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* 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>
116 lines
5.1 KiB
Go
116 lines
5.1 KiB
Go
package grpc
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import (
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"context"
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pb "github.com/mudler/LocalAI/pkg/grpc/proto"
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)
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// AnimationMetadataModel optionally reports JSON metadata without changing the legacy
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// animation interface implemented by other backends.
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type AnimationMetadataModel interface {
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Animate3DWithMetadata(*pb.Animate3DRequest) ([]byte, error)
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}
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type AIModel interface {
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Busy() bool
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Lock()
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Unlock()
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Locking() bool
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Predict(*pb.PredictOptions) (string, error)
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PredictStream(*pb.PredictOptions, chan string) error
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Load(*pb.ModelOptions) error
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Free() error
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Embeddings(*pb.PredictOptions) ([]float32, error)
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GenerateImage(*pb.GenerateImageRequest) error
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UpscaleImage(*pb.UpscaleImageRequest) error
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GenerateVideo(*pb.GenerateVideoRequest) error
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Generate3D(*pb.Generate3DRequest) error
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Animate3D(*pb.Animate3DRequest) error
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Detect(*pb.DetectOptions) (pb.DetectResponse, error)
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Depth(*pb.DepthRequest) (pb.DepthResponse, error)
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FaceVerify(*pb.FaceVerifyRequest) (pb.FaceVerifyResponse, error)
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FaceAnalyze(*pb.FaceAnalyzeRequest) (pb.FaceAnalyzeResponse, error)
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VoiceVerify(*pb.VoiceVerifyRequest) (pb.VoiceVerifyResponse, error)
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VoiceAnalyze(*pb.VoiceAnalyzeRequest) (pb.VoiceAnalyzeResponse, error)
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VoiceEmbed(*pb.VoiceEmbedRequest) (pb.VoiceEmbedResponse, error)
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AudioTranscription(context.Context, *pb.TranscriptRequest) (pb.TranscriptResult, error)
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AudioTranscriptionStream(context.Context, *pb.TranscriptRequest, chan *pb.TranscriptStreamResponse) error
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AudioTranscriptionLive(in <-chan *pb.TranscriptLiveRequest, out chan<- *pb.TranscriptLiveResponse) error
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TTS(*pb.TTSRequest) error
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TTSStream(*pb.TTSRequest, chan []byte) error
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SoundGeneration(*pb.SoundGenerationRequest) error
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TokenizeString(*pb.PredictOptions) (pb.TokenizationResponse, error)
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Detokenize(*pb.DetokenizeRequest) (pb.DetokenizeResponse, error)
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Status() (pb.StatusResponse, error)
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StoresSet(*pb.StoresSetOptions) error
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StoresDelete(*pb.StoresDeleteOptions) error
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StoresGet(*pb.StoresGetOptions) (pb.StoresGetResult, error)
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StoresFind(*pb.StoresFindOptions) (pb.StoresFindResult, error)
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VAD(*pb.VADRequest) (pb.VADResponse, error)
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Diarize(*pb.DiarizeRequest) (pb.DiarizeResponse, error)
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SoundDetection(context.Context, *pb.SoundDetectionRequest) (*pb.SoundDetectionResponse, error)
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AudioEncode(*pb.AudioEncodeRequest) (*pb.AudioEncodeResult, error)
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AudioDecode(*pb.AudioDecodeRequest) (*pb.AudioDecodeResult, error)
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AudioTransform(*pb.AudioTransformRequest) (*pb.AudioTransformResult, error)
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AudioTransformStream(in <-chan *pb.AudioTransformFrameRequest, out chan<- *pb.AudioTransformFrameResponse) error
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AudioToAudioStream(in <-chan *pb.AudioToAudioRequest, out chan<- *pb.AudioToAudioResponse) error
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// Forward proxies a raw HTTP request to an upstream provider for
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// passthrough-mode cloud-proxy backends. ctx is the gRPC stream
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// context — cancellation propagates to the upstream HTTP request
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// so client disconnect closes the upstream connection.
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Forward(ctx context.Context, in <-chan *pb.ForwardRequest, out chan<- *pb.ForwardReply) error
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ModelMetadata(*pb.ModelOptions) (*pb.ModelMetadataResponse, error)
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// Fine-tuning
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StartFineTune(*pb.FineTuneRequest) (*pb.FineTuneJobResult, error)
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FineTuneProgress(*pb.FineTuneProgressRequest, chan *pb.FineTuneProgressUpdate) error
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StopFineTune(*pb.FineTuneStopRequest) error
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ListCheckpoints(*pb.ListCheckpointsRequest) (*pb.ListCheckpointsResponse, error)
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ExportModel(*pb.ExportModelRequest) error
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// Quantization
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StartQuantization(*pb.QuantizationRequest) (*pb.QuantizationJobResult, error)
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QuantizationProgress(*pb.QuantizationProgressRequest, chan *pb.QuantizationProgressUpdate) error
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StopQuantization(*pb.QuantizationStopRequest) error
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}
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func newReply(s string) *pb.Reply {
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return &pb.Reply{Message: []byte(s)}
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}
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// AIModelRich is an optional extension to AIModel for backends that
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// can produce a full *pb.Reply — including tool-call deltas and
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// usage tokens — rather than just a content string. The gRPC server
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// type-asserts and prefers the rich path when implemented; otherwise
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// it wraps Predict's string return in a Reply.
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//
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// Cloud-proxy translate mode is the motivating use case: the upstream
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// emits structured tool_calls that would be lost through the legacy
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// (string, error) signature.
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//
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// PredictStreamRich contract: send replies into the channel and
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// return when finished. Do NOT close the channel — the server closes
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// it after the call returns. This is opposite to legacy PredictStream
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// which expects the impl to defer close().
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type AIModelRich interface {
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PredictRich(*pb.PredictOptions) (*pb.Reply, error)
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PredictStreamRich(*pb.PredictOptions, chan<- *pb.Reply) error
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}
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// ClassifyModel is an optional extension to AIModel for backends that
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// implement the TokenClassify RPC (zero-shot NER). The gRPC server
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// type-asserts to this interface; backends that do not implement it
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// fall through to the UnimplementedBackendServer default. This mirrors
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// the AIModelRich pattern: adding a method to AIModel itself would
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// break every backend, so the capability is opt-in.
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type ClassifyModel interface {
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TokenClassify(context.Context, *pb.TokenClassifyRequest) (*pb.TokenClassifyResponse, error)
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}
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