semvec/pypi-packages huggingface dataset:
lobsang 0.1.0
A simple framework to interact with conversational LLMs
Requires Python: >=3.11
Keywords: chat,framework,llm

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# Lobsang

> **Welcome to Lobsang** 🧘‍♂️
> 
> Lobsang is a framework to interact with conversational LLMs in the simplest way possible. \
> It's easy to get started, yet designed to scale up to complex use cases. Enjoy the ride! 🚀

**Note:** This project is still in early development. Expect breaking changes.

## Installation

Requires **Python 3.11** or higher.

```bash
pip install lobsang
```

## Core Concepts
Lobsang is built around a few core concepts:

- **Chat** is the main class of Lobsang. It represents a conversation between a user and a LLM. 
    It is the main entry point to the framework and stores the chat history for a single conversation. 
    After creating a `Chat` instance, you can interact with it by calling itself with one
    or more messages:
    ```python
    from lobsang import Chat, UserMessage
    from lobsang.answers import TextAnswer
    from lobsang.llms import FakeLLM
    chat = Chat(llm=FakeLLM()) 
    #                 👆 FakeLLM returns dummy responses
    # Call the chat with one message and a corresponding answer (placeholder for the LLM's response)
    chat([UserMessage("Hello"), TextAnswer()])
    print("Chat history:")  
    print(chat)
    
    # Call the chat with multiple messages
    res = chat([
        UserMessage("What is 1+1?"),
        TextAnswer(),
        UserMessage("What is 2+2?"),
        TextAnswer()
    ])
  
    print("Conversation:")
    print(res)
    ```
  - **Answers** are used to guide the LLM to generate a specific response by modifying user messages. 
    For example, you can use the `JSONAnswer`to instruct the LLM to generate a JSON response 
    (see [examples/2_directives.ipynb](./examples/2_directives.ipynb) for more details).

## Examples
We provide a few examples to get you started. You can find them in the [examples](./examples) folder.
The examples use the openai package, make sure to install it before running the examples (`pip install openai`).
You will also need an OpenAI API key, which you can get here: https://platform.openai.com/account/api-keys.