Data Characteristics
Autoimmune disease R&D data primarily comes from clinical trial reports, pathological analyses, gene sequencing data, drug mechanism of action research papers, and internal experimental records. These documents update frequently, especially clinical trial progress and new research findings, typically on a quarterly or semi-annual basis. Document structures are complex, containing extensive specialized terminology, abbreviations, charts, and cross-references. For example, clinical trial reports often follow ICH GCP guidelines, featuring fixed chapter structures such as study protocols, subject information, adverse events, and statistical analyses. Gene sequencing reports involve large amounts of sequence data, variant site information, and functional annotations. Common units include nM, μM for concentration, mg/kg for dosage, days, weeks, months for time, and expression levels for various biomarkers (e.g., IL-6, TNF-α).
Constraints on Knowledge Base Retrieval and Recall
The complex structure and specialized terminology of autoimmune R&D documents demand high standards for knowledge base chunking and vectorization. Abbreviations and synonyms frequently appearing in documents require standardization to ensure retrieval accuracy. The high update frequency necessitates an efficient incremental update mechanism for the knowledge base to prevent data lag from impacting R&D decisions. Additionally, large amounts of chart and table data are often difficult to vectorize directly, requiring an additional parsing layer for structured extraction. For instance, adverse event tables in clinical trial reports contain structured information crucial for drug safety evaluation, but direct text chunking might lose contextual relevance. Gene sequence data, due to its length and specificity, may not be effectively captured by traditional text segmentation methods, requiring tailored processing strategies to preserve biological meaning.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Balances contextual completeness and vectorization efficiency, avoiding semantic loss or excessive noise from chunks that are too long or too short. |
Chunk overlap (Chunk Overlap) | 50–100 characters | Ensures semantic continuity between adjacent chunks, reducing information fragmentation caused by splitting. |
Recall count (Recall Count) | 8–12 items | Reduces the computational burden on subsequent reranking and generation models while ensuring coverage. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | The autoimmune domain requires high precision for terminology; increasing the threshold appropriately reduces irrelevant results. |
Rerank result count (Reranked Return Count) | 4–6 items | The top few items typically have the highest precision and relevance after reranking. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large clinical trial reports or complex genomic annotation files can take a long time. |
Common Pitfalls
- After a knowledge base update, retrieval results do not include the latest information, showing outdated data. This occurs because the incremental indexing process for the knowledge base was not configured or triggered, preventing new documents from being vectorized and stored.
- When retrieving specific disease names or drug targets, the recall results contain a large amount of irrelevant content. This happens because the text preprocessing stage of the knowledge base failed to effectively identify and handle domain-specific abbreviations and synonyms, leading to inaccurate vectorized representations.
- After uploading large XLSX files, the system reports parsing failures or incomplete content recognition. This is due to complex table data structures, or the presence of merged cells, multi-level headers, and other non-standard formats, which the default parser cannot correctly extract as QA pairs.
Verification Steps
- Select a batch of test documents containing newly published clinical data, upload them to the knowledge base, and perform retrieval to verify that the latest information is accurately recalled.
- Design queries for core disease entities and drug targets. Check that the recall results exclude a large amount of irrelevant information and evaluate the professional relevance of the returned content. Define a relevance threshold.
- Use test files with complex table structures for upload. Observe log output and knowledge base content preview to confirm that key data within tables (e.g., dosage, adverse event incidence) is correctly identified and structured.
- Randomly select different types of documents from the knowledge base. Perform keyword and semantic queries. Evaluate the coverage and precision of the recall results, and collaboratively define acceptable recall and precision ranges with domain experts.
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.