ninja VS pydantic

Under the hood, Rescript uses a build system called Ninja. Ninja is similar to Make, but cross-platform and more minimal/performant.

Really? I thought most new projects were switching to ninja[^1] and have never used it.

[^1]: https://ninja-build.org/

warning: Starting with the September 2023 release, the default triplet for vcpkg libraries will change from x86-windows to the detected host triplet (x64-windows). To resolve this message, add --triplet x86-windows to keep the same behavior. Computing installation plan... The following packages will be built and installed: * egl-registry:x86-windows -> 2022-09-20 glew:x86-windows -> 2.2.0#3 * opengl:x86-windows -> 2022-12-04#3 * opengl-registry:x86-windows -> 2022-09-29#1 * vcpkg-cmake:x64-windows -> 2023-05-04 * vcpkg-cmake-config:x64-windows -> 2022-02-06#1 Additional packages (*) will be modified to complete this operation. Detecting compiler hash for triplet x86-windows... A suitable version of powershell-core was not found (required v7.2.11) Downloading portable powershell-core 7.2.11... Downloading powershell-core... https://github.com/PowerShell/PowerShell/releases/download/v7.2.11/PowerShell-7.2.11-win-x86.zip->C:\vcpkg\downloads\PowerShell-7.2.11-win-x86.zip Downloading https://github.com/PowerShell/PowerShell/releases/download/v7.2.11/PowerShell-7.2.11-win-x86.zip Extracting powershell-core... error: while detecting compiler information: The log file content at "C:\vcpkg\buildtrees\detect_compiler\stdout-x86-windows.log" is: -- Downloading https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-win.zip -> ninja-win-1.10.2.zip... -- Configuring x86-windows CMake Error at scripts/cmake/vcpkg_execute_required_process.cmake:112 (message): Command failed: C:/vcpkg/downloads/tools/ninja/1.10.2-windows/ninja.exe -v Working Directory: C:/vcpkg/buildtrees/detect_compiler/x86-windows-rel/vcpkg-parallel-configure Error code: 1 See logs for more information: C:\vcpkg\buildtrees\detect_compiler\config-x86-windows-rel-CMakeCache.txt.log C:\vcpkg\buildtrees\detect_compiler\config-x86-windows-out.log

I thought I would share my custom installer script for the latest GitHub versions of CMake, Ninja, and Meson.

Now that you have your build system all generated you can go ahead and build everything. By default Meson will use Ninja as the build tool. Ninja is similar to Make but much much faster. You can also generate additional build systems but that's outside of the scope of this post.

There are also some other tools like https://ninja-build.org/ that you might prefer using instead

I got Ninja from https://github.com/ninja-build/ninja, latest release. I'm assuming this is a false positive?

Oh excellent, then better (and more portable!) tools are available:

http://pants.build

https://ninja-build.org

https://buck.build

and, if you hate yourself: https://bazel.build

First, note the method prefix_allowed_tokens_fn. This method applies a Pydantic model to constrain/guide how the LLM generates tokens. Next, see how that constrain can be applied to txtai's LLM pipeline.

utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses

I like some of the changes from v1 to v2. But then you have something like this [0] removed from the library without proper documentation or replacement, resulting in ugly workarounds in the link that wont' work properly.

[0]: https://github.com/pydantic/pydantic/discussions/6337

The easiest implementation I found was to use a PyDantic class for my target schema — and use that as a parameter for the method call to “ChatCompletion.create()”. Here’s a fragment of the GinAI Python classes used.

Also, FastStream uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your applications, so you can serialize your input messages just using type annotations.

Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages

Not sure if it is exactly related, but there is an open issue on Github for that exact message.

Well the performance increase is so huge because pydantic1 is really really slow. And for using rust, I'd have expected more tbh…

I've been benchmarking pydantic v2 against typedload (which I write) and despite the rust, it still manages to be slower than pure python in some benchmarks.

The ones on the website are still about comparing to v1 because v2 was not out yet at the time of the last release.

pydantic's author will refuse to benchmark any library that is faster (https://github.com/pydantic/pydantic/pull/3264 https://github.com/pydantic/pydantic/pull/1525 https://github.com/pydantic/pydantic/pull/1810) and keep boasting about amazing performances.

On pypy, v2 beta was really really really slow.