Data Characteristics
Attenuated live vaccine quality documents include production batch records, inspection reports, stability study data, Standard Operating Procedures (SOPs), and deviation investigation reports. Data sources typically are electronic batch record systems from production workshops, LIMS systems from QC laboratories, and archived documents from quality management departments. These documents update infrequently, primarily during process changes, regulatory updates, or annual reviews. Document structures are highly standardized. For example, batch records contain production dates, batch numbers, critical process parameters, intermediate product test results, and finished product release data. Fields include, but are not limited to, viral titer (TCID50/mL), protein content (ug/mL), purity (%), and endotoxin (EU/mL), with units precise to two decimal places.
Constraints on Knowledge Base Retrieval and Recall
The standardized structure and low update frequency of attenuated live vaccine quality documents allow for efficient segmentation during knowledge base construction, leveraging inherent hierarchies to reduce contextual redundancy. The precision of fields and standardization of units require strict preprocessing before text embedding to prevent numerical data from being semantically misinterpreted. Due to requirements for batch traceability and regulatory compliance, retrieval results demand high accuracy and traceability, requiring highly relevant recall results. Significant duplication or similarity may exist across documents, especially in SOPs for different batches or product lines. This presents challenges for knowledge base deduplication and version management. For inspection scenarios, queries often focus on specific regulatory clauses or quality standards, requiring the knowledge base to precisely match corresponding evidentiary documents.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Accommodates longer paragraphs in batch records and SOPs, ensuring contextual completeness. |
Chunk Overlap Length (Segment Overlap Length) | 100–150 characters | Ensures contextual continuity, preventing critical information from being split. |
Recall count (Recall Count) | Top 8 | Covers potentially relevant documents while controlling computational load for subsequent processing. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Balances retrieval precision with recall comprehensiveness, reducing irrelevant results. |
Rerank result count (Reranked Return Count) | Top 5 | Focuses on the most critical evidentiary documents, improving the accuracy of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing of large PDFs or scanned documents, preventing upload failures due to timeouts. |
Common Pitfalls
- Uploading large volumes of documents results in upload failures or frozen progress bars. This typically occurs because the
UPLOAD_FILE_MAX_SIZEparameter is set too low, exceeding the single file limit, orPARSE_FILE_TIMEOUT_SECONDSis too short, leading to timeouts when parsing complex documents. - Retrieval results show multiple highly similar documents, with no clear indication of the latest version. This indicates that document versions or update dates were not effectively managed during knowledge base import, leading to semantic duplicates being recalled simultaneously.
- Queries for specific batch numbers or dates yield inaccurate or missing critical information. This usually results from an overly coarse document segmentation strategy, mixing key identifiers like batch numbers and dates with irrelevant content, or failing to effectively extract and index structured information.
Verification Steps
- Select a typical production batch record or SOP. Simulate an inspection scenario query. Check if the top recalled documents contain the expected critical information and verify its completeness.
- Upload different versions or revision dates of similar documents. Perform specific queries. Confirm the knowledge base prioritizes the latest or most relevant version and validate its sorting logic.
- Choose a query containing precise numerical fields (e.g., viral titer). Check if the recalled document snippets accurately include these values and their units. Verify the embedding and retrieval effectiveness of numerical information.
- Review knowledge base backend logs. Confirm no
ERRORorWARNINGlevel timeout, out-of-memory, or other abnormal messages occurred during file parsing and vectorization.
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.