Document Parsing and Chunking for Peptide Drug Regulations

Data Characteristics

Peptide drug regulations and SOP documents originate from regulatory documents issued by drug administration authorities, internal GMP (Good Manufacturing Practice) standards, clinical trial protocols, registration application materials, and drug analysis methods. These documents have a relatively low update frequency, typically updated with policy adjustments or new drug development milestones, ranging from months to years. Document structures are highly standardized, using chapters, appendices, tables, and figures with clear hierarchical levels. Common fields include batch number, test item, limit, detection method, storage conditions, and expiry date, strictly adhering to pharmacopoeia or international standards. Units involve mass (mg, g, kg), concentration (μg/mL, mg/mL, %), time (min, h, d), and temperature (℃), with extremely high precision requirements.

Constraints Imposed by These Characteristics on Document Parsing and Chunking

The standardized structure of peptide drug regulation documents demands high-precision document parsing. Information within chapters, appendices, and tables has strong interdependencies. The parsing process must accurately identify and maintain this context. For example, a limit value is usually closely related to its test item and detection method. If chunked separately, semantic loss occurs. The strictness of units means that strings containing numbers and units cannot be arbitrarily truncated during chunking, as this could lead to misinterpretation. The low update frequency allows for more resources to be invested in fine-grained processing during initial parsing, reducing the complexity of subsequent incremental updates. Additionally, peptide drug documents often contain complex chemical structures, flowcharts, and other non-textual information. These contents require special handling to ensure their meaning is not overlooked or incorrectly converted during plain text chunking.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Ensures each chunk contains sufficient context, preventing critical information from being split, especially when describing test methods or operational procedures.
`Chunk Overlap Length` (Chunk Overlap Length)	`100–150 characters` (characters)	Maintains contextual coherence, helping the Agent establish connections between different chunks and reducing semantic boundary ambiguity.
`maxContext`	`3500–4000 token`	Accommodates complex peptide drug regulation Q&A, ensuring it can handle lengthy regulatory clauses or SOP procedure descriptions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Handles large or structurally complex PDF and Word documents, allowing ample time for parsing completion.
`Recall count` (Recall Count)	`Top 8 entries` (top 8)	Increases the coverage of relevant regulatory clauses, especially in Q&A scenarios with multiple constraints.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances recall precision and generalization ability, avoiding interference from irrelevant information while ensuring core regulatory clauses are recalled.

Three Common Pitfalls

Document parsing remains in the "parsing" state for an extended period or ultimately shows "parsing failed." This may occur if the document is too large or has an overly complex internal structure, triggering the PARSE_FILE_TIMEOUT_SECONDS limit.
After a user query, the returned answer is missing or has incorrect units for key numerical values such as limits or batch numbers. This happens when the document is chunked, and truncation occurs between a numerical value and its unit, leading to incomplete information.
The knowledge base contains a large amount of duplicate document block content, leading to reduced retrieval efficiency and contextual redundancy. This typically occurs when identical text content is not correctly processed or identified during document upload or content updates.

How to Verify the Configuration

Upload representative peptide drug regulation documents. Check the text content of each chunk in the knowledge base to ensure critical information (e.g., batch, limit, unit) is complete and contextually coherent.
Conduct Q&A tests on sections of documents containing tables and flowcharts. Verify if the Agent can correctly extract and interpret the meaning conveyed by this structured or semi-structured information.
Use specific regulatory clauses, SOP steps, or common questions from the documents for testing. Check if the Recall count (Recall Count) and Similarity threshold (Similarity Threshold) settings accurately recall relevant document snippets, and that the recalled results contain no obvious irrelevant information.

The values provided are common starting points. Measure them against your own samples to determine the most effective 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.