Data Characteristics
R&D documents in the infectious disease domain have unique data characteristics. Data sources are extensive, including clinical trial reports, pathogen genome sequences, drug mechanism of action studies, epidemiological survey data, and various in vitro and in vivo experimental data. These documents update frequently. New research findings, especially for emerging pathogens or drug resistance studies, can be released weekly or even daily. Document structures often contain numerous specialized terms, abbreviations, and complex biochemical pathway diagrams. Fields commonly involve pathogen names (e.g., "SARS-CoV-2", "MRSA"), host response indicators (e.g., "IL-6 concentration", "CD4+ T cell count"), drug activity parameters (e.g., "IC50", "MIC"), and dosage units (e.g., "mg/kg", "μg/mL"). Some documents also include tables, charts, and chemical structure images.
Constraints on Knowledge Base Retrieval and Recall
The characteristics of infectious disease R&D documents impose specific requirements on knowledge base retrieval and recall. High update frequency necessitates efficient incremental update mechanisms to ensure the timeliness of retrieval results. The large number of specialized terms and abbreviations in documents requires strong semantic understanding from recall models. Models must identify synonyms and hierarchical relationships to prevent recall failures due to terminology mismatches. Non-textual information, such as complex biochemical pathway diagrams and chemical structures, requires multimodal embedding or OCR technology for text conversion to include them in retrieval. Numerical fields with units, such as "IC50" and "MIC", may require support for range queries or numerical comparisons during retrieval; simple text matching is insufficient. The accuracy requirements for recall results are extremely high. Incorrect drug dosage or mechanism of action information can lead to significant R&D deviations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Balances semantic completeness and segment length, avoiding redundancy from being too long or loss of context from being too short. |
Recall count | 10–15 entries | Balances recall breadth with subsequent re-ranking processing efficiency, covering potentially relevant results. |
Similarity threshold | Calibrate based on actual measurements | Infectious disease terminology varies widely; adjust based on the specific embedding model and corpus to ensure high relevance in recall. |
Rerank result count | 3–5 entries | Focuses on the most relevant information, reduces the model's processing burden, and improves final output quality. |
maxContext | 4000–8000 tokens | Ensures enough recalled segments can be accommodated within a limited context window for comprehensive analysis. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates the file size requirements of large clinical trial reports or genomic analysis documents. |
Common Pitfalls
- Symptom: The model's output language does not match the query language (e.g., answering an English question in Chinese). Reason: The
promptdoes not explicitly specify the output language, or the model's training corpus is biased towards a specific language, leading to language preference being overlooked. - Symptom: Retrieval results do not match the question, showing low semantic relevance, or even abnormal similarity scores like "0-1". Reason: Knowledge base document segmentation is unreasonable, leading to key information being truncated or semantic units being split, affecting embedding quality; alternatively, the embedding model does not match the corpus domain.
- Symptom: After a knowledge base update, retrieval results still show old information or lack new data. Reason: The knowledge base lacks an automatic or manual incremental indexing update mechanism, or the update frequency cannot keep pace with the rapid advancements in infectious disease research.
Validation
- Construct queries containing specialized terms and numerical values for typical questions related to core diseases (e.g., influenza, HIV) and drugs (e.g., antibiotics, antiviral drugs). Check if the recalled results contain correct and complete key information.
- Randomly select recently updated documents from the knowledge base. Verify whether their content can be accurately retrieved and recalled by asking questions. This confirms the effectiveness of the knowledge base's update mechanism.
- Design test questions that include synonyms, abbreviations, or hierarchical concepts. Evaluate the consistency and accuracy of recall results under different expressions to verify semantic understanding capabilities.
Note: The values provided are common starting points. Measure against your own samples for optimal configuration.
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.