notebooks/multilingual_rag_podcast.ipynb

{
  "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 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: Mixtral-8x7B-Instruct-v0.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBNKuXcmUWO7"
      },
      "source": [
        "## Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qk347f3Jo8bC"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "! pip install -U 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": 3,
      "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",
        "# whisper.warm_up()\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": 5,
      "metadata": {
        "id": "7RrwmVz0tQMT"
      },
      "outputs": [],
      "source": [
        "from haystack_integrations.document_stores.qdrant import QdrantDocumentStore\n",
        "from haystack import Document\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": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "pL3bOIsOLwBg",
        "outputId": "6d32ae2a-d470-4dca-f3ef-38294ca1b57c"
      },
      "outputs": [
        {
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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# show the pipeline\n",
        "\n",
        "from IPython.display import Image\n",
        "\n",
        "indexing_pipeline.draw('indexing_pipeline.png')\n",
        "Image('indexing_pipeline.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674,
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          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100it [00:00, 529.46it/s]\n"
          ]
        }
      ],
      "source": [
        "# Run the pipeline! 🚀\n",
        "res = indexing_pipeline.run({\"text_file_converter\":{\"sources\":[\"podcast_transcript_whisper_small.txt\"]}})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6rO9BNf0P57",
        "outputId": "8cfb58d9-5210-4230-c1cb-f7002808bd4c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "52"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "document_store.count_documents()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8r4znJWbNaFe"
      },
      "source": [
        "## RAG pipeline\n",
        "\n",
        "Finally our RAG pipeline: from an Italian podcast 🇮🇹🎧 to answering questions in English 🇬🇧\n",
        "\n",
        "- [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) transforms the query into a vector that captures its semantics, to allow vector retrieval\n",
        "- `QdrantRetriever` compares the query and Document embeddings and fetches the Documents most relevant to the query.\n",
        "- [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder) prepares the prompt for the LLM: renders a prompt template and fill in variable values.\n",
        "- [`HuggingFaceAPIGenerator`](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator) allows using LLMs via the (free) Hugging Face Serverless Inference API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "p190hhrDNZac"
      },
      "outputs": [],
      "source": [
        "from haystack.components.generators import HHuggingFaceAPIGenerator\n",
        "from haystack.components.builders.prompt_builder import PromptBuilder\n",
        "from haystack_integrations.components.retrievers.qdrant import QdrantEmbeddingRetriever\n",
        "from pprint import pprint\n",
        "from getpass import getpass\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oU__s3p02RMX",
        "outputId": "a93949e0-c092-4616-b648-b41e80c0c061"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your Hugging Face Token: https://huggingface.co/settings/tokens··········\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"HF_API_TOKEN\"] = getpass(\"Enter your Hugging Face Token: https://huggingface.co/settings/tokens \")"
      ]
    },
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          ]
        },
        "id": "C_c184dO3aDj",
        "outputId": "6f66b28d-f3f8-4ddc-d499-d29640d477bf"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c37ab5df2d54487960722f95e89e734",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.46k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c47d4f1241642d1934fa4554a6b3387",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b61373c10dbc44df96c9c75afc6cd7dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ad19402697e24c588c24cacac1e0d39a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'replies': [\".\\n\\nVim is the ultimate IDE because it's like a ninja warrior of text editors. It's fast, efficient, and deadly accurate. With its powerful modal editing system, you can quickly and easily navigate and manipulate text like a pro. And with its vast array of plugins and customization options, you can tailor it to your specific needs and workflow. Plus, it's available on almost every platform, so you can take it with you wherever you go. Vim is the ultimate IDE for those who want to take their text editing skills to the next level and become true masters of their craft. It's like having a secret weapon in your coding arsenal that gives you an unfair advantage over your competitors. So, if you're ready to join the ranks of the Vim elite, grab your keyboard and get ready to slay some text!\"],\n",
              " 'metadata': [{'model': 'mistralai/Mixtral-8x7B-Instruct-v0.1',\n",
              "   'index': 0,\n",
              "   'finish_reason': 'eos_token',\n",
              "   'usage': {'completion_tokens': 185,\n",
              "    'prompt_tokens': 14,\n",
              "    'total_tokens': 199}}]}"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load the model (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) and try the Generator\n",
        "# you first need to accept Mistral conditions here: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1\n",
        "\n",
