* feat(router): make KNN a first-class classifier with a persisted, curated corpus
Add `classifier: knn` — similarity-weighted voting over labelled
example prompts. Unlike score/colbert it needs no classifier model:
label knowledge lives in a corpus seeded and curated through the
admin API, so routing decisions are deterministic, auditable, and
grounded in graded experience rather than a model's opinion.
Epistemic gate: corpus entries below knn.similarity_threshold cannot
vote; when none clears it the classifier activates no labels and the
router uses the fallback — a prompt unlike all labelled experience is
treated as undecidable, not guessed. Decisions record
nearest_similarity (also on fallback rows) so admins can see how far
the nearest labelled experience was; the Routing tab explains
out-of-corpus fallbacks and shows per-label corpus counts.
Persistence: one JSONL file per router under
<data path>/router-corpus (text, labels, vector, embedder
fingerprint). The file is the source of truth; the local-store index
is rebuilt from it at classifier build time and stays a pure
in-memory index. Entries recorded under a different embedding model
re-embed on load. Also corrects the docs' false claim that
local-store collections persist — the embedding cache never survived
restarts (and still doesn't); the corpus does.
Corpus input is API-only by design (entries may contain example user
content): POST /api/router/{name}/corpus seeds (labels validated
against declared policies, embedded server-side, indexed
immediately), GET .../corpus/stats inspects — label counts only,
entry texts are never returned by any surface — DELETE .../corpus
wipes. Admin-gated like the sibling router endpoints, and exposed as
MCP tools (seed_router_corpus / get_router_corpus_stats /
clear_router_corpus) in both the httpapi and inproc clients with
coverage-test route mappings.
Plumbing: VectorStore gains SearchK (top-K was hardcoded to 1);
local-store gets InsertBatch/Delete as optional fast paths;
RouterConfig gains a knn block (embedding_model, k,
similarity_threshold, vote_threshold, store_name) with meta-registry
fields; the classifier dropdown now offers knn and the
previously-missing colbert; embedding_cache is ignored (with a
warning) for knn — it IS an embedding-KNN lookup; the stale
/api/instructions intelligent-routing entry is rewritten (it
described a classifier that no longer exists); swagger regenerated.
Tests: KNN vote/gate specs with hand-computed vote shares, corpus
manager suite (restart reload without re-embedding, fingerprint
re-embed, dedupe, hostile store names), middleware specs (corpus
routing, gate fallback, config validation, cache-wrap refusal),
corpus endpoint specs pinning the texts-never-returned contract, MCP
catalog + route-mapping gates, and a Playwright spec for corpus
stats and the out-of-corpus decision detail.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(router): name consulted corpus neighbours in knn decisions
Every knn decision (decision log rows and the /api/router/decide
response) now carries neighbors: the K retrieved corpus entries by
descending similarity - including ones below the epistemic gate, which
is what makes fallback decisions diagnosable - each as {id, similarity,
labels}. The id is the entry's content hash (first 8 bytes of the
SHA-256 of its text, hex): stable across reseeds and re-embeds, and
text-free, so an external platform that seeded the corpus can recompute
text->id on its own copy and bucket decisions by corpus region (per-
region reliability accounting) without corpus text ever leaving the
server. A corrupt index payload surfaces as an id-less neighbour at a
real similarity instead of disappearing.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* refactor(router): deduplicate knn plumbing and cut corpus hot-path waste
Post-review cleanup of the knn-first-class-router branch; no behaviour
changes on the API surface.
Reuse/altitude:
- RouterKNNConfig.ResolvedStoreName is now the single source of the
router-corpus-<name> default (was hand-derived in four files).
- corpus.ResolveKNNRouter + corpus.Seed carry the shared model
resolution and seed validation; the REST endpoints and the assistant
MCP client are thin transport adapters over them, with sentinel
errors mapped to HTTP statuses at the echo boundary.
- middleware.NewClassifierDeps assembles the classifier dependency set
once for all five entry points (OpenAI, Anthropic, realtime, decide,
corpus) instead of five hand-copied literals.
