Files
LocalAI/tests/e2e/mock-backend/main.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

876 lines
27 KiB
Go

package main
import (
"context"
"encoding/binary"
"encoding/json"
"flag"
"fmt"
"log"
"math"
"net"
"os"
"path/filepath"
"strconv"
"strings"
"sync"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/xlog"
"google.golang.org/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
// MockBackend implements the Backend gRPC service with mocked responses.
// When tools are present but the prompt already contains MCP tool results
// (indicated by the marker from the mock MCP server), it returns a plain
// text response instead of another tool call, letting the MCP loop complete.
type MockBackend struct {
pb.UnimplementedBackendServer
}
// lastLoadParams records the most recent LoadModel parameters so a Predict
// call can echo them back. Used by the path-resolution e2e test, which needs
// to verify that relative draft_model / mmproj / modelfile paths in the YAML
// config arrive at the backend already resolved against the models directory.
// Each backend binary serves a single model, so a single value is enough.
var (
lastLoadParamsMu sync.RWMutex
lastLoadParams *pb.ModelOptions
)
func recordLoadParams(opts *pb.ModelOptions) {
lastLoadParamsMu.Lock()
defer lastLoadParamsMu.Unlock()
lastLoadParams = opts
}
func snapshotLoadParams() *pb.ModelOptions {
lastLoadParamsMu.RLock()
defer lastLoadParamsMu.RUnlock()
return lastLoadParams
}
// promptHasToolResults checks if the prompt contains evidence of prior tool
// execution — specifically the output from the mock MCP server's get_weather tool.
func promptHasToolResults(prompt string) bool {
return strings.Contains(prompt, "Weather in")
}
func (m *MockBackend) Health(ctx context.Context, in *pb.HealthMessage) (*pb.Reply, error) {
xlog.Debug("Health check called")
return &pb.Reply{Message: []byte("OK")}, nil
}
func (m *MockBackend) LoadModel(ctx context.Context, in *pb.ModelOptions) (*pb.Result, error) {
xlog.Debug("LoadModel called",
"model", in.Model,
"modelfile", in.ModelFile,
"draft_model", in.DraftModel,
"mmproj", in.MMProj)
recordLoadParams(in)
return &pb.Result{
Message: "Model loaded successfully (mocked)",
Success: true,
}, nil
}
func (m *MockBackend) Predict(ctx context.Context, in *pb.PredictOptions) (*pb.Reply, error) {
xlog.Debug("Predict called", "prompt", in.Prompt)
if strings.Contains(in.Prompt, "MOCK_ERROR") {
return nil, fmt.Errorf("mock backend predict error: simulated failure")
}
// ECHO_LOAD_PARAMS lets path-resolution tests inspect what LoadModel
// received without adding a new RPC. The reply carries a JSON snapshot
// of the relevant ModelOptions fields so the test can assert that
// relative paths from the YAML have been resolved before reaching the
// backend.
if strings.Contains(in.Prompt, "ECHO_LOAD_PARAMS") {
opts := snapshotLoadParams()
snapshot := map[string]string{}
if opts != nil {
snapshot["model"] = opts.Model
snapshot["model_file"] = opts.ModelFile
snapshot["draft_model"] = opts.DraftModel
snapshot["mmproj"] = opts.MMProj
}
payload, err := json.Marshal(snapshot)
if err != nil {
return nil, fmt.Errorf("mock backend echo error: %w", err)
}
return &pb.Reply{
Message: payload,
Tokens: int32(len(snapshot)),
PromptTokens: 1,
}, nil
}
// ECHO_SERVED_MODEL returns the loaded model file path so router e2e
// tests can verify which candidate actually served the request without
// adding a new RPC. The router fans out to a single backend process per
// candidate, so lastLoadParams.Model is unique per candidate.
