Healthcare LLM orchestration with retrieval and validation boundaries.
A semantic-routing and vector-retrieval architecture designed to bring only verified context into an LLM workflow, then enforce structural integrity before output reached a healthcare process.
The system pattern at a glance.
Problem class
LLM assistance inside a healthcare workflow where irrelevant context and malformed output could not be treated as acceptable defaults.
Architecture pattern
Semantic routing, vector retrieval, LLM orchestration, and multi-layer typed validation before downstream use.
Disclosed stack
LangChain, Pydantic, a vector database, FastAPI, and Next.js.
Generation was only one part of the engineering problem.
A model can produce fluent text while using the wrong context or returning a response that violates the structure expected by another system. In a healthcare workflow, the architecture therefore needed explicit boundaries around context selection and response shape. The public system description focuses on those controls rather than treating a raw model response as a finished product.
Context relevance
Route each request toward the retrieval path appropriate to its intent instead of exposing one undifferentiated context pool.
Source boundaries
Use vector retrieval to bring verified context into the generation step and reduce reliance on unsupported model recall.
Output integrity
Validate structure before an answer is allowed to continue into the surrounding workflow.
A staged path from request to validated output.
Interpret and route
Semantic routing distinguishes request intent so the workflow can select a relevant retrieval path rather than applying one prompt and one source set to every request.
Retrieve bounded context
Vector embeddings and a vector database locate context from the verified material available to the system.
Orchestrate the model workflow
LangChain coordinates the disclosed retrieval and generation components across the request path.
Validate the response
Multi-layer Pydantic validation checks structural integrity before output continues into the surrounding workflow.
Expose application interfaces
FastAPI and Next.js form the disclosed API and application layers around the orchestration flow, keeping model behavior inside a conventional software boundary.
What can be evaluated publicly.
System context
A healthcare workflow with explicit requirements around context selection and response structure.
Architecture evidence
Semantic routing, vector retrieval, orchestration, and typed validation are each assigned a clear role.
Stack evidence
LangChain, Pydantic, a vector database, FastAPI, and Next.js define the publicly described implementation surface.
Experience engineering AI as a controlled software system.
This case supports team experience with semantic routing, retrieval-augmented generation, typed output validation, and full-stack integration in a domain-sensitive workflow.
