Reference and Traceability for Quality Documents in

Data Characteristics

Quality documents for tendering in the biomedical field include product registration certificates, production licenses, GMP/GSP certificates, inspection reports, quality standards, product manuals, brochures, and enterprise qualifications. These documents typically exist as PDFs, Word files, or scanned images, with varying degrees of structural organization. Data sources generally include the National Medical Products Administration (NMPA), provincial and municipal drug procurement platforms, company official websites, and offline submitted paper materials. Update frequency is influenced by policy changes, enterprise qualification updates, and product registration renewals, usually occurring quarterly or annually. Some inspection reports or temporary notices may be released immediately. Documents contain numerous technical terms, batch information, expiration dates, and measurement units (e.g., mg/mL, IU, %).

Constraints on Reference and Traceability

The complexity and specialized nature of tendering quality documents impose high demands on reference and traceability. The uncertain update frequency requires the knowledge base to support efficient incremental updates and version management, ensuring the timeliness and accuracy of referenced content. Diverse document formats and structures necessitate that text extraction and parsing modules effectively handle tables and image text from scanned documents, accurately identifying critical fields like batch numbers and expiration dates. Accurate recognition of technical terms and measurement units directly impacts recall precision and traceability reliability, requiring the model to have a deep understanding of biomedical domain knowledge. Incorrect references or untraceable snippets can lead to non-compliant submission materials, affecting the tendering process.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Size`	`300–500 characters`	Ensures each chunk contains sufficient context while avoiding information redundancy, facilitating precise matching of technical terms.
`Chunk Overlap`	`50 characters`	Enhances semantic continuity between chunks and increases the probability of recalling relevant information, preventing key information from being truncated.
`Recall Count`	`Top 8–12`	Given the potentially large number of documents, increasing the recall count improves the coverage of initial retrieval.
`Similarity Threshold`	`0.75–0.85`	Balances recall rate and accuracy, preventing interference from irrelevant documents while not missing potentially relevant information.
`Rerank Return Count`	`Top 3–5`	Further refines the most relevant reference snippets from the initial recall using a reranking model, improving the quality of the final output.
`maxContext`	`4000 tokens`	Ensures sufficient original content is included when citing sources, supporting complete traceability verification.

Common Pitfalls

Reference results are empty, and logs show No relevant chunks found. This often occurs when the Similarity Threshold is set too high, making the model overly strict and failing to recall valid snippets.
Reference snippets contain a large amount of irrelevant information, such as batch numbers or expiration dates from other products mixed into a single product manual. This might be due to an excessively large Chunk Size, causing individual chunks to contain too much unrelated content, or because document parsing failed to effectively differentiate between product information.
After a knowledge base update, reference results for specific batch numbers or certificate IDs remain unchanged, still pointing to old document versions. This indicates that the knowledge base's incremental update mechanism or version management function is not correctly configured, leading to old data not being replaced or updated promptly.

Verification Steps

Query specific product registration numbers, batch numbers, or inspection report numbers. Verify that the returned reference sources accurately point to the corresponding original document and that their content matches the original document.
Simulate a tendering application scenario. Input questions containing technical terms and measurement units. Check if the system accurately recalls snippets containing these terms and verify the completeness of their contextual semantics.
After a knowledge base update, repeat the above checks. Verify that new document content is correctly indexed and referenced, and that references to old document versions are either invalidated or correctly marked.

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.