Knowledge Base Retrieval for Academic Promotion Registration

Data Characteristics

Data for academic promotion registration and declaration document preparation primarily comes from approved drug product inserts, clinical trial reports, pharmacology and toxicology research reports, and relevant medical guidelines and expert consensuses. Document updates typically align with drug lifecycles and regulatory policy changes, such as package insert revisions or new indication approvals. Update frequencies vary from quarterly to annually. Documents are often standardized PDF or Word files, containing extensive technical terminology, dosage units (e.g., mg/kg, IU), time units (e.g., days, weeks), and complex tables and figures. Common fields include drug generic name, brand name, indications, dosage and administration, adverse reactions, contraindications, and drug interactions.

Constraints on Knowledge Base Retrieval and Recall

The specialized and standardized nature of academic promotion documents demands highly accurate knowledge base retrieval to avoid semantic deviations. Minor differences in critical information like drug names or dosages can lead to serious errors. Complex document structures, including tables and figures, mean that plain text chunking may lose context, requiring more intelligent preprocessing and embedding strategies. Irregular update frequencies necessitate efficient incremental update and version management capabilities for the knowledge base to ensure retrieval timeliness. Accurate recognition of technical terms and units challenges the domain adaptability of tokenizers and embedding models. Retrieval results containing original document paragraph IDs may expose sensitive information or confuse users, requiring output filtering.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk Size`	`500-800 characters`	Balances semantic completeness and retrieval efficiency, preventing overly long chunks from introducing noise or overly short chunks from losing context.
`Chunk Overlap`	`50-100 characters`	Ensures semantic continuity at chunk boundaries, improving recall for cross-chunk queries.
`Recall Count`	`Top 5-8`	Given the complexity of academic materials, increasing the recall count can improve coverage, followed by re-ranking for optimization.
`Similarity Threshold`	Calibrate by measurement	Adjust based on specific datasets and embedding model performance. Typically `0.7-0.8` is suitable; too low introduces irrelevant results, too high may miss relevant ones.
`Re-rank Return Count`	`3-5`	Further refines the most relevant snippets from the initial recall, reducing the burden on the large language model to process irrelevant information.
`maxContext`	`3000-4000 tokens`	Ensures sufficient context for the large language model to reason, accommodating multiple recalled chunks and user queries.

Common Pitfalls

Retrieval results show relevant paragraphs, but the model's final output is empty or irrelevant: This often occurs when the large language model fails to effectively utilize knowledge base information due to context length limitations or internal logic during result processing.
Knowledge base query functionality fails after an upgrade, or works locally but not externally: This typically relates to API interface changes in the new version, improper permission configurations, or network environment differences preventing external requests from correctly accessing the knowledge base service.
Retrieved paragraphs contain internal numbers or IDs, such as 12356, and are output directly to the user: This happens when document content is not cleaned during knowledge base construction or when post-processing filters are not applied before model output.

Verification Steps

Conduct multi-turn dialogue tests for typical questions. Check if the model's answers accurately cite factual content from the knowledge base and compare them with original documents.
In the knowledge base management interface, verify that recently updated documents are successfully chunked, embedded, and searchable. Check the creation_time field.
Use different query types (e.g., direct questions, fuzzy queries, queries with technical terms). Observe if the knowledge base's top-k recalled paragraphs contain key information for the answer and check the similarity score distribution.
Check system logs to confirm that the knowledge base retrieval service does not show 5xx error codes or timeout warnings.

Note: The values provided are common starting points and should be measured against specific 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.