From 3c51b88861b6cc3753c32451164fdaf2d812a6b0 Mon Sep 17 00:00:00 2001
From: bilgeyucel <bilgeyucel96@gmail.com>
Date: Thu, 26 Mar 2026 17:18:14 +0100
Subject: [PATCH] Add alternative LLM models

---
 index.toml                            |    2 +-
 tutorials/27_First_RAG_Pipeline.ipynb | 1802 ++++++++++++++++---------
 2 files changed, 1192 insertions(+), 612 deletions(-)

diff --git a/index.toml b/index.toml
index 2b92622..b6559e4 100644
--- a/index.toml
+++ b/index.toml
@@ -12,7 +12,7 @@ notebook = "27_First_RAG_Pipeline.ipynb"
 aliases = []
 completion_time = "10 min"
 created_at = 2023-12-05
-dependencies = ["datasets>=2.6.1", "sentence-transformers>=4.1.0"]
+dependencies = ["datasets>=2.6.1", "sentence-transformers>=4.1.0", "mistral-haystack"]
 featured = true
 
 [[tutorial]]
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
index f24153b..9e9937a 100644
--- a/tutorials/27_First_RAG_Pipeline.ipynb
+++ b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -1,617 +1,1197 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2OvkPji9O-qX"
-   },
-   "source": [
-    "# Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation\n",
-    "\n",
-    "- **Level**: Beginner\n",
-    "- **Time to complete**: 10 minutes\n",
-    "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder), [`OpenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
-    "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys).\n",
-    "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIChatGenerator to build a generative question-answering pipeline with retrieval-augmentation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LFqHcXYPO-qZ"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) for creating a template prompt, and [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) for generating responses.\n",
-    "\n",
-    "For this tutorial, you'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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Kww5B_vXO-qZ"
-   },
-   "source": [
-    "## Installing Haystack\n",
-    "\n",
-    "Install Haystack and other required packages with `pip`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "UQbU8GUfO-qZ",
-    "outputId": "c33579e9-5557-43bd-a3c5-63b8373770c7"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install haystack-ai\n",
-    "pip install \"datasets>=2.6.1\"\n",
-    "pip install \"sentence-transformers>=4.1.0\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_lvfew16O-qa"
-   },
-   "source": [
-    "## Fetching and Indexing Documents\n",
-    "\n",
-    "You'll start creating your question answering system by downloading the data and indexing the data with its embeddings to a DocumentStore. \n",
-    "\n",
-    "In this tutorial, you will take a simple approach to writing documents and their embeddings into the DocumentStore. For a full indexing pipeline with preprocessing, cleaning and splitting, check out our tutorial on [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline).\n",
-    "\n",
-    "\n",
-    "### Initializing the DocumentStore\n",
-    "\n",
-    "Initialize a DocumentStore to index your documents. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "id": "CbVN-s5LO-qa"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
-    "\n",
-    "document_store = InMemoryDocumentStore()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yL8nuJdWO-qa"
-   },
-   "source": [
-    "> `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 different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XvLVaFHTO-qb"
-   },
-   "source": [
-    "The DocumentStore is now ready. Now it's time to fill it with some Documents."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HryYZP9ZO-qb"
-   },
-   "source": [
-    "### Fetch the Data\n",
-    "\n",
-    "You'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, you don't need to perform any additional cleaning or splitting.\n",
-    "\n",
-    "Fetch the data and convert it into Haystack Documents:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "INdC3WvLO-qb",
-    "outputId": "1af43d0f-2999-4de4-d152-b3cca9fb49e6"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from haystack import Document\n",
-    "\n",
-    "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
-    "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "czMjWwnxPA-3"
-   },
-   "source": [
-    "### Initalize a Document Embedder\n",
-    "\n",
-    "To store your data in the DocumentStore with embeddings, initialize a [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) with the model name and call `warm_up()` to download the embedding model.\n",
-    "\n",
-    "> If you'd like, you can use a different [Embedder](https://docs.haystack.deepset.ai/docs/embedders) for your documents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "EUmAH9sEn3R7",
-    "outputId": "ee54b59b-4d4a-45eb-c1a9-0b7b248f1dd4"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
-    "\n",
-    "doc_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
-    "doc_embedder.warm_up()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9y4iJE_SrS4K"
-   },
-   "source": [
-    "### Write Documents to the DocumentStore\n",
-    "\n",
-    "Run the `doc_embedder` with the Documents. The embedder will create embeddings for each document and save these embeddings in Document object's `embedding` field. Then, you can write the Documents to the DocumentStore with `write_documents()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 66,
-     "referenced_widgets": [
-      "7d482188c12d4a7886f20a65d3402c59",
-      "2a3ec74419ae4a02ac0210db66133415",
-      "ddeff9a822404adbbc3cad97a939bc0c",
-      "36d341ab3a044709b5af2e8ab97559bc",
-      "88fc33e1ab78405e911b5eafa512c935",
-      "91e5d4b0ede848319ef0d3b558d57d19",
-      "d2428c21707d43f2b6f07bfafbace8bb",
