haystack-tutorials

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    "# Tutorial: Creating a Generative QA Pipeline with Retrieval-Augmentation\n",
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    "\n",
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    "- **Level**: Intermediate\n",
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    "- **Time to complete**: 15 minutes\n",
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    "- **Nodes Used**: `InMemoryDocumentStore`, `BM25Retriever`, `PromptNode`, `PromptTemplate`\n",
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    "- **Goal**: After completing this tutorial, you'll have created a generative question answering search system that uses a large language model through PromptNode with PromptTemplate."
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   ]
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    "> This tutorial is based on Haystack 1.x. If you're using Haystack 2.0-Beta and would like to follow the updated version of this tutorial, check out [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline). \n",
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    ">\n",
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    "> For more information on Haystack 2.0-Beta, you can also read the [announcement post](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent)."
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   "source": [
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    "## Overview\n",
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    "\n",
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    "Learn how to build a generative question answering pipeline using the power of LLMs with PromptNode. In this generative pipeline, BM25Retriever gets the related Documents, and PromptNode generates the answer using the retrieval augmented generation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach. In this tutorial, we'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n",
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    "\n",
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    "This tutorial introduces you to the PrompTemplate structure and explains how to use the new PrompTemplate to integrate PromptNode into a pipeline."
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   ]
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    "## Preparing the Colab Environment\n",
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    "\n",
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    "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n",
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    "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)"
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   ]
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  },
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   "source": [
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    "## Installing Haystack\n",
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    "\n",
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    "To start, let's install the latest release of Haystack with `pip`:"
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   ]
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  },
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    "%%bash\n",
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    "\n",
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    "pip install --upgrade pip\n",
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    "pip install farm-haystack[colab]\n",
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    "pip install \"datasets>=2.6.1\""
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   ]
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   "source": [
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    "### Enabling Telemetry\n",
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    "\n",
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    "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {
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    "id": "A76B4S49O-qa"
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   },
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   "outputs": [],
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   "source": [
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    "from haystack.telemetry import tutorial_running\n",
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    "\n",
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    "tutorial_running(22)"
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   ]
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  },
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   "source": [
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    "## Initializing the DocumentStore\n",
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    "\n",
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    "We'll start creating our question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, we're using the `InMemoryDocumentStore`.\n",
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    "\n",
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    "Let's initialize our DocumentStore."
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   ]
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  },
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   "source": [
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    "from haystack.document_stores import InMemoryDocumentStore\n",
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    "\n",
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    "document_store = InMemoryDocumentStore(use_bm25=True)"
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   ]
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  },
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    "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the DocumentStore and the different types of external databases that we support, see [DocumentStore](https://docs.haystack.deepset.ai/docs/document_store)."
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   ]
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  },
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    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
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   ]
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  },
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    "## Fetching and Writing Documents\n",
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    "\n",
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    "We'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, we don't need to perform any additional cleaning or splitting.\n",
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    "\n",
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    "Let's fetch the data and write it to the DocumentStore:"
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   ]
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  },
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   "source": [
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    "from datasets import load_dataset\n",
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    "\n",
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    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
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    "\n",
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    "document_store.write_documents(dataset)"
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   ]
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  },
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    "## Initializing the Retriever\n",
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    "\n",
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    "Let's initialize a BM25Retriever and make it use the InMemoryDocumentStore we initialized earlier in this tutorial:"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {
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   },
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   "source": [
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    "from haystack.nodes import BM25Retriever\n",
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    "\n",
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    "retriever = BM25Retriever(document_store=document_store, top_k=2)"
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   ]
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  },
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    "## Initializing the PromptNode\n",
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    "\n",
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    "[PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node) is the central abstraction in Haystack's large language model (LLM) support. It's possible to interact with LLMs through PromptNode by providing `model_name_or_path` and if necessary, `api_key`.\n",
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    "\n",
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    "For this tutorial, we'll use OpenAI's `gpt-3.5-turbo`, so, we need to enter a `OPENAI_API_KEY`:"
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   ]
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  },
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   "metadata": {
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    "colab": {
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    "id": "SavE_FAqfApo",
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   },
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    "import os\n",
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    "from getpass import getpass\n",
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    "\n",
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    "openai_api_key = os.getenv(\"OPENAI_API_KEY\", None) or getpass(\"Enter OpenAI API key:\")"
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   ]
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  },
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    "Let's define a custom prompt for PromptTemplate to use with PromptNode. As parameters, this prompt will accept `documents` that our Retriever fetched from our DocumentStore and `query` we pass at runtime. To join the content of the Documents, we'll use `join()` function. To learn about using functions in PromptTemplate, check out [PromptTemplate Structure](https://docs.haystack.deepset.ai/docs/prompt_node#prompttemplate-structure). Finally, we'll use [AnswerParser](https://docs.haystack.deepset.ai/reference/prompt-node-api#answerparser) to parse the output of the LLM into a Haystack Answer object.\n",
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    "\n",
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    "We'll initialize PromptNode with the PromptTemplate, the `gpt-3.5-turbo` model and the `api_key`."