if strings.Contains(in.Prompt, "ECHO_SERVED_MODEL") {
opts := snapshotLoadParams()
modelID := ""
if opts != nil {
modelID = opts.Model
}
return &pb.Reply{
Message: []byte("SERVED_MODEL=" + modelID),
Tokens: 2,
PromptTokens: 1,
}, nil
}
// Simulate C++ autoparser: tool call via ChatDeltas, empty message
if strings.Contains(in.Prompt, "AUTOPARSER_TOOL_CALL") {
toolName := mockToolNameFromRequest(in)
if toolName == "" {
toolName = "search_collections"
}
return &pb.Reply{
Message: []byte{},
Tokens: 10,
PromptTokens: 5,
ChatDeltas: []*pb.ChatDelta{
{ReasoningContent: "I need to search for information."},
{
ToolCalls: []*pb.ToolCallDelta{
{
Index: 0,
Id: "call_mock_123",
Name: toolName,
Arguments: `{"query":"localai"}`,
},
},
},
},
}, nil
}
// Simulate C++ autoparser: content via ChatDeltas, empty message
if strings.Contains(in.Prompt, "AUTOPARSER_CONTENT") {
return &pb.Reply{
Message: []byte{},
Tokens: 10,
PromptTokens: 5,
ChatDeltas: []*pb.ChatDelta{
{ReasoningContent: "Let me compose a response."},
{Content: "LocalAI is an open-source AI platform."},
},
}, nil
}
// Simulate Gemma 4 / thinking model with C++ autoparser:
// - Message contains the clean content (autoparser extracts it from OAI choices[0].message.content)
// - ChatDeltas contain both reasoning and content separately
// This reproduces the bug where Go-side PrependThinkingTokenIfNeeded
// incorrectly prepends a thinking start token to the clean content,
// causing the entire response to be classified as unclosed reasoning.
if strings.Contains(in.Prompt, "AUTOPARSER_THINKING_CONTENT") {
return &pb.Reply{
Message: []byte("I am a helpful AI assistant designed to assist you with a wide range of tasks."),
Tokens: 20,
PromptTokens: 50,
ChatDeltas: []*pb.ChatDelta{
{
ReasoningContent: "The user is asking a simple introductory question. I should respond directly.",
Content: "I am a helpful AI assistant designed to assist you with a wide range of tasks.",
},
},
}, nil
}
// Simulate multiple tool calls in a single response (Go-side JSON parser path).
if strings.Contains(in.Prompt, "MULTI_TOOL_CALL") {
return &pb.Reply{
Message: []byte(`{"name": "get_weather", "arguments": {"location": "Rome"}}
{"name": "get_weather", "arguments": {"location": "Paris"}}`),
Tokens: 30,
PromptTokens: 10,
}, nil
}
var response string
toolName := mockToolNameFromRequest(in)
if toolName != "" && !promptHasToolResults(in.Prompt) {
// First call with tools: return a tool call so the MCP loop executes it.
response = fmt.Sprintf(`{"name": "%s", "arguments": {"location": "San Francisco"}}`, toolName)
} else if toolName != "" {
// Subsequent call: tool results already in prompt, return final text.
response = "Based on the tool results, the weather in San Francisco is sunny, 72°F."
} else {
response = "This is a mocked response."
}
return &pb.Reply{
Message: []byte(response),
Tokens: 10,
PromptTokens: 5,
TimingPromptProcessing: 0.1,
TimingTokenGeneration: 0.2,
}, nil
}
func (m *MockBackend) PredictStream(in *pb.PredictOptions, stream pb.Backend_PredictStreamServer) error {
xlog.Debug("PredictStream called", "prompt", in.Prompt)
if strings.Contains(in.Prompt, "MOCK_ERROR_IMMEDIATE") {
return fmt.Errorf("mock backend stream error: simulated failure")
}
if strings.Contains(in.Prompt, "MOCK_ERROR_MIDSTREAM") {
for _, r := range "Partial resp" {
if err := stream.Send(&pb.Reply{Message: []byte(string(r))}); err != nil {
return err
}
}
return fmt.Errorf("mock backend stream error: simulated mid-stream failure")
}
// Simulate C++ autoparser behavior: tool calls delivered via ChatDeltas
// with empty message (autoparser clears raw message during parsing).
