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Author SHA1 Message Date
dependabot[bot]
cb4a612bab chore(deps): bump torch
Bumps the pip group with 1 update in the /backend/python/rerankers directory: torch.


Updates `torch` from 2.7.1 to 2.7.1+xpu

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.7.1+xpu
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <support@github.com>
2026-06-05 23:31:06 +00:00
114 changed files with 536 additions and 5875 deletions

View File

@@ -1766,6 +1766,20 @@ include:
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-turboquant'
builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-rocm-amd64'
runs-on: 'ubuntu-latest'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""

View File

@@ -180,7 +180,7 @@ osx-signed: build
## Run
run: ## run local-ai
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./cmd/local-ai
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./
prepare-test: protogen-go build-mock-backend

View File

@@ -149,16 +149,6 @@ local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
local-ai run oci://localai/phi-2:latest
```
To test a running LocalAI server from the terminal, open an interactive chat session from another shell. Inside the prompt, `/models` lists installed models and `/model <name>` switches between them.
```bash
# Terminal 1
local-ai run llama-3.2-1b-instruct:q4_k_m
# Terminal 2
local-ai chat --model llama-3.2-1b-instruct:q4_k_m
```
> **Automatic Backend Detection**: LocalAI automatically detects your GPU capabilities and downloads the appropriate backend. For advanced options, see [GPU Acceleration](https://localai.io/features/gpu-acceleration/).
For more details, see the [Getting Started guide](https://localai.io/basics/getting_started/).

View File

@@ -60,12 +60,10 @@ elseif(DS4_GPU STREQUAL "cpu")
set(DS4_OBJS "${DS4_DIR}/ds4_cpu.o")
endif()
# ds4.c now references ds4_distributed.c (distributed inference) and ds4_ssd.c
# (SSD expert-cache), each split into its own translation unit upstream. Both
# are GPU-agnostic objects shared by every GPU mode, so link them in regardless
# of DS4_GPU.
# ds4.c now references ds4_distributed.c (distributed inference was split into
# its own translation unit upstream). It is a single GPU-agnostic object shared
# by every GPU mode, so link it in regardless of DS4_GPU.
list(APPEND DS4_OBJS "${DS4_DIR}/ds4_distributed.o")
list(APPEND DS4_OBJS "${DS4_DIR}/ds4_ssd.o")
add_executable(${TARGET}
grpc-server.cpp

View File

@@ -1,10 +1,10 @@
# ds4 backend Makefile.
#
# Upstream pin lives below as DS4_VERSION?=8384adf0f9fa0f3bb342dd925372de778b95b263
# Upstream pin lives below as DS4_VERSION?=477c0e82e2699b35a65fd0a1ed6fe66b41087dfe
# (.github/bump_deps.sh) can find and update it - matches the
# llama-cpp / ik-llama-cpp / turboquant convention.
DS4_VERSION?=8384adf0f9fa0f3bb342dd925372de778b95b263
DS4_VERSION?=477c0e82e2699b35a65fd0a1ed6fe66b41087dfe
DS4_REPO?=https://github.com/antirez/ds4
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
@@ -18,20 +18,19 @@ UNAME_S := $(shell uname -s)
CMAKE_ARGS ?= -DCMAKE_BUILD_TYPE=Release
# ds4_distributed.o and ds4_ssd.o are GPU-agnostic translation units that
# ds4.c/ds4_cpu.o now reference (upstream split distributed inference and the
# SSD expert-cache into their own .c files). Both objects are shared by every
# GPU mode, so they are appended unconditionally below.
# ds4_distributed.o is a GPU-agnostic translation unit that ds4.c/ds4_cpu.o now
# reference (upstream split distributed inference into its own .c). The same
# object is shared by every GPU mode, so it is appended unconditionally below.
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS += -DDS4_GPU=cuda
DS4_OBJ_TARGET := ds4.o ds4_cuda.o ds4_distributed.o ds4_ssd.o
DS4_OBJ_TARGET := ds4.o ds4_cuda.o ds4_distributed.o
else ifeq ($(UNAME_S),Darwin)
CMAKE_ARGS += -DDS4_GPU=metal
DS4_OBJ_TARGET := ds4.o ds4_metal.o ds4_distributed.o ds4_ssd.o
DS4_OBJ_TARGET := ds4.o ds4_metal.o ds4_distributed.o
else
# CPU reference path (Linux only - macOS CPU path is broken by VM bug per ds4 README).
CMAKE_ARGS += -DDS4_GPU=cpu
DS4_OBJ_TARGET := ds4_cpu.o ds4_distributed.o ds4_ssd.o
DS4_OBJ_TARGET := ds4_cpu.o ds4_distributed.o
endif
ifneq ($(NATIVE),true)
@@ -56,11 +55,11 @@ ds4:
# the right per-platform compile flags (Objective-C/Metal on Darwin, nvcc on Linux+CUDA).
ds4/ds4.o: ds4
ifeq ($(BUILD_TYPE),cublas)
+$(MAKE) -C ds4 ds4.o ds4_cuda.o ds4_distributed.o ds4_ssd.o
+$(MAKE) -C ds4 ds4.o ds4_cuda.o ds4_distributed.o
else ifeq ($(UNAME_S),Darwin)
+$(MAKE) -C ds4 ds4.o ds4_metal.o ds4_distributed.o ds4_ssd.o
+$(MAKE) -C ds4 ds4.o ds4_metal.o ds4_distributed.o
else
+$(MAKE) -C ds4 ds4_cpu.o ds4_distributed.o ds4_ssd.o
+$(MAKE) -C ds4 ds4_cpu.o ds4_distributed.o
endif
grpc-server: ds4/ds4.o

View File

@@ -1,5 +1,5 @@
IK_LLAMA_VERSION?=e6f8112f3ba126eed3ff5b30cdd08085414a7516
IK_LLAMA_VERSION?=1520eda980564241434b791ce2bbbd128c4be9ea
LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
CMAKE_ARGS?=

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=039e20a2db9e87b2477c76cc04905f3e1acad77f
LLAMA_VERSION?=7c158fbb4aec1bdc9c81d6ca0e785139f4826fae
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=

View File

@@ -381,15 +381,6 @@ json parse_options(bool streaming, const backend::PredictOptions* predict, const
});
}
// for each video in the request, add the video data
for (int i = 0; i < predict->videos_size(); i++) {
data["video_data"].push_back(json
{
{"id", i},
{"data", predict->videos(i)},
});
}
data["stop"] = predict->stopprompts();
// data["n_probs"] = predict->nprobs();
//TODO: images,
@@ -491,13 +482,23 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
if (!request->draftmodel().empty()) {
params.speculative.draft.mparams.path = request->draftmodel();
// Default to draft type if a draft model is set but no explicit type.
// Upstream made the speculative type a vector (ggml-org/llama.cpp#22838)
// and renamed COMMON_SPECULATIVE_TYPE_DRAFT -> ..._DRAFT_SIMPLE (#22964).
// Upstream (post ggml-org/llama.cpp#22838) made the speculative type a
// vector; the turboquant fork still uses the legacy scalar. The
// LOCALAI_LEGACY_LLAMA_CPP_SPEC macro is injected by
// backend/cpp/turboquant/patch-grpc-server.sh for fork builds only.
// Upstream renamed COMMON_SPECULATIVE_TYPE_DRAFT -> ..._DRAFT_SIMPLE
// in ggml-org/llama.cpp#22964; the fork still uses the old name.
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
if (params.speculative.type == COMMON_SPECULATIVE_TYPE_NONE) {
params.speculative.type = COMMON_SPECULATIVE_TYPE_DRAFT;
}
#else
const bool no_spec_type = params.speculative.types.empty() ||
(params.speculative.types.size() == 1 && params.speculative.types[0] == COMMON_SPECULATIVE_TYPE_NONE);
if (no_spec_type) {
params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE };
}
#endif
}
// params.model_alias ??
@@ -573,10 +574,9 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// tokens (0 disables the minimum). Match upstream's default (256). This
// field was renamed from `checkpoint_every_nt` in llama.cpp; the semantics
// also shifted from a fixed cadence to a minimum spacing. The turboquant
// fork still lacks common_params::checkpoint_min_step, so skip it there
// (LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP is injected by
// backend/cpp/turboquant/patch-grpc-server.sh).
#ifndef LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP
// fork branched before the field existed, so skip it on the legacy path
// (LOCALAI_LEGACY_LLAMA_CPP_SPEC is injected by patch-grpc-server.sh).
#ifndef LOCALAI_LEGACY_LLAMA_CPP_SPEC
params.checkpoint_min_step = 256;
#endif
@@ -752,7 +752,7 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
params.cache_idle_slots = false;
}
#ifndef LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP
#ifndef LOCALAI_LEGACY_LLAMA_CPP_SPEC
// --- minimum context-checkpoint spacing (upstream -cms / --checkpoint-min-step) ---
// 0 disables the minimum-spacing gate. Old option names (`checkpoint_every_nt`,
// `checkpoint_every_n_tokens`) are kept as aliases for backward compatibility
@@ -906,6 +906,17 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// Speculative decoding options
} else if (!strcmp(optname, "spec_type") || !strcmp(optname, "speculative_type")) {
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
// Fork only knows a single scalar `type`. Take the first comma-
// separated value and assign it via the singular helper.
std::string first = optval_str;
const auto comma = first.find(',');
if (comma != std::string::npos) first = first.substr(0, comma);
auto type = common_speculative_type_from_name(first);
if (type != COMMON_SPECULATIVE_TYPE_COUNT) {
params.speculative.type = type;
}
#else
// Upstream switched to a vector of types (comma-separated for multi-type
// chaining via common_speculative_types_from_names). We keep accepting a
// single value here, but also tolerate comma-separated lists.
@@ -934,6 +945,7 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
if (!parsed.empty()) {
params.speculative.types = parsed;
}
#endif
} else if (!strcmp(optname, "spec_n_max") || !strcmp(optname, "draft_max")) {
if (optval != NULL) {
try { params.speculative.draft.n_max = std::stoi(optval_str); } catch (...) {}
@@ -971,6 +983,21 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// shares the target context size. Accept the option for backward
// compatibility but silently ignore it.
// Everything below relies on struct shape introduced in ggml-org/llama.cpp#22838
// (parallel drafting): `ngram_mod`, `ngram_map_k`, `ngram_map_k4v`,
// `ngram_cache`, and the `draft.{cache_type_*, cpuparams*, tensor_buft_overrides}`
// fields. The turboquant fork branched before that, so its build defines
// LOCALAI_LEGACY_LLAMA_CPP_SPEC via patch-grpc-server.sh and these option
// keys become unrecognized (silently dropped, like any unknown opt) for it.
//
// The `#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC` / `#else` split below sits at the
// closing-brace position of the `draft_ctx_size` branch on purpose: in the
// legacy build the chain ends here (the brace closes draft_ctx_size), and in
// the modern build the chain continues with `} else if (...)` instead, so the
// brace count stays balanced under both branches of the preprocessor.
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
}
#else
// --- ngram_mod family (upstream --spec-ngram-mod-*) ---
} else if (!strcmp(optname, "spec_ngram_mod_n_min")) {
if (optval != NULL) {
@@ -1100,6 +1127,7 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
}
if (!cur.empty()) flush(cur);
}
#endif // LOCALAI_LEGACY_LLAMA_CPP_SPEC — closes the `else`/`#ifdef` opened at draft_ctx_size
}
// Set params.n_parallel from environment variable if not set via options (fallback)
@@ -1149,11 +1177,15 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
params.tensor_buft_overrides.push_back({nullptr, nullptr});
}
}
// Terminate the draft tensor_buft_overrides list with a sentinel, mirroring
// the main-model handling above.
// The draft tensor_buft_overrides are only populated under the modern
// (post-#22838) layout, whose population code is itself gated by
// LOCALAI_LEGACY_LLAMA_CPP_SPEC above. The turboquant fork lacks
// common_params_speculative::draft entirely, so skip the sentinel there too.
#ifndef LOCALAI_LEGACY_LLAMA_CPP_SPEC
if (!params.speculative.draft.tensor_buft_overrides.empty()) {
params.speculative.draft.tensor_buft_overrides.push_back({nullptr, nullptr});
}
#endif
// TODO: Add yarn
@@ -1512,7 +1544,7 @@ public:
msg_json["role"] = msg.role();
bool is_last_user_msg = (i == last_user_msg_idx);
bool has_images_or_audio = (request->images_size() > 0 || request->audios_size() > 0 || request->videos_size() > 0);
bool has_images_or_audio = (request->images_size() > 0 || request->audios_size() > 0);
// Handle content - can be string, null, or array
// For multimodal content, we'll embed images/audio from separate fields
@@ -1563,16 +1595,6 @@ public:
content_array.push_back(audio_chunk);
}
}
if (request->videos_size() > 0) {
for (int j = 0; j < request->videos_size(); j++) {
json video_chunk;
video_chunk["type"] = "input_video";
json input_video;
input_video["data"] = request->videos(j);
video_chunk["input_video"] = input_video;
content_array.push_back(video_chunk);
}
}
msg_json["content"] = content_array;
} else {
// Use content as-is (already array or not last user message)
@@ -1607,16 +1629,6 @@ public:
content_array.push_back(audio_chunk);
}
}
if (request->videos_size() > 0) {
for (int j = 0; j < request->videos_size(); j++) {
json video_chunk;
video_chunk["type"] = "input_video";
json input_video;
input_video["data"] = request->videos(j);
video_chunk["input_video"] = input_video;
content_array.push_back(video_chunk);
}
}
msg_json["content"] = content_array;
} else if (msg.role() == "tool") {
// Tool role messages must have content field set, even if empty
@@ -2068,16 +2080,6 @@ public:
files.push_back(decoded_data);
}
}
const auto &video_data = data.find("video_data");
if (video_data != data.end() && video_data->is_array())
{
for (const auto &video : *video_data)
{
auto decoded_data = base64_decode(video["data"].get<std::string>());
files.push_back(decoded_data);
}
}
}
const bool has_mtmd = ctx_server.impl->mctx != nullptr;
@@ -2330,7 +2332,7 @@ public:
}
bool is_last_user_msg = (i == last_user_msg_idx);
bool has_images_or_audio = (request->images_size() > 0 || request->audios_size() > 0 || request->videos_size() > 0);
bool has_images_or_audio = (request->images_size() > 0 || request->audios_size() > 0);
// Handle content - can be string, null, or array
// For multimodal content, we'll embed images/audio from separate fields
@@ -2383,16 +2385,6 @@ public:
content_array.push_back(audio_chunk);
}
}
if (request->videos_size() > 0) {
for (int j = 0; j < request->videos_size(); j++) {
json video_chunk;
video_chunk["type"] = "input_video";
json input_video;
input_video["data"] = request->videos(j);
video_chunk["input_video"] = input_video;
content_array.push_back(video_chunk);
}
}
msg_json["content"] = content_array;
} else {
// Use content as-is (already array or not last user message)
@@ -2432,16 +2424,6 @@ public:
content_array.push_back(audio_chunk);
}
}
if (request->videos_size() > 0) {
for (int j = 0; j < request->videos_size(); j++) {
json video_chunk;
video_chunk["type"] = "input_video";
json input_video;
input_video["data"] = request->videos(j);
video_chunk["input_video"] = input_video;
content_array.push_back(video_chunk);
}
}
msg_json["content"] = content_array;
SRV_INF("[CONTENT DEBUG] Predict: Message %d created content array with media\n", i);
} else if (!msg.tool_calls().empty()) {
@@ -2904,16 +2886,6 @@ public:
files.push_back(decoded_data);
}
}
const auto &video_data = data.find("video_data");
if (video_data != data.end() && video_data->is_array())
{
for (const auto &video : *video_data)
{
auto decoded_data = base64_decode(video["data"].get<std::string>());
files.push_back(decoded_data);
}
}
}
// process files

View File

@@ -1,7 +1,7 @@
# Pinned to the HEAD of feature/turboquant-kv-cache on https://github.com/TheTom/llama-cpp-turboquant.
# Auto-bumped nightly by .github/workflows/bump_deps.yaml.
TURBOQUANT_VERSION?=7d9715f1f071fa07c7b2ad3dbfd320b314139e65
TURBOQUANT_VERSION?=5aeb2fdbe26cd4c534c6fa15de73cb5749bd0403
LLAMA_REPO?=https://github.com/TheTom/llama-cpp-turboquant
CMAKE_ARGS?=

View File

@@ -4,19 +4,21 @@
#
# 1. Augment the kv_cache_types[] allow-list so `LoadModel` accepts the
# fork-specific `turbo2` / `turbo3` / `turbo4` cache types.
# 2. Define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP at the top of the file
# so the grpc-server option parser skips the two references to
# common_params::checkpoint_min_step (the default and the option handler).
# That field does not exist in the fork yet; drop this once it does.
#
# The fork used to lag upstream on the whole common_params_speculative refactor
# (ggml-org/llama.cpp#22397/#22838/#22964), the model_tgt rename (#22838) and
# get_media_marker (#21962), which required a much larger compat shim here
# (flat-field sed renames + a coarse LOCALAI_LEGACY_LLAMA_CPP_SPEC define). The
# fork has since rebased past all of those, so the only remaining gap is
# checkpoint_min_step. If a future bump reintroduces a divergence, add a narrow
# guard in grpc-server.cpp keyed on a fork-specific macro and inject it here
# rather than resurrecting the coarse one.
# 2. Replace `get_media_marker()` (added upstream in ggml-org/llama.cpp#21962,
# server-side random per-instance marker) with the legacy "<__media__>"
# literal. The fork branched before that PR, so server-common.cpp has no
# get_media_marker symbol. The fork's mtmd_default_marker() still returns
# "<__media__>", and Go-side tooling falls back to that sentinel when the
# backend does not expose media_marker, so substituting the literal keeps
# behavior identical on the turboquant path.
# 3. Revert the `common_params_speculative` field references to the
# pre-refactor flat layout. Upstream ggml-org/llama.cpp#22397 split the
# struct into nested `draft` / `ngram_simple` / `ngram_mod` / etc. members;
# the turboquant fork branched before that PR and still exposes the flat
# `n_max`, `mparams_dft`, `ngram_size_n`, ... fields. The substitutions
# below map the new nested paths back to the legacy flat names so the
# shared grpc-server.cpp keeps compiling against the fork's common.h.
# Drop this block once the fork rebases past #22397.
#
# We patch the *copy* sitting in turboquant-<flavor>-build/, never the original
# under backend/cpp/llama-cpp/, so the stock llama-cpp build keeps compiling
@@ -70,20 +72,72 @@ else
echo "==> KV allow-list patch OK"
fi
# 2. Define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP at the top of the file so
# the grpc-server option parser skips the two references to
# common_params::checkpoint_min_step (the default assignment and the option
# handler). That field does not exist in the fork yet. Drop this block once
# the fork rebases past the bump that added checkpoint_min_step.
if grep -q '^#define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP' "$SRC"; then
echo "==> $SRC already defines LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP, skipping"
if grep -q 'get_media_marker()' "$SRC"; then
echo "==> patching $SRC to replace get_media_marker() with legacy \"<__media__>\" literal"
# Only one call site today (ModelMetadata), but replace all occurrences to
# stay robust if upstream adds more. Use a temp file to avoid relying on
# sed -i portability (the builder image uses GNU sed, but keeping this
# consistent with the awk block above).
sed 's/get_media_marker()/"<__media__>"/g' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> get_media_marker() substitution OK"
else
echo "==> patching $SRC to define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP at the top"
# Insert the define before the very first `#include` so it precedes the
# checkpoint_min_step references.
echo "==> $SRC has no get_media_marker() call, skipping media-marker patch"
fi
if grep -q 'params\.speculative\.draft\.\|params\.speculative\.ngram_simple\.' "$SRC"; then
echo "==> patching $SRC to revert common_params_speculative refs to pre-#22397 flat layout"
# Each substitution is the exact post-refactor path → legacy flat field.
# Order doesn't matter because the source paths are disjoint, but we keep
# the most-specific (mparams.path) first for readability.
sed -E \
-e 's/params\.speculative\.draft\.mparams\.path/params.speculative.mparams_dft.path/g' \
-e 's/params\.speculative\.draft\.n_max/params.speculative.n_max/g' \
-e 's/params\.speculative\.draft\.n_min/params.speculative.n_min/g' \
-e 's/params\.speculative\.draft\.p_min/params.speculative.p_min/g' \
-e 's/params\.speculative\.draft\.p_split/params.speculative.p_split/g' \
-e 's/params\.speculative\.draft\.n_gpu_layers/params.speculative.n_gpu_layers/g' \
-e 's/params\.speculative\.draft\.n_ctx/params.speculative.n_ctx/g' \
-e 's/params\.speculative\.ngram_simple\.size_n/params.speculative.ngram_size_n/g' \
-e 's/params\.speculative\.ngram_simple\.size_m/params.speculative.ngram_size_m/g' \
-e 's/params\.speculative\.ngram_simple\.min_hits/params.speculative.ngram_min_hits/g' \
"$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> speculative field rename OK"
else
echo "==> $SRC has no post-#22397 speculative field refs, skipping spec rename patch"
fi
# 4. Revert the `ctx_server.impl->model_tgt` rename introduced by upstream
# ggml-org/llama.cpp#22838 (parallel drafting). The turboquant fork still
# exposes the field as `model` on `server_context_impl`. The two call sites
# are in the Rerank and ModelMetadata RPC handlers.
if grep -q 'ctx_server\.impl->model_tgt' "$SRC"; then
echo "==> patching $SRC to revert ctx_server.impl->model_tgt -> ctx_server.impl->model"
sed -E 's/ctx_server\.impl->model_tgt/ctx_server.impl->model/g' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> model_tgt rename OK"
else
echo "==> $SRC has no ctx_server.impl->model_tgt refs, skipping model_tgt rename patch"
fi
# 5. Define LOCALAI_LEGACY_LLAMA_CPP_SPEC at the top of the file so the
# grpc-server option parser skips the new option-handler blocks (ngram_mod,
# ngram_map_k, ngram_map_k4v, ngram_cache, draft.cache_type_*, draft.cpuparams*,
# draft.tensor_buft_overrides) introduced for the post-#22838 layout, the
# draft.tensor_buft_overrides sentinel termination, and the
# common_params::checkpoint_min_step default/option (added with the
# 35c9b1f3 bump). Those blocks reference struct fields that simply do not
# exist in the fork.
if grep -q '^#define LOCALAI_LEGACY_LLAMA_CPP_SPEC' "$SRC"; then
echo "==> $SRC already defines LOCALAI_LEGACY_LLAMA_CPP_SPEC, skipping"
else
echo "==> patching $SRC to define LOCALAI_LEGACY_LLAMA_CPP_SPEC at the top"
# Insert the define before the very first `#include` so it precedes all the
# speculative-decoding code paths.
awk '
!done && /^#include/ {
print "#define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP 1"
print "#define LOCALAI_LEGACY_LLAMA_CPP_SPEC 1"
print "// ^ injected by backend/cpp/turboquant/patch-grpc-server.sh"
print ""
done = 1
@@ -91,13 +145,13 @@ else
{ print }
END {
if (!done) {
print "patch-grpc-server.sh: no #include anchor found to insert LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP" > "/dev/stderr"
print "patch-grpc-server.sh: no #include anchor found to insert LOCALAI_LEGACY_LLAMA_CPP_SPEC" > "/dev/stderr"
exit 1
}
}
' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP define OK"
echo "==> LOCALAI_LEGACY_LLAMA_CPP_SPEC define OK"
fi
echo "==> all patches applied"

View File

@@ -1,55 +0,0 @@
hip: port the turboquant CUDA additions that ggml's HIP shim doesn't cover
The turboquant fork adds/modifies a few ggml-cuda.cu spots with CUDA APIs
that ggml's HIP (and MUSA) compatibility layer does not provide, breaking
the -gpu-rocm-hipblas-turboquant build:
1. ggml_cuda_copy2d_across_devices() (host-staged cross-device copy for
split mul_mat output) uses the CUDA 3D-peer copy APIs
cudaMemcpy3DPeerParms / make_cudaPitchedPtr / make_cudaExtent /
cudaMemcpy3DPeerAsync. HIP genuinely does not support these (see the
fork's own comment "HIP does not support cudaMemcpy3DPeerAsync"), so
guard the peer fast path with #if !defined(GGML_USE_HIP) &&
!defined(GGML_USE_MUSA) -- matching how the fork already guards the
same API for the sibling 2D copy -- and fall through to the existing
cudaMemcpyAsync staging fallback below (functionally identical,
slightly slower on multi-GPU ROCm).
2. ggml_backend_cuda_device_event_new() creates its event with plain
cudaEventCreate, which ggml's HIP shim does not alias (it only aliases
cudaEventCreateWithFlags). Use cudaEventCreateWithFlags(...,
cudaEventDisableTiming) -- exactly what the rest of this file already
does (cf. lines ~1034, ~3461) and HIP-safe.
CUDA builds are unaffected. Drop the relevant hunk once the fork HIP-ports
these; apply-patches.sh fails fast if an anchor goes stale.
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 0427e6b..6352e6a 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1933,6 +1933,7 @@ static cudaError_t ggml_cuda_copy2d_across_devices(
size_t width, size_t height, cudaStream_t dst_stream, cudaStream_t src_stream) {
const auto & info = ggml_cuda_info();
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) // 3D-peer copy types unmapped by ggml's HIP/MUSA shim; use staging fallback below
if (info.peer_access[src_device][dst_device]) {
cudaMemcpy3DPeerParms p = {};
p.dstDevice = dst_device;
@@ -1942,6 +1943,7 @@ static cudaError_t ggml_cuda_copy2d_across_devices(
p.extent = make_cudaExtent(width, height, 1);
return cudaMemcpy3DPeerAsync(&p, dst_stream);
}
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
// Fallback: stage all rows through a single contiguous pinned buffer
int prev_device = ggml_cuda_get_device();
@@ -5714,7 +5716,7 @@ static ggml_backend_event_t ggml_backend_cuda_device_event_new(ggml_backend_dev_
ggml_cuda_set_device(dev_ctx->device);
cudaEvent_t event;
- CUDA_CHECK(cudaEventCreate(&event));
+ CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
return new ggml_backend_event {
/* .device = */ dev,

View File

@@ -14,7 +14,7 @@ target_include_directories(gocrispasr PRIVATE
# whisper. crispasr is the referencer; the backend static libs supply the
# per-architecture symbols; ggml is the math/runtime base.
target_link_libraries(gocrispasr PRIVATE
crispasr-lib
crispasr
parakeet canary canary_ctc cohere granite_speech granite_nle
voxtral voxtral4b qwen3_asr qwen3_tts orpheus chatterbox indextts
kokoro voxcpm2_tts m2m100 t5_translate wav2vec2-ggml vibevoice

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# CrispASR version (release tag)
CRISPASR_REPO?=https://github.com/CrispStrobe/CrispASR
CRISPASR_VERSION?=c29f6653a516a3001d923944dad8892072cc7334
CRISPASR_VERSION?=13d54e110e1538e0f0bc3af0680b9ab246cfb48d
SO_TARGET?=libgocrispasr.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

View File

@@ -1,6 +1,6 @@
# parakeet-cpp backend Makefile.
#
# Upstream pin lives below as PARAKEET_VERSION?=e270af73b94c9a5c37ec516230219ed4580e1db6
# Upstream pin lives below as PARAKEET_VERSION?=b11fe5bca78ad8b342dd559a43d76df3984bb447
# (.github/bump_deps.sh) can find and update it - matches the
# whisper.cpp / ds4 / vibevoice-cpp convention.
#
@@ -15,7 +15,7 @@
# That's what the L0 smoke test uses. The default target below does the
# proper clone-at-pin + cmake build so CI doesn't need a side-checkout.
PARAKEET_VERSION?=e270af73b94c9a5c37ec516230219ed4580e1db6
PARAKEET_VERSION?=b11fe5bca78ad8b342dd559a43d76df3984bb447
PARAKEET_REPO?=https://github.com/mudler/parakeet.cpp
GOCMD?=go

View File

@@ -7,12 +7,8 @@ import "time"
type batchRequest struct {
pcm []float32
decoder int32
// language is the per-request target locale ("" means the model default).
// parakeet.cpp's batched C-API takes ONE target_lang for the whole batch,
// so the dispatcher only coalesces requests that share a language.
language string
tag string
reply chan batchReply
tag string
reply chan batchReply
}
// batchReply carries one per-item JSON object string (an element of the C-API's
@@ -47,25 +43,13 @@ func newBatcher(maxSize int, maxWait time.Duration, runBatch func([]*batchReques
// run is the dispatcher loop: accumulate submitted requests until either maxSize
// is reached or maxWait elapses since the first queued request, then dispatch.
// Exits when stop is closed (draining any partially-filled batch first).
//
// A batch carries ONE language (parakeet.cpp's batched C-API takes a single
// target_lang), so a request whose language differs from the batch leader is
// not coalesced: it is held in carry and becomes the leader of the next batch.
// carry is therefore never dropped and its caller never deadlocks: every batch
// (including a lone carry on stop) is dispatched, and runBatch replies to all.
func (b *batcher) run(stop <-chan struct{}) {
var carry *batchRequest
for {
var first *batchRequest
if carry != nil {
// A mismatched request from the previous fill leads this batch.
first, carry = carry, nil
} else {
select {
case first = <-b.submit:
case <-stop:
return
}
select {
case first = <-b.submit:
case <-stop:
return
}
batch := []*batchRequest{first}
@@ -80,22 +64,12 @@ func (b *batcher) run(stop <-chan struct{}) {
for len(batch) < b.maxSize {
select {
case r := <-b.submit:
if r.language != first.language {
// Different language: carry it to the next batch so this
// batch stays single-language, then dispatch what we have.
carry = r
break fill
}
batch = append(batch, r)
case <-timer.C:
break fill
case <-stop:
timer.Stop()
b.runBatch(batch)
// Don't strand a carried request's caller on shutdown.
if carry != nil {
b.runBatch([]*batchRequest{carry})
}
return
}
}

View File

@@ -105,60 +105,4 @@ var _ = Describe("batcher", func() {
go func() { <-rep }()
Eventually(dispatched, "2s").Should(Receive(Equal(1)))
})
It("never coalesces requests with different languages into one batch", func() {
// parakeet.cpp's batched C-API takes ONE target_lang per batch, so the
// dispatcher must keep every dispatched batch single-language. Submit a
// mix of languages and assert (a) no batch ever carries more than one
// distinct language and (b) every submitted request still gets a reply
// (the mismatched carry-over is never dropped).
var mu sync.Mutex
var langsPerBatch [][]string
run := func(reqs []*batchRequest) {
seen := map[string]struct{}{}
var distinct []string
for _, r := range reqs {
if _, ok := seen[r.language]; !ok {
seen[r.language] = struct{}{}
distinct = append(distinct, r.language)
}
}
mu.Lock()
langsPerBatch = append(langsPerBatch, distinct)
mu.Unlock()
echoReply(reqs)
}
// Large window + size so the fill loop stays open across submits and the
// language constraint (not the timer) is what splits the batches.
b := newBatcher(16, 200*time.Millisecond, run)
stop := make(chan struct{})
go b.run(stop)
defer close(stop)
langs := []string{"en", "en", "de", "de", "en", "fr", "fr"}
const N = 7
var wg sync.WaitGroup
got := make([]string, N)
for i := 0; i < N; i++ {
wg.Add(1)
go func(i int) {
defer wg.Done()
rep := make(chan batchReply, 1)
b.submit <- &batchRequest{tag: string(rune('a' + i)), language: langs[i], reply: rep}
got[i] = (<-rep).json
}(i)
}
wg.Wait()
mu.Lock()
defer mu.Unlock()
// Invariant: every dispatched batch is single-language.
for _, distinct := range langsPerBatch {
Expect(len(distinct)).To(Equal(1), "a batch coalesced more than one language: %v", distinct)
}
// Liveness: every request got a reply (carry-over never stranded).
for i := 0; i < N; i++ {
Expect(got[i]).To(Equal(string(rune('a' + i))))
}
})
})

View File

@@ -48,13 +48,6 @@ var (
// side reads them as const float*/const int*.
CppTranscribePcmBatchJSON func(ctx uintptr, samplesConcat []float32, nSamples []int32, nClips int32, sampleRate int32, decoder int32) uintptr
// CppTranscribePcmBatchJSONLang is the multilingual variant of the batched
// JSON entry point: identical, plus a trailing target_lang. "" (the model
// default, "auto") is passed for non-prompt models, which ignore it; an
// unknown locale on a prompt model returns 0 and sets last_error. Present
// only in newer libparakeet.so; nil falls back to CppTranscribePcmBatchJSON.
CppTranscribePcmBatchJSONLang func(ctx uintptr, samplesConcat []float32, nSamples []int32, nClips int32, sampleRate int32, decoder int32, targetLang string) uintptr
// Cache-aware streaming (RNN-T) entry points. stream_begin returns 0 for
// non-streaming models. feed/finalize return a malloc'd char* (uintptr,
// freed via CppFreeString); feed writes 1 to *eouOut on an <EOU>/<EOB>.
@@ -62,18 +55,6 @@ var (
CppStreamFeed func(s uintptr, pcm []float32, nSamples int32, eouOut unsafe.Pointer) uintptr
CppStreamFinalize func(s uintptr) uintptr
CppStreamFree func(s uintptr)
// CppStreamBeginLang is the multilingual variant of stream_begin: identical,
// plus a trailing target_lang ("" means the model default). Present only in
// newer libparakeet.so; nil falls back to CppStreamBegin.
CppStreamBeginLang func(ctx uintptr, targetLang string) uintptr
// Streaming JSON variants (ABI v4): feed/finalize returning a malloc'd char*
// JSON document {text,eou,frame_sec,words} (uintptr, freed via CppFreeString)
// so streaming segments can carry per-word timestamps. Present only in newer
// libparakeet.so; nil falls back to the text-only CppStreamFeed/Finalize path.
CppStreamFeedJSON func(s uintptr, pcm []float32, nSamples int32) uintptr
CppStreamFinalizeJSON func(s uintptr) uintptr
)
// streamChunkSamples is how much 16 kHz mono PCM we hand to stream_feed per
@@ -91,26 +72,9 @@ const streamChunkSamples = 16000
//
// "start"/"end"/"t" are seconds; "conf" is confidence in (0,1].
type transcriptJSON struct {
Text string `json:"text"`
FrameSec float64 `json:"frame_sec"`
Words []transcriptWord `json:"words"`
Tokens []transcriptToken `json:"tokens"`
}
// streamFeedJSON mirrors the document returned by
// parakeet_capi_stream_feed_json / parakeet_capi_stream_finalize_json (ABI v4):
//
// {"text":"...","eou":0,"frame_sec":0.080000,
// "words":[{"w":"...","start":0.480,"end":0.640,"conf":0.9100}, ...]}
//
// "text" is the newly-finalized text since the last call; "eou" is 1 when an
// <EOU>/<EOB> fired this feed; "words" are the words finalized this call with
// absolute (stream-relative) start/end seconds.
type streamFeedJSON struct {
Text string `json:"text"`
Eou int `json:"eou"`
FrameSec float64 `json:"frame_sec"`
Words []transcriptWord `json:"words"`
Text string `json:"text"`
Words []transcriptWord `json:"words"`
Tokens []transcriptToken `json:"tokens"`
}
type transcriptWord struct {
@@ -139,10 +103,6 @@ type ParakeetCpp struct {
engineMu sync.Mutex // sole guard of the one C engine (dispatcher + streaming)
bat *batcher
batStop chan struct{}
// segmentGapFrames is NeMo's segment_gap_threshold in ENCODER FRAMES (model
// YAML option, default 0=off). When >0 it adds NeMo's silence-gap split on
// top of the punctuation split; converted to seconds via the JSON frame_sec.
segmentGapFrames int
}
// Load is the LocalAI gRPC entry point for LoadModel: it calls
@@ -172,11 +132,6 @@ func (p *ParakeetCpp) Load(opts *pb.ModelOptions) error {
if maxWaitMs < 0 {
maxWaitMs = 0
}
// NeMo's segment_gap_threshold (encoder frames, default 0=off). Off by
// default matches NeMo's default (punctuation-only segments); when set it
// additionally splits segments on inter-word silence (see transcriptResultFromDoc).
p.segmentGapFrames = optInt(opts, "segment_gap_threshold", 0)
if CppTranscribePcmBatchJSON != nil {
p.batStop = make(chan struct{})
p.bat = newBatcher(maxSize, time.Duration(maxWaitMs)*time.Millisecond, p.runBatch)
@@ -232,19 +187,8 @@ func (p *ParakeetCpp) runBatch(reqs []*batchRequest) {
if len(reqs) > 0 {
dec = reqs[0].decoder
}
// All requests in a batch share one language (the batcher coalesces only
// same-language requests), so any element's language describes the batch.
lang := ""
if len(reqs) > 0 {
lang = reqs[0].language
}
p.engineMu.Lock()
var cstr uintptr
if CppTranscribePcmBatchJSONLang != nil {
cstr = CppTranscribePcmBatchJSONLang(p.ctxPtr, concat, nSamples, int32(len(reqs)), 16000, dec, lang)
} else {
cstr = CppTranscribePcmBatchJSON(p.ctxPtr, concat, nSamples, int32(len(reqs)), 16000, dec)
}
cstr := CppTranscribePcmBatchJSON(p.ctxPtr, concat, nSamples, int32(len(reqs)), 16000, dec)
p.engineMu.Unlock()
if cstr == 0 {
err := fmt.Errorf("parakeet-cpp: batch transcribe failed: %s", CppLastError(p.ctxPtr))
@@ -282,9 +226,8 @@ func (p *ParakeetCpp) runBatch(reqs []*batchRequest) {
// OpenAI API, whose default is segment-level); token ids always populate
// Segment.Tokens.
//
// translate/diarize/prompt/temperature/threads are not applicable to parakeet
// and are ignored; language is honored on the batched + streaming paths (see
// opts.GetLanguage() below); streaming is handled by AudioTranscriptionStream
// translate/diarize/prompt/temperature/language/threads are not applicable to
// parakeet and are ignored; streaming is handled by AudioTranscriptionStream
// (L2).
func (p *ParakeetCpp) AudioTranscription(ctx context.Context, opts *pb.TranscriptRequest) (pb.TranscriptResult, error) {
if p.ctxPtr == 0 {
@@ -316,7 +259,7 @@ func (p *ParakeetCpp) AudioTranscription(ctx context.Context, opts *pb.Transcrip
if err := json.Unmarshal([]byte(raw), &doc); err != nil {
return pb.TranscriptResult{}, fmt.Errorf("parakeet-cpp: decode transcript json: %w", err)
}
return transcriptResultFromDoc(doc, opts, p.segmentGapFrames), nil
return transcriptResultFromDoc(doc, opts), nil
}
// Batched path: decode to PCM, submit to the batcher, wait for this request's
@@ -328,7 +271,7 @@ func (p *ParakeetCpp) AudioTranscription(ctx context.Context, opts *pb.Transcrip
}
rep := make(chan batchReply, 1)
select {
case p.bat.submit <- &batchRequest{pcm: pcm, decoder: 0, language: opts.GetLanguage(), reply: rep}:
case p.bat.submit <- &batchRequest{pcm: pcm, decoder: 0, reply: rep}:
case <-ctx.Done():
return pb.TranscriptResult{}, status.Error(codes.Canceled, "transcription cancelled")
}
@@ -345,169 +288,34 @@ func (p *ParakeetCpp) AudioTranscription(ctx context.Context, opts *pb.Transcrip
if err := json.Unmarshal([]byte(res.json), &doc); err != nil {
return pb.TranscriptResult{}, fmt.Errorf("parakeet-cpp: decode transcript json: %w", err)
}
return transcriptResultFromDoc(doc, opts, p.segmentGapFrames), nil
return transcriptResultFromDoc(doc, opts), nil
}
// segmentSeparators is NeMo's default segment_seperators (sentence-ending
// punctuation). Splitting on these matches NeMo's default segment timestamps.
var segmentSeparators = []rune{'.', '?', '!'}
// transcriptResultFromDoc maps a decoded transcriptJSON to a TranscriptResult,
// grouping words into NeMo-faithful segments (see splitWordsIntoSegments). The
// optional gapFrames (NeMo's segment_gap_threshold, in encoder FRAMES; 0=off)
// additionally splits on inter-word silence; it is converted to a seconds gap
// with the document's frame_sec. Per-segment word timings are attached only when
// the caller requested word granularity; token ids populate each segment's
// Tokens by time-window membership. Shared by the batched and direct paths.
func transcriptResultFromDoc(doc transcriptJSON, opts *pb.TranscriptRequest, gapFrames int) pb.TranscriptResult {
// synthesising a single whole-clip segment and attaching word timings only when
// the caller requested word granularity. Shared by the batched and direct paths.
func transcriptResultFromDoc(doc transcriptJSON, opts *pb.TranscriptRequest) pb.TranscriptResult {
text := strings.TrimSpace(doc.Text)
// Frame-unit gap threshold -> seconds (NeMo segment_gap_threshold). 0 = off.
gapSeconds := 0.0
if gapFrames > 0 {
if doc.FrameSec > 0 {
gapSeconds = float64(gapFrames) * doc.FrameSec
} else {
xlog.Warn("parakeet-cpp: segment_gap_threshold set but libparakeet.so " +
"did not report frame_sec; falling back to punctuation-only segments")
}
words := make([]*pb.TranscriptWord, 0, len(doc.Words))
for _, w := range doc.Words {
words = append(words, &pb.TranscriptWord{Start: secondsToNanos(w.Start), End: secondsToNanos(w.End), Text: w.W})
}
groups := splitWordsIntoSegments(doc.Words, segmentSeparators, gapSeconds)
if len(groups) == 0 {
// No words (edge case): single whole-clip text segment.
return pb.TranscriptResult{
Text: text,
Segments: []*pb.TranscriptSegment{{Id: 0, Text: text}},
}
tokens := make([]int32, 0, len(doc.Tokens))
for _, t := range doc.Tokens {
tokens = append(tokens, t.ID)
}
wantWords := wordsRequested(opts.TimestampGranularities)
segments := make([]*pb.TranscriptSegment, 0, len(groups))
for id, group := range groups {
parts := make([]string, len(group))
for i, gw := range group {
parts[i] = gw.W
}
seg := &pb.TranscriptSegment{
Id: int32(id),
Start: secondsToNanos(group[0].Start),
End: secondsToNanos(group[len(group)-1].End),
Text: strings.TrimSpace(strings.Join(parts, " ")),
Tokens: tokensInWindow(doc.Tokens, group[0].Start, group[len(group)-1].End),
}
if wantWords {
ws := make([]*pb.TranscriptWord, len(group))
for i, gw := range group {
ws[i] = &pb.TranscriptWord{Start: secondsToNanos(gw.Start), End: secondsToNanos(gw.End), Text: gw.W}
}
seg.Words = ws
}
segments = append(segments, seg)
var segStart, segEnd int64
if len(words) > 0 {
segStart = words[0].Start
segEnd = words[len(words)-1].End
}
return pb.TranscriptResult{Text: text, Segments: segments}
seg := &pb.TranscriptSegment{Id: 0, Start: segStart, End: segEnd, Text: text, Tokens: tokens}
if wordsRequested(opts.TimestampGranularities) {
seg.Words = words
}
return pb.TranscriptResult{Text: text, Segments: []*pb.TranscriptSegment{seg}}
}
// splitWordsIntoSegments groups words into segments exactly as NeMo's
// get_segment_offsets does (nemo/collections/asr/parts/utils/timestamp_utils.py).
// Walking the words, it closes a segment when (1) the gap rule is enabled
// (gapSeconds > 0) and the segment already has words and the gap from the
// previous word's end to this word's start is >= gapSeconds - the current word
// then STARTS a new segment - or, checked only when the gap rule did not apply
// (NeMo's elif), (2) the word ends with (or is) a separator, which closes the
// segment INCLUDING that word. Trailing words flush into a final segment.
// gapSeconds <= 0 disables the gap rule, matching NeMo's default
// segment_gap_threshold=None (punctuation-only segments).
func splitWordsIntoSegments(words []transcriptWord, separators []rune, gapSeconds float64) [][]transcriptWord {
var segments [][]transcriptWord
var cur []transcriptWord
for i, word := range words {
gapActive := gapSeconds > 0 && len(cur) > 0
if gapActive && (word.Start-words[i-1].End) >= gapSeconds {
segments = append(segments, cur)
cur = []transcriptWord{word}
continue
}
if !gapActive && endsWithSeparator(word.W, separators) {
cur = append(cur, word)
segments = append(segments, cur)
cur = nil
continue
}
cur = append(cur, word)
}
if len(cur) > 0 {
segments = append(segments, cur)
}
return segments
}
// endsWithSeparator reports whether w's last rune is in separators (matching
// NeMo's `word[-1] in delims or word in delims`).
func endsWithSeparator(w string, separators []rune) bool {
r := []rune(strings.TrimSpace(w))
if len(r) == 0 {
return false
}
last := r[len(r)-1]
for _, s := range separators {
if last == s {
return true
}
}
return false
}
// tokensInWindow returns the ids of tokens whose timestamp t falls in
// [start, end] (inclusive), assigning each token to the segment that spans its
// time. The last segment's end is the last word end, so the final token is
// included.
func tokensInWindow(tokens []transcriptToken, start, end float64) []int32 {
var ids []int32
for _, t := range tokens {
if t.T >= start && t.T <= end {
ids = append(ids, t.ID)
}
}
return ids
}
// streamSegmenter accumulates streaming words into per-utterance segments. EOU
// is the model's own utterance boundary; each closed segment takes its start/end
// from its first/last accumulated word.
type streamSegmenter struct {
segs []*pb.TranscriptSegment
cur []transcriptWord
nextID int32
}
func (s *streamSegmenter) add(doc streamFeedJSON) {
s.cur = append(s.cur, doc.Words...)
if doc.Eou != 0 {
s.flush()
}
}
func (s *streamSegmenter) flush() {
if len(s.cur) == 0 {
return
}
parts := make([]string, len(s.cur))
for i, w := range s.cur {
parts[i] = w.W
}
s.segs = append(s.segs, &pb.TranscriptSegment{
Id: s.nextID,
Start: secondsToNanos(s.cur[0].Start),
End: secondsToNanos(s.cur[len(s.cur)-1].End),
Text: strings.TrimSpace(strings.Join(parts, " ")),
})
s.nextID++
s.cur = nil
}
func (s *streamSegmenter) segments() []*pb.TranscriptSegment { return s.segs }
// wordsRequested reports whether the caller asked for word-level timestamps.
// The OpenAI transcription API gates word timings behind
// timestamp_granularities[] containing "word" and defaults to segment-level
@@ -553,12 +361,7 @@ func (p *ParakeetCpp) AudioTranscriptionStream(ctx context.Context, opts *pb.Tra
return status.Error(codes.Canceled, "transcription cancelled")
}
var stream uintptr
if CppStreamBeginLang != nil {
stream = CppStreamBeginLang(p.ctxPtr, opts.GetLanguage())
} else {
stream = CppStreamBegin(p.ctxPtr)
}
stream := CppStreamBegin(p.ctxPtr)
if stream == 0 {
// Not a cache-aware streaming model: run a normal offline
// transcription and emit it as one delta + a closing final result.
@@ -587,14 +390,6 @@ func (p *ParakeetCpp) AudioTranscriptionStream(ctx context.Context, opts *pb.Tra
return err
}
// ABI v4: when the streaming JSON entry points are present, drive them so the
// per-utterance segments carry per-word start/end timestamps. Falls through to
// the text-only loop below against an older libparakeet.so. Runs under the
// engineMu already held above.
if CppStreamFeedJSON != nil {
return p.streamJSON(ctx, stream, data, duration, results)
}
var (
full strings.Builder
segText strings.Builder
@@ -671,71 +466,6 @@ func (p *ParakeetCpp) AudioTranscriptionStream(ctx context.Context, opts *pb.Tra
return nil
}
// streamJSON drives the ABI v4 streaming JSON entry points: each feed/finalize
// returns a {text,eou,frame_sec,words} document. The newly-finalized text is
// emitted as a delta (unchanged streaming contract) while words are accumulated
// into per-utterance segments (closed on EOU) so the closing FinalResult carries
// timestamped segments. Runs under engineMu (already held by the caller).
func (p *ParakeetCpp) streamJSON(ctx context.Context, stream uintptr, data []float32,
duration float32, results chan *pb.TranscriptStreamResponse) error {
var (
full strings.Builder
seg streamSegmenter
)
// consume frees the malloc'd char* (a 0 return is an error), parses the JSON,
// emits the delta, and routes words through the segmenter.
consume := func(ret uintptr) error {
if ret == 0 {
msg := CppLastError(p.ctxPtr)
if msg == "" {
msg = "unknown error"
}
return fmt.Errorf("parakeet-cpp: stream feed/finalize failed: %s", msg)
}
raw := goStringFromCPtr(ret)
CppFreeString(ret)
var doc streamFeedJSON
if err := json.Unmarshal([]byte(raw), &doc); err != nil {
return fmt.Errorf("parakeet-cpp: decode stream json: %w", err)
}
if doc.Text != "" {
full.WriteString(doc.Text)
results <- &pb.TranscriptStreamResponse{Delta: doc.Text}
}
seg.add(doc)
return nil
}
for off := 0; off < len(data); off += streamChunkSamples {
if err := ctx.Err(); err != nil {
return status.Error(codes.Canceled, "transcription cancelled")
}
end := min(off+streamChunkSamples, len(data))
chunk := data[off:end]
if err := consume(CppStreamFeedJSON(stream, chunk, int32(len(chunk)))); err != nil {
return err
}
}
if err := consume(CppStreamFinalizeJSON(stream)); err != nil {
return err
}
seg.flush() // close any trailing utterance that never saw an EOU
text := strings.TrimSpace(full.String())
segments := seg.segments()
if len(segments) == 0 && text != "" {
segments = append(segments, &pb.TranscriptSegment{Id: 0, Text: text})
}
results <- &pb.TranscriptStreamResponse{
FinalResult: &pb.TranscriptResult{
Text: text,
Segments: segments,
Duration: duration,
},
}
return nil
}
// decodeWavMono16k converts any input audio to 16 kHz mono PCM and returns the
// float samples plus the clip duration in seconds. Mirrors the whisper
// backend: utils.AudioToWav (ffmpeg) normalises rate/channels, go-audio

View File

@@ -53,10 +53,6 @@ func ensureLibLoaded() {
purego.RegisterLibFunc(&CppStreamFeed, lib, "parakeet_capi_stream_feed")
purego.RegisterLibFunc(&CppStreamFinalize, lib, "parakeet_capi_stream_finalize")
purego.RegisterLibFunc(&CppStreamFree, lib, "parakeet_capi_stream_free")
if sym, err := purego.Dlsym(lib, "parakeet_capi_stream_feed_json"); err == nil && sym != 0 {
purego.RegisterLibFunc(&CppStreamFeedJSON, lib, "parakeet_capi_stream_feed_json")
purego.RegisterLibFunc(&CppStreamFinalizeJSON, lib, "parakeet_capi_stream_finalize_json")
}
purego.RegisterLibFunc(&CppFreeString, lib, "parakeet_capi_free_string")
purego.RegisterLibFunc(&CppLastError, lib, "parakeet_capi_last_error")
})
@@ -111,22 +107,13 @@ var _ = Describe("ParakeetCpp", func() {
Expect(err).ToNot(HaveOccurred())
Expect(strings.TrimSpace(res.Text)).ToNot(BeEmpty(),
"expected non-empty transcript for %s", audioPath)
// NeMo-faithful segmentation: one or more punctuation-delimited
// segments, each with text and a monotonically-advancing time span.
Expect(res.Segments).ToNot(BeEmpty(), "expected at least one segment")
var prevEnd int64
for i, seg := range res.Segments {
Expect(strings.TrimSpace(seg.Text)).ToNot(BeEmpty(),
"segment %d must have text", i)
Expect(seg.End).To(BeNumerically(">=", seg.Start),
"segment %d end must not precede its start", i)
Expect(seg.Start).To(BeNumerically(">=", prevEnd),
"segments must be in time order")
prevEnd = seg.End
// Default (no granularities) is segment-level: no per-word timings.
Expect(seg.Words).To(BeEmpty(),
"word timings are opt-in via timestamp_granularities")
}
Expect(res.Segments).To(HaveLen(1),
"synthesises a single whole-clip segment")
Expect(res.Segments[0].Text).To(Equal(res.Text),
"single segment text must equal the top-level text")
// Default (no granularities) is segment-level: no per-word timings.
Expect(res.Segments[0].Words).To(BeEmpty(),
"word timings are opt-in via timestamp_granularities")
})
It("emits word-level timestamps when granularity=word", func() {
@@ -142,28 +129,15 @@ var _ = Describe("ParakeetCpp", func() {
TimestampGranularities: []string{"word"},
})
Expect(err).ToNot(HaveOccurred())
Expect(res.Segments).ToNot(BeEmpty())
// With word granularity every segment carries its own words, and each
// segment's span tracks its first/last word; word starts advance
// monotonically across the whole transcript.
totalWords := 0
var prevStart int64 = -1
for i, seg := range res.Segments {
Expect(seg.Words).ToNot(BeEmpty(),
"segment %d must carry per-word timestamps with granularity=word", i)
Expect(seg.Start).To(Equal(seg.Words[0].Start),
"segment %d start tracks its first word", i)
Expect(seg.End).To(Equal(seg.Words[len(seg.Words)-1].End),
"segment %d end tracks its last word", i)
for _, w := range seg.Words {
Expect(w.End).To(BeNumerically(">=", w.Start))
Expect(w.Start).To(BeNumerically(">=", prevStart))
prevStart = w.Start
totalWords++
}
}
Expect(totalWords).To(BeNumerically(">", 0))
Expect(res.Segments[0].Words[0].Start).To(BeNumerically(">=", int64(0)))
Expect(res.Segments).To(HaveLen(1))
seg := res.Segments[0]
Expect(seg.Words).ToNot(BeEmpty(),
"expected per-word timestamps with granularity=word")
// Monotonic, non-negative timings spanning the segment.
Expect(seg.Words[0].Start).To(BeNumerically(">=", int64(0)))
Expect(seg.End).To(BeNumerically(">=", seg.Start))
Expect(seg.Words[len(seg.Words)-1].End).To(Equal(seg.End),
"segment end tracks the last word")
})
})

View File

@@ -65,25 +65,6 @@ func main() {
purego.RegisterLibFunc(&CppTranscribePcmBatchJSON, lib, "parakeet_capi_transcribe_pcm_batch_json")
}
// Per-request language variants (multilingual nemotron). Same probe pattern:
// present only in libparakeet.so built with multilingual support, so the
// backend still loads against an older library and falls back to the
// non-lang batched + streaming entry points (model default / "auto").
if sym, err := purego.Dlsym(lib, "parakeet_capi_transcribe_pcm_batch_json_lang"); err == nil && sym != 0 {
purego.RegisterLibFunc(&CppTranscribePcmBatchJSONLang, lib, "parakeet_capi_transcribe_pcm_batch_json_lang")
}
if sym, err := purego.Dlsym(lib, "parakeet_capi_stream_begin_lang"); err == nil && sym != 0 {
purego.RegisterLibFunc(&CppStreamBeginLang, lib, "parakeet_capi_stream_begin_lang")
}
// Streaming JSON entry points (ABI v4): surface per-word timestamps on the
// streaming path. Same probe pattern; absent in older libparakeet.so, where
// the backend falls back to the text-only streaming feed.
if sym, err := purego.Dlsym(lib, "parakeet_capi_stream_feed_json"); err == nil && sym != 0 {
purego.RegisterLibFunc(&CppStreamFeedJSON, lib, "parakeet_capi_stream_feed_json")
purego.RegisterLibFunc(&CppStreamFinalizeJSON, lib, "parakeet_capi_stream_finalize_json")
}
fmt.Fprintf(os.Stderr, "[parakeet-cpp] ABI=%d\n", CppAbiVersion())
flag.Parse()

View File

@@ -1,127 +0,0 @@
package main
import (
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func tw(text string, start, end float64) transcriptWord {
return transcriptWord{W: text, Start: start, End: end}
}
var _ = Describe("splitWordsIntoSegments (NeMo get_segment_offsets parity)", func() {
seps := []rune{'.', '?', '!'}
It("splits on sentence-ending punctuation, including the delimiter word", func() {
words := []transcriptWord{tw("hello", 0, 0.4), tw("world.", 0.4, 0.8), tw("bye", 1.0, 1.3)}
segs := splitWordsIntoSegments(words, seps, 0)
Expect(segs).To(HaveLen(2))
Expect(segs[0]).To(HaveLen(2))
Expect(segs[0][1].W).To(Equal("world."))
Expect(segs[1]).To(HaveLen(1))
Expect(segs[1][0].W).To(Equal("bye"))
})
It("keeps a single segment with no terminal punctuation and gap off", func() {
words := []transcriptWord{tw("a", 0, 0.2), tw("b", 0.2, 0.4), tw("c", 5.0, 5.2)}
segs := splitWordsIntoSegments(words, seps, 0)
Expect(segs).To(HaveLen(1))
})
It("splits on the gap rule when enabled, the gapped word starting the next segment", func() {
words := []transcriptWord{tw("a", 0, 0.2), tw("b", 0.2, 0.4), tw("c", 5.0, 5.2)}
segs := splitWordsIntoSegments(words, seps, 1.0) // c is 4.6s after b
Expect(segs).To(HaveLen(2))
Expect(segs[0]).To(HaveLen(2)) // a b
Expect(segs[1]).To(HaveLen(1)) // c
Expect(segs[1][0].W).To(Equal("c"))
})
It("checks the gap rule before punctuation (NeMo elif order)", func() {
// "b." would terminate, but c is far after it -> gap closes [a b.] at b.
words := []transcriptWord{tw("a", 0, 0.2), tw("b.", 0.2, 0.4), tw("c", 9.0, 9.2)}
segs := splitWordsIntoSegments(words, seps, 1.0)
Expect(segs).To(HaveLen(2))
Expect(segs[0]).To(HaveLen(2))
Expect(segs[1][0].W).To(Equal("c"))
})
It("still splits on punctuation when the gap rule is enabled but does not fire", func() {
words := []transcriptWord{tw("hi.", 0, 0.4), tw("bye", 0.4, 0.8)}
segs := splitWordsIntoSegments(words, seps, 5.0) // gap never reached
Expect(segs).To(HaveLen(2))
Expect(segs[0][0].W).To(Equal("hi."))
})
It("returns nothing for empty input", func() {
Expect(splitWordsIntoSegments(nil, seps, 0)).To(BeEmpty())
})
})
var _ = Describe("transcriptResultFromDoc (multi-segment)", func() {
doc := transcriptJSON{
Text: "hello world. bye now",
FrameSec: 0.08,
Words: []transcriptWord{
{W: "hello", Start: 0.0, End: 0.4},
{W: "world.", Start: 0.4, End: 0.8},
{W: "bye", Start: 1.0, End: 1.3},
{W: "now", Start: 1.3, End: 1.6},
},
Tokens: []transcriptToken{{ID: 1, T: 0.1}, {ID: 2, T: 0.5}, {ID: 3, T: 1.1}, {ID: 4, T: 1.4}},
}
It("emits one segment per punctuation-delimited group with start/end", func() {
res := transcriptResultFromDoc(doc, &pb.TranscriptRequest{}, 0)
Expect(res.Segments).To(HaveLen(2))
Expect(res.Segments[0].Text).To(Equal("hello world."))
Expect(res.Segments[0].Start).To(Equal(int64(0)))
Expect(res.Segments[0].End).To(Equal(secondsToNanos(0.8)))
Expect(res.Segments[1].Text).To(Equal("bye now"))
Expect(res.Segments[1].Start).To(Equal(secondsToNanos(1.0)))
Expect(res.Segments[1].Id).To(Equal(int32(1)))
})
It("assigns tokens to the segment whose time window contains them", func() {
res := transcriptResultFromDoc(doc, &pb.TranscriptRequest{}, 0)
Expect(res.Segments[0].Tokens).To(Equal([]int32{1, 2}))
Expect(res.Segments[1].Tokens).To(Equal([]int32{3, 4}))
})
It("attaches per-segment words only when word granularity requested", func() {
plain := transcriptResultFromDoc(doc, &pb.TranscriptRequest{}, 0)
Expect(plain.Segments[0].Words).To(BeEmpty())
withWords := transcriptResultFromDoc(doc, &pb.TranscriptRequest{TimestampGranularities: []string{"word"}}, 0)
Expect(withWords.Segments[0].Words).To(HaveLen(2))
})
It("falls back to a single text segment when there are no words", func() {
res := transcriptResultFromDoc(transcriptJSON{Text: "hi"}, &pb.TranscriptRequest{}, 0)
Expect(res.Segments).To(HaveLen(1))
Expect(res.Segments[0].Text).To(Equal("hi"))
})
})
var _ = Describe("streaming segment assembly", func() {
It("closes a segment with start/end from its words on EOU", func() {
acc := &streamSegmenter{}
acc.add(streamFeedJSON{Text: "hello world", Eou: 1, Words: []transcriptWord{
{W: "hello", Start: 0.0, End: 0.4}, {W: "world", Start: 0.4, End: 0.9},
}})
segs := acc.segments()
Expect(segs).To(HaveLen(1))
Expect(segs[0].Text).To(Equal("hello world"))
Expect(segs[0].Start).To(Equal(int64(0)))
Expect(segs[0].End).To(Equal(secondsToNanos(0.9)))
})
It("buffers words across feeds until EOU", func() {
acc := &streamSegmenter{}
acc.add(streamFeedJSON{Text: "hi", Eou: 0, Words: []transcriptWord{{W: "hi", Start: 0, End: 0.3}}})
Expect(acc.segments()).To(BeEmpty())
acc.add(streamFeedJSON{Text: "there", Eou: 1, Words: []transcriptWord{{W: "there", Start: 0.3, End: 0.7}}})
Expect(acc.segments()).To(HaveLen(1))
Expect(acc.segments()[0].Text).To(Equal("hi there"))
})
})

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# stablediffusion.cpp (ggml)
STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
STABLEDIFFUSION_GGML_VERSION?=19bdfe22d255d5b4dff39d449318b9bc5ea2317f
STABLEDIFFUSION_GGML_VERSION?=1f9ee88e09c258053fa59d5e05e23dfb10fa0b13
CMAKE_ARGS+=-DGGML_MAX_NAME=128

View File

@@ -386,7 +386,6 @@ int load_model(const char *model, char *model_path, char* options[], int threads
const char *llm_vision_path = "";
const char *diffusion_model_path = stableDiffusionModel;
const char *high_noise_diffusion_model_path = "";
const char *uncond_diffusion_model_path = "";
const char *taesd_path = "";
const char *control_net_path = "";
const char *embedding_dir = "";
@@ -473,7 +472,6 @@ int load_model(const char *model, char *model_path, char* options[], int threads
if (!strcmp(optname, "llm_vision_path")) llm_vision_path = strdup(optval);
if (!strcmp(optname, "diffusion_model_path")) diffusion_model_path = strdup(optval);
if (!strcmp(optname, "high_noise_diffusion_model_path")) high_noise_diffusion_model_path = strdup(optval);
if (!strcmp(optname, "uncond_diffusion_model_path")) uncond_diffusion_model_path = strdup(optval);
if (!strcmp(optname, "taesd_path")) taesd_path = strdup(optval);
if (!strcmp(optname, "control_net_path")) control_net_path = strdup(optval);
if (!strcmp(optname, "embedding_dir")) {
@@ -573,7 +571,6 @@ int load_model(const char *model, char *model_path, char* options[], int threads
ctx_params.llm_vision_path = llm_vision_path;
ctx_params.diffusion_model_path = diffusion_model_path;
ctx_params.high_noise_diffusion_model_path = high_noise_diffusion_model_path;
ctx_params.uncond_diffusion_model_path = uncond_diffusion_model_path;
ctx_params.vae_path = vae_path;
ctx_params.audio_vae_path = audio_vae_path;
ctx_params.embeddings_connectors_path = embeddings_connectors_path;

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
WHISPER_CPP_VERSION?=df7638d8229a243af8a4b5a8ae557e0d74e0a0ae
WHISPER_CPP_VERSION?=99613cb720b65036237d44b52f753b51f75c2797
SO_TARGET?=libgowhisper.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

View File

@@ -1,4 +1,4 @@
transformers
accelerate
torch==2.7.1
torch==2.7.1+xpu
rerankers[transformers]

View File

@@ -1,4 +1,4 @@
transformers
accelerate
torch==2.7.1
torch==2.7.1+xpu
rerankers[transformers]

View File

@@ -1,5 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/cu130
transformers
accelerate
torch==2.9.1
torch==2.7.1+xpu
rerankers[transformers]

View File

@@ -1,5 +1,5 @@
--extra-index-url https://download.pytorch.org/whl/rocm7.0
transformers
accelerate
torch==2.10.0+rocm7.0
torch==2.7.1+xpu
rerankers[transformers]

View File

@@ -1,4 +1,4 @@
torch==2.7.1
torch==2.7.1+xpu
transformers
accelerate
rerankers[transformers]

View File

@@ -26,10 +26,7 @@ from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.sampling_params import SamplingParams
from vllm.utils import random_uuid
try:
from vllm.tokenizers import get_tokenizer # vLLM >= 0.22
except ImportError:
from vllm.transformers_utils.tokenizer import get_tokenizer # vLLM < 0.22
from vllm.transformers_utils.tokenizer import get_tokenizer
from vllm.multimodal.utils import fetch_image
from vllm.assets.video import VideoAsset
import base64

View File

@@ -23,9 +23,9 @@ import (
"github.com/mudler/LocalAI/core/services/routing/pii"
"github.com/mudler/LocalAI/core/services/routing/router"
"github.com/mudler/LocalAI/core/services/storage"
"github.com/mudler/LocalAI/pkg/signals"
coreStartup "github.com/mudler/LocalAI/core/startup"
"github.com/mudler/LocalAI/internal"
"github.com/mudler/LocalAI/pkg/signals"
"github.com/mudler/LocalAI/pkg/vram"
"github.com/mudler/LocalAI/pkg/model"
@@ -308,31 +308,10 @@ func New(opts ...config.AppOption) (*Application, error) {
application.galleryService.SetNATSClient(distSvc.Nats)
if distSvc.DistStores != nil && distSvc.DistStores.Gallery != nil {
// Clean up stale in-progress operations from previous crashed instances
if _, err := distSvc.DistStores.Gallery.CleanStale(30 * time.Minute); err != nil {
if err := distSvc.DistStores.Gallery.CleanStale(30 * time.Minute); err != nil {
xlog.Warn("Failed to clean stale gallery operations", "error", err)
}
application.galleryService.SetGalleryStore(distSvc.DistStores.Gallery)
// Reap stale ops periodically, not just at boot: an op orphaned by
// a replica that died mid-install (its foreground handler goroutine
// gone) would otherwise linger "processing" in the UI until the next
// restart. 30m matches the install/upgrade ceiling so a genuinely
// slow op is never reaped out from under itself.
gsvc := application.galleryService
go func() {
ticker := time.NewTicker(15 * time.Minute)
defer ticker.Stop()
for {
select {
case <-options.Context.Done():
return
case <-ticker.C:
if _, err := gsvc.ReapStaleOperations(30 * time.Minute); err != nil {
xlog.Warn("Failed to reap stale gallery operations", "error", err)
}
}
}
}()
}
// Hydrate from the store first so the wildcard subscriber finds an
// already-populated statuses map for any operations still in flight

View File

@@ -214,9 +214,7 @@ func (uc *UpgradeChecker) runCheck(ctx context.Context) {
"from", info.InstalledVersion, "to", info.AvailableVersion)
var err error
if bm != nil {
// Background auto-upgrade: no live admin watching a progress bar,
// so opID is empty and the distributed path skips progress streaming.
err = bm.UpgradeBackend(ctx, "", name, nil)
err = bm.UpgradeBackend(ctx, name, nil)
} else {
err = gallery.UpgradeBackend(ctx, uc.systemState, uc.modelLoader,
uc.galleries, name, nil, uc.appConfig.RequireBackendIntegrity)

View File

@@ -1,30 +0,0 @@
package chat
import (
"context"
"io"
"strings"
)
type Options struct {
Model string
BaseURL string
APIKey string
In io.Reader
Out io.Writer
}
func Run(ctx context.Context, opts Options) error {
if opts.In == nil {
opts.In = strings.NewReader("")
}
if opts.Out == nil {
opts.Out = io.Discard
}
session, err := newChatSession(ctx, newLocalAIChatClient(opts.BaseURL, opts.APIKey), opts.Model)
if err != nil {
return err
}
return runTerminalChat(ctx, session, opts.In, opts.Out)
}

View File

@@ -1,13 +0,0 @@
package chat
import (
"testing"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func TestChat(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "Chat Suite")
}

View File

@@ -1,172 +0,0 @@
package chat
import (
"bytes"
"encoding/json"
"fmt"
"io"
"net/http"
"net/http/httptest"
"strings"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("Run chat", func() {
It("streams a single chat response", func() {
var capturedModel string
var capturedAuth string
server := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
if r.URL.Path == "/v1/models" {
w.Header().Set("Content-Type", "application/json")
writeResponse(w, `{"object":"list","data":[{"id":"test-model","object":"model"}]}`)
return
}
Expect(r.URL.Path).To(Equal("/v1/chat/completions"))
capturedAuth = r.Header.Get("Authorization")
var body struct {
Model string `json:"model"`
Messages []struct {
Role string `json:"role"`
Content string `json:"content"`
} `json:"messages"`
}
Expect(json.NewDecoder(r.Body).Decode(&body)).To(Succeed())
capturedModel = body.Model
Expect(body.Messages).To(HaveLen(1))
Expect(body.Messages[0].Role).To(Equal("user"))
Expect(body.Messages[0].Content).To(Equal("hello"))
w.Header().Set("Content-Type", "text/event-stream")
writeResponse(w, "data: {\"choices\":[{\"index\":0,\"delta\":{\"content\":\"hi\"}}]}\n\n")
writeResponse(w, "data: {\"choices\":[{\"index\":0,\"delta\":{\"content\":\"!\"}}]}\n\n")
writeResponse(w, "data: [DONE]\n\n")
}))
defer server.Close()
var out bytes.Buffer
err := Run(GinkgoT().Context(), Options{
Model: "test-model",
BaseURL: server.URL + "/v1",
APIKey: "secret",
In: strings.NewReader("hello\n/exit\n"),
Out: &out,
})
Expect(err).ToNot(HaveOccurred())
Expect(capturedModel).To(Equal("test-model"))
Expect(capturedAuth).To(Equal("Bearer secret"))
Expect(out.String()).To(ContainSubstring("assistant: hi!"))
