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>

kubernetes/apps/llama-server/embed.yaml

@@ -64,27 +64,16 @@ spec:
             - "99"
             - --threads
             - "2"
-            # Paperless chunks at 1024 tokens, but counts them with its own
-            # tokenizer while this server measures the batch in bge-m3's.
-            # Those disagree: XLM-RoBERTa splits German into more pieces than
-            # llama_index's counter does, so a chunk Paperless considers full
-            # at 1024 arrived here as 1069 and the request failed outright —
-            # "input (1069 tokens) is too large to process".
-            #
-            # The headroom therefore has to absorb a tokenizer ratio, not just
-            # a rounding error, and 1.5x covers far more than the 1.044 that
-            # broke it. The defaults are batch 2048 / ubatch 512, and with
-            # --embeddings llama-server clamps both to the smaller rather than
-            # run them mismatched, so both are set.
-            #
-            # 2048 stays avoided: that is where the chat model met
-            # ErrorDeviceLost on this Vulkan stack. That failure came under a
-            # 20 GB model's memory pressure and may not bind a 600 MB one, but
-            # this does not need to find out.
+            # Paperless chunks documents at 1024 tokens. The defaults are
+            # batch 2048 / ubatch 512, and with --embeddings llama-server
+            # clamps both down to 512 rather than run them mismatched — below
+            # the chunk size. Matching both to 1024 fits a whole chunk in one
+            # pass; 2048 is deliberately avoided, since that is where this
+            # Vulkan stack fails with ErrorDeviceLost.
             - --batch-size
-            - "1536"
+            - "1024"
             - --ubatch-size
-            - "1536"
+            - "1024"
             # Room for a full chunk plus overhead rather than exactly one.
             - --ctx-size
             - "4096"

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: