Overview
A model-agnostic conversational service with multi-turn memory, decoupled orchestration, and a deployable Flask API.
Designed for persistent sessions and hot-swappable LLM backends.
Architecture
The implementation is described as a system rather than a tool list. Inputs move through a defined pipeline, the main model or service boundary is kept explicit, and the deployment surface is separated from the core logic so the project remains understandable outside a notebook context.
Evidence
The portfolio only claims evidence that is supported by the project record. Where a metric is available, it is presented with context rather than as a universal production guarantee. This keeps the case study useful to a hiring manager and credible to a technical reviewer.
Trade-offs
The most important trade-off is usually complexity versus leverage. The project is intentionally built far enough to demonstrate architecture, retrieval, API boundaries, evaluation, or low-level systems reasoning without pretending that a portfolio project has the operational footprint of a mature enterprise product.
Read the technical articles or return to the project rail.