21c5495a99
* 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>
LocalAI website
LocalAI documentation website
Requirement
In this project, the Docsy theme component is pulled in as a Hugo module, together with other module dependencies:
$ hugo mod graph
hugo: collected modules in 566 ms
hugo: collected modules in 578 ms
github.com/google/docsy-example github.com/google/docsy@v0.5.1-0.20221017155306-99eacb09ffb0
github.com/google/docsy-example github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1
github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1 github.com/twbs/bootstrap@v4.6.2+incompatible
github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1 github.com/FortAwesome/Font-Awesome@v0.0.0-20220831210243-d3a7818c253f
If you want to do SCSS edits and want to publish these, you need to install PostCSS
npm install
Running the website locally
Building and running the site locally requires a recent extended version of Hugo.
You can find out more about how to install Hugo for your environment in our
Getting started guide.
From the LocalAI repository root, run:
make docs
The Hugo configuration lives in the docs directory. To invoke Hugo
directly instead, run:
cd docs
hugo server
Running a container locally
You can run docsy-example inside a Docker
container, the container runs with a volume bound to the docsy-example
folder. This approach doesn't require you to install any dependencies other
than Docker Desktop on
Windows and Mac, and Docker Compose
on Linux.
-
Build the docker image
docker-compose build -
Run the built image
docker-compose upNOTE: You can run both commands at once with
docker-compose up --build. -
Verify that the service is working.
Open your web browser and type
http://localhost:1313in your navigation bar, This opens a local instance of the docsy-example homepage. You can now make changes to the docsy example and those changes will immediately show up in your browser after you save.
Cleanup
To stop Docker Compose, on your terminal window, press Ctrl + C.
To remove the produced images run:
docker-compose rm
For more information see the Docker Compose documentation.
Troubleshooting
As you run the website locally, you may run into the following error:
➜ hugo server
INFO 2021/01/21 21:07:55 Using config file:
Building sites … INFO 2021/01/21 21:07:55 syncing static files to /
Built in 288 ms
Error: Error building site: TOCSS: failed to transform "scss/main.scss" (text/x-scss): resource "scss/scss/main.scss_9fadf33d895a46083cdd64396b57ef68" not found in file cache
This error occurs if you have not installed the extended version of Hugo. See this section of the user guide for instructions on how to install Hugo.
Or you may encounter the following error:
➜ hugo server
Error: failed to download modules: binary with name "go" not found
This error occurs if you have not installed the go programming language on your system.
See this section of the user guide for instructions on how to install go.