Citation and Traceability for Attenuated Inactivated Vaccine

Data Characteristics

Quality documents for attenuated inactivated vaccines include production batch records, inspection reports, stability study data, batch release documents, change control records, and deviation investigation reports. These documents are typically in PDF, Word, or scanned image formats. Data sources include MES systems in production workshops, LIMS systems in laboratories, and document management systems in quality assurance departments. Documents are updated frequently, especially before production batch release, when inspection reports and batch records are updated intensively. Document structures are rigorous, adhering to GMP standards, and typically contain detailed production process parameters, material batch information, and test results for intermediate and final products. Fields cover indicators such as biological activity, purity, potency, sterility, and abnormal toxicity, with units like IU/mL, TCID50/mL, and μg/mL.

Constraints on Citation and Traceability

The characteristics of attenuated inactivated vaccine quality documents impose specific requirements on citation and traceability. First, the strict document structure and high update frequency demand efficient document parsing capabilities for the knowledge base, especially for extracting key data from tables and images, to ensure accuracy of cited content. Second, the large volume of batch data and change records requires citations to be precise down to specific batch numbers and versions, preventing the citation of outdated or incorrect production data. The strong inter-document correlation, such as the binding of inspection reports to batch records, means cross-document queries may be necessary for traceability. Simultaneously, the specialized nature of biological indicators and specific units requires the RAG retrieval model to accurately match these technical terms in semantic understanding, avoiding recall bias due to synonyms or abbreviations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters`	Balances the coherence of vaccine production process descriptions with the information density of individual text blocks, ensuring critical parameters are not truncated.
`Overlap Length`	`100 characters`	Ensures contextual continuity between paragraphs, especially when describing process flows and inspection steps, preventing semantic breaks.
`Recall count` (Recall Count)	`Top 8–12 entries`	Given the complexity and volume of information in quality documents, increasing the recall count enhances retrieval comprehensiveness, particularly when searching for information across multiple batches or change records.
`Similarity threshold` (Similarity Threshold)	`Calibrate by measurement`	Vaccine documents contain many specialized terms; experimental determination is needed to establish a threshold that effectively distinguishes between relevant and irrelevant batches, avoiding interference from irrelevant information.
`Rerank result count` (Rerank Return Count)	`Top 3–5 entries`	Based on initial recall, re-sorts more relevant documents, focusing on content that best matches specific batches, inspection results, or change events.
`QUERY_MAX_LENGTH`	`200 characters`	Accommodates complex user queries that may include detailed information such as batch numbers, inspection items, and dates, ensuring the complete transmission of query intent.

Common Pitfalls

AI responses are empty, providing only citation links. This may occur if knowledge base chunks are too short or the recall count is insufficient, preventing the model from forming complete answers from limited contextual snippets.
AI responses cite irrelevant batches or outdated versions. This can happen if batch number and version number fields are not effectively extracted during document parsing, or if the retrieval model has biases in handling temporal information.
In the code execution node, specific fields from knowledge base citations cannot be selected. This usually means that after document parsing, critical fields like inspection results or production date were not correctly extracted and mapped into structured variables available for subsequent operations.

How to Confirm Proper Configuration

Select a query containing a specific batch number and inspection result. Check if the response accurately cites the corresponding inspection report and production batch record for that batch.
For a production process that has undergone changes, query for relevant documents before and after the change. Verify if the AI response can differentiate process descriptions under different version numbers.
Submit a query containing biological indicators (e.g., potency, sterility). Check if the citations in the response are precise down to the sentence or table row in the document containing the corresponding indicator values and units.
Simulate a user query asking the AI to summarize the stability study conclusions for a specific vaccine batch. Cross-reference if the cited source documents cover the complete stability data.

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.