The `llm_api_access` crate provides a unified way to interact with different large language models (LLMs) like OpenAI, Gemini, and Anthropic.
## Current Status
Currently I develop this because it is used to power an open-source coding assistant currently in active development. Gemini has been the main test target; OpenAI (including embeddings) and Anthropic are supported but have been exercised less. Development is self encouraged so updates can be far and few between, open an issue on github if you want something specific.
### LLM Enum
This enum represents the supported LLM providers:
- `OpenAI`: Represents the OpenAI language model.
- `Gemini`: Represents the Gemini language model.
- `Anthropic`: Represents the Anthropic language model.
### Access Trait
The `Access` trait defines asynchronous methods for interacting with LLMs:
- `send_single_message`: Sends a single message and returns the generated response.
- `send_convo_message`: Sends a list of messages as a conversation and returns the generated response.
- `get_model_info`: Gets information about a specific LLM model.
- `list_models`: Lists all available LLM models.
- `count_tokens`: Counts the number of tokens in a given text.
The `LLM` enum implements `Access`, providing specific implementations for each method based on the chosen LLM provider.
**Note:** Currently, `get_model_info`, `list_models`, and `count_tokens` only work for the Gemini LLM. Other providers return an error indicating this functionality is not yet supported.
### Loading API Credentials with dotenv
The `llm_api_access` crate uses the `dotenv` library to securely load API credentials from a `.env` file in your project's root directory. This file should contain key-value pairs for each LLM provider you want to use.
**Example Structure:**
```
OPEN_AI_ORG=your_openai_org
OPENAI_API_KEY=your_openai_api_key
GEMINI_API_KEY=your_gemini_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
```
**Steps:**
1. **Create `.env` File:** Create a file named `.env` at the root of your Rust project directory.
2. **Add API Keys:** Fill in the `.env` file with the following format, replacing placeholders with your actual API keys.
**Important Note:**
* **Never** commit your `.env` file to version control systems like Git. It contains sensitive information like API keys.
## Example Usage
### `send_single_message` Example
```rust
use llm_api_access::llm::{Access, LLM};
#[tokio::main]
async fn main() {
// Create an instance of the OpenAI LLM
let llm = LLM::OpenAI;
// Send a single message to the LLM
let response = llm.send_single_message("Tell me a joke about programmers").await;
match response {
Ok(joke) => println!("Joke: {}", joke),
Err(err) => eprintln!("Error: {}", err),
}
}
```
This example creates an instance of the `LLM::OpenAI` provider and sends a single message using the `send_single_message` method. It then matches the result, printing the generated joke or an error message if an error occurred.
### `send_convo_message` Example
```rust
use llm_api_access::llm::{Access, LLM};
use llm_api_access::structs::general::Message;
#[tokio::main]
async fn main() {
// Create an instance of the Gemini LLM
let llm = LLM::Gemini;
// Define the conversation messages
let messages = vec![
Message {
role: "user".to_string(),
content: "You are a helpful coding assistant.".to_string(),
},
Message {
role: "model".to_string(),
content: "You got it! I am ready to assist!".to_string(),
},
Message {
role: "user".to_string(),
content: "Generate a rust function that reverses a string.".to_string(),
},
];
// Send the conversation messages to the LLM
let response = llm.send_convo_message(messages).await;
match response {
Ok(code) => println!("Code: {}", code),
Err(err) => eprintln!("Error: {}", err),
}
}
```
**Note:** This example requires API keys and configuration for the Gemini LLM provider.
## Embeddings
The crate provides support for generating text embeddings through the OpenAI API.
### OpenAI Embeddings
The `openai` module includes functionality to generate vector embeddings:
```rust
pub async fn get_embedding(
input: String,
dimensions: Option<u32>,
) -> Result<Vec<f32>, Box<dyn std::error::Error + Send + Sync>>
```
This function takes:
- `input`: The text to generate embeddings for
- `dimensions`: Optional parameter to specify the number of dimensions (if omitted, uses the model default)
It returns a vector of floating point values representing the text embedding.
### Example Usage:
```rust
use llm_api_access::openai::get_embedding;
#[tokio::main]
async fn main() {
// Generate an embedding with default dimensions
match get_embedding("This is a sample text for embedding".to_string(), None).await {
Ok(embedding) => {
println!("Generated embedding with {} dimensions", embedding.len());
// Use embedding for semantic search, clustering, etc.
},
Err(err) => eprintln!("Error generating embedding: {}", err),
}
// Generate an embedding with custom dimensions
match get_embedding("Custom dimension embedding".to_string(), Some(64)).await {
Ok(embedding) => {
println!("Generated custom embedding with {} dimensions", embedding.len());
assert_eq!(embedding.len(), 64);
},
Err(err) => eprintln!("Error generating embedding: {}", err),
}
}
```
The function uses the "text-embedding-3-small" model by default and requires the same environment variables as other OpenAI API calls (`OPEN_AI_KEY` and `OPEN_AI_ORG`).
## Testing
The `llm_api_access` crate includes unit tests for various methods in the `Access` trait. These tests showcase usage and expected behavior with different LLM providers.