-      "7fdb2c859e454e72888709a835f7591e",
-      "6b8334e071a3438397ba6435aac69f58",
-      "5f5cfa425cac4d37b2ea29e53b4ed900",
-      "3c59a82dac5c476b9a3e3132094e1702"
-     ]
-    },
-    "id": "ETpQKftLplqh",
-    "outputId": "b9c8658c-90c8-497c-e765-97487c0daf8e"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Batches:   0%|          | 0/5 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Batches: 100%|██████████| 5/5 [00:01<00:00,  3.38it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "151"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs_with_embeddings = doc_embedder.run(docs)\n",
-    "document_store.write_documents(docs_with_embeddings[\"documents\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IdojTxg6uubn"
-   },
-   "source": [
-    "## Building the RAG Pipeline\n",
-    "\n",
-    "The next step is to build a [Pipeline](https://docs.haystack.deepset.ai/docs/pipelines) to generate answers for the user query following the RAG approach. To create the pipeline, you first need to initialize each component, add them to your pipeline, and connect them."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0uyV6-u-u56P"
-   },
-   "source": [
-    "### Initialize a Text Embedder\n",
-    "\n",
-    "Initialize a text embedder to create an embedding for the user query. The created embedding will later be used by the Retriever to retrieve relevant documents from the DocumentStore.\n",
-    "\n",
-    "> ⚠️ Notice that you used `sentence-transformers/all-MiniLM-L6-v2` model to create embeddings for your documents before. This is why you need to use the same model to embed the user queries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "LyJY2yW628dl"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
-    "\n",
-    "text_embedder = SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0_cj-5m-O-qb"
-   },
-   "source": [
-    "### Initialize the Retriever\n",
-    "\n",
-    "Initialize a [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) and make it use the InMemoryDocumentStore you initialized earlier in this tutorial. This Retriever will get the relevant documents to the query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "id": "-uo-6fjiO-qb"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
-    "\n",
-    "retriever = InMemoryEmbeddingRetriever(document_store)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6CEuQpB7O-qb"
-   },
-   "source": [
-    "### Define a Template Prompt\n",
-    "\n",
-    "Create a custom prompt for a generative question answering task using the RAG approach. The prompt should take in two parameters: `documents`, which are retrieved from a document store, and a `question` from the user. Use the Jinja2 looping syntax to combine the content of the retrieved documents in the prompt.\n",
-    "\n",
-    "Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) instance with your prompt template. The PromptBuilder, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "id": "ObahTh45FqOT"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.builders import ChatPromptBuilder\n",
-    "from haystack.dataclasses import ChatMessage\n",
-    "\n",
-    "template = [\n",
-    "    ChatMessage.from_user(\n",
-    "        \"\"\"\n",
-    "Given the following information, answer the question.\n",
-    "\n",
-    "Context:\n",
-    "{% for document in documents %}\n",
-    "    {{ document.content }}\n",
-    "{% endfor %}\n",
-    "\n",
-    "Question: {{question}}\n",
-    "Answer:\n",
-    "\"\"\"\n",
-    "    )\n",
-    "]\n",
-    "\n",
-    "prompt_builder = ChatPromptBuilder(template=template)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "HR14lbfcFtXj"
-   },
-   "source": [
-    "### Initialize a ChatGenerator\n",
-    "\n",
-    "\n",
-    "ChatGenerators are the components that interact with large language models (LLMs). Now, set `OPENAI_API_KEY` environment variable and initialize a [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) that can communicate with OpenAI GPT models. As you initialize, provide a model name:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "SavE_FAqfApo",
-    "outputId": "1afbf2e8-ae63-41ff-c37f-5123b2103356"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
-    "\n",
-    "if \"OPENAI_API_KEY\" not in os.environ:\n",
-    "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
-    "chat_generator = OpenAIChatGenerator(model=\"gpt-4o-mini\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nenbo2SvycHd"
-   },
-   "source": [
-    "> You can replace `OpenAIChatGenerator` in your pipeline with another `ChatGenerator`. Check out the full list of chat generators [here](https://docs.haystack.deepset.ai/docs/generators)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1bfHwOQwycHe"
-   },
-   "source": [
-    "### Build the Pipeline\n",
-    "\n",
-    "To build a pipeline, add all components to your pipeline and connect them. Create connections from `text_embedder`'s \"embedding\" output to \"query_embedding\" input of `retriever`, from `retriever` to `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\").\n",
-    "\n",
-    "For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines) documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "f6NFmpjEO-qb",
-    "outputId": "89fd1b48-5189-4401-9cf8-15f55c503676"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack import Pipeline\n",
-    "\n",
-    "basic_rag_pipeline = Pipeline()\n",
-    "# Add components to your pipeline\n",
-    "basic_rag_pipeline.add_component(\"text_embedder\", text_embedder)\n",
-    "basic_rag_pipeline.add_component(\"retriever\", retriever)\n",
-    "basic_rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
-    "basic_rag_pipeline.add_component(\"llm\", chat_generator)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<haystack.core.pipeline.pipeline.Pipeline object at 0x36efbc520>\n",
