Deployment and Upgrade for Medical Insurance Settlement

Data Characteristics

Medical insurance settlement regulations typically exist as structured or semi-structured documents. Examples include policy documents, implementation rules, operational procedure manuals, and FAQs. National or local medical insurance authorities usually issue these documents. Updates are frequent, potentially quarterly or annually, with more frequent updates during major policy changes. Documents contain extensive specialized terminology, legal provisions, cost codes, reimbursement ratios, and settlement flowcharts. Field definitions are precise, with units often specified down to cents, days, or occurrences. Data sources are typically government official websites, internal medical insurance bureau systems, or specific databases.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

Frequent updates to medical insurance settlement data require deployment solutions with flexible knowledge base update mechanisms, minimizing manual intervention. The complex structure and specialized terminology in documents, along with potential nested tables and image explanations, demand high document parsing capabilities. The parser must accurately extract key information. The coexistence of multiple policy versions can lead to ambiguous query results. The RAG (Retrieval Augmented Generation) system must identify policy effective dates or applicable scopes and ensure precise matching. Data involves sensitive medical expenses and personal information; therefore, the deployment environment has strict requirements for data security and compliance. Unofficial or insecure deployment methods are unacceptable.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Medical insurance policy documents are often large, containing charts and attachments. This size covers most cases and prevents upload failures.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Parsing complex policy documents, especially those with many tables and images, takes longer. Extending the timeout reduces parsing failure rates.
`maxContext`	`4000 characters`	Medical insurance policy terms are highly interconnected. Insufficient context length can lead to information loss or misunderstanding. This length maintains good context integrity.
`Chunk size`	`800–1200 characters`	Each segment contains enough information to understand a specific policy point, while avoiding excessive length that could reduce RAG efficiency.
`Recall count`	`Top 8 entries`	Medical insurance questions often involve multiple policy aspects. Increasing the number of recalled items improves the probability of retrieving relevant policies and reduces omissions.
`Similarity threshold`	Calibrated by actual measurement, `0.75` or higher recommended	Medical insurance terminology and policy descriptions are precise. A high threshold helps accurately match user queries, preventing irrelevant or outdated policies from being recalled.
`Rerank result count`	`Top 5 entries`	After recalling multiple relevant policies, re-ranking selects the most relevant ones, improving the accuracy and focus of the final answer.

Common Pitfalls

After uploading large policy files, parsing is unresponsive for an extended period or shows a request failure. This usually occurs when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, causing complex file parsing to exceed the preset time.
After a knowledge base update, query results still contain old policies or show inconsistencies. This might be due to incomplete knowledge base index reconstruction or improper cleanup of old version data, leading to a mix of new and old data.
When deploying on non-standard hardware (e.g., Huawei all-in-one machines), the MCP server fails to start or runs unstably. This typically indicates incompatibility between the underlying system environment or dependency library versions and FastGPT requirements. Adaptation for the specific hardware platform or use of a more compatible deployment method is necessary.

Verification Steps

Upload the latest medical insurance policy documents. Confirm that parsing proceeds smoothly, without timeout errors, and that document content is correctly identified and segmented.
For typical medical insurance settlement queries, such as "out-of-area medical reimbursement process" or "outpatient expense settlement ratio," verify that the system recalls currently effective policy provisions and provides accurate answers. Check if the answer cites the correct policy version and effective date.
Simulate complex queries that might occur in a medical insurance system, such as those with multiple conditions or involving cross-policy issues. Check if the system can handle such multi-hop questions and provide coherent and logically correct answers.
Review system logs. Confirm that during knowledge base updates or daily queries, there are no excessive parsing errors, connection timeouts, or memory overflow warnings.

Note: The values provided are common starting points. Measure them 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.