Data Characteristics for This Category
Medical insurance access data originates from policy documents, drug catalogs, treatment item catalogs, and payment standards publicly released by national and local medical insurance bureaus. This data updates frequently, typically quarterly or annually, with ad-hoc updates for significant policy changes. Documents come in various formats, including official PDF files, Excel spreadsheet catalogs, and online query systems provided by official websites in some regions.
Document structures vary:
- Drug catalogs typically include fields like drug code, generic name, dosage form, specification, and payment scope.
- Treatment item catalogs cover item code, item name, service content, and billing unit.
Field content often contains specialized terminology and abbreviations. Unit expressions can differ by region or document type; for example, drug dosage units might be milligrams (mg) or grams (g), and payment standards might be in yuan/time or yuan/unit.
Constraints Imposed by These Characteristics on Deployment and Upgrade
The diversity and high update frequency of medical insurance access data impose specific requirements on FastGPT's deployment and upgrade.
- The presence of multi-format documents (PDF, Excel) demands robust compatibility in the file parsing stage to extract key information from all policy files.
- The periodic and sudden nature of data updates makes continuous knowledge base synchronization a regular operation. This requires efficient file upload and incremental update mechanisms to avoid full rebuilds each time.
- The specialized nature of fields and varying units necessitates detailed preprocessing before data cleaning and vectorization to improve retrieval accuracy.
- The timeliness of policy documents makes knowledge base version management crucial, ensuring users receive the latest and most accurate medical insurance policy information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Medical insurance policy files, especially catalog files, can be large due to numerous entries. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Excel files can take considerable time; sufficient time must be allocated to prevent timeouts. |
Chunk size | 800–1200 characters | Medical insurance policy clauses are often long; this range maintains complete semantic units and reduces truncation. |
Recall count | Top 8 entries | Policy queries require high accuracy; increasing recall count improves coverage. |
Similarity threshold | 0.75 | Medical insurance policy texts often have semantically similar expressions; a higher threshold filters out irrelevant results. |
Rerank result count | Top 5 entries | From a higher recall set, select the most relevant items to return, enhancing user experience. |
Three Common Mistakes
- After uploading a large PDF file, the agent responds slowly or not at all. This occurs because
PARSE_FILE_TIMEOUT_SECONDSis set too low, causing file parsing to time out and impacting backend services. - After a knowledge base content update, query results still show old information. This likely means the knowledge base index was not rebuilt or incrementally updated after file upload, leading searches to use the old index.
- Query results for specific medical insurance terms are inaccurate or missing. This usually indicates insufficient cleaning of specialized fields during data preprocessing or that the vectorization model failed to capture the semantic features of these terms effectively.
How to Verify Correct Configuration
- Upload a comprehensive medical insurance policy document containing multiple formats (PDF, Excel). Check if the file parses correctly and if key information is retrievable from the knowledge base.
- For policy updates, upload a new version of a document. Verify that the knowledge base content updates accordingly and that old policy information no longer appears in query results.
- Use query statements containing specialized terms like medical insurance drug names or treatment item codes. Verify the accuracy and relevance of the agent's returned results, and check if the returned fields and units are correct.
- Simulate high-concurrency query scenarios. Observe system response times and resource utilization to ensure stable performance in actual use.
The values provided are common starting points and should be measured against the reader's 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.