{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "xRFiZ-FMQ68a" }, "source": [ "# 🇮🇹🇬🇧 Multilingual RAG from a 🎧 podcast\n", "\n", "\n", "*Notebook by [Stefano Fiorucci](https://github.com/anakin87)*\n", "\n", "This notebook shows how to create a multilingual Retrieval Augmented Generation application, starting from a podcast.\n", "\n", "🧰 **Stack**:\n", "- Haystack LLM framework\n", "- OpenAI Whisper model for audio transcription\n", "- Qdrant vector database\n", "- multilingual embedding model: multilingual-e5-large\n", "- multilingual LLM: Mistral Small" ] }, { "cell_type": "markdown", "metadata": { "id": "PBNKuXcmUWO7" }, "source": [ "## Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Qk347f3Jo8bC" }, "outputs": [], "source": [ "%%capture\n", "! pip install -U mistral-haystack haystack-ai qdrant-haystack \"openai-whisper>=20231106\" pytube \"sentence-transformers>=3.0.0\" \"huggingface_hub>=0.23.0\"" ] }, { "cell_type": "markdown", "metadata": { "id": "RNdKA_wMUmfN" }, "source": [ "## Podcast transcription\n", "\n", "- download the audio from Youtube using `pytube`\n", "- transcribe it locally using Haystack's [`LocalWhisperTranscriber`](https://docs.haystack.deepset.ai/docs/localwhispertranscriber) with the `whisper-small` model. We could use bigger models, which take longer to transcribe. We could also call the paid OpenAI API, using [`RemoteWhisperTranscriber`](https://docs.haystack.deepset.ai/docs/remotewhispertranscriber).\n", "\n", "Since the transcription takes some time (about 10 minutes), I commented out the following code and will provide the transcription." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "DbSVXXihpFIf" }, "outputs": [], "source": [ "# # https://www.tutorialspoint.com/download-video-in-mp3-format-using-pytube\n", "\n", "# from pytube import YouTube\n", "\n", "# url = \"https://www.youtube.com/watch?v=vrf4_XMSlE0\"\n", "\n", "\n", "# video = YouTube(url)\n", "# stream = video.streams.filter(only_audio=True).first()\n", "# stream.download(filename=f\"podcast.mp3\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2VlicBGkrOVC" }, "outputs": [], "source": [ "# from haystack.components.audio import LocalWhisperTranscriber\n", "# from haystack.utils import ComponentDevice\n", "\n", "# whisper = LocalWhisperTranscriber(model=\"small\", device=ComponentDevice.from_str(\"cuda:0\"),)\n", "# transcription = whisper.run(sources=[\"podcast.mp3\"])\n", "\n", "# with open('podcast_transcript_whisper_small.txt.txt', 'w') as fo:\n", "# fo.write(transcription[\"documents\"][0].content)" ] }, { "cell_type": "markdown", "metadata": { "id": "rNC9lC1cIcQ3" }, "source": [ "## Indexing pipeline\n", "\n", "Create an Indexing pipeline that stores chunks of the transcript in the [Qdrant vector database](https://haystack.deepset.ai/integrations/qdrant-document-store).\n", "\n", "- [`TextFileToDocument`](https://docs.haystack.deepset.ai/docs/textfiletodocument) converts the transcript into a [Haystack Document](https://docs.haystack.deepset.ai/docs/data-classes#document).\n", "- [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter) divides the original Document into smaller chunks.\n", "- [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) computes embeddings(=vector representations) of Documents using a multilingual model, to allow semantic retrieval\n", "- [`DocumentWriter`](https://docs.haystack.deepset.ai/docs/documentwriter) stores the Documents in Qdrant" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DI8sTTP1H6pg", "outputId": "4106d734-4376-4523-bdf9-5c9413b78f52" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2023-12-31 14:25:00-- https://raw.githubusercontent.com/anakin87/mistral-haystack/main/data/podcast_transcript_whisper_small.txt\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 61083 (60K) [text/plain]\n", "Saving to: ‘podcast_transcript_whisper_small.txt’\n", "\n", "\r podcast_t 0%[ ] 0 --.-KB/s \rpodcast_transcript_ 100%[===================>] 59.65K --.-KB/s in 0.01s \n", "\n", "2023-12-31 14:25:00 (5.46 MB/s) - ‘podcast_transcript_whisper_small.txt’ saved [61083/61083]\n", "\n", " Ciao e benvenuti nella puntata 183 del Pointer Podcast, torniamo oggi con degli ospiti, ma prima vi introduto Eugenio, ciao Eugenio. Ciao Luca. Come va? Tutto bene? Tutto bene, tutto bene. Oggi abbiamo due ospiti che arrivano dalla stessa azienda, che è una azienda che produce una libreria che pro" ] } ], "source": [ "# download the podcast transcript\n", "# to create the transcript, you can uncomment and run the previous section\n", "!wget \"https://raw.githubusercontent.com/deepset-ai/haystack-cookbook/main/data/multilingual_rag_podcast/podcast_transcript_whisper_small.txt\"\n", "\n", "# let's take a look at the begininning of our 🇮🇹 transcript\n", "!head --bytes 300 podcast_transcript_whisper_small.txt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7RrwmVz0tQMT" }, "outputs": [], "source": [ "from haystack_integrations.document_stores.qdrant import QdrantDocumentStore\n", "from haystack import Pipeline\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\n", "\n", "# initialize