Knowledge Base Retrieval and Recall for Autoimmune Disease

Data Characteristics for this Category

Regulatory documents and SOPs in the autoimmune disease field originate from national drug administration regulations, pharmaceutical company internal standard operating procedures, clinical trial protocols, and medical society treatment guidelines. These documents update at a relatively stable pace, with minor revisions typically occurring during regulatory changes or new drug launches, and major revisions having longer cycles. Document structures are primarily hierarchical, with distinct chapters and clauses, often including flowcharts, tables, and appendices. Fields involve drug names, dosage units (e.g., mg/kg, IU), treatment cycles, adverse event codes (e.g., MedDRA codes), and patient inclusion/exclusion criteria.

Constraints Imposed by these Characteristics on Knowledge Base Retrieval and Recall

The hierarchical structure and high density of specialized terminology in autoimmune regulation documents require the knowledge base to maintain semantic integrity during chunking, avoiding the severance of critical clauses. Precise numerical information like drug dosages and treatment cycles, along with the recall of adverse event codes, demand high retrieval accuracy; fuzzy matching can lead to misinterpretation. Although document update frequency is not high, updates often involve core clauses, necessitating a version management mechanism to ensure that the retrieved information is always the latest effective version. Additionally, parsing non-textual content such as flowcharts and tables challenges the knowledge base's file processing capabilities, directly impacting subsequent recall effectiveness.

Configuration Settings

Configuration Item	Suggested Value	Rationale for this Value
`Chunk Length`	`500–800 characters`	Ensures semantic integrity of regulatory clauses and SOP steps, preventing single sentences from being truncated.
`Overlap Length`	`50–100 characters`	Provides contextual continuity, helping the model understand logical relationships across chunks.
`Recall Count`	`Top 5–7 items`	Balances recall breadth with model processing capacity, covering most relevant clauses.
`Similarity Threshold`	`0.75–0.85`	Filters out low-relevance content, improving recall precision and avoiding noisy information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates parsing time for large SOP files, preventing processing failures due to timeouts.
`Reranked Return Count`	`Top 3 items`	Further optimizes ranking, prioritizing the most relevant core clauses for the query.

Three Common Mistakes

Knowledge base queries return empty or incomplete results, manifesting as the model answering "no relevant information found." This occurs because text in flowcharts or table structures were not correctly identified during document parsing, leading to missing key information.
The model provides seemingly correct answers without citing original text snippets. This usually happens when the reference_source field in the RAG knowledge base configuration is not correctly mapped or enabled, preventing the model from tracing information sources.
After uploading large PDF files, the system remains unresponsive for an extended period or reports a UPLOAD_FILE_MAX_SIZE error. This indicates that the file size exceeds the system's default or configured limit, requiring adjustment of the UPLOAD_FILE_MAX_SIZE parameter.

How to Confirm Correct Configuration

Construct multiple query statements containing specialized terms and acronyms for core clauses and key processes. Check if the recall results accurately include original text snippets and if the similarity_score values are within the expected range.
Upload SOP documents containing flowcharts and complex tables. Check if the knowledge base can correctly extract and index this non-pure text content by querying process steps or table content.
Simulate a regulation update scenario by uploading two versions of a document (old and new) and performing queries. Confirm that recall results prioritize relevant clauses from the latest version, verifying version management functionality.
Upload and parse multiple large regulatory documents under different network environments. Monitor if the PARSE_FILE_TIMEOUT_SECONDS parameter is sufficient to cover file processing time, avoiding timeouts.

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.