# allms: One Library to rule them aLLMs
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This Rust library is specialized in providing type-safe interactions with APIs of the following LLM providers: Anthropic, AWS Bedrock, Azure, DeepSeek, Google Gemini, Mistral, OpenAI, Perplexity, xAI. (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 Sonnet 4.6, Claude Opus 4.6, Claude Opus 4.5, Claude Sonnet 4.5, Claude Haiku 4.5, Claude Opus 4.1, Claude Sonnet 4, Claude Opus 4, Claude 3.7 Sonnet, Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 3 Opus, Claude 3 Sonnet, Claude 3 Haiku, Claude 2.0, Claude Instant 1.2
- Tools: file search, web search (including dynamic filtering), code interpreter, computer use

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

Azure OpenAI:
- APIs: Completions, Responses, 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 Gemini:
- APIs: Chat Completions (including streaming)
    - Via Vertex AI or AI Studio
- Models: Gemini 3 Pro (Preview), Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.5 Flash-Lite, Gemini 2.0 Flash, Gemini 2.0 Flash-Lite, Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 1.5 Flash-8B
    - Experimental models: Gemini 2.0 Pro, Gemini 2.0 Flash-Thinking
    - Fine-tuned models: in Vertex AI only, using endpoint constructor
- Tools: Google Search, code execution

Mistral:
- APIs: Chat Completions
- Models:  
    - Multimodal: Mistral Large 2.1, Mistral Medium 3.1, Mistral Medium 3, Mistral Small 3.2, Mistral Small 3.1, Mistral Small 3, Mistral Small 2
    - Reasoning: Magistral Medium 1.2, Magistral Medium, Magistral Small 1.2
    - Other: Codestral 2508, Codestral 2, Ministral 3B, Ministral 8B
    - Legacy models: Mistral Large, Mistral Nemo, Mistral 7B, Mixtral 8x7B, Mixtral 8x22B, Mistral Medium, Mistral Small, Mistral Tiny
- Tools: file web search, code interpreter

OpenAI:
- APIs: Chat Completions, Responses, Function Calling, Assistants (v1 & v2), Files, Vector Stores
- Models: 
    - Chat Completions & Responses only: GPT-5.2, GPT-5.2 Pro, Gpt-5.1, o1, o1 Preview, o1 Mini, o1 Pro, o3, o3 Mini, o4 Mini
    - Chat Completions, Responses & Assistants: GPT-5, GPT-5-mini, GPT-5-nano, GPT-4.5-Preview, 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)
- Tools: file search, web search, code interpreter, computer use

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

xAI:
- APIs: Chat Completions, Responses
- Models: Grok 4.1 Fast Thinking, Grok 4.1 Fast Non Thinking, Grok 4, Grok 4 Fast Thinking, Grok 4 Fast Non Thinking, Grok Code Fast 1, Grok 3, Grok 3 Mini, Grok 3 Fast, Grok 3 Mini Fast
- Tools: web search, X search

### 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)
- xAI: 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::Claude4Sonnet, &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::Gemini2_5Flash, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

let mistral_answer = Completions::new(MistralModels::MistralMedium3, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

let openai_answer = Completions::new(OpenAIModels::Gpt4_1Mini, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

let openai_responses_answer = Completions::new(OpenAIModels::Gpt4_1Mini, &API_KEY, None, None)
    .version("openai_responses")
    .get_answer::<T>(instructions)
    .await?

let perplexity_answer = Completions::new(PerplexityModels::SonarPro, &API_KEY, None, None)
    .get_answer::<T>(instructions)
    .await?

let xai_answer = Completions::new(XAIModels::Grok3Mini, &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](LICENSE-MIT) and [LICENSE-APACHE](LICENSE-APACHE) files for details.
