{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "I8Q51pZ5NI0L" }, "source": [ "# 🏆🎬 RAG with Llama 3.1 and Haystack\n", "\n", "       \n", "\n", "\n", "\n", "\n", "Simple RAG example on the Oscars using [Llama 3.1 open models](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) and the [Haystack LLM framework](https://haystack.deepset.ai/)." ] }, { "cell_type": "markdown", "metadata": { "id": "1LRwBMJdF_d1" }, "source": [ "## Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5ggXrtFs18rs", "outputId": "aa67979b-caa5-4d83-844f-6b0fcbac45e5" }, "outputs": [], "source": [ "! pip install haystack-ai \"transformers>=4.43.1\" sentence-transformers accelerate bitsandbytes" ] }, { "cell_type": "markdown", "metadata": { "id": "Lh60ZvTdGDdh" }, "source": [ "## Authorization\n", "\n", "- you need an Hugging Face account\n", "- you need to accept Meta conditions here: https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct and wait for the authorization" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6lBjYZOC3Ug2", "outputId": "417ee380-3a76-4b28-aec4-e84af3be93ab" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Your Hugging Face token··········\n" ] } ], "source": [ "import getpass, os\n", "\n", "\n", "os.environ[\"HF_API_TOKEN\"] = getpass.getpass(\"Your Hugging Face token\")" ] }, { "cell_type": "markdown", "metadata": { "id": "7XAtaoEiHE6B" }, "source": [ "## RAG with Llama-3.1-8B-Instruct (about the Oscars) 🏆🎬" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7Mlv8v0X_qGP", "outputId": "5e110d67-08a2-420a-c413-1e9962df46a8" }, "outputs": [], "source": [ "! pip install wikipedia" ] }, { "cell_type": "markdown", "metadata": { "id": "TCeqQB3kHqcz" }, "source": [ "### Load data from Wikipedia" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Uu9PclysIP4T" }, "outputs": [], "source": [ "from IPython.display import Image\n", "from pprint import pprint\n", "import rich\n", "import random" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cpTwStuJHvxR" }, "outputs": [], "source": [ "import wikipedia\n", "from haystack.dataclasses import Document\n", "\n", "title = \"96th_Academy_Awards\"\n", "page = wikipedia.page(title=title, auto_suggest=False)\n", "raw_docs = [Document(content=page.content, meta={\"title\": page.title, \"url\":page.url})]" ] }, { "cell_type": "markdown", "metadata": { "id": "h1fvmgsZH0i8" }, "source": [ "### Indexing Pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MHVRdvRNHwKT" }, "outputs": [], "source": [ "from haystack import Pipeline\n", "from haystack.document_stores.in_memory import InMemoryDocumentStore\n", "from haystack import Document\n", "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n", "from haystack.components.converters import TextFileToDocument\n", "from haystack.components.writers import DocumentWriter\n", "from haystack.components.preprocessors import DocumentSplitter\n", "from haystack.utils import ComponentDevice" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EoJjHmLjH7Ag" }, "outputs": [], "source": [ "document_store = InMemoryDocumentStore()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fBbmbJX6H9cj", "outputId": "1e675d73-e8ee-45bc-e6d5-ffb4a3f3088a" }, "outputs": [ { "data": { "text/plain": [ "\n", "🚅 Components\n", " - splitter: DocumentSplitter\n", " - embedder: SentenceTransformersDocumentEmbedder\n", " - writer: DocumentWriter\n", "🛤️ Connections\n", " - splitter.documents -> embedder.documents (List[Document])\n", " - embedder.documents -> writer.documents (List[Document])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "indexing_pipeline = Pipeline()\n", "indexing_pipeline.add_component(\"splitter\", DocumentSplitter(split_by=\"word\", split_length=200))\n", "\n", "indexing_pipeline.add_component(\n", " \"embedder\",\n", " SentenceTransformersDocumentEmbedder(\n", " model=\"Snowflake/snowflake-arctic-embed-l\", # good