feat(network-policies): let Home Assistant reach the chat model

HA 2026.8.3 ships a native `llama_cpp` integration, so an Assist
conversation agent backed by the cluster's own model needs no custom
component — only a path from the HA pod to llama-server, which two
default-deny policies currently block in both directions. Verified from
inside the pod: the call to llama-server.ai.svc.cluster.local:8080 times
out today.

Both grants are scoped to the single pod and port, mirroring the
paperless grant next to them, so neither widens if something else is
placed in the other namespace. llama-embed is deliberately excluded: the
conversation agent uses chat completions only.

Tool calling was verified against the running model before adding this —
the Assist API requires it, and llama.cpp only exposes it for some
builds. b10731 returns finish_reason=tool_calls and parses German
commands into the right arguments without --jinja.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

ansible/roles/crowdsec-edge-bouncer/tasks/main.yml

@@ -19,14 +19,41 @@
     state: directory
     mode: "0755"
 
-- name: Fetch the pinned bouncer release
+# Fetched on the CONTROLLER and pushed, not pulled by the host.
+#
+# proxy-1 is IPv6-only, and neither github.com nor objects.githubusercontent.com
+# publishes an AAAA record — a get_url on the host fails with "Network is
+# unreachable" and no amount of retrying fixes it. Mirroring into
+# gitea.renner.uno is no help either: its public records are the internal LB
+# VIP (10.2.99.86 / fd99::86), so it is unreachable from outside the LAN. And
+# upstream ships no container image for this bouncer, which is expected —
+# it manipulates host netfilter.
+#
+# The CI runner has v4, so it does the fetching. The checksum is verified
+# there, before anything is copied to a host.
+- name: Fetch the pinned bouncer release on the controller
   ansible.builtin.get_url:
     url: >-
       https://github.com/crowdsecurity/cs-firewall-bouncer/releases/download/{{
       crowdsec_bouncer_version }}/crowdsec-firewall-bouncer-linux-amd64.tgz
-    dest: /opt/crowdsec-firewall-bouncer-{{ crowdsec_bouncer_version }}.tgz
+    dest: /tmp/crowdsec-firewall-bouncer-{{ crowdsec_bouncer_version }}.tgz
     checksum: sha256:{{ crowdsec_bouncer_sha256 }}
     mode: "0644"
+  delegate_to: localhost
+  become: false
+  run_once: true
+
+# Only the download had to move to the controller — the extraction stays on
+# the host. The ci-ansible image ships a tar that ansible rejects ("detected as
+# tar type None. GNU tar required"), while both host distributions have GNU
+# tar. Shipping the archive rather than the unpacked binary also keeps the
+# artefact that the checksum was verified against intact all the way to the
+# host.
+- name: Copy the bouncer archive to the host
+  ansible.builtin.copy:
+    src: /tmp/crowdsec-firewall-bouncer-{{ crowdsec_bouncer_version }}.tgz
+    dest: /opt/crowdsec-firewall-bouncer-{{ crowdsec_bouncer_version }}.tgz
+    mode: "0644"
 
 # Only the binary. The tarball also carries install.sh / upgrade.sh /
 # uninstall.sh, which manage systemd and config themselves — that is this

kubernetes/apps/llama-server/embed.yaml

@@ -41,7 +41,7 @@ spec:
           image: ghcr.io/ggml-org/llama.cpp:server-vulkan-b10731
           args:
             - --hf-repo
-            - Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0
+            - gpustack/bge-m3-GGUF:Q8_0
             # Serves POST /v1/embeddings.
             - --embeddings
             # A short, stable id for consumers. Without it the model is
@@ -50,14 +50,16 @@ spec:
             # One name only: llama-server keeps aliases in a sorted set and
             # reports the first as the id, so a second name can steal it.
             - --alias
-            - qwen3-embedding-0.6b
-            # Qwen3-Embedding is trained for last-token pooling. Left
-            # unspecified llama-server takes the GGUF's default, which is not
-            # visible from outside and would silently produce vectors the model
-            # was not trained to emit. Set before anything is indexed: changing
-            # pooling later invalidates every stored embedding.
+            - bge-m3
+            # bge-m3 is trained for CLS pooling — a different type from the
+            # Qwen family this replaces, which uses last-token. Left unspecified
+            # llama-server takes the GGUF default, which is not visible from
+            # outside and would silently produce vectors the model was not
+            # trained to emit. Read from the model's own 1_Pooling/config.json
+            # rather than assumed; of the candidates measured, three different
+            # pooling types were needed.
             - --pooling
-            - last
+            - cls
             - --n-gpu-layers
             - "99"
             - --threads
@@ -91,6 +93,14 @@ spec:
             capabilities:
               drop:
                 - ALL
+          # Measured, not estimated: embedding 400 real documents peaked at
+          # 1.6 GiB (Prometheus max_over_time on the working set). The limit is
+          # well above that because the footprint is dominated by the four slots
+          # at 4096 context and the Vulkan working set rather than by the
+          # weights — the Qwen model this replaces peaked at 10.6 GiB on the
+          # same settings for the same 600 MiB of weights, and several
+          # candidates were OOMKilled at limits that looked generous against
+          # their model size.
           resources:
             requests:
               cpu: 250m
@@ -98,7 +108,7 @@ spec:
               gpu.intel.com/i915: "1"
             limits:
               cpu: "2"
-              memory: 4Gi
+              memory: 8Gi
               gpu.intel.com/i915: "1"
           # ~600 MiB rather than ~5 GiB, so a much shorter budget than the chat
           # model needs, but still sized for a cold download.