if strings.Contains(in.Prompt, "AUTOPARSER_TOOL_CALL") {
toolName := mockToolNameFromRequest(in)
if toolName == "" {
toolName = "search_collections"
}
// Phase 1: Stream reasoning tokens with empty message (autoparser active)
reasoning := "I need to search for information."
for _, r := range reasoning {
if err := stream.Send(&pb.Reply{
Message: []byte{}, // autoparser clears raw message
ChatDeltas: []*pb.ChatDelta{
{ReasoningContent: string(r)},
},
}); err != nil {
return err
}
}
// Phase 2: Emit tool call via ChatDeltas (no raw message)
if err := stream.Send(&pb.Reply{
Message: []byte{}, // autoparser clears raw message
ChatDeltas: []*pb.ChatDelta{
{
ToolCalls: []*pb.ToolCallDelta{
{
Index: 0,
Id: "call_mock_123",
Name: toolName,
Arguments: `{"query":"localai"}`,
},
},
},
},
}); err != nil {
return err
}
return nil
}
// Simulate C++ autoparser behavior: content delivered via ChatDeltas
// with empty message (autoparser clears raw message during parsing).
if strings.Contains(in.Prompt, "AUTOPARSER_CONTENT") {
// Phase 1: Stream reasoning via ChatDeltas
reasoning := "Let me compose a response."
for _, r := range reasoning {
if err := stream.Send(&pb.Reply{
Message: []byte{},
ChatDeltas: []*pb.ChatDelta{
{ReasoningContent: string(r)},
},
}); err != nil {
return err
}
}
// Phase 2: Stream content via ChatDeltas (no raw message)
content := "LocalAI is an open-source AI platform."
for _, r := range content {
if err := stream.Send(&pb.Reply{
Message: []byte{},
ChatDeltas: []*pb.ChatDelta{
{Content: string(r)},
},
}); err != nil {
return err
}
}
return nil
}
// Simulate tool calls streamed as whole JSON objects (Go-side parser path).
// Each object is sent as a complete chunk so the incremental parser can
// detect tool calls mid-stream (unlike char-by-char which only parses after
// streaming completes).
if strings.Contains(in.Prompt, "MULTI_TOOL_CALL") {
chunks := []string{
`{"name": "get_weather", "arguments": {"location": "Rome"}}`,
"\n",
`{"name": "get_weather", "arguments": {"location": "Paris"}}`,
}
for i, chunk := range chunks {
if err := stream.Send(&pb.Reply{
Message: []byte(chunk),
Tokens: int32(i + 1),
}); err != nil {
return err
}
}
return nil
}
// Simulate single tool call streamed as whole JSON (Go-side parser path).
if strings.Contains(in.Prompt, "SINGLE_TOOL_CALL") {
if err := stream.Send(&pb.Reply{
Message: []byte(`{"name": "get_weather", "arguments": {"location": "San Francisco"}}`),
Tokens: 1,
}); err != nil {
return err
}
return nil
}
var toStream string
toolName := mockToolNameFromRequest(in)
switch {
case toolName != "" && !promptHasToolResults(in.Prompt):
toStream = fmt.Sprintf(`{"name": "%s", "arguments": {"location": "San Francisco"}}`, toolName)
case toolName != "":
toStream = "Based on the tool results, the weather in San Francisco is sunny, 72°F."
case strings.Contains(in.Prompt, "MOCK_LEAK_EMAIL"):
// PII streaming test fixture: emit a response containing an email
// address so the streaming PII filter has something to mask. The
// content is split character-by-character below, so the mask
// must hold across chunk boundaries.
toStream = "Sure — here it is: alice@example.com is the address."
default:
toStream = "This is a mocked streaming response."
