Understanding the Data for this Category
Autoimmune product data comes from diverse sources. These include clinical trial reports, drug inserts, academic papers, patent documents, and adverse event monitoring data. Documents typically contain information on disease mechanisms, drug targets, clinical indications, dosage, contraindications, and side effects. Data updates frequently, especially with new drug launches or clinical research advancements. Document structures are complex, often containing extensive specialized terminology, tables, charts, and references. Fields and units involve dosage (e.g., mg/kg), frequency (e.g., QD, BID), treatment duration (e.g., weeks, months), biomarkers (e.g., CRP, ESR), and various clinical indices (e.g., DAS28 score).
Constraints Imposed by Data Characteristics on Vector Models and Indexing
The high update frequency of autoimmune product data requires the indexing system to support efficient incremental updates. This ensures the knowledge base remains current. Complex specialized terminology and abbreviations in documents demand strong semantic understanding from vector models. This avoids inaccurate recall due to lexical differences. Structured information in tables and charts challenges traditional text chunking methods. Multimodal or structured data embedding techniques need consideration. The diversity of numerical values and units for clinical indicators makes precise matching and range queries critical; semantic similarity alone may be insufficient. Recalling critical information like adverse events requires the model to have higher sensitivity and recall precision for negative or risk-related information.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk Length | 800–1200 characters | Ensures each chunk contains sufficient context to cover complex descriptions like disease mechanisms and drug action, while avoiding excessive length that leads to information redundancy and computational overhead. |
Chunk Overlap Length | 100–200 characters | Guarantees semantic continuity between chunks, especially when describing drug action processes or clinical pathways, preventing critical information from being split. |
Recall Count | 8–12 items | Given the complexity of autoimmune diseases and the diversity of drugs, increasing the recall count helps cover more relevant products or treatment plans, improving comprehensiveness. |
Similarity Threshold | Calibrate by actual measurement | Requires balancing recall and precision based on actual query results. This avoids confusing products with similar disease manifestations but different treatment plans. |
Rerank Return Count | 3–5 items | After reranking, the most relevant results are selected, reducing user screening effort and focusing on core products or treatment plans. |
Embedding Model | Doubao-embedding-large or text-embedding-ada-002 | Large embedding models have stronger understanding capabilities for specialized terminology and complex semantics in the biomedical field, improving vectorization quality. |
Common Pitfalls
- After enabling the embedding model, a connection test fails, displaying "API KEY or address error." This typically occurs because the entered
Custom Request AddressorAPI KEYdoes not match the actual service credentials. - During knowledge base chunking, table data is incorrectly processed as plain text. This leads to the loss of critical numerical and unit information within tables, preventing effective indexing. This happens because the default chunking strategy is not optimized for table structures.
- Refreshing the page shows "No available embedding model detected," even though one has been configured and tested successfully. This may be due to browser caching or a delay in FastGPT service internal state synchronization, causing the frontend to not update the model status promptly.
How to Verify Correct Configuration
- Upload an autoimmune product insert containing complex specialized terminology and clinical indicators. Check if the chunking results accurately retain key information, especially table and list data.
- Test with query statements containing specific drug dosages, frequencies, or biomarker values. Observe if the recall results include products with exact matches or relevant numerical ranges.
- Regularly check the index update logs. Ensure newly uploaded clinical trial reports or drug revision information are indexed promptly and completely.
- Conduct multi-turn dialogue tests for products related to different autoimmune diseases (e.g., rheumatoid arthritis, systemic lupus erythematosus). Evaluate the Agent's accuracy and comprehensiveness, and adjust the
Similarity Thresholdaccordingly.
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.