-       "🚅 Components\n",
-       "  - text_embedder: SentenceTransformersTextEmbedder\n",
-       "  - retriever: InMemoryEmbeddingRetriever\n",
-       "  - prompt_builder: ChatPromptBuilder\n",
-       "  - llm: OpenAIChatGenerator\n",
-       "🛤️ Connections\n",
-       "  - text_embedder.embedding -> retriever.query_embedding (List[float])\n",
-       "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
-       "  - prompt_builder.prompt -> llm.messages (List[ChatMessage])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation\n",
+        "\n",
+        "- **Level**: Beginner\n",
+        "- **Time to complete**: 10 minutes\n",
+        "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`ChatPromptBuilder`](https://docs.haystack.deepset.ai/docs/chatpromptbuilder), and a [`ChatGenerator`](https://docs.haystack.deepset.ai/docs/generators) such as [`OpenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/openaichatgenerator), [`MistralChatGenerator`](https://docs.haystack.deepset.ai/docs/mistralchatgenerator), or [`HuggingFaceLocalChatGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalchatgenerator).\n",
+        "- **Prerequisites**: Access to a large language model, either an **API key** from a provider or a **locally or on-premises hosted** model (for example on Colab runtime).\n",
+        "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use ChatPromptBuilder with a ChatGenerator to build a generative question-answering pipeline with retrieval-augmentation."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LFqHcXYPO-qZ"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) for fetching relevant documents, [ChatPromptBuilder](https://docs.haystack.deepset.ai/docs/chatpromptbuilder) for creating a template prompt, and a [ChatGenerator](https://docs.haystack.deepset.ai/docs/generators) for generating the final answer.\n",
+        "\n",
+        "The LLM behind that generator can be **hosted in the cloud**, for example with [OpenAI](https://haystack.deepset.ai/integrations/openai), [Anthropic](https://haystack.deepset.ai/integrations/anthropic), [Google](https://haystack.deepset.ai/integrations/google-genai), [Mistral](https://haystack.deepset.ai/integrations/mistral), or other providers, usually by setting an API key in the environment or **run locally**, for example via [Ollama](https://haystack.deepset.ai/integrations/ollama) or [vLLM](https://haystack.deepset.ai/integrations/vllm), or **on a Colab VM** by loading an open-weight model from Hugging Face. The *Initialize a ChatGenerator* section shows three concrete options (OpenAI, Mistral, and a local model).\n",
+        "\n",
+        "For this tutorial, you'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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kww5B_vXO-qZ"
+      },
+      "source": [
+        "## Installing Haystack\n",
+        "\n",
+        "Install Haystack and other required packages with `pip`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UQbU8GUfO-qZ",
+        "outputId": "7a4ef73b-8822-467e-9979-a75169b36729"
+      },
+      "outputs": [],
+      "source": [
+        "%%bash\n",
+        "\n",
+        "pip install haystack-ai mistral-haystack \"datasets>=2.6.1\" \"sentence-transformers>=4.1.0\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_lvfew16O-qa"
+      },
+      "source": [
+        "## Fetching and Indexing Documents\n",
+        "\n",
+        "You'll start creating your question answering system by downloading the data and indexing the data with its embeddings to a DocumentStore.\n",
+        "\n",
+        "In this tutorial, you will take a simple approach to writing documents and their embeddings into the DocumentStore. For a full indexing pipeline with preprocessing, cleaning and splitting, check out our tutorial on [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline).\n",
+        "\n",
+        "\n",
+        "### Initializing the DocumentStore\n",
+        "\n",
+        "Initialize a DocumentStore to index your documents. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "CbVN-s5LO-qa"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+        "\n",
+        "document_store = InMemoryDocumentStore()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yL8nuJdWO-qa"
+      },
+      "source": [
+        "> `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 different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XvLVaFHTO-qb"
+      },
+      "source": [
+        "The DocumentStore is now ready. Now it's time to fill it with some Documents."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HryYZP9ZO-qb"
+      },
+      "source": [
+        "### Fetch the Data\n",
+        "\n",
+        "You'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 it to Hugging Face as the [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders) dataset. Thus, you don't need to perform any additional cleaning or splitting.\n",
+        "\n",
+        "Fetch the data and convert it into Haystack Documents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 256,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "INdC3WvLO-qb",
+        "outputId": "d1515765-20ea-44db-c8a8-a68776fafdab"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b22af125522742d4a83e95890e582058",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "README.md:   0%|          | 0.00/46.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
+            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "c3fca5ba78d2467da72fb1abd0b01ec3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "data/train-00000-of-00001-4077bd623d5510(…):   0%|          | 0.00/119k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "041e6870096b4f8e95a10274524065ab",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/151 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "from haystack import Document\n",