}
for i, r := range toStream {
if err := stream.Send(&pb.Reply{
Message: []byte(string(r)),
Tokens: int32(i + 1),
}); err != nil {
return err
}
}
return nil
}
// mockToolNameFromRequest returns the first tool name from the request's Tools JSON (same as other endpoints).
func mockToolNameFromRequest(in *pb.PredictOptions) string {
if in.Tools == "" {
return ""
}
var tools []struct {
Function struct {
Name string `json:"name"`
} `json:"function"`
}
if err := json.Unmarshal([]byte(in.Tools), &tools); err != nil || len(tools) == 0 || tools[0].Function.Name == "" {
return ""
}
return tools[0].Function.Name
}
func (m *MockBackend) Embedding(ctx context.Context, in *pb.PredictOptions) (*pb.EmbeddingResult, error) {
xlog.Debug("Embedding called", "prompt", in.Prompt)
// Return a mock embedding vector of 768 dimensions
embeddings := make([]float32, 768)
for i := range embeddings {
embeddings[i] = float32(i%100) / 100.0 // Pattern: 0.0, 0.01, 0.02, ..., 0.99, 0.0, ...
}
return &pb.EmbeddingResult{Embeddings: embeddings}, nil
}
func (m *MockBackend) GenerateImage(ctx context.Context, in *pb.GenerateImageRequest) (*pb.Result, error) {
xlog.Debug("GenerateImage called", "prompt", in.PositivePrompt)
return &pb.Result{
Message: "Image generated successfully (mocked)",
Success: true,
}, nil
}
func (m *MockBackend) GenerateVideo(ctx context.Context, in *pb.GenerateVideoRequest) (*pb.Result, error) {
xlog.Debug("GenerateVideo called", "prompt", in.Prompt)
return &pb.Result{
Message: "Video generated successfully (mocked)",
Success: true,
}, nil
}
func (m *MockBackend) TTS(ctx context.Context, in *pb.TTSRequest) (*pb.Result, error) {
xlog.Debug("TTS called", "text", in.Text)
dst := in.GetDst()
if dst != "" {
if err := os.MkdirAll(filepath.Dir(dst), 0750); err != nil {
return &pb.Result{Message: err.Error(), Success: false}, nil
}
if err := writeMinimalWAV(dst); err != nil {
return &pb.Result{Message: err.Error(), Success: false}, nil
}
}
return &pb.Result{
Message: "TTS audio generated successfully (mocked)",
Success: true,
}, nil
}
func (m *MockBackend) TTSStream(in *pb.TTSRequest, stream pb.Backend_TTSStreamServer) error {
xlog.Debug("TTSStream called", "text", in.Text)
// Stream mock audio chunks (simplified - just send a few bytes)
chunks := [][]byte{
{0x52, 0x49, 0x46, 0x46}, // Mock WAV header start
{0x57, 0x41, 0x56, 0x45}, // Mock WAV header
{0x64, 0x61, 0x74, 0x61}, // Mock data chunk
}
for _, chunk := range chunks {
if err := stream.Send(&pb.Reply{Audio: chunk}); err != nil {
return err
}
}
return nil
}
func (m *MockBackend) SoundGeneration(ctx context.Context, in *pb.SoundGenerationRequest) (*pb.Result, error) {
xlog.Debug("SoundGeneration called",
"text", in.Text,
"caption", in.GetCaption(),
"lyrics", in.GetLyrics(),
"think", in.GetThink(),
"bpm", in.GetBpm(),
"keyscale", in.GetKeyscale(),
"language", in.GetLanguage(),
"timesignature", in.GetTimesignature(),
"instrumental", in.GetInstrumental())
dst := in.GetDst()
if dst != "" {
if err := os.MkdirAll(filepath.Dir(dst), 0750); err != nil {
return &pb.Result{Message: err.Error(), Success: false}, nil
}
if err := writeMinimalWAV(dst); err != nil {
return &pb.Result{Message: err.Error(), Success: false}, nil
}
}
return &pb.Result{
Message: "Sound generated successfully (mocked)",
Success: true,
}, nil
}
// ttsSampleRate returns the sample rate to use for TTS output, configurable
// via the MOCK_TTS_SAMPLE_RATE environment variable (default 16000).
