4 Commits

Include renames

Author	SHA1	Message	Date
3 people	21c5495a99	feat(vllm-cpp): add GLiNER2.5 NER via TokenClassify (#12140) ... * feat(vllm-cpp): add GLiNER2.5 NER via TokenClassify Wire the vllm-cpp backend to the C ABI NER surface (vllm_gliner_ner, ABI v27) so LocalAI can serve zero-shot named entity recognition through the existing TokenClassify gRPC method. backend.go: TokenClassify method on *VllmCpp calls vllm_gliner_ner with the text and labels, copies the C-owned entity array into protobuf TokenClassifyEntity messages, and frees the result. govllmcpp.go: cNerEntity and cNerResult Go POD mirrors matching the C structs; vllmGlinerNer and vllmNerResultFree purego bindings; abiVersion bumped 26 -> 27. options.go: ner_labels, ner_threshold, ner_max_width parsed from engine_args. pkg/grpc: ClassifyModel interface and TokenClassify server handler (follows the Embedding locking pattern). core/config: vllm-cpp backend declares MethodTokenClassify and UsecaseTokenClassify. docs/content/features/vllm-cpp.md: NER section documenting the engine_args keys and the host-forward contract. Assisted-by: MAKI:regolo/glm5.2 [maki] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): correct NER pointer lint directive Use the govet directive for the C-owned NER array, matching the other purego pointer conversions. The array remains valid until its deferred free; the misspelled directive caused CI to flag this conversion. Assisted-by: Codex:gpt-6 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vllm-cpp): add kev-compatible SystemOne API endpoints Add POST /v1/systemone, /v1/systemone/permute, and /v1/systemone/separate to LocalAI, mirroring the kev project's structured-extraction API. Each endpoint runs zero-shot NER over the rendered state text and builds kev-compatible answers for three question types: noul (binary entity presence), choice (pick one option), and score (pick one level). The TokenClassifyRequest proto gains a `repeated string labels` field so each question can supply its own labels at inference time, and TokenClassifier gains TokenClassifyWithLabels for per-call label selection. The vllm-cpp backend uses request labels when non-empty, falling back to configured ner_labels then the built-in defaults. Helpers (renderState, softmax, choiceConfidence, scoreConfidence, r2) are ported from kev/api.py and mirrored in vllm.cpp's api_server.cpp so both servers produce the same answer shape. Following-Agents-Protocol: true AI-Assisted: true Assisted-by: AGENT:regolo/glm5.2 [maki] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): suppress gosec G404 on seeded permutation RNG The SystemOne permute endpoint uses math/rand with a caller-supplied seed for reproducible option permutations, matching kev's random.seed. gosec flags this as G404 (weak RNG). Add #nosec with a comment naming the intent: this is reproducibility, not cryptography. Following-Agents-Protocol: true AI-Assisted: true Assisted-by: AGENT:regolo/glm5.2 [maki] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(vllm-cpp): bump vllm.cpp pin to GLiNER2.5 merge commit Advance VLLM_CPP_VERSION from f3cd97e to 5058268d, the commit that landed GLiNER2.5 zero-shot NER support (PR #3224) in vllm.cpp. This brings the DeBERTa v2 encoder, GLiNER2 boundary head, C ABI NER functions, and server endpoints into the LocalAI vllm-cpp backend. The ABI version (27) and Go struct mirrors already match. Following-Agents-Protocol: true AI-Assisted: true Assisted-by: AGENT:regolo/glm5.2 [maki] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vllm-cpp): use instruction text as NER label in SystemOne handler The SystemOne handler was passing question IDs as NER labels for noul questions and bare key names for choice questions, so the model never matched any entities. Port the label mapping from vllm.cpp's ParseSystemOneBody: - noul: use the rendered instructions field (with instr alias) as the NER label, not the question ID - choice: use optionText(name, desc) — "name: description" or "name" when the description is null/empty — not the bare key - score: already correct (rendered criteria text) - permute: shuffle indices and build parallel key/label arrays so the NER call uses the optionText labels while the response is keyed by the original option names Also add the instructions field to the SystemOneQuestion schema struct (accepted alongside the instr backward-compat alias). Verified end-to-end against the real GLiNER2.5 model: noul questions now find "Apple Inc. is" (organization, 0.999) and "Tim Cook is" (person, 0.852) where they previously returned zero entities. Following-Agents-Protocol: true AI-Assisted: true Assisted-by: AGENT:regolo/glm5.2 [maki] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- 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>	2026-09-23 12:31:28 +02:00
Richard Palethorpe	799cc9f211	feat: bound global admission and expose running backend traces (#11560) ... feat: bound backend admission and expose running traces Add process-wide backend execution admission without blocking UI or administrative HTTP work. Represent backend operations while they are in flight, surface running traces with immediate log links, and tie streaming admission leases to the gRPC receive lifecycle. Assisted-by: OpenAI Codex: GPT-5 Signed-off-by: Richard Palethorpe <io@richiejp.com>	2026-08-18 08:56:59 +02:00
mudler's LocalAI [bot]andEttore Di Giacinto	1cd7d63c7b	fix(distributed): reject wrong-model requests on the remaining modalities (#10990) ... #10970 gave the four PredictOptions RPCs a model-identity check so a backend reached through a stale distributed route rejects the request instead of answering from whatever model it holds (#10952). Every other modality shares that exposure: the route is cached by host:port, a worker can recycle a stopped backend's port for another model's backend, and a liveness-only probe cannot tell a stale row from a valid one. Extends the same mechanism to the 21 remaining request messages that reach a backend through the router, using the pattern #10970 established rather than a parallel one: - proto: ModelIdentity on each modality request message. - controller: populated from ModelConfig.Model at the call site that also builds ModelOptions, so load-time and request-time values are equal by construction. - backends: one generic guard in pkg/grpc/server.go (27 Go backends), the method set in backend/python/common (36 Python backends), llama-cpp (AudioTranscription/Stream, Rerank, Score) and privacy-filter (TokenClassify). - reconcile already drops the stale row on IsModelMismatch; no change. TTSRequest and SoundGenerationRequest get a SEPARATE ModelIdentity field rather than reusing their existing `model`: FileStagingClient rewrites `model` to a worker-local path, so comparing it would reject valid requests in exactly the configuration this guards. AudioEncode/AudioDecode are deliberately left unguarded: the opus codec backend is loaded from a literal rather than a ModelConfig, so no value carries the equality guarantee the comparison depends on. The four bidirectional stream RPCs are out of scope; they bypass reconcile. Empty means skip on both sides, so an old controller, an old backend, and the bare request structs in tests/e2e-backends all keep working. Assisted-by: Claude Code:claude-opus-4-8 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>	2026-07-20 21:58:19 +02:00
Richard Palethorpe	3fa7b2955c	feat(pii): NER tier engine — privacy-filter.cpp backend + NER-centric PII filter (#10360) ... 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>	2026-06-18 11:45:22 +01:00