{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\"Open\n" ] }, { "cell_type": "markdown", "metadata": { "id": "v9bpz99INAc1" }, "source": [ "# Install Packages and Setup Variables\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BeuFJKlj9jKz", "outputId": "6419987a-aa8c-49f8-de20-42aa9d7528c3" }, "outputs": [], "source": [ "!pip install -q llama-index==0.10.57 llama-index-llms-gemini==0.1.11 openai==1.37.0 google-generativeai==0.5.4" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "CWholrWlt2OQ" }, "outputs": [], "source": [ "import os\n", "\n", "# Set the following API Keys in the Python environment. Will be used later.\n", "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", "os.environ[\"GOOGLE_API_KEY\"] = \"\"" ] }, { "cell_type": "markdown", "metadata": { "id": "f5eV5EnvNCMM" }, "source": [ "# Load Dataset\n" ] }, { "cell_type": "markdown", "metadata": { "id": "q-7mRQ-mNJlm" }, "source": [ "## Download\n" ] }, { "cell_type": "markdown", "metadata": { "id": "3PsdOdMUNmEi" }, "source": [ "The dataset includes several articles from the TowardsAI blog, which provide an in-depth explanation of the LLaMA2 model.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3ImRCP7pACaI", "outputId": "ff52cd9a-67e0-4243-9774-98288c3cf248" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 169k 100 169k 0 0 772k 0 --:--:-- --:--:-- --:--:-- 774k\n" ] } ], "source": [ "!curl -o ./mini-dataset.csv https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv" ] }, { "cell_type": "markdown", "metadata": { "id": "bZZLK_wyEc-L" }, "source": [ "## Read File\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "miUqycqAEfr7", "outputId": "6c3068a9-a9a3-465a-8f84-8d329e0cd02a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of articles: 14\n" ] } ], "source": [ "import csv\n", "\n", "rows = []\n", "\n", "# Load the CSV file\n", "with open(\"./mini-dataset.csv\", mode=\"r\", encoding=\"utf-8\") as file:\n", " csv_reader = csv.reader(file)\n", "\n", " for idx, row in enumerate(csv_reader):\n", " if idx == 0:\n", " continue\n", " # Skip header row\n", " rows.append(row)\n", "\n", "# The number of characters in the dataset.\n", "print(\"number of articles:\", len(rows))" ] }, { "cell_type": "markdown", "metadata": { "id": "f86yksB9K571" }, "source": [ "# Generate Embedding\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "iXrr5-tnEfm9" }, "outputs": [], "source": [ "from llama_index.core import Document\n", "\n", "# Convert the texts to Document objects so the LlamaIndex framework can process them.\n", "documents = [Document(text=row[1]) for row in rows]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "6e893cde79734e408bb8d0b4305bedab", "51242f18dfd14aba963ed72b008d6dd6", "a88124e34ad24f19bdcbcd73e998168a", "fff2627bdf20445f8507a7792a17546d", "f5f3f69abfd149f281a2f0c3f58d3284", "d1a558eb15cf43f8a013a91b9262eee5", "946ebbd88b344a248564a1b2c593653e", "4e905c17eddc44c299aabf699ec33642", "ab738a29078d43aaa3364b3076f1eca0", "ae615040ed1a4a47838aaa99192fd33b", "7e3db69b3e20451f8fc88631b7915a39", "27fd17bf0eaa49868321cf2d31a5a0a1", "a0ba4f46f20b435cb6b811317a935b1e", "4026c7a3aead4dc1bb0525535c885601", "8ab7550005bf4d8f80c87716c769e2ec", "3e0e3f06c25543e9877d30ed378edd8d", "4a766f37197b41d7bfa496c0c6d393bf", "a436c3949572481cbde16838298cbf93", "ab59db85ad504297a3c56e3d63f5d474", "2b3e4d550bce4effb83939e026ea6538", "93e9287c92034d36a44a3855f38ef6d8", "12380f5aab5e4c41843036e4f12883cd" ] }, "id": "Bsa7Q-DoNWBk", "outputId": "b6f4f826-e4cd-4745-fc99-13b91c2d4d1b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/omar/Documents/ai_repos/ai-tutor-rag-system/env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "Parsing nodes: 100%|██████████| 14/14 [00:00<00:00, 247.39it/s]\n", "/Users/omar/Documents/ai_repos/ai-tutor-rag-system/env/lib/python3.12/site-packages/langchain/agents/json_chat/base.py:22: SyntaxWarning: invalid escape sequence '\\ '\n", " \"\"\"Create an agent that uses JSON to format its logic, build for Chat Models.\n", "Generating embeddings: 100%|██████████| 56/56 [00:01<00:00, 43.08it/s]\n" ] } ], "source": [ "from llama_index.core import VectorStoreIndex\n", "from llama_index.core.node_parser import SentenceSplitter\n", "from llama_index.embeddings.openai import OpenAIEmbedding\n", "\n", "\n", "# Build index / generate embeddings using OpenAI embedding model\n", "index = VectorStoreIndex.from_documents(\n", " documents,\n", " embed_model=OpenAIEmbedding(model=\"text-embedding-3-small\"),\n", " transformations=[SentenceSplitter(chunk_size=768, chunk_overlap=64)],\n", " show_progress=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "3DoUxd8KK--Q" }, "source": [ "# Query Dataset\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "bUaNH97dEfh9" }, "outputs": [], "source": [ "# Define a query engine that is responsible for retrieving related pieces of text,\n", "# and using a LLM to formulate the final answer.\n", "\n", "from llama_index.llms.gemini import Gemini\n", "\n", "llm = Gemini(model=\"models/gemini-1.5-flash\", temperature=1, max_tokens=512)\n", "\n", "query_engine = index.as_query_engine(llm=llm)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KHK4V_GRR6ZG", "outputId": "105cf2b3-3a65-4eb7-f629-38ce22bb20aa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LLaMA 2 comes in four different sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. \n", "\n" ] } ], "source": [ "response = query_engine.query(\"How many parameters LLaMA2 model has?\")\n", "print(response)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "S-BmyTBbNd9y", "outputId": "662f49d2-8c19-400a-c7fd-dd0018dcd74e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The context does not provide information about the release of Llama 3. \n", "\n" ] } ], "source": [ "response = query_engine.query(\"When will Llama3 will be released?\")\n", "print(response)" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.12.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "12380f5aab5e4c41843036e4f12883cd": { 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