Data Characteristics in This Category
Stem cell therapy data primarily originates from clinical trial reports, research literature, regulatory approval documents, and patent applications. This data often exists in various forms, including unstructured text, tables, images, and even gene sequences. Update frequency varies; clinical trial results and regulatory policy changes may see significant updates quarterly or semi-annually, while research literature is published more frequently. Document structure for clinical trial reports typically includes standardized sections such as study protocols, patient inclusion and exclusion criteria, treatment regimens, efficacy evaluation metrics, and adverse events. Fields and units are highly specialized, for example, cell line names, dosage (e.g., 1x10^6 cells/kg), treatment cycles (e.g., once weekly), efficacy indicators (e.g., Karnofsky score, ECOG PS), biomarkers (e.g., CD34+ cell count), and various biostatistical parameters (e.g., p-value, confidence interval).
Constraints Imposed by These Characteristics on Forms and Interactions
The high specialization and diversity of stem cell therapy data demand strict requirements for form design and interaction flows. Due to the wide range of data sources and varying update paces, forms must support multi-source data integration and validation. For instance, when entering new clinical trial data, the system should compare it against existing regulatory approval information. Specialized fields and units require forms to have robust data type validation capabilities to prevent input errors. For example, a cell dosage field needs to restrict input to numerical values and offer unit selection. The presence of unstructured text means traditional dropdowns or checkboxes are insufficient; free-text input fields are necessary, complemented by entity recognition and keyword extraction functions. Furthermore, due to potential ethical and compliance risks, multi-level review mechanisms before form submission, along with data traceability and version control requirements, must be reflected in the interaction design to ensure operational traceability and data accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 2048 token | Stem cell therapy reports often contain extensive professional terminology and detailed descriptions, requiring a longer context window for accurate understanding and response generation. |
Chunk size (Segment Length) | 800 characters (characters) | Ensures that a single segment can contain a relatively complete professional discussion or experimental result, reducing semantic fragmentation. |
Recall count (Recall Count) | Top 8 entries (top 8) | Considering that queries may involve multiple related factors, increasing the recall count enhances information coverage. |
Similarity threshold (Similarity Threshold) | 0.75 | The domain is highly specialized, requiring a higher similarity threshold to ensure precise matching of recalled content and avoid interference from generalized information. |
Rerank result count (Reranked Return Count) | 5 entries (5 items) | Further refines recall results, focusing on the most relevant core information to improve user efficiency in obtaining useful information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Stem cell therapy documents (e.g., clinical trial reports) are often lengthy and contain complex tables and charts, which may require longer parsing times. |
Three Common Pitfalls
- When users submit free-text queries containing a large number of specialized terms, the AI frequently responds with "cannot understand" or "insufficient information" prompts. This usually occurs because the knowledge base lacks synonyms or hypernym mappings for specific professional terms, leading to recall failures.
- After form submission, the system displays "data format does not meet requirements" or "field value out of range." This stems from a lack of detailed validation rule configuration for fields specific to the stem cell therapy domain (e.g., cell dosage units, specific biomarker values).
- In FastGPT version 4.8.14, when the workflow orchestration uses the "Code Execution" module to process inputs containing historical chat records, validation fails and execution is blocked. This may be due to the "Code Execution" module's parsing logic for input history not correctly handling complex multi-turn conversation contexts.
How to Verify Configuration
- Submit a form containing a stem cell therapy product name, dosage, and target indication. Check if all fields are correctly parsed and recorded. Observe if the system provides preliminary query results or guidance based on this information.
- Simulate a user asking about clinical safety data for a specific stem cell therapy. Verify if the AI's response accurately and completely cites literature sources and data. Compare it with original documents to confirm if the recall threshold and segment length are appropriate.
- Test form submissions of varying complexity, including scenarios with attachments (e.g., PDF clinical reports). Verify if file parsing time is within the
PARSE_FILE_TIMEOUT_SECONDSsetting and if key information extracted is correctly mapped to form fields. - Configure multi-turn conversation guidance in the FastGPT application. Attempt to progressively refine stem cell therapy product inquiry requirements through dialogue. Confirm that the
maxContextsetting supports continuous professional conversations and that each interaction correctly understands user intent.
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.