Data Characteristics
Intelligent triage registration and declaration documents come from diverse sources. These include regulatory files, technical guidelines, and approval cases published by the National Medical Products Administration (NMPA). They also include internal R&D documents, clinical trial reports, and product manuals. Data updates frequently, especially regulations and guidelines, which may be revised or supplemented annually. Regulatory documents have strict chapter and clause numbering. Technical guidelines contain many specialized terms, diagrams, and formulas. Internal company documents have more flexible formats, such as Word, PDF, and scanned images. Fields and units cover various domains like pharmacology, medicine, and statistics. Examples include dosage units (mg, g), time units (days, months), and statistical indicators (P-value, confidence interval). These require precise identification and processing.
Constraints on Knowledge Base Retrieval
Data diversity requires the knowledge base to support multi-format document import and parsing. This ensures effective content extraction from all file types. High update frequency means the knowledge base needs an efficient incremental update mechanism. This must quickly identify and synchronize the latest regulations to avoid using outdated information. The strict structure and specialized terminology of regulatory files demand higher requirements for text segmentation and embedding models. This ensures the integrity of semantic units and accurate representation of professional vocabulary, preventing key clauses from being fragmented or misunderstood. The flexible formats and image content of internal company documents require the knowledge base to have OCR capabilities. This converts text in images into retrievable text. The precision of fields and units constrains the accuracy of retrieval results. It is necessary to avoid declaration errors caused by unit confusion or misreading of values.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Balances semantic integrity and retrieval efficiency. Avoids overly long paragraphs diluting key information and overly short paragraphs losing context. |
Chunk overlap (Segment Overlap) | 50–100 characters (characters) | Ensures contextual continuity at segment boundaries, improving recall rate for cross-paragraph information. |
Recall count (Recall Count) | 10–15 entries (items) | Considering the complexity of declaration documents, appropriately increasing the recall count covers a wider range of potentially relevant information. |
Similarity threshold (Similarity Threshold) | Calibrate by measurement | Requires testing with specific embedding models and datasets to ensure recall results are relevant but not overly broad. |
Rerank result count (Reranked Return Count) | 5 entries (items) | After optimization by the reranking model, selects the most relevant few items to present to the user, improving information utilization efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses parsing needs for large PDFs or scanned documents, preventing parsing timeouts due to oversized files. |
Common Pitfalls
- Retrieval results contain many irrelevant or outdated regulatory clauses. This occurs because the knowledge base's update mechanism did not synchronize with the latest regulatory versions in time, leading to outdated data interfering with retrieval.
- After a user query, the system's results lack critical table or diagram information. This happens because the knowledge base did not effectively OCR image content, resulting in text information within images not being indexed.
- Retrieval results for specialized terms are inaccurate or missing. This is due to the embedding model's insufficient understanding of professional vocabulary in the biomedical field, or text segmentation fragmenting core terms from their context.
Verification Steps
- Select recently updated regulatory files. Query relevant clauses. Verify if recall results include the latest version content and confirm its accuracy.
- Choose internal company documents containing complex tables and diagrams. Query key data or processes within them. Check if recall results correctly identify and present information from images.
- Prepare a set of queries for specialized terms in the biomedical field. Compare recall results to ensure the context of these terms is complete and semantically correct, and check for any omissions.
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.