# Sprint 5: Backend Architecture & Local LLM Integration

## Overview
Sprint 5 established the core backend infrastructure required to operate a fully local, privacy-first AI application without relying on external cloud APIs.

## Technical Choices
*   **Backend Framework**: **FastAPI** (Python)
    *   *Reasoning*: Chosen for its native asynchronous capabilities (`async`/`await`), which are critical for handling streaming responses from the LLM without blocking other user requests. It also automatically generates OpenAPI documentation.
*   **Database**: **SQLite**
    *   *Reasoning*: Selected over heavy databases like PostgreSQL because this is a local application running on a constrained VM. SQLite is incredibly fast for read-heavy operations, requires zero setup, and naturally aligns with the "local file" privacy philosophy.
    *   *Optimization*: Enabled `WAL` (Write-Ahead Logging) mode and `check_same_thread=False` to allow FastAPI's asynchronous workers to read/write concurrently without immediate locking issues.
*   **AI Engine**: **Qwen 3.5:9B via Ollama** (Upgraded from Llama 3.1)
    *   *Reasoning*: Selected because it provides exceptional natural language understanding while fitting within the RAM constraints of the local VM. Ollama handles the model serving locally on port `11434`.
*   **Authentication**: **Custom Token-Based Auth**
    *   *Reasoning*: Rather than relying on external OAuth providers (Google/Auth0), we built a local token system (`secrets.token_urlsafe`) stored in the SQLite database to ensure zero data leaves the server.

## RAG (Retrieval-Augmented Generation) Design
To prevent the LLM from hallucinating nutritional facts:
1.  **Keyword Extraction**: The user's query is filtered for stopwords (e.g., "protein in salmon" -> ["protein", "salmon"]).
2.  **Fuzzy Search**: The SQLite database (`foods` table) is queried using `LIKE` pattern matching.
3.  **Context Injection**: The resulting verified nutritional data is secretly prepended to the user's prompt as a "System Message", forcing the AI to base its answer on local, hard data.