Data Characteristics
Phase I clinical study quality documents include research protocols, informed consent forms, ethics committee approvals, investigator brochures, case report forms (CRFs), raw data records, statistical analysis plans, investigational product management records, adverse event reports, and final study reports. These documents originate from sponsors, Contract Research Organizations (CROs), research sites, central laboratories, and ethics committees. Updates are frequent during study startup and execution, especially after protocol amendments, adverse events, or data audits. Documents are primarily in PDF, Word, and Excel formats. They have a strict structure and contain extensive medical terminology, dosage units (e.g., mg, μg/kg), time points (e.g., Day 1, Week 4), laboratory indicators (e.g., U/L, ng/mL), and subject IDs.
Constraints on Deployment and Upgrade
The rigor, diverse formats, and update frequency of Phase I clinical quality documents impose specific requirements on FastGPT deployment and upgrades. The medical terminology and abbreviations in these documents require robust text embedding models for accurate semantic understanding and retrieval. Multi-node deployment solutions must ensure knowledge base data consistency and real-time synchronization to handle frequent document updates and revisions. Historical version management is critical for tracing document content at different points in time, supporting audits and compliance reviews. Documents contain sensitive subject information, so the deployment environment must meet strict data security and privacy protection requirements, including data encryption, access control, and audit logs. Integrating vector databases like Zilliz requires attention to version compatibility and data migration strategies to prevent data loss or service interruption during upgrades.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Phase I clinical study reports and investigator brochures are often large; ensure full upload capability. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances medical term context integrity with search recall efficiency, avoiding over-segmentation or overly long paragraphs. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures precision of retrieved content and reduces interference from irrelevant medical concepts. |
Rerank result count (Rerank Return Count) | Top 5 entries (top 5) | Prioritizes the most relevant key information. Phase I clinical questions typically require highly focused answers. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Processing complex PDF and Word documents, especially those with many charts and tables, requires longer parsing times. |
EMBEDDING_MODEL_NAME | text-embedding-ada-002 or equivalent | Ensures good semantic understanding of medical professional vocabulary and clinical data. |
Common Pitfalls
- Poor search quality after knowledge base construction, or inconsistent results for different questions about the same document. This occurs due to an unsuitable text embedding model failing to fully understand medical terminology and contextual relationships, or an unreasonable segmentation strategy that fragments key information.
- After multi-node deployment, some nodes fail to load the latest document content, or knowledge base data is inconsistent. This results from improper container storage or data synchronization mechanism configuration, such as incorrect shared storage volume setup or message queue service configuration, leading to delayed data broadcast or synchronization.
- Models added cannot be enabled or throw errors in the interface. This happens due to incorrect local model path configuration or missing runtime dependencies, preventing the system from correctly loading and calling the model service.
Verification Steps
- Upload a Phase I clinical study protocol containing key medical terms and dosage units. Ask detailed questions and check if the returned results accurately include this information and correctly interpret the context.
- In a multi-node environment, update a core document. Then, query from different nodes and verify that all nodes return consistent results to confirm the data synchronization mechanism's effectiveness.
- Check the status of added local models via the FastGPT administration interface. Confirm they are "Enabled." Attempt a knowledge base query using the model and observe response speed and result quality to determine if the model is functioning correctly.
- Examine system logs for
Reached the max retries per request limitor other Zilliz Cloud connection errors to confirm stable vector database connectivity.
Note: The values provided are common starting points. Measure against your own samples for optimal performance.
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.