build python packages for llm

To get a more recent version of the llm python tool and the anthropic
module, we need to build the packages ourselves.

Refactor how we're building overlays to make it easier to add new
packages using the `pkgsUnstable` set.

1 files changed, 22 insertions, 2 deletions

diff --git a/flake.nix b/flake.nix
index 9d51051..ac1179c 100644
--- a/flake.nix
+++ b/flake.nix
@@ -4,6 +4,8 @@
   inputs = {
     nixpkgs.url = "github:nixos/nixpkgs/nixos-24.11";
 
+    nixpkgsUnstable.url = "github:nixos/nixpkgs/nixos-unstable";
+
     home-manager = {
       url = "github:nix-community/home-manager/release-24.11";
       inputs.nixpkgs.follows = "nixpkgs";
@@ -29,6 +31,7 @@
   outputs =
     {
       nixpkgs,
+      nixpkgsUnstable,
       darwin,
       treefmt-nix,
       pre-commit-hooks,
@@ -37,7 +40,24 @@
     let
       system = "aarch64-darwin";
       pkgs = import nixpkgs { inherit system; };
-      mkSystem = import ./nix/lib/mkSystem.nix { inherit nixpkgs inputs; };
+      pkgsUnstable = import nixpkgsUnstable { inherit system; };
+
+      # Define overlays here
+      overlays = [
+        (final: _prev: {
+          sapi = final.callPackage ./pkgs/sapi { };
+          hashi = final.callPackage ./pkgs/hashi { };
+          llmPython = final.callPackage ./pkgs/llmPython {
+            inherit pkgsUnstable;
+          };
+        })
+      ];
+
+      mkSystem = import ./nix/lib/mkSystem.nix {
+        inherit nixpkgs nixpkgsUnstable inputs;
+        inherit overlays;
+      };
+
       treefmtEval = treefmt-nix.lib.evalModule pkgs {
         projectRootFile = "flake.nix";
         programs = {
@@ -91,7 +111,7 @@
           git
           (writeScriptBin "nbuild" ''
             set -e
-            echo "> Running darwin-rebuild switch..."
+            echo "> Running darwin-rebuild build..."
             ${inputs.darwin.packages.${system}.darwin-rebuild}/bin/darwin-rebuild build --flake .
             echo "> darwin-rebuild build was successful ✅"
             echo "> macOS config was successfully applied 🚀"