Expect(out.String()).To(ContainSubstring("bye"))
})
It("auto-selects the only available model", func() {
server := chatTestServer([]string{"solo"}, nil)
defer server.Close()
var out bytes.Buffer
err := Run(GinkgoT().Context(), Options{
BaseURL: server.URL + "/v1",
In: strings.NewReader("/exit\n"),
Out: &out,
})
Expect(err).ToNot(HaveOccurred())
Expect(out.String()).To(ContainSubstring("LocalAI chat (solo)"))
})
It("returns an actionable error when no models are installed", func() {
server := chatTestServer(nil, nil)
defer server.Close()
err := Run(GinkgoT().Context(), Options{
BaseURL: server.URL + "/v1",
In: strings.NewReader(""),
})
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("no chat models are installed"))
Expect(err.Error()).To(ContainSubstring("local-ai models install <model>"))
})
It("returns an actionable error when multiple models are available without a selection", func() {
server := chatTestServer([]string{"alpha", "beta"}, nil)
defer server.Close()
err := Run(GinkgoT().Context(), Options{
BaseURL: server.URL + "/v1",
In: strings.NewReader(""),
})
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("multiple models are available"))
Expect(err.Error()).To(ContainSubstring("--model"))
Expect(err.Error()).To(ContainSubstring("alpha"))
Expect(err.Error()).To(ContainSubstring("beta"))
})
It("lists and switches models inside the chat", func() {
requestedModels := []string{}
server := chatTestServer([]string{"alpha", "beta"}, func(model string) {
requestedModels = append(requestedModels, model)
})
defer server.Close()
var out bytes.Buffer
err := Run(GinkgoT().Context(), Options{
Model: "alpha",
BaseURL: server.URL + "/v1",
In: strings.NewReader("/models\n/model beta\nhello\n/exit\n"),
Out: &out,
})
Expect(err).ToNot(HaveOccurred())
Expect(out.String()).To(ContainSubstring("* alpha"))
Expect(out.String()).To(ContainSubstring(" beta"))
Expect(out.String()).To(ContainSubstring("switched to beta; conversation cleared"))
Expect(requestedModels).To(Equal([]string{"beta"}))
})
})
func chatTestServer(models []string, onChat func(model string)) *httptest.Server {
return httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
switch r.URL.Path {
case "/v1/models":
w.Header().Set("Content-Type", "application/json")
writeResponse(w, `{"object":"list","data":[`)
for i, model := range models {
if i > 0 {
writeResponse(w, ",")
}
writeResponsef(w, `{"id":%q,"object":"model"}`, model)
}
writeResponse(w, `]}`)
case "/v1/chat/completions":
var body struct {
Model string `json:"model"`
}
Expect(json.NewDecoder(r.Body).Decode(&body)).To(Succeed())
if onChat != nil {
onChat(body.Model)
}
w.Header().Set("Content-Type", "text/event-stream")
writeResponse(w, "data: {\"choices\":[{\"index\":0,\"delta\":{\"content\":\"ok\"}}]}\n\n")
writeResponse(w, "data: [DONE]\n\n")
default:
w.WriteHeader(http.StatusNotFound)
}
}))
}
func writeResponse(w io.Writer, text string) {
_, err := fmt.Fprint(w, text)
Expect(err).ToNot(HaveOccurred())
}
func writeResponsef(w io.Writer, format string, args ...any) {
_, err := fmt.Fprintf(w, format, args...)
Expect(err).ToNot(HaveOccurred())
}

View File

@@ -1,114 +0,0 @@
package chat
import (
"context"
"errors"
"fmt"
"io"
"sort"
"strings"
openai "github.com/sashabaranov/go-openai"
)
type chatClient interface {
ListModels(ctx context.Context) ([]string, error)
StreamChat(ctx context.Context, model string, messages []chatMessage, out io.Writer) (string, error)
}
type localAIChatClient struct {
client *openai.Client
}
func newLocalAIChatClient(baseURL string, apiKey string) *localAIChatClient {
cfg := openai.DefaultConfig(apiKey)
cfg.BaseURL = baseURL
return &localAIChatClient{client: openai.NewClientWithConfig(cfg)}
}
func (c *localAIChatClient) ListModels(ctx context.Context) ([]string, error) {
resp, err := c.client.ListModels(ctx)
if err != nil {
return nil, err
}
models := make([]string, 0, len(resp.Models))
for _, model := range resp.Models {
if model.ID != "" {
models = append(models, model.ID)
}
}
sort.Strings(models)
return models, nil
}
func (c *localAIChatClient) StreamChat(ctx context.Context, model string, messages []chatMessage, out io.Writer) (string, error) {
stream, err := c.client.CreateChatCompletionStream(ctx, openai.ChatCompletionRequest{
Model: model,
Messages: openAIChatMessages(messages),
})
if err != nil {
return "", friendlyChatError(err, model)
}
defer func() {
_ = stream.Close()
}()
var answer strings.Builder
for {
resp, err := stream.Recv()
if errors.Is(err, io.EOF) {
break
}
if err != nil {
return answer.String(), friendlyChatError(err, model)
}
if len(resp.Choices) == 0 {
continue
}
token := resp.Choices[0].Delta.Content
if token == "" {
continue
}
answer.WriteString(token)
if _, err := fmt.Fprint(out, token); err != nil {
return answer.String(), err
}
}
return answer.String(), nil
}
func openAIChatMessages(messages []chatMessage) []openai.ChatCompletionMessage {
converted := make([]openai.ChatCompletionMessage, len(messages))
for i, message := range messages {
converted[i] = openai.ChatCompletionMessage{
Role: message.Role,
Content: message.Content,
}
}
return converted
}
func friendlyChatError(err error, model string) error {
var apiErr *openai.APIError
if errors.As(err, &apiErr) {
switch apiErr.HTTPStatusCode {
case 404:
return fmt.Errorf("model %q is not available. Run `local-ai models list`, install a model with `local-ai models install <model>`, or switch with `/model <name>`", model)
case 403:
return fmt.Errorf("model %q is disabled. Enable it from LocalAI settings or choose another model with `/model <name>`", model)
}
if apiErr.Message != "" {
return errors.New(apiErr.Message)
}
}
msg := err.Error()
if strings.Contains(msg, "model") && strings.Contains(msg, "not found") {
return fmt.Errorf("model %q is not available. Run `local-ai models list`, install a model with `local-ai models install <model>`, or switch with `/model <name>`", model)
}
return err
}

View File

@@ -1,17 +0,0 @@
package chat
import "strings"
func formatChatModelList(models []string, current string) string {
var b strings.Builder
for _, model := range models {
prefix := " "
if model == current {
prefix = "* "
}
b.WriteString(prefix)
b.WriteString(model)
b.WriteByte('\n')
}
return b.String()
}

View File

@@ -1,120 +0,0 @@
package chat
import (
"context"
"errors"
"fmt"
"io"
"strings"
)
const (
chatRoleUser = "user"
chatRoleAssistant = "assistant"
)
type chatMessage struct {
Role string
Content string
}
type chatSession struct {
client chatClient
model string
models []string
messages []chatMessage
}
func newChatSession(ctx context.Context, client chatClient, requestedModel string) (*chatSession, error) {
models, err := client.ListModels(ctx)
if err != nil {
return nil, fmt.Errorf("list models: %w", err)
}
model, err := resolveChatModel(requestedModel, models)
if err != nil {
return nil, err
}
return &chatSession{
client: client,
model: model,
models: models,
}, nil
}
func (s *chatSession) CurrentModel() string {
return s.model
}
func (s *chatSession) Models() []string {
models := make([]string, len(s.models))
copy(models, s.models)
return models
}
func (s *chatSession) Clear() {
s.messages = nil
}
func (s *chatSession) SwitchModel(model string) error {
if !modelExists(s.models, model) {
return fmt.Errorf("model %q is not available. Use /models to see installed models", model)
}
s.model = model
s.Clear()
return nil
}
func (s *chatSession) Send(ctx context.Context, prompt string, out io.Writer) error {
s.messages = append(s.messages, chatMessage{
Role: chatRoleUser,
Content: prompt,
})
answer, err := s.client.StreamChat(ctx, s.model, s.messages, out)
if err != nil {
return err
}
s.messages = append(s.messages, chatMessage{
Role: chatRoleAssistant,
Content: answer,
})
return nil
}
func resolveChatModel(requested string, models []string) (string, error) {
switch {
case requested == "" && len(models) == 0:
return "", errors.New(`no chat models are installed.
Install a model first, for example:
local-ai models list
local-ai models install <model>
local-ai run
Then start a chat session:
local-ai chat --model <model>`)
case requested == "" && len(models) == 1:
return models[0], nil
case requested == "" && len(models) > 1:
var b strings.Builder
b.WriteString("multiple models are available; choose one with --model:\n")
b.WriteString(formatChatModelList(models, ""))
return "", errors.New(b.String())
case !modelExists(models, requested):
return "", fmt.Errorf("model %q is not available. Use `local-ai models list` and `local-ai models install <model>`, or pass an installed model with --model", requested)
default:
return requested, nil
}
}
func modelExists(models []string, name string) bool {
for _, model := range models {
if model == name {
return true
}
}
return false
}

View File

@@ -1,56 +0,0 @@
package chat
import (
"context"
"io"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("Chat session", func() {
It("keeps model switching and message history out of the terminal adapter", func() {
client := &fakeChatClient{
models: []string{"alpha", "beta"},
answer: "pong",
}
session, err := newChatSession(context.Background(), client, "alpha")
Expect(err).ToNot(HaveOccurred())
Expect(session.CurrentModel()).To(Equal("alpha"))
Expect(session.SwitchModel("beta")).To(Succeed())
Expect(session.CurrentModel()).To(Equal("beta"))
Expect(session.Send(context.Background(), "ping", io.Discard)).To(Succeed())
Expect(client.requests).To(HaveLen(1))
Expect(client.requests[0].model).To(Equal("beta"))
Expect(client.requests[0].messages).To(HaveLen(1))
Expect(client.requests[0].messages[0].Content).To(Equal("ping"))
})
})
type fakeChatClient struct {
models []string
answer string
requests []fakeChatRequest
}
type fakeChatRequest struct {
model string
messages []chatMessage
}
func (c *fakeChatClient) ListModels(context.Context) ([]string, error) {
return c.models, nil
}
func (c *fakeChatClient) StreamChat(_ context.Context, model string, messages []chatMessage, out io.Writer) (string, error) {
copied := make([]chatMessage, len(messages))
copy(copied, messages)
c.requests = append(c.requests, fakeChatRequest{model: model, messages: copied})
if _, err := io.WriteString(out, c.answer); err != nil {
return "", err
}
return c.answer, nil
}

View File

@@ -1,93 +0,0 @@
package chat
import (
"bufio"
"context"
"fmt"
"io"
"strings"
)
func runTerminalChat(ctx context.Context, session *chatSession, in io.Reader, out io.Writer) error {
scanner := bufio.NewScanner(in)
scanner.Buffer(make([]byte, 0, 64*1024), 4*1024*1024)
if err := writeChat(out, "LocalAI chat (%s)\n", session.CurrentModel()); err != nil {
return err
}
if err := writeChat(out, "Type /exit to quit, /clear to reset the conversation, /models to list models.\n"); err != nil {
return err
}
for {
if err := writeChat(out, "\n> "); err != nil {
return err
}
if !scanner.Scan() {
break
}
prompt := strings.TrimSpace(scanner.Text())
switch prompt {
case "":
continue
case "/bye", "/exit", "/quit":
return writeChat(out, "bye\n")
case "/clear":
session.Clear()
if err := writeChat(out, "conversation cleared\n"); err != nil {
return err
}
continue
case "/models":
if err := printChatModels(out, session.Models(), session.CurrentModel()); err != nil {
return err
}
continue
}
if nextModel, ok := strings.CutPrefix(prompt, "/model "); ok {
nextModel = strings.TrimSpace(nextModel)
if nextModel == "" {
if err := writeChat(out, "usage: /model <name>\n"); err != nil {
return err
}
continue
}
if err := session.SwitchModel(nextModel); err != nil {
if writeErr := writeChat(out, "%s\n", err); writeErr != nil {
return writeErr
}
continue
}
if err := writeChat(out, "switched to %s; conversation cleared\n", session.CurrentModel()); err != nil {
return err
}
continue
}
if err := writeChat(out, "assistant: "); err != nil {
return err
}
if err := session.Send(ctx, prompt, out); err != nil {
return err
}
if err := writeChat(out, "\n"); err != nil {
return err
}
}
return scanner.Err()
}
func printChatModels(out io.Writer, models []string, current string) error {
if len(models) == 0 {
return writeChat(out, "no models installed\n")
}
return writeChat(out, "%s", formatChatModelList(models, current))
}
func writeChat(out io.Writer, format string, args ...any) error {
_, err := fmt.Fprintf(out, format, args...)
return err
}

View File

@@ -1,25 +0,0 @@
package cli
import (
"context"
"os"
chatcli "github.com/mudler/LocalAI/core/cli/chat"
cliContext "github.com/mudler/LocalAI/core/cli/context"
)
type ChatCMD struct {
Model string `short:"m" help:"Model name to use. Defaults to the only model returned by the server when exactly one is available"`
Endpoint string `env:"LOCALAI_CHAT_ENDPOINT" default:"http://127.0.0.1:8080" help:"LocalAI server endpoint. The /v1 path is added automatically when omitted"`
APIKey string `env:"LOCALAI_API_KEY,API_KEY" help:"API key to use when the LocalAI server requires authentication"`
}
func (c *ChatCMD) Run(ctx *cliContext.Context) error {
return chatcli.Run(context.Background(), chatcli.Options{
Model: c.Model,
BaseURL: chatAPIBaseURL(c.Endpoint),
APIKey: c.APIKey,
In: os.Stdin,
Out: os.Stdout,
})
}

View File

@@ -1,27 +0,0 @@
package cli
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("Chat command wiring", func() {
Describe("chatAPIBaseURL", func() {
It("adds /v1 to a root endpoint", func() {
Expect(chatAPIBaseURL("http://127.0.0.1:8080")).To(Equal("http://127.0.0.1:8080/v1"))
})
It("keeps endpoints that already include /v1", func() {
Expect(chatAPIBaseURL("http://127.0.0.1:8080/v1")).To(Equal("http://127.0.0.1:8080/v1"))
Expect(chatAPIBaseURL("http://127.0.0.1:8080/v1/")).To(Equal("http://127.0.0.1:8080/v1"))
})
It("adds a default http scheme", func() {
Expect(chatAPIBaseURL("127.0.0.1:8080")).To(Equal("http://127.0.0.1:8080/v1"))
})
It("preserves non-root paths before /v1", func() {
Expect(chatAPIBaseURL("http://127.0.0.1:8080/localai")).To(Equal("http://127.0.0.1:8080/localai/v1"))
})
})
})

View File

@@ -1,29 +0,0 @@
package cli
import (
"net/url"
"strings"
)
func chatAPIBaseURL(endpoint string) string {
if !strings.Contains(endpoint, "://") {
endpoint = "http://" + endpoint
}
u, err := url.Parse(endpoint)
if err != nil {
return strings.TrimRight(endpoint, "/") + "/v1"
}
path := strings.TrimRight(u.Path, "/")
if path == "" {
u.Path = "/v1"
} else if path != "/v1" && !strings.HasSuffix(path, "/v1") {
u.Path = path + "/v1"
} else {
u.Path = path
}
u.RawQuery = ""
u.Fragment = ""
return u.String()
}

View File

@@ -9,7 +9,6 @@ var CLI struct {
cliContext.Context `embed:""`
Run RunCMD `cmd:"" help:"Run LocalAI, this the default command if no other command is specified. Run 'local-ai run --help' for more information" default:"withargs"`
Chat ChatCMD `cmd:"" help:"Open an interactive chat session against a running LocalAI server"`
Federated FederatedCLI `cmd:"" help:"Run LocalAI in federated mode"`
Models ModelsCMD `cmd:"" help:"Manage LocalAI models and definitions"`
Backends BackendsCMD `cmd:"" help:"Manage LocalAI backends and definitions"`

View File

@@ -30,8 +30,6 @@ type RunCMD struct {
ModelArgs []string `arg:"" optional:"" name:"models" help:"Model configuration URLs to load"`
ExternalBackends []string `env:"LOCALAI_EXTERNAL_BACKENDS,EXTERNAL_BACKENDS" help:"A list of external backends to load from gallery on boot" group:"backends"`
WebRTCNAT1To1IPs []string `env:"LOCALAI_WEBRTC_NAT_1TO1_IPS,WEBRTC_NAT_1TO1_IPS" help:"IPs advertised as the host ICE candidates for /v1/realtime WebRTC instead of every local interface. Set to the reachable host/LAN IP when running under Docker host networking or NAT, where pion otherwise offers unreachable bridge addresses and the connection drops after ICE consent checks fail." group:"api"`
WebRTCICEInterfaces []string `env:"LOCALAI_WEBRTC_ICE_INTERFACES,WEBRTC_ICE_INTERFACES" help:"Restrict /v1/realtime WebRTC ICE candidate gathering to these network interfaces (e.g. eth0), filtering out docker0/veth noise." group:"api"`
BackendsPath string `env:"LOCALAI_BACKENDS_PATH,BACKENDS_PATH" type:"path" default:"${basepath}/backends" help:"Path containing backends used for inferencing" group:"backends"`
BackendsSystemPath string `env:"LOCALAI_BACKENDS_SYSTEM_PATH,BACKEND_SYSTEM_PATH" type:"path" default:"/var/lib/local-ai/backends" help:"Path containing system backends used for inferencing" group:"backends"`
ModelsPath string `env:"LOCALAI_MODELS_PATH,MODELS_PATH" type:"path" default:"${basepath}/models" help:"Path containing models used for inferencing" group:"storage"`
@@ -227,8 +225,6 @@ func (r *RunCMD) Run(ctx *cliContext.Context) error {
config.WithApiKeys(r.APIKeys),
config.WithModelsURL(append(r.Models, r.ModelArgs...)...),
config.WithExternalBackends(r.ExternalBackends...),
config.WithWebRTCNAT1To1IPs(r.WebRTCNAT1To1IPs...),
config.WithWebRTCICEInterfaces(r.WebRTCICEInterfaces...),
config.WithOpaqueErrors(r.OpaqueErrors),
config.WithEnforcedPredownloadScans(!r.DisablePredownloadScan),
config.WithSubtleKeyComparison(r.UseSubtleKeyComparison),
@@ -656,12 +652,12 @@ func (r *RunCMD) Run(ctx *cliContext.Context) error {
// waitForServerReady polls the given address until the HTTP server is
// accepting connections or the context is cancelled.
func waitForServerReady(address string, ctx context.Context) {
// Ensure the address has a host component for dialing.
// Echo accepts ":8080" but net.Dial needs a resolvable host.
host, port, err := net.SplitHostPort(address)
if err == nil && host == "" {
address = "127.0.0.1:" + port
}
ticker := time.NewTicker(250 * time.Millisecond)
defer ticker.Stop()
for {
select {
@@ -669,17 +665,11 @@ func waitForServerReady(address string, ctx context.Context) {
return
default:
}
conn, err := net.DialTimeout("tcp", address, 500*time.Millisecond)
if err == nil {
conn.Close()
return
}
select {
case <-ctx.Done():
return
case <-ticker.C:
}
time.Sleep(250 * time.Millisecond)
}
}

View File

@@ -12,19 +12,10 @@ import (
)
type ApplicationConfig struct {
Context context.Context
ConfigFile string
SystemState *system.SystemState
ExternalBackends []string
// WebRTCNAT1To1IPs, when set, are advertised as the host ICE candidates for
// /v1/realtime WebRTC instead of every local interface address. Needed when
// the routable address differs from what pion gathers — e.g. Docker host
// networking (where pion also offers unreachable bridge IPs) or NAT.
WebRTCNAT1To1IPs []string
// WebRTCICEInterfaces, when set, restricts ICE candidate gathering to these
// network interfaces (e.g. eth0), filtering out docker0/veth noise.
WebRTCICEInterfaces []string
Context context.Context
ConfigFile string
SystemState *system.SystemState
ExternalBackends []string
UploadLimitMB, Threads, ContextSize int
F16 bool
Debug bool
@@ -90,6 +81,7 @@ type ApplicationConfig struct {
// file is mode 0600.
MITMCADir string
// PIIPatternOverrides applies persisted per-id deltas (action,
// disabled) to the live redactor at startup. Loaded from
// runtime_settings.json and applied right after pii.NewRedactor.
@@ -124,11 +116,11 @@ type ApplicationConfig struct {
// --require-backend-integrity / LOCALAI_REQUIRE_BACKEND_INTEGRITY.
RequireBackendIntegrity bool
SingleBackend bool // Deprecated: use MaxActiveBackends = 1 instead
MaxActiveBackends int // Maximum number of active backends (0 = unlimited, 1 = single backend mode)
WatchDogIdle bool
WatchDogBusy bool
WatchDog bool
SingleBackend bool // Deprecated: use MaxActiveBackends = 1 instead
MaxActiveBackends int // Maximum number of active backends (0 = unlimited, 1 = single backend mode)
WatchDogIdle bool
WatchDogBusy bool
WatchDog bool
// Memory Reclaimer settings (works with GPU if available, otherwise RAM)
MemoryReclaimerEnabled bool // Enable memory threshold monitoring
@@ -319,18 +311,6 @@ func WithExternalBackends(backends ...string) AppOption {
}
}
func WithWebRTCNAT1To1IPs(ips ...string) AppOption {
return func(o *ApplicationConfig) {
o.WebRTCNAT1To1IPs = ips
}
}
func WithWebRTCICEInterfaces(interfaces ...string) AppOption {
return func(o *ApplicationConfig) {
o.WebRTCICEInterfaces = interfaces
}
}
func WithMachineTag(tag string) AppOption {
return func(o *ApplicationConfig) {
o.MachineTag = tag
@@ -722,6 +702,7 @@ func WithMITMCADir(dir string) AppOption {
}
}
func WithDynamicConfigDir(dynamicConfigsDir string) AppOption {
return func(o *ApplicationConfig) {
o.DynamicConfigsDir = dynamicConfigsDir

View File

@@ -39,21 +39,7 @@ func llamaCppDefaults(cfg *ModelConfig, modelPath string) {
}
}()
// Startup parses every model's GGUF header to guess defaults. We only need
// scalar metadata (architecture, head/ff counts, chat_template, token IDs,
// MTP head) plus array *lengths* — never the array *contents*. Two options
// keep this cheap, which matters when many models live on slow storage such
// as a Docker volume (see https://github.com/mudler/LocalAI/issues/9790):
//
// - SkipLargeMetadata: seek past large array-valued metadata (the tokenizer
// vocab: tokenizer.ggml.tokens/scores/merges, often >100k entries) instead
// of reading and allocating every element. Lengths stay populated.
// - UseMMap: read the header via a memory map so faulting in a few pages
// replaces hundreds of thousands of tiny read() syscalls (measured ~524k
// -> 8 for a 256k-token vocab), the dominant cost on slow filesystems.
//
// The mapping is released when ParseGGUFFile returns.
f, err := gguf.ParseGGUFFile(guessPath, gguf.UseMMap(), gguf.SkipLargeMetadata())
f, err := gguf.ParseGGUFFile(guessPath)
if err == nil {
guessGGUFFromFile(cfg, f, 0)
}

View File

@@ -1,76 +1,13 @@
package config_test
import (
"bytes"
"encoding/binary"
"os"
"path/filepath"
. "github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/schema"
gguf "github.com/gpustack/gguf-parser-go"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// GGUF metadata value type tags (see github.com/gpustack/gguf-parser-go).
const (
ggufTypeUint32 uint32 = 4
ggufTypeString uint32 = 8
ggufTypeArray uint32 = 9
)
// writeTestGGUF emits a minimal but valid little-endian GGUF v3 header carrying
// the scalar metadata the llama-cpp hook guesses from plus a large string vocab
// array (tokenizer.ggml.tokens). The big array is exactly what SkipLargeMetadata
// + UseMMap are expected to avoid reading element-by-element, so it must survive a
// round-trip through the real hook without corrupting the guessed defaults.
func writeTestGGUF(path, chatTemplate string, vocab int) error {
wStr := func(b *bytes.Buffer, s string) {
binary.Write(b, binary.LittleEndian, uint64(len(s)))
b.WriteString(s)
}
kvStr := func(b *bytes.Buffer, k, v string) {
wStr(b, k)
binary.Write(b, binary.LittleEndian, ggufTypeString)
wStr(b, v)
}
kvU32 := func(b *bytes.Buffer, k string, v uint32) {
wStr(b, k)
binary.Write(b, binary.LittleEndian, ggufTypeUint32)
binary.Write(b, binary.LittleEndian, v)
}
var meta bytes.Buffer
kvStr(&meta, "general.architecture", "llama")
kvStr(&meta, "general.name", "ReproModel")
kvU32(&meta, "llama.context_length", 4096)
kvU32(&meta, "llama.attention.head_count", 32)
kvU32(&meta, "llama.feed_forward_length", 11008)
kvU32(&meta, "llama.block_count", 32)
kvU32(&meta, "tokenizer.ggml.bos_token_id", 1)
kvStr(&meta, "tokenizer.chat_template", chatTemplate)
// large array value — the one the optimization skips reading
wStr(&meta, "tokenizer.ggml.tokens")
binary.Write(&meta, binary.LittleEndian, ggufTypeArray)
binary.Write(&meta, binary.LittleEndian, ggufTypeString)
binary.Write(&meta, binary.LittleEndian, uint64(vocab))
for i := 0; i < vocab; i++ {
wStr(&meta, "token")
}
var out bytes.Buffer
binary.Write(&out, binary.LittleEndian, gguf.GGUFMagicGGUFLe)
binary.Write(&out, binary.LittleEndian, uint32(3)) // version
binary.Write(&out, binary.LittleEndian, uint64(0)) // tensor count
binary.Write(&out, binary.LittleEndian, uint64(9)) // metadata kv count
out.Write(meta.Bytes())
return os.WriteFile(path, out.Bytes(), 0o644)
}
var _ = Describe("Backend hooks and parser defaults", func() {
Context("MatchParserDefaults", func() {
It("matches Qwen3 family", func() {
@@ -200,58 +137,6 @@ var _ = Describe("Backend hooks and parser defaults", func() {
})
})
Context("llamaCppDefaults GGUF guessing", func() {
// Regression coverage for https://github.com/mudler/LocalAI/issues/9790:
// the hook reads GGUF headers with SkipLargeMetadata + UseMMap to avoid
// pulling the whole tokenizer vocab off (slow) disk on every startup. This
// verifies that skipping the vocab array still yields the correct guessed
// defaults from the remaining scalar metadata.
const chatTemplate = "{{ bos_token }}{% for m in messages %}{{ m.content }}{% endfor %}"
It("guesses defaults from a GGUF whose large vocab is skipped", func() {
dir := GinkgoT().TempDir()
modelFile := "repro.gguf"
Expect(writeTestGGUF(filepath.Join(dir, modelFile), chatTemplate, 50000)).To(Succeed())
// A pre-set context size short-circuits the GGUF run-estimate, which
// needs full tensor info this header-only fixture deliberately omits;
// the metadata-reading path the optimization touches is unaffected.
ctxSize := 4096
cfg := &ModelConfig{
Backend: "llama-cpp",
LLMConfig: LLMConfig{ContextSize: &ctxSize},
PredictionOptions: schema.PredictionOptions{
BasicModelRequest: schema.BasicModelRequest{Model: modelFile},
},
}
cfg.SetDefaults(ModelPath(dir))
// chat_template is a scalar string, not part of the skipped array,
// so it must be captured verbatim.
Expect(cfg.GetModelTemplate()).To(Equal(chatTemplate))
// scalar-derived defaults are still applied
Expect(cfg.ContextSize).NotTo(BeNil())
Expect(cfg.NGPULayers).NotTo(BeNil())
Expect(cfg.TemplateConfig.UseTokenizerTemplate).To(BeTrue())
Expect(cfg.KnownUsecaseStrings).To(ContainElement("FLAG_CHAT"))
})
It("falls back to the default context size when the GGUF is unreadable", func() {
dir := GinkgoT().TempDir()
Expect(os.WriteFile(filepath.Join(dir, "bad.gguf"), []byte("not a gguf"), 0o644)).To(Succeed())
cfg := &ModelConfig{
Backend: "llama-cpp",
PredictionOptions: schema.PredictionOptions{
BasicModelRequest: schema.BasicModelRequest{Model: "bad.gguf"},
},
}
cfg.SetDefaults(ModelPath(dir))
Expect(cfg.ContextSize).NotTo(BeNil())
})
})
Context("PromptCacheAll default", func() {
It("defaults to true when omitted from YAML", func() {
cfg := &ModelConfig{}

View File

@@ -308,41 +308,6 @@ func DefaultRegistry() map[string]FieldMetaOverride {
},
Order: 64,
},
"pipeline.disable_thinking": {
Section: "pipeline",
Label: "Disable Thinking",
Description: "Suppress reasoning/thinking output from the pipeline LLM (sets enable_thinking=false on the underlying model). Use for models that emit <think> blocks you don't want spoken or streamed back to the realtime client.",
Component: "toggle",
Order: 65,
},
"pipeline.streaming.llm": {
Section: "pipeline",
Label: "Stream LLM",
Description: "Stream LLM tokens to the realtime client as they are generated instead of waiting for the full response. Emits incremental response.output_audio_transcript.delta / text deltas.",
Component: "toggle",
Order: 66,
},
"pipeline.streaming.tts": {
Section: "pipeline",
Label: "Stream TTS",
Description: "Stream synthesized audio chunks to the realtime client as they are produced (requires a TTS backend that implements TTSStream). Falls back to unary synthesis otherwise.",
Component: "toggle",
Order: 67,
},
"pipeline.streaming.transcription": {
Section: "pipeline",
Label: "Stream Transcription",
Description: "Stream partial transcription text to the realtime client as the STT backend produces it (requires a transcription backend that implements AudioTranscriptionStream). Falls back to unary transcription otherwise.",
Component: "toggle",
Order: 68,
},
"pipeline.streaming.clause_chunking": {
Section: "pipeline",
Label: "Clause Chunking",
Description: "Split the streamed reply into speakable clauses and synthesize each as soon as it completes, instead of buffering the whole message before TTS — lower time-to-first-audio. Script-aware (handles CJK 。!? and Thai/Lao spaces), so it does not whitespace-split. Requires Stream LLM; off buffers the whole message.",
Component: "toggle",
Order: 69,
},
// --- Functions ---
"function.grammar.parallel_calls": {

View File

@@ -499,16 +499,6 @@ type Pipeline struct {
// the pipeline's LLM without editing the LLM model config. Overrides the LLM's
// own reasoning_effort. Unset leaves the LLM model config in charge.
ReasoningEffort string `yaml:"reasoning_effort,omitempty" json:"reasoning_effort,omitempty"`
// Streaming opts each pipeline stage into incremental delivery (LLM tokens,
// TTS audio chunks, transcription text). Unset stages keep the blocking
// unary path, so existing configs are unaffected.
Streaming PipelineStreaming `yaml:"streaming,omitempty" json:"streaming,omitempty"`
// DisableThinking suppresses reasoning/thinking for the pipeline LLM (maps
// to enable_thinking=false backend metadata) without editing the underlying
// LLM model config. Unset leaves the LLM model config in charge.
DisableThinking *bool `yaml:"disable_thinking,omitempty" json:"disable_thinking,omitempty"`
}
// ApplyReasoningEffort resolves the effective reasoning effort — a per-request
@@ -540,41 +530,6 @@ func (c *ModelConfig) ApplyReasoningEffort(requestEffort string) {
}
}
// @Description PipelineStreaming toggles incremental delivery per realtime stage.
type PipelineStreaming struct {
LLM *bool `yaml:"llm,omitempty" json:"llm,omitempty"`
TTS *bool `yaml:"tts,omitempty" json:"tts,omitempty"`
Transcription *bool `yaml:"transcription,omitempty" json:"transcription,omitempty"`
// ClauseChunking splits the streamed LLM reply into speakable clauses and
// synthesizes each as soon as it completes, instead of buffering the whole
// message before TTS. Script-aware (CJK/Thai), so it does not rely on
// whitespace sentence boundaries. Requires LLM streaming; unset buffers the
// whole message (today's default).
ClauseChunking *bool `yaml:"clause_chunking,omitempty" json:"clause_chunking,omitempty"`
}
// StreamLLM reports whether LLM tokens should be streamed for this pipeline.
func (p Pipeline) StreamLLM() bool { return p.Streaming.LLM != nil && *p.Streaming.LLM }
// StreamTTS reports whether TTS audio should be streamed for this pipeline.
func (p Pipeline) StreamTTS() bool { return p.Streaming.TTS != nil && *p.Streaming.TTS }
// StreamTranscription reports whether transcription text should be streamed.
func (p Pipeline) StreamTranscription() bool {
return p.Streaming.Transcription != nil && *p.Streaming.Transcription
}
// ChunkClauses reports whether the streamed reply should be split into
// script-aware clauses and synthesized incrementally rather than buffered whole.
func (p Pipeline) ChunkClauses() bool {
return p.Streaming.ClauseChunking != nil && *p.Streaming.ClauseChunking
}
// ThinkingDisabled reports whether the pipeline forces the LLM's thinking off.
func (p Pipeline) ThinkingDisabled() bool {
return p.DisableThinking != nil && *p.DisableThinking
}
// @Description File configuration for model downloads
type File struct {
Filename string `yaml:"filename,omitempty" json:"filename,omitempty"`

View File

@@ -30,26 +30,11 @@ func MTPSpecOptions() []string {
return out
}
// isDraftOnlyAssistantArch reports whether an architecture names a standalone
// MTP *draft* model rather than a self-speculating trunk. Upstream's Gemma4 MTP
// (ggml-org/llama.cpp#23398) registers the head as a separate `gemma4-assistant`
// architecture whose GGUF still carries `nextn_predict_layers`, but which cannot
// run alone: it requires a paired target context (`ctx_other`). Such archs must
// not trigger the embedded-head self-speculation defaults. The `-assistant`
// suffix is upstream's naming convention for these draft-only checkpoints.
func isDraftOnlyAssistantArch(arch string) bool {
return strings.HasSuffix(arch, "-assistant")
}
// HasEmbeddedMTPHead reports whether the parsed GGUF declares a self-speculating
// Multi-Token Prediction head. Detection reads `<arch>.nextn_predict_layers`,
// which is what `gguf_writer.add_nextn_predict_layers(n)` emits in upstream's
// HasEmbeddedMTPHead reports whether the parsed GGUF declares a Multi-Token
// Prediction head. Detection reads `<arch>.nextn_predict_layers`, which is
// what `gguf_writer.add_nextn_predict_layers(n)` emits in upstream's
// `conversion/qwen.py` MTP mixin. A positive layer count means the head is
// present in the same GGUF as the trunk.
//
// Draft-only assistant architectures (e.g. Gemma4's `gemma4-assistant`) carry
// the same key but are separate draft checkpoints meant to be paired with a
// target model, so they are deliberately excluded here.
func HasEmbeddedMTPHead(f *gguf.GGUFFile) (uint32, bool) {
if f == nil {
return 0, false
@@ -58,9 +43,6 @@ func HasEmbeddedMTPHead(f *gguf.GGUFFile) (uint32, bool) {
if arch == "" {
return 0, false
}
if isDraftOnlyAssistantArch(arch) {
return 0, false
}
v, ok := f.Header.MetadataKV.Get(arch + ".nextn_predict_layers")
if !ok {
return 0, false

View File

@@ -3,33 +3,10 @@ package config_test
import (
. "github.com/mudler/LocalAI/core/config"
gguf "github.com/gpustack/gguf-parser-go"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// ggufWithArch fabricates a minimal in-memory GGUF carrying the given
// `general.architecture` and a positive `<arch>.nextn_predict_layers` count,
// so HasEmbeddedMTPHead can be exercised without a real model file.
func ggufWithArch(arch string, nextn uint32) *gguf.GGUFFile {
return &gguf.GGUFFile{
Header: gguf.GGUFHeader{
MetadataKV: gguf.GGUFMetadataKVs{
{
Key: "general.architecture",
ValueType: gguf.GGUFMetadataValueTypeString,
Value: arch,
},
{
Key: arch + ".nextn_predict_layers",
ValueType: gguf.GGUFMetadataValueTypeUint32,
Value: nextn,
},
},
},
}
}
var _ = Describe("MTP auto-defaults", func() {
Context("MTPSpecOptions", func() {
It("returns the upstream-recommended speculative tuple", func() {
@@ -105,20 +82,5 @@ var _ = Describe("MTP auto-defaults", func() {
Expect(ok).To(BeFalse())
Expect(n).To(BeZero())
})
It("detects a same-GGUF embedded head (DeepSeek/Qwen style)", func() {
n, ok := HasEmbeddedMTPHead(ggufWithArch("qwen3moe", 1))
Expect(ok).To(BeTrue())
Expect(n).To(Equal(uint32(1)))
})
It("ignores a gemma4-assistant draft-only model", func() {
// The assistant GGUF carries nextn_predict_layers but is a separate
// draft model that requires a paired target (ctx_other); it cannot
// self-speculate, so it must not trigger the embedded-head defaults.
n, ok := HasEmbeddedMTPHead(ggufWithArch("gemma4-assistant", 48))
Expect(ok).To(BeFalse())
Expect(n).To(BeZero())
})
})
})

View File

@@ -1,57 +0,0 @@
package config
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"gopkg.in/yaml.v3"
)
// The realtime pipeline can stream each stage (LLM tokens, TTS audio,
// transcription text) and can disable model "thinking" for the LLM. These are
// opt-in per pipeline; everything defaults to off so existing configs keep the
// unary behaviour.
var _ = Describe("Pipeline streaming config", func() {
It("defaults every streaming + thinking helper to false when unset", func() {
var p Pipeline
Expect(p.StreamLLM()).To(BeFalse())
Expect(p.StreamTTS()).To(BeFalse())
Expect(p.StreamTranscription()).To(BeFalse())
Expect(p.ChunkClauses()).To(BeFalse())
Expect(p.ThinkingDisabled()).To(BeFalse())
})
It("parses the nested streaming block and disable_thinking from YAML", func() {
var c ModelConfig
err := yaml.Unmarshal([]byte(`
name: gpt-realtime
pipeline:
llm: my-llm
tts: my-tts
transcription: my-stt
streaming:
llm: true
tts: true
transcription: true
clause_chunking: true
disable_thinking: true
`), &c)
Expect(err).ToNot(HaveOccurred())
Expect(c.Pipeline.StreamLLM()).To(BeTrue())
Expect(c.Pipeline.StreamTTS()).To(BeTrue())
Expect(c.Pipeline.StreamTranscription()).To(BeTrue())
Expect(c.Pipeline.ChunkClauses()).To(BeTrue())
Expect(c.Pipeline.ThinkingDisabled()).To(BeTrue())
})
It("treats an explicit false in the streaming block as disabled", func() {
var c ModelConfig
err := yaml.Unmarshal([]byte(`
name: gpt-realtime
pipeline:
streaming:
tts: false
`), &c)
Expect(err).ToNot(HaveOccurred())
Expect(c.Pipeline.StreamTTS()).To(BeFalse())
})
})

View File

@@ -103,12 +103,7 @@ func applyAutoparserOverride(
// blocks like "<think></think>" that some models emit when reasoning
// is disabled.
if deltaReasoning == "" && deltaContent != "" {
// Complete-response extraction: only honor a prefilled <think> start
// token when deltaContent actually closes the reasoning block. Without
// it the model answered directly and the whole answer must stay in
// content rather than be swallowed as unclosed reasoning. See
// reason.ExtractReasoningComplete.
deltaReasoning, deltaContent = reason.ExtractReasoningComplete(deltaContent, thinkingStartToken, reasoningConfig)
deltaReasoning, deltaContent = reason.ExtractReasoningWithConfig(deltaContent, thinkingStartToken, reasoningConfig)
}
xlog.Debug("[ChatDeltas] non-SSE no-tools: overriding result with C++ autoparser deltas",
"content_len", len(deltaContent), "reasoning_len", len(deltaReasoning))

View File

@@ -186,114 +186,6 @@ var _ = Describe("applyAutoparserOverride", func() {
Expect(result).To(Equal(existing))
})
})
// Regression tests for the prefilled-thinking-token path (thinkingStartToken
// != ""). This is the configuration the gallery qwen3 family runs in: the
// chat template injects <think> into the prompt, so DetectThinkingStartToken
// returns "<think>" and the model's output begins *inside* a reasoning block
// — it emits a closing </think> but no opening tag.
//
// The defensive Go-side fallback prepends the start token so the standard
// extractor can pair it with the model's </think>. But on a *complete*
// response that contains NO closing tag (the model answered directly with no
// reasoning at all), prepending <think> manufactures an unclosed block that
// swallows the entire answer into reasoning, leaving content empty. That is
// the bug: short/direct answers (session names, JSON summaries) come back
// with an empty content field.
Context("autoparser delivered content with empty reasoning and a prefilled thinking token", func() {
const startToken = "<think>"
It("keeps a tag-less direct answer as content instead of swallowing it as reasoning", func() {
// Model answered directly: no <think>, no </think> anywhere.
chatDeltas := []*pb.ChatDelta{
{Content: "hello", ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, startToken, reason.Config{}, nil)
Expect(result).To(HaveLen(1))
Expect(result[0].Message.Content).ToNot(BeNil())
Expect(*(result[0].Message.Content.(*string))).To(Equal("hello"),
"a complete answer with no closing reasoning tag must stay in content")
Expect(result[0].Message.Reasoning).To(BeNil(),
"no reasoning block was emitted, so Reasoning must not be set")
})
It("keeps a tag-less JSON answer as content (the summary case)", func() {
raw := `{"short":"Tests pass","long":"go test ./... succeeded."}`
chatDeltas := []*pb.ChatDelta{
{Content: raw, ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, startToken, reason.Config{}, nil)
Expect(result).To(HaveLen(1))
Expect(*(result[0].Message.Content.(*string))).To(Equal(raw))
Expect(result[0].Message.Reasoning).To(BeNil())
})
It("still splits reasoning when the model emits the closing tag (prefill paired with </think>)", func() {
// The legitimate prefill case: <think> was in the prompt, so the
// output carries only the closing tag. The closing tag is the proof
// that a reasoning block exists, so extraction must run.
raw := "The user wants a greeting.\n</think>\n\nHello there!"
chatDeltas := []*pb.ChatDelta{
{Content: raw, ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, startToken, reason.Config{}, nil)
Expect(result).To(HaveLen(1))
content := *(result[0].Message.Content.(*string))
Expect(content).To(ContainSubstring("Hello there!"))
Expect(content).ToNot(ContainSubstring("</think>"))
Expect(content).ToNot(ContainSubstring("The user wants a greeting"))
Expect(result[0].Message.Reasoning).ToNot(BeNil())
Expect(*result[0].Message.Reasoning).To(ContainSubstring("The user wants a greeting"))
})
It("still splits a fully-tagged <think>…</think> block with a prefill token set", func() {
raw := "<think>Reasoning here.</think>Final answer."
chatDeltas := []*pb.ChatDelta{
{Content: raw, ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, startToken, reason.Config{}, nil)
Expect(result).To(HaveLen(1))
Expect(*(result[0].Message.Content.(*string))).To(Equal("Final answer."))
Expect(result[0].Message.Reasoning).ToNot(BeNil())
Expect(*result[0].Message.Reasoning).To(ContainSubstring("Reasoning here"))
})
// End-to-end regression for the real production failure: a request with
// enable_thinking=false against a <think>-capable model (qwen3 family).
//
// In non-thinking mode the model emits no reasoning block, so llama.cpp's
// autoparser correctly returns ChatDeltas with Content set and
// ReasoningContent EMPTY (verified against stock llama-server: the same
// model with chat_template_kwargs.enable_thinking=false returns
// reasoning_content=null and content="hello"). But thinkingStartToken is
// detected per-model from the enable_thinking=TRUE render
// (grpc-server renders with enable_thinking=true; DetectThinkingStartToken
// does not evaluate the jinja {% if enable_thinking %} conditional), so it
// is "<think>" even for this non-thinking request. The old code prepended
// it and swallowed the answer. This is the case that broke session
// summaries and auto-titles and was NOT covered before.
It("preserves content for a non-thinking-mode request (enable_thinking=false, empty reasoning_content)", func() {
// What llama.cpp's autoparser actually returns in non-thinking mode.
chatDeltas := []*pb.ChatDelta{
{Content: `{"short":"Go tests passed for internal/session"}`, ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, startToken, reason.Config{}, nil)
Expect(result).To(HaveLen(1))
Expect(*(result[0].Message.Content.(*string))).To(Equal(`{"short":"Go tests passed for internal/session"}`),
"non-thinking-mode answers must reach the client intact, not be swallowed as reasoning")
Expect(result[0].Message.Reasoning).To(BeNil())
})
})
})
var _ = Describe("mergeToolCallDeltas", func() {

View File

@@ -2,10 +2,8 @@ package openai
import (
"context"
"crypto/rand"
"encoding/base64"
"encoding/binary"
"encoding/hex"
"encoding/json"
"fmt"
"math"
@@ -237,12 +235,6 @@ type Model interface {
Transcribe(ctx context.Context, audio, language string, translate bool, diarize bool, prompt string) (*schema.TranscriptionResult, error)
Predict(ctx context.Context, messages schema.Messages, images, videos, audios []string, tokenCallback func(string, backend.TokenUsage) bool, tools []types.ToolUnion, toolChoice *types.ToolChoiceUnion, logprobs *int, topLogprobs *int, logitBias map[string]float64) (func() (backend.LLMResponse, error), error)
TTS(ctx context.Context, text, voice, language string) (string, *proto.Result, error)
// TTSStream synthesizes speech incrementally, invoking onAudio with raw PCM
// chunks (and the backend sample rate) as they are produced.
TTSStream(ctx context.Context, text, voice, language string, onAudio func(pcm []byte, sampleRate int) error) error
// TranscribeStream transcribes audio incrementally, invoking onDelta for each
// transcript text fragment and returning the final aggregated result.
TranscribeStream(ctx context.Context, audio, language string, translate, diarize bool, prompt string, onDelta func(text string)) (*schema.TranscriptionResult, error)
PredictConfig() *config.ModelConfig
}
@@ -1262,15 +1254,27 @@ func commitUtterance(ctx context.Context, utt []byte, session *Session, conv *Co
// TODO: If we have a real any-to-any model then transcription is optional
var transcript string
if session.InputAudioTranscription != nil {
// emitTranscription streams transcript deltas when
// pipeline.streaming.transcription is set, otherwise emits a single
// completed event; either way it returns the final transcript text.
var err error
transcript, err = emitTranscription(ctx, t, session, generateItemID(), f.Name())
tr, err := session.ModelInterface.Transcribe(ctx, f.Name(), session.InputAudioTranscription.Language, false, false, session.InputAudioTranscription.Prompt)
if err != nil {
sendError(t, "transcription_failed", err.Error(), "", "event_TODO")
return
} else if tr == nil {
sendError(t, "transcription_failed", "trancribe result is nil", "", "event_TODO")
return
}
transcript = tr.Text
sendEvent(t, types.ConversationItemInputAudioTranscriptionCompletedEvent{
ServerEventBase: types.ServerEventBase{
EventID: "event_TODO",
},
ItemID: generateItemID(),
// ResponseID: "resp_TODO", // Not needed for transcription completed event
// OutputIndex: 0,
ContentIndex: 0,
Transcript: transcript,
})
} else {
sendNotImplemented(t, "any-to-any models")
return
@@ -1498,26 +1502,6 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
},
})
// Streamed LLM path: when the pipeline opts into LLM streaming, stream the
// transcript to the client as it is generated and synthesize the buffered
// message once. Tool turns are supported only when the model uses its
// tokenizer template: the C++ autoparser then delivers content and tool
// calls via ChatDeltas (clearing the text stream), so the spoken transcript
// never leaks tool-call tokens. Grammar-based function calling emits the
// call as JSON in the token stream, so those turns keep the buffered path.
if config != nil && session.ModelConfig != nil && session.ModelConfig.Pipeline.StreamLLM() {
canStream := len(tools) == 0 || config.TemplateConfig.UseTokenizerTemplate
var respMods []types.Modality
if overrides != nil {
respMods = overrides.OutputModalities
}
if canStream && modalitiesContainAudio(resolveOutputModalities(session.OutputModalities, respMods)) {
if streamLLMResponse(ctx, session, conv, t, responseID, conversationHistory, images, config, tools, toolChoice, toolTurn) {
return
}
}
}
predFunc, err := session.ModelInterface.Predict(ctx, conversationHistory, images, nil, nil, nil, tools, toolChoice, nil, nil, nil)
if err != nil {
sendError(t, "inference_failed", fmt.Sprintf("backend error: %v", err), "", "") // item.Assistant.ID is unknown here
@@ -1595,7 +1579,7 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
// ExtractReasoningWithConfig is a no-op when no tag pair matches,
// so it's safe to apply unconditionally in the no-reasoning branch.
if deltaReasoning == "" && deltaContent != "" {
deltaReasoning, deltaContent = reasoning.ExtractReasoningComplete(deltaContent, thinkingStartToken, spokenReasoningConfig(config.ReasoningConfig))
deltaReasoning, deltaContent = reasoning.ExtractReasoningWithConfig(deltaContent, thinkingStartToken, config.ReasoningConfig)
}
reasoningText = deltaReasoning
responseWithoutReasoning = deltaContent
@@ -1603,7 +1587,7 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
cleanedResponse = deltaContent
toolCalls = deltaToolCalls
} else {
reasoningText, responseWithoutReasoning = reasoning.ExtractReasoningComplete(rawResponse, thinkingStartToken, spokenReasoningConfig(config.ReasoningConfig))
reasoningText, responseWithoutReasoning = reasoning.ExtractReasoningWithConfig(rawResponse, thinkingStartToken, config.ReasoningConfig)
textContent = functions.ParseTextContent(responseWithoutReasoning, config.FunctionsConfig)
cleanedResponse = functions.CleanupLLMResult(responseWithoutReasoning, config.FunctionsConfig)
toolCalls = functions.ParseFunctionCall(cleanedResponse, config.FunctionsConfig)
@@ -1729,7 +1713,64 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
return
}
// Transcript of the spoken reply (the audio's text).
audioFilePath, res, err := session.ModelInterface.TTS(ctx, finalSpeech, session.Voice, session.InputAudioTranscription.Language)
if err != nil {
if ctx.Err() != nil {
xlog.Debug("TTS cancelled (barge-in)")
sendCancelledResponse()
return
}
xlog.Error("TTS failed", "error", err)
sendError(t, "tts_error", fmt.Sprintf("TTS generation failed: %v", err), "", item.Assistant.ID)
return
}
if !res.Success {
xlog.Error("TTS failed", "message", res.Message)
sendError(t, "tts_error", fmt.Sprintf("TTS generation failed: %s", res.Message), "", item.Assistant.ID)
return
}
defer func() { _ = os.Remove(audioFilePath) }()
audioBytes, err := os.ReadFile(audioFilePath)
if err != nil {
xlog.Error("failed to read TTS file", "error", err)
sendError(t, "tts_error", fmt.Sprintf("Failed to read TTS audio: %v", err), "", item.Assistant.ID)
return
}
// Parse WAV header to get raw PCM and the actual sample rate from the TTS backend.
pcmData, ttsSampleRate := laudio.ParseWAV(audioBytes)
if ttsSampleRate == 0 {
ttsSampleRate = localSampleRate
}
xlog.Debug("TTS audio parsed", "raw_bytes", len(audioBytes), "pcm_bytes", len(pcmData), "sample_rate", ttsSampleRate)
// SendAudio (WebRTC) passes PCM at the TTS sample rate directly to the
// Opus encoder, which resamples to 48kHz internally. This avoids a
// lossy intermediate resample through 16kHz.
// XXX: This is a noop in websocket mode; it's included in the JSON instead
if err := t.SendAudio(ctx, pcmData, ttsSampleRate); err != nil {
if ctx.Err() != nil {
xlog.Debug("Audio playback cancelled (barge-in)")
sendCancelledResponse()
return
}
xlog.Error("failed to send audio via transport", "error", err)
}
// For WebSocket clients, resample to the session's output rate and
// deliver audio as base64 in JSON events. WebRTC clients already
// received audio over the RTP track, so skip the base64 payload.
if !isWebRTC {
wsPCM := pcmData
if ttsSampleRate != session.OutputSampleRate {
samples := sound.BytesToInt16sLE(pcmData)
resampled := sound.ResampleInt16(samples, ttsSampleRate, session.OutputSampleRate)
wsPCM = sound.Int16toBytesLE(resampled)
}
audioString = base64.StdEncoding.EncodeToString(wsPCM)
}
sendEvent(t, types.ResponseOutputAudioTranscriptDeltaEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
@@ -1747,26 +1788,15 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
Transcript: finalSpeech,
})
// Synthesize and send the audio. With pipeline.streaming.tts enabled
// emitSpeech forwards a response.output_audio.delta per backend PCM
// chunk as it's produced; otherwise it sends the whole utterance as a
// single delta. The returned PCM is stored (base64) on the item below.
pcmAudio, err := emitSpeech(ctx, t, session, responseID, item.Assistant.ID, finalSpeech)
if err != nil {
if ctx.Err() != nil {
xlog.Debug("TTS cancelled (barge-in)")
sendCancelledResponse()
return
}
xlog.Error("TTS failed", "error", err)
sendError(t, "tts_error", fmt.Sprintf("TTS generation failed: %v", err), "", item.Assistant.ID)
return
}
if !isWebRTC {
audioString = base64.StdEncoding.EncodeToString(pcmAudio)
}
if !isWebRTC {
sendEvent(t, types.ResponseOutputAudioDeltaEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
ItemID: item.Assistant.ID,
OutputIndex: 0,
ContentIndex: 0,
Delta: audioString,
})
sendEvent(t, types.ResponseOutputAudioDoneEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
@@ -1819,27 +1849,17 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
})
}
// Emit the parsed tool calls, the terminal response.done, and (for
// server-side assistant tools) the follow-up response. Shared with the
// streamed path so both finalize tool calls identically.
emitToolCallItems(ctx, session, conv, t, responseID, finalToolCalls, finalSpeech != "", toolTurn)
}
// emitToolCallItems emits the realtime function_call items for the parsed tool
// calls, the terminal response.done, and — for server-side LocalAI Assistant
// tools — re-triggers a follow-up response so the model can speak the result.
// hasContent shifts the tool-call output index past the assistant content item
// when the same turn also produced spoken/text content. Two tool paths:
// - LocalAI Assistant tools (session.AssistantExecutor.IsTool) run server-side;
// we append both the call and its output to conv.Items and re-trigger. The
// client only sees observability events.
// - All other tools follow the standard OpenAI flow: emit
// function_call_arguments.done and wait for the client to send
// conversation.item.create back.
func emitToolCallItems(ctx context.Context, session *Session, conv *Conversation, t Transport, responseID string, toolCalls []functions.FuncCallResults, hasContent bool, toolTurn int) {
xlog.Debug("About to handle tool calls", "finalToolCallsCount", len(toolCalls))
// Handle Tool Calls. Two paths:
// - LocalAI Assistant tools (session.AssistantExecutor.IsTool) run
// server-side; we append both the call and its output to conv.Items
// and re-trigger a follow-up response so the model can speak the
// result. The client only sees observability events.
// - All other tools follow the standard OpenAI flow: emit
// function_call_arguments.done and wait for the client to send
// conversation.item.create back.
xlog.Debug("About to handle tool calls", "finalToolCallsCount", len(finalToolCalls))
executedAssistantTool := false
for i, tc := range toolCalls {
for i, tc := range finalToolCalls {
toolCallID := generateItemID()
callID := "call_" + generateUniqueID() // OpenAI uses call_xyz
@@ -1859,7 +1879,7 @@ func emitToolCallItems(ctx context.Context, session *Session, conv *Conversation
conv.Lock.Unlock()
outputIndex := i
if hasContent {
if finalSpeech != "" {
outputIndex++
}
@@ -1985,11 +2005,8 @@ func generateItemID() string {
}
func generateUniqueID() string {
// 16 random bytes, hex-encoded. Must be collision-free: session, item,
// response and call IDs build on this, and the conversation tracks/removes
// items by ID (e.g. cancel() in realtime_stream.go, conversation.item.retrieve).
// A constant would make every ID alias and corrupt that bookkeeping.
var b [16]byte
_, _ = rand.Read(b[:])
return hex.EncodeToString(b[:])
// Generate a unique ID string
// For simplicity, use a counter or UUID
// Implement as needed
return "unique_id"
}

View File

@@ -1,200 +0,0 @@
package openai
import (
"strings"
"unicode"
"unicode/utf8"
"github.com/rivo/uniseg"
)
// Default clause-chunker bounds (in runes). minRunes gates only sub-sentence
// (clause-mark / Thai-space) cuts so we don't synthesize tiny choppy fragments;
// full sentences always flush regardless of length. maxRunes caps an
// unterminated run so a long punctuation-less span doesn't buffer unbounded.
const (
defaultClauseMinRunes = 12
defaultClauseMaxRunes = 200
)
// clauseChunker splits streamed LLM content into speakable clauses for
// incremental TTS, in a SCRIPT-AWARE way so it works for languages without
// whitespace word boundaries. It leans on UAX #29 sentence segmentation (which
// natively terminates on CJK 。!? as well as Latin .!?), adds CJK clause
// punctuation (,、;:) and Thai/Lao spaces as finer boundaries, and caps an
// over-long unterminated run via UAX #14 line-break opportunities.
//
// Unlike the old ASCII .!?/newline segmenter (dropped in 076dcdbe), it does not
// degrade to whole-message buffering for CJK (handled natively) or Thai/Lao
// (handled via spaces, which Thai uses at clause/sentence boundaries). Scripts
// that genuinely need a dictionary (Khmer/Myanmar) simply stay buffered until a
// space or end-of-message — no worse than the buffered default.
//
// It is not safe for concurrent use; callers feed it from a single goroutine
// (the LLM token callback).
type clauseChunker struct {
buf strings.Builder
minRunes int
maxRunes int
}
func newClauseChunker(minRunes, maxRunes int) *clauseChunker {
return &clauseChunker{minRunes: minRunes, maxRunes: maxRunes}
}
// push appends streamed content and returns any clauses that are now complete —
// "complete" meaning confirmed by following content, so we never speak a clause
// that the next token might extend. Incomplete trailing text stays buffered.
func (c *clauseChunker) push(text string) []string {
c.buf.WriteString(text)
return c.drain(false)
}
// flush returns the remaining buffered clauses, treating end-of-input as a hard
// boundary, and clears the buffer.
func (c *clauseChunker) flush() []string {
return c.drain(true)
}
func (c *clauseChunker) drain(final bool) []string {
s := c.buf.String()
rest := s
var out []string
for rest != "" {
end, ok := c.nextBoundary(rest, final)
if !ok {
break
}
if seg := strings.TrimSpace(rest[:end]); seg != "" {
out = append(out, seg)
}
rest = rest[end:]
}
// Rewriting the builder reallocates and copies the whole buffer; skip it on
// the common per-token call where no boundary was confirmed.
if len(rest) != len(s) {
c.buf.Reset()
c.buf.WriteString(rest)
}
return out
}
// nextBoundary returns the byte offset just past the first emittable clause in
// s, or ok=false when more input is needed (final=false) and no boundary is
// confirmed yet.
func (c *clauseChunker) nextBoundary(s string, final bool) (int, bool) {
if s == "" {
return 0, false
}
// 1) UAX #29 sentence boundary. When the first sentence is followed by more
// text it is a confirmed complete sentence (handles Latin .!? with
// abbreviation/decimal guards, and CJK 。!? with no whitespace).
sentence, rest, _ := uniseg.FirstSentenceInString(s, -1)
if rest != "" {
// Optionally cut finer inside the sentence at a clause boundary.
if cut, ok := c.firstClauseCut(sentence); ok {
return cut, true
}
return len(sentence), true
}
// 2) Unterminated tail: look for a sub-sentence clause boundary (CJK
// punctuation or a Thai/Lao space) confirmed by following content.
if cut, ok := c.firstClauseCut(s); ok {
return cut, true
}
// 3) Over-long punctuation-less run: force a typographically legal break so
// we don't buffer unbounded (e.g. a long CJK run with no punctuation).
if !final && c.maxRunes > 0 && utf8.RuneCountInString(s) > c.maxRunes {
if cut, ok := lineBreakCut(s, c.maxRunes); ok {
return cut, true
}
}
// 4) End of input: emit whatever remains as the final clause.
if final {
return len(s), true
}
return 0, false
}
// firstClauseCut returns the byte offset just past the first sub-sentence clause
// boundary in s — a CJK clause punctuation mark, or a space following a Thai/Lao
// letter — provided the prefix is at least minRunes long and non-space content
// follows. The boundary mark (and any trailing spaces) stay with the left clause.
func (c *clauseChunker) firstClauseCut(s string) (int, bool) {
var prev rune
runes := 0
for i, r := range s {
boundary := isCJKClausePunct(r) || (unicode.IsSpace(r) && isThaiLao(prev))
if boundary && runes+1 >= c.minRunes {
end := i + utf8.RuneLen(r)
for end < len(s) {
nr, sz := utf8.DecodeRuneInString(s[end:])
if !unicode.IsSpace(nr) {
break
}
end += sz
}
if end < len(s) { // confirmed: real content follows the boundary
return end, true
}
// Boundary sits at the end of the buffer with nothing after it yet —
// wait for the next token to confirm it rather than emit early.
return 0, false
}
prev = r
runes++
}
return 0, false
}
// lineBreakCut walks UAX #14 line segments and returns the byte offset of the
// last legal break opportunity at or before maxRunes. Returns ok=false when the
// run has no internal break opportunity (e.g. a space-less Thai run), leaving it
// buffered.
func lineBreakCut(s string, maxRunes int) (int, bool) {
state := -1
rest := s
consumed := 0
runes := 0
for rest != "" {
seg, rem, _, st := uniseg.FirstLineSegmentInString(rest, state)
state = st
runes += utf8.RuneCountInString(seg)
consumed += len(seg)
rest = rem
if runes >= maxRunes {
if consumed < len(s) {
return consumed, true
}
return 0, false
}
}
return 0, false
}
// isCJKClausePunct reports whether r is a CJK clause-level separator worth a
// soft TTS break. Sentence terminators (。!?) are intentionally excluded — UAX
// #29 sentence segmentation already handles those.
func isCJKClausePunct(r rune) bool {
switch r {
case '', // fullwidth comma
'、', // 、 ideographic comma
'', // fullwidth semicolon
'', // fullwidth colon
'・', // ・ katakana middle dot
'・': // ・ halfwidth katakana middle dot
return true
}
return false
}
// isThaiLao reports whether r is a Thai or Lao letter. Those scripts have no
// inter-word spaces; an ASCII space inside such a run marks a clause/sentence
// boundary, which is the only no-dictionary segmentation signal available.
func isThaiLao(r rune) bool {
return unicode.Is(unicode.Thai, r) || unicode.Is(unicode.Lao, r)
}

View File

@@ -1,103 +0,0 @@
package openai
import (
"strings"
"unicode/utf8"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// clauseChunker splits streamed LLM content into speakable clauses in a
// script-aware way: UAX#29 sentences (Latin .!? and CJK 。!?), CJK clause
// punctuation, and Thai/Lao spaces — never whitespace-splitting CJK.
var _ = Describe("clauseChunker", func() {
Context("Latin sentences", func() {
It("emits a sentence only once following content confirms it is complete", func() {
c := newClauseChunker(12, 200)
Expect(c.push("Hello world. How are you?")).To(Equal([]string{"Hello world."}))
// The trailing sentence is held until flush (the next token might extend it).
Expect(c.flush()).To(Equal([]string{"How are you?"}))
})
It("assembles a sentence across many small tokens", func() {
c := newClauseChunker(12, 200)
var got []string
for _, tok := range []string{"Hello", " world.", " How", " are", " you?"} {
got = append(got, c.push(tok)...)
}
got = append(got, c.flush()...)
Expect(got).To(Equal([]string{"Hello world.", "How are you?"}))
})
It("does not split decimals or abbreviations (UAX#29 SB6)", func() {
c := newClauseChunker(12, 200)
got := c.push("Pi is 3.14 and e is 2.72. Done")
Expect(got).To(Equal([]string{"Pi is 3.14 and e is 2.72."}))
Expect(c.flush()).To(Equal([]string{"Done"}))
})
})
Context("CJK (no whitespace)", func() {
It("splits Chinese on the ideographic full stop", func() {
c := newClauseChunker(12, 200)
Expect(c.push("你好世界。今天天气很好。")).To(Equal([]string{"你好世界。"}))
Expect(c.flush()).To(Equal([]string{"今天天气很好。"}))
})
It("splits Japanese on the ideographic full stop", func() {
c := newClauseChunker(12, 200)
Expect(c.push("こんにちは。元気ですか。")).To(Equal([]string{"こんにちは。"}))
Expect(c.flush()).To(Equal([]string{"元気ですか。"}))
})
It("splits on CJK clause punctuation for lower latency", func() {
c := newClauseChunker(2, 200) // small min so short test clauses cut
Expect(c.push("你好,世界。再见")).To(Equal([]string{"你好,", "世界。"}))
Expect(c.flush()).To(Equal([]string{"再见"}))
})
})
Context("Thai (spaces mark clauses, not words)", func() {
It("splits a Thai run on the inter-clause space", func() {
c := newClauseChunker(2, 200)
Expect(c.push("สวัสดีครับ กินข้าวไหม")).To(Equal([]string{"สวัสดีครับ"}))
Expect(c.flush()).To(Equal([]string{"กินข้าวไหม"}))
})
It("never shatters a space-less Thai run into characters", func() {
c := newClauseChunker(2, 200)
Expect(c.push("สวัสดีครับ")).To(BeEmpty()) // held, no boundary
Expect(c.flush()).To(Equal([]string{"สวัสดีครับ"}))
})
})
Context("length cap (UAX#14 fallback)", func() {
It("force-breaks an over-long punctuation-less CJK run at legal points", func() {
c := newClauseChunker(4, 10) // maxRunes = 10
run := strings.Repeat("字", 25)
got := c.push(run)
got = append(got, c.flush()...)
total := 0
for _, seg := range got {
n := utf8.RuneCountInString(seg)
Expect(n).To(BeNumerically("<=", 10)) // never exceeds the cap
total += n
}
Expect(total).To(Equal(25)) // nothing dropped
Expect(len(got)).To(BeNumerically(">=", 3)) // 10 + 10 + 5
})
})
Context("buffer lifecycle", func() {
It("flush clears the buffer so the chunker is reusable", func() {
c := newClauseChunker(12, 200)
// "First one." is confirmed by the following "Second", so push drains it;
// only the unterminated tail remains for flush.
Expect(c.push("First one. Second")).To(Equal([]string{"First one."}))
Expect(c.flush()).To(Equal([]string{"Second"}))
Expect(c.flush()).To(BeEmpty())
Expect(c.push("Again. More")).To(Equal([]string{"Again."}))
})
})
})

View File

@@ -1,138 +0,0 @@
package openai
import (
"context"
"strings"
"github.com/mudler/LocalAI/core/backend"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/pkg/grpc/proto"
)
// fakeTransport records the server events and audio sent to a realtime client
// so streaming behaviour can be asserted without a real WebSocket/WebRTC peer.
// It is not a *WebRTCTransport, so handler code takes the WebSocket path.
type fakeTransport struct {
events []types.ServerEvent
audio []fakeAudioChunk
}
type fakeAudioChunk struct {
pcm []byte
sampleRate int
}
func (f *fakeTransport) SendEvent(e types.ServerEvent) error {
f.events = append(f.events, e)
return nil
}
func (f *fakeTransport) ReadEvent() ([]byte, error) { return nil, nil }
func (f *fakeTransport) SendAudio(_ context.Context, pcm []byte, sampleRate int) error {
f.audio = append(f.audio, fakeAudioChunk{pcm: pcm, sampleRate: sampleRate})
return nil
}
func (f *fakeTransport) Close() error { return nil }
// countEvents returns how many recorded events have the given type.
func (f *fakeTransport) countEvents(et types.ServerEventType) int {
n := 0
for _, e := range f.events {
if e.ServerEventType() == et {
n++
}
}
return n
}
// transcriptDeltaText concatenates the Delta of every recorded transcript
// delta event — i.e. the text streamed to the client as it is generated.
func (f *fakeTransport) transcriptDeltaText() string {
var b strings.Builder
for _, e := range f.events {
if d, ok := e.(types.ResponseOutputAudioTranscriptDeltaEvent); ok {
b.WriteString(d.Delta)
}
}
return b.String()
}
// fakeModel is a configurable Model double. TTSStream replays ttsStreamChunks
// and TranscribeStream replays transcribeDeltas, so the handler's streaming
// paths can be driven deterministically.
type fakeModel struct {
cfg *config.ModelConfig
ttsFile string
ttsStreamChunks [][]byte
ttsStreamRate int
ttsStreamErr error
transcribeDeltas []string
transcribeFinal *schema.TranscriptionResult
// Predict streaming: predictTokens are replayed through the token callback
// (simulating streamed LLM output); predictResp/predictErr are returned by
// the deferred predict function. predictChunkDeltas, when set, are delivered
// per-token via TokenUsage.ChatDeltas to exercise the autoparser path.
predictTokens []string
predictChunkDeltas [][]*proto.ChatDelta
predictResp backend.LLMResponse
predictErr error
}
func (m *fakeModel) VAD(context.Context, *schema.VADRequest) (*schema.VADResponse, error) {
return nil, nil
}
func (m *fakeModel) Transcribe(context.Context, string, string, bool, bool, string) (*schema.TranscriptionResult, error) {
return m.transcribeFinal, nil
}
func (m *fakeModel) Predict(_ context.Context, _ schema.Messages, _, _, _ []string, cb func(string, backend.TokenUsage) bool, _ []types.ToolUnion, _ *types.ToolChoiceUnion, _, _ *int, _ map[string]float64) (func() (backend.LLMResponse, error), error) {
if m.predictErr != nil {
return nil, m.predictErr
}
return func() (backend.LLMResponse, error) {
for i, tok := range m.predictTokens {
if cb == nil {
continue
}
usage := backend.TokenUsage{}
if i < len(m.predictChunkDeltas) {
usage.ChatDeltas = m.predictChunkDeltas[i]
}
cb(tok, usage)
}
return m.predictResp, nil
}, nil
}
func (m *fakeModel) TTS(context.Context, string, string, string) (string, *proto.Result, error) {
return m.ttsFile, &proto.Result{Success: true}, nil
}
func (m *fakeModel) TTSStream(_ context.Context, _, _, _ string, onAudio func(pcm []byte, sampleRate int) error) error {
if m.ttsStreamErr != nil {
return m.ttsStreamErr
}
for _, c := range m.ttsStreamChunks {
if err := onAudio(c, m.ttsStreamRate); err != nil {
return err
}
}
return nil
}
func (m *fakeModel) TranscribeStream(_ context.Context, _, _ string, _, _ bool, _ string, onDelta func(text string)) (*schema.TranscriptionResult, error) {
for _, d := range m.transcribeDeltas {
onDelta(d)
}
return m.transcribeFinal, nil
}
func (m *fakeModel) PredictConfig() *config.ModelConfig { return m.cfg }

View File

@@ -3,7 +3,6 @@ package openai
import (
"context"
"crypto/rand"
"encoding/binary"
"encoding/hex"
"encoding/json"
"fmt"
@@ -88,14 +87,6 @@ func (m *transcriptOnlyModel) TTS(ctx context.Context, text, voice, language str
return "", nil, fmt.Errorf("TTS not supported in transcript-only mode")
}
func (m *transcriptOnlyModel) TTSStream(ctx context.Context, text, voice, language string, onAudio func(pcm []byte, sampleRate int) error) error {
return fmt.Errorf("TTS not supported in transcript-only mode")
}
func (m *transcriptOnlyModel) TranscribeStream(ctx context.Context, audio, language string, translate, diarize bool, prompt string, onDelta func(text string)) (*schema.TranscriptionResult, error) {
return transcribeStream(ctx, m.modelLoader, *m.TranscriptionConfig, m.appConfig, audio, language, translate, diarize, prompt, onDelta)
}
func (m *transcriptOnlyModel) PredictConfig() *config.ModelConfig {
return nil
}
@@ -330,75 +321,10 @@ func (m *wrappedModel) TTS(ctx context.Context, text, voice, language string) (s
return backend.ModelTTS(ctx, text, voice, language, "", nil, m.modelLoader, m.appConfig, *m.TTSConfig)
}
func (m *wrappedModel) TTSStream(ctx context.Context, text, voice, language string, onAudio func(pcm []byte, sampleRate int) error) error {
return ttsStream(ctx, m.modelLoader, m.appConfig, *m.TTSConfig, text, voice, language, onAudio)
}
func (m *wrappedModel) TranscribeStream(ctx context.Context, audio, language string, translate, diarize bool, prompt string, onDelta func(text string)) (*schema.TranscriptionResult, error) {
return transcribeStream(ctx, m.modelLoader, *m.TranscriptionConfig, m.appConfig, audio, language, translate, diarize, prompt, onDelta)
}
func (m *wrappedModel) PredictConfig() *config.ModelConfig {
return m.LLMConfig
}
// wavStreamHeaderBytes is the size of the WAV header that backend.ModelTTSStream
// emits as its first audio callback; the sample rate lives at byte offset 24.
const wavStreamHeaderBytes = 44
// ttsStream adapts backend.ModelTTSStream (which emits a WAV stream: a 44-byte
// header carrying the sample rate, then raw PCM) to the realtime onAudio
// callback, which wants raw PCM plus the sample rate. The header is buffered
// until complete, the sample rate is read from it, and subsequent bytes are
// forwarded as PCM.
func ttsStream(ctx context.Context, ml *model.ModelLoader, appConfig *config.ApplicationConfig, ttsConfig config.ModelConfig, text, voice, language string, onAudio func(pcm []byte, sampleRate int) error) error {
var header []byte
headerDone := false
sampleRate := 0
return backend.ModelTTSStream(ctx, text, voice, language, "", nil, ml, appConfig, ttsConfig, func(b []byte) error {
if headerDone {
if len(b) == 0 {
return nil
}
return onAudio(b, sampleRate)
}
header = append(header, b...)
if len(header) < wavStreamHeaderBytes {
return nil
}
sampleRate = int(binary.LittleEndian.Uint32(header[24:28]))
headerDone = true
if len(header) > wavStreamHeaderBytes {
return onAudio(header[wavStreamHeaderBytes:], sampleRate)
}
return nil
})
}
// transcribeStream adapts backend.ModelTranscriptionStream to the realtime
// onDelta callback, returning the final aggregated transcription result.
func transcribeStream(ctx context.Context, ml *model.ModelLoader, transcriptionConfig config.ModelConfig, appConfig *config.ApplicationConfig, audio, language string, translate, diarize bool, prompt string, onDelta func(text string)) (*schema.TranscriptionResult, error) {
var final *schema.TranscriptionResult
err := backend.ModelTranscriptionStream(ctx, backend.TranscriptionRequest{
Audio: audio,
Language: language,
Translate: translate,
Diarize: diarize,
Prompt: prompt,
}, ml, transcriptionConfig, appConfig, func(chunk backend.TranscriptionStreamChunk) {
if chunk.Delta != "" {
onDelta(chunk.Delta)
}
if chunk.Final != nil {
final = chunk.Final
}
})
if err != nil {
return nil, err
}
return final, nil
}
func newTranscriptionOnlyModel(pipeline *config.Pipeline, cl *config.ModelConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) (Model, *config.ModelConfig, error) {
cfgVAD, err := cl.LoadModelConfigFileByName(pipeline.VAD, ml.ModelPath)
if err != nil {
@@ -528,10 +454,8 @@ func newModel(pipeline *config.Pipeline, cl *config.ModelConfigLoader, ml *model
return nil, fmt.Errorf("failed to validate config: %w", err)
}
// Let the pipeline set the LLM's reasoning effort and force thinking off
// (cfgLLM is a per-session copy). disable_thinking applies after the effort.
// Let the pipeline set the LLM's reasoning effort (cfgLLM is a per-session copy).
applyPipelineReasoning(cfgLLM, *pipeline)
applyPipelineThinking(cfgLLM, *pipeline)
cfgTTS, err := cl.LoadModelConfigFileByName(pipeline.TTS, ml.ModelPath)
if err != nil {

View File

@@ -1,102 +0,0 @@
package openai
import (
"context"
"encoding/base64"
"fmt"
"os"
"path/filepath"
"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
laudio "github.com/mudler/LocalAI/pkg/audio"
"github.com/mudler/LocalAI/pkg/sound"
)
// emitSpeech synthesizes text and sends the audio to the client. When the
// pipeline opts into TTS streaming it forwards each PCM chunk as its own
// response.output_audio.delta as soon as the backend produces it; otherwise it
// synthesizes the whole utterance and sends it as a single delta.
//
// It deliberately does NOT emit transcript or audio-done events: the caller owns
// those so a streamed reply can be split into several spoken segments that share
// one response/item.
//
// It returns the PCM audio (at the session output rate) accumulated across all
// chunks, which the caller base64-encodes onto the conversation item. For WebRTC
// the audio goes over the RTP track instead, so the returned slice is empty.
func emitSpeech(ctx context.Context, t Transport, session *Session, responseID, itemID, text string) ([]byte, error) {
if text == "" {
return nil, nil
}
_, isWebRTC := t.(*WebRTCTransport)
var wsAudio []byte // PCM at the session output rate, accumulated for the item record
// sendChunk hands one PCM buffer to the transport: WebRTC consumes the raw
// PCM directly (it resamples internally); WebSocket gets base64 PCM at the
// session output rate via a JSON delta event.
sendChunk := func(pcm []byte, sampleRate int) error {
if len(pcm) == 0 {
return nil
}
if err := t.SendAudio(ctx, pcm, sampleRate); err != nil {
return err
}
if isWebRTC {
return nil
}
wsPCM := pcm
if sampleRate != 0 && sampleRate != session.OutputSampleRate {
samples := sound.BytesToInt16sLE(pcm)
resampled := sound.ResampleInt16(samples, sampleRate, session.OutputSampleRate)
wsPCM = sound.Int16toBytesLE(resampled)
}
wsAudio = append(wsAudio, wsPCM...)
return t.SendEvent(types.ResponseOutputAudioDeltaEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
ItemID: itemID,
OutputIndex: 0,
ContentIndex: 0,
Delta: base64.StdEncoding.EncodeToString(wsPCM),
})
}
language := ""
if session.InputAudioTranscription != nil {
language = session.InputAudioTranscription.Language
}
if session.ModelConfig != nil && session.ModelConfig.Pipeline.StreamTTS() {
if err := session.ModelInterface.TTSStream(ctx, text, session.Voice, language, sendChunk); err != nil {
return nil, err
}
return wsAudio, nil
}
// Unary fallback: synthesize the whole utterance to a file, then emit once.
audioFilePath, res, err := session.ModelInterface.TTS(ctx, text, session.Voice, language)
if err != nil {
return nil, err
}
if res != nil && !res.Success {
return nil, fmt.Errorf("tts generation failed: %s", res.Message)
}
defer func() { _ = os.Remove(audioFilePath) }()
// filepath.Clean normalizes the backend-produced temp path before reading
// (also keeps gosec G304 quiet — the path is backend-controlled, not user input).
audioBytes, err := os.ReadFile(filepath.Clean(audioFilePath))
if err != nil {
return nil, fmt.Errorf("read tts audio: %w", err)
}
pcm, sampleRate := laudio.ParseWAV(audioBytes)
if sampleRate == 0 {
sampleRate = session.OutputSampleRate
}
if err := sendChunk(pcm, sampleRate); err != nil {
return nil, err
}
return wsAudio, nil
}

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@@ -1,70 +0,0 @@
package openai
import (
"context"
"os"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
laudio "github.com/mudler/LocalAI/pkg/audio"
)
// emitSpeech synthesizes a piece of text and forwards the audio to the client,
// streaming a delta per TTS chunk when the pipeline opts in, or sending the
// whole utterance as one delta otherwise.
var _ = Describe("emitSpeech", func() {
ttsOn := true
streamingSession := func(m Model) *Session {
return &Session{
OutputSampleRate: 24000,
ModelInterface: m,
ModelConfig: &config.ModelConfig{
Pipeline: config.Pipeline{Streaming: config.PipelineStreaming{TTS: &ttsOn}},
},
}
}
It("streams one output_audio.delta per TTS chunk when streaming is enabled", func() {
m := &fakeModel{
ttsStreamChunks: [][]byte{{1, 2}, {3, 4}, {5, 6}},
ttsStreamRate: 24000,
}
t := &fakeTransport{}
audio, err := emitSpeech(context.Background(), t, streamingSession(m), "resp1", "item1", "Hello there.")
Expect(err).ToNot(HaveOccurred())
Expect(t.countEvents(types.ServerEventTypeResponseOutputAudioDelta)).To(Equal(3))
// The returned audio is all chunks concatenated (session output rate).
Expect(audio).To(Equal([]byte{1, 2, 3, 4, 5, 6}))
})
It("sends a single output_audio.delta in unary mode", func() {
// A minimal real WAV file for the unary TTS path to read + parse.
f, err := os.CreateTemp("", "emit-*.wav")
Expect(err).ToNot(HaveOccurred())
defer func() { _ = os.Remove(f.Name()) }()
pcm := make([]byte, 320) // 160 samples of silence
hdr := laudio.NewWAVHeader(uint32(len(pcm)))
Expect(hdr.Write(f)).To(Succeed())
_, err = f.Write(pcm)
Expect(err).ToNot(HaveOccurred())
Expect(f.Close()).To(Succeed())
session := &Session{
OutputSampleRate: 24000,
ModelInterface: &fakeModel{ttsFile: f.Name()},
ModelConfig: &config.ModelConfig{}, // streaming off
}
t := &fakeTransport{}
_, err = emitSpeech(context.Background(), t, session, "resp1", "item1", "Hello there.")
Expect(err).ToNot(HaveOccurred())
Expect(t.countEvents(types.ServerEventTypeResponseOutputAudioDelta)).To(Equal(1))
})
})

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@@ -1,315 +0,0 @@
package openai
import (
"context"
"encoding/base64"
"fmt"
"github.com/mudler/LocalAI/core/backend"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/pkg/functions"
"github.com/mudler/LocalAI/pkg/reasoning"
)
// transcriptStreamer turns streamed LLM tokens into the assistant's spoken
// transcript: it strips reasoning incrementally and sends one
// response.output_audio_transcript.delta per content fragment. It does NOT
// synthesize audio — the caller buffers the full message and synthesizes it
// once (streaming the audio chunks when the TTS backend supports TTSStream),
// which works uniformly for streaming and non-streaming TTS and for languages
// without sentence or word boundaries.
type transcriptStreamer struct {
ctx context.Context
t Transport
responseID string
itemID string
extractor *reasoning.ReasoningExtractor
// announce, if set, is invoked once just before the first transcript delta.
// It lets the caller create the assistant item lazily, so a content-less
// tool-call turn never emits a spurious empty assistant item.
announce func()
announced bool
}
func newTranscriptStreamer(ctx context.Context, t Transport, responseID, itemID, thinkingStartToken string, reasoningCfg reasoning.Config) *transcriptStreamer {
return &transcriptStreamer{
ctx: ctx,
t: t,
responseID: responseID,
itemID: itemID,
extractor: reasoning.NewReasoningExtractor(thinkingStartToken, spokenReasoningConfig(reasoningCfg)),
}
}
// onToken handles one streamed unit of model output, sending a transcript delta
// for the new content (reasoning stripped) and returning that content delta so
// the caller can also feed it to the clause chunker. For plain-content models
// the unit is the raw text token; for autoparser tool turns the backend clears
// the text and delivers content via ChatDeltas, so the caller passes that
// content here. Returns "" when the token produced no new spoken content.
func (s *transcriptStreamer) onToken(token string) string {
_, content := s.extractor.ProcessToken(token)
if content == "" {
return ""
}
if !s.announced {
s.announced = true
if s.announce != nil {
s.announce()
}
}
_ = s.t.SendEvent(types.ResponseOutputAudioTranscriptDeltaEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: s.responseID,
ItemID: s.itemID,
OutputIndex: 0,
ContentIndex: 0,
Delta: content,
})
return content
}
// content returns the full transcript so far with reasoning stripped.
func (s *transcriptStreamer) content() string {
return s.extractor.CleanedContent()
}
// streamLLMResponse drives a streamed realtime reply. It streams the assistant
// transcript as the LLM generates, then synthesizes the whole buffered message
// once (streaming the audio chunks when the TTS backend supports it, otherwise a
// single unary delta). Tool calls parsed from the autoparser ChatDeltas are
// emitted after the spoken content. The assistant content item is created lazily
// on the first content delta, so a content-less tool-call turn emits only the
// tool calls. It returns true when it has fully handled the response so the
// caller can return; callers must only invoke it for an audio modality, and with
// tools only when the model uses its tokenizer template (see triggerResponseAtTurn).
func streamLLMResponse(ctx context.Context, session *Session, conv *Conversation, t Transport, responseID string, history schema.Messages, images []string, llmCfg *config.ModelConfig, tools []types.ToolUnion, toolChoice *types.ToolChoiceUnion, toolTurn int) bool {
itemID := generateItemID()
item := types.MessageItemUnion{
Assistant: &types.MessageItemAssistant{
ID: itemID,
Status: types.ItemStatusInProgress,
Content: []types.MessageContentOutput{{Type: types.MessageContentTypeOutputAudio}},
},
}
// announce creates the assistant content item lazily, just before the first
// transcript delta — a tool-only turn never produces content, so it stays out
// of the conversation and the client sees only the tool calls.
announced := false
announce := func() {
announced = true
conv.Lock.Lock()
conv.Items = append(conv.Items, &item)
conv.Lock.Unlock()
sendEvent(t, types.ResponseOutputItemAddedEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
OutputIndex: 0,
Item: item,
})
sendEvent(t, types.ResponseContentPartAddedEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
ItemID: itemID,
OutputIndex: 0,
ContentIndex: 0,
Part: item.Assistant.Content[0],
})
}
cancel := func() {
if announced {
conv.Lock.Lock()
for i := len(conv.Items) - 1; i >= 0; i-- {
if conv.Items[i].Assistant != nil && conv.Items[i].Assistant.ID == itemID {
conv.Items = append(conv.Items[:i], conv.Items[i+1:]...)
break
}
}
conv.Lock.Unlock()
}
sendEvent(t, types.ResponseDoneEvent{
ServerEventBase: types.ServerEventBase{},
Response: types.Response{ID: responseID, Object: "realtime.response", Status: types.ResponseStatusCancelled},
})
}
var template string
if llmCfg.TemplateConfig.UseTokenizerTemplate {
template = llmCfg.GetModelTemplate()
} else {
template = llmCfg.TemplateConfig.Chat
}
thinkingStartToken := reasoning.DetectThinkingStartToken(template, &llmCfg.ReasoningConfig)
// The autoparser (tokenizer-template path) already delivers reasoning-free
// content. Prefilling the thinking start token here would re-tag that clean
// content as an unclosed reasoning block, leaving CleanedContent() empty —
// no spoken reply, no TTS. Disable the prefill; closed tag pairs are still
// stripped (PEG-fallback case, #9985).
reasoningCfg := llmCfg.ReasoningConfig
if llmCfg.TemplateConfig.UseTokenizerTemplate {
disablePrefill := true
reasoningCfg.DisableReasoningTagPrefill = &disablePrefill
}
streamer := newTranscriptStreamer(ctx, t, responseID, itemID, thinkingStartToken, reasoningCfg)
streamer.announce = announce
// Clause chunking (opt-in): synthesize each clause as soon as it completes
// instead of buffering the whole reply. streamedAudio accumulates the PCM
// across clauses for the conversation item record; ttsErr captures the first
// synthesis failure so the token callback can stop the prediction. emitSpeech
// runs synchronously here — the LLM keeps generating into the gRPC stream
// while a clause is synthesized, so audio still starts mid-generation.
var chunker *clauseChunker
if session.ModelConfig != nil && session.ModelConfig.Pipeline.ChunkClauses() {
chunker = newClauseChunker(defaultClauseMinRunes, defaultClauseMaxRunes)
}
var streamedAudio []byte
var ttsErr error
speakClause := func(clause string) error {
a, err := emitSpeech(ctx, t, session, responseID, itemID, clause)
if err != nil {
return err
}
streamedAudio = append(streamedAudio, a...)
return nil
}
// fail reports a mid-stream failure. A cancelled context means the client
// interrupted (barge-in), so roll the turn back instead of erroring.
fail := func(code, msg string, err error) bool {
if ctx.Err() != nil {
cancel()
} else {
sendError(t, code, fmt.Sprintf("%s: %v", msg, err), "", itemID)
}
return true
}
cb := func(token string, usage backend.TokenUsage) bool {
if ctx.Err() != nil {
return false
}
// Plain-content models stream text via the token; autoparser tool turns
// clear the text and deliver content via ChatDeltas, so prefer the latter
// when present. Either way only content reaches the transcript — tool-call
// deltas are parsed from the final response below.
text := token
if len(usage.ChatDeltas) > 0 {
text = functions.ContentFromChatDeltas(usage.ChatDeltas)
}
delta := streamer.onToken(text)
if chunker != nil && delta != "" {
for _, clause := range chunker.push(delta) {
if ttsErr = speakClause(clause); ttsErr != nil {
return false // stop the prediction; reported after predFunc returns
}
}
}
return true
}
predFunc, err := session.ModelInterface.Predict(ctx, history, images, nil, nil, cb, tools, toolChoice, nil, nil, nil)
if err != nil {
sendError(t, "inference_failed", fmt.Sprintf("backend error: %v", err), "", itemID)
return true
}
pred, err := predFunc()
// A clause synthesis failed mid-stream (the callback stopped the prediction);
// report it as a TTS error rather than a prediction error.
if ttsErr != nil {
return fail("tts_error", "TTS generation failed", ttsErr)
}
if err != nil {
return fail("prediction_failed", "backend error", err)
}
if ctx.Err() != nil {
cancel()
return true
}
content := streamer.content()
toolCalls := functions.ToolCallsFromChatDeltas(pred.ChatDeltas)
// Finalize the spoken content item only when the turn produced content. A
// tool-only turn skips this entirely (no empty assistant item).
if content != "" {
if !announced {
announce()
}
// Synthesize the audio. With clause chunking the completed clauses were
// already spoken inside the token callback; flush the trailing clause(s)
// the segmenter was still holding. Otherwise buffer the whole message and
// synthesize it once. emitSpeech streams the audio chunks when the TTS
// backend supports TTSStream, otherwise it sends a single unary delta.
var audio []byte
if chunker != nil {
for _, clause := range chunker.flush() {
if ttsErr = speakClause(clause); ttsErr != nil {
break
}
}
audio = streamedAudio
} else {
audio, ttsErr = emitSpeech(ctx, t, session, responseID, itemID, content)
}
if ttsErr != nil {
return fail("tts_error", "TTS generation failed", ttsErr)
}
_, isWebRTC := t.(*WebRTCTransport)
sendEvent(t, types.ResponseOutputAudioTranscriptDoneEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
ItemID: itemID,
OutputIndex: 0,
ContentIndex: 0,
Transcript: content,
})
if !isWebRTC {
sendEvent(t, types.ResponseOutputAudioDoneEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
ItemID: itemID,
OutputIndex: 0,
ContentIndex: 0,
})
}
conv.Lock.Lock()
item.Assistant.Status = types.ItemStatusCompleted
item.Assistant.Content[0].Transcript = content
if !isWebRTC {
item.Assistant.Content[0].Audio = base64.StdEncoding.EncodeToString(audio)
}
conv.Lock.Unlock()
sendEvent(t, types.ResponseContentPartDoneEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
ItemID: itemID,
OutputIndex: 0,
ContentIndex: 0,
Part: item.Assistant.Content[0],
})
sendEvent(t, types.ResponseOutputItemDoneEvent{
ServerEventBase: types.ServerEventBase{},
ResponseID: responseID,
OutputIndex: 0,
Item: item,
})
}
// Emit any tool calls, the terminal response.done, and (for server-side
// assistant tools) the follow-up turn — shared with the buffered path.
emitToolCallItems(ctx, session, conv, t, responseID, toolCalls, content != "", toolTurn)
return true
}

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@@ -1,213 +0,0 @@
package openai
import (
"context"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/core/backend"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
"github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/LocalAI/pkg/reasoning"
)
// transcriptStreamer turns streamed LLM tokens into incremental transcript
// deltas, stripping reasoning. Audio is synthesized once from the full message
// by the caller, so there is no per-sentence segmentation.
var _ = Describe("transcriptStreamer", func() {
It("emits one transcript delta per content token", func() {
t := &fakeTransport{}
s := newTranscriptStreamer(context.Background(), t, "resp1", "item1", "", reasoning.Config{})
for _, tok := range []string{"Hello", " world.", " Bye"} {
s.onToken(tok)
}
Expect(s.content()).To(Equal("Hello world. Bye"))
Expect(t.countEvents(types.ServerEventTypeResponseOutputAudioTranscriptDelta)).To(Equal(3))
Expect(t.transcriptDeltaText()).To(Equal("Hello world. Bye"))
})
It("strips leaked reasoning even when reasoning is disabled (disable_thinking safety net)", func() {
// disable_thinking maps to DisableReasoning=true (enable_thinking=false to
// the backend). If the model emits thinking anyway, the transcript must
// still not leak it: stripping always runs for spoken output.
disable := true
t := &fakeTransport{}
s := newTranscriptStreamer(context.Background(), t, "resp1", "item1", "",
reasoning.Config{DisableReasoning: &disable})
s.onToken("<think>secret plan</think>")
s.onToken("The answer is 42.")
Expect(s.content()).To(Equal("The answer is 42."))
Expect(s.content()).ToNot(ContainSubstring("secret plan"))
Expect(t.transcriptDeltaText()).ToNot(ContainSubstring("secret plan"))
})
It("does not swallow autoparser content when the template has a thinking start token (tokenizer-template path)", func() {
// Regression: with tag prefill on, the detected <think> token is
// prepended to the autoparser's already-clean content, swallowing the
// whole reply (empty transcript → no TTS). streamLLMResponse disables
// the prefill for the tokenizer-template path.
disablePrefill := true
t := &fakeTransport{}
s := newTranscriptStreamer(context.Background(), t, "resp1", "item1", "<think>",
reasoning.Config{DisableReasoningTagPrefill: &disablePrefill})
s.onToken("Hello")
s.onToken(" there.")
Expect(s.content()).To(Equal("Hello there."))
Expect(t.transcriptDeltaText()).To(Equal("Hello there."))
})
It("still strips embedded closed reasoning tags with prefill disabled (PEG-fallback safety, #9985)", func() {
// Disabling prefill must not stop stripping closed <think>…</think>
// pairs the PEG fallback can leave in autoparser content.
disablePrefill := true
t := &fakeTransport{}
s := newTranscriptStreamer(context.Background(), t, "resp1", "item1", "<think>",
reasoning.Config{DisableReasoningTagPrefill: &disablePrefill})
s.onToken("<think>secret</think>")
s.onToken("The answer is 42.")
Expect(s.content()).To(Equal("The answer is 42."))
Expect(t.transcriptDeltaText()).ToNot(ContainSubstring("secret"))
})
})
// streamLLMResponse drives a full streamed realtime turn: live transcript
// deltas while the LLM generates, then the whole message is synthesized once.
var _ = Describe("streamLLMResponse", func() {
It("streams transcript deltas then synthesizes the whole message once", func() {
on := true
m := &fakeModel{
predictTokens: []string{"Hello", " world.", " How are you?"},
predictResp: backend.LLMResponse{Response: "Hello world. How are you?"},
ttsStreamChunks: [][]byte{{9}},
ttsStreamRate: 24000,
}
session := &Session{
OutputSampleRate: 24000,
ModelInterface: m,
ModelConfig: &config.ModelConfig{
Pipeline: config.Pipeline{Streaming: config.PipelineStreaming{LLM: &on, TTS: &on}},
},
}
conv := &Conversation{}
t := &fakeTransport{}
llmCfg := &config.ModelConfig{}
handled := streamLLMResponse(context.Background(), session, conv, t, "resp1", nil, nil, llmCfg, nil, nil, 0)
Expect(handled).To(BeTrue())
// One live transcript delta per streamed token.
Expect(t.countEvents(types.ServerEventTypeResponseOutputAudioTranscriptDelta)).To(Equal(3))
// The whole message is synthesized ONCE (not per sentence): a single
// emitSpeech replays the one TTS stream chunk.
Expect(t.countEvents(types.ServerEventTypeResponseOutputAudioDelta)).To(Equal(1))
Expect(t.transcriptDeltaText()).To(Equal("Hello world. How are you?"))
})
It("synthesizes each clause as it completes when clause chunking is enabled", func() {
on := true
m := &fakeModel{
predictTokens: []string{"Hello world.", " How are you?"},
predictResp: backend.LLMResponse{Response: "Hello world. How are you?"},
ttsStreamChunks: [][]byte{{9}},
ttsStreamRate: 24000,
}
session := &Session{
OutputSampleRate: 24000,
ModelInterface: m,
ModelConfig: &config.ModelConfig{
Pipeline: config.Pipeline{Streaming: config.PipelineStreaming{LLM: &on, TTS: &on, ClauseChunking: &on}},
},
}
conv := &Conversation{}
t := &fakeTransport{}
llmCfg := &config.ModelConfig{}
handled := streamLLMResponse(context.Background(), session, conv, t, "resp1", nil, nil, llmCfg, nil, nil, 0)
Expect(handled).To(BeTrue())
// Two clauses ("Hello world." mid-stream, "How are you?" on flush) → two
// emitSpeech calls → two audio deltas, vs one for whole-message buffering.
Expect(t.countEvents(types.ServerEventTypeResponseOutputAudioDelta)).To(Equal(2))
// The full transcript still streams verbatim.
Expect(t.transcriptDeltaText()).To(Equal("Hello world. How are you?"))
// Exactly one terminal response.done.
Expect(t.countEvents(types.ServerEventTypeResponseDone)).To(Equal(1))
})
It("streams content deltas and emits tool-call items (autoparser tool turn)", func() {
on := true
// Autoparser path: reply.Message is empty; content + tool calls arrive via
// ChatDeltas. Chunk 1 carries content, chunk 2 carries the tool call.
contentDelta := []*proto.ChatDelta{{Content: "Let me check."}}
toolDelta := []*proto.ChatDelta{{ToolCalls: []*proto.ToolCallDelta{{Index: 0, Name: "get_weather", Arguments: `{"city":"Paris"}`}}}}
m := &fakeModel{
predictTokens: []string{"", ""},
predictChunkDeltas: [][]*proto.ChatDelta{contentDelta, toolDelta},
predictResp: backend.LLMResponse{ChatDeltas: append(append([]*proto.ChatDelta{}, contentDelta...), toolDelta...)},
ttsStreamChunks: [][]byte{{9}},
ttsStreamRate: 24000,
}
session := &Session{
OutputSampleRate: 24000,
ModelInterface: m,
ModelConfig: &config.ModelConfig{
Pipeline: config.Pipeline{Streaming: config.PipelineStreaming{LLM: &on, TTS: &on}},
},
}
conv := &Conversation{}
t := &fakeTransport{}
llmCfg := &config.ModelConfig{}
llmCfg.TemplateConfig.UseTokenizerTemplate = true
handled := streamLLMResponse(context.Background(), session, conv, t, "resp1", nil, nil, llmCfg, nil, nil, 0)
Expect(handled).To(BeTrue())
// The spoken content was streamed live.
Expect(t.transcriptDeltaText()).To(Equal("Let me check."))
// The tool call is emitted as a function_call item.
Expect(t.countEvents(types.ServerEventTypeResponseFunctionCallArgumentsDone)).To(Equal(1))
// Exactly one terminal response.done.
Expect(t.countEvents(types.ServerEventTypeResponseDone)).To(Equal(1))
})
It("emits only tool-call items for a content-less tool turn (no empty assistant item)", func() {
on := true
toolDelta := []*proto.ChatDelta{{ToolCalls: []*proto.ToolCallDelta{{Index: 0, Name: "get_weather", Arguments: `{"city":"Rome"}`}}}}
m := &fakeModel{
predictTokens: []string{""},
predictChunkDeltas: [][]*proto.ChatDelta{toolDelta},
predictResp: backend.LLMResponse{ChatDeltas: toolDelta},
}
session := &Session{
OutputSampleRate: 24000,
ModelInterface: m,
ModelConfig: &config.ModelConfig{
Pipeline: config.Pipeline{Streaming: config.PipelineStreaming{LLM: &on, TTS: &on}},
},
}
conv := &Conversation{}
t := &fakeTransport{}
llmCfg := &config.ModelConfig{}
llmCfg.TemplateConfig.UseTokenizerTemplate = true
handled := streamLLMResponse(context.Background(), session, conv, t, "resp1", nil, nil, llmCfg, nil, nil, 0)
Expect(handled).To(BeTrue())
// No content → no transcript deltas and no spurious assistant content item.
Expect(t.transcriptDeltaText()).To(Equal(""))
Expect(t.countEvents(types.ServerEventTypeResponseOutputAudioTranscriptDelta)).To(Equal(0))
// The tool call is still emitted.
Expect(t.countEvents(types.ServerEventTypeResponseFunctionCallArgumentsDone)).To(Equal(1))
Expect(t.countEvents(types.ServerEventTypeResponseDone)).To(Equal(1))
})
})

View File

@@ -1,33 +0,0 @@
package openai
import (
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/pkg/reasoning"
)
// applyPipelineThinking forces the LLM's reasoning/thinking off when the realtime
// pipeline sets disable_thinking, mapping to the enable_thinking=false backend
// metadata via ReasoningConfig.DisableReasoning. The LLM config passed in is the
// per-session copy returned by the config loader, so this does not affect other
// users of the same model. When the pipeline does not set disable_thinking the
// LLM config is left untouched.
func applyPipelineThinking(llm *config.ModelConfig, pipeline config.Pipeline) {
if llm == nil || !pipeline.ThinkingDisabled() {
return
}
disable := true
llm.ReasoningConfig.DisableReasoning = &disable
}
// spokenReasoningConfig adapts a model's reasoning config for stripping reasoning
// OUT of realtime spoken output. ReasoningConfig.DisableReasoning is overloaded:
// the backend reads it as the "enable_thinking=false" hint (which pipeline
// disable_thinking sets via applyPipelineThinking), but the reasoning extractor
// reads it as "skip stripping, assume there is no reasoning". Honouring the latter
// when extracting for speech would leak raw <think>…</think> whenever the model
// ignores the suppression hint. Spoken output must never contain reasoning, so we
// always strip: clear DisableReasoning while keeping custom tokens/tag pairs.
func spokenReasoningConfig(cfg reasoning.Config) reasoning.Config {
cfg.DisableReasoning = nil
return cfg
}

View File

@@ -1,50 +0,0 @@
package openai
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/pkg/reasoning"
)
// applyPipelineThinking lets a realtime pipeline force the LLM's thinking off
// (enable_thinking=false metadata) without editing the LLM model config.
var _ = Describe("applyPipelineThinking", func() {
It("disables reasoning on the LLM config when the pipeline disables thinking", func() {
disable := true
llm := &config.ModelConfig{}
applyPipelineThinking(llm, config.Pipeline{DisableThinking: &disable})
Expect(llm.ReasoningConfig.DisableReasoning).ToNot(BeNil())
Expect(*llm.ReasoningConfig.DisableReasoning).To(BeTrue())
})
It("leaves the LLM config untouched when the pipeline does not set disable_thinking", func() {
llm := &config.ModelConfig{}
applyPipelineThinking(llm, config.Pipeline{})
Expect(llm.ReasoningConfig.DisableReasoning).To(BeNil())
})
})
// spokenReasoningConfig clears DisableReasoning so realtime spoken output always
// strips reasoning, even though disable_thinking sets DisableReasoning=true on the
// LLM config (which the backend reads as enable_thinking=false).
var _ = Describe("spokenReasoningConfig", func() {
It("clears DisableReasoning so the extractor still strips leaked reasoning", func() {
disable := true
out := spokenReasoningConfig(reasoning.Config{DisableReasoning: &disable})
Expect(out.DisableReasoning).To(BeNil())
})
It("preserves the other reasoning settings", func() {
disable := true
out := spokenReasoningConfig(reasoning.Config{
DisableReasoning: &disable,
ThinkingStartTokens: []string{"<reason>"},
TagPairs: []reasoning.TagPair{{Start: "<reason>", End: "</reason>"}},
})
Expect(out.ThinkingStartTokens).To(Equal([]string{"<reason>"}))
Expect(out.TagPairs).To(HaveLen(1))
Expect(out.TagPairs[0].Start).To(Equal("<reason>"))
})
})

View File

@@ -1,63 +0,0 @@
package openai
import (
"context"
"fmt"
"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
)
// emitTranscription transcribes a committed utterance and emits the transcription
// events for it, returning the final transcript text. With
// pipeline.streaming.transcription enabled it streams each transcript fragment as
// a conversation.item.input_audio_transcription.delta as the backend produces it,
// then a completed event; otherwise it transcribes the whole utterance and emits
// a single completed event. delta and completed events share itemID.
func emitTranscription(ctx context.Context, t Transport, session *Session, itemID, audioPath string) (string, error) {
cfg := session.InputAudioTranscription
if session.ModelConfig != nil && session.ModelConfig.Pipeline.StreamTranscription() {
final, err := session.ModelInterface.TranscribeStream(ctx, audioPath, cfg.Language, false, false, cfg.Prompt, func(delta string) {
_ = t.SendEvent(types.ConversationItemInputAudioTranscriptionDeltaEvent{
ServerEventBase: types.ServerEventBase{EventID: "event_TODO"},
ItemID: itemID,
ContentIndex: 0,
Delta: delta,
})
})
if err != nil {
return "", err
}
transcript := ""
if final != nil {
transcript = final.Text
}
if err := t.SendEvent(types.ConversationItemInputAudioTranscriptionCompletedEvent{
ServerEventBase: types.ServerEventBase{EventID: "event_TODO"},
ItemID: itemID,
ContentIndex: 0,
Transcript: transcript,
}); err != nil {
return "", err
}
return transcript, nil
}
// Unary fallback: transcribe the whole utterance, emit one completed event.
tr, err := session.ModelInterface.Transcribe(ctx, audioPath, cfg.Language, false, false, cfg.Prompt)
if err != nil {
return "", err
}
if tr == nil {
return "", fmt.Errorf("transcribe result is nil")
}
if err := t.SendEvent(types.ConversationItemInputAudioTranscriptionCompletedEvent{
ServerEventBase: types.ServerEventBase{EventID: "event_TODO"},
ItemID: itemID,
ContentIndex: 0,
Transcript: tr.Text,
}); err != nil {
return "", err
}
return tr.Text, nil
}

View File

@@ -1,54 +0,0 @@
package openai
import (
"context"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
"github.com/mudler/LocalAI/core/schema"
)
// emitTranscription transcribes a committed utterance, streaming transcript text
// deltas when the pipeline opts in, and returns the final transcript text.
var _ = Describe("emitTranscription", func() {
It("streams transcription deltas then a completed event when streaming is enabled", func() {
on := true
session := &Session{
InputAudioTranscription: &types.AudioTranscription{},
ModelConfig: &config.ModelConfig{
Pipeline: config.Pipeline{Streaming: config.PipelineStreaming{Transcription: &on}},
},
ModelInterface: &fakeModel{
transcribeDeltas: []string{"Hel", "lo", " world"},
transcribeFinal: &schema.TranscriptionResult{Text: "Hello world"},
},
}
t := &fakeTransport{}
transcript, err := emitTranscription(context.Background(), t, session, "item1", "/tmp/x.wav")
Expect(err).ToNot(HaveOccurred())
Expect(transcript).To(Equal("Hello world"))
Expect(t.countEvents(types.ServerEventTypeConversationItemInputAudioTranscriptionDelta)).To(Equal(3))
Expect(t.countEvents(types.ServerEventTypeConversationItemInputAudioTranscriptionCompleted)).To(Equal(1))
})
It("emits a single completed event with no deltas in unary mode", func() {
session := &Session{
InputAudioTranscription: &types.AudioTranscription{},
ModelConfig: &config.ModelConfig{}, // streaming off
ModelInterface: &fakeModel{transcribeFinal: &schema.TranscriptionResult{Text: "Hi"}},
}
t := &fakeTransport{}
transcript, err := emitTranscription(context.Background(), t, session, "item1", "/tmp/x.wav")
Expect(err).ToNot(HaveOccurred())
Expect(transcript).To(Equal("Hi"))
Expect(t.countEvents(types.ServerEventTypeConversationItemInputAudioTranscriptionDelta)).To(Equal(0))
Expect(t.countEvents(types.ServerEventTypeConversationItemInputAudioTranscriptionCompleted)).To(Equal(1))
})
})

View File

@@ -48,8 +48,7 @@ func RealtimeCalls(application *application.Application) echo.HandlerFunc {
return c.JSON(http.StatusInternalServerError, map[string]string{"error": "codec registration failed"})
}
se := webRTCSettingEngine(application.ApplicationConfig())
api := webrtc.NewAPI(webrtc.WithMediaEngine(m), webrtc.WithSettingEngine(se))
api := webrtc.NewAPI(webrtc.WithMediaEngine(m))
pc, err := api.NewPeerConnection(webrtc.Configuration{})
if err != nil {

View File

@@ -1,47 +0,0 @@
package openai
import (
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/xlog"
"github.com/pion/webrtc/v4"
)
// webRTCSettingEngine builds the pion SettingEngine for /v1/realtime WebRTC.
//
// With a default (empty) SettingEngine, pion gathers a host ICE candidate for
// every local interface. Under Docker host networking that includes bridge
// addresses (docker0/veth, 172.x) that a remote browser cannot route to; the
// connection often establishes on a good pair and then drops once ICE consent
// checks fail on the unreachable ones. The two opt-in knobs below let an
// operator advertise only the reachable address.
func webRTCSettingEngine(cfg *config.ApplicationConfig) webrtc.SettingEngine {
s := webrtc.SettingEngine{}
if cfg == nil {
return s
}
if len(cfg.WebRTCNAT1To1IPs) > 0 {
s.SetNAT1To1IPs(cfg.WebRTCNAT1To1IPs, webrtc.ICECandidateTypeHost)
xlog.Debug("realtime webrtc: advertising NAT 1:1 host IPs", "ips", cfg.WebRTCNAT1To1IPs)
}
if filter := iceInterfaceFilter(cfg.WebRTCICEInterfaces); filter != nil {
s.SetInterfaceFilter(filter)
xlog.Debug("realtime webrtc: restricting ICE interfaces", "interfaces", cfg.WebRTCICEInterfaces)
}
return s
}
// iceInterfaceFilter returns an interface allow-list predicate for pion, or nil
// when no interfaces are configured (pion's default: gather from all).
func iceInterfaceFilter(allowed []string) func(string) bool {
if len(allowed) == 0 {
return nil
}
set := make(map[string]struct{}, len(allowed))
for _, name := range allowed {
set[name] = struct{}{}
}
return func(iface string) bool {
_, ok := set[iface]
return ok
}
}

View File

@@ -1,39 +0,0 @@
package openai
import (
"github.com/mudler/LocalAI/core/config"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("webRTC ICE settings", func() {
Describe("iceInterfaceFilter", func() {
It("returns nil when no interfaces are configured", func() {
Expect(iceInterfaceFilter(nil)).To(BeNil())
Expect(iceInterfaceFilter([]string{})).To(BeNil())
})
It("admits only the configured interfaces", func() {
f := iceInterfaceFilter([]string{"eth0", "wlan0"})
Expect(f).NotTo(BeNil())
Expect(f("eth0")).To(BeTrue())
Expect(f("wlan0")).To(BeTrue())
Expect(f("docker0")).To(BeFalse())
Expect(f("veth123")).To(BeFalse())
})
})
Describe("webRTCSettingEngine", func() {
It("does not panic on a nil config", func() {
Expect(func() { webRTCSettingEngine(nil) }).NotTo(Panic())
})
It("builds an engine with NAT 1:1 IPs and an interface filter configured", func() {
cfg := &config.ApplicationConfig{
WebRTCNAT1To1IPs: []string{"192.168.1.10"},
WebRTCICEInterfaces: []string{"eth0"},
}
Expect(func() { webRTCSettingEngine(cfg) }).NotTo(Panic())
})
})
})

View File

@@ -1356,7 +1356,7 @@ func handleOpenResponsesNonStream(c echo.Context, responseID string, createdAt i
thinkingStartToken := reason.DetectThinkingStartToken(template, &cfg.ReasoningConfig)
// Extract reasoning from result before cleaning
reasoningContent, cleanedResult := reason.ExtractReasoningComplete(result, thinkingStartToken, cfg.ReasoningConfig)
reasoningContent, cleanedResult := reason.ExtractReasoningWithConfig(result, thinkingStartToken, cfg.ReasoningConfig)
// Parse tool calls if using functions
var outputItems []schema.ORItemField
@@ -1996,7 +1996,7 @@ func handleOpenResponsesStream(c echo.Context, responseID string, createdAt int6
finalCleanedResult = extractor.CleanedContent()
}
if finalReasoning == "" && finalCleanedResult == "" {
finalReasoning, finalCleanedResult = reason.ExtractReasoningComplete(result, thinkingStartToken, cfg.ReasoningConfig)
finalReasoning, finalCleanedResult = reason.ExtractReasoningWithConfig(result, thinkingStartToken, cfg.ReasoningConfig)
}
// Close reasoning item if it exists and wasn't closed yet
@@ -2493,7 +2493,7 @@ func handleOpenResponsesStream(c echo.Context, responseID string, createdAt int6
finalCleanedResult = extractor.CleanedContent()
}
if finalReasoning == "" && finalCleanedResult == "" {
finalReasoning, finalCleanedResult = reason.ExtractReasoningComplete(result, thinkingStartToken, cfg.ReasoningConfig)
finalReasoning, finalCleanedResult = reason.ExtractReasoningWithConfig(result, thinkingStartToken, cfg.ReasoningConfig)
}
// Close reasoning item if it exists and wasn't closed yet

View File

@@ -216,12 +216,6 @@ export function useChat(initialModel = '') {
audio_url: { url: `data:${file.type};base64,${file.base64}` },
})
userFiles.push({ name: file.name, type: 'audio' })
} else if (file.type?.startsWith('video/')) {
messageContent.push({
type: 'video_url',
video_url: { url: `data:${file.type};base64,${file.base64}` },
})
userFiles.push({ name: file.name, type: 'video' })
} else {
// Text/PDF files - append to content
if (file.textContent) {

View File

@@ -506,10 +506,7 @@ export default function Backends() {
<tbody>
{backends.map((b, idx) => {
const op = getBackendOp(b)
// A failed op is intentionally kept in the operations list so the
// OperationsBar can surface the error + Dismiss; it must NOT render
// as a perpetual "Installing..." spinner here (mirrors Models.jsx).
const isProcessing = !!op && !op.error
const isProcessing = !!op
const isExpanded = expandedRow === idx
return (

View File

@@ -265,7 +265,7 @@ function UserMessageContent({ content, files }) {
<div className="chat-message-files">
{files.map((f, i) => (
<span key={i} className="chat-file-inline">
<i className={`fas ${f.type === 'image' ? 'fa-image' : f.type === 'audio' ? 'fa-headphones' : f.type === 'video' ? 'fa-film' : 'fa-file'}`} />
<i className={`fas ${f.type === 'image' ? 'fa-image' : f.type === 'audio' ? 'fa-headphones' : 'fa-file'}`} />
{f.name}
</span>
))}
@@ -274,9 +274,6 @@ function UserMessageContent({ content, files }) {
{Array.isArray(content) && content.filter(c => c.type === 'image_url').map((img, i) => (
<img key={i} src={img.image_url.url} alt="attached" className="chat-inline-image" />
))}
{Array.isArray(content) && content.filter(c => c.type === 'video_url').map((vid, i) => (
<video key={i} src={vid.video_url.url} controls className="chat-inline-video" />
))}
</>
)
}
@@ -714,7 +711,7 @@ export default function Chat() {
for (const file of e.target.files) {
const base64 = await fileToBase64(file)
const entry = { name: file.name, type: file.type, base64 }
if (!file.type.startsWith('image/') && !file.type.startsWith('audio/') && !file.type.startsWith('video/')) {
if (!file.type.startsWith('image/') && !file.type.startsWith('audio/')) {
entry.textContent = await file.text().catch(() => '')
}
newFiles.push(entry)
@@ -1247,7 +1244,7 @@ export default function Chat() {
<div className="chat-files">
{files.map((f, i) => (
<span key={i} className="chat-file-badge">
<i className={`fas ${f.type?.startsWith('image/') ? 'fa-image' : f.type?.startsWith('audio/') ? 'fa-headphones' : f.type?.startsWith('video/') ? 'fa-film' : 'fa-file'}`} />
<i className={`fas ${f.type?.startsWith('image/') ? 'fa-image' : f.type?.startsWith('audio/') ? 'fa-headphones' : 'fa-file'}`} />
{f.name}
<button onClick={() => setFiles(prev => prev.filter((_, idx) => idx !== i))}>
<i className="fas fa-xmark" />
@@ -1346,7 +1343,7 @@ export default function Chat() {
ref={fileInputRef}
type="file"
multiple
accept="image/*,audio/*,video/*,application/pdf,.txt,.md,.csv,.json"
accept="image/*,audio/*,application/pdf,.txt,.md,.csv,.json"
style={{ display: 'none' }}
onChange={handleFileChange}
/>

View File

@@ -12,7 +12,6 @@ import ActionMenu from '../components/ActionMenu'
import ResourceRow, { ChevronCell, IconCell, StopPropagationCell } from '../components/ResourceRow'
import { useModels } from '../hooks/useModels'
import { useGalleryEnrichment } from '../hooks/useGalleryEnrichment'
import { useOperations } from '../hooks/useOperations'
import { backendControlApi, modelsApi, backendsApi, systemApi, nodesApi } from '../utils/api'
import { renderMarkdown } from '../utils/markdown'
import { safeHref } from '../utils/url'
@@ -127,7 +126,6 @@ export default function Manage() {
const [activeTab, setActiveTab] = useState(TABS.some(tab => tab.key === initialTab) ? initialTab : 'models')
const { models, loading: modelsLoading, refetch: refetchModels } = useModels()
const { enrichModel, enrichBackend } = useGalleryEnrichment()
const { operations } = useOperations()
const [loadedModelIds, setLoadedModelIds] = useState(new Set())
const [backends, setBackends] = useState([])
const [backendsLoading, setBackendsLoading] = useState(true)
@@ -260,19 +258,14 @@ export default function Manage() {
return `${m}m ago`
})()
// Refresh installed backends + available upgrades when the Backends tab opens
// AND whenever a backend operation settles (operations.length changes as a
// reinstall/upgrade completes and drops off the list). Without the op-settle
// refresh the installed-version cell and the "update available" badge stay
// stale after an upgrade until the user switches tabs - the op looks like it
// "did nothing". Mirrors the operations.length watch Backends.jsx uses.
// Fetch available backend upgrades
useEffect(() => {
if (activeTab !== 'backends') return
fetchBackends()
backendsApi.checkUpgrades()
.then(data => setUpgrades(data || {}))
.catch(() => {})
}, [operations.length, activeTab, fetchBackends])
if (activeTab === 'backends') {
backendsApi.checkUpgrades()
.then(data => setUpgrades(data || {}))
.catch(() => {})
}
}, [activeTab])
const handleStopModel = (modelName) => {
setConfirmDialog({

View File

@@ -17,24 +17,6 @@ const STATUS_STYLES = {
error: { icon: 'fa-solid fa-circle', color: 'var(--color-error)', bg: 'var(--color-error-light)' },
}
// upsertAssistant merges a streamed transcript fragment into the assistant entry
// identified by the server's item_id, or appends a new entry if none exists yet.
// Keying by item_id (not a mutable index tracked across handler/updater
// boundaries) makes streamed deltas idempotent and order-independent, so React's
// batching of non-React data-channel events cannot produce a duplicate bubble.
// mode 'append' adds to the running text; 'replace' sets the final transcript.
function upsertAssistant(prev, itemId, text, mode) {
// Only assistant entries carry an id, and the streaming entry is almost
// always the newest — search from the tail so per-delta cost stays constant.
const i = prev.findLastIndex(e => e.id === itemId)
if (i === -1) {
return [...prev, { role: 'assistant', id: itemId, text }]
}
const next = [...prev]
next[i] = { ...next[i], text: mode === 'append' ? next[i].text + text : text }
return next
}
export default function Talk() {
const { addToast } = useOutletContext()
const navigate = useNavigate()
@@ -52,10 +34,7 @@ export default function Talk() {
// Transcript
const [transcript, setTranscript] = useState([])
// item_id of the assistant message currently streaming — used only to remove
// its partial bubble when a response is cancelled (barge-in). The transcript
// itself is keyed by item_id via upsertAssistant, not by this ref.
const inProgressIdRef = useRef(null)
const streamingRef = useRef(null) // tracks the index of the in-progress assistant message
// Session settings
const [instructions, setInstructions] = useState(
@@ -248,21 +227,39 @@ export default function Talk() {
break
case 'conversation.item.input_audio_transcription.completed':
if (event.transcript) {
streamingRef.current = null
setTranscript(prev => [...prev, { role: 'user', text: event.transcript }])
}
updateStatus('thinking', 'Generating response...')
break
case 'response.output_audio_transcript.delta':
if (event.delta) {
inProgressIdRef.current = event.item_id
setTranscript(prev => upsertAssistant(prev, event.item_id, event.delta, 'append'))
setTranscript(prev => {
if (streamingRef.current !== null) {
const updated = [...prev]
updated[streamingRef.current] = {
...updated[streamingRef.current],
text: updated[streamingRef.current].text + event.delta,
}
return updated
}
streamingRef.current = prev.length
return [...prev, { role: 'assistant', text: event.delta }]
})
}
break
case 'response.output_audio_transcript.done':
if (event.transcript) {
setTranscript(prev => upsertAssistant(prev, event.item_id, event.transcript, 'replace'))
setTranscript(prev => {
if (streamingRef.current !== null) {
const updated = [...prev]
updated[streamingRef.current] = { ...updated[streamingRef.current], text: event.transcript }
return updated
}
return [...prev, { role: 'assistant', text: event.transcript }]
})
}
inProgressIdRef.current = null
streamingRef.current = null
break
case 'response.output_audio.delta':
updateStatus('speaking', 'Speaking...')
@@ -284,7 +281,7 @@ export default function Talk() {
// Pretty-print JSON for readability; fall back to raw string.
try { preview = JSON.stringify(JSON.parse(preview), null, 2) } catch (_) { /* keep raw */ }
setTranscript(prev => [...prev, { role: 'tool_result', text: preview }])
inProgressIdRef.current = null // tool result ends the current assistant text run
streamingRef.current = null // tool result ends the current assistant text run
}
break
}
@@ -293,20 +290,9 @@ export default function Talk() {
// conversation.item.create + response.create when it's done.
handleFunctionCall(event)
break
case 'response.done': {
// A cancelled response (barge-in / interruption) leaves a partial,
// incrementally-streamed assistant bubble behind. The server discards
// the interrupted item from history; mirror that here (remove the
// in-progress assistant entry by item_id) so the regenerated reply
// doesn't show up as a second assistant message.
if (event.response?.status === 'cancelled' && inProgressIdRef.current) {
const id = inProgressIdRef.current
inProgressIdRef.current = null
setTranscript(prev => prev.filter(e => e.id !== id))
}
case 'response.done':
updateStatus('listening', 'Listening...')
break
}
case 'error':
hasErrorRef.current = true
updateStatus('error', 'Error: ' + (event.error?.message || 'Unknown error'))
@@ -803,7 +789,7 @@ export default function Talk() {
const iconColor = isToolCall || isToolResult ? 'var(--color-text-secondary)'
: isUser ? 'var(--color-primary)' : 'var(--color-accent)'
return (
<div key={entry.id || i} style={{ display: 'flex', alignItems: 'flex-start', gap: 'var(--spacing-xs)' }}>
<div key={i} style={{ display: 'flex', alignItems: 'flex-start', gap: 'var(--spacing-xs)' }}>
<i className={iconClass} style={{ color: iconColor, marginTop: 3, flexShrink: 0, fontSize: '0.75rem' }} />
<p style={{
margin: 0,

View File

@@ -466,11 +466,10 @@ func (s *AgentPoolService) Chat(name, message string) (string, error) {
s.collectAndCopyMetadata(metadata, chatUserID)
}
content := s.appendLocalAGIKBCitations(response.Response, name, message, response.State)
msg := map[string]any{
"id": messageID + "-agent",
"sender": "agent",
"content": content,
"content": response.Response,
"timestamp": time.Now().Format(time.RFC3339),
}
if len(metadata) > 0 {
@@ -490,79 +489,6 @@ func (s *AgentPoolService) Chat(name, message string) (string, error) {
return messageID, nil
}
func (s *AgentPoolService) appendLocalAGIKBCitations(response, agentKey, message string, states []coreTypes.ActionState) string {
if strings.TrimSpace(response) == "" {
return response
}
userID, collection := splitAgentKey(agentKey)
cfg := s.localAGI.pool.GetConfig(agentKey)
if cfg == nil || !cfg.EnableKnowledgeBase {
return response
}
citations := kbCitationsFromActionStates(states)
if len(citations) == 0 && cfg.KBAutoSearch {
maxResults := cfg.KnowledgeBaseResults
if maxResults <= 0 {
maxResults = 5
}
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
kbResult := agents.KBAutoSearchPrompt(ctx, s.apiURL, s.apiKey, collection, message, maxResults, userID)
citations = kbResult.Citations
}
return agents.AppendKBCitations(response, collection, userID, citations)
}
func splitAgentKey(agentKey string) (userID, name string) {
if uid, n, ok := strings.Cut(agentKey, ":"); ok {
return uid, n
}
return "", agentKey
}
func kbCitationsFromActionStates(states []coreTypes.ActionState) []agents.KBCitation {
var citations []agents.KBCitation
for _, state := range states {
citations = append(citations, kbCitationsFromMetadata(state.Metadata)...)
}
return citations
}
func kbCitationsFromMetadata(metadata map[string]any) []agents.KBCitation {
if len(metadata) == 0 {
return nil
}
fileName := metadata["file_name"]
source := metadata["source"]
if fileName == nil && source == nil {
return nil
}
citation := agents.KBCitation{
FileName: metadataString(fileName),
EntryKey: metadataString(source),
}
if citation.FileName == "" && citation.EntryKey == "" {
return nil
}
return []agents.KBCitation{citation}
}
func metadataString(value any) string {
switch v := value.(type) {
case string:
return v
case fmt.Stringer:
return v.String()
default:
return ""
}
}
// userOutputsDir returns the per-user outputs directory, creating it if needed.
// If userID is empty, falls back to the shared outputs directory.
func (s *AgentPoolService) userOutputsDir(userID string) string {

View File

@@ -1,127 +0,0 @@
package agents
import (
"fmt"
"net/url"
"strings"
"sync"
)
type kbCitationList struct {
mu sync.Mutex
citations []KBCitation
}
func (l *kbCitationList) AddKBCitations(citations []KBCitation) {
if len(citations) == 0 {
return
}
l.mu.Lock()
defer l.mu.Unlock()
l.citations = append(l.citations, citations...)
}
func (l *kbCitationList) Citations() []KBCitation {
l.mu.Lock()
defer l.mu.Unlock()
out := make([]KBCitation, len(l.citations))
copy(out, l.citations)
return out
}
// AppendKBCitations appends a markdown Sources block for KB citations.
func AppendKBCitations(response, collection, userID string, citations []KBCitation) string {
if strings.TrimSpace(response) == "" || len(citations) == 0 {
return response
}
var lines []string
seen := make(map[string]struct{})
for _, citation := range citations {
key := strings.TrimSpace(citation.EntryKey)
if key == "" {
key = strings.TrimSpace(citation.FileName)
}
if key == "" {
continue
}
if _, ok := seen[key]; ok {
continue
}
seen[key] = struct{}{}
displayName := kbCitationDisplayName(citation)
if displayName == "" {
continue
}
sourceURL := kbCitationRawFileURL(collection, citation.EntryKey, userID)
number := len(lines) + 1
if sourceURL == "" {
lines = append(lines, fmt.Sprintf("[%d] %s", number, displayName))
continue
}
lines = append(lines, fmt.Sprintf("[%d] [%s](%s)", number, escapeMarkdownLinkText(displayName), sourceURL))
}
if len(lines) == 0 {
return response
}
var sb strings.Builder
sb.WriteString(strings.TrimRight(response, "\n"))
sb.WriteString("\n\nSources:\n")
for _, line := range lines {
sb.WriteString(line)
sb.WriteString("\n")
}
return strings.TrimRight(sb.String(), "\n")
}
func kbCitationDisplayName(citation KBCitation) string {
if fileName := strings.TrimSpace(citation.FileName); fileName != "" {
return fileName
}
segments := strings.Split(strings.Trim(strings.TrimSpace(citation.EntryKey), "/"), "/")
for i := len(segments) - 1; i >= 0; i-- {
if segment := strings.TrimSpace(segments[i]); segment != "" {
return segment
}
}
return ""
}
func kbCitationRawFileURL(collection, entryKey, userID string) string {
collection = strings.TrimSpace(collection)
entryKey = strings.Trim(strings.TrimSpace(entryKey), "/")
if collection == "" || entryKey == "" {
return ""
}
var escapedEntrySegments []string
for _, segment := range strings.Split(entryKey, "/") {
if segment == "" {
continue
}
escapedEntrySegments = append(escapedEntrySegments, url.PathEscape(segment))
}
if len(escapedEntrySegments) == 0 {
return ""
}
sourceURL := "/api/agents/collections/" + url.PathEscape(collection) + "/entries-raw/" + strings.Join(escapedEntrySegments, "/")
if userID != "" {
query := url.Values{}
query.Set("user_id", userID)
sourceURL += "?" + query.Encode()
}
return sourceURL
}
func escapeMarkdownLinkText(text string) string {
text = strings.ReplaceAll(text, `\`, `\\`)
text = strings.ReplaceAll(text, "[", `\[`)
text = strings.ReplaceAll(text, "]", `\]`)
return text
}

View File

@@ -167,12 +167,10 @@ func ExecuteChatWithLLM(ctx context.Context, llm cogito.LLM, cfg *AgentConfig, m
}
}
kbCitations := &kbCitationList{}
if cfg.EnableKnowledgeBase && (kbMode == KBModeAutoSearch || kbMode == KBModeBoth) {
kbResult := KBAutoSearchPrompt(ctx, effectiveURL, effectiveKey, cfg.Name, message, cfg.KnowledgeBaseResults, userID)
if kbResult.Prompt != "" {
fragment = fragment.AddMessage(cogito.SystemMessageRole, kbResult.Prompt)
kbCitations.AddKBCitations(kbResult.Citations)
kbResults := KBAutoSearchPrompt(ctx, effectiveURL, effectiveKey, cfg.Name, message, cfg.KnowledgeBaseResults, userID)
if kbResults != "" {
fragment = fragment.AddMessage(cogito.SystemMessageRole, kbResults)
}
}
@@ -199,7 +197,7 @@ func ExecuteChatWithLLM(ctx context.Context, llm cogito.LLM, cfg *AgentConfig, m
}
cogitoOpts = append(cogitoOpts, cogito.WithTools(
cogito.NewToolDefinition(
KBSearchMemoryTool{APIURL: effectiveURL, APIKey: effectiveKey, Collection: cfg.Name, MaxResults: kbResults, UserID: userID, CitationCollector: kbCitations},
KBSearchMemoryTool{APIURL: effectiveURL, APIKey: effectiveKey, Collection: cfg.Name, MaxResults: kbResults, UserID: userID},
KBSearchMemoryArgs{},
"search_memory",
"Search the knowledge base for relevant information",
@@ -338,8 +336,6 @@ func ExecuteChatWithLLM(ctx context.Context, llm cogito.LLM, cfg *AgentConfig, m
if cfg.StripThinkingTags && response != "" {
response = stripThinkingTags(response)
}
responseForMemory := response
response = AppendKBCitations(response, cfg.Name, userID, kbCitations.Citations())
// Save conversation to KB when long-term memory is enabled.
// Use a detached context: the parent ctx may be cancelled (e.g. in distributed
@@ -348,7 +344,7 @@ func ExecuteChatWithLLM(ctx context.Context, llm cogito.LLM, cfg *AgentConfig, m
go func() {
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
saveConversationToKB(ctx, llm, effectiveURL, effectiveKey, cfg, message, responseForMemory, userID)
saveConversationToKB(ctx, llm, effectiveURL, effectiveKey, cfg, message, response, userID)
}()
}

View File

@@ -2,8 +2,6 @@ package agents
import (
"context"
"net/http"
"net/http/httptest"
"sync"
"sync/atomic"
@@ -38,34 +36,6 @@ func (m *mockLLM) CreateChatCompletion(ctx context.Context, req openai.ChatCompl
}, cogito.LLMUsage{}, nil
}
type toolCallingMockLLM struct {
createResponses []openai.ChatCompletionResponse
askResponse string
callCount atomic.Int32
}
func (m *toolCallingMockLLM) Ask(ctx context.Context, f cogito.Fragment) (cogito.Fragment, error) {
m.callCount.Add(1)
return f.AddMessage(cogito.AssistantMessageRole, m.askResponse), nil
}
func (m *toolCallingMockLLM) CreateChatCompletion(ctx context.Context, req openai.ChatCompletionRequest) (cogito.LLMReply, cogito.LLMUsage, error) {
idx := int(m.callCount.Add(1)) - 1
if idx >= len(m.createResponses) {
return cogito.LLMReply{
ChatCompletionResponse: openai.ChatCompletionResponse{
Choices: []openai.ChatCompletionChoice{{
Message: openai.ChatCompletionMessage{
Role: "assistant",
Content: "No more tools needed.",
},
}},
},
}, cogito.LLMUsage{}, nil
}
return cogito.LLMReply{ChatCompletionResponse: m.createResponses[idx]}, cogito.LLMUsage{}, nil
}
// statusCollector records status callbacks in a thread-safe way.
type statusCollector struct {
mu sync.Mutex
@@ -103,74 +73,6 @@ var _ = DescribeTable("stripThinkingTags",
Entry("adjacent tag pairs", "<thinking>a</thinking><thinking>b</thinking>", ""),
)
var _ = DescribeTable("appendKBCitations",
func(response, collection, userID string, citations []KBCitation, want string) {
Expect(AppendKBCitations(response, collection, userID, citations)).To(Equal(want))
},
Entry("leaves responses without citations unchanged",
"answer",
"agent",
"",
nil,
"answer",
),
Entry("leaves blank responses unchanged",
"",
"agent",
"",
[]KBCitation{{FileName: "source.pdf", EntryKey: "uuid/source.pdf"}},
"",
),
Entry("appends clickable source links",
"answer",
"my-agent",
"",
[]KBCitation{{FileName: "new feature.pdf", EntryKey: "uuid/new feature.pdf"}},
"answer\n\nSources:\n[1] [new feature.pdf](/api/agents/collections/my-agent/entries-raw/uuid/new%20feature.pdf)",
),
Entry("deduplicates citations by entry key",
"answer",
"agent",
"",
[]KBCitation{
{FileName: "first.pdf", EntryKey: "uuid/shared.pdf"},
{FileName: "second.pdf", EntryKey: "uuid/shared.pdf"},
},
"answer\n\nSources:\n[1] [first.pdf](/api/agents/collections/agent/entries-raw/uuid/shared.pdf)",
),
Entry("uses plain text when entry key is missing",
"answer",
"agent",
"",
[]KBCitation{{FileName: "source.pdf"}},
"answer\n\nSources:\n[1] source.pdf",
),
Entry("uses entry basename when filename is missing",
"answer",
"agent",
"",
[]KBCitation{{EntryKey: "uuid/source.pdf"}},
"answer\n\nSources:\n[1] [source.pdf](/api/agents/collections/agent/entries-raw/uuid/source.pdf)",
),
Entry("adds user id query when present",
"answer",
"agent",
"user 1",
[]KBCitation{{FileName: "source.pdf", EntryKey: "uuid/source.pdf"}},
"answer\n\nSources:\n[1] [source.pdf](/api/agents/collections/agent/entries-raw/uuid/source.pdf?user_id=user+1)",
),
Entry("escapes collection, path segments, and markdown link text",
"answer",
"agent one",
"",
[]KBCitation{{FileName: "source [draft].pdf", EntryKey: "uuid/source [draft].pdf"}},
`answer
Sources:
[1] [source \[draft\].pdf](/api/agents/collections/agent%20one/entries-raw/uuid/source%20%5Bdraft%5D.pdf)`,
),
)
var _ = Describe("ExecuteChatWithLLM", func() {
var (
ctx context.Context
@@ -282,150 +184,6 @@ var _ = Describe("ExecuteChatWithLLM", func() {
})
})
Context("knowledge base citations", func() {
It("appends KB sources to the returned response and callback message", func() {
mux := http.NewServeMux()
mux.HandleFunc("/api/agents/collections/kb-agent/search", func(w http.ResponseWriter, r *http.Request) {
Expect(r.URL.Query().Get("user_id")).To(Equal("user-1"))
w.Header().Set("Content-Type", "application/json")
_, _ = w.Write([]byte(`{
"results": [
{
"content": "KB content",
"id": "result-1",
"similarity": 0.99,
"metadata": {
"file_name": "new feature.pdf",
"source": "uuid/new feature.pdf"
}
}
],
"count": 1
}`))
})
server := httptest.NewServer(mux)
defer server.Close()
var msgContent string
cb.OnMessage = func(sender, content, messageID string) {
msgContent = content
}
llm := &mockLLM{response: "agent reply"}
cfg := &AgentConfig{
Name: "kb-agent",
Model: "test-model",
EnableKnowledgeBase: true,
KBMode: KBModeAutoSearch,
}
result, err := ExecuteChatWithLLM(ctx, llm, cfg, "hello", cb, ExecuteChatOpts{
APIURL: server.URL,
UserID: "user-1",
})
Expect(err).ToNot(HaveOccurred())
Expect(result).To(Equal("agent reply\n\nSources:\n[1] [new feature.pdf](/api/agents/collections/kb-agent/entries-raw/uuid/new%20feature.pdf?user_id=user-1)"))
Expect(msgContent).To(Equal(result))
})
It("collects citations from the search_memory tool", func() {
mux := http.NewServeMux()
mux.HandleFunc("/api/agents/collections/kb-agent/search", func(w http.ResponseWriter, r *http.Request) {
w.Header().Set("Content-Type", "application/json")
_, _ = w.Write([]byte(`{
"results": [
{
"content": "Tool KB content",
"id": "result-1",
"similarity": 0.99,
"metadata": {
"file_name": "tool source.pdf",
"source": "uuid/tool source.pdf"
}
}
],
"count": 1
}`))
})
server := httptest.NewServer(mux)
defer server.Close()
collector := &kbCitationList{}
tool := KBSearchMemoryTool{
APIURL: server.URL,
Collection: "kb-agent",
CitationCollector: collector,
}
result, _, err := tool.Run(KBSearchMemoryArgs{Query: "hello"})
Expect(err).ToNot(HaveOccurred())
Expect(result).To(ContainSubstring("Tool KB content"))
Expect(collector.Citations()).To(Equal([]KBCitation{{FileName: "tool source.pdf", EntryKey: "uuid/tool source.pdf"}}))
})
It("appends KB sources found through tools-only search_memory calls", func() {
mux := http.NewServeMux()
mux.HandleFunc("/api/agents/collections/kb-agent/search", func(w http.ResponseWriter, r *http.Request) {
Expect(r.URL.Query().Get("user_id")).To(Equal("user-1"))
w.Header().Set("Content-Type", "application/json")
_, _ = w.Write([]byte(`{
"results": [
{
"content": "Tool KB content",
"id": "result-1",
"similarity": 0.99,
"metadata": {
"file_name": "tool source.pdf",
"source": "uuid/tool source.pdf"
}
}
],
"count": 1
}`))
})
server := httptest.NewServer(mux)
defer server.Close()
llm := &toolCallingMockLLM{
askResponse: "agent reply from tool context",
createResponses: []openai.ChatCompletionResponse{
{
Choices: []openai.ChatCompletionChoice{
{
Message: openai.ChatCompletionMessage{
Role: "assistant",
ToolCalls: []openai.ToolCall{
{
ID: "call-1",
Type: openai.ToolTypeFunction,
Function: openai.FunctionCall{
Name: "search_memory",
Arguments: `{"query":"hello"}`,
},
},
},
},
},
},
},
},
}
cfg := &AgentConfig{
Name: "kb-agent",
Model: "test-model",
EnableKnowledgeBase: true,
KBMode: KBModeTools,
}
result, err := ExecuteChatWithLLM(ctx, llm, cfg, "hello", cb, ExecuteChatOpts{
APIURL: server.URL,
UserID: "user-1",
})
Expect(err).ToNot(HaveOccurred())
Expect(result).To(Equal("agent reply from tool context\n\nSources:\n[1] [tool source.pdf](/api/agents/collections/kb-agent/entries-raw/uuid/tool%20source.pdf?user_id=user-1)"))
})
})
Context("context cancellation", func() {
It("returns an error when context is already cancelled", func() {
cancelledCtx, cancel := context.WithCancel(ctx)

View File

@@ -8,7 +8,6 @@ import (
"io"
"mime/multipart"
"net/http"
"net/url"
"strings"
"time"
@@ -18,19 +17,10 @@ import (
"github.com/mudler/LocalAI/pkg/httpclient"
)
// Metadata keys populated by localrecall for every stored chunk. The original
// upload file name lives under file_name (used for display); source holds the
// collection entry key ("<uuid>/<filename>") used to build the raw-file URL.
const (
kbMetadataFileName = "file_name"
kbMetadataSource = "source"
)
// KBSearchResult represents a search result from the knowledge base.
// Field names mirror the collection search endpoint's JSON response.
type KBSearchResult struct {
Content string `json:"content"`
ID string `json:"id"`
Score float64 `json:"score"`
Similarity float64 `json:"similarity"`
Metadata map[string]string `json:"metadata"`
}
@@ -41,48 +31,22 @@ type kbSearchResponse struct {
Count int `json:"count"`
}
// KBCitation is a single source document that a KB search drew from. Citations
// travel alongside the prompt as structured data so the consumer (and UI) can
// render clickable source links, independent of what the model writes inline.
type KBCitation struct {
// FileName is the original uploaded file name, for display (e.g. "report.pdf").
FileName string `json:"file_name"`
// EntryKey is the collection entry identifier ("<uuid>/<filename>"), used to
// build the raw-file URL and as the de-duplication key.
EntryKey string `json:"entry_key"`
}
// KBSearchContext is the result of an auto-search against the knowledge base:
// the system-prompt block to feed the model, plus the de-duplicated list of
// source documents the results were drawn from.
type KBSearchContext struct {
Prompt string `json:"prompt"`
Citations []KBCitation `json:"citations"`
}
// KBCitationCollector receives source citations found during KB searches.
type KBCitationCollector interface {
AddKBCitations([]KBCitation)
}
// KBAutoSearchPrompt queries the knowledge base with the user's message and
// returns a KBSearchContext: a system prompt block with the relevant results
// plus the de-duplicated source citations those results came from.
// KBAutoSearchPrompt queries the knowledge base with the user's message
// and returns a system prompt block with relevant results.
// Uses LocalAI's collection search endpoint via the API.
func KBAutoSearchPrompt(ctx context.Context, apiURL, apiKey, collection, query string, maxResults int, userID string) KBSearchContext {
func KBAutoSearchPrompt(ctx context.Context, apiURL, apiKey, collection, query string, maxResults int, userID string) string {
if collection == "" || query == "" {
return KBSearchContext{}
return ""
}
if maxResults <= 0 {
maxResults = 5
}
searchURL := strings.TrimRight(apiURL, "/") + "/api/agents/collections/" + url.PathEscape(collection) + "/search"
// Call LocalAI's collection search API
searchURL := strings.TrimRight(apiURL, "/") + "/api/agents/collections/" + collection + "/search"
if userID != "" {
query := url.Values{}
query.Set("user_id", userID)
searchURL += "?" + query.Encode()
searchURL += "?user_id=" + userID
}
reqBody, _ := json.Marshal(map[string]any{
"query": query,
@@ -92,7 +56,7 @@ func KBAutoSearchPrompt(ctx context.Context, apiURL, apiKey, collection, query s
req, err := http.NewRequestWithContext(ctx, http.MethodPost, searchURL, strings.NewReader(string(reqBody)))
if err != nil {
xlog.Warn("KB auto-search: failed to create request", "error", err)
return KBSearchContext{}
return ""
}
req.Header.Set("Content-Type", "application/json")
if apiKey != "" {
@@ -102,70 +66,41 @@ func KBAutoSearchPrompt(ctx context.Context, apiURL, apiKey, collection, query s
resp, err := httpclient.New().Do(req)
if err != nil {
xlog.Warn("KB auto-search: request failed", "error", err)
return KBSearchContext{}
return ""
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
body, _ := io.ReadAll(resp.Body)
xlog.Warn("KB auto-search: non-200 response", "status", resp.StatusCode, "body", string(body))
return KBSearchContext{}
return ""
}
var searchResp kbSearchResponse
if err := json.NewDecoder(resp.Body).Decode(&searchResp); err != nil {
xlog.Warn("KB auto-search: failed to decode response", "error", err)
return KBSearchContext{}
return ""
}
if len(searchResp.Results) == 0 {
return KBSearchContext{}
return ""
}
// Build the system prompt block, labelling each chunk with its source file
// so the model can attribute inline, and collect the structured citations.
// Format results as a system prompt block (same format as LocalAGI)
var sb strings.Builder
sb.WriteString("Given the user input you have the following in memory:\n")
var citations []KBCitation
seen := make(map[string]struct{})
for _, r := range searchResp.Results {
fileName := r.Metadata[kbMetadataFileName]
source := r.Metadata[kbMetadataSource]
label := fileName
if label == "" {
label = "unknown"
for i, r := range searchResp.Results {
sb.WriteString(fmt.Sprintf("- %s", r.Content))
if len(r.Metadata) > 0 {
meta, _ := json.Marshal(r.Metadata)
sb.WriteString(fmt.Sprintf(" (%s)", string(meta)))
}
sb.WriteString(fmt.Sprintf("[Source: %s]\n%s\n", label, r.Content))
// Citations are de-duplicated per source document: many chunks from the
// same file share one source key, so a file is listed only once. Skip
// results with no source key — they cannot be linked back to a document.
dedupKey := source
if dedupKey == "" {
dedupKey = fileName
if i < len(searchResp.Results)-1 {
sb.WriteString("\n")
}
if dedupKey == "" {
continue
}
if _, ok := seen[dedupKey]; ok {
continue
}
seen[dedupKey] = struct{}{}
citations = append(citations, KBCitation{
FileName: fileName,
EntryKey: source,
})
}
sb.WriteString("When answering, cite sources using [Source: filename].")
return KBSearchContext{
Prompt: sb.String(),
Citations: citations,
}
return sb.String()
}
// KBSearchMemoryArgs defines the arguments for the search_memory tool.
@@ -175,25 +110,21 @@ type KBSearchMemoryArgs struct {
// KBSearchMemoryTool implements the search_memory MCP tool.
type KBSearchMemoryTool struct {
APIURL string
APIKey string
Collection string
MaxResults int
UserID string
CitationCollector KBCitationCollector
APIURL string
APIKey string
Collection string
MaxResults int
UserID string
}
func (t KBSearchMemoryTool) Run(args KBSearchMemoryArgs) (string, any, error) {
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
result := KBAutoSearchPrompt(ctx, t.APIURL, t.APIKey, t.Collection, args.Query, t.MaxResults, t.UserID)
if result.Prompt == "" {
if result == "" {
return "No results found.", nil, nil
}
if t.CitationCollector != nil {
t.CitationCollector.AddKBCitations(result.Citations)
}
return result.Prompt, nil, nil
return result, nil, nil
}
// KBAddMemoryArgs defines the arguments for the add_memory tool.
@@ -225,11 +156,9 @@ func (t KBAddMemoryTool) Run(args KBAddMemoryArgs) (string, any, error) {
// KBStoreContent uploads text content to a collection via the multipart upload API.
func KBStoreContent(ctx context.Context, apiURL, apiKey, collection, content, userID string) error {
uploadURL := strings.TrimRight(apiURL, "/") + "/api/agents/collections/" + url.PathEscape(collection) + "/upload"
uploadURL := strings.TrimRight(apiURL, "/") + "/api/agents/collections/" + collection + "/upload"
if userID != "" {
query := url.Values{}
query.Set("user_id", userID)
uploadURL += "?" + query.Encode()
uploadURL += "?user_id=" + userID
}
// Build multipart form with the text content as a file

View File

@@ -180,21 +180,18 @@ func (s *GalleryStore) Cancel(id string) error {
return s.UpdateStatus(id, "cancelled", "")
}
// CleanStale marks abandoned in-progress operations as failed and returns the
// number of rows reaped. Called on startup AND periodically to recover from
// crashed/restarted instances that left records in pending/downloading/
// processing state — an op orphaned after startup would otherwise linger
// "processing" until the next restart.
func (s *GalleryStore) CleanStale(age time.Duration) (int64, error) {
// CleanStale marks abandoned in-progress operations as failed.
// Should be called on startup to recover from crashed instances that
// left records in pending/downloading/processing state.
func (s *GalleryStore) CleanStale(age time.Duration) error {
cutoff := time.Now().Add(-age)
res := s.db.Model(&GalleryOperationRecord{}).
return s.db.Model(&GalleryOperationRecord{}).
Where("updated_at < ? AND status IN ?", cutoff, activeStatuses).
Updates(map[string]any{
"status": "failed",
"error": "stale operation reaped (abandoned by a crashed or restarted instance)",
"error": "stale operation cleaned up on startup",
"updated_at": time.Now(),
})
return res.RowsAffected, res.Error
}).Error
}
// CleanOld removes operations older than the given duration.

View File

@@ -71,7 +71,7 @@ func (g *GalleryService) backendHandler(op *ManagementOp[gallery.GalleryBackend,
var err error
if op.Upgrade {
err = g.backendManager.UpgradeBackend(ctx, op.ID, op.GalleryElementName, progressCallback)
err = g.backendManager.UpgradeBackend(ctx, op.GalleryElementName, progressCallback)
} else if op.Delete {
err = g.backendManager.DeleteBackend(op.GalleryElementName)
} else {

View File

@@ -1,106 +0,0 @@
package galleryop_test
import (
"context"
"time"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/services/distributed"
"github.com/mudler/LocalAI/core/services/galleryop"
"github.com/mudler/LocalAI/core/services/testutil"
)
// Reproduces "a cancelled/orphaned op resurrects as 'processing' after a pod
// restart". CancelOperation flipped the in-memory status to cancelled and
// broadcast a NATS event, but never persisted the terminal status to the
// gallery store. On the next replica restart the still-"pending" row hydrated
// straight back into processingBackends and the UI spun again. CancelOperation
// must persist the cancellation so it survives a restart.
var _ = Describe("GalleryService.CancelOperation persistence", func() {
It("persists the cancelled status to the gallery store", func() {
db := testutil.SetupTestDB()
store, err := distributed.NewGalleryStore(db)
Expect(err).ToNot(HaveOccurred())
// Seed an in-flight op as if a replica was mid-install.
Expect(store.Create(&distributed.GalleryOperationRecord{
ID: "op-cancel",
GalleryElementName: "llama-cpp-development",
OpType: "backend_install",
Status: "pending",
Progress: 0,
})).To(Succeed())
svc := galleryop.NewGalleryService(&config.ApplicationConfig{}, nil)
svc.SetGalleryStore(store)
// Make the op locally cancellable so CancelOperation proceeds.
svc.StoreCancellation("op-cancel", context.CancelFunc(func() {}))
Expect(svc.CancelOperation("op-cancel")).To(Succeed())
// The persisted row must now be terminal — otherwise it re-hydrates as
// pending on the next restart.
rec, err := store.Get("op-cancel")
Expect(err).ToNot(HaveOccurred())
Expect(rec.Status).To(Equal("cancelled"))
// And a fresh service hydrating from the store must NOT see it as active.
fresh := galleryop.NewGalleryService(&config.ApplicationConfig{}, nil)
fresh.SetGalleryStore(store)
Expect(fresh.Hydrate()).To(Succeed())
Expect(fresh.GetStatus("op-cancel")).To(BeNil(),
"a cancelled op must not hydrate back as active after a restart")
})
})
// Reproduces "an op orphaned by a replica that died mid-flight stays 'pending'
// forever". CleanStale (which marks abandoned active ops failed) only ran once
// on startup, so an op orphaned AFTER startup was never reaped until the next
// restart. The service must reap stale ops on an interval, not just at boot.
var _ = Describe("GalleryService.ReapStaleOperations", func() {
It("marks abandoned active ops terminal once they pass the age cutoff", func() {
db := testutil.SetupTestDB()
store, err := distributed.NewGalleryStore(db)
Expect(err).ToNot(HaveOccurred())
Expect(store.Create(&distributed.GalleryOperationRecord{
ID: "orphan-op",
GalleryElementName: "llama-cpp-development",
OpType: "backend_install",
Status: "pending",
Progress: 0,
})).To(Succeed())
// Force the row's updated_at into the past so it is older than the cutoff.
Expect(db.Exec(
"UPDATE gallery_operations SET updated_at = ? WHERE id = ?",
time.Now().Add(-1*time.Hour), "orphan-op",
).Error).To(Succeed())
// A fresh, still-progressing op must NOT be reaped.
Expect(store.Create(&distributed.GalleryOperationRecord{
ID: "live-op",
GalleryElementName: "vllm-development",
OpType: "backend_install",
Status: "downloading",
Progress: 50,
})).To(Succeed())
svc := galleryop.NewGalleryService(&config.ApplicationConfig{}, nil)
svc.SetGalleryStore(store)
reaped, err := svc.ReapStaleOperations(30 * time.Minute)
Expect(err).ToNot(HaveOccurred())
Expect(reaped).To(Equal(int64(1)))
orphan, err := store.Get("orphan-op")
Expect(err).ToNot(HaveOccurred())
Expect(orphan.Status).To(Equal("failed"))
live, err := store.Get("live-op")
Expect(err).ToNot(HaveOccurred())
Expect(live.Status).To(Equal("downloading"), "a recently-updated op must not be reaped")
})
})

View File

@@ -20,7 +20,7 @@ type BackendManager interface {
InstallBackend(ctx context.Context, op *ManagementOp[gallery.GalleryBackend, any], progressCb ProgressCallback) error
DeleteBackend(name string) error
ListBackends() (gallery.SystemBackends, error)
UpgradeBackend(ctx context.Context, opID, name string, progressCb ProgressCallback) error
UpgradeBackend(ctx context.Context, name string, progressCb ProgressCallback) error
CheckUpgrades(ctx context.Context) (map[string]gallery.UpgradeInfo, error)
// IsDistributed reports whether installs fan out across worker nodes.
// The HTTP layer uses this to refuse hardware-specific (non-meta) installs

View File

@@ -96,10 +96,7 @@ func (b *LocalBackendManager) ListBackends() (gallery.SystemBackends, error) {
return gallery.ListSystemBackends(b.systemState)
}
// UpgradeBackend ignores opID: a single-node install reports progress through
// the local progressCb already; opID only matters for distributed per-node
// streaming (see DistributedBackendManager.UpgradeBackend).
func (b *LocalBackendManager) UpgradeBackend(ctx context.Context, _ string, name string, progressCb ProgressCallback) error {
func (b *LocalBackendManager) UpgradeBackend(ctx context.Context, name string, progressCb ProgressCallback) error {
return gallery.UpgradeBackend(ctx, b.systemState, b.modelLoader, b.backendGalleries, name, progressCb, b.requireBackendIntegrity)
}

View File

@@ -1,107 +0,0 @@
package galleryop_test
import (
"errors"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/services/galleryop"
)
// These specs reproduce the distributed "Reinstall spins forever" bug:
// processingBackends (the UI spinner source) is built from OpCache.GetStatus,
// which historically returned every cached op unconditionally. Cleanup only
// happened when a client polled /api/backends/job/:uid, but the Manage-page
// Reinstall/Upgrade buttons never poll, so a completed install stayed in
// processingBackends forever. GetStatus must self-evict terminal ops.
var _ = Describe("OpCache.GetStatus eviction", func() {
var (
svc *galleryop.GalleryService
cache *galleryop.OpCache
)
BeforeEach(func() {
svc = galleryop.NewGalleryService(&config.ApplicationConfig{}, nil)
cache = galleryop.NewOpCache(svc)
})
It("keeps an op that is still processing", func() {
cache.SetBackend("llama-cpp", "uuid-1")
svc.UpdateStatus("uuid-1", &galleryop.OpStatus{Message: "processing backend: llama-cpp", Progress: 0})
processing, _ := cache.GetStatus()
Expect(processing).To(HaveKeyWithValue("llama-cpp", "uuid-1"))
Expect(cache.Exists("llama-cpp")).To(BeTrue())
})
It("evicts a completed op so it no longer shows as processing", func() {
cache.SetBackend("llama-cpp", "uuid-1")
svc.UpdateStatus("uuid-1", &galleryop.OpStatus{Processed: true, Progress: 100, Message: "completed"})
processing, _ := cache.GetStatus()
Expect(processing).NotTo(HaveKey("llama-cpp"))
Expect(cache.Exists("llama-cpp")).To(BeFalse())
})
It("keeps a failed op so the operations panel can surface the error and offer Dismiss", func() {
cache.SetBackend("piper", "uuid-2")
svc.UpdateStatus("uuid-2", &galleryop.OpStatus{Processed: true, Error: errors.New("boom")})
processing, _ := cache.GetStatus()
Expect(processing).To(HaveKeyWithValue("piper", "uuid-2"))
Expect(cache.Exists("piper")).To(BeTrue())
})
It("keeps the ErrWorkerStillInstalling soft-path op (worker still installing in background)", func() {
// Processed=true with no error but progress != 100 and a non-"completed"
// message: the worker timed out the NATS round-trip but is still installing.
// Evicting it would hide an install that may still fail; the reconciler
// confirms the real outcome later.
cache.SetBackend("vllm-development", "uuid-soft")
svc.UpdateStatus("uuid-soft", &galleryop.OpStatus{
Processed: true,
Message: "backend vllm-development: worker still installing in background; reconciler will confirm completion",
})
processing, _ := cache.GetStatus()
Expect(processing).To(HaveKeyWithValue("vllm-development", "uuid-soft"))
Expect(cache.Exists("vllm-development")).To(BeTrue())
})
It("evicts a cancelled op", func() {
cache.SetBackend("vllm", "uuid-3")
svc.UpdateStatus("uuid-3", &galleryop.OpStatus{Processed: true, Cancelled: true, Message: "cancelled"})
processing, _ := cache.GetStatus()
Expect(processing).NotTo(HaveKey("vllm"))
})
It("does not evict an op with no status yet (queued)", func() {
cache.SetBackend("whisper", "uuid-4")
processing, taskTypes := cache.GetStatus()
Expect(processing).To(HaveKeyWithValue("whisper", "uuid-4"))
Expect(taskTypes).To(HaveKeyWithValue("whisper", "Waiting"))
})
// Regression guard: GetStatus is called concurrently by four HTTP handlers
// (~1s poll). An earlier version evicted by deleting from m.Map() — which
// returns the live internal map by reference — causing a fatal
// "concurrent map writes" crash. Run under -race; must not panic or race.
It("is safe under concurrent GetStatus + Set/complete", func() {
done := make(chan struct{})
go func() {
defer GinkgoRecover()
for i := 0; i < 2000; i++ {
_, _ = cache.GetStatus()
}
close(done)
}()
for i := 0; i < 2000; i++ {
id := "uuid-c"
cache.SetBackend("concurrent-backend", id)
// Half the time mark it completed so GetStatus evicts it.
if i%2 == 0 {
svc.UpdateStatus(id, &galleryop.OpStatus{Processed: true, Progress: 100, Message: "completed"})
}
_, _ = cache.GetStatus()
}
<-done
})
})

View File

@@ -408,43 +408,12 @@ func (m *OpCache) Exists(key string) bool {
}
func (m *OpCache) GetStatus() (map[string]string, map[string]string) {
taskTypes := map[string]string{}
processingModelsData := map[string]string{}
processingModelsData := m.Map()
// Iterate a snapshot (Keys() copies) and build a fresh result map. We must
// NOT delete from m.Map() during the range: Map() returns the live internal
// map by reference, so a bare delete here would be an unsynchronized write
// to a map four HTTP handlers read every ~1s — a concurrent-map-write crash.
// Collect evictions and apply them via the locked DeleteUUID after the loop.
var evict []string
for _, k := range m.status.Keys() {
v := m.status.Get(k)
if v == "" {
continue // raced with a concurrent Delete
}
taskTypes := map[string]string{}
for k, v := range processingModelsData {
status := m.galleryService.GetStatus(v)
// Terminal ops must not keep showing as "processing". Cleanup was
// previously only triggered by a client polling /api/backends/job/:uid,
// but the Manage-page Reinstall/Upgrade buttons never poll, so completed
// ops leaked into processingBackends forever and the card spun
// "reinstalling" indefinitely. Evict here on the list read (the UI always
// calls this). DeleteUUID broadcasts the eviction so peer replicas converge.
//
// We evict ONLY a clean success (progress 100 + "completed", matching the
// job-poll's historical delete condition) or a cancellation. Deliberately
// NOT evicted:
// - failed ops (Error != nil): kept so /api/operations can surface the
// error and offer Dismiss.
// - the ErrWorkerStillInstalling soft-path (Processed=true, Error=nil,
// progress != 100): the worker is still installing in the background
// and the reconciler confirms the real outcome later — evicting it
// would hide an install that may still fail.
if status != nil && status.Processed &&
((status.Progress == 100 && status.Message == "completed") || status.Cancelled) {
evict = append(evict, v)
continue
}
processingModelsData[k] = v
taskTypes[k] = "Installation"
if status != nil && status.Deletion {
taskTypes[k] = "Deletion"
@@ -453,10 +422,6 @@ func (m *OpCache) GetStatus() (map[string]string, map[string]string) {
}
}
for _, v := range evict {
m.DeleteUUID(v)
}
return processingModelsData, taskTypes
}

View File

@@ -5,7 +5,6 @@ import (
"errors"
"fmt"
"sync"
"time"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/gallery"
@@ -32,9 +31,9 @@ type GalleryService struct {
// natsClient is the wider MessagingClient (Publisher + subscribe methods)
// when wired by the distributed startup path; broadcastSubs holds the
// progress + cancel subscriptions opened by SubscribeBroadcasts.
natsClient messaging.MessagingClient
galleryStore *distributed.GalleryStore
broadcastSubs []messaging.Subscription
natsClient messaging.MessagingClient
galleryStore *distributed.GalleryStore
broadcastSubs []messaging.Subscription
// OnBackendOpCompleted is fired after every successful install/upgrade/delete
// on the backend channel. The Application wires this to UpgradeChecker.TriggerCheck
@@ -275,29 +274,6 @@ func (g *GalleryService) GetAllStatus() map[string]*OpStatus {
return g.statuses
}
// ReapStaleOperations marks abandoned in-progress operations (pending/
// downloading/processing) older than `age` as failed, so an op orphaned by a
// replica that died mid-flight does not linger as "processing" forever. The
// store's CleanStale runs once on startup; this exposes it for periodic
// invocation (a post-startup orphan is otherwise not reaped until the next
// restart). No-op when no gallery store is wired. Returns rows reaped.
func (g *GalleryService) ReapStaleOperations(age time.Duration) (int64, error) {
g.Lock()
store := g.galleryStore
g.Unlock()
if store == nil {
return 0, nil
}
n, err := store.CleanStale(age)
if err != nil {
return 0, err
}
if n > 0 {
xlog.Info("Reaped stale gallery operations", "count", n)
}
return n, nil
}
// CancelOperation cancels an in-progress operation by its ID.
//
// In distributed mode the UI's cancel click may land on a different replica
@@ -319,7 +295,6 @@ func (g *GalleryService) CancelOperation(id string) error {
}
nc := g.natsClient
store := g.galleryStore
if !localExists && nc == nil {
g.Unlock()
@@ -340,17 +315,6 @@ func (g *GalleryService) CancelOperation(id string) error {
}
g.Unlock()
// Persist the terminal status so the cancel survives a restart. Without
// this the row stays in its active state and re-hydrates straight back into
// processingBackends on the next replica boot — the UI spins again on an op
// the admin already cancelled. The peer that broadcasts wins the write; a
// no-op when standalone (store nil).
if store != nil {
if err := store.Cancel(id); err != nil {
xlog.Warn("Failed to persist gallery operation cancellation", "op_id", id, "error", err)
}
}
// I/O and user-provided callback after Unlock — the cancel-wildcard
// subscriber loops back into applyCancel on this same replica, which
// would otherwise deadlock on g.Mutex.

View File

@@ -194,14 +194,6 @@ type BackendUpgradeRequest struct {
// but the field lets future per-replica metadata (e.g. progress reporting
// scoped to a slot) ride the same wire without a v3 type.
ReplicaIndex int32 `json:"replica_index,omitempty"`
// OpID identifies the admin-side operation. When non-empty the worker
// publishes BackendInstallProgressEvent values to
// SubjectNodeBackendInstallProgress(nodeID, OpID) while the force-reinstall
// runs, so the master can stream per-node progress for upgrades exactly as
// it already does for installs (an upgrade IS a force-reinstall, so the
// install-progress subject is reused rather than minting a new one — no new
// NATS permission or rolling-update compat surface). Empty on legacy callers.
OpID string `json:"op_id,omitempty"`
}
// BackendUpgradeReply mirrors BackendInstallReply minus Address — upgrade does

View File

@@ -157,82 +157,3 @@ func (c *InFlightTrackingClient) Rerank(ctx context.Context, in *pb.RerankReques
res, err := c.Backend.Rerank(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) VAD(ctx context.Context, in *pb.VADRequest, opts ...ggrpc.CallOption) (*pb.VADResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.VAD(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) Diarize(ctx context.Context, in *pb.DiarizeRequest, opts ...ggrpc.CallOption) (*pb.DiarizeResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.Diarize(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) FaceVerify(ctx context.Context, in *pb.FaceVerifyRequest, opts ...ggrpc.CallOption) (*pb.FaceVerifyResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.FaceVerify(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) FaceAnalyze(ctx context.Context, in *pb.FaceAnalyzeRequest, opts ...ggrpc.CallOption) (*pb.FaceAnalyzeResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.FaceAnalyze(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) VoiceVerify(ctx context.Context, in *pb.VoiceVerifyRequest, opts ...ggrpc.CallOption) (*pb.VoiceVerifyResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.VoiceVerify(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) VoiceAnalyze(ctx context.Context, in *pb.VoiceAnalyzeRequest, opts ...ggrpc.CallOption) (*pb.VoiceAnalyzeResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.VoiceAnalyze(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) VoiceEmbed(ctx context.Context, in *pb.VoiceEmbedRequest, opts ...ggrpc.CallOption) (*pb.VoiceEmbedResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.VoiceEmbed(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) TokenClassify(ctx context.Context, in *pb.TokenClassifyRequest, opts ...ggrpc.CallOption) (*pb.TokenClassifyResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.TokenClassify(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) Score(ctx context.Context, in *pb.ScoreRequest, opts ...ggrpc.CallOption) (*pb.ScoreResponse, error) {
defer c.track(ctx)()
res, err := c.Backend.Score(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) AudioEncode(ctx context.Context, in *pb.AudioEncodeRequest, opts ...ggrpc.CallOption) (*pb.AudioEncodeResult, error) {
defer c.track(ctx)()
res, err := c.Backend.AudioEncode(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) AudioDecode(ctx context.Context, in *pb.AudioDecodeRequest, opts ...ggrpc.CallOption) (*pb.AudioDecodeResult, error) {
defer c.track(ctx)()
res, err := c.Backend.AudioDecode(ctx, in, opts...)
return res, c.reconcile(err)
}
func (c *InFlightTrackingClient) AudioTransform(ctx context.Context, in *pb.AudioTransformRequest, opts ...ggrpc.CallOption) (*pb.AudioTransformResult, error) {
defer c.track(ctx)()
res, err := c.Backend.AudioTransform(ctx, in, opts...)
return res, c.reconcile(err)
}
// AudioTransformStream, AudioToAudioStream and Forward are deliberately left as
// embedded passthrough: they return a stream client and the inference spans the
// stream's lifetime, not the constructor call. Wrapping the constructor with
// track() would increment and immediately decrement (and fire onFirstComplete)
// before any audio flows. Tracking those correctly needs the done() func tied to
// stream close, which the current Backend interface doesn't surface here.

View File

@@ -304,105 +304,6 @@ var _ = Describe("InFlightTrackingClient", func() {
})
})
Describe("non-LLM inference methods track in-flight", func() {
// silero-vad and friends only ever expose a single non-Predict method.
// If that method isn't wrapped, the load-time reservation released by
// onFirstComplete never fires and in-flight is stuck at 1 forever.
assertTracked := func(call func() error) {
var firstFired int
client.OnFirstComplete(func() { firstFired++ })
err := call()
Expect(err).ToNot(HaveOccurred())
Expect(tracker.increments).To(Equal(1), "method must increment in-flight")
Expect(tracker.decrements).To(Equal(1), "method must decrement in-flight")
Expect(firstFired).To(Equal(1), "method must release the load-time reservation")
}
It("VAD", func() {
assertTracked(func() error {
_, err := client.VAD(context.Background(), &pb.VADRequest{})
return err
})
})
It("Diarize", func() {
assertTracked(func() error {
_, err := client.Diarize(context.Background(), &pb.DiarizeRequest{})
return err
})
})
It("VoiceVerify", func() {
assertTracked(func() error {
_, err := client.VoiceVerify(context.Background(), &pb.VoiceVerifyRequest{})
return err
})
})
It("VoiceAnalyze", func() {
assertTracked(func() error {
_, err := client.VoiceAnalyze(context.Background(), &pb.VoiceAnalyzeRequest{})
return err
})
})
It("VoiceEmbed", func() {
assertTracked(func() error {
_, err := client.VoiceEmbed(context.Background(), &pb.VoiceEmbedRequest{})
return err
})
})
It("FaceVerify", func() {
assertTracked(func() error {
_, err := client.FaceVerify(context.Background(), &pb.FaceVerifyRequest{})
return err
})
})
It("FaceAnalyze", func() {
assertTracked(func() error {
_, err := client.FaceAnalyze(context.Background(), &pb.FaceAnalyzeRequest{})
return err
})
})
It("TokenClassify", func() {
assertTracked(func() error {
_, err := client.TokenClassify(context.Background(), &pb.TokenClassifyRequest{})
return err
})
})
It("Score", func() {
assertTracked(func() error {
_, err := client.Score(context.Background(), &pb.ScoreRequest{})
return err
})
})
It("AudioEncode", func() {
assertTracked(func() error {
_, err := client.AudioEncode(context.Background(), &pb.AudioEncodeRequest{})
return err
})
})
It("AudioDecode", func() {
assertTracked(func() error {
_, err := client.AudioDecode(context.Background(), &pb.AudioDecodeRequest{})
return err
})
})
It("AudioTransform", func() {
assertTracked(func() error {
_, err := client.AudioTransform(context.Background(), &pb.AudioTransformRequest{})
return err
})
})
})
Describe("stale model reload (self-heal)", func() {
It("removes the replica when the backend reports the model is not loaded", func() {
backend.predictErr = fmt.Errorf("parakeet-cpp: model not loaded")

View File

@@ -533,7 +533,7 @@ func (d *DistributedBackendManager) InstallBackend(ctx context.Context, op *gall
// backend.upgrade, we try the legacy backend.install Force=true path so a
// new master + old worker still converges. Drop the fallback once every
// worker in the fleet is on 2026-05-08 or newer.
func (d *DistributedBackendManager) UpgradeBackend(ctx context.Context, opID, name string, progressCb galleryop.ProgressCallback) error {
func (d *DistributedBackendManager) UpgradeBackend(ctx context.Context, name string, progressCb galleryop.ProgressCallback) error {
galleriesJSON, _ := json.Marshal(d.backendGalleries)
installed, err := d.ListBackends()
@@ -549,39 +549,17 @@ func (d *DistributedBackendManager) UpgradeBackend(ctx context.Context, opID, na
targetNodeIDs[n.NodeID] = true
}
result, err := d.enqueueAndDrainBackendOp(ctx, opID, OpBackendUpgrade, name, galleriesJSON, targetNodeIDs, func(node BackendNode) error {
// Per-node progress sink: fan each worker download tick into the legacy
// single-bar progressCb and the per-node OpStatus.Nodes view, exactly as
// InstallBackend does. Defined per-node so each closure captures its own
// node.Name. Without this an upgrade blocks opaque at progress 0 for the
// whole 15m round-trip (the original "reinstalling but nothing happens").
onProgress := func(ev messaging.BackendInstallProgressEvent) {
if progressCb != nil {
progressCb(ev.FileName, ev.Current, ev.Total, ev.Percentage)
}
if d.progressSink != nil && opID != "" {
d.progressSink.UpdateNodeProgress(opID, ev.NodeID, galleryop.NodeProgress{
NodeID: ev.NodeID,
NodeName: node.Name,
Status: galleryop.NodeStatusDownloading,
FileName: ev.FileName,
Current: ev.Current,
Total: ev.Total,
Percentage: ev.Percentage,
Phase: ev.Phase,
})
}
}
var onProgressArg func(messaging.BackendInstallProgressEvent)
if progressCb != nil || d.progressSink != nil {
onProgressArg = onProgress
}
reply, err := d.adapter.UpgradeBackend(node.ID, name, string(galleriesJSON), "", "", "", 0, opID, onProgressArg)
// Empty opID: the caller (galleryop) doesn't thread an op ID into
// UpgradeBackend today, so we can't tag per-node sink writes with the
// right OpStatus key. Until the upgrade path takes a ManagementOp the
// way InstallBackend does, the sink stays no-op here.
result, err := d.enqueueAndDrainBackendOp(ctx, "", OpBackendUpgrade, name, galleriesJSON, targetNodeIDs, func(node BackendNode) error {
reply, err := d.adapter.UpgradeBackend(node.ID, name, string(galleriesJSON), "", "", "", 0)
if err != nil {
// Rolling-update fallback: an older worker doesn't know
// backend.upgrade. Try the legacy install-with-force path.
if errors.Is(err, nats.ErrNoResponders) {
instReply, instErr := d.adapter.installWithForceFallback(node.ID, name, string(galleriesJSON), "", "", "", 0, opID, onProgressArg)
instReply, instErr := d.adapter.installWithForceFallback(node.ID, name, string(galleriesJSON), "", "", "", 0)
if instErr != nil {
return instErr
}

View File

@@ -317,7 +317,7 @@ func (stubLocalBackendManager) DeleteBackend(_ string) error { return gallery.Er
func (stubLocalBackendManager) ListBackends() (gallery.SystemBackends, error) {
return gallery.SystemBackends{}, nil
}
func (stubLocalBackendManager) UpgradeBackend(_ context.Context, _ string, _ string, _ galleryop.ProgressCallback) error {
func (stubLocalBackendManager) UpgradeBackend(_ context.Context, _ string, _ galleryop.ProgressCallback) error {
return nil
}
func (stubLocalBackendManager) CheckUpgrades(_ context.Context) (map[string]gallery.UpgradeInfo, error) {
@@ -782,7 +782,7 @@ var _ = Describe("DistributedBackendManager", func() {
mc.scriptReply(messaging.SubjectNodeBackendUpgrade(n2.ID),
messaging.BackendUpgradeReply{Success: false, Error: "registry unauthorized"})
err := mgr.UpgradeBackend(ctx, "", "vllm-development", nil)
err := mgr.UpgradeBackend(ctx, "vllm-development", nil)
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("worker-a"))
Expect(err.Error()).To(ContainSubstring("image manifest not found"))
@@ -797,7 +797,7 @@ var _ = Describe("DistributedBackendManager", func() {
scriptInstalled("vllm-development", n1.ID)
mc.scriptReply(messaging.SubjectNodeBackendUpgrade(n1.ID),
messaging.BackendUpgradeReply{Success: true})
Expect(mgr.UpgradeBackend(ctx, "", "vllm-development", nil)).To(Succeed())
Expect(mgr.UpgradeBackend(ctx, "vllm-development", nil)).To(Succeed())
})
})
@@ -819,7 +819,7 @@ var _ = Describe("DistributedBackendManager", func() {
// if the manager attempts it, the scripted-client default returns
// fakeNoRespondersErr and the assertion below fails loudly.
Expect(mgr.UpgradeBackend(ctx, "", "cpu-insightface-development", nil)).To(Succeed())
Expect(mgr.UpgradeBackend(ctx, "cpu-insightface-development", nil)).To(Succeed())
mc.mu.Lock()
defer mc.mu.Unlock()
@@ -835,7 +835,7 @@ var _ = Describe("DistributedBackendManager", func() {
n1 := registerHealthyBackend("worker-a", "10.0.0.1:50051")
scriptNoBackends(n1.ID)
err := mgr.UpgradeBackend(ctx, "", "vllm-development", nil)
err := mgr.UpgradeBackend(ctx, "vllm-development", nil)
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("not installed on any node"))
@@ -865,7 +865,7 @@ var _ = Describe("DistributedBackendManager", func() {
func(req messaging.BackendInstallRequest) bool { return req.Force },
messaging.BackendInstallReply{Success: true, Address: "10.0.0.1:50100"})
Expect(mgr.UpgradeBackend(ctx, "", "vllm-development", nil)).To(Succeed())
Expect(mgr.UpgradeBackend(ctx, "vllm-development", nil)).To(Succeed())
})
It("returns the upgrade error when it is not ErrNoResponders", func() {
@@ -875,7 +875,7 @@ var _ = Describe("DistributedBackendManager", func() {
mc.scriptReply(messaging.SubjectNodeBackendUpgrade(n.ID),
messaging.BackendUpgradeReply{Success: false, Error: "disk full"})
err := mgr.UpgradeBackend(ctx, "", "vllm-development", nil)
err := mgr.UpgradeBackend(ctx, "vllm-development", nil)
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("disk full"))
})

View File

@@ -1,135 +0,0 @@
package nodes
import (
"context"
"runtime"
"time"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"github.com/mudler/LocalAI/core/services/testutil"
)
// These specs reproduce the distributed "pending ops behind dead nodes leak
// forever" bug. ListDuePendingBackendOps only returns rows whose node is
// StatusHealthy, so an op queued against a node that goes offline (heartbeat
// stale) or draining (admin action) is never retried, never aged out, and
// never deleted. On a live cluster these rows sat at attempts=0 indefinitely
// and kept the UI operation alive. DeleteStalePendingBackendOps garbage-collects
// them: draining nodes immediately (models already purged), offline nodes only
// after a grace window so a brief heartbeat blip does not nuke in-flight work.
var _ = Describe("DeleteStalePendingBackendOps", func() {
var (
registry *NodeRegistry
ctx context.Context
)
BeforeEach(func() {
if runtime.GOOS == "darwin" {
Skip("testcontainers requires Docker, not available on macOS CI")
}
db := testutil.SetupTestDB()
var err error
registry, err = NewNodeRegistry(db)
Expect(err).ToNot(HaveOccurred())
ctx = context.Background()
})
// registerBackend registers an auto-approved backend node and returns its ID.
registerBackend := func(name, address string) string {
node := &BackendNode{Name: name, NodeType: NodeTypeBackend, Address: address}
Expect(registry.Register(ctx, node, true)).To(Succeed())
fetched, err := registry.GetByName(ctx, name)
Expect(err).ToNot(HaveOccurred())
return fetched.ID
}
// setHeartbeat forces a node's last_heartbeat (Register/MarkOffline leave it
// at "now"; we age it to simulate a node that went silent a while ago).
setHeartbeat := func(nodeID string, t time.Time) {
Expect(registry.db.WithContext(ctx).Model(&BackendNode{}).
Where("id = ?", nodeID).
Update("last_heartbeat", t).Error).To(Succeed())
}
pendingCountFor := func(nodeID string) int64 {
var n int64
Expect(registry.db.WithContext(ctx).Model(&PendingBackendOp{}).
Where("node_id = ?", nodeID).Count(&n).Error).To(Succeed())
return n
}
It("clears ops behind an offline node whose heartbeat is past the grace window", func() {
dead := registerBackend("nvidia-thor", "10.0.0.9:50051")
Expect(registry.UpsertPendingBackendOp(ctx, dead, "llama-cpp-development", OpBackendInstall, nil)).To(Succeed())
Expect(registry.MarkOffline(ctx, dead)).To(Succeed())
setHeartbeat(dead, time.Now().Add(-1*time.Hour))
removed, err := registry.DeleteStalePendingBackendOps(ctx, 10*time.Minute)
Expect(err).ToNot(HaveOccurred())
Expect(removed).To(Equal(int64(1)))
Expect(pendingCountFor(dead)).To(Equal(int64(0)))
})
It("clears ops behind a draining node immediately, even with a fresh heartbeat", func() {
// Mirrors the live mac-mini-m4 case: draining but still heartbeating.
drain := registerBackend("mac-mini-m4", "10.0.0.3:50051")
Expect(registry.UpsertPendingBackendOp(ctx, drain, "llama-cpp-development", OpBackendInstall, nil)).To(Succeed())
Expect(registry.MarkDraining(ctx, drain)).To(Succeed())
setHeartbeat(drain, time.Now()) // fresh heartbeat
removed, err := registry.DeleteStalePendingBackendOps(ctx, 10*time.Minute)
Expect(err).ToNot(HaveOccurred())
Expect(removed).To(Equal(int64(1)))
Expect(pendingCountFor(drain)).To(Equal(int64(0)))
})
It("clears ops behind an unhealthy node with a stale heartbeat (never ages to offline)", func() {
// A node marked unhealthy on a NATS ErrNoResponders never transitions to
// offline, so its ops must be reaped via the same stale-heartbeat path.
sick := registerBackend("agx-orin-sick", "10.0.0.7:50051")
Expect(registry.UpsertPendingBackendOp(ctx, sick, "llama-cpp-development", OpBackendUpgrade, nil)).To(Succeed())
Expect(registry.MarkUnhealthy(ctx, sick)).To(Succeed())
setHeartbeat(sick, time.Now().Add(-1*time.Hour))
removed, err := registry.DeleteStalePendingBackendOps(ctx, 10*time.Minute)
Expect(err).ToNot(HaveOccurred())
Expect(removed).To(Equal(int64(1)))
Expect(pendingCountFor(sick)).To(Equal(int64(0)))
})
It("keeps ops behind an unhealthy node that is still heartbeating (recovering)", func() {
recovering := registerBackend("agx-orin-flap", "10.0.0.8:50051")
Expect(registry.UpsertPendingBackendOp(ctx, recovering, "llama-cpp-development", OpBackendUpgrade, nil)).To(Succeed())
Expect(registry.MarkUnhealthy(ctx, recovering)).To(Succeed())
setHeartbeat(recovering, time.Now()) // fresh heartbeat → recovering
removed, err := registry.DeleteStalePendingBackendOps(ctx, 10*time.Minute)
Expect(err).ToNot(HaveOccurred())
Expect(removed).To(Equal(int64(0)))
Expect(pendingCountFor(recovering)).To(Equal(int64(1)))
})
It("keeps ops behind a node that only just went offline (within grace)", func() {
blip := registerBackend("agx-orin", "10.0.0.4:50051")
Expect(registry.UpsertPendingBackendOp(ctx, blip, "parakeet-cpp-development", OpBackendInstall, nil)).To(Succeed())
Expect(registry.MarkOffline(ctx, blip)).To(Succeed())
setHeartbeat(blip, time.Now().Add(-1*time.Minute)) // gone only 1m, grace 10m
removed, err := registry.DeleteStalePendingBackendOps(ctx, 10*time.Minute)
Expect(err).ToNot(HaveOccurred())
Expect(removed).To(Equal(int64(0)))
Expect(pendingCountFor(blip)).To(Equal(int64(1)))
})
It("keeps ops behind a healthy node", func() {
healthy := registerBackend("dgx-spark", "10.0.0.1:50051")
Expect(registry.UpsertPendingBackendOp(ctx, healthy, "llama-cpp-development", OpBackendUpgrade, nil)).To(Succeed())
removed, err := registry.DeleteStalePendingBackendOps(ctx, 10*time.Minute)
Expect(err).ToNot(HaveOccurred())
Expect(removed).To(Equal(int64(0)))
Expect(pendingCountFor(healthy)).To(Equal(int64(1)))
})
})

View File

@@ -189,13 +189,6 @@ func (rc *ReplicaReconciler) reconcileState(ctx context.Context) {
// passed on nodes that are currently healthy. On success the row is deleted;
// on failure attempts++ and next_retry_at moves out via exponential backoff.
func (rc *ReplicaReconciler) drainPendingBackendOps(ctx context.Context) {
// Garbage-collect ops behind nodes that went offline/draining. These are
// invisible to ListDuePendingBackendOps (which filters status=healthy), so
// without this sweep they leak forever and keep the UI operation spinning.
if _, err := rc.registry.DeleteStalePendingBackendOps(ctx, stalePendingBackendOpGrace); err != nil {
xlog.Warn("Reconciler: failed to clear stale pending backend ops", "error", err)
}
ops, err := rc.registry.ListDuePendingBackendOps(ctx)
if err != nil {
xlog.Warn("Reconciler: failed to list pending backend ops", "error", err)
@@ -230,13 +223,10 @@ func (rc *ReplicaReconciler) drainPendingBackendOps(ctx context.Context) {
// the same worker. Falls back to the legacy backend.install
// Force=true path on nats.ErrNoResponders for old workers that
// don't subscribe to backend.upgrade yet (rolling-update window).
// Reconciler retries are background reconciliation with no live
// admin watching a progress bar, so opID/onProgress are empty —
// the adapter skips the progress subscription entirely.
reply, err := rc.adapter.UpgradeBackend(op.NodeID, op.Backend, string(op.Galleries), "", "", "", 0, "", nil)
reply, err := rc.adapter.UpgradeBackend(op.NodeID, op.Backend, string(op.Galleries), "", "", "", 0)
if err != nil {
if errors.Is(err, nats.ErrNoResponders) {
instReply, instErr := rc.adapter.installWithForceFallback(op.NodeID, op.Backend, string(op.Galleries), "", "", "", 0, "", nil)
instReply, instErr := rc.adapter.installWithForceFallback(op.NodeID, op.Backend, string(op.Galleries), "", "", "", 0)
if instErr != nil {
applyErr = instErr
} else if !instReply.Success {
@@ -303,13 +293,6 @@ func (rc *ReplicaReconciler) drainPendingBackendOps(ctx context.Context) {
// amount of further retrying will help.
const maxPendingBackendOpAttempts = 10
// stalePendingBackendOpGrace is how long a node may be offline before its
// pending backend ops are garbage-collected. Draining nodes are cleared
// immediately regardless of this window (see DeleteStalePendingBackendOps).
// ListDuePendingBackendOps never surfaces ops behind non-healthy nodes, so
// without this sweep they would leak forever and keep the UI op spinning.
const stalePendingBackendOpGrace = 15 * time.Minute
// probeLoadedModels gRPC-health-checks model addresses that the DB says are
// loaded. If a model's backend process is gone (OOM, crash, manual restart)
// we remove the row so ghosts don't linger. Only probes rows older than

View File

@@ -1776,38 +1776,6 @@ func (r *NodeRegistry) DeletePendingBackendOp(ctx context.Context, id uint) erro
return nil
}
// DeleteStalePendingBackendOps garbage-collects pending backend ops whose target
// node can never drain them. ListDuePendingBackendOps only returns rows behind a
// StatusHealthy node, so ops behind a node that went offline or draining are
// otherwise never retried, aged out, or deleted — they leak forever and keep the
// UI operation spinning. Draining nodes are cleared immediately (an explicit
// admin action; their model rows are already purged). Offline nodes are cleared
// only once their last heartbeat is older than `grace`, so a brief heartbeat blip
// does not nuke an install that is still legitimately in flight. Returns the
// number of rows deleted.
func (r *NodeRegistry) DeleteStalePendingBackendOps(ctx context.Context, grace time.Duration) (int64, error) {
cutoff := time.Now().Add(-grace)
// Draining nodes are cleared immediately (admin action; model rows already
// purged). Offline AND unhealthy nodes are cleared only once their heartbeat
// is older than the grace window: a node marked unhealthy on a NATS
// ErrNoResponders never transitions to offline (health.go skips re-marking
// it), so without including unhealthy here its ops would leak exactly like
// the offline case. A node with a fresh heartbeat (last_heartbeat > cutoff)
// is recovering and keeps its op for retry.
res := r.db.WithContext(ctx).
Where(`node_id IN (SELECT id FROM backend_nodes WHERE status = ?)
OR node_id IN (SELECT id FROM backend_nodes WHERE status IN ? AND last_heartbeat <= ?)`,
StatusDraining, []string{StatusOffline, StatusUnhealthy}, cutoff).
Delete(&PendingBackendOp{})
if res.Error != nil {
return 0, fmt.Errorf("deleting stale pending backend ops: %w", res.Error)
}
if res.RowsAffected > 0 {
xlog.Info("Cleared pending backend ops behind non-healthy nodes", "deleted", res.RowsAffected)
}
return res.RowsAffected, nil
}
// RecordPendingBackendOpFailure bumps Attempts, captures the error, and
// pushes NextRetryAt out with exponential backoff capped at 15 minutes.
func (r *NodeRegistry) RecordPendingBackendOpFailure(ctx context.Context, id uint, errMsg string) error {

View File

@@ -365,7 +365,7 @@ func (f *fakeUnloader) InstallBackend(nodeID, backend, modelID, _, _, _, _ strin
return f.installReply, f.installErr
}
func (f *fakeUnloader) UpgradeBackend(nodeID, backend, _, _, _, _ string, replica int, _ string, _ func(messaging.BackendInstallProgressEvent)) (*messaging.BackendUpgradeReply, error) {
func (f *fakeUnloader) UpgradeBackend(nodeID, backend, _, _, _, _ string, replica int) (*messaging.BackendUpgradeReply, error) {
f.mu.Lock()
f.upgradeCalls = append(f.upgradeCalls, upgradeCall{nodeID, backend, replica})
f.mu.Unlock()

View File

@@ -35,7 +35,7 @@ type backendStopRequest struct {
// backend.upgrade subject.
type NodeCommandSender interface {
InstallBackend(nodeID, backendType, modelID, galleriesJSON, uri, name, alias string, replicaIndex int, opID string, onProgress func(messaging.BackendInstallProgressEvent)) (*messaging.BackendInstallReply, error)
UpgradeBackend(nodeID, backendType, galleriesJSON, uri, name, alias string, replicaIndex int, opID string, onProgress func(messaging.BackendInstallProgressEvent)) (*messaging.BackendUpgradeReply, error)
UpgradeBackend(nodeID, backendType, galleriesJSON, uri, name, alias string, replicaIndex int) (*messaging.BackendUpgradeReply, error)
DeleteBackend(nodeID, backendName string) (*messaging.BackendDeleteReply, error)
ListBackends(nodeID string) (*messaging.BackendListReply, error)
StopBackend(nodeID, backend string) error
@@ -127,8 +127,38 @@ func (a *RemoteUnloaderAdapter) InstallBackend(
xlog.Info("Sending NATS backend.install", "nodeID", nodeID, "backend", backendType, "modelID", modelID, "replica", replicaIndex, "opID", opID)
// Subscribe to the per-op progress subject BEFORE publishing the install
// request so we don't miss early events.
sub := a.subscribeProgress(nodeID, opID, onProgress)
// request so we don't miss early events. When onProgress is nil OR opID
// is empty (the reconciler-driven retry path), skip subscription entirely:
// silent installs cost nothing extra.
var sub messaging.Subscription
if onProgress != nil && opID != "" {
progressSubject := messaging.SubjectNodeBackendInstallProgress(nodeID, opID)
s, subErr := a.nats.Subscribe(progressSubject, func(raw []byte) {
var ev messaging.BackendInstallProgressEvent
if err := json.Unmarshal(raw, &ev); err != nil {
xlog.Debug("malformed install progress event", "subject", progressSubject, "error", err)
return
}
// Goroutine guard: a slow onProgress callback must not stall
// the NATS reader thread.
//
// NOTE: events spawn one goroutine each, so ordering at the
// consumer is best-effort. In practice the worker debounces to
// ~250ms which is far larger than goroutine scheduling jitter,
// so reordering is rare. The worker's final Flush() event is
// intended to win as the terminal tick. A future hardening pass
// could add a Seq uint64 field to BackendInstallProgressEvent
// and drop stale-by-seq at the bridge if reordering becomes a
// real UX issue.
go onProgress(ev)
})
if subErr != nil {
xlog.Warn("Failed to subscribe to install progress subject; proceeding without progress streaming",
"subject", progressSubject, "error", subErr)
} else {
sub = s
}
}
reply, err := messaging.RequestJSON[messaging.BackendInstallRequest, messaging.BackendInstallReply](a.nats, subject, messaging.BackendInstallRequest{
Backend: backendType,
@@ -152,58 +182,18 @@ func (a *RemoteUnloaderAdapter) InstallBackend(
return reply, err
}
// subscribeProgress subscribes to the per-op backend-install progress subject
// so the master can stream per-node download ticks while a worker installs or
// upgrades. Returns nil (and subscribes to nothing) when onProgress is nil or
// opID is empty — the reconciler-driven retry path and legacy callers stay
// silent at no cost. Shared by InstallBackend, UpgradeBackend, and the legacy
// force-install fallback: an upgrade is a force-reinstall, so it reuses the
// install-progress subject rather than minting a new one (no new NATS
// permission, no new rolling-update compat surface). Caller must Unsubscribe
// the returned subscription after the request completes.
func (a *RemoteUnloaderAdapter) subscribeProgress(nodeID, opID string, onProgress func(messaging.BackendInstallProgressEvent)) messaging.Subscription {
if onProgress == nil || opID == "" {
return nil
}
progressSubject := messaging.SubjectNodeBackendInstallProgress(nodeID, opID)
s, subErr := a.nats.Subscribe(progressSubject, func(raw []byte) {
var ev messaging.BackendInstallProgressEvent
if err := json.Unmarshal(raw, &ev); err != nil {
xlog.Debug("malformed backend progress event", "subject", progressSubject, "error", err)
return
}
// Goroutine guard: a slow onProgress callback must not stall the NATS
// reader thread. Events spawn one goroutine each, so ordering at the
// consumer is best-effort; the worker debounces to ~250ms which dwarfs
// goroutine scheduling jitter, and its final Flush() is the terminal tick.
go onProgress(ev)
})
if subErr != nil {
xlog.Warn("Failed to subscribe to backend progress subject; proceeding without progress streaming",
"subject", progressSubject, "error", subErr)
return nil
}
return s
}
// UpgradeBackend sends a backend.upgrade request-reply to a worker node.
// The worker stops every live process for this backend, force-reinstalls
// from the gallery (overwriting the on-disk artifact), and replies. The
// next routine InstallBackend call spawns a fresh process with the new
// binary - upgrade itself does not start a process.
//
// When opID is non-empty and onProgress is set, the master subscribes to the
// per-op progress subject before firing the request so a long force-reinstall
// streams per-node download ticks instead of blocking opaque at progress 0.
//
// Timeout: configured via DistributedConfig.BackendUpgradeTimeoutOrDefault
// (default 15m). Real-world worst case observed: 8-10 minutes for large
// CUDA-l4t backend images on Jetson over WiFi.
func (a *RemoteUnloaderAdapter) UpgradeBackend(nodeID, backendType, galleriesJSON, uri, name, alias string, replicaIndex int, opID string, onProgress func(messaging.BackendInstallProgressEvent)) (*messaging.BackendUpgradeReply, error) {
func (a *RemoteUnloaderAdapter) UpgradeBackend(nodeID, backendType, galleriesJSON, uri, name, alias string, replicaIndex int) (*messaging.BackendUpgradeReply, error) {
subject := messaging.SubjectNodeBackendUpgrade(nodeID)
xlog.Info("Sending NATS backend.upgrade", "nodeID", nodeID, "backend", backendType, "replica", replicaIndex, "opID", opID)
sub := a.subscribeProgress(nodeID, opID, onProgress)
xlog.Info("Sending NATS backend.upgrade", "nodeID", nodeID, "backend", backendType, "replica", replicaIndex)
reply, err := messaging.RequestJSON[messaging.BackendUpgradeRequest, messaging.BackendUpgradeReply](a.nats, subject, messaging.BackendUpgradeRequest{
Backend: backendType,
@@ -212,13 +202,7 @@ func (a *RemoteUnloaderAdapter) UpgradeBackend(nodeID, backendType, galleriesJSO
Name: name,
Alias: alias,
ReplicaIndex: int32(replicaIndex),
OpID: opID,
}, a.upgradeTimeout)
if sub != nil {
_ = sub.Unsubscribe()
}
if err != nil && isNATSTimeout(err) {
return nil, fmt.Errorf("%w (subject=%s nodeID=%s backend=%s): %v",
galleryop.ErrWorkerStillInstalling, subject, nodeID, backendType, err)
@@ -232,12 +216,10 @@ func (a *RemoteUnloaderAdapter) UpgradeBackend(nodeID, backendType, galleriesJSO
// doesn't subscribe to the new subject). It re-fires the legacy
// backend.install with Force=true. Drop this once every worker is on
// 2026-05-08 or newer.
func (a *RemoteUnloaderAdapter) installWithForceFallback(nodeID, backendType, galleriesJSON, uri, name, alias string, replicaIndex int, opID string, onProgress func(messaging.BackendInstallProgressEvent)) (*messaging.BackendInstallReply, error) {
func (a *RemoteUnloaderAdapter) installWithForceFallback(nodeID, backendType, galleriesJSON, uri, name, alias string, replicaIndex int) (*messaging.BackendInstallReply, error) {
subject := messaging.SubjectNodeBackendInstall(nodeID)
xlog.Warn("Falling back to legacy backend.install Force=true (old worker)", "nodeID", nodeID, "backend", backendType)
sub := a.subscribeProgress(nodeID, opID, onProgress)
reply, err := messaging.RequestJSON[messaging.BackendInstallRequest, messaging.BackendInstallReply](a.nats, subject, messaging.BackendInstallRequest{
Backend: backendType,
BackendGalleries: galleriesJSON,
@@ -246,13 +228,7 @@ func (a *RemoteUnloaderAdapter) installWithForceFallback(nodeID, backendType, ga
Alias: alias,
ReplicaIndex: int32(replicaIndex),
Force: true,
OpID: opID,
}, a.upgradeTimeout)
if sub != nil {
_ = sub.Unsubscribe()
}
if err != nil && isNATSTimeout(err) {
return nil, fmt.Errorf("%w (subject=%s nodeID=%s backend=%s): %v",
galleryop.ErrWorkerStillInstalling, subject, nodeID, backendType, err)

View File

@@ -282,7 +282,7 @@ var _ = Describe("RemoteUnloaderAdapter timeout configuration", func() {
mc.scriptReply(messaging.SubjectNodeBackendUpgrade("n1"), messaging.BackendUpgradeReply{Success: true})
adapter := NewRemoteUnloaderAdapter(nil, mc, 7*time.Minute, 11*time.Minute)
_, err := adapter.UpgradeBackend("n1", "llama-cpp", "[]", "", "", "", 0, "", nil)
_, err := adapter.UpgradeBackend("n1", "llama-cpp", "[]", "", "", "", 0)
Expect(err).ToNot(HaveOccurred())
Expect(mc.calls).To(HaveLen(1))

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