fix(llama-embed): raise the memory limit to match its own context and batch

#739 raised the context from 2048 to 4096 and the micro-batch from an effective
512 to 1024, both to match how Paperless chunks documents. It left memory at
2Gi/4Gi, which was sized for the smaller values.

The pod ran fine interactively and was OOMKilled (exit 137) partway through the
first sustained run, around 300 documents into an embedding pass. Nothing in the
server log marked it — the process was killed outright, so the last line is a
half-finished task and the reason is only visible in the container's
lastState.terminated.

This would have surfaced when Paperless first indexed the archive, i.e. on the
first real use rather than under test.

The weights are only ~600 MiB. What sets the footprint is four slots at 4096
context with a 1024-token micro-batch, plus Vulkan allocating the iGPU working
set from system RAM where it counts against this cgroup — the same effect that
put llama-server at 32Gi for 19 GiB of weights.

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

kubernetes/apps/llama-server/embed.yaml

@@ -44,12 +44,37 @@ spec:
             - Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0
             # Serves POST /v1/embeddings.
             - --embeddings
+            # A short, stable id for consumers. Without it the model is
+            # advertised as the whole --hf-repo string, which would end up
+            # verbatim in Paperless's config and break on the next model swap.
+            # 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.
+            - --pooling
+            - last
             - --n-gpu-layers
             - "99"
             - --threads
             - "2"
+            # 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
+            - "1024"
+            - --ubatch-size
+            - "1024"
+            # Room for a full chunk plus overhead rather than exactly one.
             - --ctx-size
-            - "2048"
+            - "4096"
             # No browser ever talks to this Service, so the WebUI is dead weight
             # and one more unauthenticated surface.
             - --no-webui
@@ -66,14 +91,24 @@ spec:
             capabilities:
               drop:
                 - ALL
+          # The weights are only ~600 MiB, but that is not what sets the
+          # footprint: four slots at 4096 context with a 1024-token micro-batch,
+          # and Vulkan allocating the iGPU's working set from system RAM where
+          # it counts against this cgroup.
+          #
+          # 4Gi was sized for the earlier 2048 context and 512 micro-batch. Both
+          # were raised to match Paperless's chunking without raising this, and
+          # the pod then survived interactive use and was OOMKilled (137) partway
+          # through the first sustained run — roughly 300 documents in. Indexing
+          # the archive would have hit it on day one.
           resources:
             requests:
               cpu: 250m
-              memory: 2Gi
+              memory: 4Gi
               gpu.intel.com/i915: "1"
             limits:
               cpu: "2"
-              memory: 4Gi
+              memory: 10Gi
               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/paperless/network-policy.yaml

@@ -57,6 +57,20 @@ spec:
               protocol: UDP
             - port: "53"
               protocol: TCP
+    # The cluster's own LLM, for AI suggestions and RAG embeddings. Scoped to
+    # the two pods and the one port rather than the ai namespace as a whole, so
+    # this grant cannot widen if something else is ever placed there.
+    - toEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": ai
+            app.kubernetes.io/name: llama-server
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": ai
+            app.kubernetes.io/name: llama-embed
+      toPorts:
+        - ports:
+            - port: "8080"
+              protocol: TCP
     # External HTTPS only — Tika model downloads, runtime image
     # fetches, etc. RFC1918 + ULA are excluded from `world` via the
     # CIDR-deny pattern. `world` covers public IPs / DNS-resolvable

kubernetes/apps/paperless/paperless.yaml

@@ -46,6 +46,44 @@ data:
   PAPERLESS_TIKA_ENABLED: "1"
   PAPERLESS_TIKA_ENDPOINT: "http://tika:9998"
   PAPERLESS_TIKA_GOTENBERG_ENDPOINT: "http://gotenberg:3000"
+  # AI suggestions against the cluster's own LLM (kubernetes/apps/llama-server).
+  # Nothing leaves the cluster: both endpoints are ClusterIP Services, so
+  # document text is never sent to a third party and there is no API key to
+  # rotate — llama-server has no authentication, and the ai namespace's network
+  # policy is what restricts who may reach it.
+  #
+  # CAVEAT, and it is a real one: paperless reads these from an
+  # ApplicationConfiguration row in the database and falls back to the
+  # environment only when that row is empty. A value set in the admin UI
+  # therefore overrides this ConfigMap silently. Configure AI in ONE place —
+  # here — and leave those fields untouched in the UI.
+  PAPERLESS_AI_ENABLED: "true"
+  PAPERLESS_AI_LLM_BACKEND: "openai-like"
+  PAPERLESS_AI_LLM_ENDPOINT: "http://llama-server.ai.svc.cluster.local:8080/v1"
+  # Must match --alias in kubernetes/apps/llama-server/deployment.yaml. The
+  # server ignores the field on the wire, but paperless sends it and the value
+  # is what appears in its logs.
+  PAPERLESS_AI_LLM_MODEL: "qwen3.6-35b-a3b"
+  # ClusterIP addresses are private, and paperless blocks non-public endpoints
+  # unless this is set. Default is already true; explicit because the whole
+  # integration silently stops working if it ever flips.
+  PAPERLESS_AI_LLM_ALLOW_INTERNAL_ENDPOINTS: "true"
+  # Under llama-server's own 16384, leaving room for the response.
+  PAPERLESS_AI_LLM_CONTEXT_SIZE: "12288"
+  # The 120s default is too tight at ~11 tok/s: a classification is roughly
+  # 2-4k prompt tokens plus a second round trip for localisation.
+  PAPERLESS_AI_LLM_REQUEST_TIMEOUT: "300"
+  # Matched against Django's settings.LANGUAGES, so the region is required —
+  # "de" alone does not resolve. Without it the model returns German tags but
+  # English document types, which was reproducible against this endpoint.
+  PAPERLESS_AI_LLM_OUTPUT_LANGUAGE: "de-de"
+  # RAG embeddings from llama-embed. Its Qwen3-Embedding covers 100+ languages,
+  # where paperless's default all-MiniLM-L6-v2 is documented as suited to
+  # 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"
+  PAPERLESS_AI_LLM_EMBEDDING_CHUNK_SIZE: "1024"
 ---
 apiVersion: apps/v1
 kind: Deployment
@@ -58,6 +96,13 @@ metadata:
     # Reloader restarts paperless when paperless-secrets changes (e.g. after a
     # secret-key rotation). See kubernetes/infrastructure/openbao/rotation/paperless.yaml.
     secret.reloader.stakater.com/reload: paperless-secrets
+    # And when paperless-config changes. The ConfigMap is consumed with
+    # envFrom, and Kubernetes does not restart a pod when a ConfigMap it reads
+    # that way is updated — the running container keeps the environment it
+    # started with. Without this, a settings change applies cleanly in git and
+    # in the cluster while the application never sees it, which is how the AI
+    # settings landed with no effect at all.
+    configmap.reloader.stakater.com/reload: paperless-config
 spec:
   replicas: 1
   strategy:

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

@@ -24,6 +24,16 @@ spec:
         - ports:
             - port: "8080"
               protocol: TCP
+    # Paperless calls both models for AI suggestions and RAG embeddings. The
+    # matching egress grant on its side is scoped to these two pods on :8080.
+    - fromEndpoints:
+        - matchLabels:
+            "k8s:io.kubernetes.pod.namespace": paperless
+            app.kubernetes.io/name: paperless
+      toPorts:
+        - ports:
+            - port: "8080"
+              protocol: TCP
     - fromEndpoints:
         - matchLabels:
             "k8s:io.kubernetes.pod.namespace": monitoring