Langchain

Work in progress interface for building langchain framework tools. Will attempt 1-1 correspondence with the python langchain framework (including serialisation)

Objectives:

• [ ] 1-1 correspondence with python langchain framwork interface
• [ ] Serialisation / Deserialisation of langchain objects

Non-objectives:

• [ ] Reference / concrete implementations of various modules. Those are delegated to other crates
• [ ] Visualisation, UI tools

Architecture:

The vast majority of code written when using this framework will be writing tools, working with memory, and llm models. The abstraction provided is described in the Model trait. Most of the code written with this will be adapting tools, models, and others to memory. It is the core mode of communication between entities.

```

[asynctrait::asynctrait]

pub trait Model where M: Memory, { async fn run(&self, memory: &mut M) -> Result<(), LanguageModelError>; } ```

This is describing that an LLM model is abstract over its own internal implementation and the memory it operates on. Memory is a serialisation of state or a "session" of a model and is expected to be passed around if required.

A very simple agent implementation would be something like

```

pub fn answer_question(question: &str) { let model = langchain::model::openai::ChatModel; let memory = langchain::model::openai::ChatMemory;

let mut agent = langchain::agent::Agent::default()
    .with_tool(SearchWeb) // implement this yourself 

let result = agent.execute( AgentRequest { memory, question: "How do birds fly?" } ).await;

```