func ttsSampleRate() int {
if s := os.Getenv("MOCK_TTS_SAMPLE_RATE"); s != "" {
if v, err := strconv.Atoi(s); err == nil && v > 0 {
return v
}
}
return 16000
}
// writeMinimalWAV writes a WAV file containing a 440Hz sine wave (0.5s)
// so that tests can verify audio integrity end-to-end. The sample rate
// is configurable via MOCK_TTS_SAMPLE_RATE to test rate mismatch bugs.
func writeMinimalWAV(path string) error {
sampleRate := ttsSampleRate()
const numChannels = 1
const bitsPerSample = 16
const freq = 440.0
const durationSec = 0.5
numSamples := int(float64(sampleRate) * durationSec)
dataSize := numSamples * numChannels * (bitsPerSample / 8)
const headerLen = 44
f, err := os.Create(path)
if err != nil {
return err
}
defer f.Close()
// RIFF header
_, _ = f.Write([]byte("RIFF"))
_ = binary.Write(f, binary.LittleEndian, uint32(headerLen-8+dataSize))
_, _ = f.Write([]byte("WAVE"))
// fmt chunk
_, _ = f.Write([]byte("fmt "))
_ = binary.Write(f, binary.LittleEndian, uint32(16))
_ = binary.Write(f, binary.LittleEndian, uint16(1))
_ = binary.Write(f, binary.LittleEndian, uint16(numChannels))
_ = binary.Write(f, binary.LittleEndian, uint32(sampleRate))
_ = binary.Write(f, binary.LittleEndian, uint32(sampleRate*numChannels*(bitsPerSample/8)))
_ = binary.Write(f, binary.LittleEndian, uint16(numChannels*(bitsPerSample/8)))
_ = binary.Write(f, binary.LittleEndian, uint16(bitsPerSample))
// data chunk — 440Hz sine wave
_, _ = f.Write([]byte("data"))
_ = binary.Write(f, binary.LittleEndian, uint32(dataSize))
for i := range numSamples {
t := float64(i) / float64(sampleRate)
sample := int16(math.MaxInt16 / 2 * math.Sin(2*math.Pi*freq*t))
_ = binary.Write(f, binary.LittleEndian, sample)
}
return nil
}
func (m *MockBackend) AudioTranscription(ctx context.Context, in *pb.TranscriptRequest) (*pb.TranscriptResult, error) {
dst := in.GetDst()
wavSR := 0
dataLen := 0
rms := 0.0
if dst != "" {
if data, err := os.ReadFile(dst); err == nil {
if len(data) >= 44 {
wavSR = int(binary.LittleEndian.Uint32(data[24:28]))
dataLen = int(binary.LittleEndian.Uint32(data[40:44]))
// Compute RMS of the PCM payload (16-bit LE samples)
pcm := data[44:]
var sumSq float64
nSamples := len(pcm) / 2
for i := range nSamples {
s := int16(pcm[2*i]) | int16(pcm[2*i+1])<<8
v := float64(s)
sumSq += v * v
}
if nSamples > 0 {
rms = math.Sqrt(sumSq / float64(nSamples))
}
}
}
}
xlog.Debug("AudioTranscription called", "dst", dst, "wav_sample_rate", wavSR, "data_len", dataLen, "rms", rms)
text := fmt.Sprintf("transcribed: rms=%.1f samples=%d sr=%d", rms, dataLen/2, wavSR)
return &pb.TranscriptResult{
Text: text,
Segments: []*pb.TranscriptSegment{
{
Id: 0,
Start: 0,
End: 3000,
Text: text,
Tokens: []int32{1, 2, 3, 4, 5, 6},
},
},
}, nil
}
func (m *MockBackend) TokenizeString(ctx context.Context, in *pb.PredictOptions) (*pb.TokenizationResponse, error) {
xlog.Debug("TokenizeString called", "prompt_len", len(in.Prompt))
// Approximate BPE: ~4 chars/token, minimum 1. Realistic enough for the
// router's fitMessages to exercise the budget/rune-pretrim path with
// recognisable counts that scale with input size.
