* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): adapt to the common JSON API
The llama.cpp bump replaces its nlohmann JSON alias with common_json. Update the gRPC adapter for the new exception, iterator, conversion, and container APIs.
Assisted-by: Codex:gpt-5.6 [systematic-debugging]
* fix(turboquant): adapt the JSON exception type
The shared gRPC source now follows the upstream common_json API. The
TurboQuant fork still exposes nlohmann JSON and cannot compile the new
exception type.
Translate that exception in the fork-specific source patch so both
llama.cpp variants compile from the shared adapter.
Assisted-by: Codex:gpt-5.6 [systematic-debugging]
* fix(bonsai): adapt the JSON exception type
The shared gRPC source uses upstream's common_json wrapper. The Bonsai fork still exposes nlohmann JSON and cannot compile that exception type.\n\nTranslate the exception in the fork-specific preparation step and verify that repeated preparation stays idempotent.\n\nAssisted-by: Codex:gpt-5.6 [systematic-debugging]
* fix(llama-cpp): let prepare register gRPC
The score patch duplicated the gRPC CMake registration that prepare.sh already owns. Its stale context rejects the current upstream tools file on Darwin before compilation starts.
Assisted-by: Codex:gpt-5
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
The llama-cpp gRPC backend reconstructs OpenAI messages from proto for the
tokenizer-template path and blindly json::parse'd each message's content
string. LocalAI's Go layer always flattens content to a plain string, so a
user prompt that merely looks like JSON (e.g. mealie's ingredient array
["1/4 cup brown sugar", ...]) was reinterpreted as structured content parts and
rejected by oaicompat_chat_params_parse with "unsupported content[].type".
Normalize content per role instead: user/system/developer content is opaque
text and is never JSON-sniffed; assistant/tool content still collapses a literal
JSON null/object (tool-call bookkeeping) to a string, but a plain string is
never turned into an array/scalar. The array defense is role-independent, so the
role gate only governs the benign null/object case.
While here, extract the duplicated per-message reconstruction and the
pre-template content sanitization into shared, unit-tested helpers
(message_content.h) so the streaming (PredictStream) and non-streaming (Predict)
paths cannot drift. This removes ~490 lines of copy-pasted defensive code, the
dead tool-role parse branches, and the redundant Predict-only tool_calls branch,
while preserving the prior #7324 (null content -> "") and #7528 (tool array
content -> string) fixes.
Tests:
- backend/cpp/llama-cpp/message_content_test.cpp: standalone C++ unit tests for
all three helpers (#10524, #7324, #7528, multimodal), discovered and run by
`make test-backend-cpp` and a new generic tests-backend-cpp CI job. Also wired
as an opt-in CMake/ctest target (-DLLAMA_GRPC_BUILD_TESTS=ON).
- core/schema/message_test.go: Go regression pinning that ToProto flattens a
JSON-array-looking text part to the verbatim string.
- prepare.sh now copies message_content.h into the build tree.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
