Add CPU-only Docker image and fix release tag naming

- Add Dockerfile.cpu and compose.cpu.yaml for CPU-only deployments
- Use sentence-transformers[onnx] + CPU-only torch for ~4x smaller image
- Fix release script: separate git tags (engine-v*) from Docker tags (v*)
- Add CPU image to release build/push pipeline
- Update README with CPU deployment instructions

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

README.md

@@ -2,7 +2,7 @@
 
 Personal knowledge base with hybrid search (full-text + semantic vector search).
 
-v2 uses a client-server architecture: a **FastAPI engine** running in Docker (with GPU acceleration) and a lightweight **Go CLI client** that talks to it over HTTP.
+v2 uses a client-server architecture: a **FastAPI engine** running in Docker (with optional GPU acceleration) and a lightweight **Go CLI client** that talks to it over HTTP.
 
 ## Architecture
 
@@ -10,7 +10,7 @@ v2 uses a client-server architecture: a **FastAPI engine** running in Docker (wi
 Go CLI (kb) ──HTTP──▶ FastAPI Engine (Docker) ──▶ SQLite + GPU
 ```
 
-- **Engine**: Keeps the embedding model warm in GPU memory. Handles search, ingestion, and document management via REST API. Runs in Docker with NVIDIA or AMD GPU support.
+- **Engine**: Keeps the embedding model warm in memory. Handles search, ingestion, and document management via REST API. Runs in Docker with NVIDIA GPU, AMD GPU (ROCm), or CPU-only support.
 - **Client**: Single static Go binary. No Python, no ML dependencies, instant startup. Talks to the engine over HTTP.
 - **Storage**: Single SQLite database with FTS5 (keyword search) and sqlite-vec (vector search). Portable via bind mount — just copy the data directory between hosts.
 
@@ -43,49 +43,33 @@ docker run -d --name kb-engine \
   -e KB_API_KEY=your-secret-key \
   --restart unless-stopped \
   docker.dcglab.co.uk/dcg/kb/engine:latest-rocm
+
+# CPU only (no GPU required — smaller image)
+docker run -d --name kb-engine \
+  -p 8000:8000 \
+  -v ~/kb-data:/data \
+  -e KB_MODEL=all-MiniLM-L6-v2 \
+  -e KB_API_KEY=your-secret-key \
+  --restart unless-stopped \
+  docker.dcglab.co.uk/dcg/kb/engine:latest-cpu
 ```
 
-Or use a compose file — create `compose.yaml`:
+Or use a compose file from the repo:
-
-```yaml
-services:
-  kb-engine:
-    image: docker.dcglab.co.uk/dcg/kb/engine:latest-nvidia  # or latest-rocm
-    runtime: nvidia  # remove for ROCm
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    # For ROCm, replace the above runtime/deploy block with:
-    # devices:
-    #   - "/dev/kfd"
-    #   - "/dev/dri"
-    # group_add:
-    #   - "video"
-    ports:
-      - "${KB_PORT:-8000}:8000"
-    volumes:
-      - ${KB_DATA_PATH:-./data}:/data
-    environment:
-      - KB_MODEL=${KB_MODEL:-all-MiniLM-L6-v2}
-      - KB_DEVICE=${KB_DEVICE:-auto}
-      - KB_INGEST_DEVICE=${KB_INGEST_DEVICE:-auto}
-      - KB_API_KEY=${KB_API_KEY:-}
-      - KB_SEARCH_THRESHOLD=${KB_SEARCH_THRESHOLD:-0.01}
-      - HF_HUB_OFFLINE=${HF_HUB_OFFLINE:-}
-    restart: unless-stopped
-```
 
 ```bash
-KB_DATA_PATH=~/kb-data docker compose up -d
+# NVIDIA GPU
+KB_DATA_PATH=~/kb-data docker compose -f engine/compose.nvidia.yaml up -d
+
+# AMD GPU (ROCm)
+KB_DATA_PATH=~/kb-data docker compose -f engine/compose.rocm.yaml up -d
+
+# CPU only
+KB_DATA_PATH=~/kb-data docker compose -f engine/compose.cpu.yaml up -d
 ```
 
 See [DEVELOPER.md](DEVELOPER.md) to run the engine from source.
 
