Data Characteristics
Medical insurance access regulation data originates from official documents published by national and local medical insurance bureaus. These include policy documents, drug catalogs, diagnostic project catalogs, and payment standards. Documents are typically in PDF, Word, or HTML formats. Update frequencies vary; national policies usually adjust annually or biennially, while local regulations may update more frequently. Document structures are complex, containing extensive unstructured text, tables, and figures. For example, drug catalogs list generic names, dosages, specifications, payment scopes, and restricted payment conditions. Payment standards may include coding and corresponding prices for Diagnosis-Related Groups (DRG) or Diagnosis-Intervention Packet (DIP) based payments. These data sources typically lack unified API interfaces, requiring web scraping or manual entry for structured processing.
Constraints on Tool Calling and Plugins
The diverse and heterogeneous nature of medical insurance access data requires robust document parsing capabilities from tool calling and plugins. They must process multiple file formats and extract critical information. Uncertain update frequencies necessitate flexible data synchronization mechanisms to ensure knowledge base timeliness. Complex tables and nested structures within documents demand high accuracy in information extraction, combining natural language processing with table structure recognition techniques. Furthermore, medical insurance payment conditions and restricted payment scopes are often described in natural language, involving medical terminology and policy interpretation. This requires tool calling to accurately understand semantic meaning during answer generation to avoid misinterpretation. The absence of standardized API interfaces means integrating external data sources into FastGPT relies more on custom web scraping or data import plugins.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4096 tokens | Medical insurance policy texts are often long, requiring a larger context window. |
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness with recall efficiency, avoiding excessive truncation. |
Recall count (Recall Count) | Top 8 entries (Top 8) | Ensures coverage of relevant policy clauses and reduces omissions. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall precision and generalization, reducing interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (600 seconds) | Processing large PDF or Word documents may require a longer time. |
Rerank result count (Rerank Return Count) | Top 3 entries (Top 3) | Focuses on the most relevant policy clauses, improving answer accuracy. |
Common Pitfalls
- Plugin calls return a "cross-origin error," with logs showing
CORS policyrejection. This indicates the FastGPT backend service is not correctly configured to allow the origin address. - Knowledge base search results are empty or irrelevant, even when the document contains the required information. This can be due to failed document parsing leading to inaccurate knowledge chunking, or chunking strategies failing to capture key information units within medical insurance policies.
- Calls to external APIs for medical drug information return an
HTTP 403 Forbiddenstatus code. This typically indicates an invalid API key, missing necessary authentication information in the request header, or an IP address not whitelisted.
Verification Steps
- Upload a medical insurance policy PDF file containing complex tables. Verify that knowledge chunking accurately identifies and extracts table content.
- Ask about the payment scope and restricted conditions for a specific drug within medical insurance access. Cross-reference FastGPT's answer with the original policy text.
- In the application workflow, simulate a call to an external medical insurance catalog query plugin. Observe if drug codes and price information are correctly retrieved, and check if the returned data structure meets expectations.
The values provided are common starting points. Measure them against your own samples for optimal configuration.
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.