{ "cells": [ { "cell_type": "markdown", "id": "40a27d3c-4e5c-4b96-b290-4c49d4fd7219", "metadata": {}, "source": [ "## HuggingFace Tools\n", "\n", "[Huggingface Tools](https://huggingface.co/docs/transformers/v4.29.0/en/custom_tools) supporting text I/O can be\n", "loaded directly using the `load_huggingface_tool` function." ] }, { "cell_type": "code", "execution_count": null, "id": "d1055b75-362c-452a-b40d-c9a359706a3a", "metadata": {}, "outputs": [], "source": [ "# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1\n", "!pip install --uprade transformers huggingface_hub > /dev/null" ] }, { "cell_type": "code", "execution_count": 1, "id": "f964bb45-fba3-4919-b022-70a602ed4354", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It takes the name of the category (such as text-classification, depth-estimation, etc), and returns the name of the checkpoint\n" ] } ], "source": [ "from langchain.agents import load_huggingface_tool\n", "\n", "tool = load_huggingface_tool(\"lysandre/hf-model-downloads\")\n", "\n", "print(f\"{tool.name}: {tool.description}\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "641d9d79-95bb-469d-b40a-50f37375de7f", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'facebook/bart-large-mnli'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tool.run(\"text-classification\")" ] }, { "cell_type": "code", "execution_count": null, "id": "88724222-7c10-4aff-8713-751911dc8b63", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 5 }