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   ]
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  },
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   "metadata": {
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   },
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   "outputs": [],
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   "source": [
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    "from haystack.nodes import PromptNode, PromptTemplate, AnswerParser\n",
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    "\n",
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    "rag_prompt = PromptTemplate(\n",
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    "    prompt=\"\"\"Synthesize a comprehensive answer from the following text for the given question.\n",
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    "                             Provide a clear and concise response that summarizes the key points and information presented in the text.\n",
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    "                             Your answer should be in your own words and be no longer than 50 words.\n",
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    "                             \\n\\n Related text: {join(documents)} \\n\\n Question: {query} \\n\\n Answer:\"\"\",\n",
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    "    output_parser=AnswerParser(),\n",
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    ")\n",
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    "\n",
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    "prompt_node = PromptNode(model_name_or_path=\"gpt-3.5-turbo\", api_key=openai_api_key, default_prompt_template=rag_prompt)"
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   ]
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  },
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   "source": [
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    ">To learn about how to use custom templates with PromptNode, check out [Customizing PromptNode for NLP Tasks](https://haystack.deepset.ai/tutorials/21_customizing_promptnode) tutorial."
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   ]
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  },
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  {
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   "source": [
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    "## Defining the Pipeline\n",
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    "\n",
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    "We'll use a custom pipeline with the Retriever, and PromptNode."
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   ]
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  },
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  {
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   "metadata": {
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   },
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   "outputs": [],
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   "source": [
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    "from haystack.pipelines import Pipeline\n",
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    "\n",
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    "pipe = Pipeline()\n",
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    "pipe.add_node(component=retriever, name=\"retriever\", inputs=[\"Query\"])\n",
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    "pipe.add_node(component=prompt_node, name=\"prompt_node\", inputs=[\"retriever\"])"
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   ]
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  },
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  {
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   "source": [
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    "That's it! The pipeline's ready to generate answers to questions!"
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   ]
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  },
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  {
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   "source": [
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    "## Asking a Question\n",
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    "\n",
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    "We use the pipeline `run()` method to ask a question."
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   ]
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  },
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  {
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   "metadata": {
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   "outputs": [],
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   "source": [
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    "output = pipe.run(query=\"What does Rhodes Statue look like?\")\n",
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    "\n",
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    "print(output[\"answers\"][0].answer)"
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   ]
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  },
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   "source": [
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    "Here are some other example queries to test:"
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   ]
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  },
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   "source": [
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    "examples = [\n",
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    "    \"Where is Gardens of Babylon?\",\n",
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    "    \"Why did people build Great Pyramid of Giza?\",\n",
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    "    \"What does Rhodes Statue look like?\",\n",
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    "    \"Why did people visit the Temple of Artemis?\",\n",
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    "    \"What is the importance of Colossus of Rhodes?\",\n",
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    "    \"What happened to the Tomb of Mausolus?\",\n",
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    "    \"How did Colossus of Rhodes collapse?\",\n",
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    "]"
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   ]
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  },
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   "source": [
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    "🎉 Congratulations! You've learned how to create a generative QA system for your documents with PromptNode."
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   ]
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  }
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