        "generator = HHuggingFaceAPIGenerator(\n",
        "        api_type=\"serverless_inference_api\",\n",
        "        api_params={\"model\": \"mistralai/Mistral-7B-Instruct-v0.1\"},\n",
        "        generation_kwargs={\"max_new_tokens\":500})\n",
        "\n",
        "generator.run(\"Please explain in a fun way why vim is the ultimate IDE\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "K49xqRiJ3q3i"
      },
      "outputs": [],
      "source": [
        "# define a multilingual prompt template\n",
        "\n",
        "prompt_template = \"\"\"\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": 13,
      "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\", PromptBuilder(template=prompt_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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hb7Gm0tMVmdG",
        "outputId": "8b3fcf4b-4e14-4a03-b719-ccf0bbd32734"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# show the pipeline\n",
        "\n",
        "from IPython.display import Image\n",
        "\n",
        "query_pipeline.draw('query_pipeline.png')\n",
        "Image('query_pipeline.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
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        },
        "id": "1DiyjLz52bB6",
        "outputId": "e1588a0c-b162-4341-d22d-b26688e22412"
      },
      "outputs": [
        {
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          "output_type": "stream",
          "text": [
            "(' The Pointer Podcast is an Italian language podcast available on Apple '\n",
            " 'Podcast, Google Podcast, and Spotify. It covers various topics, including '\n",
            " 'NLP (Natural Language Processing) and LLM (Large Language Model). The hosts '\n",
            " 'interview experts in the field, and listeners can contact the podcast via '\n",
            " 'email or social media.')\n"
          ]
        }
      ],
      "source": [
        "# try the pipeline\n",
        "\n",
        "question = \"What is Pointer Podcast?\"\n",
        "results = query_pipeline.run(\n",
        "    {   \"text_embedder\": {\"text\": question},\n",
        "        \"prompt_builder\": {\"question\": question},\n",
        "    }\n",
        ")\n",
        "\n",
        "for d in results['generator']['replies']:\n",
        "  pprint(d)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqU6fBjeT5ph"
      },
      "source": [
        "✨ Nice!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "SVctNnyCT_fk"
      },
      "outputs": [],
      "source": [
        "# let's create a simple wrapper to call the pipeline and show the answers\n",
        "\n",
        "def ask_rag(question: str):\n",
        "  results = query_pipeline.run(\n",
        "      {\n",
        "          \"text_embedder\": {\"text\": question},\n",
        "          \"prompt_builder\": {\"question\": question},\n",
        "      }\n",
        "  )\n",
        "\n",
        "  for d in results[\"generator\"][\"replies\"]:\n",
        "      pprint(d)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-t0tryLtUmLw"
      },
      "source": [
        "## Try our multilingual RAG application!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "HwOKsp6WU0kA"
      },
      "outputs": [],
      "source": [
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "gw5yqC4zUtJ1"
      },
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      "source": [
        "questions=\"\"\"What are some interesting directions in Large Language Models?\n",
        "What is Haystack?\n",
        "What is Ollama?\n",
        "How did Stefano end up working at deepset?\n",
        "Will open source models achieve the quality of closed ones?\n",
        "What are the main features of Haystack 2.0?\n",
        "Summarize in a bulleted list the main stages of training a Large Language Model\n",
        "What is Zephyr?\n",
        "What is it and why is the quantization of Large Language Models interesting?\n",
        "Could you point out the names of the hosts and guests of the podcast?\"\"\".split(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Will open source models achieve the quality of closed ones?\n"
          ]
        },
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          "text": [
            "(' The documents suggest that open source models are reaching the performance '\n",
            " 'levels of closed models, and there is a paper from Aggin Face that '\n",
            " 'demonstrates a technique for distilling the characteristics of larger models '\n",
            " 'into smaller ones using human preference alignment with minimal human '\n",
            " 'supervision. This allows for the creation of smaller models that replicate '\n",
            " 'some of the capabilities of larger models, which can be useful for the end '\n",
            " 'user. The report and recipe for this technique have been released and can be '\n",
            " 'replicated with a relatively small budget. The documents also indicate that '\n",
            " 'open source models are having an important impact on the field, not just '\n",
            " 'from big companies like Open AI and Microsoft, but also from smaller '\n",
            " 'companies or individuals releasing open source models. However, there is a '\n",
            " 'tension between open source and proprietary models that is not likely to be '\n",
            " 'resolved soon. The quality and variety of open source models continue to '\n",
            " 'improve, despite the cost and difficulty of training and fine-tuning them.')\n"
          ]
        }
      ],
      "source": [
        "q = random.choice(questions)\n",
        "print(q)\n",
        "ask_rag(q)"
      ]
    }
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