fix(paperless-eval): score a right answer the matcher threw away

The AI engine reports an unresolved guess as a name in
`suggested_correspondents` rather than an id in `correspondents`, and
paperless_ai.matching only resolves it when difflib.get_close_matches
clears MATCH_THRESHOLD = 0.8. A correct but more verbose answer does not:
"Generali Deutschland Krankenversicherung AG" fails against the existing
"Generali AG", leaving `correspondents` empty.

Scored as it was, that counts as a miss, which would have blamed the
model for a matcher limit. Score both instead — the id as offered, and
the name matched wordwise against the true label — so the two causes stay
separable. The classic engine emits no `suggested_*` fields, so its two
columns coincide and the comparison stays fair either way.

Names now come out of postgres alongside the ids, tab-separated because
'|' occurs in correspondent names.

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

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

@@ -19,40 +19,13 @@
     state: directory
     mode: "0755"
 
-# 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
+- name: Fetch the pinned bouncer release
   ansible.builtin.get_url:
     url: >-
       https://github.com/crowdsecurity/cs-firewall-bouncer/releases/download/{{
       crowdsec_bouncer_version }}/crowdsec-firewall-bouncer-linux-amd64.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
+    checksum: sha256:{{ crowdsec_bouncer_sha256 }}
     mode: "0644"
 
 # Only the binary. The tarball also carries install.sh / upgrade.sh /

kubernetes/apps/llama-server/embed.yaml

@@ -41,7 +41,7 @@ spec:
           image: ghcr.io/ggml-org/llama.cpp:server-vulkan-b10731
           args:
             - --hf-repo
-            - gpustack/bge-m3-GGUF:Q8_0
+            - Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0
             # Serves POST /v1/embeddings.
             - --embeddings
             # A short, stable id for consumers. Without it the model is
@@ -50,16 +50,14 @@ 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
-            - 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.
+            - 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
-            - cls
+            - last
             - --n-gpu-layers
             - "99"
             - --threads
@@ -93,14 +91,6 @@ 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
@@ -108,7 +98,7 @@ spec:
               gpu.intel.com/i915: "1"
             limits:
               cpu: "2"
-              memory: 8Gi
+              memory: 4Gi
               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

@@ -82,10 +82,7 @@ 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"
-  # 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_MODEL: "qwen3-embedding-0.6b"
   PAPERLESS_AI_LLM_EMBEDDING_CHUNK_SIZE: "1024"
 ---
 apiVersion: apps/v1

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

@@ -34,17 +34,6 @@ 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

@@ -167,18 +167,6 @@ 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:

scripts/paperless-eval.sh

@@ -0,0 +1,207 @@
+#!/usr/bin/env bash
+# End-to-end evaluation of Paperless document suggestions against the real
+# archive, comparing the two engines Paperless ships:
+#
+#   /api/documents/<id>/suggestions/      the built-in scikit-learn classifier,
+#                                         trained on the existing corpus
+#   /api/documents/<id>/ai_suggestions/   the LLM path, optionally with RAG
+#
+# WHY THIS EXISTS
+#
+# scripts/embed-eval-real.sh measures whether a document's nearest neighbours
+# carry its labels. That is a proxy: Paperless does not act on the neighbours
+# directly, it hands them to an LLM as context, alongside the document text and
+# a candidate list of existing tags and correspondents. The LLM can override
+# poor context, or fail with good context. So a better embedding score does not
+# automatically mean better suggestions, and nothing so far has verified that it
+# does.
+#
+# This measures the thing that is actually wanted: does Paperless suggest the
+# labels a human already chose? Everything else — MTEB scores, retrieval
+# accuracy — is only interesting insofar as it predicts this number.
+#
+# It also answers a question that predates the LLM work: with ten years of
+# hand-curated labels, the built-in classifier has excellent training data. It
+# cannot invent a new tag or a title, which is the LLM's advantage — but for
+# assigning labels that already exist it may simply be better. Running both on
+# the same documents settles that rather than assuming it.
+#
+# Ground truth is the current filing. Where the two disagree with it, that is
+# not automatically an error: over ten years filing conventions change, and an
+# old document may carry an older convention. Treat the absolute numbers as
+# indicative and the comparison between engines as the result.
+#
+# Requires a Paperless API token in PAPERLESS_TOKEN. The API is only reachable
+# from inside the cluster (the namespace policy admits the Authentik gate
+# outpost alone), so requests are issued from within the paperless pod.
+#
+# Usage:
+#   PAPERLESS_TOKEN=... scripts/paperless-eval.sh [-n 100] [--engine both|classic|ai]
+set -uo pipefail
+
+SAMPLE="${PAPERLESS_EVAL_SAMPLE:-100}"
+ENGINE=both
+PG_POD="${PAPERLESS_EVAL_PG_POD:-pprl-pg-2}"
+APP_POD="${PAPERLESS_EVAL_APP_POD:-deploy/paperless}"
+
+while [ $# -gt 0 ]; do
+    case "$1" in
+        -n|--sample) SAMPLE="$2"; shift 2 ;;
+        --engine)    ENGINE="$2"; shift 2 ;;
+        -h|--help)   sed -n '2,40p' "$0"; exit 0 ;;
+        *) echo "unknown argument: $1 (try --help)" >&2; exit 2 ;;
+    esac
+done
+
+if [ -z "${PAPERLESS_TOKEN:-}" ]; then
+    echo "PAPERLESS_TOKEN is not set." >&2
+    echo "Create one for an existing user, then remove it when finished." >&2
+    exit 2
+fi
+
+WORK="$(mktemp -d)"
+trap 'rm -rf "$WORK"' EXIT
+
+# Deterministic sample, so repeated runs and different engines see the same
+# documents. Only documents that carry both labels are useful as ground truth.
+echo "reading $SAMPLE labelled documents from postgres ($PG_POD)..."
+# Names come along with the ids because the AI engine reports an unresolved
+# guess as a name string rather than an id; see the lenient scoring below.
+# Tab-separated rather than '|', which occurs in correspondent names.
+kubectl -n paperless exec "$PG_POD" -- psql -U postgres -d paperless -tA -F$'\t' -c \
+  "select d.id, d.correspondent_id, d.document_type_id, c.name, t.name
+   from documents_document d
+   join documents_correspondent c on c.id = d.correspondent_id
+   join documents_documenttype  t on t.id = d.document_type_id
+   where d.correspondent_id is not null
+     and d.document_type_id is not null
+     and length(d.content) > 400
+   order by md5(d.id::text)
+   limit $SAMPLE;" > "$WORK/docs.txt" 2>"$WORK/pg-err.txt"
+if ! [ -s "$WORK/docs.txt" ]; then
+    echo "no documents returned from postgres." >&2
+    [ -s "$WORK/pg-err.txt" ] && cat "$WORK/pg-err.txt" >&2
+    exit 2
+fi
+N=$(grep -c . "$WORK/docs.txt")
+
+# The floor: always guessing the most frequent label. Without it a number like
+# "70% correct" cannot be read, because a skewed archive gives that away for
+# free — one correspondent holds several hundred of ~3900 documents.
+kubectl -n paperless exec "$PG_POD" -- psql -U postgres -d paperless -tAc \
+  "select max(c) from (select count(*) c from documents_document
+   where correspondent_id is not null group by correspondent_id) x;" \
+  > "$WORK/corr_max.txt" 2>"$WORK/pg-err.txt"
+kubectl -n paperless exec "$PG_POD" -- psql -U postgres -d paperless -tAc \
+  "select count(*) from documents_document where correspondent_id is not null;" \
+  > "$WORK/corr_total.txt" 2>"$WORK/pg-err.txt"
+
+api() { # path -> body on stdout
+    kubectl -n paperless exec "$APP_POD" -c paperless -- \
+        curl -sS -m 300 -H "Authorization: Token $PAPERLESS_TOKEN" \
+        "http://127.0.0.1:8000$1" 2>"$WORK/err.txt"
+}
+
+# A hit under two definitions, because the AI engine can be right in a way the
+# API response does not record as a suggestion:
+#
+#   strict   the true label's id appears in `.correspondents` /
+#            `.document_types`. This is what the UI actually offers, so it is
+#            the number that describes the feature as experienced.
+#
+#   lenient  strict, OR the true label's name is contained wordwise in one of
+#            `.suggested_correspondents` / `.suggested_document_types`.
+#
+# The gap between them is one specific failure: the LLM names the right entity
+# but paperless_ai.matching cannot resolve the name to the existing object,
+# because difflib.get_close_matches runs at MATCH_THRESHOLD = 0.8 and a correct
+# but more verbose answer falls below it — observed with "Generali Deutschland
+# Krankenversicherung AG" against the existing "Generali AG", which yielded an
+# empty `correspondents` list.
+#
+# Reporting only the strict number would blame the model for a matcher problem;
+# reporting only the lenient one would claim a usefulness the UI does not
+# deliver. The classic engine emits no `suggested_*` fields at all, so both
+# numbers coincide for it and the comparison stays fair in either column.
+LENIENT_JQ='
+def norm: ascii_downcase | gsub("[^\\p{L}\\p{N}]+"; " ") | split(" ") | map(select(length > 0));
+def contains_all($want): . as $have | $want | all(. as $t | $have | index([$t]) != null);
+($want_name | norm) as $w
+| ((.[$id_field] // []) | index($want_id) != null)
+  or ([(.[$name_field] // [])[] | norm] | any(contains_all($w)))
+'
+
+# score <engine> <url-suffix>
+score() {
+    local engine="$1" suffix="$2"
+    local n=0 corr_hit=0 type_hit=0 corr_len=0 type_len=0 fails=0 start elapsed
+    echo ""
+    echo "=== $engine"
+    start=$(date +%s)
+    while IFS=$'\t' read -r id want_corr want_type want_corr_name want_type_name; do
+        [ -n "$id" ] || continue
+        n=$((n + 1))
+        body=$(api "/api/documents/$id/$suffix")
+        if ! printf '%s' "$body" | jq -e . >/dev/null 2>&1; then
+            fails=$((fails + 1))
+            [ "$fails" -le 3 ] && printf '    %s: keine gueltige Antwort: %s\n' \
+                "$id" "$(printf '%s' "$body" | tr '\n' ' ' | head -c 120)" >&2
+            continue
+        fi
+        if printf '%s' "$body" | jq -e --argjson w "$want_corr" \
+               '.correspondents // [] | index($w) != null' >/dev/null 2>&1; then
+            corr_hit=$((corr_hit + 1))
+        fi
+        if printf '%s' "$body" | jq -e --argjson w "$want_type" \
+               '.document_types // [] | index($w) != null' >/dev/null 2>&1; then
+            type_hit=$((type_hit + 1))
+        fi
+        if printf '%s' "$body" | jq -e \
+               --argjson want_id "$want_corr" --arg want_name "$want_corr_name" \
+               --arg id_field correspondents --arg name_field suggested_correspondents \
+               "$LENIENT_JQ" >/dev/null 2>&1; then
+            corr_len=$((corr_len + 1))
+        fi
+        if printf '%s' "$body" | jq -e \
+               --argjson want_id "$want_type" --arg want_name "$want_type_name" \
+               --arg id_field document_types --arg name_field suggested_document_types \
+               "$LENIENT_JQ" >/dev/null 2>&1; then
+            type_len=$((type_len + 1))
+        fi
+        [ $((n % 25)) -eq 0 ] && echo "    $n/$N"
+    done < "$WORK/docs.txt"
+    elapsed=$(( $(date +%s) - start ))
+    pct() { awk -v a="$1" -v b="$2" 'BEGIN{if (b>0) printf "%.1f", 100*a/b; else print "0.0"}'; }
+    printf '    correspondent, offered  : %5s%%   recognised: %5s%%\n' \
+        "$(pct "$corr_hit" "$n")" "$(pct "$corr_len" "$n")"
+    printf '    document type, offered  : %5s%%   recognised: %5s%%\n' \
+        "$(pct "$type_hit" "$n")" "$(pct "$type_len" "$n")"
+    printf '    no usable answer        : %s of %s\n' "$fails" "$n"
+    printf '    %ss total, %.1fs per document\n' "$elapsed" "$(awk -v e="$elapsed" -v n="$n" 'BEGIN{print e/n}')"
+    printf '%-9s %7s %7s %7s %7s %6s %8ss\n' "$engine" \
+        "$(pct "$corr_hit" "$n")" "$(pct "$corr_len" "$n")" \
+        "$(pct "$type_hit" "$n")" "$(pct "$type_len" "$n")" \
+        "$fails" "$elapsed" >> "$WORK/summary.txt"
+}
+
+: > "$WORK/summary.txt"
+FLOOR=$(awk -v m="$(cat "$WORK/corr_max.txt")" -v t="$(cat "$WORK/corr_total.txt")" \
+    'BEGIN{if (t>0) printf "%.1f", 100*m/t; else print "?"}')
+echo "documents: $N   most-frequent-correspondent floor: ${FLOOR}%"
+
+case "$ENGINE" in
+    both)    score classic suggestions/; score ai ai_suggestions/ ;;
+    classic) score classic suggestions/ ;;
+    ai)      score ai ai_suggestions/ ;;
+    *) echo "unknown engine: $ENGINE" >&2; exit 2 ;;
+esac
+
+echo ""
+echo "================================================================"
+printf '%-9s %7s %7s %7s %7s %6s %9s\n' \
+    ENGINE CORR CORR+ TYPE TYPE+ FAILS TIME
+cat "$WORK/summary.txt"
+printf '%-9s %7s\n' "floor" "$FLOOR"
+echo "================================================================"
+echo "CORR/TYPE  = true label offered as a resolved id (what the UI shows)"
+echo "CORR+/TYPE+ = also counting names the LLM got right but matching dropped"