n := max((len(in.Prompt)+3)/4, 1)
tokens := make([]int32, n)
for i := range tokens {
tokens[i] = int32(i + 1)
}
return &pb.TokenizationResponse{
Length: int32(n),
Tokens: tokens,
}, nil
}
// Score implements deterministic marker-driven ranking for router e2e
// tests. The Score RPC receives the full rendered routing prompt (system
// prompt + chat envelope + user turn), and the system prompt by construction
// lists every policy label — so any keyword-against-prompt heuristic would
// match every candidate. Instead we look for an explicit `ROUTE_HINT=<label>`
// marker, which only appears when a test deliberately places one in a user
// message. The candidate whose extracted label equals the hint gets a large
// log-prob boost; all others stay at the base. With no hint, every candidate
// scores equally, softmax is uniform, and (with a sensible activation
// threshold) the router falls back.
func (m *MockBackend) Score(ctx context.Context, in *pb.ScoreRequest) (*pb.ScoreResponse, error) {
xlog.Debug("Score called", "candidates", len(in.Candidates))
hint := extractRouteHint(in.Prompt)
out := &pb.ScoreResponse{Candidates: make([]*pb.CandidateScore, len(in.Candidates))}
for i, c := range in.Candidates {
label := extractRouteLabel(c)
// Base -5 (softmax ≈ 0.003), hint match +5 → 0 (softmax ≈ 0.99).
logProb := -5.0
if hint != "" && label == hint {
logProb = 0.0
}
// num_tokens matches TokenizeString's heuristic so per-token mean
// log-prob consumers see consistent values.
nTok := max((len(c)+3)/4, 1)
out.Candidates[i] = &pb.CandidateScore{
LogProb: logProb,
NumTokens: int32(nTok),
LengthNormalizedLogProb: logProb / float64(nTok),
}
}
return out, nil
}
// extractRouteHint returns the label after the LAST occurrence of
// `ROUTE_HINT=` in the prompt, terminated by whitespace or end-of-string.
// Using the last occurrence makes the marker stable across long
// conversations: the *newest* user message's hint wins, mirroring how the
// router's fitMessages keeps the newest turn whole.
func extractRouteHint(prompt string) string {
const key = "ROUTE_HINT="
i := strings.LastIndex(prompt, key)
if i < 0 {
return ""
}
rest := prompt[i+len(key):]
end := strings.IndexAny(rest, " \t\r\n<")
if end < 0 {
return rest
}
return rest[:end]
}
// extractRouteLabel returns the label inside `{"route": "<label>"}`. Returns
// "" on any shape it doesn't recognise — the caller treats that as a no-match.
