Files
LocalAI/core/backend/tokenize.go
Richard Palethorpe 085fc53bbc fix(router): production-ready request router + auto-size batch for embedding/rerank (#10104)
* 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>
2026-06-12 16:21:15 +02:00

87 lines
2.4 KiB
Go

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