Knowledge Base Retrieval and Recall for High-Value

Data Characteristics

High-value consumable data for clinical trial pre-screening primarily comes from product manuals, registration certificates, clinical study protocols, adverse event reports, and relevant domestic and international regulations and guidelines. These documents are typically in PDF, DOCX, or structured database formats. Data update frequency is relatively low, occurring mainly when new products launch, indications expand, or regulations change. Document structures often include standardized fields in product manuals, such as product model, specifications, intended use, contraindications, precautions, and operating procedures. Clinical study protocols cover inclusion/exclusion criteria, follow-up plans, and evaluation metrics. The data frequently involves specific medical terminology, units of measurement (e.g., millimeters, milliliters, joules), and proprietary product serial numbers and batch information.

Constraints on Knowledge Base Retrieval and Recall

The authoritative and rigorous nature of high-value consumable data sources demands highly precise knowledge base retrieval results. Ambiguous or speculative content is unacceptable. The large volume of specialized terminology and units of measurement in documents challenges tokenization and entity recognition, potentially leading to insufficient or excessive retrieval of irrelevant information. The low data update frequency means knowledge base construction must prioritize historical version management, ensuring retrieval of currently valid or time-specific information. The coexistence of structured and unstructured data requires the knowledge base to effectively process different document formats and accurately extract key information. For example, strict inclusion/exclusion criteria in clinical study protocols require precise matching of patient characteristics; any deviation can affect the reliability of pre-screening results.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	High-value consumable documents have strong logical paragraph structures. This range avoids breaking semantic integrity with overly short segments and reduces noise from overly long ones.
`Recall count` (Recall Count)	`5–8 items`	Clinical trial pre-screening demands high information precision. This ensures sufficient context recall while avoiding redundant information interference.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Guarantees high relevance between retrieval results and queries, reducing imprecise matches, especially useful for documents with extensive technical terminology.
`Rerank result count` (Reranked Return Count)	`3 items`	Further refines recall results, presenting the most relevant limited information to the large language model, improving output quality.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides ample parsing time for large PDFs or manuals containing complex diagrams.
`maxContext`	`4000 characters`	Ensures the large language model receives and processes sufficiently long contexts to handle complex clinical trial standards.

Common Pitfalls

Incomplete knowledge base query results, with critical information missing. This typically results from an inappropriate Chunk size (Segment Length) setting, causing important information to be split across different segments, or single segments being too short and losing context.
AI responses containing speculative content inconsistent with the query. This may occur if the Similarity threshold (Similarity Threshold) is set too low, recalling many weakly related document fragments, leading to misinterpretation by the large language model.
System unresponsiveness or errors when uploading large product manuals or clinical study protocols. This relates to the PARSE_FILE_TIMEOUT_SECONDS parameter being set too low, not providing enough time for file parsing.

Verification Steps

Perform knowledge base queries for typical high-value consumable pre-screening questions. Check if the recalled document segments fully cover all information required for the answer.
Upload a product manual containing complex tables and specialized terminology. Observe if the file parses normally and is successfully ingested into the knowledge base. Check the File Parsing Log.
Manually evaluate recall results. Confirm that the Similarity threshold (Similarity Threshold) effectively filters irrelevant content while maintaining a high recall rate, without significant noise.
Use a series of queries with varying lengths and complexities. Verify that the large language model's answers, based on the knowledge base, are accurate, free of redundancy, and correctly cite sources.

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.