Data Characteristics
Site Management Organizations (SMO) prepare registration and declaration documents using data from clinical trial protocols, informed consent forms, ethics approval documents, investigator brochures, clinical study reports, adverse event reports, data management plans, and statistical analysis plans. These documents are typically in PDF, Word, or Excel formats. Some data may reside in Electronic Data Capture (EDC) or Laboratory Information Management System (LIMS) systems. Data updates are frequent, especially during clinical trials, as protocol amendments, subject enrollment, data entry, and adverse event reporting lead to real-time changes. Document structures are complex, containing specialized terminology, abbreviations, charts, and appendices. Fields include medical terms, dosage units (e.g., mg, mL), time units (e.g., days, weeks), and statistical indicators. Data formats can vary across different phases and trials.
Constraints on Deployment and Upgrade
SMO registration and declaration data characteristics impose specific constraints on FastGPT deployment and upgrades. First, the large volume and frequent updates of documents require the deployment environment to support efficient file upload, parsing, and indexing. It must also support incremental updates to avoid full knowledge base rebuilds, which reduces system load and improves data timeliness. Second, diverse document formats and complex content, especially scanned PDFs and specialized charts, demand high accuracy for text extraction and Optical Character Recognition (OCR). OCR engine integration and configuration must be correct during deployment. Additionally, medical terminology and abbreviations in the data require the knowledge base to have strong semantic understanding. This prevents ambiguity or errors during retrieval and generation, often achieved through pre-trained models or domain-specific fine-tuning. When upgrading, new version optimizations and compatibility for these capabilities are key considerations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Individual registration and declaration documents, such as clinical study reports, can be several hundred megabytes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDFs or complex Word documents can take a long time. |
Chunk size (Segment Length) | 800–1200 characters | Retains sufficient context while preventing overly long segments from affecting recall precision, ensuring the integrity of medical terminology. |
Recall count (Recall Count) | Top 5 | Ensures refined and relevant retrieval results, reducing interference from irrelevant information. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall and precision, ensuring high semantic relevance between retrieval results and queries, especially in the medical domain. |
Rerank result count (Rerank Return Count) | 3 | Further selects the most relevant segments to improve the quality of the final answer. |
Common Misconfigurations
- Issue: Third-party model API keys (e.g., Deepseek) fail to connect after configuration, returning "API key invalid" or "connection refused" errors. Cause: The deployment environment's network policies restrict outbound access, or proxy configurations are incorrect, preventing FastGPT from connecting to the model provider's API endpoint.
- Issue: After uploading large PDF files, knowledge base construction is unresponsive for extended periods or returns a "file parse error." Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, or the OCR engine is not correctly installed or configured, leading to file parsing timeouts or failures. - Issue: New users cannot register, or existing users cannot log in, with a "user service unavailable" message. Cause: During Docker Compose deployment, the user service container did not start correctly, or its data volume configuration is incorrect, leading to an inaccessible user database.
Verification Steps
- Upload a typical clinical study report (e.g., a PDF with charts and tables). Observe if parsing and knowledge base construction complete successfully. Check the completeness of the content in the knowledge base.
- Perform retrieval using queries containing medical terminology. Verify the relevance and accuracy of the returned results. Evaluate if the similarity threshold is appropriate.
- Test conversations with different third-party large models. Confirm that API key configurations are valid and models respond correctly. Check system logs for connection errors.
The values provided are common starting points. Measure them against your 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.