Data Characteristics
Market access data in the biopharmaceutical sector originates from multiple sources. These include regulations, guidelines, and approval results from national or regional drug regulatory agencies, medical insurance payment policies, and compliance requirements from industry associations. Data updates are frequent. Regulations may be revised annually, and payment catalogs adjusted quarterly or yearly. Document structures vary, primarily consisting of policy texts in PDF, payment catalog lists in Excel, and online query systems from regulatory agency websites. Fields and units are highly specialized, including generic drug names, brand names, ATC classification codes, medical insurance payment codes, reimbursement ratios, indication descriptions, clinical trial data requirements, and post-market surveillance requirements. This data often includes timestamps such as version numbers, effective dates, and expiration dates.
Constraints from "HTTP API and External Systems"
The multi-source nature and high update frequency of market access data require HTTP APIs to have flexible data source integration capabilities. The system must pull data from various external systems on a scheduled or on-demand basis. For example, configuring multiple API endpoints is necessary to connect with public databases of regulatory agencies and policy release platforms of medical insurance bureaus. Diverse document structures complicate data preprocessing, requiring support for PDF parsing and Excel table extraction. Non-structured text must convert into retrievable knowledge fragments. Specialized fields and units require FastGPT to accurately identify and retain this information during data ingestion to ensure precise subsequent Q&A and prevent semantic loss. Managing and querying historical data versions is also crucial to ensure users retrieve regulations effective at specific points in time.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
data_source_url | External API address, e.g., https://regulatory.example.com/api/policies | Connects to specific regulatory agency policy release interfaces. |
update_frequency_cron | 0 0 * * 0 (every Sunday at midnight) | Policy and regulation update cycles are typically weekly or monthly. This avoids frequent pulling, which would increase server load. |
parser_config.document_type | pdf_ocr, excel_table | Market access documents are often in PDF and Excel formats, requiring text recognition and table parsing support. |
chunk_size | 800-1200 characters | Ensures knowledge fragments contain sufficient context while avoiding excessive length that could impact recall efficiency. |
max_retries | 3 | External interfaces may temporarily fail due to network fluctuations or service maintenance. Retries increase stability. |
timeout_seconds | 60 seconds | External interface responses can be slow. This provides enough time for data transfer and processing. |
Common Pitfalls
- HTTP request returns a 500 error with "Post 'https://xxx' tls: failed to verify": This usually occurs because the external API uses a self-signed certificate or an internal enterprise CA certificate that the FastGPT container environment does not trust.
- After knowledge base import, some key fields (e.g., "medical insurance payment code") show empty or inaccurate query results: This happens when field mapping or regular expression extraction rules are not correctly configured during data preprocessing, leading to the loss of specialized field information.
- Updated policy text does not reflect in Q&A results promptly: This typically means
update_frequency_cronis set too long, or the external data source interface changed butdata_source_urlwas not updated accordingly.
Verification Steps
- Perform a manual data synchronization and check FastGPT backend logs for HTTP errors or parsing failures.
- Randomly select several market access policy documents in the knowledge base. Use FastGPT's preview function to check segmentation effectiveness and the accuracy of key information extraction.
- Construct query statements containing specific fields like medical insurance payment codes and generic drug names. Verify that FastGPT's recall results include correct and complete relevant information.
The values provided are common starting points and should be measured 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.