+        "\n",
+        "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
+        "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "czMjWwnxPA-3"
+      },
+      "source": [
+        "### Initialize a Document Embedder\n",
+        "\n",
+        "To store your data in the DocumentStore with embeddings, initialize a [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) with the model name.\n",
+        "\n",
+        "> If you'd like, you can use a different [Embedder](https://docs.haystack.deepset.ai/docs/embedders) for your documents."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "EUmAH9sEn3R7"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
+        "\n",
+        "doc_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9y4iJE_SrS4K"
+      },
+      "source": [
+        "### Write Documents to the DocumentStore\n",
+        "\n",
+        "Run the `doc_embedder` with the Documents. The embedder will create embeddings for each document and store them in that document's `embedding` field. Then, write the Documents to the DocumentStore with the `write_documents()` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
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+        "outputId": "2b73c450-364e-4483-80b1-449dccff6e6c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2cc35d93724b4e0f9608fe6999e96baa",
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+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+            },
+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "65d85c964cfb46dab8b9ea69c9506ada",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading weights:   0%|          | 0/103 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "BertModel LOAD REPORT from: sentence-transformers/all-MiniLM-L6-v2\n",
+            "Key                     | Status     |  | \n",
+            "------------------------+------------+--+-\n",
+            "embeddings.position_ids | UNEXPECTED |  | \n",
+            "\n",
+            "Notes:\n",
+            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "98895c06137b4d80b5451f56f06324a4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8c9072b8a35849cfbda6f5eb9179f528",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "vocab.txt: 0.00B [00:00, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6ad2981514934ba3a99d9af804b71b83",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json: 0.00B [00:00, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ba3552f1892e4334a82d7ff298cd3b5b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "962f0a845a8141fb9a616a548cf586ae",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "347ff1dbd39d441eb77d5cef02415a5e",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/5 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "151"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "docs_with_embeddings = doc_embedder.run(docs)\n",
+        "document_store.write_documents(docs_with_embeddings[\"documents\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IdojTxg6uubn"
+      },
+      "source": [
+        "## Building the RAG Pipeline\n",
+        "\n",
+        "The next step is to build a [Pipeline](https://docs.haystack.deepset.ai/docs/pipelines) to generate answers for the user query following the RAG approach. To create the pipeline, you first need to initialize each component, add them to your pipeline, and connect them."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0uyV6-u-u56P"
+      },
+      "source": [
+        "### Initialize a Text Embedder\n",
+        "\n",
+        "Initialize a text embedder to create an embedding for the user query. The created embedding will later be used by the Retriever to retrieve relevant documents from the DocumentStore.\n",
+        "\n",
+        "> ⚠️ Notice that you used `sentence-transformers/all-MiniLM-L6-v2` model to create embeddings for your documents before. This is why you need to use the same model to embed the user queries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "LyJY2yW628dl"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
+        "\n",
+        "text_embedder = SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0_cj-5m-O-qb"
+      },
+      "source": [
+        "### Initialize the Retriever\n",
+        "\n",
+        "Initialize an [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) and make it use the `InMemoryDocumentStore` you initialized earlier in this tutorial. This Retriever will fetch the documents most relevant to the query."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "-uo-6fjiO-qb"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+        "\n",
+        "retriever = InMemoryEmbeddingRetriever(document_store)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6CEuQpB7O-qb"
+      },
+      "source": [
+        "### Define a Template Prompt\n",
+        "\n",
+        "Create a [ChatMessage](https://docs.haystack.deepset.ai/docs/chatmessage) object with the `from_user` method and pass the custom prompt for a question answering task using the RAG approach. The prompt should take in two parameters: `documents`, which are retrieved from a document store, and a `question` from the user. Use the Jinja2 looping syntax to combine the content of the retrieved documents in the prompt.\n",
+        "\n",
+        "Next, initialize a [ChatPromptBuilder](https://docs.haystack.deepset.ai/docs/chatpromptbuilder) instance with your prompt template. The `ChatPromptBuilder`, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience.\n",
+        "\n",
