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/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