func extractRouteLabel(candidate string) string {
_, rest, ok := strings.Cut(candidate, `"route"`)
if !ok {
return ""
}
_, rest, ok = strings.Cut(rest, `"`)
if !ok {
return ""
}
label, _, ok := strings.Cut(rest, `"`)
if !ok {
return ""
}
return label
}
func (m *MockBackend) Status(ctx context.Context, in *pb.HealthMessage) (*pb.StatusResponse, error) {
xlog.Debug("Status called")
return &pb.StatusResponse{
State: pb.StatusResponse_READY,
Memory: &pb.MemoryUsageData{
Total: 1024 * 1024 * 100, // 100MB
Breakdown: map[string]uint64{
"mock": 1024 * 1024 * 50,
},
},
}, nil
}
func (m *MockBackend) Detect(ctx context.Context, in *pb.DetectOptions) (*pb.DetectResponse, error) {
xlog.Debug("Detect called", "src", in.Src)
return &pb.DetectResponse{
Detections: []*pb.Detection{
{
X: 10.0,
Y: 20.0,
Width: 100.0,
Height: 200.0,
Confidence: 0.95,
ClassName: "mocked_object",
},
},
}, nil
}
func (m *MockBackend) StoresSet(ctx context.Context, in *pb.StoresSetOptions) (*pb.Result, error) {
xlog.Debug("StoresSet called", "keys", len(in.Keys))
return &pb.Result{
Message: "Keys set successfully (mocked)",
Success: true,
}, nil
}
func (m *MockBackend) StoresDelete(ctx context.Context, in *pb.StoresDeleteOptions) (*pb.Result, error) {
xlog.Debug("StoresDelete called", "keys", len(in.Keys))
return &pb.Result{
Message: "Keys deleted successfully (mocked)",
Success: true,
}, nil
}
func (m *MockBackend) StoresGet(ctx context.Context, in *pb.StoresGetOptions) (*pb.StoresGetResult, error) {
xlog.Debug("StoresGet called", "keys", len(in.Keys))
// Return mock keys and values
keys := make([]*pb.StoresKey, len(in.Keys))
values := make([]*pb.StoresValue, len(in.Keys))
for i := range in.Keys {
keys[i] = in.Keys[i]
values[i] = &pb.StoresValue{
Bytes: []byte(fmt.Sprintf("mocked_value_%d", i)),
}
}
return &pb.StoresGetResult{
Keys: keys,
Values: values,
}, nil
}
func (m *MockBackend) StoresFind(ctx context.Context, in *pb.StoresFindOptions) (*pb.StoresFindResult, error) {
xlog.Debug("StoresFind called", "topK", in.TopK)
// Return mock similar keys
keys := []*pb.StoresKey{
{Floats: []float32{0.1, 0.2, 0.3}},
{Floats: []float32{0.4, 0.5, 0.6}},
}
values := []*pb.StoresValue{
{Bytes: []byte("mocked_value_1")},
{Bytes: []byte("mocked_value_2")},
}
similarities := []float32{0.95, 0.85}
return &pb.StoresFindResult{
Keys: keys,
Values: values,
Similarities: similarities,
}, nil
}
func (m *MockBackend) Rerank(ctx context.Context, in *pb.RerankRequest) (*pb.RerankResult, error) {
xlog.Debug("Rerank called", "query", in.Query, "documents", len(in.Documents))
// Return mock reranking results
results := make([]*pb.DocumentResult, len(in.Documents))
for i, doc := range in.Documents {
results[i] = &pb.DocumentResult{
Index: int32(i),
Text: doc,
RelevanceScore: 0.9 - float32(i)*0.1, // Decreasing scores
}
}
return &pb.RerankResult{
Usage: &pb.Usage{
TotalTokens: int32(len(in.Documents) * 10),
PromptTokens: int32(len(in.Documents) * 10),
},
Results: results,
}, nil
}
func (m *MockBackend) GetMetrics(ctx context.Context, in *pb.MetricsRequest) (*pb.MetricsResponse, error) {
xlog.Debug("GetMetrics called")
return &pb.MetricsResponse{
SlotId: 0,
PromptJsonForSlot: `{"prompt":"mocked"}`,
TokensPerSecond: 10.0,
TokensGenerated: 100,
PromptTokensProcessed: 50,
}, nil
}
func (m *MockBackend) VAD(ctx context.Context, in *pb.VADRequest) (*pb.VADResponse, error) {
// Compute RMS of the received float32 audio to decide whether speech is present.
var sumSq float64
for _, s := range in.Audio {
v := float64(s)
sumSq += v * v
}
rms := 0.0
if len(in.Audio) > 0 {
rms = math.Sqrt(sumSq / float64(len(in.Audio)))
}
xlog.Debug("VAD called", "audio_length", len(in.Audio), "rms", rms)
// If audio is near-silence, return no segments (no speech detected).
if rms < 0.001 {
return &pb.VADResponse{}, nil
}
// Audio has signal — return a single segment covering the duration.
