger the incremental update process, or caching mechanisms prevented timely invalidation of old data.

How to Verify Configuration

Upload representative siRNA quality documents (e.g., stability reports, batch release reports). In the knowledge base management interface, check if the content of each chunk is complete, especially whether specialized terminology, sequence information, and table data are correctly extracted.
Test with query statements containing specific batch numbers, test items, and results. Verify that key fields (e.g., Batch Number, Test Item, Result, Unit) in the returned JSON results are accurate and complete, and compare them with the original document.
Attempt to upload a PDF document containing numerous charts and complex layouts. Check if the OCR-recognized text content is clear and readable, without obvious typos or garbled characters, and confirm recognition accuracy by comparing with the original image.

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.