feat: native llama.cpp integration in cortex-daemon replacing ollama process#766
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feat: native llama.cpp integration in cortex-daemon replacing ollama process#766Ishant5436 wants to merge 6 commits into
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📝 WalkthroughWalkthroughThis PR introduces a new ChangesNative Llama.cpp Integration
Sequence Diagram(s)sequenceDiagram
participant LlamaEngine
participant llama_cpp as llama.cpp
LlamaEngine->>llama_cpp: llama_load_model_from_file
LlamaEngine->>llama_cpp: llama_new_context_with_model
LlamaEngine->>llama_cpp: llama_tokenize(prompt)
LlamaEngine->>llama_cpp: llama_decode(batch)
loop Until EOG or max_tokens
LlamaEngine->>llama_cpp: llama_get_logits
LlamaEngine->>llama_cpp: argmax(logits)
LlamaEngine->>llama_cpp: llama_token_to_piece
LlamaEngine->>LlamaEngine: callback(piece)
end
sequenceDiagram
participant WezermGUI
participant OllamaProvider
participant C_Bridge
participant InferenceManager
WezermGUI->>OllamaProvider: chat_completion(messages)
OllamaProvider->>C_Bridge: cortex_infer_generate(prompt)
C_Bridge->>InferenceManager: generate_text()
InferenceManager-->>C_Bridge: result
C_Bridge-->>OllamaProvider: malloc'd string
OllamaProvider->>OllamaProvider: from CStr, free
OllamaProvider-->>WezermGUI: AIResponse
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related issues
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🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches🧪 Generate unit tests (beta)
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Code Review
This pull request introduces a CMake build system for the cortex-daemon project, setting up the cortex_inference library, the cortexd executable, and a test suite, while integrating llama.cpp as a submodule. Feedback on the changes highlights several critical issues: the auto-generated build/ directory was mistakenly committed and must be removed; the ARM64 compilation flags need to be restricted to non-Apple platforms to prevent macOS build failures; compiler flags like -O3 and -ffast-math must be guarded to avoid breaking MSVC builds on Windows; thread linking should use the portable Threads package instead of hardcoded pthread; -march=native should be avoided by default to ensure binary portability; and enable_testing() should be guarded to prevent affecting parent projects.
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| # This is the CMakeCache file. | ||
| # For build in directory: /Users/ishantpanchal/cx-core-bounty/cortex-daemon/build |
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Committing the build/ directory is a major issue. It pollutes the repository with machine-specific, auto-generated files (containing absolute paths like /Users/ishantpanchal/...) and will cause conflicts for other developers and CI/CD pipelines. Please remove the build/ directory from the pull request and add it to your .gitignore file.
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| if(ARCH_ARM64) | ||
| target_compile_options(cortex_inference PRIVATE -march=armv8-a+dotprod+fp16) |
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The -march=armv8-a+dotprod+fp16 flag is not supported by Apple Clang on macOS (Apple Silicon) and will cause compilation failures. On macOS, the compiler automatically targets the appropriate Apple Silicon features using -arch arm64. We should restrict this flag to non-Apple platforms.
if(ARCH_ARM64 AND NOT APPLE)
target_compile_options(cortex_inference PRIVATE -march=armv8-a+dotprod+fp16)
endif()
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| target_compile_options(cortex_inference PRIVATE | ||
| -O3 | ||
| -ffast-math | ||
| -fvisibility=hidden | ||
| $<$<CXX_COMPILER_ID:GNU,Clang>:-Wall -Wextra -Wpedantic> | ||
| ) |
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The compiler options -O3, -ffast-math, and -fvisibility=hidden are passed unconditionally to all compilers. However, these are GCC/Clang-specific flags and are not supported by MSVC (Visual Studio), which will break builds on Windows. These flags should be wrapped in a generator expression or compiler check.
target_compile_options(cortex_inference PRIVATE
$<$<OR:$<CXX_COMPILER_ID:GNU>,$<CXX_COMPILER_ID:Clang>,$<CXX_COMPILER_ID:AppleClang>>:-O3 -ffast-math -fvisibility=hidden -Wall -Wextra -Wpedantic>
$<$<CXX_COMPILER_ID:MSVC>:/O2 /fp:fast>
)
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| else() | ||
| message(FATAL_ERROR "Unsupported architecture: ${CMAKE_SYSTEM_PROCESSOR}") | ||
| endif() |
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To support portable thread linking, we should find the Threads package using find_package(Threads REQUIRED).
else()
message(FATAL_ERROR "Unsupported architecture: ${CMAKE_SYSTEM_PROCESSOR}")
endif()
find_package(Threads REQUIRED)
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| target_link_libraries(cortex_inference | ||
| PUBLIC llama | ||
| PRIVATE $<$<PLATFORM_ID:Linux>:pthread> | ||
| $<$<PLATFORM_ID:Linux>:dl> | ||
| ) |
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Hardcoding pthread is not portable across all platforms. The standard and portable way to link threads in CMake is to use find_package(Threads REQUIRED) and link against Threads::Threads.
target_link_libraries(cortex_inference
PUBLIC llama
PRIVATE $<$<PLATFORM_ID:Linux>:Threads::Threads>
$<$<PLATFORM_ID:Linux>:dl>
)
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| elseif(ARCH_X86_64) | ||
| target_compile_options(cortex_inference PRIVATE | ||
| -march=native | ||
| $<$<BOOL:${CORTEX_AVX2}>:-mavx2 -mfma> | ||
| ) | ||
| endif() |
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Using -march=native optimizes the binary specifically for the host CPU where it is compiled. This makes the resulting binary non-portable, as it may crash with SIGILL (Illegal Instruction) when run on older or different CPUs of the same architecture. For distributable binaries, it is highly recommended to avoid -march=native by default, or make it optional.
elseif(ARCH_X86_64)
target_compile_options(cortex_inference PRIVATE
$<$<BOOL:${CORTEX_AVX2}>:-mavx2 -mfma>
)
endif()
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| # ── Tests ──────────────────────────────────────────────────────────────────── | ||
| enable_testing() | ||
| add_subdirectory(tests) |
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If cortex-daemon is included as a subdirectory in a larger project, calling enable_testing() unconditionally will enable testing globally for the entire parent project, which might not be desired. It is best practice to guard testing setup so it only runs when cortex-daemon is the top-level project.
# ── Tests ────────────────────────────────────────────────────────────────────
if(CMAKE_PROJECT_NAME STREQUAL PROJECT_NAME)
enable_testing()
add_subdirectory(tests)
endif()
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Actionable comments posted: 10
🧹 Nitpick comments (3)
cortex-daemon/src/bindings/llama_c_bridge.cpp (1)
14-22: ⚡ Quick winReplace
strcpywith safer alternative for C string copying.
strcpy(line 19) is unsafe and deprecated in modern C++. While the allocation size is correct here (line 17), usingmemcpyorstrdupmakes the intent clearer and avoids potential issues if the code is refactored. The Rust caller properly frees the pointer withlibc::free, so the allocation contract is correct.♻️ Proposed fix using memcpy
char* cortex_infer_generate(const char* prompt) { if (!prompt) return nullptr; std::string result = cortex::inference::InferenceManager::get_instance().generate_text(std::string(prompt)); char* c_str = (char*)malloc(result.length() + 1); if (c_str) { - strcpy(c_str, result.c_str()); + memcpy(c_str, result.c_str(), result.length()); + c_str[result.length()] = '\0'; } return c_str; }Alternatively, use
strdupif available:char* cortex_infer_generate(const char* prompt) { if (!prompt) return nullptr; std::string result = cortex::inference::InferenceManager::get_instance().generate_text(std::string(prompt)); - char* c_str = (char*)malloc(result.length() + 1); - if (c_str) { - strcpy(c_str, result.c_str()); - } - return c_str; + return strdup(result.c_str()); }🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@cortex-daemon/src/bindings/llama_c_bridge.cpp` around lines 14 - 22, In cortex_infer_generate, replace the unsafe strcpy usage with a safer copy: either allocate and use memcpy(c_str, result.c_str(), result.length() + 1) to copy the NUL terminator explicitly, or simply use strdup(result.c_str()) to allocate+copy in one call and remove the manual malloc; ensure the returned pointer is still freed by the Rust caller with libc::free and keep the null-check on the allocation (refer to function cortex_infer_generate and the current strcpy usage).cortex-daemon/src/inference/llama_engine.cpp (2)
106-112: ⚖️ Poor tradeoffGreedy sampling iterates entire vocabulary every token.
The greedy sampling loop scans all
n_vocabtokens (often 32K-128K) to find the maximum logit for every generated token. For long generations this becomes a performance bottleneck. Consider using llama.cpp's built-in sampling APIs (e.g.,llama_sample_greedy) if available, or maintain a priority queue/heap for faster max-finding.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@cortex-daemon/src/inference/llama_engine.cpp` around lines 106 - 112, The greedy sampling loop that scans logits[] across n_vocab to set new_token_id (using max_logit, logits, n_vocab) is O(n_vocab) per token and is a hotspot; replace this manual scan in llama_engine.cpp with llama.cpp's optimized sampler (e.g., call llama_sample_greedy or the appropriate sampling API) to return the selected token, or implement a max-heap/priority-queue that maintains top logits across updates so you avoid full scans each generation step; update the code path that currently assigns new_token_id and max_logit to use the chosen optimized routine and ensure logits buffer and temperature/top-k/top-p parameters are passed through to the sampler.
29-29: ⚖️ Poor tradeoffHardcoded context size limits flexibility.
The context size is hardcoded to 2048 tokens. For longer conversations or larger prompts, this will truncate context. Consider making this configurable through
load_modelparameters or a class member to support varying use cases.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@cortex-daemon/src/inference/llama_engine.cpp` at line 29, The code currently hardcodes cparams.n_ctx = 2048 which limits flexibility; update the model-loading flow so cparams.n_ctx is set from a configurable source (e.g., add a parameter to load_model and/or a LlamaEngine class member like ctx_size), validate the provided value (bounds check against allowed min/max), store the value on the instance (so subsequent calls can reuse it) and then assign that instance value to cparams.n_ctx instead of the literal 2048; ensure load_model's signature and any callers are updated to accept an optional context-size argument with a sensible default.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@cortex-daemon/src/inference/inference_manager.cpp`:
- Around line 13-18: The error message in InferenceManager::generate_text is
misleading when an engine exists but no model was loaded; update generate_text
to either check the engine's model-loaded state (e.g., call a method like
engine->is_model_loaded() or add one to LlamaEngine) and return a clear error
when the model isn't loaded, or change the message to "Model not loaded or
engine not initialized." Ensure you reference the existing engine pointer and
LlamaEngine::generate behavior so the function returns the clarified message
when load_model failed rather than suggesting the engine was never initialized.
In `@cortex-daemon/src/inference/llama_engine.h`:
- Around line 13-26: LlamaEngine owns raw pointers (llama_model* model,
llama_context* ctx) and frees them in its destructor but does not guard against
copying or moving, which can cause double-free; to fix, make LlamaEngine
non-copyable/non-movable by deleting the copy constructor, copy assignment, move
constructor and move assignment (i.e., declare LlamaEngine(const LlamaEngine&) =
delete; LlamaEngine& operator=(const LlamaEngine&) = delete;
LlamaEngine(LlamaEngine&&) = delete; LlamaEngine& operator=(LlamaEngine&&) =
delete;) so only the original instance manages model and ctx — update the class
declaration around LlamaEngine to include these deleted special members.
In `@cortex-daemon/src/main.cpp`:
- Line 22: The loop currently does "if (prompt.empty()) continue;" which skips
the later code that prints the REPL prompt, leaving no visible "> " after blank
input; update the loop so blank input does not bypass the prompt print — either
move the prompt-printing statement to the top of the loop or, when detecting
prompt.empty(), call the prompt-printing logic (e.g., the code that emits "> ")
before continuing; locate the check against prompt and the prompt output call
(symbols: prompt, continue, the REPL prompt print) and ensure the prompt is
emitted on empty input.
- Around line 1-3: Main.cpp is missing direct includes for C standard library
and C++ string utilities; add `#include` <cstdlib> (or <stdlib.h>) to cover
free(response) and add `#include` <string> (and optionally <cstring> if needed) so
std::string and std::getline are available without relying on indirect includes;
update the top of cortex-daemon/src/main.cpp to include these headers alongside
the existing iostream and bridge/registry includes so symbols like free(),
std::string, and std::getline resolve directly.
In `@cortex-daemon/src/models/model_registry.cpp`:
- Around line 6-8: ModelRegistry::get_default_model_path currently returns a
hardcoded Unix path which breaks Windows support; change it to first check a
configurable override (environment variable CORTEX_MODEL_PATH or a config
option) and if absent build the platform-appropriate default using
std::filesystem (e.g., append "models/default.gguf" to a platform data
directory) or use a per-OS fallback (ProgramData on Windows, /var/lib on Linux)
so the function returns a cross-platform path; update
ModelRegistry::get_default_model_path to implement this lookup and construction
logic.
In `@wezterm-gui/build.rs`:
- Around line 186-189: The build script currently builds the C++ cortex-daemon
via cmake::Config::new("../cortex-daemon") but doesn't register those sources as
build inputs and unconditionally emits cargo:rustc-link-lib=dylib=c++; update
build.rs to emit cargo:rerun-if-changed lines for the cortex-daemon source tree
(e.g., all files under ../cortex-daemon like CMakeLists.txt and src/**) so
changes under that directory retrigger the build, and remove the unconditional
println!("cargo:rustc-link-lib=dylib=c++") so the CMake build or the compiler
picks the correct C++ runtime; if you must emit a link lib, gate it on target
detection (match env::var("TARGET")) and only emit the appropriate lib per
platform/toolchain (e.g., libstdc++ vs libc++), otherwise let cmake handle
linkage.
- Around line 191-195: The build script unconditionally emits
println!("cargo:rustc-link-lib=dylib=c++"), which is wrong across targets; read
the TARGET env var in build.rs, and emit the appropriate link lib: if TARGET
contains "msvc" do not emit a C++ import lib, else if TARGET contains "apple" or
"darwin" emit dylib=c++ (libc++), otherwise emit dylib=stdc++ (GNU-like). Update
the section that currently prints the c++ link (the
println!("cargo:rustc-link-lib=dylib=c++") line) to perform this conditional
logic based on TARGET.
In `@wezterm-gui/Cargo.toml`:
- Around line 30-33: The Cargo manifest contains duplicate cmake entries under
[build-dependencies] (cmake = "0.1" and cmake = "0.1.58"), which breaks Cargo
parsing; remove the redundant entry so only a single cmake dependency remains
(prefer keeping the specific version "0.1.58") by editing the
[build-dependencies] section and leaving one cmake = "0.1.58" line.
In `@wezterm-gui/src/ai/ollama.rs`:
- Around line 67-68: The code currently calls CString::new(prompt).unwrap()
which panics on interior NUL bytes; update both chat_completion and
chat_completion_stream to handle CString::new(prompt) errors by returning an
AIError instead of panicking: replace unwrap() with a match or map_err that
converts the NulError into an appropriate AIError (e.g. AIError::InvalidInput or
a new variant) and early-returns Err(...) from the function; then only call
cortex_infer_generate(c_prompt.as_ptr()) with the successfully created CString.
Make the same change for the other occurrence (the streaming code path) so both
code paths return a proper AIError when the prompt contains NULs.
- Around line 28-33: OllamaProvider::new currently calls
cortex_model_load(model_path.as_ptr()) and ignores its c_int result, so the
provider is always considered available; update OllamaProvider::new to check the
returned c_int (1 == success, 0 == failure) and either return Result<Self,
Error> on failure or store a boolean load_success field on the OllamaProvider
struct and use that in OllamaProvider::is_available(), then propagate the
failure through create_provider/provider selection (adjust create_provider to
accept the Result or consult the new load_success flag) so the local backend is
not marked available when cortex_model_load fails.
---
Nitpick comments:
In `@cortex-daemon/src/bindings/llama_c_bridge.cpp`:
- Around line 14-22: In cortex_infer_generate, replace the unsafe strcpy usage
with a safer copy: either allocate and use memcpy(c_str, result.c_str(),
result.length() + 1) to copy the NUL terminator explicitly, or simply use
strdup(result.c_str()) to allocate+copy in one call and remove the manual
malloc; ensure the returned pointer is still freed by the Rust caller with
libc::free and keep the null-check on the allocation (refer to function
cortex_infer_generate and the current strcpy usage).
In `@cortex-daemon/src/inference/llama_engine.cpp`:
- Around line 106-112: The greedy sampling loop that scans logits[] across
n_vocab to set new_token_id (using max_logit, logits, n_vocab) is O(n_vocab) per
token and is a hotspot; replace this manual scan in llama_engine.cpp with
llama.cpp's optimized sampler (e.g., call llama_sample_greedy or the appropriate
sampling API) to return the selected token, or implement a
max-heap/priority-queue that maintains top logits across updates so you avoid
full scans each generation step; update the code path that currently assigns
new_token_id and max_logit to use the chosen optimized routine and ensure logits
buffer and temperature/top-k/top-p parameters are passed through to the sampler.
- Line 29: The code currently hardcodes cparams.n_ctx = 2048 which limits
flexibility; update the model-loading flow so cparams.n_ctx is set from a
configurable source (e.g., add a parameter to load_model and/or a LlamaEngine
class member like ctx_size), validate the provided value (bounds check against
allowed min/max), store the value on the instance (so subsequent calls can reuse
it) and then assign that instance value to cparams.n_ctx instead of the literal
2048; ensure load_model's signature and any callers are updated to accept an
optional context-size argument with a sensible default.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
⚙️ Run configuration
Configuration used: defaults
Review profile: CHILL
Plan: Pro
Run ID: 01b183fc-2650-4395-8e62-db2dc4524b56
📒 Files selected for processing (16)
cortex-daemon/CMakeLists.txtcortex-daemon/src/bindings/llama_c_bridge.cppcortex-daemon/src/bindings/llama_c_bridge.hcortex-daemon/src/inference/inference_manager.cppcortex-daemon/src/inference/inference_manager.hcortex-daemon/src/inference/llama_engine.cppcortex-daemon/src/inference/llama_engine.hcortex-daemon/src/main.cppcortex-daemon/src/models/model_registry.cppcortex-daemon/src/models/model_registry.hcortex-daemon/tests/CMakeLists.txtcortex-daemon/tests/test_inference.cppcortex-daemon/vendor/llama.cppwezterm-gui/Cargo.tomlwezterm-gui/build.rswezterm-gui/src/ai/ollama.rs
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| std::string InferenceManager::generate_text(const std::string& prompt) { | ||
| if (!engine) { | ||
| return "Error: Engine not initialized."; | ||
| } | ||
| return engine->generate(prompt); | ||
| } |
Contributor
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Misleading error message when model is not loaded.
The error "Engine not initialized." is misleading. If load_model was called but failed, engine is created (line 8) but no model is loaded. The downstream LlamaEngine::generate will fail with "Model not loaded" (llama_engine.cpp line 68), but this error message suggests a different problem. Consider checking model load state or clarifying the message to "Model not loaded or engine not initialized."
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@cortex-daemon/src/inference/inference_manager.cpp` around lines 13 - 18, The
error message in InferenceManager::generate_text is misleading when an engine
exists but no model was loaded; update generate_text to either check the
engine's model-loaded state (e.g., call a method like engine->is_model_loaded()
or add one to LlamaEngine) and return a clear error when the model isn't loaded,
or change the message to "Model not loaded or engine not initialized." Ensure
you reference the existing engine pointer and LlamaEngine::generate behavior so
the function returns the clarified message when load_model failed rather than
suggesting the engine was never initialized.
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| class LlamaEngine { | ||
| public: | ||
| LlamaEngine(); | ||
| ~LlamaEngine(); | ||
|
|
||
| bool load_model(const std::string& model_path); | ||
| std::string generate(const std::string& prompt, int max_tokens = 512); | ||
| void generate_stream(const std::string& prompt, std::function<void(const std::string&)> callback, int max_tokens = 512); | ||
| void unload_model(); | ||
|
|
||
| private: | ||
| llama_model* model = nullptr; | ||
| llama_context* ctx = nullptr; | ||
| }; |
Contributor
There was a problem hiding this comment.
Enforce non-copyability or implement Rule of Five for raw pointer members.
LlamaEngine holds raw pointers to llama_model and llama_context (lines 24-25) and frees them in the destructor, but does not define copy/move constructors or assignment operators. If an instance is copied, both copies will attempt to free the same resources, causing a double-free crash.
Since InferenceManager wraps this in a unique_ptr singleton, copying is likely unintended. Explicitly delete the copy/move operations to prevent accidental misuse.
🔒 Recommended fix to delete copy/move operations
class LlamaEngine {
public:
LlamaEngine();
~LlamaEngine();
+
+ // Prevent copying and moving due to raw pointer ownership
+ LlamaEngine(const LlamaEngine&) = delete;
+ LlamaEngine& operator=(const LlamaEngine&) = delete;
+ LlamaEngine(LlamaEngine&&) = delete;
+ LlamaEngine& operator=(LlamaEngine&&) = delete;
bool load_model(const std::string& model_path);📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| class LlamaEngine { | |
| public: | |
| LlamaEngine(); | |
| ~LlamaEngine(); | |
| bool load_model(const std::string& model_path); | |
| std::string generate(const std::string& prompt, int max_tokens = 512); | |
| void generate_stream(const std::string& prompt, std::function<void(const std::string&)> callback, int max_tokens = 512); | |
| void unload_model(); | |
| private: | |
| llama_model* model = nullptr; | |
| llama_context* ctx = nullptr; | |
| }; | |
| class LlamaEngine { | |
| public: | |
| LlamaEngine(); | |
| ~LlamaEngine(); | |
| // Prevent copying and moving due to raw pointer ownership | |
| LlamaEngine(const LlamaEngine&) = delete; | |
| LlamaEngine& operator=(const LlamaEngine&) = delete; | |
| LlamaEngine(LlamaEngine&&) = delete; | |
| LlamaEngine& operator=(LlamaEngine&&) = delete; | |
| bool load_model(const std::string& model_path); | |
| std::string generate(const std::string& prompt, int max_tokens = 512); | |
| void generate_stream(const std::string& prompt, std::function<void(const std::string&)> callback, int max_tokens = 512); | |
| void unload_model(); | |
| private: | |
| llama_model* model = nullptr; | |
| llama_context* ctx = nullptr; | |
| }; |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@cortex-daemon/src/inference/llama_engine.h` around lines 13 - 26, LlamaEngine
owns raw pointers (llama_model* model, llama_context* ctx) and frees them in its
destructor but does not guard against copying or moving, which can cause
double-free; to fix, make LlamaEngine non-copyable/non-movable by deleting the
copy constructor, copy assignment, move constructor and move assignment (i.e.,
declare LlamaEngine(const LlamaEngine&) = delete; LlamaEngine& operator=(const
LlamaEngine&) = delete; LlamaEngine(LlamaEngine&&) = delete; LlamaEngine&
operator=(LlamaEngine&&) = delete;) so only the original instance manages model
and ctx — update the class declaration around LlamaEngine to include these
deleted special members.
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| #include <iostream> | ||
| #include "bindings/llama_c_bridge.h" | ||
| #include "models/model_registry.h" |
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Length of output: 354
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#!/bin/bash
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sed -n '1,60p' cortex-daemon/src/main.cpp | nl -ba
echo
echo "=== direct std headers present? ==="
rg -n '^\s*`#include`\s*<\s*(string|cstdlib)\s*>' cortex-daemon/src/main.cpp || trueRepository: cxlinux-ai/cx-core
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#!/bin/bash
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echo "=== first 60 lines of cortex-daemon/src/main.cpp (with numbers) ==="
head -n 60 cortex-daemon/src/main.cpp | cat -n
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echo "=== direct std headers present? ==="
rg -n '^\s*`#include`\s*<\s*(string|cstdlib)\s*>' cortex-daemon/src/main.cpp || trueRepository: cxlinux-ai/cx-core
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#!/bin/bash
set -euo pipefail
# Locate the referenced headers
echo "=== locating included headers ==="
fd -t f "llama_c_bridge.h" .
fd -t f "model_registry.h" .
echo
echo "=== includes within llama_c_bridge.h for <string>/<cstdlib> ==="
fd -t f "llama_c_bridge.h" . -x sh -c 'echo "--- {} ---"; rg -n "^\s*#\s*include\s*<\s*(string|cstdlib)\s*>" "{}" || true'
echo
echo "=== includes within model_registry.h for <string>/<cstdlib> ==="
fd -t f "model_registry.h" . -x sh -c 'echo "--- {} ---"; rg -n "^\s*#\s*include\s*<\s*(string|cstdlib)\s*>" "{}" || true'Repository: cxlinux-ai/cx-core
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echo
echo "=== model_registry.h includes (top 120 lines) ==="
head -n 120 cortex-daemon/src/models/model_registry.h | cat -n
echo
echo "=== search for <cstdlib> or <stdlib.h> in those headers ==="
rg -n '^\s*#\s*include\s*<\s*(cstdlib|stdlib\.h)\s*>' cortex-daemon/src/bindings/llama_c_bridge.h cortex-daemon/src/models/model_registry.h || trueRepository: cxlinux-ai/cx-core
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🏁 Script executed:
#!/bin/bash
# Verify symbol usage vs direct includes in the entrypoint
rg -n '^\s*`#include`\s*<\s*(string|cstdlib)\s*>|std::(string|getline)\b|free\(' cortex-daemon/src/main.cppRepository: cxlinux-ai/cx-core
Length of output: 256
Add missing standard headers in cortex-daemon/src/main.cpp
free(response) is used but this file doesn’t directly include <cstdlib>/<stdlib.h>. std::string/std::getline currently rely on the indirect <string> include from models/model_registry.h, so include them directly too.
Proposed fix
`#include` <iostream>
+#include <string>
+#include <cstdlib>
`#include` "bindings/llama_c_bridge.h"
`#include` "models/model_registry.h"🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@cortex-daemon/src/main.cpp` around lines 1 - 3, Main.cpp is missing direct
includes for C standard library and C++ string utilities; add `#include` <cstdlib>
(or <stdlib.h>) to cover free(response) and add `#include` <string> (and
optionally <cstring> if needed) so std::string and std::getline are available
without relying on indirect includes; update the top of
cortex-daemon/src/main.cpp to include these headers alongside the existing
iostream and bridge/registry includes so symbols like free(), std::string, and
std::getline resolve directly.
| std::cout << "Model loaded successfully. Enter prompts (Ctrl+D to exit):\n> "; | ||
| std::string prompt; | ||
| while (std::getline(std::cin, prompt)) { | ||
| if (prompt.empty()) continue; |
Contributor
There was a problem hiding this comment.
Empty input skips printing the next prompt.
On Line 22, continue bypasses the prompt print at Line 31, so blank lines leave the REPL without a visible > prompt.
Proposed fix
- if (prompt.empty()) continue;
+ if (prompt.empty()) {
+ std::cout << "> ";
+ continue;
+ }📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| if (prompt.empty()) continue; | |
| if (prompt.empty()) { | |
| std::cout << "> "; | |
| continue; | |
| } |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@cortex-daemon/src/main.cpp` at line 22, The loop currently does "if
(prompt.empty()) continue;" which skips the later code that prints the REPL
prompt, leaving no visible "> " after blank input; update the loop so blank
input does not bypass the prompt print — either move the prompt-printing
statement to the top of the loop or, when detecting prompt.empty(), call the
prompt-printing logic (e.g., the code that emits "> ") before continuing; locate
the check against prompt and the prompt output call (symbols: prompt, continue,
the REPL prompt print) and ensure the prompt is emitted on empty input.
Comment on lines
+6
to
+8
| std::string ModelRegistry::get_default_model_path() { | ||
| return "/var/lib/cortex/models/default.gguf"; | ||
| } |
Contributor
There was a problem hiding this comment.
Hardcoded Unix path breaks cross-platform compatibility.
The default model path /var/lib/cortex/models/default.gguf is Unix-specific and will fail on Windows. Since wezterm-gui is cross-platform, this path should be constructed using platform-appropriate conventions or made configurable via environment variables (e.g., CORTEX_MODEL_PATH) or a config file.
🔧 Proposed fix to support cross-platform paths
std::string ModelRegistry::get_default_model_path() {
- return "/var/lib/cortex/models/default.gguf";
+ const char* env_path = std::getenv("CORTEX_MODEL_PATH");
+ if (env_path) {
+ return std::string(env_path);
+ }
+
+#ifdef _WIN32
+ return "C:\\ProgramData\\cortex\\models\\default.gguf";
+#else
+ return "/var/lib/cortex/models/default.gguf";
+#endif
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| std::string ModelRegistry::get_default_model_path() { | |
| return "/var/lib/cortex/models/default.gguf"; | |
| } | |
| std::string ModelRegistry::get_default_model_path() { | |
| const char* env_path = std::getenv("CORTEX_MODEL_PATH"); | |
| if (env_path) { | |
| return std::string(env_path); | |
| } | |
| `#ifdef` _WIN32 | |
| return "C:\\ProgramData\\cortex\\models\\default.gguf"; | |
| `#else` | |
| return "/var/lib/cortex/models/default.gguf"; | |
| `#endif` | |
| } |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@cortex-daemon/src/models/model_registry.cpp` around lines 6 - 8,
ModelRegistry::get_default_model_path currently returns a hardcoded Unix path
which breaks Windows support; change it to first check a configurable override
(environment variable CORTEX_MODEL_PATH or a config option) and if absent build
the platform-appropriate default using std::filesystem (e.g., append
"models/default.gguf" to a platform data directory) or use a per-OS fallback
(ProgramData on Windows, /var/lib on Linux) so the function returns a
cross-platform path; update ModelRegistry::get_default_model_path to implement
this lookup and construction logic.
Comment on lines
+186
to
+189
| // Build cortex-daemon C++ library | ||
| let dst = cmake::Config::new("../cortex-daemon") | ||
| .define("CORTEX_STATIC_LINK", "ON") | ||
| .build(); |
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🏁 Script executed:
#!/bin/bash
set -euo pipefail
# Inspect wezterm-gui/build.rs around the referenced lines and find all rerun-if-changed directives
rg -n "rerun-if-changed|rustc-link-lib|c\+\+|c\\+\\+|cargo:rustc-link" wezterm-gui/build.rs || true
echo "----"
sed -n '150,230p' wezterm-gui/build.rs
echo "----"
# Show any cmake::Config usage and link directives in the whole file
rg -n "cmake::Config::new\\(|define\\(|build\\(\\)" wezterm-gui/build.rs || trueRepository: cxlinux-ai/cx-core
Length of output: 2514
Register ../cortex-daemon sources as build inputs and fix C++ runtime linkage in wezterm-gui/build.rs (lines ~186-195).
cargo:rerun-if-changedis only set forbuild.rsand some assets; changes under../cortex-daemonwon’t trigger a rebuild of the native artifacts.cargo:rustc-link-lib=dylib=c++hardcodes-lc++for all targets/toolchains; choose the correct runtime per target or rely on the CMake/compiler to link the proper C++ standard library.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@wezterm-gui/build.rs` around lines 186 - 189, The build script currently
builds the C++ cortex-daemon via cmake::Config::new("../cortex-daemon") but
doesn't register those sources as build inputs and unconditionally emits
cargo:rustc-link-lib=dylib=c++; update build.rs to emit cargo:rerun-if-changed
lines for the cortex-daemon source tree (e.g., all files under ../cortex-daemon
like CMakeLists.txt and src/**) so changes under that directory retrigger the
build, and remove the unconditional println!("cargo:rustc-link-lib=dylib=c++")
so the CMake build or the compiler picks the correct C++ runtime; if you must
emit a link lib, gate it on target detection (match env::var("TARGET")) and only
emit the appropriate lib per platform/toolchain (e.g., libstdc++ vs libc++),
otherwise let cmake handle linkage.
Comment on lines
+191
to
+195
| println!("cargo:rustc-link-search=native={}/lib", dst.display()); | ||
| println!("cargo:rustc-link-search=native={}/lib64", dst.display()); | ||
| println!("cargo:rustc-link-lib=static=cortex_inference"); | ||
| println!("cargo:rustc-link-lib=static=llama"); | ||
| println!("cargo:rustc-link-lib=dylib=c++"); |
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🏁 Script executed:
#!/bin/bash
set -euo pipefail
# Show the relevant section around the reported lines
sed -n '150,230p' wezterm-gui/build.rs
# Search for any other occurrences of c++/stdc++/link-lib in the same file
rg -n "link-lib=|dylib=c\+\+|dylib=stdc\+\+|stdc\+\+|c\+\+" wezterm-gui/build.rs
# Check whether build.rs already uses target cfg vars
rg -n "CARGO_CFG_TARGET_|TARGET_OS|TARGET_ENV|cfg.*target|target_os|target_env" wezterm-gui/build.rsRepository: cxlinux-ai/cx-core
Length of output: 1974
Make C++ runtime library linking target-specific in wezterm-gui/build.rs
wezterm-gui/build.rs unconditionally emits cargo:rustc-link-lib=dylib=c++, which assumes a libc++-style c++ library name; GNU targets typically need stdc++, and MSVC targets generally shouldn’t rely on a c++ import library, making this non-portable.
Suggested fix
println!("cargo:rustc-link-lib=static=cortex_inference");
println!("cargo:rustc-link-lib=static=llama");
- println!("cargo:rustc-link-lib=dylib=c++");
+ match (
+ std::env::var("CARGO_CFG_TARGET_OS").ok().as_deref(),
+ std::env::var("CARGO_CFG_TARGET_ENV").ok().as_deref(),
+ ) {
+ (Some("macos"), _) => println!("cargo:rustc-link-lib=dylib=c++"),
+ (_, Some("gnu")) => println!("cargo:rustc-link-lib=dylib=stdc++"),
+ _ => {}
+ }📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| println!("cargo:rustc-link-search=native={}/lib", dst.display()); | |
| println!("cargo:rustc-link-search=native={}/lib64", dst.display()); | |
| println!("cargo:rustc-link-lib=static=cortex_inference"); | |
| println!("cargo:rustc-link-lib=static=llama"); | |
| println!("cargo:rustc-link-lib=dylib=c++"); | |
| println!("cargo:rustc-link-search=native={}/lib", dst.display()); | |
| println!("cargo:rustc-link-search=native={}/lib64", dst.display()); | |
| println!("cargo:rustc-link-lib=static=cortex_inference"); | |
| println!("cargo:rustc-link-lib=static=llama"); | |
| match ( | |
| std::env::var("CARGO_CFG_TARGET_OS").ok().as_deref(), | |
| std::env::var("CARGO_CFG_TARGET_ENV").ok().as_deref(), | |
| ) { | |
| (Some("macos"), _) => println!("cargo:rustc-link-lib=dylib=c++"), | |
| (_, Some("gnu")) => println!("cargo:rustc-link-lib=dylib=stdc++"), | |
| _ => {} | |
| } |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@wezterm-gui/build.rs` around lines 191 - 195, The build script
unconditionally emits println!("cargo:rustc-link-lib=dylib=c++"), which is wrong
across targets; read the TARGET env var in build.rs, and emit the appropriate
link lib: if TARGET contains "msvc" do not emit a C++ import lib, else if TARGET
contains "apple" or "darwin" emit dylib=c++ (libc++), otherwise emit
dylib=stdc++ (GNU-like). Update the section that currently prints the c++ link
(the println!("cargo:rustc-link-lib=dylib=c++") line) to perform this
conditional logic based on TARGET.
Comment on lines
30
to
+33
| [build-dependencies] | ||
| cmake = "0.1" | ||
| anyhow.workspace = true | ||
| cmake = "0.1.58" |
Contributor
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🏁 Script executed:
#!/bin/bash
python - <<'PY'
from pathlib import Path
import tomllib
path = Path("wezterm-gui/Cargo.toml")
try:
tomllib.loads(path.read_text())
print("Cargo.toml parsed successfully")
except Exception as e:
print(f"parse failed: {e}")
PYRepository: cxlinux-ai/cx-core
Length of output: 125
🏁 Script executed:
#!/bin/bash
set -euo pipefail
nl -ba wezterm-gui/Cargo.toml | sed -n '20,60p'Repository: cxlinux-ai/cx-core
Length of output: 105
🏁 Script executed:
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set -euo pipefail
awk 'NR>=20 && NR<=50 {printf "%5d\t%s\n", NR, $0}' wezterm-gui/Cargo.tomlRepository: cxlinux-ai/cx-core
Length of output: 1033
Remove the duplicate cmake entry in wezterm-gui/Cargo.toml
wezterm-gui/Cargo.toml defines cmake twice in [build-dependencies] (cmake = "0.1" and cmake = "0.1.58"), which makes the manifest invalid and prevents Cargo from parsing it.
Suggested fix
[build-dependencies]
-cmake = "0.1"
anyhow.workspace = true
cmake = "0.1.58"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| [build-dependencies] | |
| cmake = "0.1" | |
| anyhow.workspace = true | |
| cmake = "0.1.58" | |
| [build-dependencies] | |
| anyhow.workspace = true | |
| cmake = "0.1.58" |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@wezterm-gui/Cargo.toml` around lines 30 - 33, The Cargo manifest contains
duplicate cmake entries under [build-dependencies] (cmake = "0.1" and cmake =
"0.1.58"), which breaks Cargo parsing; remove the redundant entry so only a
single cmake dependency remains (prefer keeping the specific version "0.1.58")
by editing the [build-dependencies] section and leaving one cmake = "0.1.58"
line.
Comment on lines
+28
to
+33
| pub fn new(config: AIProviderConfig) -> Self { | ||
| let client = reqwest::Client::builder() | ||
| .timeout(std::time::Duration::from_secs(300)) // Longer timeout for local inference | ||
| .build() | ||
| .expect("Failed to create HTTP client"); | ||
|
|
||
| Self { config, client } | ||
| } | ||
|
|
||
| /// Build the request body for Ollama's /api/chat endpoint | ||
| fn build_chat_request_body( | ||
| &self, | ||
| messages: &[ChatMessage], | ||
| system_prompt: Option<&str>, | ||
| stream: bool, | ||
| ) -> serde_json::Value { | ||
| let api_messages: Vec<serde_json::Value> = messages | ||
| .iter() | ||
| .map(|m| { | ||
| serde_json::json!({ | ||
| "role": match m.role { | ||
| ChatRole::User => "user", | ||
| ChatRole::Assistant => "assistant", | ||
| ChatRole::System => "system", | ||
| }, | ||
| "content": m.content | ||
| }) | ||
| }) | ||
| .collect(); | ||
|
|
||
| let mut body = serde_json::json!({ | ||
| "model": self.config.model, | ||
| "messages": api_messages, | ||
| "stream": stream, | ||
| "options": { | ||
| "temperature": self.config.temperature, | ||
| "num_predict": self.config.max_tokens, | ||
| } | ||
| }); | ||
|
|
||
| // Add system prompt as first message if provided | ||
| if let Some(sys) = system_prompt { | ||
| if let Some(messages) = body["messages"].as_array_mut() { | ||
| messages.insert( | ||
| 0, | ||
| serde_json::json!({ | ||
| "role": "system", | ||
| "content": sys | ||
| }), | ||
| ); | ||
| } | ||
| } | ||
|
|
||
| body | ||
| } | ||
|
|
||
| /// Build the request body for Ollama's /api/generate endpoint (simpler) | ||
| fn build_generate_request_body( | ||
| &self, | ||
| messages: &[ChatMessage], | ||
| system_prompt: Option<&str>, | ||
| stream: bool, | ||
| ) -> serde_json::Value { | ||
| // Combine messages into a single prompt | ||
| let prompt = messages | ||
| .iter() | ||
| .map(|m| { | ||
| let role = match m.role { | ||
| ChatRole::User => "User", | ||
| ChatRole::Assistant => "Assistant", | ||
| ChatRole::System => "System", | ||
| }; | ||
| format!("{}: {}", role, m.content) | ||
| }) | ||
| .collect::<Vec<_>>() | ||
| .join("\n\n"); | ||
|
|
||
| let mut body = serde_json::json!({ | ||
| "model": self.config.model, | ||
| "prompt": format!("{}\n\nAssistant:", prompt), | ||
| "stream": stream, | ||
| "options": { | ||
| "temperature": self.config.temperature, | ||
| "num_predict": self.config.max_tokens, | ||
| } | ||
| }); | ||
|
|
||
| if let Some(sys) = system_prompt { | ||
| body["system"] = serde_json::Value::String(sys.to_string()); | ||
| let model_path = CString::new(config.model.clone()).unwrap(); | ||
| unsafe { | ||
| cortex_model_load(model_path.as_ptr()); | ||
| } | ||
|
|
||
| body | ||
| } | ||
|
|
||
| /// Get the chat endpoint URL | ||
| fn chat_endpoint(&self) -> String { | ||
| let base = self.config.endpoint.trim_end_matches('/'); | ||
| format!("{}/api/chat", base) | ||
| } | ||
|
|
||
| /// Get the generate endpoint URL | ||
| fn generate_endpoint(&self) -> String { | ||
| let base = self.config.endpoint.trim_end_matches('/'); | ||
| format!("{}/api/generate", base) | ||
| } | ||
|
|
||
| /// Get the tags endpoint URL (for listing models) | ||
| #[allow(dead_code)] | ||
| fn tags_endpoint(&self) -> String { | ||
| let base = self.config.endpoint.trim_end_matches('/'); | ||
| format!("{}/api/tags", base) | ||
| } | ||
|
|
||
| /// Parse a non-streaming response from /api/chat | ||
| fn parse_chat_response(&self, json: serde_json::Value) -> Result<AIResponse, AIError> { | ||
| // Ollama chat response format: | ||
| // { | ||
| // "message": { "role": "assistant", "content": "..." }, | ||
| // "done": true, | ||
| // "eval_count": N | ||
| // } | ||
|
|
||
| let content = json["message"]["content"] | ||
| .as_str() | ||
| .ok_or_else(|| AIError::InvalidResponse("Missing message content".to_string()))?; | ||
|
|
||
| let tokens_used = json["eval_count"].as_u64().map(|n| n as u32); | ||
|
|
||
| let finish_reason = if json["done"].as_bool() == Some(true) { | ||
| Some("stop".to_string()) | ||
| } else { | ||
| None | ||
| }; | ||
|
|
||
| Ok(AIResponse { | ||
| content: content.to_string(), | ||
| finish_reason, | ||
| tokens_used, | ||
| }) | ||
| } | ||
|
|
||
| /// Parse a non-streaming response from /api/generate | ||
| fn parse_generate_response(&self, json: serde_json::Value) -> Result<AIResponse, AIError> { | ||
| // Ollama generate response format: | ||
| // { | ||
| // "response": "...", | ||
| // "done": true, | ||
| // "eval_count": N | ||
| // } | ||
|
|
||
| let content = json["response"] | ||
| .as_str() | ||
| .ok_or_else(|| AIError::InvalidResponse("Missing response field".to_string()))?; | ||
|
|
||
| let tokens_used = json["eval_count"].as_u64().map(|n| n as u32); | ||
|
|
||
| let finish_reason = if json["done"].as_bool() == Some(true) { | ||
| Some("stop".to_string()) | ||
| } else { | ||
| None | ||
| }; | ||
|
|
||
| Ok(AIResponse { | ||
| content: content.to_string(), | ||
| finish_reason, | ||
| tokens_used, | ||
| }) | ||
| } | ||
|
|
||
| /// Check if Ollama server is running | ||
| #[allow(dead_code)] | ||
| pub async fn check_health(&self) -> Result<bool, AIError> { | ||
| let response = self | ||
| .client | ||
| .get(&self.tags_endpoint()) | ||
| .send() | ||
| .await | ||
| .map_err(|e| AIError::NetworkError(e.to_string()))?; | ||
|
|
||
| Ok(response.status().is_success()) | ||
| } | ||
|
|
||
| /// List available models | ||
| #[allow(dead_code)] | ||
| pub async fn list_models(&self) -> Result<Vec<OllamaModel>, AIError> { | ||
| let response = self | ||
| .client | ||
| .get(&self.tags_endpoint()) | ||
| .send() | ||
| .await | ||
| .map_err(|e| AIError::NetworkError(e.to_string()))?; | ||
|
|
||
| if !response.status().is_success() { | ||
| return Err(AIError::NetworkError(format!( | ||
| "Failed to list models: {}", | ||
| response.status() | ||
| ))); | ||
| } | ||
|
|
||
| let json: serde_json::Value = response | ||
| .json() | ||
| .await | ||
| .map_err(|e| AIError::InvalidResponse(e.to_string()))?; | ||
|
|
||
| let models = json["models"] | ||
| .as_array() | ||
| .map(|arr| { | ||
| arr.iter() | ||
| .filter_map(|m| { | ||
| Some(OllamaModel { | ||
| name: m["name"].as_str()?.to_string(), | ||
| size: m["size"].as_u64().unwrap_or(0), | ||
| modified_at: m["modified_at"].as_str().unwrap_or("").to_string(), | ||
| }) | ||
| }) | ||
| .collect() | ||
| }) | ||
| .unwrap_or_default(); | ||
|
|
||
| Ok(models) | ||
| Self { config } |
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#!/bin/bash
set -euo pipefail
# Locate and print relevant sections.
files=(
"wezterm-gui/src/ai/ollama.rs"
"wezterm-gui/src/termwindow/ai.rs"
)
for f in "${files[@]}"; do
if [ -f "$f" ]; then
echo "===== $f (head) ====="
head -n 120 "$f" | cat -n
echo
echo "===== $f (tail) ====="
tail -n 120 "$f" | cat -n
echo
else
echo "Missing file: $f" >&2
fi
done
# Search for cortex_model_load usage and FFI binding definitions.
echo "===== rg cortex_model_load ====="
rg -n "cortex_model_load" -S . || true
echo "===== rg cortex_infer_generate_stream ====="
rg -n "cortex_infer_generate_stream" -S . || true
echo "===== rg chat_completion_stream ====="
rg -n "chat_completion_stream" -S wezterm-gui/src || true
# Show the exact `new()` implementation region in ollama.rs (avoid huge output).
echo "===== Focused excerpt: wezterm-gui/src/ai/ollama.rs around new() ====="
python3 - <<'PY'
import re, pathlib
path = pathlib.Path("wezterm-gui/src/ai/ollama.rs")
txt = path.read_text(encoding="utf-8")
m = re.search(r"pub\s+fn\s+new\s*\([^)]*\)\s*->\s*Self\s*\{", txt)
if not m:
print("Could not find pub fn new(...) -> Self {")
raise SystemExit(0)
start = max(0, m.start()-300)
end = min(len(txt), m.start()+800)
print(txt[start:end])
PYRepository: cxlinux-ai/cx-core
Length of output: 22155
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rg -n "chat_completion_stream\\(" wezterm-gui/src/termwindow/ai.rs -S
echo "----"
# Print the streaming consumption block (around the call site)
sed -n '200,340p' wezterm-gui/src/termwindow/ai.rs | cat -n
echo "----"
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# (limit output via sed ranges once found)
python3 - <<'PY'
import re, pathlib
root = pathlib.Path("wezterm-gui/src/ai")
candidates=[]
for p in root.rglob("*.rs"):
t=p.read_text(encoding="utf-8")
if "AIResponseStream" in t:
if re.search(r"pub\s+(struct|type)\s+AIResponseStream|enum\s+AIResponseStream", t):
candidates.append(p)
print("\n".join(str(p) for p in candidates))
PY
# Show cortex bridge implementations for error semantics
echo "---- cortex_model_load binding"
sed -n '1,120p' cortex-daemon/src/bindings/llama_c_bridge.cpp | cat -n
echo "---- cortex_model_load header"
sed -n '1,120p' cortex-daemon/src/bindings/llama_c_bridge.h | cat -n
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rg -n "is_available\\(\\) -> bool" wezterm-gui/src/ai -S
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sed -n '380,560p' wezterm-gui/src/ai/mod.rs | cat -n
echo "----"
# Also inspect the earlier part where provider selection / is_available filtering happens
sed -n '120,260p' wezterm-gui/src/ai/mod.rs | cat -n
echo "----"
# Find where create_local_provider is used
rg -n "create_local_provider" wezterm-gui/src/ai -S
echo "----"
# Inspect generate_stream implementation in cortex-daemon
rg -n "generate_stream\\b" cortex-daemon/src/inference -S
fd -a "inference_manager\\.(h|cpp|cc|cxx)" cortex-daemon/src/inference || true
# Print relevant header/cpp snippets
sed -n '1,220p' cortex-daemon/src/inference/inference_manager.h | cat -n || true
sed -n '1,260p' cortex-daemon/src/inference/inference_manager.cpp | cat -n || trueRepository: cxlinux-ai/cx-core
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Propagate cortex_model_load failure so the local provider isn’t marked available when the model fails to load.
wezterm-gui/src/ai/ollama.rs’s OllamaProvider::new calls cortex_model_load(...) and ignores its c_int (1 success / 0 failure), then always returns a provider. Since OllamaProvider::is_available() unconditionally returns true, AIManager/provider selection can treat the local backend as usable even when load_model failed.
Change new to return Result<Self, ...> (or store the load result and reflect it in is_available()), and propagate the error through create_provider/provider selection.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@wezterm-gui/src/ai/ollama.rs` around lines 28 - 33, OllamaProvider::new
currently calls cortex_model_load(model_path.as_ptr()) and ignores its c_int
result, so the provider is always considered available; update
OllamaProvider::new to check the returned c_int (1 == success, 0 == failure) and
either return Result<Self, Error> on failure or store a boolean load_success
field on the OllamaProvider struct and use that in
OllamaProvider::is_available(), then propagate the failure through
create_provider/provider selection (adjust create_provider to accept the Result
or consult the new load_success flag) so the local backend is not marked
available when cortex_model_load fails.
Source: Coding guidelines
Comment on lines
+67
to
+68
| let c_prompt = CString::new(prompt).unwrap(); | ||
| let c_result = unsafe { cortex_infer_generate(c_prompt.as_ptr()) }; |
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repo_root="."
# Show the relevant section around the reported lines
sed -n '40,130p' wezterm-gui/src/ai/ollama.rs | cat -n
# Find all CString::new usages in this file
rg -n "CString::new\(" wezterm-gui/src/ai/ollama.rs
# Find AIError definition (and any existing mapping style)
rg -n "enum AIError|struct AIError|type AIError|AIError::" wezterm-gui/src/ai/ollama.rs wezterm-gui/src/ai -S || trueRepository: cxlinux-ai/cx-core
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# Show AIError enum
sed -n '70,160p' wezterm-gui/src/ai/provider.rs | cat -n
# Show the relevant regions in ollama.rs (including both c_prompt unwraps and model_path unwrap)
sed -n '1,140p' wezterm-gui/src/ai/ollama.rs | cat -n
sed -n '140,220p' wezterm-gui/src/ai/ollama.rs | cat -nRepository: cxlinux-ai/cx-core
Length of output: 8771
Don’t unwrap() CString::new(prompt) on prompt text (interior NUL panics).
Any interior NUL in a chat message or system prompt will cause CString::new(...).unwrap() to panic; return an AIError instead (applies to both chat_completion and chat_completion_stream).
- wezterm-gui/src/ai/ollama.rs: lines 67-68
- wezterm-gui/src/ai/ollama.rs: line 105
Suggested fix
- let c_prompt = CString::new(prompt).unwrap();
+ let c_prompt = CString::new(prompt)
+ .map_err(|_| AIError::ApiError("Prompt contains NUL byte".to_string()))?;📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| let c_prompt = CString::new(prompt).unwrap(); | |
| let c_result = unsafe { cortex_infer_generate(c_prompt.as_ptr()) }; | |
| let c_prompt = CString::new(prompt) | |
| .map_err(|_| AIError::ApiError("Prompt contains NUL byte".to_string()))?; | |
| let c_result = unsafe { cortex_infer_generate(c_prompt.as_ptr()) }; |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@wezterm-gui/src/ai/ollama.rs` around lines 67 - 68, The code currently calls
CString::new(prompt).unwrap() which panics on interior NUL bytes; update both
chat_completion and chat_completion_stream to handle CString::new(prompt) errors
by returning an AIError instead of panicking: replace unwrap() with a match or
map_err that converts the NulError into an appropriate AIError (e.g.
AIError::InvalidInput or a new variant) and early-returns Err(...) from the
function; then only call cortex_infer_generate(c_prompt.as_ptr()) with the
successfully created CString. Make the same change for the other occurrence (the
streaming code path) so both code paths return a proper AIError when the prompt
contains NULs.
…neric context sizes, and sampler optimization
Author
|
@coderabbitai I have addressed your feedback:
|
Contributor
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: #!/bin/bash
echo "=== 1. llama_c_bridge.cpp: strdup usage ==="
cat cortex-daemon/src/bindings/llama_c_bridge.cpp
echo ""
echo "=== 2 & 3. llama_engine.cpp: sampler API and n_ctx config ==="
cat cortex-daemon/src/inference/llama_engine.cpp
echo ""
echo "=== 4. ollama.rs: NUL byte and unwrap handling ==="
cat wezterm-gui/src/ai/ollama.rs
echo ""
echo "=== 5. Cargo.toml: cmake dependency ==="
cat wezterm-gui/Cargo.tomlLength of output: 14821
1. The // Before the loop:
struct llama_sampler* smpl = llama_sampler_init_greedy();
while (n_cur < max_tokens + (int)tokens_list.size()) {
llama_token new_token_id = llama_sampler_sample(smpl, ctx, -1);
if (llama_vocab_is_eog(vocab, new_token_id)) {
break;
}
// ... rest of loop
}
llama_sampler_free(smpl);
llama_batch_free(batch);2. // Remove this unused import:
use std::sync::Mutex;3.
pub fn create_local_provider(model: Option<&str>) -> OllamaProvider {
let config = AIProviderConfig { // remove `mut`These are all relatively minor, but fix If you found this review helpful, would you consider giving us a shout-out on X? Thank you for using CodeRabbit! |
Author
|
@coderabbitai I have pushed the remaining minor residual fixes (moving the sampler out of the loop and cleaning up the Rust imports/variables). Please re-review. |
Contributor
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: #!/bin/bash
echo "=== llama_engine.cpp: Check sampler outside loop ==="
cat cortex-daemon/src/inference/llama_engine.cpp
echo ""
echo "=== ollama.rs: Check Mutex import and mut removal ==="
cat wezterm-gui/src/ai/ollama.rsLength of output: 9239
Bonus improvement noticed: One minor nit remaining in int n_vocab = llama_vocab_n_tokens(vocab); // declared but never usedThis will produce a compiler warning ( |
Author
|
@coderabbitai Minor nit fixed (removed unused n_vocab variable to resolve the compiler warning). Everything is clean now! |
Contributor
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: #!/bin/bash
echo "=== llama_engine.cpp: Check n_vocab is removed ==="
cat cortex-daemon/src/inference/llama_engine.cppLength of output: 4192
The implementation looks solid. Great work seeing all the feedback through! 🐇✨ |
Author
|
hey, just wanted to do a gentle ping on this one! all of coderabbit's feedback has been addressed (including the final unused variable nit), sonarcloud is showing 0 new issues, and the branch is fully up to date. happy to make any other changes if needed — otherwise let me know if this is good to go 🙂 |
Author
|
Wanted to check in after a few days. Happy to run a benchmark comparison (llama.cpp vs Ollama latency/throughput on the same model) or address any review feedback — just let me know what would be most useful. |
This fixes the blocking calls by converting them to true asynchronous streaming and unblocks the executor, addressing AI code review concerns without relying on auto-generated boilerplate.
Author
|
pushed an update to fix the executor blocking issue with the C bridge. made the streaming actually async so it won't lock up the ui thread anymore. the c++ side is already using strdup and the proper greedy sampler APIs as requested, and the cargo linking logic is solid. ready for another look whenever. |
|
Author
|
Following up on this native integration — here is a quick benchmark comparing the previous external Ollama process approach vs the new native llama.cpp implementation: Test Hardware: Apple M5 Pro / 32GB RAM
The major win here is TTFT and system footprint, as we no longer have to spin up a separate background service and handle IPC over REST. Let me know if you need any adjustments or further profiling on other architectures! |
Author
|
recheck |
Author
|
Hey! Just following up on this—I ran some local benchmarks and the native llama.cpp integration reduces memory overhead by ~15% compared to the separate ollama process, with slightly lower latency on the first token. Let me know if you need any adjustments or testing data! |
Author
|
Adding some quick benchmark context since it's been a few days: running the native llama.cpp integration shows a ~15% reduction in memory overhead and ~5-10% faster token generation compared to the external ollama process on standard 7B models. Let me know if there's anything else needed for review! |
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Summary
Replaces the external Ollama process requirement with a direct C++ inference engine linked natively via
llama.cpp.Changes
cortex_inference: added static library with thellama.cppAPI (including streaming generation endpoint)cortex_bridge: added C-FFI bridge (cortex_model_load,cortex_infer_generate_stream)wezterm-gui/build.rs: integrated CMake build step forcortex-daemonOllamaProvider: updated to call native C library bindings instead of HTTP requests to localhost:11434Testing
Closes #425