Add GPU device control, Docker support, and v2 client-server design

- Add configurable device selection for embeddings (embedding.device) and
  Docling ingestion (ingestion.device) with env var overrides (KB_DEVICE,
  KB_INGEST_DEVICE) to control GPU/CPU usage per component
- Add `kb doctor` command for safe GPU diagnostics
- Add Dockerfile (NVIDIA CUDA) and compose.yaml for containerised GPU usage
- Add OpenSpec v2 change (kb-v2-client-server): proposal, design, specs, and
  tasks for client-server architecture with Go CLI, FastAPI engine, async
  ingestion queue, and GPU-vendor-agnostic Docker deployment
- Add uv.lock for reproducible installs
- Gitignore examples/ directory (test data only)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Dockerfile

@@ -0,0 +1,44 @@
+FROM nvidia/cuda:13.0.1-runtime-ubuntu24.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# System deps for docling (poppler for PDF, build tools for native wheels)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3.12 python3.12-venv python3.12-dev python3-pip \
+    libpoppler-cpp-dev poppler-utils \
+    libgl1 libglib2.0-0 \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv for fast dependency resolution
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
+
+WORKDIR /app
+
+# Copy project files
+COPY pyproject.toml uv.lock ./
+COPY src/ src/
+
+# Create venv, install deps, overlay onnxruntime-gpu
+RUN uv venv .venv && \
+    . .venv/bin/activate && \
+    uv sync && \
+    uv pip install --no-deps onnxruntime-gpu
+
+# Put venv on PATH
+ENV PATH="/app/.venv/bin:$PATH"
+ENV VIRTUAL_ENV="/app/.venv"
+
+# GPU enabled by default in the container
+ENV KB_DEVICE=auto
+ENV KB_INGEST_DEVICE=auto
+
+# Model cache persisted via volume
+ENV HF_HOME=/data/hf_cache
+ENV KB_DATA_DIR=/data/kb
+
+VOLUME ["/data"]
+
+ENTRYPOINT ["kb"]
+CMD ["--help"]