Data Characteristics for This Category
Quality documents for attenuated inactivated vaccines primarily include batch production records, inspection reports, stability study data, deviation and change records, validation documents, and regulatory compliance documents. These documents typically originate from the quality management and R&D departments of vaccine manufacturers. Data update frequency is closely tied to batch production cycles and regulatory requirements. For example, batch records are generated with each batch, inspection reports are issued upon completion of tests, and stability data is updated periodically at predefined time points. Document structures are mainly structured and semi-structured, such as manufacturing process flow charts, standard operating procedures (SOPs) for inspection, and batch production instructions. Fields and units within documents are highly standardized, including batch number, production date, expiry date, test items, test results (e.g., virus titer TCID50/mL, protein content mg/mL), temperature ℃, and humidity %RH. Strict adherence to pharmacopoeia and relevant regulatory requirements is essential.
Constraints Imposed by These Characteristics on Model Access and Configuration
The highly standardized and structured nature of attenuated inactivated vaccine quality documents demands precise matching and efficient parsing from the model. The extensive use of specialized terminology and measurement units in documents requires the model to have accurate entity recognition and relationship extraction capabilities to avoid incorrect recall due to comprehension errors. The frequent updates to batch data necessitate that the knowledge base supports incremental updates and version management, ensuring the model always responds based on the latest data. Time-series data from stability studies requires the model to consider data timeliness and trends when processing related queries. Furthermore, strict regulatory compliance requirements mean the model must adhere closely to the original document content when generating answers, avoiding hallucinations or inaccurate information. This requires high precision in recall and strong answer traceability.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000 tokens | Ensures complex batch records and inspection reports can be fully accommodated, preventing context truncation. |
Chunk size (Chunk Length) | 800–1000 characters | Balances semantic integrity with model processing efficiency, suitable for longer descriptive texts. |
Recall count (Recall Count) | 8 entries | Covers multiple relevant document segments, increasing recall comprehensiveness while controlling model input length. |
Similarity threshold (Similarity Threshold) | 0.75 | Precisely matches specialized terminology and key data, reducing interference from irrelevant documents. |
Rerank result count (Reranked Return Count) | 3 entries | Prioritizes the most relevant core information, improving answer accuracy. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates the upload of large batch records or comprehensive documents containing charts. |
Common Pitfalls
- After enabling voice input for the agent, speech is not converted to text. This occurs when the
Speech-to-Textvoice model is incorrectly configured or not enabled, preventing proper recognition of the audio stream. - The model sometimes cites outdated or non-latest version data when answering batch compliance questions. This happens when the knowledge base's document version management mechanism is not effectively enabled, or incremental updates fail to synchronize the latest batch data in a timely manner.
- Numerical errors appear in batch numbers or test results within the model's answers. This is typically due to the
PARSE_FILE_TIMEOUT_SECONDSparameter being set too short, leading to incomplete parsing of large documents and failure to extract some critical fields correctly.
Verification Steps
- Upload an attenuated inactivated vaccine production batch record containing information for multiple batches. Ask the model about the production date and key test indicators for a specific batch. Verify if the answer is accurate and traceable to the original document.
- Simulate a query about long-term trends in stability study data. Check if the model can provide a coherent and logical answer by combining reports from different time points, and verify the cited data sources.
- Test uploading and parsing inspection reports in different formats (e.g., PDF, Word). Check if document content (especially tabular data) is fully recognized and can be used to answer relevant queries.
- Ask compliance questions involving specialized terminology and measurement units (e.g.,
TCID50/mL,mg/mL). Evaluate if the model's understanding and citation of these specific fields are precise and accurate.
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.