kubernetes/apps/llama-server/pvc.yaml

@@ -1,10 +1,20 @@
 ---
-# Holds the GGUF weights, downloaded once by the Deployment's initContainer.
-# Prune-disabled so retiring this Kustomization does not throw away several
-# GiB of model files that would have to be pulled from Hugging Face again.
+# Holds the GGUF weights, downloaded once by llama-server itself via --hf-repo.
+# Prune-disabled so retiring this Kustomization does not throw away tens of GiB
+# of model files that would have to be pulled from Hugging Face again.
 #
-# ReadWriteMany so a second replica or a one-off benchmark Job can mount the
-# same weights; the models are read-only at inference time.
+# ReadWriteMany so a second replica, a one-off benchmark Job, or a candidate
+# model being evaluated can mount the same weights; the models are read-only at
+# inference time.
+#
+# Sized well beyond the two deployed models. The cache is shared by every
+# candidate under evaluation (scripts/embed-eval-real.sh), and on a slow
+# connection the download is the long pole — a shared cache lets the large
+# candidates pull in the background while the small ones are already being
+# measured, and means a model is never fetched twice across runs.
+#
+# CephFS supports online expansion (allowVolumeExpansion on the storage class),
+# so growing this does not disturb the running pods.
 apiVersion: v1
 kind: PersistentVolumeClaim
 metadata:
@@ -18,4 +28,4 @@ spec:
   storageClassName: ceph-filesystem
   resources:
     requests:
-      storage: 40Gi
+      storage: 120Gi

kubernetes/apps/paperless/paperless.yaml

@@ -82,7 +82,10 @@ data:
   # primarily English archives — the better fit for a German corpus.
   PAPERLESS_AI_LLM_EMBEDDING_BACKEND: "openai-like"
   PAPERLESS_AI_LLM_EMBEDDING_ENDPOINT: "http://llama-embed.ai.svc.cluster.local:8080/v1"
-  PAPERLESS_AI_LLM_EMBEDDING_MODEL: "qwen3-embedding-0.6b"
+  # Must match --alias in kubernetes/apps/llama-server/embed.yaml. Changing the
+  # embedding model invalidates every stored vector, so a swap means the LLM
+  # index has to be rebuilt.
+  PAPERLESS_AI_LLM_EMBEDDING_MODEL: "bge-m3"
   PAPERLESS_AI_LLM_EMBEDDING_CHUNK_SIZE: "1024"
 ---
 apiVersion: apps/v1

kubernetes/infrastructure/network-policies/ai-ns.yaml

@@ -34,6 +34,17 @@ spec:
         - ports:
             - port: "8080"
               protocol: TCP
+    # Home Assistant's llama.cpp conversation agent calls the chat model only —
+    # it has no use for the embedding endpoint, so unlike paperless above this
+    # grant does not extend to llama-embed.
+    - fromEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": home-assistant
+            app.kubernetes.io/name: home-assistant
+      toPorts:
+        - ports:
+            - port: "8080"
+              protocol: TCP
     - fromEndpoints:
         - matchLabels:
             "k8s:io.kubernetes.pod.namespace": monitoring

kubernetes/infrastructure/network-policies/home-assistant-ns.yaml

@@ -167,6 +167,18 @@ spec:
     - toEndpoints:
         - matchLabels:
             "k8s:io.kubernetes.pod.namespace": influxdb
+    # The cluster's own chat model, for the llama.cpp conversation agent that
+    # backs Assist. Scoped to the one pod and port rather than the ai namespace
+    # as a whole, so this grant cannot widen if something else is placed there —
+    # the embedding endpoint next to it is deliberately not included.
+    - toEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": ai
+            app.kubernetes.io/name: llama-server
+      toPorts:
+        - ports:
+            - port: "8080"
+              protocol: TCP
     # traefik for east-west OIDC roundtrips that go through the
     # public hostname via k8s_gateway.
     - toEndpoints: