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.

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.

This is the output of calling the assistant api with metallica.pdf

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

This program will send this press release to OpenAI Assistant API and get the data requested in the response type back:

pub struct ConcertInfo {
    dates: Vec<String>,
    band: String,
    venue: String,
    city: String,
    country: String,
    ticket_price: String,
}

Output:

Running `target/debug/examples/use_openai_assistant`

ConcertInfo { dates: ["Friday September 6, 2019"], band: "Metallica and the San Francisco Symphony", venue: "Chase Center", city: "San Francisco", country: "USA", ticket_price: "Information not available" }

License

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