Data Characteristics
Psychiatric R&D document data primarily comes from clinical trial reports, drug mechanism of action studies, patient medical records, genomics and proteomics data, and drug interaction analyses. These documents update frequently, especially clinical trial progress and new research findings. Document structures are complex, often containing large amounts of unstructured text like disease progression descriptions, diagnostic evaluations, and efficacy observations. They also contain semi-structured data such as trial protocols, scale scores, and drug dosages. Fields and units are highly specialized, for example, scale scores (e.g., HAM-D scores for Hamilton Depression Rating Scale), drug concentrations (ng/mL), gene expression levels (RPKM or TPM), and clinical symptom descriptions (e.g., "hallucination," "delusion"). Documents frequently include medical acronyms, specialized terminology, and disease-specific expressions.
Constraints on Knowledge Base Retrieval and Recall
The complexity of psychiatric R&D documents imposes multiple constraints on knowledge base retrieval and recall. High-frequency updates require efficient document synchronization and indexing mechanisms to ensure retrieval result timeliness. Unstructured text and specialized terminology in documents make traditional keyword matching insufficient for accurate recall. Semi-structured data like scale scores and gene expression levels require support for numerical range queries or specific conditional filtering; pure text vector similarity retrieval may not effectively capture this information. Disease-specific expressions and acronyms demand semantic understanding and synonym expansion from the knowledge base to avoid recall omissions due to inconsistent terminology. Long documents have strong contextual relevance, requiring more refined segmentation strategies to ensure retrieved segment completeness and relevance.
Configuration Strategy
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Psychiatric R&D documents have strong contextual relevance. This length ensures context completeness while reducing information overload in a single segment. |
Chunk Overlap Length | 100–150 characters | Ensures semantic continuity between adjacent segments, preventing critical information from being truncated at segment boundaries. |
Recall count | 10–15 entries | Given specialized terminology and complex semantics, increasing the number of recalled items improves initial recall coverage, providing more candidates for subsequent re-ranking. |
Similarity threshold | Calibrate by actual measurement | Psychiatric terminology is extensive with synonymous expressions. Small-sample testing and manual evaluation are necessary to balance recall rate and precision. |
Rerank result count | 5–8 entries | After initial recall, large language models perform semantic re-ranking to select the most relevant results, avoiding interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Psychiatric documents (e.g., clinical trial reports) can be lengthy, potentially requiring longer parsing times. This value prevents parsing failures due to timeouts. |
Common Pitfalls
- Empty or irrelevant retrieval results often stem from improper knowledge base segmentation strategies that fail to capture key information, or from outdated indexes.
- Retrieval results containing excessive irrelevant information occur when the similarity threshold is set too low, recalling semantically similar but actually irrelevant content, or when effective re-ranking is not performed.
- Failure to query specific numerical or structured data typically happens because the knowledge base does not effectively extract and index semi-structured information like scale scores or gene expression levels, preventing precise matching or range queries.
Validation Steps
- Select a batch of representative queries. Compare retrieval results against original documents to verify inclusion of all relevant key information.
- For queries involving specific scale scores, drug dosages, or other semi-structured information, confirm the knowledge base accurately recalls document segments containing these numerical ranges or specific conditions.
- Regularly track knowledge base update logs to confirm newly uploaded or modified R&D documents are indexed promptly and become effective in retrieval.
- Simulate actual usage scenarios. Observe the ranking and quantity of retrieval results to evaluate if the re-ranking mechanism effectively prioritizes the most relevant information.
Note: The values provided are common starting points. Measure them against your 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.