* feat(distributed): add per-request node ID context holder
Introduce pkg/distributedhdr, a leaf package carrying a per-request
*atomic.Value holder for the picked worker node ID from the
SmartRouter (core/services/nodes) up to the HTTP response writer
wrapper (core/http/middleware). Avoids the import cycle that a shared
key in either consumer would create.
Exposes NewHolder, WithHolder, Holder, Stamp, Load, Inherit. The
holder is atomic.Value so cross-goroutine publish from the router to
the response writer wrapper is race-clean.
Assisted-by: Claude:claude-opus-4-7[1m]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(distributed): add ExposeNodeHeader middleware + response writer wrapper
New ApplicationConfig.ExposeNodeHeader bool + --expose-node-header CLI
flag / LOCALAI_EXPOSE_NODE_HEADER env var (default off; the node ID
reveals internal topology and is opt-in).
The middleware creates a per-request *atomic.Value holder, attaches it
to c.Request().Context() via distributedhdr.WithHolder, and wraps
c.Response().Writer with a custom http.ResponseWriter that sets the
X-LocalAI-Node header on first Write / WriteHeader / Flush by reading
the holder. Implements http.Flusher, http.Hijacker, Unwrap so it
composes cleanly with Echo and http.NewResponseController.
request.go propagates the holder onto derived contexts via
distributedhdr.Inherit so the holder survives the correlation-ID
context replacement.
Unit + race-clean concurrency + integration specs.
Assisted-by: Claude:claude-opus-4-7[1m]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(distributed): stamp node ID in router and wire middleware to inference routes
ModelRouterAdapter.Route stamps the picked node ID into the
per-request holder via distributedhdr.Stamp(ctx, result.Node.ID) right
after replica selection.
Wire ExposeNodeHeader middleware to:
- OpenAI chat/completion/embeddings + audio transcriptions/speech + image generations/inpainting
- Anthropic /v1/messages
- Ollama /api/chat, /api/generate, /api/embed, /api/embeddings
- Jina /v1/rerank
- LocalAI /v1/vad
The middleware's wrapper reads the holder on first byte and sets the
X-LocalAI-Node response header before delegating to the underlying
writer. Per-request scope means no race under concurrent multi-replica
routing.
Assisted-by: Claude:claude-opus-4-7[1m]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): thread request context through backend Load + cover ctx propagation
Five non-OpenAI backend helpers were silently using app.Context instead
of the request context for the gRPC backend call: transcription, TTS,
image generation, rerank, VAD. Effect: distributedhdr.Stamp in the
router callback was a silent no-op for these paths, AND client
cancellation didn't propagate to in-flight inference.
Thread c.Request().Context() (or the equivalent input.Context after
the request middleware has installed the correlation-ID derived
context) through each helper and into ModelOptions via
model.WithContext(ctx). ImageGeneration's signature gains a leading
ctx parameter; in-tree callers (openai image, openai inpainting,
openai inpainting_test) are updated to match.
ModelEmbedding gains a leading ctx parameter for the same reason; the
openai and ollama embedding handlers pass the request context through.
chat_stream_workers.go defers the initial role=assistant chunk
emission until the first token callback so the wrapper's lazy
X-LocalAI-Node lookup against the loader runs AFTER ml.Load has
stamped the per-modelID node ID; semantically identical for clients
(role still arrives before any text).
Regression test core/backend/ctx_propagation_test.go pins ctx
propagation for all five helpers.
Docs updated to enumerate the full endpoint coverage of the
--expose-node-header flag.
Assisted-by: Claude:claude-opus-4-7[1m]
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>
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>
The OpenAI Node.js SDK v4+ sends encoding_format=base64 by default.
LocalAI previously ignored this parameter and always returned a float
JSON array, causing a silent data corruption bug in any Node.js client
(AnythingLLM Desktop, LangChain.js, LlamaIndex.TS, …):
// What the client does when it expects base64 but receives a float array:
Buffer.from(floatArray, 'base64')
Node.js treats a non-string first argument as a byte array — each
float32 value is truncated to a single byte — and Float32Array then
reads those bytes as floats, yielding dims/4 values. Vector databases
(Qdrant, pgvector, …) then create collections with the wrong dimension,
causing all similarity searches to fail silently.
e.g. granite-embedding-107m (384 dims) → 96 stored in Qdrant
jina-embeddings-v3 (1024 dims) → 256 stored in Qdrant
Changes:
- core/schema/prediction.go: add EncodingFormat string field to
PredictionOptions so the request parameter is parsed and available
throughout the request pipeline
- core/schema/openai.go: add EmbeddingBase64 string field to Item;
add MarshalJSON so the "embedding" JSON key emits either []float32
or a base64 string depending on which field is populated — all other
Item consumers (image, video endpoints) are unaffected
- core/http/endpoints/openai/embeddings.go: add floatsToBase64()
which packs a float32 slice as little-endian bytes and base64-encodes
it; add embeddingItem() helper; both InputToken and InputStrings loops
now honour encoding_format=base64
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add a system backend path
- Refactor and consolidate system information in system state
- Use system state in all the components to figure out the system paths
to used whenever needed
- Refactor BackendConfig -> ModelConfig. This was otherway misleading as
now we do have a backend configuration which is not the model config.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
fix(model-list): be consistent, skip known files from listing
This changeset does two things:
- Removes the dependency of listing models from the OpenAI schema.
- Tries to reduce confusion between ListModels() in model loader and in
the service - now there is only one ListModels which is in services
and does not depend anymore on the OpenAI schema
- The OpenAI-schema functions were moved nearby the OpenAI specific
endpoints that needs the schema
- Drops the ListModel Service structure as there was no real need for
it.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* core 1
* api/openai/files fix
* core 2 - core/config
* move over core api.go and tests to the start of core/http
* move over localai specific endpoints to core/http, begin the service/endpoint split there
* refactor big chunk on the plane
* refactor chunk 2 on plane, next step: port and modify changes to request.go
* easy fixes for request.go, major changes not done yet
* lintfix
* json tag lintfix?
* gitignore and .keep files
* strange fix attempt: rename the config dir?
