python/notebooks/06-memory-and-embeddings.ipynb

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  {
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   "cell_type": "markdown",
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   "source": [
    "# Building Semantic Memory with Embeddings\n",
    "\n",
    "So far, we've mostly been treating the kernel as a stateless orchestration engine.\n",
    "We send text into a model API and receive text out. \n",
    "\n",
    "In a [previous notebook](04-context-variables-chat.ipynb), we used `context variables` to pass in additional\n",
    "text into prompts to enrich them with more context. This allowed us to create a basic chat experience. \n",
    "\n",
    "However, if you solely relied on context variables, you would quickly realize that eventually your prompt\n",
    "would grow so large that you would run into the model's token limit. What we need is a way to persist state\n",
    "and build both short-term and long-term memory to empower even more intelligent applications. \n",
    "\n",
    "To do this, we dive into the key concept of `Semantic Memory` in the Semantic Kernel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a77bdf89",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m pip install semantic-kernel==0.4.5.dev0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508ad44f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple\n",
    "\n",
    "import semantic_kernel as sk\n",
    "from semantic_kernel.connectors.ai.open_ai import (\n",
    "    OpenAIChatCompletion,\n",
    "    OpenAITextEmbedding,\n",
    "    AzureChatCompletion,\n",
    "    AzureTextEmbedding,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8ddffc1",
   "metadata": {},
   "source": [
    "In order to use memory, we need to instantiate the Kernel with a Memory Storage\n",
    "and an Embedding service. In this example, we make use of the `VolatileMemoryStore` which can be thought of as a temporary in-memory storage. This memory is not written to disk and is only available during the app session.\n",
    "\n",
    "When developing your app you will have the option to plug in persistent storage like Azure AI Search, Azure Cosmos Db, PostgreSQL, SQLite, etc. Semantic Memory allows also to index external data sources, without duplicating all the information as you will see further down in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f8dcbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = sk.Kernel()\n",
    "\n",
    "useAzureOpenAI = False\n",
    "\n",
    "# Configure AI service used by the kernel\n",
    "if useAzureOpenAI:\n",
    "    deployment, api_key, endpoint = sk.azure_openai_settings_from_dot_env()\n",
    "    # next line assumes chat deployment name is \"turbo\", adjust the deployment name to the value of your chat model if needed\n",
    "    azure_chat_service = AzureChatCompletion(deployment_name=\"turbo\", endpoint=endpoint, api_key=api_key)\n",
    "    # next line assumes embeddings deployment name is \"text-embedding\", adjust the deployment name to the value of your chat model if needed\n",
    "    azure_text_embedding = AzureTextEmbedding(deployment_name=\"text-embedding\", endpoint=endpoint, api_key=api_key)\n",
    "    kernel.add_chat_service(\"chat_completion\", azure_chat_service)\n",
    "    kernel.add_text_embedding_generation_service(\"ada\", azure_text_embedding)\n",
    "else:\n",
    "    api_key, org_id = sk.openai_settings_from_dot_env()\n",
    "    oai_chat_service = OpenAIChatCompletion(ai_model_id=\"gpt-3.5-turbo\", api_key=api_key, org_id=org_id)\n",
    "    oai_text_embedding = OpenAITextEmbedding(ai_model_id=\"text-embedding-ada-002\", api_key=api_key, org_id=org_id)\n",
    "    kernel.add_chat_service(\"chat-gpt\", oai_chat_service)\n",
    "    kernel.add_text_embedding_generation_service(\"ada\", oai_text_embedding)\n",
    "\n",
    "kernel.register_memory_store(memory_store=sk.memory.VolatileMemoryStore())\n",
    "kernel.import_skill(sk.core_skills.TextMemorySkill())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e7fefb6a",
   "metadata": {},
   "source": [
    "At its core, Semantic Memory is a set of data structures that allow you to store the meaning of text that come from different data sources, and optionally to store the source text too. These texts can be from the web, e-mail providers, chats, a database, or from your local directory, and are hooked up to the Semantic Kernel through data source connectors.\n",
    "\n",
    "The texts are embedded or compressed into a vector of floats representing mathematically the texts' contents and meaning. You can read more about embeddings [here](https://aka.ms/sk/embeddings)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2a7e7ca4",
   "metadata": {},
   "source": [
    "### Manually adding memories\n",
    "Let's create some initial memories \"About Me\". We can add memories to our `VolatileMemoryStore` by using `SaveInformationAsync`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d096504c",
   "metadata": {},
   "outputs": [],
   "source": [
    "async def populate_memory(kernel: sk.Kernel) -> None:\n",
    "    # Add some documents to the semantic memory\n",
    "    await kernel.memory.save_information_async(collection=\"aboutMe\", id=\"info1\", text=\"My name is Andrea\")\n",
    "    await kernel.memory.save_information_async(\n",
    "        collection=\"aboutMe\", id=\"info2\", text=\"I currently work as a tour guide\"\n",
    "    )\n",
    "    await kernel.memory.save_information_async(\n",
    "        collection=\"aboutMe\", id=\"info3\", text=\"I've been living in Seattle since 2005\"\n",
    "    )\n",
    "    await kernel.memory.save_information_async(\n",
    "        collection=\"aboutMe\",\n",
    "        id=\"info4\",\n",
    "        text=\"I visited France and Italy five times since 2015\",\n",
    "    )\n",
    "    await kernel.memory.save_information_async(collection=\"aboutMe\", id=\"info5\", text=\"My family is from New York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await populate_memory(kernel)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2calf857",
   "metadata": {},
   "source": [
    "Let's try searching the memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "628c843e",
   "metadata": {},
   "outputs": [],
   "source": [
    "async def search_memory_examples(kernel: sk.Kernel) -> None:\n",
    "    questions = [\n",
    "        \"what's my name\",\n",
    "        \"where do I live?\",\n",
    "        \"where's my family from?\",\n",
    "        \"where have I traveled?\",\n",
    "        \"what do I do for work\",\n",
    "    ]\n",
    "\n",
    "    for question in questions:\n",
    "        print(f\"Question: {question}\")\n",
    "        result = await kernel.memory.search_async(\"aboutMe\", question)\n",
    "        print(f\"Answer: {result[0].text}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await search_memory_examples(kernel)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e70c2b22",
   "metadata": {},
   "source": [
    "Let's now revisit the our chat sample from the [previous notebook](04-context-variables-chat.ipynb).\n",
    "If you remember, we used context variables to fill the prompt with a `history` that continuously got populated as we chatted with the bot. Let's add also memory to it!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1ed54a32",
   "metadata": {},
   "source": [
    "This is done by using the `TextMemorySkill` which exposes the `recall` native function.\n",
    "\n",
    "`recall` takes an input ask and performs a similarity search on the contents that have\n",
    "been embedded in the Memory Store and returns the most relevant memory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb8549b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "async def setup_chat_with_memory(\n",
    "    kernel: sk.Kernel,\n",
    ") -> Tuple[sk.SKFunctionBase, sk.SKContext]:\n",
    "    sk_prompt = \"\"\"\n",
    "    ChatBot can have a conversation with you about any topic.\n",
    "    It can give explicit instructions or say 'I don't know' if\n",
    "    it does not have an answer.\n",
    "\n",
    "    Information about me, from previous conversations:\n",
    "    - {{$fact1}} {{recall $fact1}}\n",
    "    - {{$fact2}} {{recall $fact2}}\n",
    "    - {{$fact3}} {{recall $fact3}}\n",
    "    - {{$fact4}} {{recall $fact4}}\n",
    "    - {{$fact5}} {{recall $fact5}}\n",
    "\n",
    "    Chat:\n",
    "    {{$chat_history}}\n",
    "    User: {{$user_input}}\n",
    "    ChatBot: \"\"\".strip()\n",
    "\n",
    "    chat_func = kernel.create_semantic_function(sk_prompt, max_tokens=200, temperature=0.8)\n",
    "\n",
    "    context = kernel.create_new_context()\n",
    "    context[\"fact1\"] = \"what is my name?\"\n",
    "    context[\"fact2\"] = \"where do I live?\"\n",
    "    context[\"fact3\"] = \"where's my family from?\"\n",
    "    context[\"fact4\"] = \"where have I traveled?\"\n",
    "    context[\"fact5\"] = \"what do I do for work?\"\n",
    "\n",
    "    context[sk.core_skills.TextMemorySkill.COLLECTION_PARAM] = \"aboutMe\"\n",
    "    context[sk.core_skills.TextMemorySkill.RELEVANCE_PARAM] = \"0.8\"\n",
    "\n",
    "    context[\"chat_history\"] = \"\"\n",
    "\n",
    "    return chat_func, context"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1ac62457",
   "metadata": {},
   "source": [
    "The `RelevanceParam` is used in memory search and is a measure of the relevance score from 0.0 to 1.0, where 1.0 means a perfect match. We encourage users to experiment with different values."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "645b55a1",
   "metadata": {},
   "source": [
    "Now that we've included our memories, let's chat!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75267a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "async def chat(kernel: sk.Kernel, chat_func: sk.SKFunctionBase, context: sk.SKContext) -> bool:\n",
    "    try:\n",
    "        user_input = input(\"User:> \")\n",
    "        context[\"user_input\"] = user_input\n",
    "        print(f\"User:> {user_input}\")\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"\\n\\nExiting chat...\")\n",
    "        return False\n",
    "    except EOFError:\n",
    "        print(\"\\n\\nExiting chat...\")\n",
    "        return False\n",
    "\n",
    "    if user_input == \"exit\":\n",
    "        print(\"\\n\\nExiting chat...\")\n",
    "        return False\n",
    "\n",
    "    answer = await kernel.run_async(chat_func, input_vars=context.variables)\n",
    "    context[\"chat_history\"] += f\"\\nUser:> {user_input}\\nChatBot:> {answer}\\n\"\n",
    "\n",
    "    print(f\"ChatBot:> {answer}\")\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3875a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Populating memory...\")\n",
    "await populate_memory(kernel)\n",
    "\n",
    "print(\"Asking questions... (manually)\")\n",
    "await search_memory_examples(kernel)\n",
    "\n",
    "print(\"Setting up a chat (with memory!)\")\n",
    "chat_func, context = await setup_chat_with_memory(kernel)\n",
    "\n",
    "print(\"Begin chatting (type 'exit' to exit):\\n\")\n",
    "chatting = True\n",
    "while chatting:\n",
    "    chatting = await chat(kernel, chat_func, context)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0a51542b",
   "metadata": {},
   "source": [
    "### Adding documents to your memory\n",
    "\n",
    "Many times in your applications you'll want to bring in external documents into your memory. Let's see how we can do this using our VolatileMemoryStore.\n",
    "\n",
    "Let's first get some data using some of the links in the Semantic Kernel repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3d5a1b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "github_files = {}\n",
    "github_files[\n",
    "    \"https://github.com/microsoft/semantic-kernel/blob/main/README.md\"\n",
    "] = \"README: Installation, getting started, and how to contribute\"\n",
    "github_files[\n",
    "    \"https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks/02-running-prompts-from-file.ipynb\"\n",
    "] = \"Jupyter notebook describing how to pass prompts from a file to a semantic skill or function\"\n",
    "github_files[\n",
    "    \"https://github.com/microsoft/semantic-kernel/blob/main/dotnet/notebooks/00-getting-started.ipynb\"\n",
    "] = \"Jupyter notebook describing how to get started with the Semantic Kernel\"\n",
    "github_files[\n",
    "    \"https://github.com/microsoft/semantic-kernel/tree/main/samples/skills/ChatSkill/ChatGPT\"\n",
    "] = \"Sample demonstrating how to create a chat skill interfacing with ChatGPT\"\n",
    "github_files[\n",
    "    \"https://github.com/microsoft/semantic-kernel/blob/main/dotnet/src/SemanticKernel/Memory/Volatile/VolatileMemoryStore.cs\"\n",
    "] = \"C# class that defines a volatile embedding store\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "75f3ea5e",
   "metadata": {},
   "source": [
    "Now let's add these files to our VolatileMemoryStore using `SaveReferenceAsync`. We'll separate these memories from the chat memories by putting them in a different collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "170e7142",
   "metadata": {},
   "outputs": [],
   "source": [
    "memory_collection_name = \"SKGitHub\"\n",
    "print(\"Adding some GitHub file URLs and their descriptions to a volatile Semantic Memory.\")\n",
    "i = 0\n",
    "for entry, value in github_files.items():\n",
    "    await kernel.memory.save_reference_async(\n",
    "        collection=memory_collection_name,\n",
    "        description=value,\n",
    "        text=value,\n",
    "        external_id=entry,\n",
    "        external_source_name=\"GitHub\",\n",
    "    )\n",
    "    i += 1\n",
    "    print(\"  URL {} saved\".format(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "143911c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ask = \"I love Jupyter notebooks, how should I get started?\"\n",
    "print(\"===========================\\n\" + \"Query: \" + ask + \"\\n\")\n",
    "\n",
    "memories = await kernel.memory.search_async(memory_collection_name, ask, limit=5, min_relevance_score=0.77)\n",
    "\n",
    "i = 0\n",
    "for memory in memories:\n",
    "    i += 1\n",
    "    print(f\"Result {i}:\")\n",
    "    print(\"  URL:     : \" + memory.id)\n",
    "    print(\"  Title    : \" + memory.description)\n",
    "    print(\"  Relevance: \" + str(memory.relevance))\n",
    "    print()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "59294dac",
   "metadata": {},
   "source": [
    "Now you might be wondering what happens if you have so much data that it doesn't fit into your RAM? That's where you want to make use of an external Vector Database made specifically for storing and retrieving embeddings. Fortunately, semantic kernel makes this easy thanks to an extensive list of available connectors. In the following section, we will connect to an existing Azure AI Search service that we will use as an external Vector Database to store and retrieve embeddings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_kernel.connectors.memory.azure_cognitive_search import (\n",
    "    AzureCognitiveSearchMemoryStore,\n",
    ")\n",
    "\n",
    "azure_ai_search_api_key, azure_ai_search_url = sk.azure_aisearch_settings_from_dot_env()\n",
    "\n",
    "# text-embedding-ada-002 uses a 1536-dimensional embedding vector\n",
    "kernel.register_memory_store(\n",
    "    memory_store=AzureCognitiveSearchMemoryStore(\n",
    "        vector_size=1536,\n",
    "        search_endpoint=azure_ai_search_url,\n",
    "        admin_key=azure_ai_search_api_key,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The implementation of Semantic Kernel allows to easily swap memory store for another. Here, we will re-use the functions we initially created for `VolatileMemoryStore` with our new external Vector Store leveraging Azure AI Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await populate_memory(kernel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our function created an \"About Me\" index and that our five pieces of information have been indexed (note that it can take a few minutes for the UI to reflect the document count and storage size)."
