Data Characteristics
Phase I clinical trial registration submission documents include study protocols, ethics approvals, informed consent forms, investigator brochures, Clinical Study Reports (CSRs), and related statistical analysis files. These documents are primarily in PDF format; some data tables may be in Excel. Data sources are mainly CRO companies, sponsor internal R&D departments, and collaborating clinical institutions. Update frequency is relatively low. After trial design, major documents are revised once or minimally after trial execution and data analysis. Document structure is rigorous, adhering to ICH guidelines and regulatory requirements from bodies like FDA, EMA, or NMPA. They have fixed chapter divisions and content organization. Fields include dosage, administration route, pharmacokinetic parameters (e.g., Cmax, AUC), pharmacodynamic indicators, and adverse event (AE) records. Units are strictly standardized, for example, blood drug concentration in ng/mL and time in hours.
Constraints from These Characteristics on "Citing Sources and Traceability"
The strictness of Phase I clinical data requires precise source citations. Fixed document structures mean citations must quickly locate specific chapters or page numbers. Low data update frequency reduces the risk of outdated citations due to document version changes, but demands high accuracy for initial indexing. The large volume of PDF documents makes text extraction and parsing critical, requiring handling of complex charts and nested structures. Specialized fields like pharmacokinetics necessitate that the knowledge base understands and associates these terms to accurately cite relevant data points in responses. Any citation deviation can lead to significant issues in registration submissions. Therefore, the system must provide a highly reliable traceability mechanism, ensuring each citation can be traced to the exact location in the original file and display the original text snippet.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 500–800 characters | Ensures each knowledge chunk contains sufficient context, preventing information fragmentation from over-segmentation, while maintaining the integrity of complex charts and tables. |
Recall count | Top 8–12 entries | Phase I clinical data is highly specialized and knowledge-intensive. Increasing recall improves coverage and reduces missed citations. |
Similarity threshold | 0.75–0.85 | A high threshold ensures recalled content is highly relevant to the query, reducing inaccurate or irrelevant citations, which aligns with the strict requirements for submission documents. |
Rerank result count | Top 5 entries | Re-ranks recall results to ensure the most relevant and authoritative evidence appears first, improving citation quality and efficiency. |
maxContext | 4000–8000 Token | Allows the model to process longer contexts, accommodating complex logical relationships and data descriptions in Phase I clinical reports. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Phase I clinical documents are often large and contain many charts, making parsing time-consuming. Extending the timeout prevents parsing interruptions. |
Three Common Mistakes
- Missing or incorrect file citations in knowledge base answers. This often results from incorrect metadata extraction during file parsing or mapping errors during indexing.
- External API calls returning unparseable citation content. This occurs when the
detail: trueparameter returns a structure that the caller does not correctly process, leading to loss of critical citation information. - Citations appearing in answers even when "display citations" is unchecked in the FastGPT interface. This may indicate a version compatibility issue or improper front-end logic.
How to Verify Correct Configuration
- Upload a Phase I clinical trial report PDF containing complex tables and charts. Query key data points from the report. Check if the returned citations accurately locate the relevant pages or paragraphs in the original file.
- Use FastGPT's API with the
detail: trueparameter. Verify that thequotefield in the returned JSON structure contains complete and parseable filenames, page numbers, and original text snippets. - In the knowledge base management interface, randomly select several uploaded Phase I clinical documents. Manually check their segmentation and the accuracy of indexed content, especially ensuring that specialized terms and data units are correctly identified.
The values given 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.