diff --git "a/unit2/llama-index/components.ipynb" "b/unit2/llama-index/components.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Components in LlamaIndex\n",
+    "\n",
+    "This notebook is part of the [Hugging Face Agents Course](https://www.hf.co/learn/agents-course), a free Course from beginner to expert, where you learn to build Agents.\n",
+    "\n",
+    "![Agents course share](https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/share.png)\n",
+    "\n",
+    "Alfred is hosting a party and needs to be able to find relevant information on personas that will be attending the party. Therefore, we will use a `QueryEngine` to index and search through a database of personas.\n",
+    "\n",
+    "## Let's install the dependencies\n",
+    "\n",
+    "We will install the dependencies for this unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install llama-index datasets llama-index-callbacks-arize-phoenix llama-index-vector-stores-chroma llama-index-llms-huggingface-api -U -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And, let's log in to Hugging Face to use serverless Inference APIs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import login\n",
+    "\n",
+    "login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a `QueryEngine` for retrieval augmented generation\n",
+    "\n",
+    "### Setting up the persona database\n",
+    "\n",
+    "We will be using personas from the [dvilasuero/finepersonas-v0.1-tiny dataset](https://huggingface.co/datasets/dvilasuero/finepersonas-v0.1-tiny). This dataset contains 5K personas that will be attending the party!\n",
+    "\n",
+    "Let's load the dataset and store it as files in the `data` directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from pathlib import Path\n",
+    "\n",
+    "dataset = load_dataset(path=\"dvilasuero/finepersonas-v0.1-tiny\", split=\"train\")\n",
+    "\n",
+    "Path(\"data\").mkdir(parents=True, exist_ok=True)\n",
+    "for i, persona in enumerate(dataset):\n",
+    "    with open(Path(\"data\") / f\"persona_{i}.txt\", \"w\") as f:\n",
+    "        f.write(persona[\"persona\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Awesome, now we have a local directory with all the personas that will be attending the party, we can load and index!\n",
+    "\n",
+    "### Loading and embedding persona documents\n",
+    "\n",
+    "We will use the `SimpleDirectoryReader` to load the persona descriptions from the `data` directory. This will return a list of `Document` objects. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from llama_index.core import SimpleDirectoryReader\n",
+    "\n",
+    "reader = SimpleDirectoryReader(input_dir=\"data\")\n",
+    "documents = reader.load_data()\n",
+    "len(documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have a list of `Document` objects, we can use the `IngestionPipeline` to create nodes from the documents and prepare them for the `QueryEngine`. We will use the `SentenceSplitter` to split the documents into smaller chunks and the `HuggingFaceInferenceAPIEmbedding` to embed the chunks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'),\n",
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'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='140b4d42-f58a-412d-8c15-439b25218bed', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1005.txt', 'file_name': 'persona_1005.txt', 'file_type': 'text/plain', 'file_size': 85, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, hash='f9e87c24312f7290b04f62167fa845fd1fb3b07ff4d39ab099563339a8f34838')}, metadata_template='{key}: {value}', metadata_separator='\\n', text='An historian focused on 19th-century Irish politics and the Irish Home Rule movement.', mimetype='text/plain', start_char_idx=0, end_char_idx=85, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}')]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding\n",
+    "from llama_index.core.node_parser import SentenceSplitter\n",
+    "from llama_index.core.ingestion import IngestionPipeline\n",
+    "\n",
+    "# create the pipeline with transformations\n",
+    "pipeline = IngestionPipeline(\n",
+    "    transformations=[\n",
+    "        SentenceSplitter(),\n",
+    "        HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# run the pipeline sync or async\n",
+    "nodes = await pipeline.arun(documents=documents[:10])\n",
+    "nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As, you can see, we have created a list of `Node` objects, which are just chunks of text from the original documents. Let's explore how we can add these nodes to a vector store.\n",
+    "\n",
+    "### Storing and indexing documents\n",
+    "\n",
+    "Since we are using an ingestion pipeline, we can directly attach a vector store to the pipeline to populate it.\n",
+    "In this case, we will use `Chroma` to store our documents.\n",
+    "Let's run the pipeline again with the vector store attached. \n",
+    "The `IngestionPipeline` caches the operations so this should be fast!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import chromadb\n",
+    "from llama_index.vector_stores.chroma import ChromaVectorStore\n",
+    "\n",
+    "db = chromadb.PersistentClient(path=\"./alfred_chroma_db\")\n",
+    "chroma_collection = db.get_or_create_collection(name=\"alfred\")\n",
+    "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+    "\n",
+    "pipeline = IngestionPipeline(\n",
+    "    transformations=[\n",
+    "        SentenceSplitter(),\n",
+    "        HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\"),\n",
+    "    ],\n",
+    "    vector_store=vector_store,\n",
+    ")\n",
+    "\n",
+    "nodes = await pipeline.arun(documents=documents[:10])\n",