-The engine will download the embedding model on first start (~90MB) and load it onto the GPU. Check readiness:
+The engine will download the embedding model on first start (~90MB) and load it into memory (GPU or CPU). Check readiness:
 
 ```bash
 curl http://localhost:8000/api/v1/health
@@ -196,7 +180,7 @@ rsync -a ~/kb-data/ user@target:/home/user/kb-data/
 KB_DATA_PATH=~/kb-data docker compose -f compose.nvidia.yaml up -d
 ```
 
-Data is GPU-vendor-agnostic — you can ingest on NVIDIA and serve from AMD (or vice versa) with the same data directory.
+Data is device-agnostic — you can ingest on NVIDIA and serve from AMD or CPU (or any combination) with the same data directory.
 
 ## Claude Code skill
 

engine/Dockerfile.cpu

@@ -0,0 +1,36 @@
+FROM ubuntu:24.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+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/*
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
+
+WORKDIR /app
+
+COPY pyproject.toml ./
+COPY kb/ kb/
+COPY main.py ./
+COPY VERSION ./
+
+RUN uv venv .venv && \
+    . .venv/bin/activate && \
+    uv pip install -e . && \
+    uv pip install "sentence-transformers[onnx]" && \
+    uv pip install --reinstall torch torchvision --index-url https://download.pytorch.org/whl/cpu
+
+ENV PATH="/app/.venv/bin:$PATH"
+ENV VIRTUAL_ENV="/app/.venv"
+ENV KB_DEVICE=cpu
+ENV KB_INGEST_DEVICE=cpu
+ENV KB_DATA_DIR=/data
+
+EXPOSE 8000
+VOLUME ["/data"]
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

engine/compose.cpu.yaml

@@ -0,0 +1,17 @@
+services:
+  kb-engine:
+    build:
+      context: .
+      dockerfile: Dockerfile.cpu
+    ports:
+      - "${KB_PORT:-8000}:8000"
+    volumes:
+      - ${KB_DATA_PATH:-./data}:/data
+    environment:
+      - KB_MODEL=${KB_MODEL:-all-MiniLM-L6-v2}
+      - KB_DEVICE=cpu
+      - KB_INGEST_DEVICE=cpu
+      - KB_API_KEY=${KB_API_KEY:-}
+      - KB_SEARCH_THRESHOLD=${KB_SEARCH_THRESHOLD:-0.01}
+      - HF_HUB_OFFLINE=${HF_HUB_OFFLINE:-}
+    restart: unless-stopped

release-engine.sh

@@ -111,9 +111,11 @@ else
     echo "==> Engine version: $VERSION (no increment)"
 fi
 
-TAG="engine-v${VERSION}"
+GIT_TAG="engine-v${VERSION}"
+DOCKER_TAG="v${VERSION}"
 
-echo "    Tag:       $TAG"
+echo "    Git tag:   $GIT_TAG"
+echo "    Image tag: $DOCKER_TAG"
 echo "    Registry:  $IMAGE_BASE"
 echo "    Forge CLI: $FORGE"
 echo "    Dry run:   $DRY_RUN"
@@ -125,8 +127,8 @@ echo ""
 echo "==> Pre-flight checks"
 
 if [[ "$DRY_RUN" == false ]]; then
-    if git -C "$SCRIPT_DIR" rev-parse "$TAG" &>/dev/null; then
+    if git -C "$SCRIPT_DIR" rev-parse "$GIT_TAG" &>/dev/null; then
-        echo "Error: tag $TAG already exists"
+        echo "Error: tag $GIT_TAG already exists"
         exit 1
     fi
 fi
@@ -148,29 +150,32 @@ fi
 #──────────────────────────────────────────────────────────────────────
 echo "==> Building Docker engine images ($VERSION)"
 
