Knowledge Base Retrieval and Recall for Medical Information

Data Characteristics

MI response tracing data originates from interactions between medical professionals and external consultants. This data is typically unstructured text. It includes consultation questions, professional responses, cited medical literature snippets, and internal approval comments. Data update frequency varies. New drug approvals, clinical guideline updates, or adverse event reports can trigger intensive data updates. Document structure usually includes fields like date, anonymized questioner background, specific question description, answer content, cited literature (DOI or PMID), and internal review status. Some data may contain images or charts, but the core content remains text. Date formats are typically standardized. Literature citations follow medical journal standards. Measurement units strictly conform to the International System of Units.

Constraints on Knowledge Base Retrieval and Recall

MI response tracing data is diverse and unstructured. This requires the knowledge base to have robust text parsing and vectorization capabilities to handle various formats and lengths. The uncertain data update frequency means the knowledge base index needs to support incremental updates and efficient batch rebuilding. This ensures real-time accuracy of recalled content. Documents contain cited literature and internal approval comments. Retrieval must match semantics and precisely match specific fields, such as quickly locating original literature via DOI. Accurate recognition of medical terms and abbreviations is crucial for recall relevance. This avoids missed recalls due to synonyms or near-synonyms. Consultation scenarios demand highly rigorous answers. Recalled results must be relevant, authoritative, and reliable. Therefore, source tracing and quality evaluation of recalled knowledge snippets are necessary.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Size`	500–800 characters	Balances contextual completeness of medical text with retrieval efficiency. Avoids noise from overly long paragraphs.
`Overlap Size`	50–100 characters	Ensures semantic coherence. Prevents loss of critical information at paragraph boundaries.
`Recall Count`	5–10 items	Provides diverse relevant candidates for subsequent re-ranking and manual filtering.
`Similarity Threshold`	Calibrate based on actual measurements	Balances recall and precision based on specific datasets and evaluation metrics.
`Rerank Return Count`	3–5 items	Refines recall results. Prioritizes the most relevant and authoritative knowledge snippets.
`Vector Model`	`bge-large-zh` or equivalent	Suitable for semantic understanding of Chinese medical text. Provides high-dimensional vector representations.

Common Pitfalls

The knowledge base file details show "Invalid dataset file key." This usually occurs after a private deployment version upgrade. File storage paths or metadata indexes may not have been migrated or updated correctly.
Retrieval results contain many irrelevant historical responses. This may be due to an overly large chunk size, causing individual knowledge blocks to contain too much irrelevant content. Alternatively, the vector model may have insufficient understanding of medical terminology.
Existing document libraries cannot be retrieved via API. This may be due to improper API permission configuration. Request parameters may also not match the system's expected format, such as missing a required dataset_id or authentication token.

Verification Steps

Upload various MI response tracing documents (e.g., PDFs or DOCXs with charts, cited literature, approval comments) via the FastGPT interface. Check if file parsing results are complete and accurate.
Conduct retrieval tests using a series of questions containing medical terms, abbreviations, and different phrasings. Check if the similarity score distribution and recall count of the results meet expectations.
Randomly select 10 retrieval questions. Manually evaluate the top 3 recalled knowledge snippets for relevance, authority, and completeness. Adjust the similarity threshold based on this evaluation.
Check the index status in the knowledge base management interface. Confirm that all documents are successfully indexed. Verify that incremental update operations trigger and complete normally.

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.