* feat: return 429 when backends are saturated
When backends are at capacity (per-model max_concurrent or the
process-wide --max-concurrent-backend-requests ceiling), the response
was 503. The OpenAI SDK, litellm, and most agent harnesses key on 429
for rate-limit backoff and treat 503 as a hard error.
Both saturation paths now return 429 with the existing Retry-After
header and type: "rate_limit_error" in the JSON body. The per-model
admission middleware keeps admission_rejected as the code field so
existing alerts that match on it still fire.
Non-saturation 503s are unchanged: model cold-loading (with progress
body), model-load failure cooldown, PII detector fail-closed, and
classifier unavailable. These mean "not ready" rather than "busy".
Assisted-by: AGENT:regolo/glm5.2 [TOOL]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: return 503 when scheduler has no available nodes
When the scheduler cannot find any healthy node to serve a model —
all nodes are full and eviction cannot free a slot, or a node_selector
excludes every candidate — the error fell through to 500. A 500 tells
clients something is broken when the condition is transient and
retryable.
The router now wraps these errors with a new ErrNoAvailableNodes
sentinel. The HTTP error handler maps it to 503 via applyNoAvailableNodes,
following the same pattern as applyBackendAdmission (429). Unrelated
scheduler errors (DB timeouts, registry lookups) still return 500.
Three return sites are wrapped:
- resolveSelectorCandidates: selector matches zero healthy nodes
- scheduleNewModel eviction-busy: all models have in-flight requests
- scheduleNewModel eviction-failed: eviction itself errored
The existing scheduleAndLoad wrapper ("no available nodes: %w") preserves
the sentinel through the chain via errors.Is, as does ModelRouterAdapter.
Assisted-by: AGENT:regolo/glm5.2 [TOOL]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(http): use Ginkgo for admission tests
Replace forbidden testing.T calls with Ginkgo and Gomega so the lint
check accepts the admission handler tests.
Assisted-by: Codex:GPT-6 forbidigo
* fix(middleware): show the recorded status for admission rejections
The admission audit row now records 429, but the Middleware page still
printed a hard-coded 503. Read the status from the event, and update
the two package comments that still said 503.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5-5 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
* feat(pii): restore request-scoped pseudonyms
Replace masked request values with unique per-request tokens when response restoration is enabled, then restore them across JSON and SSE write boundaries. Document the opt-in model setting and expose it in config metadata.\n\nAssisted-by: Codex:gpt-5
* fix(pii): wrap reversible redaction tokens
Use configurable token delimiters to avoid restoring ordinary model text that happens to match an internal identifier. Rename the option and document the confidentiality tradeoff.
Assisted-by: Codex:gpt-5
---------
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
The PII EventStore ring buffer is capacity-bound and meant for
recent-audit browsing via /api/pii/events; operators also want a
monotonic, scrape-friendly signal on /metrics — how many
detections/masks/blocks per hour, per origin, and whether the filter
stopped firing after a deploy (silent-failure class).
EventStore.Record is the single choke point every producer already goes
through (request middleware, response scrubbing, MITM proxy
connects/intercepts), so one lazily-initialised counter there covers all
paths without touching any producer:
localai_pii_events_total{kind, origin, action, direction}
Same lazy otel.Meter pattern as core/services/routing/billing, so the
counter lands on the Prometheus-backed global MeterProvider installed by
the monitoring service. No behaviour change; label cardinality is
bounded (enum-like fields only, no pattern IDs or user IDs).
Assisted-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: stefanwalcz <stefan.walcz@walcz.de>
Squashed feat/pii-ner-tier-engine rebased onto master (was 45 commits; see
backup/pii-ner-tier-engine-prerebase). Net change:
- privacy-filter.cpp: standalone GGML engine for the openai-privacy-filter
PII/NER token classifier, wired as a LocalAI gRPC backend (CPU/CUDA/Vulkan).
TokenClassify moves off the patched llama.cpp path onto this backend.
- PII filter reworked to be NER-centric (encoder/NER detection tier scanning
whole conversations as one document), with a recreated bounded restricted-
regex secret-matching pattern detector tier alongside it (per-model
pii_detection.builtins / .patterns + core/services/routing/piipattern).
- Detection labelled by source (ner vs pattern); backend trace / confidence /
debug observability; analyze/redact exposed as a synchronous API.
- Instance-wide default detector policy + per-usecase default-on; request
filtering extended to completions, embeddings, edits & Ollama.
- React UI: NER-centric PII editor, detector-models table, pattern/builtins
editor, middleware default-policy UI.
- Gallery: privacy-filter-multilingual token-classify model + NER install
filter; token_classify known_usecase; batch sized to context for NER models.
privacy-filter backend registered in the backend gallery (cpu/vulkan/cuda-13
meta + image entries with a capabilities map) matching its CI matrix jobs,
and an /import-model auto-detect importer (PrivacyFilterImporter, narrow
privacy-filter GGUF detection) replacing the prior pref-only registration.
