allms: One Library to rule them aLLMs

This Rust library is specialized in providing type-safe interactions with APIs of the following LLM providers: OpenAI, Anthropic, Mistral, Google Gemini. (More providers to be added in the future.) It's designed to simplify the process of experimenting with different models. It de-risks the process of migrating between providers reducing vendor lock-in issues. It also standardizes serialization of sending requests to LLM APIs and interpreting the responses, ensuring that the JSON data is handled in a type-safe manner. With allms you can focus on creating effective prompts and providing LLM with the right context, instead of worrying about differences in API implementations.

Features

Support for various LLM models including OpenAI (GPT-3.5, GPT-4), Anthropic (Claude, Claude Instant), Mistral, or Google GeminiPro.
Easy-to-use functions for chat/text completions and assistants. Use the same struct and methods regardless of which model you choose.
Automated response deserialization to custom types.
Standardized approach to providing context with support of function calling, tools, and file uploads.
Enhanced developer productivity with automated token calculations, rate limits and debug mode.
Extensibility enabling easy adoption of other models with standardized trait.
Asynchronous support using Tokio.

Foundational Models

OpenAI:

APIs: Chat Completions, Function Calling, Assistants (v1 & v2), Files, Vector Stores, Tools (file_search)
Models: GPT-4o, GPT-4, GPT-4 32k, GPT-4 Turbo, GPT-3.5 Turbo, GPT-3.5 Turbo 16k

Anthropic:

APIs: Text Completions
Models: Claude 2.0, Claude Instant 1.2 (Claude 3 coming soon)

Mistral:

APIs: Chat Completions
Models: Mistral Small, Mistral Medium, Mistral Large (Mistral 7B & Mixtral 8x coming soon)

Google Vertex AI / AI Studio:

APIs: Chat Completions (including streaming)
Models: Gemini 1.0 Pro (Gemini 1.5 Pro coming soon)

Prerequisites

OpenAI: API key (passed in model constructor)
Anthropic: API key (passed in model constructor)
Mistral: API key (passed in model constructor)
Google AI Studio: API key (passed in model constructor)
Google Vertex AI: GCP service account key (used to obtain access token) + GCP project ID (set as environment variable)

Examples

Explore the examples directory to see more use cases and how to use different LLM providers and endpoint types.

Using Completions API with different foundational models:

let openai_answer = Completions::new(OpenAIModels::Gpt4o, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

let anthropic_answer = Completions::new(AnthropicModels::Claude2, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

let mistral_answer = Completions::new(MistralModels::MistralSmall, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

let google_answer = Completions::new(GoogleModels::GeminiPro, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

Example:

RUST_LOG=info RUST_BACKTRACE=1 cargo run --example use_completions

Using Assistant API to analyze your files with File and VectorStore capabilities:

// Create a File
let openai_file = OpenAIFile::new(None, &API_KEY)
    .upload(&file_name, bytes)
    .await?;

// Create a Vector Store
let openai_vector_store = OpenAIVectorStore::new(None, "Name", &API_KEY)
    .upload(&[openai_file.id.clone().unwrap_or_default()])
    .await?;

// Extract data using Assistant 
let openai_answer = OpenAIAssistant::new(OpenAIModels::Gpt4o, &API_KEY)
    .version(OpenAIAssistantVersion::V2)
    .vector_store(openai_vector_store.clone())
    .await?
    .get_answer::<T>(instructions, &[])
    .await?;