feat(stack): drop qdrant and embedding-server, use OmniRoute's built-in memory

OmniRoute's memory feature is self-contained: its bundled sqlite-vec
vector store plus a local ONNX embedding model (Transformers.js,
~400MB, fetched into the omniroute-data volume on first use) replace
the external qdrant + bge-small-en-v1.5 embedding-server pair, which
was never wired up in the dashboard. Two fewer containers, no
second GGUF download, no EMBEDDING_MODEL_FILE var. Memory stays
opt-in via the dashboard (Settings -> Memory, transformers source);
nothing here changes the gateway's static config.

docker-compose.yml

@@ -54,50 +54,7 @@ services:
         test -f /models/${LLAMA_MODEL_FILE:-Qwen3.8-27B-UD-Q4_K_XL.gguf} &&
         echo "already downloaded, skipping" ||
         curl -L --fail --create-dirs -o /models/${LLAMA_MODEL_FILE:-Qwen3.8-27B-UD-Q4_K_XL.gguf}
-        https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/resolve/main/${LLAMA_MODEL_FILE:-Qwen3.8-27B-UD-Q4_K_XL.gguf};
-        test -f /models/${EMBEDDING_MODEL_FILE:-bge-small-en-v1.5-q8_0.gguf} &&
-        echo "already downloaded, skipping" ||
-        curl -L --fail --create-dirs -o /models/${EMBEDDING_MODEL_FILE:-bge-small-en-v1.5-q8_0.gguf}
-        https://huggingface.co/ggml-org/bge-small-en-v1.5-Q8_0-GGUF/resolve/main/${EMBEDDING_MODEL_FILE:-bge-small-en-v1.5-q8_0.gguf}
-
-  # Dedicated embedding model for OmniRoute's memory feature. llama.cpp loads
-  # one model per process and the already-running Qwen3.8-27B chat model
-  # isn't embedding-trained, so this is a second, separate llama-server
-  # instance rather than adding --embeddings to the chat one — see
-  # docs/research/litellm-knowledgebase.md #3. bge-small-en-v1.5 outputs
-  # 384-dim vectors — use that as the Qdrant collection size.
-  # ponytail: CPU image, no GPU devices — a 33M-param embedding model is
-  # fast enough on CPU and this avoids VRAM contention with llama-server's
-  # 27B chat model on the same GPU.
-  embedding-server:
-    image: ghcr.io/ggml-org/llama.cpp:server
-    container_name: embedding-server
-    volumes:
-      - models:/models
-    command: >
-      -m /models/${EMBEDDING_MODEL_FILE:-bge-small-en-v1.5-q8_0.gguf}
-      --host 0.0.0.0
-      --port 8081
-      --embeddings
-      --pooling mean
-    restart: unless-stopped
-    networks: [ai-stack]
-
-  # Vector store for OmniRoute's memory feature — wired up in the OmniRoute
-  # dashboard as a memory service, not via static config here. Not published
-  # to the host: only OmniRoute (same ai-stack network) talks to it.
-  qdrant:
-    image: qdrant/qdrant:latest
-    container_name: qdrant
-    volumes:
-      - qdrant-data:/qdrant/storage
-    restart: unless-stopped
-    networks: [ai-stack]
-    healthcheck:
-      test: ["CMD-SHELL", "bash -c 'exec 3<>/dev/tcp/localhost/6333'"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
+        https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/resolve/main/${LLAMA_MODEL_FILE:-Qwen3.8-27B-UD-Q4_K_XL.gguf}
 
   # Replaces litellm — see issue #31 (wayfinder map) for the full migration
   # rationale/findings. No static config.yaml equivalent: provider routing
@@ -110,8 +67,6 @@ services:
     depends_on:
       llama-server:
         condition: service_started
-      qdrant:
-        condition: service_healthy
     volumes:
       - omniroute-data:/app/data
     env_file: .env
@@ -191,4 +146,3 @@ networks:
 volumes:
   models:
   omniroute-data:
-  qdrant-data: