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feat(ecosystem): compliant Praxis/Hexis integration + vendored build

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Bring the Nexus/Praxis/Hexis integration in line with
MAVEN_ECOSYSTEM_ARCHITECTURE.md:

- Praxis over HTTP: drop the in-process praxis.db open (praxisstore/
  praxistools) and call praxisd's /api/v1/tools/* API via a new praxisClient.
  Honors the "no component reads another's DB" invariant (AC#12).
  PraxisConfig.DBPath -> URL.
- Hexis confirmation gate: mutating capabilities (ReadOnly=false) now park a
  bound pendingHexis confirmation and require a spoken "да" before executing;
  read-only run immediately (AC#7, no auto attention->action).
- Capability safety: >1 verb match is ambiguous -> ask instead of firing the
  first; ambiguous Nexus resolution asks for clarification (AC#2).
- Correlation IDs on Hexis execute, recorded in the cross-service trace.
- Bug: importance arrives as JSON float64 over HTTP, not int.
- Tests: confirm-gate, decline, read-only, and ambiguity paths.

Build: vendor/ bakes in the hexis client (replace-directed at a sibling repo
outside the Docker context); Dockerfile builds from vendor and no longer
`go mod download`s the unreachable replace paths.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
kami
2026-07-19 19:32:05 +04:00
parent 0c65387a5f
commit 6c92f85d10
2071 changed files with 3915438 additions and 61 deletions
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vendor/github.com/yalue/onnxruntime_go/README.md
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Cross-Platform `onnxruntime` Wrapper for Go
===========================================
About
-----
This library seeks to provide an interface for loading and executing neural
networks from Go(lang) code, while remaining as simple to use as possible.
A few example applications using this library can be found in the
[`onnxruntime_go_examples` repository](https://github.com/yalue/onnxruntime_go_examples).
The [onnxruntime](https://github.com/microsoft/onnxruntime) library provides a
way to load and execute ONNX-format neural networks, though the library
primarily supports C and C++ APIs. Several efforts exist to have written
Go(lang) wrappers for the `onnxruntime` library, but as far as I can tell, none
of these existing Go wrappers support Windows. This is due to the fact that
Microsoft's `onnxruntime` library assumes the user will be using the MSVC
compiler on Windows systems, while CGo on Windows requires using Mingw.
This wrapper works around the issues by manually loading the `onnxruntime`
shared library, removing any dependency on the `onnxruntime` source code beyond
the header files. Naturally, this approach works equally well on non-Windows
systems.
Additionally, this library uses Go's recent addition of generics to support
multiple Tensor data types; see the `NewTensor` or `NewEmptyTensor` functions.
Several accelerated execution providers (including TensorRT, CUDA and CoreML)
are tested and confirmed to work with `onnxruntime_go`. The "Requirements"
portion of this README (below) has a few more details.
Note on onnxruntime Library Versions
------------------------------------
At the time of writing, this library uses version 1.26.0 of the onnxruntime
C API headers. So, it will probably only work with version 1.26.0 of the
onnxruntime shared libraries, as well. If you need to use a different version,
or if I get behind on updating this repository, updating or changing the
onnxruntime version should be fairly easy:
1. Replace the `onnxruntime_c_api.h` and `onnxruntime_ep_c_api.h` files with
the versions corresponding to the onnxruntime version you wish to use.
2. Replace the `test_data/onnxruntime.dll` (or `test_data/onnxruntime*.so`,
`test_data/onnxruntime*.dylib`) file with the version corresponding to the
onnxruntime version you wish to use.
3. (If you care about DirectML support) Verify that the entries in the
`DummyOrtDMLAPI` struct in `onnxruntime_wrapper.c` match the order in which
they appear in the `OrtDmlApi` struct from the `dml_provider_factory.h`
header in the official repo. See the comment on this struct in
`onnxruntime_wrapper.c` for more information.
Note that both the C API headers and the shared library files are available to
download from the releases page in the
[official repo](https://github.com/microsoft/onnxruntime). Download the archive
for the release you want to use, and extract it. The header files are located
in the "include" subdirectory, and the shared library will be located in the
"lib" subdirectory. (On Linux systems, you'll need the version of the .so with
the appended version numbers, e.g., `libonnxruntime.so.1.26.0`, and _not_ the
`libonnxruntime.so`, which is just a symbolic link.) The archive will contain
several other files containing C++ headers, debug symbols, and so on, but you
shouldn't need anything other than the single onnxruntime shared library and
the two `_c_api.h` header files. (The exception is if you're wanting to enable
GPU support, where you may need other shared-library files, such as
`execution_providers_cuda.dll` and `execution_providers_shared.dll` (or their
equivalents for Linux or OSX).
Requirements
------------
To use this library, you'll need a version of Go with cgo support. You'll also
need a copy of the correct version of the onnxruntime shared library or DLL for
your operating system and architecture. Prior to initializing
`onnxruntime_go`, you need to provide a path to this shared library. See the
first couple lines (i.e., `ort.SetSharedLibraryPath(...)`) of the following
example.
If you want to use CUDA, you'll need to be using a version of the onnxruntime
shared library with CUDA support, as well as be using a CUDA version supported
by the underlying version of your onnxruntime library. For example, version
1.23.2 of the onnxruntime library only supports CUDA versions 12.x. See
[the onnxruntime CUDA support documentation](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html)
for more specifics.
Similarly to CUDA, other execution providers have their own separate
requirements. All of these requirements are too numerous to document in this
README. Please ensure that you are successfully able to use your execution
provider of choice in a python script before raising issues about it here.
Example Usage
-------------
The full documentation can be found at [pkg.go.dev](https://pkg.go.dev/github.com/yalue/onnxruntime_go).
Additionally, several example command-line applications complete with necessary
networks and data can be found in the
[`onnxruntime_go_examples` repository](https://github.com/yalue/onnxruntime_go_examples).
The following example illustrates how this library can be used to load and run
an ONNX network taking a single input tensor and producing a single output
tensor, both of which contain 32-bit floating point values. Note that error
handling is omitted; each of the functions returns an err value, which will be
non-nil in the case of failure.
```go
import (
"fmt"
ort "github.com/yalue/onnxruntime_go"
"os"
)
func main() {
// This line _may_ be optional; by default the library will try to load
// "onnxruntime.dll" on Windows, and "onnxruntime.so" on any other system.
// For stability, programs should always set this explicitly.
ort.SetSharedLibraryPath("path/to/onnxruntime.so")
err := ort.InitializeEnvironment()
if err != nil {
panic(err)
}
defer ort.DestroyEnvironment()
// For a slight performance boost and convenience when re-using existing
// tensors, this library expects the user to create all input and output
// tensors prior to creating the session. If this isn't ideal for your use
// case, see the DynamicAdvancedSession type in the documnentation, which
// allows input and output tensors to be specified when calling Run()
// rather than when initializing a session.
inputData := []float32{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}
inputShape := ort.NewShape(2, 5)
inputTensor, err := ort.NewTensor(inputShape, inputData)
defer inputTensor.Destroy()
// This hypothetical network maps a 2x5 input -> 2x3x4 output.
outputShape := ort.NewShape(2, 3, 4)
outputTensor, err := ort.NewEmptyTensor[float32](outputShape)
defer outputTensor.Destroy()
session, err := ort.NewAdvancedSession("path/to/network.onnx",
[]string{"Input 1 Name"}, []string{"Output 1 Name"},
[]ort.Value{inputTensor}, []ort.Value{outputTensor}, nil)
defer session.Destroy()
// Calling Run() will run the network, reading the current contents of the
// input tensors and modifying the contents of the output tensors.
err = session.Run()
// Get a slice view of the output tensor's data.
outputData := outputTensor.GetData()
// If you want to run the network on a different input, all you need to do
// is modify the input tensor data (available via inputTensor.GetData())
// and call Run() again.
// ...
}
```
Deprecated APIs
---------------
**Typed `Session[t]`:** Older versions of this library used a typed
`Session[T]` struct to keep track of sessions. In retrospect, associating type
parameters with Sessions was unnecessary, and the `AdvancedSession` type, along
with its associated APIs, was added to rectify this mistake. For backwards
compatibility, the old typed `Session[T]` and `DynamicSession[T]` types are
still included and unlikely to be removed. However, they now delegate their
functionality to `AdvancedSession` internally. New code should always favor
using `AdvancedSession` directly.
**Onnxruntime's training API:** The training API has been deprecated as of
onnxruntime version 1.20. Rather than continuing to maintain wrappers for a
deprecated API, `onnxruntime_go` has replaced the wrapper functions for the
training API with stubs that return an error. Users who need to continue to
use the training API will need to use an older version. For example the
following versions should be compatible with training:
- Version `v1.12.1` of `onnxruntime_go`, and
- Version 1.19.2 of `onnxruntime`.
Running Tests and System Compatibility for Testing
--------------------------------------------------
Navigate to this directory and run `go test -v`, or optionally
`go test -v -bench=.`. All tests should pass; tests relating to CUDA or other
accelerator support will be skipped on systems or onnxruntime builds that don't
support them.
Currently, this repository includes a copy of the onnxruntime shared libraries
for a few systems, including AMD64 windows, ARM64 Linux, and ARM64 darwin.
These should allow tests to pass on those systems without users needing to copy
additional libraries beyond cloning this repository. In the future, however,
this may change if support for more systems are added or removed.
You may want to use a different version of the `onnxruntime` shared library for
a couple reasons. In particular:
1. The included shared library copies do not include support for CUDA or other
accelerated execution providers, so CUDA-related tests will always be
skipped if you use the default libraries in this repo.
2. Many systems, including AMD64 and i386 Linux, and x86 osx, do not currently
have shared libraries included in `test_data/` in the first place. (I would
like to keep this directory, and the overall repo, smaller by keeping the
number of shared libraries small.)
If these or other reasons apply to you, the test code will check the
`ONNXRUNTIME_SHARED_LIBRARY_PATH` environment variable before attempting to
load a library from `test_data/`. So, if you are using one of these systems or
want accelerator-related tests to run, you should set the environment variable
to the path to the onnxruntime shared library. Afterwards, `go test -v` should
run and pass.