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* 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>
87 lines
2.4 KiB
Go
87 lines
2.4 KiB
Go
package backend
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import (
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"time"
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"github.com/mudler/LocalAI/core/config"
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"github.com/mudler/LocalAI/core/schema"
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"github.com/mudler/LocalAI/core/trace"
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"github.com/mudler/LocalAI/pkg/grpc"
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pb "github.com/mudler/LocalAI/pkg/grpc/proto"
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"github.com/mudler/LocalAI/pkg/model"
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)
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// tokenizeTokenCount returns the number of tokens in a backend response,
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// treating a nil response as zero. The gRPC client returns (nil, err) on
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// failure, and the tracing block below runs before that error is returned —
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// so the count must be read nil-safely here. Reading resp.Tokens on a nil
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// resp previously panicked the whole HTTP handler when tracing was enabled
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// (e.g. a transient tokenize failure during router probe-budget sizing).
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func tokenizeTokenCount(resp *pb.TokenizationResponse) int {
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if resp == nil {
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return 0
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}
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return len(resp.Tokens)
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}
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func ModelTokenize(s string, loader *model.ModelLoader, modelConfig config.ModelConfig, appConfig *config.ApplicationConfig) (schema.TokenizeResponse, error) {
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var inferenceModel grpc.Backend
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var err error
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opts := ModelOptions(modelConfig, appConfig)
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inferenceModel, err = loader.Load(opts...)
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if err != nil {
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recordModelLoadFailure(appConfig, modelConfig.Name, modelConfig.Backend, err, nil)
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return schema.TokenizeResponse{}, err
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}
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predictOptions := gRPCPredictOpts(modelConfig, loader.ModelPath)
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predictOptions.Prompt = s
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var startTime time.Time
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if appConfig.EnableTracing {
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trace.InitBackendTracingIfEnabled(appConfig.TracingMaxItems, appConfig.TracingMaxBodyBytes)
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startTime = time.Now()
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}
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// tokenize the string
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resp, err := inferenceModel.TokenizeString(appConfig.Context, predictOptions)
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if appConfig.EnableTracing {
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errStr := ""
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if err != nil {
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errStr = err.Error()
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}
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tokenCount := tokenizeTokenCount(resp)
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trace.RecordBackendTrace(trace.BackendTrace{
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Timestamp: startTime,
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Duration: time.Since(startTime),
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Type: trace.BackendTraceTokenize,
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ModelName: modelConfig.Name,
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Backend: modelConfig.Backend,
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Summary: trace.TruncateString(s, 200),
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Error: errStr,
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Data: map[string]any{
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"input_text": trace.TruncateString(s, 1000),
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"token_count": tokenCount,
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},
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})
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}
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if err != nil {
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return schema.TokenizeResponse{}, err
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}
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if resp == nil || resp.Tokens == nil {
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return schema.TokenizeResponse{Tokens: make([]int32, 0)}, nil
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}
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return schema.TokenizeResponse{
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Tokens: resp.Tokens,
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}, nil
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}
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