   ]
  },
  {
   "attachments": {
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aXuPfhvtDW3PSv+uZ3rK79bY3T13tsn4tCk0rQH13lINGYw29tZWeLmd4IKahWsWcVea84NBcHe2WKuRdxc5ddY16xpmJnurpnQTWri+87g2erG8raqtv9avHdNcFdiLlOc3U3brdX9foqUmTr7HjVeCd2CfZdVt5QecCe8UdPCnerqGh6ns/QLS1E6X3qceV1+jD7GBNi79/2V5Yes7bt1ZF26+q8LCRleUmZLQ640S9/0W+naqOfGXsPlHNz7KTlfhwVmJh7cJYPWb7x1T8+pUp/L0o+Z6f7x0zF2Qr2wunrc7ztpjtjdtZ4S4Y/ljhNszjlbfcseNMnW4rpXLTPlBuiwvVLXQrS9vz1Gz/3lJBo6rL+bal6xPF6JLQYe8Hm/3aS60GUK/VyxqN7PVr9WHoStfT47a7pXVrw04CbRVddGFaWqtW1e9rUJbzi2/nglbRdqxsq8YUVdF3MrUohSeuVhp6WyvNnCVNPcKOp6GwMXiQm5qMjtER2pW5v/on2q/26+1/R+iyKDtdicI+Gl74+3DWc8WakMp//argw2fUL7TuxYma+eE2ZZmV4x7T0h5rg/MqumPFe29DWfXYtVt78yPV+Aw7+cU7tonDDKY2o5xQUNXljP3hTSfJ+mR/gfZ9ZecBqJfqYdDI1rp12/wmjMKTzo3P6EPvRDK3sFOoPYkUylJW2M9mlzxBZW+v8umqQiWvsLOzw5pOvPDj3xVhq+j9ofAOCXPFnq1evbL14BHeSmuYsJH6hqPmkvZNNPSbvXpmS3V+liWgdSl7tefiJlVuCirkBYCpvVZqbmGfiYpevyrwXpti63pDYc3Ur17wp/3A8T1e/zrPr3GIUhtbO/HDtglFnUX9oX9RzUWYqi7na3FZiWVv0zWhGg4A9VL9Cxq2GjzUVh8alg5vazuFtlKPHt7480VXp36zhh0PdjAMDyXm7pXwQHBk/GaU8L4ifvOOHff3Kbw/R3HZzz2kma1u16THbi/d36S6Gtq/Na6xxvY+Rf+7dLuWF9iiSuxN3657PjxFSdc1tiXV0+OxaWo16yHv9aj49auU349nlMZWcndSRc0oxzzTeTNtv86/IvjDX81aR3llGyrtrFnV5XymWeWLd7Ty+3+cABxD6l3QMJ1AL+jRs9SVbPiJ3lyhTm1VVFU+VrcX9dEwfTJevlvZhdXwa9WzOn00ymM6JZrbMW+02x0v3V7YR6P0Pvn7ZU58ppPjrFa2ycT0FzHbcPvbIEes/bla1PmQRs3I1LwvK/oBD/MT5Jnq8dohDR167vf4VdCe3mvoBQzv9cgu7/WrkpLvuTfYZpJid7KMkabWxu9wmDtCzC9/lvxrZx+Z4N0ghU0eH5yua8JrIsxjdD1Nn6wIW6asH+mqaDlvXvsW4XedxOia+BjtTwtb9nv9HgiAY0EDfoIcNcsLEe/u0O3J30htTtfY2P9Rt6iTdbIJEwcOaFPm15q3cq/+97+naNqQc5XYjP/npEzmltE3Duvy/t11ti0q05dvacmbDfkJcgB1FkEDbhzar+V/y9OiLd/o7T0B7TJlJzRQjxan6NpuTflv4iu1W5uSV+tfed/Z6XKccJL+56KrlXCRuX0UAOqeOhU0AABA/VIP7zoBAAB1BUEDAAA4Q9AAAADOEDQAAIAzBA0AAOAMQQMAADhD0AAAAM4QNAAAgDMEDQAA4EyN/zJoenq6HQMAAMc7foIcAAA4Q9MJAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcKbGfxn0zTdTteSll1RQkG9LAKBuiYxspP433aTLL4+1JQBcqfGgMeKeu3X/rx9Qq+hoWwIAdUt2Vpae+OMf9OTTz9gSAK7UeNOJqckgZACoy8x3FLWuwNFBHw0AAOAMQQMAADhD0AAAAM7UmaBx4MvduuePW3Vx0jZv8P7+MVvPfHjQzgUAAMeiOhE09qZnq9sL3ypx0AV6f1xbb2in94c21oE123VPutuwkfrkLzXkybf98R1LxhSO16y39cSgMXrpUzsJAMBxovaDxn+/UlKKlHTnuepxRkNb6Gn8P3pg+Nk6PSVHiw/YsiO2Qy/9xgsUg8KG3yz1SqXYEX/R/BHdgosVE1zniTQ7CQAAqq32g8a/8rWp/Rm69mQ7He6Exrqn83+14r3vbMH30UK9Jnqh4k92eLyfzrVzAACAG7UeNDJyvlNMi0Z2qrSzG5+oXftqImiUzTSdjF5i6jbCmaaOcVq1U/q/WUW1H8HyUK1IWFPIp0s1etAMvWSaXrx55daCfGGWK16jElo31V8gqLwmnFC539xTch88ReVh2/cE11taVKvjzwur5Qlb1vCXD23HSVMSAOB4UetBIybqJL2/Y5+dKi3760Ne2DjJTh0t3XT/n5LUq7n00+Gh2g9zYp4lmWlTIzLxUqWPDw8I7yldd/nz7u9qi4r5Qqteke62NSpDWryqh81J/JxL1am5t25hONmhtHSpV5+ymnM86bOU3jm4jYcTpFUzi0JQuoYH983su17VM+EBKv1d6a6ieQ8PmllsunDZtBl6OP1SPRzaT80qI4gBAFA1td908pPG6vZRnpYX2Olw//1az6SfqL6X1ETQ8E704+1V+pFcqae9olXqrcRQiPADwhfaWVijcIkS+lfUGNNCve4qaq6J7dNbzbyTf6pX0rVTC+38zJ7MP33XCwyXqus5wclSOg0vDDLndrlUzXZ+boOGF44K+5oEt1lMp966yd+mnVdiOtfbjgk5L73ynn7aJ2w/O19i5wEAUH21HzROOFU9ov6joc9ka3HOIVtob3edtlsH4loosaz+G9VWoo9GmR1AK7HT1ASEwoppWvlCX3xh51XXOS3U3I6awGBqHExg2LHxXS8EXFp4oq9Q2DaM8CaPh5OPdMdsc1Hoec56z8toX9gwAwBA9dRy0Diod5Z/qmdOOUsZQxtp55rs4G9oeEP80m/VbVAbPd0pwi5bB3QKNU0UDWU3k1TBp19oZ/OWwUBxTj8ltHhXaZ8Gm006dalSzCjGhIyHd/Yu3K+HE0rUaFRZiUBmBjrOAgCOUC0GjWDISFJTLUpsorObnaEHBrcJ/oaGN6y9+1wNPCvsdtfa1vVS/TR91ve43dX00Qg11+zQSzNfLVZzEdu5hdJfeUXpLUJNGtWzY+cXauYFFzvlBZYjqdEwzSjh/T6KK+o4G3brbxmdWQEACKmloFE8ZJxuS+uWc3VTn0vC7jrppvsn9tbO8GaFEndrVKyFejV/1647TqtaDNeU8D4dXpBpnv6emnc+giYdT+yI4WqePM5uf6a+aHFkNRrn9p8c7Kgaeo7ewG+JAACOVIOAx47XCHNiMtXt5TsWQkZtMLfOvqtOfxqpWFsCwJ3Kv6sA1ISjXqOx960cQkYZdix5VTsT+hAyAAD1ylEPGqd3b60VhIwifh+HX/q/XXF3hbfHAgBw7Knlu05g7jiZwp0dAIB6iqABAACcqfGgERnZSNlZWXYKAOoe8x1lvqsAuFfjd528+Waqlrz0kgoK8m0JANQtJmT0v+kmXX453a8B12o8aAAAAITQRwMAADhD0AAAAM4QNAAAgDMEDQAA4AxBAwAAOEPQAAAAzhA0AACAMwQNAADgDEEDAAA4Q9AAAADOEDQAAIAzBA0AAOAMQQMAADhD0AAAAM4QNAAAgDMEDQAA4AxBAwAAOEPQAAAAzhA0AACAMwQNAADgDEEDAAA4Q9AAAADOEDQAAIAzBA0AAOAMQQMAADhD0AAA4KjKUcq0cZqzPt9O1291IGhkas4NbdUuJl5T022RlTUr0SsfpRQ7DdSe0HFaNHS58maNnpemvMN2kWNenlIm3KzYCRV/4g5mJStpaLy6XGhfi87xGv1anp3rzuZ5g5UweLay7DTqt4KN8zX65u7qaD9vHS+fqFR/TtWO07qg/OeQo42vLtWyjVv9qfquDtVoZGr+hMnaXG++tFEvXTVeqevf0rI503TnVd7J74nBir15tjLqxXG7T1n/2qy8b+xkGQpWjdN114/Ssq9jdNvYaZoyeZrGDYmRcvfZJdzJzUhTlvuHQV3w+SKNGDpZaaf205Qlbyl1yRyNuDLSzqz8OK0TKnwOXTXuzW1a/ZuudrqeC9S6TwKz+1wQiLnjnsCIyy4IJM7YYssDgcyZNwRi2j4QWO1P7Qts+OOgQPyl3rJtveHSGwIT1+3z5wQ+mhVI9MpGzFgYGHb1T735Pw3EjV8dyM0ITV8QiLtjYWDroeDigUO5gdVJNwQ6t/e20/7SQOL41wOfheYBZbLH6QPBozEkf+UDgcvNsbfMHovm2Jo6KBB3cfA47XztHYHZaXae8fU7gdnDrw0ee+Y4nfqOV1h628WO/Ro8vlc/4JX1eTTwfGj+xaHPkd0Hb1uh4cGVwXUKHXojMM77jMYMXxjILe/zUtHzt8+jaLvFn3fwOd8TmP6XO4Lrt+8ZGPaX4PeBv99h+5Y48xO/HPWU97mKaXttYHqGnS5UznFa4edudeBBc8w88GhgWE9vfp9ZgcyKziee/HWPBhL9ed5n7YEnAuOKfT4PBLbOtceomT98ViDtazsrXLnPwbD75B/HwfHw52SGcSlmuSo+Vh1Xd2o0Iq9X0tg45cwapxnbbFkxuco/NU5JL/5DG99eoGEtt2rx2Bm2Gioo5c0cDZy1XLNvba2cJfcq9p40xf5hrZaNvUL7Uidq9qvB9rDUSX00clVLjVz6T216baSarRmlO5/c7M8DqiOy1626rqV37KVv9Kf9Y2tBvuKeWKHU9cs1MnqLZgwdocWfezMPb9WcOwZrxr+ideefvPkrnlRcowJ/vaqoseN729+0OTpJK16bo4HneJ+jSbO0Wa01+LkFGtLGm29rbcZfGVy80PoULdsTpSFDb1HThrashAqff5WkKHXXQM1esVzjYvO97c3QMu8lih37lsZd5c1uM0TzvX2bM6h1cHHUTz/7hfo2zdSce4ZpTmqODtpilXOcVuW4y9i4TwkvbtPWv96p6IrOJ6Ym4t5Fyu0yXsvWr9WMTpnaEHZOylk4TH2fyPUea402vTlHsTuna8iEpSr1SS73OZQUq/He8zDPJfVvSUo4xXyvTNNo73gv77GONXWqM2hk/CSNvtL7Mv7t9DKqolsr4Y5b1CG6kSKbdNV1V7eT9uwr9ubFDR6j2OjWir3xasV40zF979PAC5sqZsB1MhVUOV/mev+mKOXlPMUM9Oa1jVBE9C3q203KejvNbAKopkg1MrWh/oEYPLaiBk/Qg7Gt1fTsdho4dri6HE5Tyvo86e/L9PwHURo47SkN6eTNj75CD94RZ1asku97fBf2bWjTT3cOaOc//kDzOfo8Rzne5y2iaTM1MgEi4kxv35sqMiK4eEhWlmlPjlFMh+B0aZU8/yqJ05D7r1C0Wff6WG/a2zfvZBHRpKmamf1p2FjNvH1r6n0Zox6LjNPEvy7QyNaZmjHsKnX9xTDN/yD4bV/6OK3acRfV+1YlNLUTFZxPslYs08bDV2jkY7coxtt+zIDh6muCjS9TK171zhW9hnuPFeXtS9fgZ2j9ZpU6g1TwHIqLUKQ5pr1h64LpSo6I0/gJCd43S/mPdaypY3edNFLfSUmK/Xi2xj27tUQCPKis1ybrzpuv0tVXdlbCk6U70USUuMpq3cpe9TT0vnCDY9LHmdrqfalmPBlf2Klv5Aqv/FBwNlA9Ocr90vsSO7tZ4bEVE34mPvtMeXOUX7BPWds2eVc9MepwSXBWddXY8d3w5KLlfZ/r8yr0sIxu2dL7N0uZHwenS6nk+VeN91yKPc9Mfb7djuL44p1Yh83xruT/Os27yk/T1JuHlV0zVsXjLvLUUP8Io/zzSeYOb7xNjNoVLh6hkwuPSe/4/8D7s+Lews+Xv+7hA8HZJVX1OXgK1ozTuBe+U9zYSUpoYkqq+Vh1WB0LGp4m/TT1d1co45lxWpxpyzwFy0cpYcxGRd01R4tX/EPJI7xkdyTatPayrNRh1BptzdhWNPz1zuB8oBoKlizW4j2tFR/nfcl5x1Y77wspIyPsimPXbpl6hmbeVVj0OdHeWIbCZxdzuCha5+Tm2LFqquD4No/+vXTqqi7eiX/Z3JTS1cRGJc8/5KB3UggKhjSgIhFtEzTx+dGKPZymZSvCTgohVTzuwlV0Pok6y/sEeeEls/A4LfACix31PkWt23qhpf+c4p+vjGmqqG6y0uewJ0UTH1kqxU9SUnwjW1j+Yx1r6l7Q8ET2n6qJsVu9N6QoZebmBWNgZERj7yDaoGXriuZVTxfFeW/k5gXjNCc1U3m78pSxaqLmr7ezgYoc3B08ZlKTNX9ComInbFDMiKkaeZGZGae4G5sqZ8GjmuofW8H+Dxub9lPfXt6Xh99mm+OtN07JH+Qp74MUJT1rbtFrrWiTDlKe14yNecpKnVzY36L6vs/x3ViR3sdLGWlK3ZVfokbRc/YtGu19Ied5V1jxN3vbfC1Zyd6weNYojZ5nvjwref7ek2ztnRBSFkzXxl2ZSp02S8uq8TQjGnnb+HiT0rLywsIK6qU1kzVw0nylpHvHUdZmLZ62WKlqqo4XmQ9KyeO0kuOuDBWdT2K6dPUeKUVPPpKiLP84fVTzC2shWivu6nZeUJmi8a9t9T9fWenzNWNJGeGhwucQLl8pkx5R8jdXaMjtHXXQ26bZbsHBajxWHVcng4bfhDJ2vLoUVld531H9R2rghTmaM7i74h7w3q4OR1ij4W07btICPfiTXD1/V7xir+yuwU/lKCLKzgYqsmaif8z0vcv78vkoWiPmvaVlw4uOxdixL2pK4kl6/X5zbCVqxq6fa8qiJMWZaljTZrtomvqeslqj+3dX7KDJymwYvOKKuz9JCS0z/eN7wPMR6jugNo7vprru5n6K2rVId17ZWUlrbHGYmOHLtXraLWpd8LqmjvEChjfMeDlHzdoEn0eFz79hnEY8mqCorNkactUAzT+pnwYWtn1XLvbG29TllA3eFV93DfSDDeqtZmcqYuM8jb7VO47ib9aMtyPVd9qLGtfFzCx9nFZ43JWhwvNJl/GaM/YKv8kiwRynDftqYFs7zxN9x7OaMSBSGx7xLjTMd8F9f9N3Z5VRc1LhcwizcYYmrsiTvtmgqTd43wveNs0w0bs4qPJj1XENzK0ndhwAAIQ7vEHjLx+m1b3maOMEL4Cg2upojQYAALXg4/kaPWGRNmflBZs8JkzRsj2tNbA3IeNIUaMBAEDIrhSNv+8RJf8zz++nFBndVf3un6YHryq8NxbVRNAAAADO0HQCAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcqfH/vXV/Qb4dAwAAxzv+m3gAAOAMTScAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAmTodNMau3q2+Cz+zUwAA4Fhz3NZoLNq8T11nZmnvgcO2BKjYzJnPqF1M28JhyOBBKigosHMB1LSdO3fqml6/0OjRD9qSoMo+i+WtV9L76enqdllXf3slhT9GeZ/1VStXFnuM8Mc1y5v1wvezrMc5HtB0AlTDPffeq60Z2/xh/oI/KTIy0s4BUJPMSfm2IYPV9bLLbElx5X0WK1svxISBp556Uh0vvtiWFDHb2L59e7U+6yZYPPTb3+i666/XlClT/bJTTj1VCxcu8rexdt16rV+3zg83x5s6ETRME0mD327xhx89/pG27/mPnRNkmk9C801NRLjweeHrbsj+t+LmbS+ssTB/zbQpN49360s52vjZt2ryyDZ/m6a8/ROfaPCSz/1tNX44o3DZ8OkQM27KSj4u6i/zxRPdKtpOAXDprrvu1spVf1OzZs1sSZGKPosVrRfOhAETIE71wkA4Uythtj9+/ARbUjUTJz6qSzt39h+/LKeddpo/7N6925YcP2o9aJgAkO8Ne37XVoHft1fnqB/qgRW77Fxp+ZZ8JV7YyJ/3xh3naczKLwtP+CYEfJ7/n8J1H+91lm5+4bNKm0MmXX2mFt4UpS4/+qG/7i0dGvvlOfv+o5aNf+Bv655uZ+jnz273y0PT5rHNtk2ouOuVnXpt0DnVelwc+3796/v9KlBTPWq+kADUDlefxX9u3qx///vfGnHvPYVNHqaJpCKh5pPyQobx8Ucf+TUcP+ve3ZYcP2o9aJx+ckM9dX1z/69hQkW4xPaNCoPAFa1O9cPB6o//7Z/sX/FCyORrzipcN77taYqMOEH/3HnAn66uKC9k/OrSJv741W1OVbszI4pNFxz8r/Z++1899+4etW0W4e+P8bNzT/H/mnmov8wVUKgq1VSPmmpS+mgAR5/Lz2JWdpY2vf++7r13hL990/Qxb97ccsNM8uuv61//93+6//5f25Ii33iB5dZbb/HDivl7XcJ1x2Vza51oOjFNF6HmD9OkUZG23sk/xISKc08/yU4FQ0ujiIbK2XfIlrhjalpC+9xqykfK2H1QO/Z+Z+eivvvlL/+ff3VirlIA1B4Xn8Xul1+uizt18sfb/PjHfpOHqekoS8J115UbdsL7aJhh3fp1x2WH0FoPGiZkPPX2V4XNH6ZJoyLbvBO6qWkwTA1D+Mndb4Y5eFhRjU+0Je48dGUzf39Dw76HYwprOFD/7d+/X7u//NJOAagtNf1ZPJJ+WKbJpNmZZ/rNLRXVrPS4soff/+N4U+tBY6sXHFo2+kFh88fyD/L9vyFrPtlf2CfDhJJ/5HzrN1Wc1+QHfvNFqN+EsWLbfj98XNT8ZL+m44v8Q4XNKFNTv9I/PvvWH/++TDPKn9L3FuscivrNVJv+Ydo0OyX99a/L/ascc7UD4Og50s+iudujf7++lfbnuKhDB78pJNQv4+9vvaVdu3b55RUxzTkmbJhOoeUxNRrnnXeenTp+1HrQeDD2DL9DZ6gZoqTOP/qhHybMvLte+UILb2rphwxj2a0/8kOKuXPEzDc1I6tuO9cPLWaZPu0b+R06zTzT4dRsK8T05zBCd51Uh6m5MH1DQts2Az8sVv+tWZNS2Dns3X/8Q08+9TS3twK1wOVnsXnz5po06TE9+ugj/vZnzJiu5+cv8MsrY+5Uyd292//9DFPTEt5HwwxGRR1G66sGAY8dBwAAqFF1ojMoAAConwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcKbG/6+Tr/N22zEAAHC84z9VAwAAztB0AgAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwhqABAACcIWgAAABnCBoAAMAZggYAAHCGoAEAAJwhaAAAAGcIGgAAwBmCBgAAcIagAQAAnCFoAAAAZwgaAADAGYIGAABwRPr/EuE4d8kNtv0AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can see that embeddings have been conveniently created to allow for semantic search."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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sRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GhXH1jPH0V50SSMG5mNYUPFNHIM5m49rxZ2lv1YIve9aL9qdyUnHUtHaEB5vvqsQ9M0lH8MnFo/DcPyX8Qp9cx4tOfGeY9aW95l9i2yvfZF4hMP0447znJNxDayMX19g1qo2J/TFe9FJx1DW743jhJ6fxbhNctr0H9GluzTGklK/R4LTVG+xw7kzO9S+Hfm9PWvau+rk74bSfvzd4X2L4ry+1bTHX5u2+DjFzFd+9mW/37rP+/Ga3fsv7/Bq/H/KoNPjckKDnXnB7+/4WfBn1X8LPh62cLg4pfq1RM6y9vBxdniOBa+rdpdyUnH0hHqgy9OzQoufks1r1BDWX7c96i15V3h7YXic81eKD5hk7fE9129F7GWDxXfB8v7JedNXR9sUM3gyfXBaabnaK/dvFx4u8za7mhOOAZnkz/n+cEXT6qmRfv8jHQZ7ftoe21vrY/xWruW/JmbVtbZ/960jdN+lyXDe9ahtN/Jtt/Tkva9jzK/DZLnvY3/O8qJ//5KV1dhPVWDD88D4/5xA576h8mYPGUyZj+8As99K1M9gah7kH9xzm2Yhz01KzBRzdPcJb7vd4n/71uEuXsnY22U5SdWT8ae8jgVjJsewkOTxN/3jfpf/Q0NJzD0zokYpLV0Ex9+yNLuaE44Bkf7uAEN4t0YdJNqdycN9ajJvhMTza/trodQ2B1fayfQfpYy+ZPjGHd5IH4jY7+tknrqnbdRM8lj/f3dbZ1CQ+0wZCZZVLu6wPrlL6O3+N/BN15GzZ/1WRF+uwc/mDUGX5Ol55FjMPuZn+P32oJL+Oi1RZg99qt6l9PwHEwPLVMl6aHT8PSPn8J0OdxAlafP/+JF8Y97TmidhyzDD36H91fPwa3D9WUP/OsJNT+Ki/pQBuO5k4teR81fxPy//BxPjxXzJq3BR/ozcfCHY8RzCrH9Yvi41v5iP56eqR/H1zyFKH//knp2pNN7n8IDpmOeXPQi3hfb0qmut/lrsKnwDm3bsns95vEJsd6bLie7SC1dS7bhA/GO09L9HNn9KLtxQss7vfuqAfvfPoHJhbED2/539mDoYw9H/2Unf0HWvo398nNNQGbmMNS8vb9Lu+gSOQbLZ2L+zOINJVDfkXK1rtYFZfneyJ8H8TOwz+iukpP9+2DurjaeG6vLWm1PdXmF1gl9FqobcL3aX+g4bN9d8zryeKesQY34R2+uXCZfn+U1RNJ/Zo1tWY+1a7/bUWQOxtBWvq8xX496H/aHlqv3Tc43nm/7PWDdlrlLNt53Qf985u4Fap6fJOab9mN5D22fY7z313yMYgodh22+9fjbeozq+2YOS+btG++dsQ+t29b6fdHer9AxqP2vV9vQ5tu/u219z8w/K+b1JeP4Y/08xf+uO8NETJwE7HnHfGTqd/wdxm9w63vQ2pCtJftivLdRnmv5/qj33vL70Mb4vC3vayLbiLVf7Tv1qPjtdQJrp4j52mcf5XtpEfv96NTPW1Var9iJV78bnHhzVnDozbcHC57+WbDhT2qBdOHt4GI5ZGDGvwR/3tAcPLHpkeDY7KzgnFf/XSysD7688HvB139ZH2z+5NfB1x+5zdJ1qpWkZdf6jNXBE2qbl/Z/T1t/7IOrgz8T6514a3XwuTdk+V11w9/8t8GCJ98Onqh+O/jUDNk9+w/B1z/R17X4f78OviiXj3kk+Hp1c7Dh5/8SnCZew9gnK7XFxn4K32gJdddqjwX9uEYFc+55JPjiz38dPPHz9cHH5Gv83wuDb1+Sz7AOCbj01kJtW5OWvhz8eXV98MNN3wtOku/XDKN7VT0/+7bg4j3N2pzWji/ae9N54nQlWLq69eeFu0es61m6HOxdNOo9tyw3dU83vPV2J3dNx+v+lVrrArYtt72eaF1U+vCCru1eincM2jJTl1FD2Xr9+LXXYn6vbN8DbbntvbK8H+rnwfY9Cu9LXx5eXy2P2ZVn/HyZllveb7W++fOI2Kdg/4zs3YeW16Cvb/mum7Yf8d03L+v073YM2uu1vy+6Vl+PWM/4vLVlcjvG8ojhBuK9WmjbVmjbrX0X9O+h5fsZ5XMwL485rEU7buvn+6L8/LT5cb7PbT5Gfbnld0HE98r+fpmWC5b329i/aX9yncW2/Vneg7jvmb4983LrZ6Jen+mYtN8FxnL78Yrvs/nYHcP+vlva+nsQ/h0j26bvQJTPTPuuCFHf27b+PrQxfobC201gG63t1/6a1HLL7yzbdyzq+9HJn/dVn3Q19Jvl+NnudSgcJ/6yeG2R+AtlDjbV6stObV2PPedvweI1j2PcoH4YWvAEHroVeL9ij0j0mXhgxdOYPTIT/W4YhtmPfRNDxTpGt6iuN2Y/Og9DvyAfn8fPXtuG865vYPm/zsNksd7Qu+Zh8UxTTfumh/H0P03E0OETMffevxMzjuIjo0xqdmAT1h7vjek/XIfZw/th0LjHxfN74/wb+7W/Dr58xxL8YFo/HHz+h/jB6jWoufUH+P5MWUsOu3ORfM3DMHTcQ3j6MbGvi3tw8H21MKQBm368B+cHiec88wDGDc/ELQVP48X5twDHt2H7MfU06daHsWRSP/1xK8enM783nW/P/PBfW9a/zpWP9+Pt2sl46GHj88lEYeFk21+1uojq5E0PYfljw1RDqa0X76Zu0F3WruqkVLsGk4337x0PTtiGEkxcUSvm7cWdb9v+Wu9EMY9B/IX+ohz+sCJ8xIMefkg7fv2zXG7qPtY/d0u1NnseHpLDKGIahrnPGdVsfX00NOjr7/Nhj2V9sfw58XOgWtGJ7e02vb93PYy52dYuQUv1XH13za8v2jqJiazOD3r4YUze6wv/LDvxuy2HstTUYs+db2vf03BFJYHXIz6f5ernftAdd4rPRrz/heq9vGki7sw+IT9ORXx+K0zbks83vR9xvwut2bceaxE+Fin6sJYGlJfvweTVpu+I+B1UKL5jCX2fr+IYE/rd1yrT+yuJbTxn+r078c5hqGlI6B0L/XyZ3zPt87VV3M3v1cRC8fNn+cxO4ZTxXPF9Nh2Zc9iGBVg+h4jfMRPx0GPA2+/IVxj7uxJN+/w+FCatw3bbv6XxtpHY9zZBcd8PqfM+73a5rNUXBomAWPYOju79Acb95Sie/pfXRbwUH+1J2SX/EZbfYYSbMXhahrr/J9f6M07tfhZLvjUJd94xBl/TutjsBiDtRvUQH6Lml+J/t4zGrZ/T50TIzAz9Mvryl7+sHkU69bE8rkvYXmQcVzYe2nwJ+Muf9CeIMDhx/sO49fwebD8gPuhF3xBHYjYAA0wzjH39WXtdZqdwWr6ooTdDRNSQAS658mk0N+ttTb+/hoqrCRyfZH5vOt/k1TLMqGlnlH8E5Dg4o8vUmObvifKLXLTF9z4zI/wLMoI2DhRqW10R3gYh0/KPrJ343onDt/6xZRZlvJD4BbBHe//WiX/sH43RFSO+ezvFc1YPwtopXdW1FuUYtDGOg8USuxifpdbFbP4H7cqdahTfHtPP+ZXRP6+Yor6+1j7jWORnb/sDT+uOU7/ku/y7Hd+gh3do39HM5yep72grr8fupkz5zsUf62vuuoz678CVSfy7Ems8X0d/nxP43XeFzMNMJj8fZ2icTfT3rLXffyYivG3ffSfell3NYt+xu5i7mgxdw1QBZT/27x0mcoj+OWjvgbmgoN5DPfTH+q5E04HfH7kN9TBS++437vvRyZ93uwRWwxcGfQOTbxUPflkj4qV4f4bIvxRHY/Huwzh4wDT96AGk7ViEyY9vx6WvP4MXX9uFg1tbq5IMwgD5hJpfh8aWXqlBN8nj6ofp62zHdeCfME57xiXsX70e7w/6O4wbpFdJjbG1uku4ZBqyejpwWvy3N3r30tth0Y9Zf37sL33rx5cE5A9HKJSZpohwGz0IyBMVLFTF54T2w9HZ/7DrVYpo1WHDxDvi/PWq/YVqO4klZCKekydlzY8TSLVqQIxA0FnMxxDzF1+cUBc14F4h+x892h9HbaH/Qo8pzi/2tocL+Y+9rPDafg5qdoSrH1363U6EPuZP/1wTeD1tIcNq+eDw74ndrf07kLhBGeI3TUKVzliBrHO+z63+7msjGVZfzNwb+mz2tKFiG/s9a8MJOjLEaPteB4g/bJwaWrVqvuwZsP1+1t6DSetC719o0npc2hDeO/L7E/d3XvvuN/77IXTi5311gfWdpzC96Fm8snsP9ohp0+o5eE4cbL+8vxMxVbzQ8eIL8bmjeOVfXtOuJiBPivpw55N4/aMvoPl8QM4QKfev0PvyaWwrf62Vf3QyMSlPbPXUi1hSuAZ7jjWgZt8aLN+a0DfH6ta/w/R+57F99Rq8HWgRM1pw+sB6rD3QDNnD/vt9P8RTO/4Lkx9djuWPTha/VH+I7+81n1R1Gpue/j72HD+vHcMPNog4Omg2JkekyfAxf/97r+Dg8QZ8tPspzF0tnn/rdEyK9Qu+leNLCrLrD2uwOOr17qy0E3yeXx8ObFqXs3osnFq/KPyPuFax6Xx6t+ejkcMfxD+42g+oCB1rM8VfoVGWD5t/ytRlGIVcd9IezFWD4vcvsoVX+QtVrN2ZZ6THPQatWzd8vNKp9S9qz9eC+/OLTaGrAeVL1gC2Kw5cKb3LeA1eDP1S1Lvo4juBteXmY12sdRXH7IaL8vrk5yivAjGxta67CPKPHWDtkijDZgQnfLcjGN/pEFmBEq9EC+vxX09b2St62pna6vFV0052tP4O2i++p5HHrf4gNf/RKH4HlYv3oGO/z6q7Ps7vPv07YRqKIpYvjlsxtVfX9CEcCYvynuk/L7H+4LaxfHdkuFMPncj4OZ+/x3pFFPkexOn1ivVdiabdvj9715u2sR9LxDHHOwm4Xb+38d6PTv68ry6wpg1A79M/w4tLxUE/vghPb25G5jdXYNMPJ0LrJBfJe8OP52HY2U2YO3MMxo0vwFM//wIGDBQvbeZczB4OHHxmGsbNWoNLX/e0+pf1gG+WY/s/TUa/E69gyTcnYfo/7UfzF1LV0jb48kQ89foKTP9yJZaL7YwbPwmFrzWgnysNuLgfTz2zB+fHL8EP7uqNL981D3NvvYT9z3wPe0Jn9g/D9Hv7YfuDYzB9/os4ccM3sPYn8yzd/gZ5zHuenYHeB9fgoZmTMPtJH74wbQX2lNuHGZjEO76kIbuS9a5EoxshVpeB7Hbc85gIdcbzlkDrrjEMyhD/QKouB9n9iNVXWM25KhPxnPgrUgulxnHKqXxwKPjIMZ/GmL/Q8vni2BOoPk1coQ8NkGMFMzNN70VoG9Yxrh0t/jHIz3Yv5jaIAK+WT35bzhW0Lm45fMBYdxLevnOvafzVVZJ/zWsVaWP7iwE5hk4tjm4Y5mb61PNld5Y4vmjDWEIiX9/VfAby+x3xvVFh2BnfbZvMwWiwdfljda1++TYh3utpq9Afgmo7ixsGtanCKsdPwn5Wdoj8mbX+DprbEA7HZhG/g6aoL3QHf5+191L+sWrs1/a7T3sN5u97xHI78d0ttP58NGRan9/W92zy23diT9yfFxPLd0d/r4zvjfPo4VP+fjCGA+jEe7B7nu1nIPxexfyuCBHvbXt9fyaJvxKXGNt4FA2PtfK+tuv3Ns770cmfd4o880o9pgTISzhMfh6Yu9sB/7AQkfZXvtatHPUfVXk5lvXI5M8rJQmjS7/d/sijpKZljoaHw13wPVi7jmElIupceveY/SYHRElJG3pir/gRkcTASkRJxHoBa6N7jNUoSkqyd8D8fZbj3dkbQBQVhwQQERERkaOxwkpEREREjsbASkRERESOxsBKRERERI7GwEpEREREjsbASkRERESOxsBKRERERI7GwEpEREREjsbASkRERESOdkU3Dkgp+bV6RE4TLL1ZPSIiIiLqHlhhJSIiIiJH461ZiYiIiMjRWGElIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdLCQrqccKq/+3fcPDnB3D58mU1h4iIiIioY1xRYPWuW4vpM+7FDTfeqOYQEREREXWMKxoSICurDKtERERE1Bk4hpWIiIiIHI2BlYiIiIgcrVMD65/O/Q6PrDoB99O1YhL/X9UIb92f1VIiIiIiokidFlj/41gjvr75j5j2D1nwfz9bTMPg/1Yq/vROEx45xtBKRERERNF1TmD97FM8vR94+qGBmHDd59RMIbUvFj58A/7n/tPY9Cc1Lxn9dhuW/MNaHFRNIiIiImo/nRNYqy/hw5uvwz1fVG2za1LxSM5n2PvL/1IzroIWHO/DA8b0/C/UAiIiIiJKVp0SWGtO/xeG9u+tWpFuSP3/cLblKgOrDKs/+AAjn3oVr/xEnx648Qx+oxYTERERUXLqlMA6dMBfwf+bFtWK1Hjh/4nQ+leqdRVuHIXRX1GPhXEzZ2Cgegz8AqtD1dfH8dPfqtnCwedNVdl/3BYKuXL+kq3b9PVC883buQ+rj2ozdUfXhrfD6i4RERFRu+icIQEjUvH1k+ex4/eqbfbZBXiP/X+Y/r+uMrB+ZRRGogL/HDUo/gY//cf1wMOq+vrUKBz7gTHm9Bc4hodVVfZp3CW24d0arss27wlgpFz2LzL8yrBq3k4e+qvnAb/EK1WjQvPTjlVYQjERERERXZnOCazX/A9MGPDf+Ja3EZtO/z81U13masXv8KeJ/TEt2vjWNhmIv/+XV/EA1kdWOI/uwj7kYdpo1Zbh9sYz+EQLlF/H/Me+rs2W2xg9MhxBpbTJUzFOPcbRD/BvIx/G/NB2ZuDvjcf4X3jA2I7a/pkzepOIiIiIrlwnBNY/4//u+C28va5Hzbd645N3GvVrsIpp0rY/4uv/cBNeGPkF9dyrN+4xvfqpBVdT9z4+qcA/G931//B97PskHCh/s/XxUFf+P++JnTJ/8+9nkHajS7XiGYgbrbmXiIiIiK5QBwdWPaw+jX54fVof3JB2HRbOuUm/BquY3i0eiNnXmy5z1Y7GPSa79z/AUaNbfqTR7R+eZKVUhtV//iQvNO+fJ8dPms2fBNQjIiIiIuoMHRhYrWH1f6q5HeboWusJUL/9AMc+UY9Hj8LfHltvXa785hNz1fQ3OHosdoV14K2jkGbezm+34adRtklERERE7aeDAmsnh1Vp9FT032V0+YtJu8TVs/h77aoBX8f8p/LwyXrTcjVcYNxjD+PGPd9X88twpn+cCutXZuA583bEPkxnXRERERFRB0gJCupxwpY/W4rFj5eoVqT/ONyAb/yub+eFVSIiIiLqtjqkwvo/x2RiL8MqEREREbWDTrhKABERERHRlWNgJSIiIiJHY2AlIiIiIke7osDaq1cvnP3EuGYUEREREVHHuaKrBFT/27/h4M8P4PLly2oOEREREVHHuKLASkRERETUWTiGlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiBwtCQLru/ieuwA/rldNIiIiIupRrjCwNuLHBV/DSLdpKnhJzO0E+5d23r6IiIiIqMtdRYU1G0VvfIhjfn1anuHF937EGElERERE7avdhgRMGH836hpPqRbQ+KOCcPV16btqrlD/EmaHKrNL8Z4xz1I1lRXcyGEA2jYffwuo82KGWP97++VcOWTA2B6HDhARERF1N+0UWEXAfPkt3DX+dr25fylmvHc7thnVVyzCbK36KsLlve/i9lBldhkm6GskJOM7m3Hs2buBrGJt289MlMF2EfCssb3N+NZg9WQiIiIi6hauIrDWouzecKUUT8kAKeer8Prgt5GhPc9cfR2EQVm1ONVuIwcykCF20tjEoQhERERE3VW7jGFdfpcIry+buv2FfY8bYVZMshu/sVFE2Qx8a/MKQC3Tu/SvzoRlO3D7e9O07elVXCIiIiLqTtplSMCEZStw176XTONHrSdkadNmo+J6O57R5sng2h5jTmUIltvTg2t7hGAiIiIico52GsMqQuizGSj7gTxxKgMTJkA9jsc0PGBwBjLq3sV7Krw2/mgpyur0x4nj8AAiIiKi7qidAqsw8Tsoghczlr6rnRwlL3Mlz+Q3hgVEntE/De9O2KHGvd6Obz8sQq4aE/s9fBtFWXJ+FMZ+tG1arwe7GCuw6TvGyFkiIiIi6g5SgoJ6TERERETkOO1XYSUiIiIi6gAMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GhXdB3WM2fOqEdERERERB3rigNr//43oqWlRc3pWrXvH8ZWZOIHt96o5nS8Q4cOY+zYMarVMf5w6BU04FaknjyFL31rMq5X83HuMH71/MdIfewBpMuZJ/bgl68fxefvmouvjk3Tn6M5gdrvvY8vGs9T5HZrT96E7G+NwbXaczbhDxiNv3lG7qMZTT9ei/Nps/G/pgxTa7Tdr7bOw8uHx+LBtdPxVTUvMedw4IVSfDpuDWb8rZpFREREPdoVB9bU1N7a45SUFO3/nensLyvxxInLqiW4bkb57QNVo3Ps3fsmJk26R7U6yi/x4f3r0DLhUeTO+V9qnu70hn9A/XuqMXQWbrxxCz7961J8/Z7+wLFXUbn2HbXQMAw3LnscWQPk4zOoKy3BJzVq/ty/xadrz+MrG++DttiyXPdXs9S2E/R/X5yKf3nrHvxjxXfxv9W8xJzGru8/gnOTd6FwtJpFREREPdoVB9brruurWj3TG29sxb33zlQtaj+NeO2he3Hm7z/AkvFqFhEREfVoVxxY09L6dUl11Sl+8pON+Id/uF+1qP3U40cFE/HvcxrxQ4+aRURERD3aFQfW66//6x4dWH/0o5fwne98W7Wo/ZyEN+9/4weHVPPBn+DCismqQURERD3RFQfWG264vkcH1vXrX8TDDz+kWkRERETUUa44sMqrBPRkL7zgxSOPFKsWEREREXUU3jiAiIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgTXZ7XwUKSnXiOlR7FCziIiIiLqTJA+sZ1G2wovBJa+i7BM1q0fZgUemeVG84zMEg+swTc0lIiIi6k6SOrA27N2PVf0GYIpq9zjHG1GDYkzIV20iIiKibih5A+snBzH/MLDAM0jNICIiIqLuKEkD61mUvV4NjJmIop59/wIiIiKibi85A+ux97EKI7B60g1qRg/1cRPeG5+OoapJRERE1B0lYWCtxoJtl7DgG+OQqeb0OMdX4nZ5ZYD94xF8byEDKxEREXVrKUFBPU7YmTNn0L9/F/XFH6vA4G2nVcMmaxzq54xQjY71wgtePPJIsWp1EXlJq3XpOMHQSkRERN1Y8lVYR+ahvrTYNI3DFPTGgsfE404Kq45xUzomHGhCjWoSERERdUfJe5UAIiIiIuoRukFgHYFVpffxagFERERE3RQrrMlseAaGogaNx1WbiIiIqBtiYE1q0/DCjqFYNOIapKQ8ih1qLhEREVF3wsCa7PLXIRj8TEzrRHwlIiIi6n4YWImIiIjI0RhYk9Dx479Wj4iIiIi6PwZWIiIiInI0BlYiIiIicjQGViIiIiJyNAbWHmkvfH99PbZ7TwK1ZdguHvveVIsMav5r2lSSBLd/PYmj06K8ju7gzRLxGczA0Vqg2TsjST4PIiKi9pOcgfWTg5hS4sVg87ShWi2kqyYD0rh/x7DfncM35bQBODatDM1qMXU3RzC/dDZ6lW2FyMRERESOk7wV1utGwFdajHpjmjNCLaDW3YTUMcC1GUOA7IG4FmOROkgtkpXKlz7G4IOlGKrm4J4HMBjvoYFppmsM+ht8SX5m2UBaxk3AmL9BP7XoqvnXoFfpB0h3u9QMIiIi5+GQgB5pCEbvOAfPPfLxJHh+tw2jRRjSvPkK6gc/EG5rhiDzdiDw7km9qXVRG8MF1NACE73bOtZwAr3rPrTcUrkNd+vXLDGeo3eFh+nDGaKvL5mXt7HrvJXXFXffliEUYlqyVy0QtGXyWEzr24477nuWXYTpv1N/QNxTim/uKEKatsDk+ErcnnINbl/elld8BPOPuuAvmQePmkNEROREDKxkUVP5MtJyJ4lH1nAFWdnTiPlzZAVWDRcQ0/TiIWqZIEKf78mbMFIt8zzxsWU4QbP3FWCZse4hDMaTOGgPvHOux4mBh7TnjJxzCPUvG+FPHtOD+MMT+jJtsoW35jmvIFU7NrHtMS/jRETojKWV1xV332LZuCdx7QZjXfEebnjQFnhfxrG/No5NLD/8JPzGeNtW3rOOcxtWF82E5W8TIiIiB0rewPppNTyhMawV8KnZdDVOoqVeHx5Qs0SFMxG8MHigraJ3KFxttdEC74bwcIK04gdEOAsPJ0grLjVVb2Xldiz++JuPVVuZ83IoLA7NFfuv/40W3mTYbR7zBMZZgqRV2gajWhxj23HFfl3x9m0sc2sVa2kSPOJ9++O771hCZ/jYJuErc4A/NOr7au09S8jwhXg3+BneXRwayEFERNRtJGdgvXEcdpvGr/rGXEIxQ2s7kWMl9+K3InANE+GsufFjfGmgUV2VRBg7+ATw5Ngo3dcy8