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genai

Multi-AI Providers Library for Rust. (Ollama, OpenAI, Anthropic, Groq, Gemini, ...)

19 releases

new 0.1.4 Jul 20, 2024
0.1.3 Jul 18, 2024
0.1.1 Jun 13, 2024
0.0.14 Jun 10, 2024

#236 in Web programming

Download history 505/week @ 2024-05-30 1127/week @ 2024-06-06 313/week @ 2024-06-13 41/week @ 2024-06-20 22/week @ 2024-06-27 196/week @ 2024-07-04 96/week @ 2024-07-11

360 downloads per month

MIT/Apache

130KB
3K SLoC

genai - Multi-AI Providers Library for Rust.

Currently supports natively: Ollama, OpenAI, Anthropic, groq, Gemini, Cohere (more to come)

Static Badge Static Badge

# cargo.toml
genai = "=0.1.4" # Version lock for `0.1.x`

The goal of this library is to provide a common and ergonomic single API to many generative AI Providers, such as OpenAI, Anthropic, Cohere, Ollama.

  • IMPORTANT 1 0.1.x will still have some breaking changes in patches, so make sure to lock your version, e.g., genai = "=0.1.4". In short, 0.1.x can be considered "beta releases." Version 0.2.x will follow semver more strictly.

  • IMPORTANT 2 genai is focused on normalizing chat completion APIs across AI providers and is not intended to be a full representation of a given AI provider. For this, there are excellent libraries such as async-openai for OpenAI and ollama-rs for Ollama.

Examples | Thanks | Library Focus | Changelog | ChatRequestOptions Provider Mapping

Examples

examples/c00-readme.rs

use genai::chat::{ChatMessage, ChatRequest};
use genai::utils::{print_chat_stream, PrintChatStreamOptions};
use genai::Client;

const MODEL_OPENAI: &str = "gpt-4o-mini";
const MODEL_ANTHROPIC: &str = "claude-3-haiku-20240307";
const MODEL_COHERE: &str = "command-light";
const MODEL_GEMINI: &str = "gemini-1.5-flash-latest";
const MODEL_GROQ: &str = "gemma-7b-it";
const MODEL_OLLAMA: &str = "gemma:2b"; // sh: `ollama pull gemma:2b`

// NOTE: Those are the default environment keys for each AI Adapter Type.
//       Can be customized, see `examples/c02-auth.rs`
const MODEL_AND_KEY_ENV_NAME_LIST: &[(&str, &str)] = &[
	// -- de/activate models/providers
	(MODEL_OPENAI, "OPENAI_API_KEY"),
	(MODEL_ANTHROPIC, "ANTHROPIC_API_KEY"),
	(MODEL_COHERE, "COHERE_API_KEY"),
	(MODEL_GEMINI, "GEMINI_API_KEY"),
	(MODEL_GROQ, "GROQ_API_KEY"),
	(MODEL_OLLAMA, ""),
];

// NOTE: Model to AdapterKind (AI Provider) type mapping rule
//  - starts_with "gpt"      -> OpenAI
//  - starts_with "claude"   -> Anthropic
//  - starts_with "command"  -> Cohere
//  - starts_with "gemini"   -> Gemini
//  - model in Groq models   -> Groq
//  - For anything else      -> Ollama
//
// Can be customized, see `examples/c03-kind.rs`

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
	let question = "Why is the sky red?";

	let chat_req = ChatRequest::new(vec![
		// -- Messages (de/activate to see the differences)
		ChatMessage::system("Answer in one sentence"),
		ChatMessage::user(question),
	]);

	let client = Client::default();

	let print_options = PrintChatStreamOptions::from_print_events(false);

	for (model, env_name) in MODEL_AND_KEY_ENV_NAME_LIST {
		// Skip if does not have the environment name set
		if !env_name.is_empty() && std::env::var(env_name).is_err() {
			println!("===== Skipping model: {model} (env var not set: {env_name})");
			continue;
		}

		let adapter_kind = client.resolve_model_info(model)?.adapter_kind;

		println!("\n===== MODEL: {model} ({adapter_kind}) =====");

		println!("\n--- Question:\n{question}");

		println!("\n--- Answer:");
		let chat_res = client.exec_chat(model, chat_req.clone(), None).await?;
		println!("{}", chat_res.content_text_as_str().unwrap_or("NO ANSWER"));

		println!("\n--- Answer: (streaming)");
		let chat_res = client.exec_chat_stream(model, chat_req.clone(), None).await?;
		print_chat_stream(chat_res, Some(&print_options)).await?;

		println!();
	}

	Ok(())
}

More Examples

Thanks

Library Focus:

  • Focuses on standardizing chat completion APIs across major AI Services.

  • Native implementation, meaning no per-service SDKs.

    • Reason: While there are some variations between all of the various APIs, they all follow the same pattern and high-level flow and constructs. Managing the differences at a lower layer is actually simpler and more cumulative accross services than doing sdks gymnastic.
  • Prioritizes ergonomics and commonality, with depth being secondary. (If you require complete client API, consider using async-openai and ollama-rs; they are both excellent and easy to use.)

  • Initially, this library will mostly focus on text chat API (images, or even function calling in the first stage).

  • The 0.1.x version will work, but the APIs will change in the patch version, not following semver strictly.

  • Version 0.2.x will follow semver more strictly.

ChatRequestOptions

Property OpenAI Anthropic Ollama Groq Gemini Cohere
temperature temperature temperature temperature temperature generationConfig.temperature temperature
max_tokens max_tokens max_tokens (default 1024) max_tokens max_tokens generationConfig.maxOutputTokens max_tokens
top_p top_p top_p top_p top_p generationConfig.topP p

MetaUsage

Property OpenAI
usage.
Ollama
usage.
Groq x_groq.usage. Anthropic usage. Gemini usageMetadata. Cohere meta.tokens.
input_tokens prompt_tokens prompt_tokens (1) prompt_tokens input_tokens (added) promptTokenCount (2) input_tokens
output_tokens completion_tokens completion_tokens (1) completion_tokens output_tokens (added) candidatesTokenCount (2) output_tokens
total_tokens total_tokens total_tokens (1) completion_tokens (computed) totalTokenCount (2) (computed)

Note (1): At this point, Ollama does not emit input/output tokens when streaming due to the Ollama OpenAI compatibility layer limitation. (see ollama #4448 - Streaming Chat Completion via OpenAI API should support stream option to include Usage)

Note (2) Right now, with Gemini Stream API, it's not really clear if the usage for each event is cumulative or needs to be added. Currently, it appears to be cumulative (i.e., the last message has the total amount of input, output, and total tokens), so that will be the assumption. See possible tweet answer for more info.

Notes on Possible Direction

  • Will add more data on ChatResponse and ChatStream, especially metadata about usage.
  • Add vision/image support to chat messages and responses.
  • Add function calling support to chat messages and responses.
  • Add embbed and embbed_batch
  • Add the AWS Bedrock variants (e.g., Mistral, and Anthropic). Most of the work will be on "interesting" token signature scheme (without having to drag big SDKs, might be below feature).
  • Add the Google VertexAI variants.
  • (might) add the Azure OpenAI variant (not sure yet).

Dependencies

~7–19MB
~281K SLoC