+        "> By default, all prompt variables are treated as optional. Set `required_variables=\"*\"` to ensure that all prompt variables are mandatory for the prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "ObahTh45FqOT"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack.components.builders import ChatPromptBuilder\n",
+        "from haystack.dataclasses import ChatMessage\n",
+        "\n",
+        "template = [\n",
+        "    ChatMessage.from_user(\n",
+        "        \"\"\"\n",
+        "Given the following information, answer the question.\n",
+        "\n",
+        "Context:\n",
+        "{% for document in documents %}\n",
+        "    {{ document.content }}\n",
+        "{% endfor %}\n",
+        "\n",
+        "Question: {{question}}\n",
+        "Answer:\n",
+        "\"\"\"\n",
+        "    )\n",
+        "]\n",
+        "\n",
+        "prompt_builder = ChatPromptBuilder(template=template, required_variables=\"*\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HR14lbfcFtXj"
+      },
+      "source": [
+        "### Initialize a ChatGenerator\n",
+        "\n",
+        "[ChatGenerators](https://docs.haystack.deepset.ai/docs/generators) are the components that call large language models (LLMs) and return chat completions.\n",
+        "\n",
+        "**Before you run the pipeline, decide how you will access the LLM:**\n",
+        "\n",
+        "- **Hosted provider API** — Create an API key with a provider. In Colab, you can store it under *Secrets* tab or set the matching environment variable (`OPENAI_API_KEY`, `MISTRAL_API_KEY`, …). The cells below prompt for a key if it is not already set.\n",
+        "- **Local or self-hosted (including on Colab)** — If you prefer not to use a remote API, you can run an open-weight model on your machine or the Colab runtime with [`HuggingFaceLocalChatGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalchatgenerator). See the [generators documentation](https://docs.haystack.deepset.ai/docs/generators) for more integrations.\n",
+        "\n",
+        "The next three sections show **OpenAI**, **Mistral**, and **Hugging Face** as examples. Run **only one** of them to define `chat_generator`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Use OpenAI's GPT models (requires an API key)**\n",
+        "\n",
+        "[Get an OpenAI API key](https://platform.openai.com/api-keys) and set it as the `OPENAI_API_KEY` environment variable. Then initialize `OpenAIChatGenerator` with the model name you want to use. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SavE_FAqfApo"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "from haystack.components.generators.chat import OpenAIChatGenerator\n",
+        "\n",
+        "if \"OPENAI_API_KEY\" not in os.environ:\n",
+        "    os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n",
+        "    \n",
+        "chat_generator = OpenAIChatGenerator(model=\"gpt-4o-mini\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Use Mistral models (requires a free API key)**\n",
+        "\n",
+        "[Get a Mistral API key](https://docs.mistral.ai/) (free tier available) and set it as the `MISTRAL_API_KEY` environment variable. Then initialize `MistralChatGenerator` with the model name you want to use. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0-zl-0WsZI7T",
+        "outputId": "dffb77e3-8c03-4fc2-d038-692e1d3cc2e7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter Mistral API key:··········\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from getpass import getpass\n",
+        "from haystack_integrations.components.generators.mistral import MistralChatGenerator\n",
+        "\n",
+        "if \"MISTRAL_API_KEY\" not in os.environ:\n",
+        "  os.environ[\"MISTRAL_API_KEY\"] = getpass(\"Enter Mistral API key:\")\n",
+        "  \n",
+        "chat_generator = MistralChatGenerator(model=\"mistral-small-latest\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**Use open-weight models from Hugging Face (no API key required for local inference)**\n",
+        "\n",
+        "Initialize `HuggingFaceLocalChatGenerator` with an open-weight LLM from Hugging Face, such as [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). To call models through the **Hugging Face Inference API** instead, use [`HuggingFaceAPIChatGenerator`](https://docs.haystack.deepset.ai/docs/huggingfaceapichatgenerator), which requires a Hugging Face API token."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from haystack.components.generators.chat import HuggingFaceLocalChatGenerator\n",
+        "\n",
+        "chat_generator = HuggingFaceLocalChatGenerator(model=\"Qwen/Qwen3-4B-Instruct-2507\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nenbo2SvycHd"
+      },
+      "source": [
+        "> You can replace the examples above with any Haystack `ChatGenerator` that fits your setup: another API provider or a local / Colab-hosted backend. See the full list of chat generators [here](https://docs.haystack.deepset.ai/docs/generators)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1bfHwOQwycHe"
+      },
+      "source": [
+        "### Build the Pipeline\n",
+        "\n",
+        "To build a pipeline, add all components to your pipeline and connect them. Create connections from `text_embedder`'s \"embedding\" output to \"query_embedding\" input of `retriever`, from `retriever` to `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\").\n",
+        "\n",
+        "For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines) documentation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "f6NFmpjEO-qb"
+      },
+      "outputs": [],
+      "source": [
+        "from haystack import Pipeline\n",
+        "\n",
+        "basic_rag_pipeline = Pipeline()\n",
+        "# Add components to your pipeline\n",
+        "basic_rag_pipeline.add_component(\"text_embedder\", text_embedder)\n",
