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, Perplexity. (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 foundational LLM providers including Anthropic, AWS Bedrock, Azure, DeepSeek, Google Gemini, OpenAI, Mistral, and Perplexity.
• 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

Anthropic:

• APIs: Messages, Text Completions
• Models: Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku, Claude 2.0, Claude Instant 1.2

AWS Bedrock:

• APIs: Converse
• Models: Nova Micro, Nova Lite, Nova Pro (additional models to be added)

Azure OpenAI:

• APIs: Assistants, Files, Vector Stores, Tools
  • API version can be set using AzureVersion variant
• Models: as per model deployments in Azure OpenAI Studio
  • If using custom model deployment names please use the Custom variant of OpenAIModels

DeepSeek:

• APIs: Chat Completion
• Models: DeepSeek-V3, DeepSeek-R1

Google Vertex AI / AI Studio:

• APIs: Chat Completions (including streaming)
• Models: Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 1.0 Pro

Mistral:

• APIs: Chat Completions
• Models: Mistral Large, Mistral Nemo, Mistral 7B, Mixtral 8x7B, Mixtral 8x22B, Mistral Medium, Mistral Small, Mistral Tiny

OpenAI:

• APIs: Chat Completions, Function Calling, Assistants (v1 & v2), Files, Vector Stores, Tools (file_search)
• Models:
  • Chat Completions only: o1, o1 Preview, o1 Mini, o3 Mini
  • Chat Completions & Assistants: GPT-4o, GPT-4, GPT-4 32k, GPT-4 Turbo, GPT-3.5 Turbo, GPT-3.5 Turbo 16k, fine-tuned models (via Custom variant)

Perplexity:

• APIs: Chat Completions
• Models: Sonar, Sonar Pro, Sonar Reasoning. The following legacy models will be supported until February 22, 2025: Llama 3.1 Sonar Small, Llama 3.1 Sonar Large, Llama 3.1 Sonar Huge

Prerequisites

• Anthropic: API key (passed in model constructor)
• AWS Bedrock: environment variables AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_REGION set as per AWS settings.
• Azure OpenAI: environment variable OPENAI_API_URL set to your Azure OpenAI resource endpoint. Endpoint key passed in constructor
• DeepSeek: 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)
• Mistral: API key (passed in model constructor)
• OpenAI: API key (passed in model constructor)
• Perplexity: API key (passed in model constructor)

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

let aws_bedrock_answer = Completions::new(AwsBedrockModels::NovaLite, "", None, None)
    .get_answer::<T>(instructions)
    .await?

let deepseek_answer = Completions::new(DeepSeekModels::DeepSeekReasoner, &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?

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

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

let perplexity_answer = Completions::new(PerplexityModels::Llama3_1SonarSmall, &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?;

Example:

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

License

This project is licensed under dual MIT/Apache-2.0 license. See the LICENSE-MIT and LICENSE-APACHE files for details.