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Declare zai as a project dependency with
zig fetch:# latest version zig fetch --save git+https://github.com/AdjectiveAllison/zai.git#main # specific commit zig fetch --save git+https://github.com/AdjectiveAllison/zai.git#COMMIT
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Expose zai as a module in your project's
build.zig:pub fn build(b: *std.Build) void { const target = b.standardTargetOptions(.{}); const optimize = b.standardOptimizeOption(.{}); const opts = .{ .target = target, .optimize = optimize }; // 👈 const zai = b.dependency("zai", opts).module("zai"); // 👈 const exe = b.addExecutable(.{ .name = "my-project", .root_source_file = b.path("src/main.zig"), .target = target, .optimize = optimize, }); exe.root_module.addImport("zai", zai); // 👈 // ... }
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Import Zig AI into your code:
const zai = @import("zai");
- Support for chat completions and embeddings
- Streaming capabilities for real-time responses
- Easy integration with Zig projects
- Configurable model selection
- Automatic provider-specific API handling (in the works)
Here's a basic example of using zai for chat completion:
const std = @import("std");
const zai = @import("zai");
pub fn main() !void {
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
defer _ = gpa.deinit();
const allocator = gpa.allocator();
var ai: zai.AI = undefined;
try ai.init(allocator, zai.Provider.OctoAI);
defer ai.deinit();
var messages = [_]zai.Message{
.{
.role = "system",
.content = "You are a helpful AI assistant.",
},
.{
.role = "user",
.content = "Write one sentence about how cool it would be to use zig to call language models.",
},
};
const payload = zai.CompletionPayload{
.model = "meta-llama-3.1-8b-instruct",
.messages = &messages,
.temperature = 0.1,
.stream = true,
};
var chat_completion: zai.ChatCompletion = undefined;
chat_completion.init(allocator);
defer chat_completion.deinit();
try chat_completion.streamAndPrint(&ai, payload);
}Here's how to use zai for generating embeddings:
const std = @import("std");
const zai = @import("zai");
pub fn main() !void {
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
defer _ = gpa.deinit();
const allocator = gpa.allocator();
var ai: zai.AI = undefined;
try ai.init(allocator, zai.Provider.OctoAI);
defer ai.deinit();
const input_text = "Zig is a general-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.";
const payload = zai.Embeddings.EmbeddingsPayload{
.input = input_text,
.model = "thenlper/gte-large",
};
var embeddings: zai.Embeddings = undefined;
embeddings.init(allocator);
defer embeddings.deinit();
try embeddings.request(&ai, payload);
std.debug.print("Embedding vector length: {d}\n", .{embeddings.embedding.len});
}You can find more detailed examples in the examples/ directory:
To run an example, use:
zig build chat-completion
# or
zig build embeddingszai uses environment variables for API keys (right now). Set the appropriate environment variable for your chosen provider:
- OpenAI:
OPENAI_API_KEY - OctoAI:
OCTOAI_TOKEN - TogetherAI:
TOGETHER_API_KEY - OpenRouter:
OPENROUTER_API_KEY
zai is released under the MIT License. See the LICENSE file for details.