+        "basic_rag_pipeline.add_component(\"retriever\", retriever)\n",
+        "basic_rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
+        "basic_rag_pipeline.add_component(\"llm\", chat_generator)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CyqzFsp1qWVM",
+        "outputId": "591f9e19-13d6-4232-f00a-d6becd89694b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7a7fb2c19730>\n",
+              "🚅 Components\n",
+              "  - text_embedder: SentenceTransformersTextEmbedder\n",
+              "  - retriever: InMemoryEmbeddingRetriever\n",
+              "  - prompt_builder: ChatPromptBuilder\n",
+              "  - llm: MistralChatGenerator\n",
+              "🛤️ Connections\n",
+              "  - text_embedder.embedding -> retriever.query_embedding (list[float])\n",
+              "  - retriever.documents -> prompt_builder.documents (list[Document])\n",
+              "  - prompt_builder.prompt -> llm.messages (list[ChatMessage])"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Now, connect the components to each other\n",
+        "basic_rag_pipeline.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n",
+        "basic_rag_pipeline.connect(\"retriever\", \"prompt_builder\")\n",
+        "basic_rag_pipeline.connect(\"prompt_builder.prompt\", \"llm.messages\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6NqyLhx7O-qc"
+      },
+      "source": [
+        "That's it! Your RAG pipeline is ready to generate answers to questions!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DBAyF5tVO-qc"
+      },
+      "source": [
+        "## Asking a Question\n",
+        "\n",
+        "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `text_embedder` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question.\n",
+        "\n",
+        "> ⚠️ If you host the model on the Colab runtime (for example with HuggingFaceLocalChatGenerator), the first pipeline run can take longer as the LLM is loaded and prepared for inference."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86,
+          "referenced_widgets": [
+            "4e6e97b6d54f4f80bb7e8b25aba8e616",
+            "1a820c06a7a049d8b6c9ff300284d06e",
+            "58ff4e0603a74978a134f63533859be5",
+            "8bdb8bfae31d4f4cb6c3b0bf43120eed",
+            "39a68d9a5c274e2dafaa2d1f86eea768",
+            "d0cfe5dacdfc431a91b4c4741123e2d0",
+            "e7f1e1a14bb740d18827dd78bbe7b2e3",
+            "3fda06f905b445a488efdd2dd08c0939",
+            "2bc341a780f7498ba9cd475468841bb5",
+            "d7218475e23b420a8c03d00ca4ab8718",
+            "a694abaf765f4d1b82fa0138e59c6793"
+          ]
+        },
+        "id": "Vnt283M5O-qc",
+        "outputId": "d2843a73-3ad5-4daa-8d1e-a58de7aa2bb0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Batches: 100%|██████████| 1/1 [00:00<00:00,  1.77it/s]\n",
+            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+            "To disable this warning, you can either:\n",
+            "\t- Avoid using `tokenizers` before the fork if possible\n",
+            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The Colossus of Rhodes was a statue of the Greek sun-god Helios, standing approximately 70 cubits (about 33 meters or 108 feet) tall. Although no complete descriptions of its appearance exist, scholars believe it featured the following characteristics:\n",
+            "\n",
+            "1. **Facial Features**: The head of the statue likely had curly hair, with spikes resembling bronze or silver flames radiating outward. This style is similar to depictions found on contemporary Rhodian coins.\n",
+            "\n",
+            "2. **Posture**: While the exact pose is uncertain, it is suggested that the statue may have been constructed in a pose where Helios is depicted shielding his eyes with one hand, a common representation of someone looking toward the sun.\n",
+            "\n",
+            "3. **Construction Materials**: The structure was built using iron tie bars and brass plates, which formed the skin of the statue. The interior was filled with stone blocks.\n",
+            "\n",
+            "4. **Height and Scale**: The Colossus was positioned on a 15-metre-high (49-foot) pedestal, making it one of the tallest statues of the ancient world, towering over the harbor entrance.\n",
+            "\n",
+            "5. **Symbolic Representation**: The statue was meant to symbolize the victory and freedom of the Rhodians after successfully defending their city against an invader.\n",
+            "\n",
+            "Overall, the Colossus of Rhodes was an impressive and monumental statue designed to celebrate and symbolize the strength and resilience of the city of Rhodes.\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"What does Rhodes Statue look like?\"\n",
+        "\n",
+        "response = basic_rag_pipeline.run({\"text_embedder\": {\"text\": question}, \"prompt_builder\": {\"question\": question}})\n",
+        "\n",
+        "print(response[\"llm\"][\"replies\"][0].text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 712,
+          "referenced_widgets": [
+            "3a404870f30f48aeb5fce11bcb794a1a",
+            "6b9eb9888076445c92b80f9aa29121ce",
+            "1839264932db40d0a40ccbfc08b50896",
+            "0a893c45730f4ce5a36060dcc880add1",
+            "699de9f0c89e4cc294b341932c4decc7",
+            "d283295d0bec454d9bd84256f14904ea",
+            "b4d7b68ea70b449b95eadc54e37954d6",
+            "0c9be40eb1064e50a70fe4de5cf9c760",
+            "96d50cf0bf05451a91c9eed788d36ed0",
+            "d891ea3f48314c7199f0963277063df8",
+            "24d82ad3686c4616aff7987647485df6"
+          ]
+        },
+        "id": "v6bQceW8ZduN",