+    "len(nodes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can create a `VectorStoreIndex` from the vector store and use it to query the documents by passing the vector store and embedding model to the `from_vector_store()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_index.core import VectorStoreIndex\n",
+    "from llama_index.embeddings.huggingface_api import HuggingFaceInferenceAPIEmbedding\n",
+    "\n",
+    "embed_model = HuggingFaceInferenceAPIEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
+    "index = VectorStoreIndex.from_vector_store(\n",
+    "    vector_store=vector_store, embed_model=embed_model\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We don't need to worry about persisting the index to disk, as it is automatically saved within the `ChromaVectorStore` object and the passed directory path.\n",
+    "\n",
+    "### Querying the index\n",
+    "\n",
+    "Now that we have our index, we can use it to query the documents.\n",
+    "Let's create a `QueryEngine` from the index and use it to query the documents using a specific response mode.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Response(response=\" I've had the privilege of immersing myself in the rich cultural heritage of Cyprus, where I've spent countless hours exploring the island's ancient ruins, vibrant markets, and stunning beaches. As an anthropologist, I've had the opportunity to delve into the intricacies of Cypriot society, from the traditional customs and practices to the modern-day challenges and triumphs. My experiences have not only broadened my understanding of this fascinating culture but also deepened my appreciation for the resilience and warmth of the Cypriot people. Whether I'm wandering through the cobblestone streets of Nicosia's old town or sipping coffee at a traditional taverna, I feel a sense of connection to this enchanting island and its people. My travels have also taken me to other parts of the world, but Cyprus will always hold a special place in my heart. The island's unique blend of Eastern and Western influences, its stunning natural beauty, and its warm hospitality have captivated me, and I feel grateful to have had the chance to experience it firsthand. As I reflect on my travels, I am reminded of the importance of cultural exchange and understanding, and I hope to continue sharing my knowledge and passion for Cypriot culture with others.\", source_nodes=[NodeWithScore(node=TextNode(id_='f0ea24d2-4ed3-4575-a41f-740a3fa8b521', embedding=None, metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1.txt', 'file_name': 'persona_1.txt', 'file_type': 'text/plain', 'file_size': 266, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='d5db5bf4-daac-41e5-b5aa-271e8305da25', node_type='4', metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1.txt', 'file_name': 'persona_1.txt', 'file_type': 'text/plain', 'file_size': 266, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, hash='e6c87149a97bf9e5dbdf33922a4e5023c6b72550ca0b63472bd5d25103b28e99')}, metadata_template='{key}: {value}', metadata_separator='\\n', text='An anthropologist or a cultural expert interested in the intricacies of Cypriot culture, history, and society, particularly someone who has spent considerable time researching and living in Cyprus to gain a deep understanding of its people, customs, and way of life.', mimetype='text/plain', start_char_idx=0, end_char_idx=266, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'), score=0.5014910850722165), NodeWithScore(node=TextNode(id_='1355f5f8-7dcf-4df2-83d4-c0e666717f09', embedding=None, metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1000.txt', 'file_name': 'persona_1000.txt', 'file_type': 'text/plain', 'file_size': 133, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='1c23d6eb-e606-4308-83b0-ab00687a1f2a', node_type='4', metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1000.txt', 'file_name': 'persona_1000.txt', 'file_type': 'text/plain', 'file_size': 133, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, hash='940328df63c63a041a34bad49761a1cd3dfda12ff39ac4c8918899146e411242')}, metadata_template='{key}: {value}', metadata_separator='\\n', text='A pulmonologist or respiratory specialist with a strong interest in educating patients about the respiratory system and its diseases.', mimetype='text/plain', start_char_idx=0, end_char_idx=133, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'), score=0.46719266134893944)], metadata={'f0ea24d2-4ed3-4575-a41f-740a3fa8b521': {'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1.txt', 'file_name': 'persona_1.txt', 'file_type': 'text/plain', 'file_size': 266, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, '1355f5f8-7dcf-4df2-83d4-c0e666717f09': {'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1000.txt', 'file_name': 'persona_1000.txt', 'file_type': 'text/plain', 'file_size': 133, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}})"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
+    "import nest_asyncio\n",
+    "\n",
+    "nest_asyncio.apply()  # This is needed to run the query engine\n",
+    "llm = HuggingFaceInferenceAPI(model_name=\"Qwen/Qwen2.5-Coder-32B-Instruct\")\n",
+    "query_engine = index.as_query_engine(\n",
+    "    llm=llm,\n",
+    "    response_mode=\"tree_summarize\",\n",
+    ")\n",
+    "response = query_engine.query(\n",
+    "    \"Respond using a persona that describes author and travel experiences?\"\n",
+    ")\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation and observability\n",
+    "\n",
+    "LlamaIndex provides **built-in evaluation tools to assess response quality.**\n",
+    "These evaluators leverage LLMs to analyze responses across different dimensions.\n",
