langchain-huggy 0.1.3

A LangChain integration for HuggingChat models

langchain_huggy

langchain_huggy is a Python package that provides an easy-to-use interface for interacting with HuggingChat models through the LangChain framework.

Available Models

langchain_huggy comes with several pre-configured models:

1. 'meta-llama/Meta-Llama-3.1-70B-Instruct'
2. 'CohereForAI/c4ai-command-r-plus-08-2024'
3. 'Qwen/Qwen2.5-72B-Instruct'
4. 'meta-llama/Llama-3.2-11B-Vision-Instruct'
5. 'NousResearch/Hermes-3-Llama-3.1-8B'
6. 'mistralai/Mistral-Nemo-Instruct-2407'
7. 'microsoft/Phi-3.5-mini-instruct'

You can choose any of these models when initializing the HuggingChat instance.

Installation

Install the package using pip:

pip install langchain_huggy

Quick Start

Here's a simple example to get you started:

from langchain_huggy import HuggingChat

# Initialize the HuggingChat model
llm = HuggingChat(
    hf_email = "[e-mail]",
    hf_password = "your_huggingface_password",
    model = "Qwen/Qwen2.5-72B-Instruct"  # Optional: specify a model
)

# Stream the response to a question
llm.pstream("Who is Modi?")

This will print the streamed response to your question about Modi using the specified model.

Features

• Easy integration with LangChain
• Supports streaming responses
• Uses HuggingChat models
• Customizable with different model options

Configuration

You can configure the HuggingChat instance with the following parameters:

• hf_email: Your HuggingFace account email
• hf_password: Your HuggingFace account password
• model: (Optional) Specify a particular model to use from the available models list

Viewing Available Models

You can view the list of available models at any time using:

print(llm.get_available_models)

Note

Make sure to keep your HuggingFace credentials secure and never share them in public repositories.

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

If you encounter any problems or have any questions, please open an issue on the GitHub repository.

Happy chatting with langchain_huggy!