the Document store\n", "document_store = QdrantDocumentStore(\n", " \":memory:\",\n", " embedding_dim=1024, # the embedding_dim should match that of the embedding model\n", ")\n", "\n", "indexing_pipeline = Pipeline()\n", "indexing_pipeline.add_component(\"text_file_converter\", TextFileToDocument())\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=\"intfloat/multilingual-e5-large\", # good multilingual model: https://huggingface.co/intfloat/multilingual-e5-large\n", " device=ComponentDevice.from_str(\"cuda:0\"), # load the model on GPU\n", " prefix=\"passage:\", # as explained in the model card (https://huggingface.co/intfloat/multilingual-e5-large#faq), documents should be prefixed with \"passage:\"\n", " ))\n", "indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store))\n", "\n", "# connect the components\n", "indexing_pipeline.connect(\"text_file_converter\", \"splitter\")\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": 956 }, "id": "pL3bOIsOLwBg", "outputId": "6d32ae2a-d470-4dca-f3ef-38294ca1b57c" }, "outputs": [], "source": [ "# show the pipeline\n", "\n", "# indexing_pipeline.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 674, "referenced_widgets": [ "34a252cb13434deda19f999c41128464", "0b7af4ca43984a9d9b55fecb0b952ed5", "1d95ff955c7e4398bd485f538272494e", "b50396eb7ba249ea88310622c03543e5", 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"outputId": "c4ce16d8-548e-4494-df28-30c9ac2c5179" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "34a252cb13434deda19f999c41128464", "version_major": 2, "version_minor": 0 }, "text/plain": [ ".gitattributes: 0%| | 0.00/1.63k [00:00, _content=[TextContent(text='Oh, absolutely! Let’s dive into why **Vim** is the *ultimate* IDE—because it’s not just an editor, it’s a *way of life*. Here’s the fun breakdown:\n", "\n", "### **1. Vim is Like a Ninja’s Sword—Lightning-Fast & Deadly**\n", "- **No mouse? No problem!** Your hands never leave the keyboard, making you code like a samurai slicing through bugs...')], _name=None, _meta={'model': 'mistral-small-latest', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 712, 'prompt_tokens': 15, 'total_tokens': 727, 'completion_tokens_details': None, 'prompt_tokens_details': None}})]}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "generator = MistralChatGenerator(model=\"mistral-small-latest\")\n", "\n", "generator.run([ChatMessage.from_user(\"Please explain in a fun way why vim is the ultimate IDE\")])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "K49xqRiJ3q3i" }, "outputs": [], "source": [ "# define a multilingual template\n", "\n", "template = [ChatMessage.from_user(\"\"\"\n", "Using only the information contained in these documents in Italian, answer the question using English.\n", "If the answer cannot be inferred from the documents, respond \\\"I don't know\\\".\n", "Documents:\n", "{% for doc in documents %}\n", " {{ doc.content }}\n", "{% endfor %}\n", "Question: {{question}}\n", "Answer:\n", "\"\"\")]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Dkk8HUb80u95" }, "outputs": [], "source": [ "# define the query pipeline\n", "query_pipeline = Pipeline()\n", "\n", "query_pipeline.add_component(\n", " \"text_embedder\",\n", " SentenceTransformersTextEmbedder(\n", " model=\"intfloat/multilingual-e5-large\", # good multilingual model: https://huggingface.co/intfloat/multilingual-e5-large\n", " device=ComponentDevice.from_str(\"cuda:0\"), # load the model on GPU\n", " prefix=\"query:\", # as explained in the model card (https://huggingface.co/intfloat/multilingual-e5-large#faq), queries should be prefixed with \"query:\"\n", " ))\n", "query_pipeline.add_component(\"retriever\", QdrantEmbeddingRetriever(document_store=document_store))\n", "query_pipeline.add_component(\"prompt_builder\", ChatPromptBuilder(template=template))\n", "query_pipeline.add_component(\"generator\", generator)\n", "\n", "# connect the components\n", "query_pipeline.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n", "query_pipeline.connect(\"retriever.documents\", \"prompt_builder.documents\")\n", "query_pipeline.connect(\"prompt_builder\", \"generator\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "Hb7Gm0tMVmdG", "outputId": "8b3fcf4b-4e14-4a03-b719-ccf0bbd32734" }, "outputs": [], "source": [ "# show the pipeline\n", "\n", "# query_pipeline.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 136, "referenced_widgets": [ "8040f122ce2c422fa16a979d1fdb09b6", "e51765476e814fcda5b53d7f74380cdb", "692dedaa672e4492a78aaca96df1b6a9", "d1ebc08542084693a3c6bbd7808cb8c4", "e5f01284579346af89d7357039cf7ddb", "3fc1a8a807bd45618b3498e225d43623", "69414b11c451453d9876a2b623d81536", "7ce10fb797d842ed9249c24adafe9b29", "28e3c633ea5b45b68d4672bf5add1345", "a7e202f2175348c3842f11db7801bcbe", "b89b59d15e784f268cad3ad42e66eb9d" ] }, "id": "1DiyjLz52bB6", "outputId": "e1588a0c-b162-4341-d22d-b26688e22412" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8040f122ce2c422fa16a979d1fdb09b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Batches: 0%| | 0/1 [00:00