Data Characteristics for This Category
Data in the stem cell therapy field primarily comes from clinical trial reports, research papers, patent literature, drug instructions, and technical guidelines from regulatory bodies. This data has a relatively high update frequency, especially clinical trial progress and regulatory policy adjustments, which are often released quarterly or semi-annually. Document structures are complex, containing numerous specialized terms, experimental data, charts, and references. Specific fields include cell line numbers, administration protocols, dosage units (e.g., cells/kg), treatment cycles, safety indicators (e.g., immunogenicity, tumor formation risk), and efficacy evaluation indicators (e.g., CR, PR, SD). Units involve various forms such as biological concentration, time, temperature, and cell count.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The high update frequency of stem cell therapy data requires vector indexes to support efficient incremental updates, ensuring knowledge base timeliness. Complex document structures and numerous specialized terms make precise text segmentation and semantic understanding challenging. Traditional keyword-based retrieval often misses critical information, necessitating stronger semantic matching capabilities. Additionally, specific fields like dosage units and efficacy indicators require the vector model to capture and differentiate these key numerical details, preventing loss of their unique meaning during vectorization. This demands more refined text normalization and entity recognition during the vectorization preprocessing stage to improve subsequent retrieval accuracy and relevance.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters (characters) | Balances contextual completeness with vector model processing efficiency, adapting to professional document paragraph lengths. |
Recall count (Retrieval Count) | 10 entries (items) | Ensures coverage of sufficient potentially relevant information, providing rich candidates for reranking. |
Similarity threshold (Similarity Threshold) | Calibrated by actual measurement | Determined by testing against specific business requirements for retrieval precision. |
Rerank result count (Rerank Return Count) | 3–5 entries (items) | Filters for the most relevant few results, improving user experience and reducing information overload. |
Max Concurrent Indexing Tasks | 2–4 | Prevents excessive system resource pressure during high update frequency, ensuring stability. |
Index Update Cycle | weekly | Adapts to the update frequency of clinical progress and regulatory policies, maintaining knowledge base timeliness. |
Common Pitfalls
- Rerank model not active: The reranking function was not enabled in the interface during knowledge base indexing, or the selected model does not match the current service configuration.
- Inaccurate retrieval results: Chunk size is set too long or too short, leading to truncation of key information or insufficient context for complete semantic expression.
- Multimodal vector model integration failure: Attempting to add a third-party multimodal vector model incompatible with the current FastGPT architecture, resulting in model loading errors or mismatched API calls.
Verification Steps
- Perform online retrieval tests to check if results include expected key information and specialized terms, and verify the sorting logic of reranked results.
- Monitor index update logs to confirm incremental update tasks execute as expected, without significant errors or timeouts.
- Compare retrieval results at different
Similarity threshold(Similarity Threshold) values to determine a threshold that effectively filters irrelevant information without missing critical data. - In simulated user inquiry scenarios, verify the system's accuracy and completeness when responding to questions about stem cell therapy products and reagents.
Note: The values provided are common starting points. Measure against your own samples to determine optimal configurations.
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.