Reconciled against master's independent evolution:
- Dropped master's PIIPatternOverrides feature (global-pattern runtime
overrides + /api/pii/patterns API + runtime_settings.json persistence). The
per-model NER + pattern-detector design supersedes it; it was built on the
global redactor pattern set this branch replaced.
- Reverted the llama.cpp Score carry-patch (0006-server-task-type-score):
removed the patch and restored master's grpc-server.cpp Score RPC (direct
llama_decode, slot-loop bypass) and LLAMA_VERSION pin, plus master's
model_config validation forbidding score + chat/completion/embeddings on
llama-cpp. token_classify is unaffected (it runs on the privacy-filter
backend, not llama-cpp).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(router): score classifier production-readiness
Conversation trimming runs through the classifier model's chat template
and trims by exact token count, sized to the model's n_batch which is
now scaled to context so long probes can't crash the backend. Missing
chat_message templates are a hard error at router build time. Router-
facing factories (Embedder/Scorer/Reranker/TokenCounter) re-resolve
ModelConfig per call so a model installed post-startup doesn't bind a
stub Backend="" config and silently fall into the loader's auto-
iterate path.
New 'vector_store' backend trace recorded inside localVectorStore on
every Search/Insert — including the backend-load-failure path that
previously vanished into an xlog.Warn — with outcome tagging
(hit/miss/empty_store/backend_load_error/find_error/insert_error/ok).
Companion cleanup drops misleading similarity:0 and input_tokens_count:0
from non-hit and text-mode traces.
Gallery local-store-development aliases to 'local-store' so the master
image satisfies pkg/model.LocalStoreBackend lookups from the embedding
cache.
Misc: llama-cpp TokenizeString reads the correct 'prompt' JSON key
(the original bug); ModelTokenize nil-guard; non-fatal mitm proxy
startup; PII 'route_local' renamed to 'allow' with docs/UI in sync;
model-editor footer no longer eats the edit area on small screens;
several config-editor template/dropdown/section fixes.
Tests: e2e router specs (casual/code-hint + long-conversation trim),
vector_store trace specs, lazy-factory specs, gallery dev-alias
resolution, Playwright trace badge + scroll regression.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(backend): auto-size batch to context for embedding and rerank models
Embedding and rerank models pool over the whole input in a single physical batch (n_ubatch). With batch left at the 512 default, the backend rejects longer inputs with "input is too large to process", silently capping a large-context embedder (e.g. 8k/32k) at 512 tokens. Size n_batch to the context for these single-pass usecases, mirroring the existing FLAG_SCORE behaviour; an explicit batch: still wins.
Extracts EffectiveContextSize/EffectiveBatchSize from grpcModelOpts so the effective decode window has one home for other callers to reuse.
Adds an e2e-aio regression test that embeds a >512-token input. The AIO embedding model is switched to nomic-embed-text-v1.5 (2048 context) because the previous granite model was capped at 512 tokens and could not exercise the larger batch.
Assisted-by: claude-code:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(gallery): raise arch-router scoring output cap via parallel:64
Scoring decodes the whole prompt+candidate in a single llama_decode and
reads one logit row per candidate token. The vendored llama.cpp server
caps causal output rows at n_parallel, so the default of 1 aborts with
GGML_ASSERT(n_outputs_max <= cparams.n_outputs_max) on multi-token route
labels. Set options: [parallel:64] on both arch-router quant entries to
lift the cap; kv_unified (the grpc-server default) keeps the full context
per sequence, so this does not split the KV cache.
Assisted-by: claude-code:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Add a routing middleware stack and a cloud-proxy backend.
* cloud-proxy: a Go gRPC backend that forwards OpenAI- and
Anthropic-shaped chat requests to upstream providers, with an
optional translate mode (OpenAI request -> Anthropic /v1/messages
-> OpenAI response) and full tool-calling support.
* routing: admission control, content-aware model routing
(embedding cache + classifier + rerank + Arch-Router score),
PII detection/redaction (regex + NER) with streaming filter and
OpenAI/Anthropic adapters, and a per-user/per-key billing recorder
backed by GORM or in-memory storage.
* middleware: UsageMiddleware records usage via the billing recorder,
plus admission, route-model, usage-stamp and trace middlewares.
* observability: BackendTrace ring buffer stores full request bodies
(capped), MITM proxy emits structured trace events, and router
classifier decisions surface at /api/router/decide.
* gallery: Arch-Router-1.5B (Q4_K_M and Q8_0).
* UI: cloud-proxy model-editor fields, classifier system-prompt and
score-normalization config, and a Traces page rendering request
bodies.
Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
