Data Characteristics for This Category
Quality documents for live attenuated vaccines include production batch records, inspection reports, stability study data, deviation handling reports, change control documents, and annual product quality review reports. These documents originate from pharmaceutical companies' quality management systems. Update frequency aligns with batch production, inspection cycles, and regulatory requirements; for example, batch records generate upon batch completion, and annual review reports update yearly. Document structures are rigorous, often adhering to GMP (Good Manufacturing Practice) standards, containing extensive tabular data, flowcharts, and normative text. Fields include batch number, production date, expiry date, inspection items, inspection results, judgment criteria, deviation descriptions, and corrective actions. Units strictly follow pharmacopoeia or internal standards, such as CFU/mL, EU/mL, pH value, and OD value.
Constraints Imposed by These Characteristics on Multiturn Conversations and Prompts
The rigor and specialized nature of live attenuated vaccine quality documents impose specific requirements on multiturn conversation and prompt design. First, the extensive use of specialized terminology and abbreviations (e.g., "potency," "sterility," "endotoxin") in documents demands precise semantic understanding from the model. Prompts must guide the model to focus on the biomedical context. Second, the mix of structured data and unstructured text means simple keyword matching is insufficient for complex queries. Multiturn conversations must support cross-referencing tabular data and logical reasoning. Furthermore, numerical fields in batch records and inspection reports, such as "potency ≥ 10^7 TCID50/mL," require prompts to handle numerical comparisons and range judgments. Finally, the cyclical nature of document updates means the knowledge base content is not static. Multiturn conversations need to reflect the latest batch data and change information promptly to ensure query results are current and accurate.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Balances semantic completeness with retrieval efficiency; avoids text truncation. |
Recall count (Recall Count) | Top 5 | Covers core relevant documents; reduces interference from irrelevant information. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Precisely matches professional terms and complex queries; reduces false positives. |
maxContext | 6 | Maintains conversational coherence in multiturn interactions; supports complex follow-up questions. |
Rerank result count (Rerank Return Count) | 3 | Optimizes result ranking; prioritizes the most relevant key information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large batch records and reports. |
Three Common Mistakes
- Symptom: Model responses include batch information or inspection items unrelated to the query. Reason: The
Similarity threshold(Similarity Threshold) is set too low, leading to the retrieval of semantically irrelevant document chunks. - Symptom: When a user asks a follow-up question in a multiturn conversation about the historical trend of a specific inspection indicator, the model fails to provide correct time-series data. Reason: The prompt did not effectively guide the model to extract and integrate time-dimension data from multiple batch inspection reports.
- Symptom: Uploading large batch production record files results in parsing failure or excessive processing time. Reason: The
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, failing to accommodate the parsing requirements of large documents.
How to Confirm Proper Configuration
- For typical queries, such as "Query the potency and sterility inspection results for batch number XX of live attenuated vaccine," verify that the model accurately extracts and presents relevant numerical values and judgment conclusions from the knowledge base.
- Conduct multiturn follow-up tests, such as "Does the endotoxin level of this vaccine batch meet pharmacopoeia requirements?" Check if the model can make logical judgments based on prior context and provide correct responses.
- Upload different types (e.g., batch records, inspection reports, deviation reports) and sizes of live attenuated vaccine quality documents. Check if file parsing is successful and if the parsed content can be properly indexed and queried by the knowledge base.
- Simulate complex query scenarios, such as "Compare stability test data for all batches of live attenuated vaccine over the past three months for significant fluctuations." Verify if the model can handle complex information integration across documents and batches.
Note: The values provided are common starting points. Measure them against your 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.