-NVIDIA_IMAGE="${IMAGE_BASE}/engine:${TAG}-nvidia"
+NVIDIA_IMAGE="${IMAGE_BASE}/engine:${DOCKER_TAG}-nvidia"
-ROCM_IMAGE="${IMAGE_BASE}/engine:${TAG}-rocm"
+ROCM_IMAGE="${IMAGE_BASE}/engine:${DOCKER_TAG}-rocm"
+CPU_IMAGE="${IMAGE_BASE}/engine:${DOCKER_TAG}-cpu"
 NVIDIA_LATEST="${IMAGE_BASE}/engine:latest-nvidia"
 ROCM_LATEST="${IMAGE_BASE}/engine:latest-rocm"
+CPU_LATEST="${IMAGE_BASE}/engine:latest-cpu"
 
 run docker build -t "$NVIDIA_IMAGE" -t "$NVIDIA_LATEST" -f "$ENGINE_DIR/Dockerfile.nvidia" "$ENGINE_DIR"
 run docker build -t "$ROCM_IMAGE" -t "$ROCM_LATEST" -f "$ENGINE_DIR/Dockerfile.rocm" "$ENGINE_DIR"
+run docker build -t "$CPU_IMAGE" -t "$CPU_LATEST" -f "$ENGINE_DIR/Dockerfile.cpu" "$ENGINE_DIR"
 
 echo ""
 
 #──────────────────────────────────────────────────────────────────────
 # 4. Commit, tag, and push
 #──────────────────────────────────────────────────────────────────────
-echo "==> Committing and tagging $TAG"
+echo "==> Committing and tagging $GIT_TAG"
 
 if [[ "$INCREMENT" == true ]]; then
     run git -C "$SCRIPT_DIR" add "$VERSION_FILE"
     run git -C "$SCRIPT_DIR" commit -m "Bump engine version to $VERSION"
 fi
 
-run git -C "$SCRIPT_DIR" tag -a "$TAG" -m "Release $TAG"
+run git -C "$SCRIPT_DIR" tag -a "$GIT_TAG" -m "Release $GIT_TAG"
 run git -C "$SCRIPT_DIR" push origin HEAD
-run git -C "$SCRIPT_DIR" push origin "$TAG"
+run git -C "$SCRIPT_DIR" push origin "$GIT_TAG"
 
 echo ""
 
@@ -179,7 +184,7 @@ echo ""
 #──────────────────────────────────────────────────────────────────────
 echo "==> Creating release via $FORGE"
 
-RELEASE_TITLE="Engine $TAG"
+RELEASE_TITLE="Engine $GIT_TAG"
 RELEASE_NOTES="## Docker images
 
 \`\`\`bash
@@ -188,16 +193,19 @@ docker pull ${NVIDIA_IMAGE}
 
 # AMD GPU (ROCm)
 docker pull ${ROCM_IMAGE}
+
+# CPU only
+docker pull ${CPU_IMAGE}
 \`\`\`"
 
 if [[ "$FORGE" == "gh" ]]; then
-    run gh release create "$TAG" \
+    run gh release create "$GIT_TAG" \
         --title "$RELEASE_TITLE" \
         --notes "$RELEASE_NOTES"
 
 elif [[ "$FORGE" == "tea" ]]; then
     run tea release create \
-        --tag "$TAG" \
+        --tag "$GIT_TAG" \
         --title "$RELEASE_TITLE" \
         --note "$RELEASE_NOTES"
 fi
@@ -213,10 +221,13 @@ run docker push "$NVIDIA_IMAGE"
 run docker push "$NVIDIA_LATEST"
 run docker push "$ROCM_IMAGE"
 run docker push "$ROCM_LATEST"
+run docker push "$CPU_IMAGE"
+run docker push "$CPU_LATEST"
 
 echo ""
-echo "==> Release $TAG complete!"
+echo "==> Release $GIT_TAG complete!"
 echo ""
 echo "    Images:"
 echo "      $NVIDIA_IMAGE"
 echo "      $ROCM_IMAGE"
+echo "      $CPU_IMAGE"