Data Characteristics
Market access quality documentation includes registration application materials for drugs and medical devices, clinical trial reports, Good Manufacturing Practice (GMP) documents, post-market surveillance requirements, and change documents. Data sources are primarily regulatory guidelines from official agencies, internal R&D and production records, and third-party assessment reports. These documents are typically in PDF, Word, and Excel formats, featuring a mix of highly structured and semi-structured content. Updates are usually quarterly or annually, driven by regulatory revisions, product life cycles, and internal processes. Emergency regulatory changes can trigger ad-hoc updates. Documents contain extensive specialized terminology, dosage units, chemical structures, and clinical indicators. Field names and units strictly adhere to industry standards.
Constraints on Deployment and Upgrades
The characteristics of market access quality documentation impose specific requirements on FastGPT deployment and upgrades. Regular updates to regulations and guidelines mean the knowledge base update frequency must synchronize with the external regulatory environment. The deployment solution must support efficient incremental updates and version management. Specialized terminology and complex structures, such as nested tables, images, and formulas, challenge document parsing capabilities. This requires optimizing text extraction and vectorization strategies. Standardized fields and units necessitate a focus on entity recognition and relationship extraction during knowledge base construction to ensure retrieval accuracy. The coexistence of various document formats demands robust file processing capabilities in the deployment environment to prevent data loss or parsing failures due to compatibility issues.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Registration application materials often include large attachments, requiring support for large file uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF parsing can be time-consuming; this prevents processing failures due to timeouts. |
Chunk size (Segment Length) | 800 characters (characters) | Balances semantic completeness and recall accuracy, preventing key information dilution in long paragraphs. |
Recall count (Recall Count) | Top 8 entries (top 8) | Ensures coverage of multiple relevant information points, such as regulatory clauses and clinical data. |
Similarity threshold (Similarity Threshold) | Calibrate by actual measurement | Adjust based on retrieval effectiveness in specific scenarios using a test set to ensure high recall. |
Rerank result count (Rerank Return Count) | Top 4 entries (top 4) | Improves the precision of final results, focusing on the most relevant content. |
Common Configuration Mistakes
- After local deployment, workflow nodes fail to output results. Logs show "no running result" or empty output. This typically occurs due to network isolation between the FastGPT container and the external code execution environment, preventing correct calls or response reception.
- After Docker deployment, changing database or service passwords causes system startup failures or errors. This is usually because passwords in configuration files are not updated synchronously, or environment variable settings do not match the actual configuration.
- Knowledge base source links are inaccessible or fail to download after Nginx proxying. The browser shows a 404 error or download failure. This happens when the Nginx configuration does not correctly handle FastGPT's internal download link redirects or path mappings.
Verification Steps
- Upload a PDF registration application document containing complex tables and specialized terminology. Check if the knowledge base segment preview is complete and semantically coherent.
- Perform question-answering tests using core regulatory clauses from the document. Observe if recall results include source links, have high content relevance, and check the
similarityscore. - Attempt to update an existing GMP document. Verify that the knowledge base version management function correctly records changes and that new and old content versions are distinguishable.
- Simulate high-concurrency access. Monitor system resource usage to confirm stable FastGPT container operation, without frequent restarts or memory overflow logs.
The values provided are common starting points. Measure them against specific samples to determine optimal settings for your use case.
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.