- router.AllClassifiers feeds both the status endpoint and the
unknown-classifier error, ending the classifier-list drift.
- Per-classifier requirements moved out of validateRouterPolicies into
their buildClassifier arms; the knn arm owns its embedding_cache
opt-out instead of a name-check in the shared wrap tail.
- adminOnly replaces four inline copies of the admin gate in the
middleware routes.
- localVectorStore.Search delegates to SearchK (identical traces).
Efficiency:
- Manager.Add embeds outside the manager mutex and appends to the
JSONL file (O(new) instead of O(corpus) rewrite); a torn tail from a
crash mid-append is tolerated on read and repaired on next write.
- Stats memoises per store keyed on the file's stat fingerprint and no
longer takes the manager mutex, so the 5s status poll stops parsing
vector-laden JSONL and stops blocking behind seeds.
- KNN Classify decodes each neighbour payload once (was twice) and
builds refs and votes in a single pass with one fallback return.
- Corpus file writes fsync before rename/close.
- The corpus manager is built eagerly in newApplication (sync.Once
dropped); test helper dead branch removed.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(router): bind knn corpus vectors to an embedder fingerprint and fail closed on mismatch
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(mcp): align corpus tool prompts and the mutating-tool safety list
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(proto,backend): report embedding shape from the llama-cpp backend
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(embeddings): Go-side pooling — mean/last/decayed_mean with half-life
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(embeddings): accept chat messages[] and per-request pooling on /v1/embeddings
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(middleware): name the failing fields when post-merge validation 400s
An intermittent post-merge validation failure surfaced as an opaque 400
during integration (pooling scheme mismatch that no client had sent).
Log the model, the request's pooling override, and the merged config's
pooling fields at the failure point so the next occurrence identifies
whether the request or the stored config carried the bad value.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(embeddings): scheme override must not inherit the config's half-life
A model config defaulting to decayed_mean pooling carries
pooling_half_life_tokens; a request overriding the scheme to mean/last
without its own half-life inherited that value, and post-merge
validation rejected the pair the server itself had assembled. Zero the
inherited half-life when the overridden scheme is not decayed_mean; a
request that explicitly pairs a half-life with a non-decayed scheme
still 400s.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix embedding pooling validation and router bounds
Declare backend embedding layouts and reject incompatible pooling modes. Reset local-store dimensions after a full clear, validate KNN thresholds, and add real backend and store integration coverage.
Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci: run local-store integration tests
Build and install the local-store backend in the Linux test job, then run the existing store integration suite so new specs are discovered automatically.
Assisted-by: Codex:gpt-5
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>
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>
* feat: wire min_p
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: inferencing defaults
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(refactor): re-use iterative parser
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: generate automatically inference defaults from unsloth
Instead of trying to re-invent the wheel and maintain here the inference
defaults, prefer to consume unsloth ones, and contribute there as
necessary.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: apply defaults also to models installed via gallery
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: be consistent and apply fallback to all endpoint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add support to logprobs in results
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add support to logitbias
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: initial hook to install elements directly
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* WIP: ui changes
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Move HF api client to pkg
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add simple importer for gguf files
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add opcache
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* wire importers to CLI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add omitempty to config fields
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add MLX importer
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Small refactors to star to use HF for discovery
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Common preferences
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add support to bare HF repos
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(importer/llama.cpp): add support for mmproj files
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* add mmproj quants to common preferences
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix vlm usage in tokenizer mode with llama.cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(defaults): set better defaults for inferencing
This changeset aim to have better defaults and to properly detect when
no inference settings are provided with the model.
If not specified, we defaults to mirostat sampling, and offload all the
GPU layers (if a GPU is detected).
Related to https://github.com/mudler/LocalAI/issues/1373 and https://github.com/mudler/LocalAI/issues/1723
* Adapt tests
* Also pre-initialize default seed
* 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?
Richard Palethorpe