Data Characteristics for this Category
Pharmacoeconomics regulation data primarily originates from official documents, guidelines, review standards, and drug reimbursement catalogs published by national and regional drug regulatory agencies and Health Technology Assessment (HTA) bodies. This data has a relatively low update frequency, typically revised annually or in response to new drug launches and policy adjustments. Document structures are mainly PDF, Word, or structured XML files, containing extensive textual descriptions, tabular data, and references. Specific fields include ICER (Incremental Cost-Effectiveness Ratio), QALY (Quality-Adjusted Life Year), and LYG (Life Year Gained). Units involve currencies (e.g., USD, EUR, RMB), life years, and quality-adjusted life years, with different regions potentially using different currency symbols and measurement standards.
Constraints Imposed by these Characteristics on "HTTP Interface and External Systems"
The low update frequency of pharmacoeconomics data means frequent full synchronization is unnecessary. The focus should be on incremental updates and version management. Most documents are unstructured or semi-structured, demanding high capabilities from external systems for data extraction and parsing, especially for identifying tables and specific metrics. The diversity of fields and units, along with regional differences, requires HTTP interfaces to clearly label data source, currency type, and units during transmission to prevent data confusion and misuse. Additionally, due to potentially large data volumes and sensitive policy information, interface design must consider stability and security, supporting paginated queries and authentication. Garbled text issues often arise from inconsistent character encoding or improper file format handling, particularly when processing multilingual or special characters.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Pharmacoeconomics documents often contain numerous charts and extensive text, resulting in large file sizes, requiring sufficient upload capacity. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large files can be time-consuming; this prevents parsing failures due to timeouts. |
Chunk size | 800–1200 characters | Balances textual semantic completeness and retrieval efficiency, avoiding overly long or short segments. |
Similarity threshold | 0.75 | Ensures that recall results are highly relevant to pharmacoeconomics queries, reducing noise. |
maxContext | 3000 Tokens | Pharmacoeconomics regulation Q&A requires a longer context to understand complex policy details and multi-party arguments. |
API_KEY_AUTH_ENABLED | true | Ensures the security of external system access to the interface, preventing unauthorized data leakage or tampering. |
Three Common Pitfalls
- Garbled content appears after uploading text or files via API. This usually occurs because the external system did not correctly set the character encoding in the
Content-Typeheader when sending the request, or FastGPT's receiving end did not parse the encoding as expected. - Significant differences exist between online chat and API call Q&A results. This may be due to incorrect settings for the
promptorknowledgeBaseIdparameters during API calls, preventing the model from correctly understanding the question or associating it with the specified pharmacoeconomics knowledge base. - API call results return empty fields or missing key metrics. This typically happens when the data parsing stage fails to correctly identify specific tables or text areas within PDF or Word documents, leading to key fields like ICER and QALY not being extracted and stored.
How to Verify Configuration
- Upload a typical pharmacoeconomics PDF document. Query the document content via API or web interface, verifying that key policy terms, ICER values, and QALY values are completely and legibly extracted and presented.
- Use a test set containing specific pharmacoeconomics regulation questions. Perform batch Q&A via API calls, comparing the returned results with expected answers to assess accuracy and relevance.
- Check the knowledge base indexing status and document processing logs in the FastGPT backend. Confirm that file upload, parsing, and vectorization processes completed without errors, and all documents are successfully indexed.
- Simulate queries with different currency units and measurement standards. Check if the data returned by the API correctly identifies and labels the corresponding units and regional information.
The values provided are common starting points and should be measured against specific 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.