Data Characteristics
Phase II-III clinical trial data primarily comes from core documents of multi-center clinical studies. These include trial protocols, investigator brochures, case report forms (CRFs), medical monitoring reports, statistical analysis plans (SAPs), and clinical study reports (CSRs). Documents are typically in PDF, Word, or scanned image formats. Data updates are frequent during trials, involving protocol amendments and data cleaning/verification before database lock. Document structures are highly standardized, adhering to international guidelines like ICH GCP, with clear chapter divisions and expression paradigms. Fields and units have strong biostatistical and medical professional attributes, such as dosage units (mg/kg, IU), time points (weeks, months), and biomarkers (ng/mL, U/L). Specific medical terminology and abbreviations are common.
Constraints Imposed on Knowledge Base Retrieval and Recall
The specialized and standardized nature of Phase II-III clinical documents requires knowledge base retrieval to have high accuracy and strong semantic understanding. Frequent document updates, especially during protocol amendments and data verification, demand efficient incremental updates and robust version management for the knowledge base. Complex table structures and graphical information mean traditional text segmentation methods can lose context. Accurate matching of biostatistical and medical professional fields requires the knowledge base to identify and associate synonyms and abbreviations with different expressions. The large number of medical terms and units in documents, if not processed effectively, can lead to ambiguity or insufficient recall during retrieval. Additionally, the data volume from multi-center trials challenges knowledge base indexing efficiency and query response speed.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Balances contextual completeness and information density per segment, avoiding excessive length that introduces noise or insufficient length that loses context. |
Overlap Length | 100–150 characters | Ensures semantic continuity between segments, particularly for connecting table and list content. |
Recall count (Recall Count) | 10 entries | Covers a wider range of potentially relevant results, improving the input quality for subsequent re-ranking. |
Similarity threshold (Similarity Threshold) | Calibrate by measurement | Requires determination through test sets based on specific data and query scenarios to balance recall rate and accuracy. |
Rerank result count (Re-rank Return Count) | 3–5 entries | Focuses on a few highly relevant, high-quality results, reducing the processing burden on subsequent models. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates the parsing time for complex documents like large clinical study reports. |
Three Common Mistakes
- The knowledge base training status remains "training" for an extended period, with no corresponding records in the call logs. This usually indicates a blocked background task queue or an abnormal worker process exit, failing to correctly process training commands.
- Knowledge base export fails with a server problem error. This may be due to the exported file size exceeding the server's temporary storage limit, or a memory overflow when the backend service processes large-scale data.
- Retrieval results do not fully cover relevant content in the knowledge base. This typically results from an improper segmentation strategy, causing critical information to be split or context to be lost, affecting the quality of vector embeddings.
How to Verify Configuration
- Randomly select core documents. Use key sentences from these documents as queries. Check if the recalled results include the original paragraphs and verify the semantic completeness of the returned paragraphs.
- For specific medical terms or trial indicators, construct queries with various expressions. Confirm that the knowledge base consistently recalls paragraphs containing this information and check the similarity scores of the recalled results.
- Monitor the average query response time of the knowledge base under simulated high-concurrency query scenarios. Ensure it remains within an acceptable range to validate indexing and recall performance.
- Regularly check the incremental update logs of the knowledge base. Confirm that newly uploaded or modified documents are indexed promptly, and verify their content can be recalled through queries.
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.