+        "outputId": "f8ca4d07-ca67-4810-d07c-354c7888f80f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "3a404870f30f48aeb5fce11bcb794a1a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The **Colossus of Rhodes** was a massive bronze statue of the Greek sun-god **Helios**, the patron deity of Rhodes. While no surviving images or detailed descriptions of the statue exist, historians and archaeologists have pieced together its likely appearance based on:\n",
+            "\n",
+            "### **1. General Description**\n",
+            "- **Height**: Approximately **32–33 meters (105–108 feet)** tall (about the height of the Statue of Liberty from feet to crown).\n",
+            "- **Material**: Constructed with **iron tie bars** for structural support, covered in **bronze plates** (likely hammered bronze sheets).\n",
+            "- **Pose**: Most scholars believe it stood in a **contrapposto pose** (weight on one leg, with the other slightly bent), possibly with one hand raised to shield its eyes from the sun (similar to depictions on Rhodian coins).\n",
+            "- **Head & Face**: Based on contemporary Rhodian coins, Helios was depicted with **curly hair** and **radiant spikes** (possibly representing sun rays) on his head.\n",
+            "\n",
+            "### **2. Possible Depictions**\n",
+            "- **Torch or Crown?**\n",
+            "  - Some ancient texts mention the Rhodians \"kindled the torch of freedom\" after the siege, leading to speculation that the statue may have held a **torch aloft** (like the Statue of Liberty).\n",
+            "  - However, no direct evidence supports this; it may have been a symbolic reference rather than a physical torch.\n",
+            "- **Helios’ Standard Pose**:\n",
+            "  - A **relief in a nearby temple** shows Helios standing with one hand shielding his eyes, suggesting the Colossus may have been in a similar pose.\n",
+            "\n",
+            "### **3. Comparison to Other Ancient Statues**\n",
+            "- Similar to other **Hellenistic bronze statues** (e.g., the **Zeus at Olympia** or **Athena Parthenos**), the Colossus likely had:\n",
+            "  - A **muscular, idealized human form** (Helios was often depicted as youthful and athletic).\n",
+            "  - **Detailed drapery** (if wearing a robe, though no records confirm this).\n",
+            "  - **A crown or radiant headpiece** (symbolizing the sun).\n",
+            "\n",
+            "### **4. Controversies & Alternative Theories**\n",
+            "- **Harbour-Straddling Myth**:\n",
+            "  - Ancient accounts of the Colossus **spanning the harbor entrance** are considered **unrealistic** by modern scholars due to engineering constraints (the statue would collapse under its own weight).\n",
+            "- **Location**:\n",
+            "  - Likely stood near the **harbor entrance** (possibly on a **15-meter marble pedestal**), but not straddling it.\n",
+            "  - Some propose it was built on a **breakwater** or near the **Fortress of St. Nicholas**.\n",
+            "\n",
+            "### **5. Legacy & Influence**\n",
+            "- The Colossus inspired later monumental statues, including the **Statue of Liberty** (which shares its height and torch symbolism).\n",
+            "- Its **fall and destruction** (after the 226 BC earthquake) left a lasting myth, with debates over whether its ruins blocked the harbor.\n",
+            "\n",
+            "### **Conclusion**\n",
+            "The Colossus of Rhodes was a **towering bronze statue of Helios**, likely standing in a **heroic, contrapposto pose** with a **radiant headpiece**, possibly with one hand raised. While its exact appearance remains speculative, it was one of the most impressive feats of ancient engineering—a testament to Rhodes' wealth and devotion to its patron god.\n",
+            "\n",
+            "Would you like details on how it was constructed or its cultural significance?\n"
+          ]
+        }
+      ],
+      "source": [
+        "question = \"What does Rhodes Statue look like?\"\n",
+        "\n",
+        "response = basic_rag_pipeline.run({\"text_embedder\": {\"text\": question}, \"prompt_builder\": {\"question\": question}})\n",
+        "\n",
+        "print(response[\"llm\"][\"replies\"][0].text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IWQN-aoGO-qc"
+      },
+      "source": [
+        "Here are some other example questions to test:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_OHUQ5xxO-qc"
+      },
+      "outputs": [],
+      "source": [
+        "examples = [\n",
+        "    \"Where is Gardens of Babylon?\",\n",
+        "    \"Why did people build Great Pyramid of Giza?\",\n",
+        "    \"What does Rhodes Statue look like?\",\n",
+        "    \"Why did people visit the Temple of Artemis?\",\n",
+        "    \"What is the importance of Colossus of Rhodes?\",\n",
+        "    \"What happened to the Tomb of Mausolus?\",\n",
+        "    \"How did Colossus of Rhodes collapse?\",\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XueCK3y4O-qc"
+      },
+      "source": [
+        "## What's next\n",
+        "\n",
+        "🎉 Congratulations! You've learned how to create a generative QA system for your documents with the RAG approach.\n",
+        "\n",
+        "If you liked this tutorial, you may also enjoy:\n",
+        "- [Filtering Documents with Metadata](https://haystack.deepset.ai/tutorials/31_metadata_filtering)\n",
+        "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
+        "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
+        "\n",
+        "To stay up to date on the latest Haystack developments, you can [subscribe to our newsletter](https://landing.deepset.ai/haystack-community-updates) and [join the Haystack Discord community](https://discord.com/invite/xYvH6drSmA).\n",