embedding model: https://huggingface.co/Snowflake/snowflake-arctic-embed-l\n", " device=ComponentDevice.from_str(\"cuda:0\"), # load the model on GPU\n", " ))\n", "indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store))\n", "\n", "# connect the components\n", "indexing_pipeline.connect(\"splitter\", \"embedder\")\n", "indexing_pipeline.connect(\"embedder\", \"writer\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 188, "referenced_widgets": [ "34bf50d636a74d1798bfa50d10fa9705", "4dd853e36a9043ab9929a69dceb23c7f", "a32067bfcafb4bc8aac4729cefa98709", "060fcc065c764e2dada10c596d407d5f", "b4947faca0aa4c81bf674879426e59e4", "3105755b1ef34876a99579b8e65325e9", "9f6150b2e29c43c6ab36a08bfd6f6c43", "6107db463a064c3fb6547a1ddaf140fe", "d90fe99c19b84e7b8f451ed4c683d9cd", "aa6260505c8649fd9e0c40f29579dc2c", "3687f567ac304c48a28600c9ed43e1fa" ] }, "id": "ddizanfoIFz3", "outputId": "ad859aa6-64c8-4c50-9854-eb5342d9196a" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/sentence_transformers/SentenceTransformer.py:174: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v3 of SentenceTransformers.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:81: UserWarning: \n", "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n", "You will not be requested again.\n", "Please restart the session if you want to be prompted again.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "34bf50d636a74d1798bfa50d10fa9705", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00**Oppenheimer** won the Best Picture Award in 2024.\n", "\n" ], "text/plain": [ "**Oppenheimer** won the Best Picture Award in \u001b[1;36m2024\u001b[0m.\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_generative_answer(\"Who won the Best Picture Award in 2024?\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 338, "referenced_widgets": [ "486a8be63cd448568d5b0d64fd577cc2", "fbcd10be335945d4844b4e0fe316cb20", "da7c81a409594da4b0ed4d004a951b6a", "ed859fb7cea546ed83c977901027e605", "63b2cafb16f142f095316b661ca7aa39", "13d1e2752990458da15297283f21ea02", "567b701a2c4a4212b40fae1109b0b9db", "ecefedb95a70485eaeddbc7e7ca1cf85", "42a67c486b134689a742094df51f5df0", "4172659560db41e08965b2dae33591fb", "9fe9f23f067c4f0ba91040c6c180f8ee" ] }, "id": "k_YBFqjOmz9m", "outputId": "d9c0e573-d91b-4df9-eacb-7527c8e21c78" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "486a8be63cd448568d5b0d64fd577cc2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00The box office performance of the Best Picture nominees was as follows:\n", "\n", "* Nine of the ten films nominated for Best Picture had earned a combined gross of $1.09 billion at the American and\n", "Canadian box offices at the time of the nominations.\n", "* The highest-grossing film among the Best Picture nominees was Barbie with $636 million in domestic box office \n", "receipts.\n", "* The other nominees' box office performances were:\n", " + Oppenheimer: $326 million\n", " + Killers of the Flower Moon: $67 million\n", " + Poor Things: $20.4 million\n", " + The Holdovers: $18.7 million\n", " + Past Lives: $10.9 million\n", " + American Fiction: $7.9 million\n", " + Anatomy of a Fall: $3.9 million\n", " + The Zone of Interest: $1.6 million\n", "* The box office performance of Maestro was not available due to its distributor Netflix's policy of not releasing \n", "such figures.\n", "\n" ], "text/plain": [ "The box office performance of the Best Picture nominees was as follows:\n", "\n", "* Nine of the ten films nominated for Best Picture had earned a combined gross of $\u001b[1;36m1.09\u001b[0m billion at the American and\n", "Canadian box offices at the time of the nominations.\n", "* The highest-grossing film among the Best Picture nominees was Barbie with $\u001b[1;36m636\u001b[0m million in domestic box office \n", "receipts.\n", "* The other nominees' box office performances were:\n", " + Oppenheimer: $\u001b[1;36m326\u001b[0m million\n", " + Killers of the Flower Moon: $\u001b[1;36m67\u001b[0m million\n", " + Poor Things: $\u001b[1;36m20.4\u001b[0m million\n", " + The Holdovers: $\u001b[1;36m18.7\u001b[0m million\n", " + Past Lives: $\u001b[1;36m10.9\u001b[0m million\n", " + American Fiction: $\u001b[1;36m7.9\u001b[0m million\n", " + Anatomy of a Fall: $\u001b[1;36m3.9\u001b[0m million\n", " + The Zone of Interest: $\u001b[1;36m1.6\u001b[0m million\n", "* The box office performance of Maestro was not available due to its distributor Netflix's policy of not releasing \n", "such figures.\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_generative_answer(\"What was the box office performance of the Best Picture nominees?\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 178, "referenced_widgets": [ "efcfc6fce81e4ee2976514326474ce7c", "ae16b7faf18a4cbb8b385f7fee7708f0", "c6a1125b15c14d9e82b32e8ddcfca2ba", "8ddad974381d4c57bdd0fb75c8fdef1e", "6d7d5e5a8aaf421fadd5f5354ccdca25", "9bedf121b1c3471bb0853d2888b307c4", "dfe034adf9cb47ff9eb84e221908f109", "08524754166540e4ac39ccb3ceac15d4", "85a92b5fd43c4d57bf7ec171497892a6", "ef9c146eeae94ec6b45ebe43d6043fc3", "58cf51f3a00e4dbbb4479e7c8aa936e9" ] }, "id": "ljbW5JRMoeoR", "outputId": "8a7fa254-841a-49aa-d7f6-2d4ac01b2843" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "efcfc6fce81e4ee2976514326474ce7c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00The reception of the ceremony was generally positive. Television critic Robert Lloyd of the Los Angeles Times \n", "commented that the show was \"not the slog it often is\" and praised Jimmy Kimmel's performance as host, stating that\n", "he was \"a reliable, relatable presence liable to stir no controversy.\" Alison Herman of Variety noted that despite \n", "the lack of surprises among the winners, the show delivered \"entertainment and emotion in spades, if not surprise.\"\n", "Daniel Fienberg of The Hollywood Reporter lauded Kimmel's hosting and the ceremony's entertainment, stating that it\n", "was \"a maximalist, infectiously goofy singalong\" that was the \"ideal way to channel the feel-good energy of an \n", "Oscars where none of the bonhomie felt forced.\"\n", "\n" ], "text/plain": [ "The reception of the ceremony was generally positive. Television critic Robert Lloyd of the Los Angeles Times \n", "commented that the show was \u001b[32m\"not the slog it often is\"\u001b[0m and praised Jimmy Kimmel's performance as host, stating that\n", "he was \u001b[32m\"a reliable, relatable presence liable to stir no controversy.\"\u001b[0m Alison Herman of Variety noted that despite \n", "the lack of surprises among the winners, the show delivered \u001b[32m\"entertainment and emotion in spades, if not surprise.\"\u001b[0m\n", "Daniel Fienberg of The Hollywood Reporter lauded Kimmel's hosting and the ceremony's entertainment, stating that it\n", "was \u001b[32m\"a maximalist, infectiously goofy singalong\"\u001b[0m that was the \u001b[32m\"ideal way to channel the feel-good energy of an \u001b[0m\n", "\u001b[32mOscars where none of the bonhomie felt forced.