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claude-botandClaude Opus 5 0a101eb8ee fix(llama-embed): size the batch for bge-m3's tokenizer, not Paperless's
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The LLM index rebuild ran for 79 minutes and then died:

    input (1069 tokens) is too large to process.
    increase the physical batch size (current batch size: 1024)

Setting the batch to Paperless's chunk size looked like an exact match
and was not. Paperless counts its 1024 tokens with llama_index's
tokenizer; this server measures the batch in bge-m3's, which is
XLM-RoBERTa and splits German into more pieces. A chunk Paperless
considered full arrived here 4.4% over the limit, and the request failed
rather than truncating.

1536 absorbs a tokenizer ratio rather than a rounding error. 2048 stays
out of reach: that is where the chat model hit ErrorDeviceLost on this
Vulkan stack, under a 20 GB model's memory pressure that a 600 MB one may
well not reproduce — but there is no reason to go looking.

The index is left partial by the failed run and has to be rebuilt with
`document_llmindex rebuild` once this is live.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-02 15:13:54 +02:00
2 changed files with 21 additions and 21 deletions
+2 -13
View File
@@ -120,14 +120,7 @@ spec:
server:
resources:
requests:
# 500m, not 200m: the HPA below targets 70% of *this* request, so at
# 200m the scale-up threshold sat at 140m — below the pod's own
# ordinary peak. Measured over 7d, a warm server pod p99s at ~108m
# and peaks at ~190m, and a freshly-started one burns ~300m booting
# Django. That made the HPA oscillate 2<->3<->4 all day and pin at
# maxReplicas (KubeHpaMaxedOut, issue #761). At 500m the threshold
# is 350m/pod, above the boot burst, so only real load scales out.
cpu: 500m
cpu: 200m
memory: 1Gi
limits:
memory: 2Gi
@@ -161,11 +154,7 @@ spec:
worker:
resources:
requests:
# Same reasoning as server above, and the worker is burstier still:
# over 7d its per-pod p99 reaches ~336m and peaks hit ~630m while
# running blueprint applies and scheduled tasks. 200m understated
# that badly enough that routine task batches read as saturation.
cpu: 500m
cpu: 200m
memory: 1Gi
limits:
memory: 2Gi
+19 -8
View File
@@ -64,16 +64,27 @@ spec:
- "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.
# 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.
- --batch-size
- "1024"
- "1536"
- --ubatch-size
- "1024"
- "1536"
# Room for a full chunk plus overhead rather than exactly one.
- --ctx-size
- "4096"