duration := float64(len(in.Audio)) / 16000.0
return &pb.VADResponse{
Segments: []*pb.VADSegment{
{
Start: 0.0,
End: float32(duration),
},
},
}, nil
}
// Diarize returns a deterministic two-speaker layout that exercises the
// HTTP layer's normalisation: raw labels "5" and "2" should become
// SPEAKER_00 and SPEAKER_01 in first-seen order, the SPEAKER_00 totals
// should reflect two segments (1.0s + 1.5s = 2.5s), and IncludeText must
// gate the per-segment Text field.
func (m *MockBackend) Diarize(ctx context.Context, in *pb.DiarizeRequest) (*pb.DiarizeResponse, error) {
xlog.Debug("Diarize called",
"dst", in.Dst,
"num_speakers", in.NumSpeakers,
"include_text", in.IncludeText)
seg := func(start, end float32, speaker, text string) *pb.DiarizeSegment {
out := &pb.DiarizeSegment{Start: start, End: end, Speaker: speaker}
if in.IncludeText {
out.Text = text
}
return out
}
return &pb.DiarizeResponse{
Segments: []*pb.DiarizeSegment{
seg(0.0, 1.0, "5", "hello there"),
seg(1.0, 2.0, "2", "general kenobi"),
seg(2.0, 3.5, "5", "you are a bold one"),
},
NumSpeakers: 2,
Duration: 3.5,
Language: in.Language,
}, nil
}
func (m *MockBackend) AudioEncode(ctx context.Context, in *pb.AudioEncodeRequest) (*pb.AudioEncodeResult, error) {
xlog.Debug("AudioEncode called", "pcm_len", len(in.PcmData), "sample_rate", in.SampleRate)
// Return a single mock Opus frame per 960-sample chunk (20ms at 48kHz).
numSamples := len(in.PcmData) / 2 // 16-bit samples
frameSize := 960
var frames [][]byte
for offset := 0; offset+frameSize <= numSamples; offset += frameSize {
// Minimal mock frame — just enough bytes to be non-empty.
frames = append(frames, []byte{0xFC, 0xFF, 0xFE})
}
return &pb.AudioEncodeResult{
Frames: frames,
SampleRate: 48000,
SamplesPerFrame: int32(frameSize),
}, nil
}
func (m *MockBackend) AudioDecode(ctx context.Context, in *pb.AudioDecodeRequest) (*pb.AudioDecodeResult, error) {
xlog.Debug("AudioDecode called", "frames", len(in.Frames))
// Return silent PCM (960 samples per frame at 48kHz, 16-bit LE).
samplesPerFrame := 960
totalSamples := len(in.Frames) * samplesPerFrame
pcm := make([]byte, totalSamples*2)
return &pb.AudioDecodeResult{
PcmData: pcm,
SampleRate: 48000,
SamplesPerFrame: int32(samplesPerFrame),
}, nil
}
func (m *MockBackend) ModelMetadata(ctx context.Context, in *pb.ModelOptions) (*pb.ModelMetadataResponse, error) {
xlog.Debug("ModelMetadata called", "model", in.Model)
return &pb.ModelMetadataResponse{
SupportsThinking: false,
RenderedTemplate: "",
}, nil
}
func main() {
xlog.SetLogger(xlog.NewLogger(xlog.LogLevel(os.Getenv("LOCALAI_LOG_LEVEL")), os.Getenv("LOCALAI_LOG_FORMAT")))
flag.Parse()
lis, err := net.Listen("tcp", *addr)
if err != nil {
log.Fatalf("failed to listen: %v", err)
}
s := grpc.NewServer(
grpc.MaxRecvMsgSize(50*1024*1024), // 50MB
grpc.MaxSendMsgSize(50*1024*1024), // 50MB
)
pb.RegisterBackendServer(s, &MockBackend{})
xlog.Info("Mock gRPC Server listening", "address", lis.Addr())
if err := s.Serve(lis); err != nil {
log.Fatalf("failed to serve: %v", err)
}
}