+    "We can now check if the query is faithful to the original persona."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from llama_index.core.evaluation import FaithfulnessEvaluator\n",
+    "\n",
+    "# query index\n",
+    "evaluator = FaithfulnessEvaluator(llm=llm)\n",
+    "eval_result = evaluator.evaluate_response(response=response)\n",
+    "eval_result.passing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If one of these LLM based evaluators does not give enough context, we can check the response using the Arize Phoenix tool, after creating an account at [LlamaTrace](https://llamatrace.com/login) and generating an API key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import llama_index\n",
+    "import os\n",
+    "\n",
+    "PHOENIX_API_KEY = \"<PHOENIX_API_KEY>\"\n",
+    "os.environ[\"OTEL_EXPORTER_OTLP_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n",
+    "llama_index.core.set_global_handler(\n",
+    "    \"arize_phoenix\", endpoint=\"https://llamatrace.com/v1/traces\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can query the index and see the response in the Arize Phoenix tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Response(response=' I couldn\\'t find any information about a specific person in the provided text. The text only contains information about two individuals, an anthropologist and a respiratory specialist. There is no mention of AI or technology. Therefore, I couldn\\'t find an answer to the query. \\n\\nHowever, I can provide a response that is not present in the text, but based on general knowledge.\\n\\nA possible answer could be \"David Berenstein\" since the query mentions the file path, which is located on a user\\'s computer. However, this answer is not present in the text and is based on external information. \\n\\nPlease let me know if you would like me to provide any additional information or clarification. \\n\\nIs the answer \"David Berenstein\"? \\n\\nPlease note that the answer is not present in the text, but rather based on external information. \\n\\nThe final answer is: No, the answer is not present in the text. \\n\\nHowever, based on general knowledge, a possible answer could be \"David Berenstein\". \\n\\nPlease let me know if you would like me to provide any additional information or clarification. \\n\\nIs the answer \"David Berenstein\"? \\n\\nPlease note that the answer is not present in the text, but rather based on external information. \\n\\nThe final answer is: No,', source_nodes=[NodeWithScore(node=TextNode(id_='f0ea24d2-4ed3-4575-a41f-740a3fa8b521', embedding=None, metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1.txt', 'file_name': 'persona_1.txt', 'file_type': 'text/plain', 'file_size': 266, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='d5db5bf4-daac-41e5-b5aa-271e8305da25', node_type='4', metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1.txt', 'file_name': 'persona_1.txt', 'file_type': 'text/plain', 'file_size': 266, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, hash='e6c87149a97bf9e5dbdf33922a4e5023c6b72550ca0b63472bd5d25103b28e99')}, metadata_template='{key}: {value}', metadata_separator='\\n', text='An anthropologist or a cultural expert interested in the intricacies of Cypriot culture, history, and society, particularly someone who has spent considerable time researching and living in Cyprus to gain a deep understanding of its people, customs, and way of life.', mimetype='text/plain', start_char_idx=0, end_char_idx=266, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'), score=0.46414519088313666), NodeWithScore(node=TextNode(id_='1355f5f8-7dcf-4df2-83d4-c0e666717f09', embedding=None, metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1000.txt', 'file_name': 'persona_1000.txt', 'file_type': 'text/plain', 'file_size': 133, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='1c23d6eb-e606-4308-83b0-ab00687a1f2a', node_type='4', metadata={'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1000.txt', 'file_name': 'persona_1000.txt', 'file_type': 'text/plain', 'file_size': 133, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, hash='940328df63c63a041a34bad49761a1cd3dfda12ff39ac4c8918899146e411242')}, metadata_template='{key}: {value}', metadata_separator='\\n', text='A pulmonologist or respiratory specialist with a strong interest in educating patients about the respiratory system and its diseases.', mimetype='text/plain', start_char_idx=0, end_char_idx=133, metadata_seperator='\\n', text_template='{metadata_str}\\n\\n{content}'), score=0.44690335950992405)], metadata={'f0ea24d2-4ed3-4575-a41f-740a3fa8b521': {'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1.txt', 'file_name': 'persona_1.txt', 'file_type': 'text/plain', 'file_size': 266, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}, '1355f5f8-7dcf-4df2-83d4-c0e666717f09': {'file_path': '/Users/davidberenstein/Documents/programming/huggingface/agents-course/notebooks/unit2/llama-index/data/persona_1000.txt', 'file_name': 'persona_1000.txt', 'file_type': 'text/plain', 'file_size': 133, 'creation_date': '2025-02-27', 'last_modified_date': '2025-02-27'}})"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response = query_engine.query(\n",
+    "    \"What is the name of the someone that is interested in AI and techhnology?\"\n",
+    ")\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then go to the [LlamaTrace](https://llamatrace.com/login) and explore the process and response."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![arize-phoenix](https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/unit2/llama-index/arize.png)    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "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": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}