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085fc53bbc
* fix(router): score classifier production-readiness Conversation trimming runs through the classifier model's chat template and trims by exact token count, sized to the model's n_batch which is now scaled to context so long probes can't crash the backend. Missing chat_message templates are a hard error at router build time. Router- facing factories (Embedder/Scorer/Reranker/TokenCounter) re-resolve ModelConfig per call so a model installed post-startup doesn't bind a stub Backend="" config and silently fall into the loader's auto- iterate path. New 'vector_store' backend trace recorded inside localVectorStore on every Search/Insert — including the backend-load-failure path that previously vanished into an xlog.Warn — with outcome tagging (hit/miss/empty_store/backend_load_error/find_error/insert_error/ok). Companion cleanup drops misleading similarity:0 and input_tokens_count:0 from non-hit and text-mode traces. Gallery local-store-development aliases to 'local-store' so the master image satisfies pkg/model.LocalStoreBackend lookups from the embedding cache. Misc: llama-cpp TokenizeString reads the correct 'prompt' JSON key (the original bug); ModelTokenize nil-guard; non-fatal mitm proxy startup; PII 'route_local' renamed to 'allow' with docs/UI in sync; model-editor footer no longer eats the edit area on small screens; several config-editor template/dropdown/section fixes. Tests: e2e router specs (casual/code-hint + long-conversation trim), vector_store trace specs, lazy-factory specs, gallery dev-alias resolution, Playwright trace badge + scroll regression. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(backend): auto-size batch to context for embedding and rerank models Embedding and rerank models pool over the whole input in a single physical batch (n_ubatch). With batch left at the 512 default, the backend rejects longer inputs with "input is too large to process", silently capping a large-context embedder (e.g. 8k/32k) at 512 tokens. Size n_batch to the context for these single-pass usecases, mirroring the existing FLAG_SCORE behaviour; an explicit batch: still wins. Extracts EffectiveContextSize/EffectiveBatchSize from grpcModelOpts so the effective decode window has one home for other callers to reuse. Adds an e2e-aio regression test that embeds a >512-token input. The AIO embedding model is switched to nomic-embed-text-v1.5 (2048 context) because the previous granite model was capped at 512 tokens and could not exercise the larger batch. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(gallery): raise arch-router scoring output cap via parallel:64 Scoring decodes the whole prompt+candidate in a single llama_decode and reads one logit row per candidate token. The vendored llama.cpp server caps causal output rows at n_parallel, so the default of 1 aborts with GGML_ASSERT(n_outputs_max <= cparams.n_outputs_max) on multi-token route labels. Set options: [parallel:64] on both arch-router quant entries to lift the cap; kv_unified (the grpc-server default) keeps the full context per sequence, so this does not split the KV cache. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com>
59 lines
2.1 KiB
Go
59 lines
2.1 KiB
Go
package application
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import (
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"github.com/mudler/LocalAI/core/config"
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"github.com/mudler/LocalAI/pkg/system"
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. "github.com/onsi/ginkgo/v2"
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. "github.com/onsi/gomega"
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)
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// minimal Application wired enough for startMITMProxy: an empty model
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// config loader (no host claims), CA written under a temp DataPath.
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func newMITMTestApp(dataPath string) (*Application, *config.ApplicationConfig) {
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state, err := system.GetSystemState()
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Expect(err).NotTo(HaveOccurred())
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state.Model.ModelsPath = dataPath
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opts := config.NewApplicationConfig(
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config.WithSystemState(state),
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config.WithDataPath(dataPath),
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)
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return newApplication(opts), opts
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}
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var _ = Describe("startMITMIfConfigured", func() {
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It("does nothing when no listen address is configured", func() {
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app, opts := newMITMTestApp(GinkgoT().TempDir())
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opts.MITMListen = ""
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Expect(func() { startMITMIfConfigured(app, opts) }).NotTo(Panic())
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Expect(app.mitmServer.Load()).To(BeNil(), "no listener should be stored when disabled")
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})
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// Regression: a persisted-but-unbindable MITM address (e.g. a LAN host
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// inside a container) must not abort startup. startMITMIfConfigured
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// swallows the bind error so the rest of LocalAI still comes up and the
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// admin can fix the address via the Settings UI.
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It("logs and continues when the listen address cannot be bound", func() {
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app, opts := newMITMTestApp(GinkgoT().TempDir())
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// 192.0.2.1 is TEST-NET-1 (RFC 5737): guaranteed not assigned to any
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// local interface, so bind fails deterministically without DNS.
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opts.MITMListen = "192.0.2.1:8082"
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Expect(func() { startMITMIfConfigured(app, opts) }).NotTo(Panic())
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Expect(app.mitmServer.Load()).To(BeNil(), "failed listener must not be stored")
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})
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It("starts and stores the listener on a bindable address", func() {
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app, opts := newMITMTestApp(GinkgoT().TempDir())
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opts.MITMListen = "127.0.0.1:0" // OS-assigned free port
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startMITMIfConfigured(app, opts)
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srv := app.mitmServer.Load()
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Expect(srv).NotTo(BeNil(), "listener should be stored on success")
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DeferCleanup(srv.Stop)
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Expect(srv.Addr()).NotTo(BeEmpty())
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})
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})
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