espionox

Simplifying Ai Agents in Rust 🕵🏼

espionox is an attempt to make building Ai applications in Rust just as approachable as it is with other libraries such as LangChain.

Why would I use Espionox?

• Making an LLM application in Rust
• Experimenting with with complex 'prompt flows' such as Chain/Tree of thought

Usage

First you need to initialize an Agent

Agent::new accepts two arguments:

1. Optional content of a system prompt, if this is left None your agent will have no system prompt
2. A CompletionModel whichever provider you wish to use (As of writing, only OpenAi and Anthropic providers are supported).

use espionox::prelude::*;

let api_key = std::env::var("OPENAI_KEY").unwrap();
let agent = Agent::new(Some("This is the system message"), CompletionModel::default_openai(api_key));

Now, In order to prompt your agent you will call any of the following 3 methods:

• io_completion
• stream_completion
• function_completion

Io Completion

impl Agent {
    pub async fn io_completion(&mut self) -> AgentResult<String>;
}

This is the most straightforward way to get a completion from a model, it will simply request a completion from the associated endpoint with the models' current context.

Stream Completion

impl Agent {
    pub async fn stream_completion(&mut self) -> AgentResult<ProviderStreamHandler>;
}

This will return a stream response handler object that needs to be polled for tokens, for example:

let mut response: ProviderStreamHandler = a.stream_completion().await.unwrap();
while let Ok(Some(res)) = response.receive(&mut a).await {
    println!("Received token: {res:?}")
}

When the stream completes, the finished message will automatically be added to the agent's context, so you do not have to worry about making sure the agent is given the completed response

Function Completion

Currently only available with OpenAi models

impl Agent {
    pub async fn function_completion(&mut self, function: Function) -> AgentResult<serde_json::Value>;
}

This is a feature built on top of OpenAi's function calling API. Instead of needing to write functions as raw JSON, espionox allows you to use it's own language which get's compiled into the correct JSON format when fed to the model. The structure of a function is as follows:

<function name>([<argname: type>])
    i = <description of what function does>
    [<optional descriptions of each arg>]

If an arg's name is followed by a !, it means that argument is required.

Supported argument types:

• bool
• int
• string
• enum - with variants defined in single quotes, separated by |

Weather Function Example

Imagine you want to use the function given in OpenAi's example

{
    "name": "get_current_weather",
         "description": "Get the current weather in a given location",
         "parameters": {
           "type": "object",
           "properties": {
             "location": {
               "type": "string",
               "description": "The city and state, e.g. San Francisco, CA"
             },
             "unit": {
               "type": "string",
               "enum": ["celcius", "fahrenheit"]
             }
           },
           "required": ["location"]
       }
}

Instead of hand writing the above JSON object, you can use espionox's function language

let weather_function_str = r#"
    get_current_weather(location!: string, unit: 'celcius' | 'farenheight') 
            i = 'Get the current weather in a given location'
            location = 'the city and state, e.g. San Francisco, CA'
"#;
let weather_function = Function::try_from(weather_function_str);
let json_response = agent.function_completion(weather_function).await?;

The returned serde_json::Value contains the specified args and their values as key value pairs. For example, json_response might look something like:

{
    "location": "San Francisco, CA",
    "unit": "fahrenheit"
}

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espionox is very early in development and everything may be subject to change Please feel free to reach out with any questions, suggestions, issues or anything else :)

Most Recent Change