+        "\n",
+        "Thanks for reading!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pwWunnaubzFD"
+      },
+      "outputs": [],
+      "source": []
     }
-   ],
-   "source": [
-    "# Now, connect the components to each other\n",
-    "basic_rag_pipeline.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n",
-    "basic_rag_pipeline.connect(\"retriever\", \"prompt_builder\")\n",
-    "basic_rag_pipeline.connect(\"prompt_builder.prompt\", \"llm.messages\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6NqyLhx7O-qc"
-   },
-   "source": [
-    "That's it! Your RAG pipeline is ready to generate answers to questions!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "DBAyF5tVO-qc"
-   },
-   "source": [
-    "## Asking a Question\n",
-    "\n",
-    "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `text_embedder` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
+  ],
+  "metadata": {
+    "accelerator": "GPU",
     "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 86,
-     "referenced_widgets": [
-      "4e6e97b6d54f4f80bb7e8b25aba8e616",
-      "1a820c06a7a049d8b6c9ff300284d06e",
-      "58ff4e0603a74978a134f63533859be5",
-      "8bdb8bfae31d4f4cb6c3b0bf43120eed",
-      "39a68d9a5c274e2dafaa2d1f86eea768",
-      "d0cfe5dacdfc431a91b4c4741123e2d0",
-      "e7f1e1a14bb740d18827dd78bbe7b2e3",
-      "3fda06f905b445a488efdd2dd08c0939",
-      "2bc341a780f7498ba9cd475468841bb5",
-      "d7218475e23b420a8c03d00ca4ab8718",
-      "a694abaf765f4d1b82fa0138e59c6793"
-     ]
-    },
-    "id": "Vnt283M5O-qc",
-    "outputId": "d2843a73-3ad5-4daa-8d1e-a58de7aa2bb0"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Batches: 100%|██████████| 1/1 [00:00<00:00,  1.77it/s]\n",
-      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-      "To disable this warning, you can either:\n",
-      "\t- Avoid using `tokenizers` before the fork if possible\n",
-      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The Colossus of Rhodes was a statue of the Greek sun-god Helios, standing approximately 70 cubits (about 33 meters or 108 feet) tall. Although no complete descriptions of its appearance exist, scholars believe it featured the following characteristics:\n",
-      "\n",
-      "1. **Facial Features**: The head of the statue likely had curly hair, with spikes resembling bronze or silver flames radiating outward. This style is similar to depictions found on contemporary Rhodian coins.\n",
-      "\n",
-      "2. **Posture**: While the exact pose is uncertain, it is suggested that the statue may have been constructed in a pose where Helios is depicted shielding his eyes with one hand, a common representation of someone looking toward the sun.\n",
-      "\n",
-      "3. **Construction Materials**: The structure was built using iron tie bars and brass plates, which formed the skin of the statue. The interior was filled with stone blocks.\n",
-      "\n",
-      "4. **Height and Scale**: The Colossus was positioned on a 15-metre-high (49-foot) pedestal, making it one of the tallest statues of the ancient world, towering over the harbor entrance.\n",
-      "\n",
-      "5. **Symbolic Representation**: The statue was meant to symbolize the victory and freedom of the Rhodians after successfully defending their city against an invader.\n",
-      "\n",
-      "Overall, the Colossus of Rhodes was an impressive and monumental statue designed to celebrate and symbolize the strength and resilience of the city of Rhodes.\n"
-     ]
+      "gpuType": "T4",
+      "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": "3.9.6"
+    },
+    "orig_nbformat": 4,
+    "vscode": {
+      "interpreter": {
+        "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+      }
     }
-   ],
-   "source": [
-    "question = \"What does Rhodes Statue look like?\"\n",
-    "\n",
-    "response = basic_rag_pipeline.run({\"text_embedder\": {\"text\": question}, \"prompt_builder\": {\"question\": question}})\n",
-    "\n",
-    "print(response[\"llm\"][\"replies\"][0].text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IWQN-aoGO-qc"
-   },
-   "source": [
-    "Here are some other example questions to test:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "_OHUQ5xxO-qc"
-   },
-   "outputs": [],
-   "source": [
-    "examples = [\n",
-    "    \"Where is Gardens of Babylon?\",\n",
-    "    \"Why did people build Great Pyramid of Giza?\",\n",
-    "    \"What does Rhodes Statue look like?\",\n",
-    "    \"Why did people visit the Temple of Artemis?\",\n",
-    "    \"What is the importance of Colossus of Rhodes?\",\n",
-    "    \"What happened to the Tomb of Mausolus?\",\n",
-    "    \"How did Colossus of Rhodes collapse?\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XueCK3y4O-qc"
-   },
-   "source": [
-    "## What's next\n",
-    "\n",
-    "🎉 Congratulations! You've learned how to create a generative QA system for your documents with the RAG approach.\n",
-    "\n",
-    "If you liked this tutorial, you may also enjoy:\n",
-    "- [Filtering Documents with Metadata](https://haystack.deepset.ai/tutorials/31_metadata_filtering)\n",
-    "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
-    "- [Creating a Hybrid Retrieval Pipeline](https://haystack.deepset.ai/tutorials/33_hybrid_retrieval)\n",
-    "\n",
-    "To stay up to date on the latest Haystack developments, you can [subscribe to our newsletter](https://landing.deepset.ai/haystack-community-updates) and [join Haystack discord community](https://discord.gg/haystack).\n",
-    "\n",
-    "Thanks for reading!"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "T4",
-   "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": "3.9.6"
   },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
+  "nbformat": 4,
+  "nbformat_minor": 0
 }