{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quick Start in 30s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# install this if you are using colab\n", "! pip install leann\n", "\n", "# For Colab environment, we need to set some environment variables\n", "import os\n", "os.environ['LEANN_LOG_LEVEL'] = 'INFO' # Enable more detailed logging" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the index" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from leann.api import LeannBuilder\n", "\n", "builder = LeannBuilder(backend_name=\"hnsw\")\n", "builder.add_text(\"C# is a powerful programming language and it is good at game development\")\n", "builder.add_text(\"Python is a powerful programming language and it is good at machine learning tasks\")\n", "builder.add_text(\"Machine learning transforms industries\")\n", "builder.add_text(\"Neural networks process complex data\")\n", "builder.add_text(\"Leann is a great storage saving engine for RAG on your MacBook\")\n", "builder.build_index(\"knowledge.leann\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Search with real-time embeddings" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from leann.api import LeannSearcher\n", "\n", "searcher = LeannSearcher(\"knowledge.leann\")\n", "results = searcher.search(\"programming languages\", top_k=2)\n", "results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chat with LEANN using retrieved results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from leann.api import LeannChat\n", "\n", "llm_config = {\n", " \"type\": \"hf\",\n", " \"model\": \"Qwen/Qwen3-0.6B\",\n", "}\n", "\n", "chat = LeannChat(index_path=\"knowledge.leann\", llm_config=llm_config)\n", "response = chat.ask(\n", " \"Compare the two retrieved programming languages and tell me their advantages.\",\n", " top_k=2,\n", " llm_kwargs={\"max_tokens\": 128}\n", ")\n", "response" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.11.12" } }, "nbformat": 4, "nbformat_minor": 2 }