\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_generative_answer(\"What was the reception of the ceremony\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 130, "referenced_widgets": [ "32acbe5d4671427ab376ee7a5b824377", "e159e03cf8a44ee7a79fe992409f0bc6", "4e953fb69d164ee9909e7307c4820831", "4bc8f7db982c4822b3be51370f0b5ca1", "66fc27d68690441a832c4bb000146fa7", "31eba8177e4f46fcbd3f06b6dab4d94e", "19760d255fd64662b78df89f21b1eacf", "7e0a4063cee040b4953f38116a836034", "56241a5836374572bdb5a22ceda10607", "6aedd953bc6c4c7dbf1a770221e54c5b", "c1db5c855f2d4df1849c83eb09fe7801" ] }, "id": "5oCgs5DNo4rZ", "outputId": "d2c6a51d-2b16-41f4-cbc8-57ea01990de9" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "32acbe5d4671427ab376ee7a5b824377", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00According to the text, two films received multiple nominations:\n", "\n", "1. Oppenheimer - 13 nominations\n", "2. Poor Things - 11 nominations\n", "\n" ], "text/plain": [ "According to the text, two films received multiple nominations:\n", "\n", "\u001b[1;36m1\u001b[0m. Oppenheimer - \u001b[1;36m13\u001b[0m nominations\n", "\u001b[1;36m2\u001b[0m. Poor Things - \u001b[1;36m11\u001b[0m nominations\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_generative_answer(\"Can you name some of the films that got multiple nominations?\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 114, "referenced_widgets": [ "dafa087092104ed2ad83e53933bfc129", "ecf7b5da1d7b4b2682cfb644e107c1c3", "8032805454c44d42b7df26d814d06a67", "a8a413163dce4ef4afdd14e3411959d9", "c4fb99da0db540b7a5caa86e65e146e3", "22f07c0acd764107b2f100e57dd9e47f", "d38e434101fe47dbbbae9d8e6e67e551", "f07beef2d43e48f2a05b0d79f6cdc3f9", "aadfb82bf5e64da8b10b93c4652fd98a", "9ea86d6397684e9f91f4ea3c793e7373", "1dd723d556ce4a7c9d827c1b1633e2b6" ] }, "id": "LXxvLGuNKrKK", "outputId": "121f4202-a0aa-4f33-b097-52f5aa78183f" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dafa087092104ed2ad83e53933bfc129", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00There is no information in the provided context about the formation of Audioslave or its sound in comparison to \n", "other bands. The context only discusses the 96th Academy Awards and related events, and does not mention Audioslave\n", "at all.\n", "\n" ], "text/plain": [ "There is no information in the provided context about the formation of Audioslave or its sound in comparison to \n", "other bands. The context only discusses the 96th Academy Awards and related events, and does not mention Audioslave\n", "at all.\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# unrelated question: let's see how our RAG pipeline performs.\n", "\n", "get_generative_answer(\"Audioslave was formed by members of two iconic bands. Can you name the bands and discuss the sound of Audioslave in comparison?\")" ] }, { "cell_type": "markdown", "metadata": { "id": "8Ap55HKoMSgx" }, "source": [ "---\n", "This is a simple demo.\n", "We can improve the RAG Pipeline in several ways, including better preprocessing the input.\n", "\n", "To use Llama 3 models in Haystack, you also have **other options**:\n", "- [LlamaCppGenerator](https://docs.haystack.deepset.ai/docs/llamacppgenerator) and [OllamaGenerator](https://docs.haystack.deepset.ai/docs/ollamagenerator): using the GGUF quantized format, these solutions are ideal to run LLMs on standard machines (even without GPUs).\n", "- [HuggingFaceAPIChatGenerator](https://docs.haystack.deepset.ai/docs/huggingfaceapichatgenerator), which allows you to query a the Hugging Face API, a local TGI container or a (paid) HF Inference Endpoint. TGI is a toolkit for efficiently deploying and serving LLMs in production.\n", "- [vLLM via OpenAIChatGenerator](https://haystack.deepset.ai/integrations/vllm): high-throughput and memory-efficient inference and serving engine for LLMs.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Wo9JfscmOEb3" }, "source": [ "(*Notebook by [Stefano Fiorucci](https://github.com/anakin87)*)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "054579aa9bb54f339fe83492c7ed5783": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", 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