OoV0nD39oPWSqGt69z35CG1IEyv8CqmLvDzvxHPjQjP7SXe60pg360e14P4SijQiiD+nFHBTeA9IyIi6uG6xZCATPdADFOP6Wp8jJbD4n+1v8Ef9BlaUJMnZ8ngqp2kJWljKk3d17ZwlxbqGjcmo7Kod53D1K3ueWKstk4i+g1M/LlXJM7ranXfqgpskO9XW8R+z4iIiKgbBNazKHu9GieyBvHEkaumXz0gTFZa5f9PouFdmK4kEKadtR6id8M3z7GdNGQTCr61ZTgYpcIaS9rtE/ClDQ9e3bVWjROrzCdFRWF9XfH3rS0zj0kV75tfvK60b0c5OSpCYu9Zq67opKsE7XwUKWLbKUU71AwiIqLOlZSBtWHvq6ZrsG7HvqHTeVmrdvMxWnAHXGPUSUJPPCjC1NjwlQNsZ9K/pp2oZB5/uU1VJ03PCZ1ANAnuJ2Q4U/PH/TtcbaiwahXQg0/gD+bu8/Y6OamV1xV33xHL9PG/+lUYWhf/PetITVhbNhu9SmfD7Q8ALTvhjnY91vzxKJb/r228ulBNRER0hVKCgnqcsDNnzqB//xtVq2d64QUvHnlE+2e80x0//msMH36zarUveTkpeYa+9Qx56tlqsHLCzViUvQ3BsmlqHhERUefpFmNYqf0MfVCeeLTUdO3TvfC10n1O3VfN8juQksKwSkREXYuBlaxk9/aGm1A/Lty9DfNZ+9SjDF38DoLBzxhWiYioSzGwUiR5KSnTGeuJjsUkIiIi6ggMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKw90l74/vp6bPee1G6Pul08vuLbnWrrm28r2o7bjnAeqx/djM8/K/dWg8embcadr5/XF3Up/bgeS/wus+2q9vWd+Py0/dgpHu98Vrw/j1ZZ71RFRESU5JI6sPo2GLdnFdOGajWXrs5JHJ1mukWomrQAmrRiBUo5fydWN8rHegD+vHnqlOCn79d6bNHmERER9VxJGljPomyFF8UYh/rSYn2aM0Ito9bdhNQxwLUZQ4DsgbgWY5E6SC3CEIzeIa+/egiDxXO+9MQh7Vqsid+qNd62r1Y/pA8Axg9IE4/TkOESu/hKP31RAmp/21o1th/e2FGA/zamdTnIVkucLPsr1wKuVAwVj4cO+CIw4MtJcdxERESJSs7Aeux9rOonwipD6hXSQ6l+Q4BJ8PxuG0a3JeG8WRKuvo57En9Us3WJbHsHHkm5BilFO1Q7cfmPF+Dtb8iQ2g/z1xXg+bH6fJ0aMhBRIdWDbqRmNAbUw7hkxXM/Vmtd73IYQo3aj1GdNZirtHoXvebQfjXPNLVn9XbsxFC4zv5GPv77cRldiYiIuo+kDKy+6tMYlnYRC4zhAGJacEwtpI4lx6XO+RiDD6o7YR18Al9Si7pa7esfAguMCulEPIcGPBYxxtU8DEC6FukZ6mFc57HkdDb+e34/HNj+ERrvLcAbt/4Jb/0ivP31q2uRsVLf/xu3nse92lhbQQbKUOVWHJfri3huQXJUb4mIiJwgCQPrWTSIjHDi8EV4jOEAMwZg97ZXUfaJegp1mJqXnwSeWNa2imyEaXihA+5Pn/2NiZgfCp/9cPfoL+LA6WatJbvKtceNp/BWwBo0w0TIDFVB7dVTETLvVZVLVyaKLJVd3cPz80P7z/96P+D90+Eqq1L7+iG8NXqs6ThbE2VsrZx4YhUREfUgSXvS1ZQZefCoxxh5KxZcdwn1Z1SbeqbGKtxpCnUjtv9JLTA53YLs+bcg++gp1Db+HrVq7KeItHg+VAUV0/xrsWShPbS2wYBUjFcPQ8TxPXbUhee1IQ2Jsh2XMSXJ+FoiIqL2kISB9QZkin/v68+eVW0iqQaPLWwApoe736unf1EtUycmCTt/AYwfOxTjBwTw1mltVnRjB+Bh9fCKiGB8IBSGpfNYvSqAuyOGAugnj0X6IjK0cbessBIRESVlhdUzYgBOHH4fPtXWTsL6dAA8I1WbOky/gWPxx3ffgd7Rvhe+iJOuEnHlJ121JnTVAFnNtFdYT9di/WnjbHrgrV+06POj2PnsR1jvcuHuhLvuzUQ4feM8xo8eFAqnO5/dH2MogH5C2PpfhK9kW/u6OM7QvtunwrqjSLzf4j1/xD5GgYiIKAkk55CAkXnwjbmEYuOkq22At9Q0RICugnEd1rGoPwz88cmxluuwphUvw2A8qd0c4LW/fgWpB1+GvMhU1xuKoulfxPrVqgK5sAV3myqsmoAIsCpEZn/dBbx/HgeMS0DZzuS/F7e0ORSG9j1NhtOJ6moGgtj2ve8DB7ab9xEebpD/+EQ8d/qj0LIR26/FG+3c5T9tYrH2/5r6cDAmIiJKFilBQT1O2JkzZ9C//42q1TO98IIXjzyih4DOdvz4rzF8+M2qRZSA4ytx+4jFGLrjM7yQr+YREREliaQ96YqIElGDlROuQQrDKhERJTEGVqJubSgWvvcZgkGGVSIiSl4MrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrD3SXu1OVdrdq2rLsF089r2pFnWxZu8MvPbXJZD3Y6pZcj1em1ambgN79Wpf3wl5Fyp5d9Kdz27ugvvxn8fqR407XYnp2Xa861RjFe6Mtk1jvjbpr7296e/rZjx2SM1oVx34npGJ/j7f+fp51bZTn4P2/tfgMfFZxH4uEVH7S8LAehZlK9QtWS3Tqyj7RD2FrtybJdqtWCMnPUS2BxlEjVu9OoUWYM2BTgW99vxHufb1Q1iCTFTvKMB/y+nxoWpJB8rIwdtyXyszMV7NSiZd8p7FE+0PA3lb3w7/4ydGSFTH0zF/LBAROUcSBtYbULSoGPWlpmnGADG/NzJ79t1i2+AmpI4Brs0YAmQPxLUYi9RBatE9pfjm786J6RAGi+d86YlDql2KzogKaRk3AWP+BvIu/P0GjgUGD0SavuiqZX/lWsCVqr2OoQO+CAz4srpf/3k0nf4ixrv+gCZ1f//aXwTEc8Vz2lHN6T9h/OhBap/tzAimXRDosr+Rr4XJ58XH1d469D27YuK7cvp0h1SrYxuK5+f3w4HtH1r2u/ONBhy49ZYOee+t+iFd/JodP0D+NKYhwyV/nuRPKRFR5+gWQwJ81acxbMyt8Kg2tWYIRu84B8898vEkeH63DaPbkggsVdgZOBoqLZ3E0WlinrkbXz1XDjnQu/uvx7ENwB+fHBvexpK96smCDMw7irSQmla8Dd98bpI+36Rm+R1ISbkDK4+rGYkaOxH/vS5HCz9ayLKFu7tHX4u3fiErWOfx1mkXHh4NHDgtX4le3bJWsaLNi0eGYvUwCqNbXZ92YrUKzlr17tkq1S0u5h9SFT5TRU+vDutTmyrCctv2YQLR5sWkvwcRx2xifV1trQTGec+MqqZ5yINluID52MRker/kMd35epVaLl6r9prt68dzrfiu/AHrY77X1n2HPhPtWKO8t8ZrUc2Yxn4Nz7nOh/crtrf+/S/iuXvD3+O473er75mZ8RrCx5v/eAHe/ob2pyTmr+uYP1CIiGJJ/sD6yUF46wageNINagZ1KDnmdc7HGHxQVl3FtOEm1I8zhgvIIPwy0g4/iYNal/9e+OaI9gY9HGsBVKwzco65ciumKKG0S3x9ALKPnhL/oJ9C44BBporyUIy/FVj/C3M38GmsF/9wj2/1H+3wP/xLAiIAb1fhyBzwRIAow1i9y1tM1dOBJatMAeb9BjTeW4A3bv0TlqxuwcM7bsHDgQDeUuvLICHXe0McY5uMHYCHRSg8YAo1O2Vgv3UAEruL61A8rx2zOB41x0K8rse2X4s31OtKvAqbwHsmBRowYqF8P8S25ZCH92vVcjne8iPUThd/oGj7nYjnIJ5rCmcHtgeQsVLMFwHw3jdSUa2tn3jVNP0b2fp3RbXD7Pu+Bdni+LXgmCEr+uEqftuJoHhvuMqqV1ezMT9DXyoD6Qjz+z2/H9avTvQ9M5Pvv/EaJib4XSAi6lhJH1h9vmqA1dVO0/zue8ATy8IV2XsewOAxL+O3oZO2JsFz8AngyVdw1PsKmkVg1Su57Wfo4ncQDL6DhcPVjKvWjEYRjLRgOiCAsjdakPF1a3dn/r3WQCOD3fjpX0vgH3Mj1MlwBLGOEWTyw0EjIwfPa5UrXfbXXRgfaFF/BAiuTBSpoJfYPhM1FEXTv2gK4jU4YKvYXT1rIE5MAu+ZRhyrCJ3a+5ExCHe7/oRGWZE99KEIuv3wcOg9FUFvgS2QmoLew/fqVfe2kd+VBpTZX5v4Y+ctZJo+T/N7rHela0zV1trf/sE0PKUVYyeKP0zOY/2z+23VVRGU37B9J7Xn/kn1GhhivGchp0JhVa+mEhE5Q3IHVq262ht3uVld7Sznf3PI2p3/12NRf1gtNGQXYdwTH6P+yZsw0inV0wTlf/1arD+dirtFmNHGvBq0f9yN8CWDnTkQXS11BrZWRRTTwgYcUEs6mhaOjSAnq8bmit3VkuNqtSqfel2JdHu3hculfU46Wze1GqvckeQfMbXmqrt0ugUHZBXT+CzFNGL7n9RCOQ5UD4i1v2hB9q0IVVv1saGJkfvF+yKETh8b8Vm1Oq403nsmHNjegPUi1N5t+4ONiKirJXVgldXVE1m3oIgnW3UqS3e+mixV1DdL4Ht3AkaK0HqsHS9L1bGuRbr8h9w0zlVz+vcqZPXD3aNVpUwLdol2m7du57P7rWfCd+YZ/aYgLqvGD3+9nWOefD/V63pjgAhy7R1aYzFXqCUZJNXDdiPfu9O1WP1b1Ta4TJ+lManx0vJkv9rf1uCtoyKk3puKRvGeyxPL2nQCkza0IHo4rf2tuZoaf9x0NLKarQ1JWZjoOGYios6RvIFVVVcXeEaoGdQZhuY+iD8+udR0opWdGrf67SIMLV6GwTDGs4bJs///+O47Vxxkr/ikq6uU/Y1sPPx+Le584w/t3G0umK5YsHpV51VYtSrbvf2w/o2dWH86PPSgI2hXZugMamxu+KSoKN3l7UK+d9eK8PkH1RbkvgMNeCzuyW+/R+MA8QePCJ44+iEOnP4iMuSFTq6K/geV5SoCEUMjEiNPSJTDDu5t8x8XNVg54Zou+dkkou4vSQPrWZS9zupql5Bn8WsnWhlDAuRknHQlb0jwoGnc6hCM/rYMuGMt111NU0FW3rwg4ioBXaHRqKK2Rp589ScRJs3dqldPHx/7kepC3o/G0W2psIbPSL/3/fAJSsaZ6aGzxrVhBiKEqH1YqmdayPoT0NbLRxln10/7COvxJyxZKB+HT/Kxn7GunRBkrl53GDkGVj/ZSd/3fiwZcEvHjMkU7122fO9C7PvWJ+NsfTnMRHa7Q6tky3GwIlgHVHX/KsmgWT39D+ozFtNq8YeVMV61jfIfVyeqxbj6Q3RDcc/dE8T/30Pjx/ocIqL2khIU1OOEnTlzBv379+yk+MILXjzySLFqda7jx3+N4cNvVi3qTPISUusHdLcTUmTorUXGSvtJTURttPNRpEyrwYrq9jwpkoioG1wlgKjTHNqPe9v1ZCtn2PnsR+17shX1QDvwSMo1DKtE1GEYWIlaY3R9X0UXqxMZNxy493Qmqrv6lqeU5KbhheBn7Xy5OSKiMA4JuEIcEkBERETUOVhhJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYO2R5B2prtfvPlVbhu3ise9NtegqNXtntOOdq07i6LQ4x/ZmCV6bVma7xav+2iLnX62Oe8808oLr8jqW2sRbWxIREZklbWD1bfBicEl4WnBMLaCrVrPEuOVqeDLfWpU6yPjlOMFrWRIREUVIysDasPdVFGMc6kuL9WnGAOzeVgGfWk6tuQmpY4BrM4YA2QNxLcYidZBapHzpiUP45u/OhabpxeK5SWESPPKYdxQhTc1pH62/Z0RERNQxkjKw1jdfwrA0Uxzp3wfD1ENKxBCM3nEOnnvkYxnwtmF0tragFbKLfgaOekv0yuuSvaFqrL173FyltS7Tu/mNZZHDB1SXvjaNRf1hNVvRhhwYy+e8rObqLMuibrcENao7X3uObdiAZX1jCm0ngffs+ErcnnINbl9eo2YQERFRe0jKwOoZMQAnDm9XwwDOouz1apzIGgSPtpQ61iHUv/s38Bx8Al/a8CBODDwEzxNj0VxpCohqvladFY+b54igqBbVLBEhdPDLqnJ7CIPrHzQNN5Bh9kH8IVTdFcvHqEXSmyXwPXkTRmrL9G2bpRVv0+bL44nuZRwb9+8Ypq3/MtIOPwm/EaZFkD34JDD4oL7tkXPEvDFPwPPcJH05ERERdZnkHMM6Mg/1peOAbXL86nbsGzod9XNGqIXUHv745FhLpdFcJU37ttHd/iCGRRsqMOfl8BCCe8aI576M32rr78VvRcgcGQqBQzD62w/ij+++o1c633wF9XgC46IOPxBh9iURMjeU4srvej9WBFJj/Un4igilf2jUw3Lzu+/hj2MmIFNVTYfmijB8+N9xXm8mZvhCvBv8DO8u5n35iYiI2lNyBtZjFSKofoTBj8kxrOMw+PB2DF5xEA1qMV09+xhWvSv8SuhjPzW1v8EfZJXTFITt3fpdJS3jJhFQ30NDrd6uqRTHNWfMVYRjIiIiai9JGFjPouzAaQwbMxFFN8r2CKwSoXXKp9Xw8koBDvQxWg6bT1B6MNylb0ztfoLUFRj0N/iSHO4wTg/SxyyVYCIiIupKyVlhFU40m06XOXYKu9Ebg/urNjlGs/cVNBtd7dl3wDXmZRyLdZ1WGRrNVU453jV00tUQpA4W2zPGysqTp9qxOlvz8pOApap8BUMPeNIVERFRh0jCwHoDihaNw5S6g+HrsG67hAWP3acqrtQe7GNY23Qd1g0PhtbzvTsBnlAFVZ5pr59oFXXb2UUY9wRCVU554pZ28pMy9Dk5hlWtK0+ekid+qWXmqw/4njwUPoZY4dhm6INix7bX/Jq8qoBaTkRERF0nJSioxwk7c+YM+vfv2enwhRe8eOSRYtXqXMeP/xrDh9+sWtQe5GW4ZEA2X29WzjuGl/HNzhgaIO90tS4dJ95byHGzRERENkk7JICo/ZxES716GCKvaAB8aeBNqk1ERERdhYGVSA5VWGYfEqBfD7ZT7/B1YDGGpVyDlJQ7sPK4mkdEREQcEnClOCSAiIiIqHOwwkpEREREjsbASkRERESOxsBKRERERI7GwEpEREREjsbASkRERESOxsBKRERERI6WtJe18m3worhONa4bAd+icchUzc7Ay1rFVutbDLc/oFo52FwyD3mq1bojmF/qRblq5bqX4U1PumrF37Z1ma7Qswmr3aoR2Ip7Nu5EpWpalgn29duyb42x/YxiXJ51m5op2PaL1Hz4i2YiWzWBJqwtW4qSFtW0r6+xvi/2YyciIurWZGBtq0AgIP77WZdN9Xt+EsxcXhmsV+19r6wLZr7yq4jndeS0bt26qPM7Y6quro463xHTsVXBLy1bFdyl2rs2zwp+yfvTYI35OTGnU8E13lnBu/ed0tunfxq8e9ms4Lxjankr267ZtzC8bsR0KDjPvC1t2wuDa06rtty2+TijLW9l37K9Rs7ffEhfJ8akrWt6jvW49eO0vA77+8CJEydOnDj1sCkJhwRUw3v4EqaMD1dUPZ4RGFZ3Cj7Vpq7ShLVHq5DrnhqqPOaNzUduSxV81sJndP5dKGnJQaFR1XTNRGEGUH7yiGhc3bZrfbtQnpqPQqMqqW07AN/xJq1Z2yw20rd/uOrp6o8s9bDVfQe2YsHFqbhcNBPhemxsWX1c6pEu27PcVMm9DbniNVdeDL+oikM7kcWKKhER9WBJOoa1Nwb3Vw+lG/tgMC6h4RPVpi4SQFOLC57hRvg6gvlaV7iYf07NikMLjRmjLF38BY3iwYUzqL3KbWvMgVSQwdEIhtnDc5Db6MU9Pj3AVmzxioCbA4+WLVvZtwi/b0Z04cfSBF9DAIVDEn3+EVQ2upDevAa9SmeraQ0q1FIiIqKeIAkD6wh4si5hla9ateV41oPYrR6TE8gxmTJYeQHPJmzOAOqa9SCYEL8eztwNOfDfLyuZARjDlVvbdqV/qQp1YtoiK7M6PZDuwlqjcCmroubxrjJ0liyDp0FfvwDFWsXUHHCv6nWp19SrdClKYKr02onnFYiAWjpWBdrAGfHaAyi5OAqXSzZp0+aMKhSUbRUhnoiIqGdIygqrZ844TKk7iMElXm3yjRBt9EZm154H1mNUbFGBUE1GVVInwtXGpWgarYer1e4mNF0AstIS6SwXGr3oddQFvwxnMjCeC6Ay1aW65+NvW3atG6HusgifpRfEtozQKgLpKhESSzaq466ACI0u5Brd8/LEKBEm9W2rdS2VzKt8Xe554WMbHYA7WpVUHoOvCoXidcy1jBrIwWZTBVcfjmAO8URERN1bkg4JGIFVpcWoV9Oq/hdRf50cFkCdIW+WCl5qCo+/dCE9VT+7PjzeUu9OT79eNePITpMpTYQzU2UzPLa0rdtOhyczcqxo6LjFPnAxEAqccpyoPLtf33Y65haJ0JpahXItjF/d64rgHoVCub6pwGtcSQCWfQjaWFrbc4mIiHqYJA2sJp8cxJTnf4O7vtG5l7WiaPSQWOn3hrre9ZOdjLGgilbNnI1e9m5tLchVoSDUlX8E5X5jvGeC2zaoLv9YY0VllVh2+1vCoTZWVgl8AF+LUUFt475bEbGuKayaL6OlkydhBVByKDy8QQ/X4bG+mp2PIiXlGqQU7VAziIiIuo+kvA5rw95X4Tl8SbUGwFuaB49qdRZehzU2y/VKI645KqiAVhltmTyhKc71RmNvW44vNV3LFC6U3m/uWrdu136N1cj127Jv67ZDjOupyvGrvio1U7C9bi08y5PLLMzHbzu2qNdp3YFHUmbAO345Try3EEPVXCIiou4gaW8c0NUYWMlZarByws1YlL0NwbJpah4REVH3kPxDAoh6uJrldyAlhWGViIi6LwZWoiQ3dPE7CAY/Y1glIqJui4GViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiByNgZWIiIiIHI2BlYiIiIgcjYGViIiIiBzN0Xe6Mm7BOmVGMVaNVDOVrr49K+90FZvlFqbIweaSedb73scV/xaq8bdtv0WqdXnkLVDDtz+NfnvUyNuz6s+z3/ZVMG43q5r29QzGfizLbetqot5+lYiIqGdyaIX1LMpWeDEfAzFFzbE4ViHCam8RUotRLyZv1mkUrziIBrWYupB/jQiULhEUN+GymDZnVKGgbCtq1eL45D3zvagTIVWue/n+fLG9pZjvV4vjbltfFyIIausay7cc0ZYaZFA0ll8uCYfOvFnm+WIS+84VgTb9en25HoZno7KPnG8nlonAmWVsW6xbJ4L1WiNXG8TxF1zIQWGqapul5sNv3j/DKhERUYgjA2vD3v2oH1+M3ZP6qDlmIsweOI1hY24NVVQ9nhEY9ulv4PtEzaAuIkLj0SrkuqeGqpp5Y0XAa6mCzx7eovHvQkmLCHRGRdU1E4UZQPlJGTpb23YATS3mgAlk9TGXQNum9ngVKjOmhgJtxZZdSL9/E1YP19tmtb5dKBeBs9ComGrHHYDveJOaIYlQ66tC4eipCNeLiYiIKBGODKyZk+6LGAIQ1oz6T3vjLvcNql2NBc9X4wQuof6MmkVdRA+NnuFGJNMrj5Vy/jk1K47aZpE8M0aFAqns/te66S+cQW2r274NuSIklmxcgwrZDGzFAn8AhUOupFJ5BOV+oHRseN28WbYhAHZ9+yNbPZRkWK68GE7pFVu8KM8ojjpMgIiIiOJL4pOu9GEDg0sOAjPksACg/uxZtYy6luyen41epXoX/eYMoK7ZXG1shX+NWHc23A058Muu+ZYA6tSieNvWuvU9QIFYt9fGKnhkRdQWEMt9cl19uscX/Zi0iqmputqa7OE5yG3cFR4CoMJyiGiXN+Zgc7xu/padcKvj6lWqQjcRERFpkjSwXsKq57drwwbkGNZVI8+i4Tww+Aaj6kodSZ44ZIS+yOAnq5xL0TRaH4u52t2EpgtAVlqCHeGNXvQ66tLHcxbNRPa5ACpTXRB/jwjxt60dl7GuxyWeaz026zjVYmT5l0YJrbK62sbKrGsmVolgLPenvScibRa6XcjVhiSIgF0hx7fGOfFMrP9m6Lg2we8OiNDN0EpERGRIwsCahsHXAcPGTDcNG9CHCQzur5rUoewnKIXP4nchPVU/sz9c2YwcWxpLdpoMeDnYLIOqPksfJqB1t7eyba2K6UJpnlrXPU+E1hxU+nfFCH5yCIF6aBIxHjVB2Z7l4fdEHD8uBvQgHfgAvhZzZXcpSox2jJPRtIqtekxERERJGVhvgGdob5w4vB9l6iSrhr0fYfd1A+Hp+CttUVzp8GS6REj0hrrH9QCYA4+5e11exkmGN3tgc49CIcxn9purnYls2zpWtuJkFRCqztrIM/ZFwA2PiZXU/kaHA/OV0C5dBTVe1VY9vVyyDKUieGtXKzAF8zC9IltpGsurOb4St6dcg5QJK1GjZhEREfUUzrwO67EKDN52WjXCzNdjtVyH9boR8C0ah0y91Sl4HdbYLNdKlZdrsgczGVjlCVPRlsmTqUzXUrVfzzTutuXYV58IqQbLcut2o10fVguaF6Idk22/Icb1WK3btl871kqOwdWHNRivy77t6OvXYOWEm7HoQDG2B9dhmppLRETUEzgzsCYBBlbqbDuKrsH02uU48d5CDFXziIiIeoIkvkoAUQ+x81GkpDCsEhFRz8XASuR0+esQDH6GIMMqERH1UAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORojg6s8varg0u8WHBMzbDxbfCK5a+i7BM1gxxB3mq0V+lsNa1BhZqfGHmbU2Pd2bjH16Tm61rftlq/bCtq1ZwI8hau8jlbjqgZVvIWrXL5fL+aocTbt7FOeFqMtbY7uRrr27crtWXb0dYnIiLqzhwaWM+ibIUX8zEQU9Qcq2osEEHWlzYCw9QccggRBt1+FzaXbMJlMW3OqEJBvPBoIe+z70Wde5m27uX788X2loYDWmvb1oLoB0h3u9SMaESg9QVQmBHjOWIbBRdyUJiq2oYEXlehR1+mT8sxN7QL+bpmYwHEdtUci1a2nTfLtF3xntSJcGsPw0RERN2ZIwNrw979qB9fjN2T+qg5Vr4NH2HwY8VY5VYzyCFEMDtahVz3VOSpOXlj85HbUgWREVvn34WSFhHqPOl62zVTBEug/KSshLa2bRFEj7rgL5kHj7Y0uootXpRnTEVh1K+WDLNVKBw9FeoIlKt7XbU+L5pGb8Kbnv5qjlkbt+3qjyz1kIiIqKdwZGDNnHQfVo1UjSg8c+5D0Y2qQQ4SQFOLC57hRtwTAXDjTlTK+efUrDhqm0VCyxgVCm6ym7ygUTy4cAa1rW77NqwumolsbVkMsnramIPNs25TM6z0MFuM1RF/CF3d68r2LI+yTUMbt+3/AOWpOfDEKyITERF1MzzpijqA3gXeq9Qr/rqQXdxAXbN1LGpcWtf+bLgbcuC/X1YbA6hTi65823ols9AzLxSILQJbUR4nzOri77vcFx5nah9727p42zaWiUmrALcSzImIiLoZBlZqM/tJQNZwFkDJxqVaF7gcc7na3YSmC0BWmrWTPaZGL3ppXftifVkxPRdAZapLdYNf+bZlt3xJ32jVU0kEwoqdyIoVZjXx920ZZ1pSjCz/0jaE1tZeVzrmFhnbXob0o1cSiImIiJIXAyu1mTWcybGZRrByIT0VyHUvMwVDvcs7/XrVjCM7TfZz52CzqWtfGybQt79oX822m+BrENuRYViFbLffaC/G2o8+gK/FXCFdihKjrZ381NZ934bcDPWwVW3ddjo8mS5UXow2wJWIiKh7YmCldqTClN8bOou91rcrcsxlYCvukcHQfvUA9ygUogoFoctNHUG5CJaFQ2Q3fYLbjspcodQnv7ySQEaxeLwcc2+ZiTdNy2QVs1SESO2sfy08t3Hf2lhZ87jUeNr6uszvicnOR5GScg1SinaoGURERN1HSlBQjxN25swZ9O/fgWc9HavA4G2nVSNsyoxi7WQseX1Wz+FLaq6hNxY81nknY73wghePPFKsWp3r+PFfY/jwm1XLeeTJUloFU0rNh99+MpQMrPLEomjL5ElHpV6Uq5YMjeZu/NjbluM89cqoRdR9qO1cnIrLUces6tuSXfSJ7dt6zFqVuMQ0vECOyfVVqUaY+bW15XXZ3xPdDjySMgPe8ctx4r2FGKrmEhERdQfODKxJgIGVnKUGKyfcjEXZ2xAsm6bmERERdQ8cEkCU5GqW34GUFIZVIiLqvhhYiZLc0MXvIBj8jGGViIi6LQZWIiIiInI0BlYiIiIicjQGViIiIiJyNAZWIiIiInI0BlYiIiIicjQGViIiIiJyNAZWIiIiInI0R9/pyrgFq3FL1rCzKFuxHas+Vc2scaifM0I1OgfvdBWb5Taj9tuUtsp6m9Nc9zK86THuyW+7TWlGcfRbq0a79WuM26NatmF5jgul9y/H3Ij7+RvHYF5uv31q5Lqx35PIW6/qYu2fiIio53FohVUGUi/mYyCmqDlmDXv3Y9/Q6agvLRbTOEypO4gpe8+qpdSlROhz+10ikG3CZTFtzqhCQdlW1KrF8cnw5kWdCKly3cv354vtLcV8v7601ueFL1MtKylGYaMX9/ia9IUhYhsVO5GVkaPainueWs+YlqE0VQTiPioRypDrC4iQqC/3u4GSjWtQoS8NkcdQ0jcHhaqtS8fcItO2PS7rurb3xO8OmN4T27ra+vLYXUhnWCUiItI4MrDKQFo/vhi7J/VRc6wyJ90nlt2gWiPgyQJONDerNnUdERaPViHXPTVUUc0bm4/cliqILNg6/y6UtIgwaFRUXTNRmAGUnzyiNbM9y03V1tuQK5ZVXrRuWAuUyEfhEDUjlsAH8Jn2VXFoJyozpoYqmtmeqSKUVqFShWWNCLULRLt07Cg1I4brXchVD6O9J9q247wnFSetzyciIurpHBlYZSC1DgGg5BBAU4sLnuFGqDyC+bJrXs4/p2bFUdssElzGqFBQk93oBY3iwYUziVVojUCZp4YBxCEDKkKhsAlNFyBCrjG8QK/0ymEJdc1GBVev3MJd3Go3fe1xEXRNr0PKSjPeE8mF9NQY74l4DeWNptBORERE3eCkq2MVKK7rjQWezh3DSvHIwDcbvUq9gEcOCzAHvwTIsaSls+FuyIH/flmhDaBOLQoRzylodKF0bHgMqx5CWw+U8UJhxRZ53Eu1oQd+tytcwZXVX+RjVcwgKcfeynXFcWtVWOO40uHJdKH8aHhYhFYFjhizqrMGaSIiIpKSO7B+chBTtp3GlBn3oajjzwEjRQ914ck6jjSAko1L0TRaH4+52q1XL60Vxjgaveh11AW/HMspT5g6F0BlqgtZarFGG29aJQKn6aQkGWAvxAuUYXr3v7UCKpX7ZqO8jz5GVg49qBNhVR/jKsKoHN8at3J7G1YbY1BLpqJp4+zQ2NtsTzFKsRNu9X4twFSUiteUfr2+PEQL0uYKNREREUnJG1hlWH2+GhgzncMHOlneLCOY6VN4XKns6tbP7F/tVrPUMIGIcBZFdpoMhznYbJzZL2jDBPr2DwdFGVY3yiqkeR8ihJ6sAlrCoVA721+1jeCoUaHQXJmVVdD0vuJ/GcWWKxKEgrb/A5RrQVxtW7uKgWpv0cfXWunja8NVZeuJVW96xNble2KrBNvH0RIREZEuOQOrKayGT76irqd3f1f6vViretJrfbtQnpoDjzmEydApg5/96gHuUdqJTgWhEHgE5f5AeGypKayGg6XOHqK1M+3lZa3EY0uwjREK84aI5zd6w+FWnQCWK9eNuMJAsThOedkp8TjGZbViV0r1y3bBY7vUV9QgbXJ8JW5PuQYpE1aiRs0iIiLqKZx5HdZjFRi87bRqhBnXY/Vt8KI4YlBjbyx4rPOGBvA6rLFZrjlqvhaqQQVPy3VSQ6zXYS30hAOnHIqgnYRlEeN6pXIcrBxaYN6+nKdduirG9U215cZ1WONdP1Ye4y6kh7ZjPeaIY7JsN9rxyjG/S1HSN8Z1ZTU1WDnhZiw6UIztwXWYpuYSERH1BI6+cYCTMbBSZ9tRdA2m1y7HifcWYqiaR0RE1BMk/1UCiLq7nY8iJYVhlYiIei4GViKny1+HYPAzBBlWiYioh2JgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHc/Sdrhr2vgrP4UuhW7KG2G/dmjUO9XNGqEbn4J2uYrPcmjXuLU6jsd7mNNe9DG96rPfk12/RGu0Wp/ZbpNr3HW/b9nWFqLeOVbdRbYmyf+OWsxmRt1i1vie2fRvr6S1dlG0QERH1VA6tsJ5F2Qov5mMgpqg5FiPzUF9arKbpWHD+IKbsPasWUpfyrxHBzCWC4iZcFtPmjCoUlG1FrVocnwyDXtSJMCfXvXx/vtjeUsz3q8VaqJyNyj75yFVzwvR14dH3G9r3liOW5bG3LcmAG17/ckRYlcHTi5K+OShUbYMMpL0qxO4z1Awz+Z405MBvbFfbtxdrw/lVD8fmfTOsEhERhTgysDbs3Y/68cXYPamPmhPPDcjspx5SFxOh8GgVct1TQ1XNvLEiXLZUwWcOZ7H4d6GkRYRBo/LomolCEQDLT+qhs2LLLqTfvwmrh2tNmwCaWlxIv141haw+pvJnK9tOSGArFoiAWzp2lJqhyPkXp2oB11oL1tU2ixfft384/Lr6I0s9JCIiotY5MrBmTrrPOgQgrmr46nrjLvcNqk1dRw+NnuGmbnatq1vMP6dmxaEFu4xRobArq5YFjeLBhTNahTZvln0IgNltyM0IoGTjGlTIphYuAygcolcqW9t260QYr9gJuIsjj0GE3zfjVESzh+cgt9GLe3xNWrtiixflqTnwxHwtREREZJa0J13J8a2DS7xiOojdWbegqOOH1FLCZPf7bPQq1bvoN2cAdc16WEuIf41Yd7bejX6/rNAGUKcWxZM3axMue4ACsW6vjVXwyGqsWy00xN12lb6uNi22dtnLCi3ysco2njYhMtCWLIOnYam27QIURw43aNkJd2jfKnQTERGRJmkDq6zCGuNYfWkfYfCKg2hQy6hjyZOe9GClT0blUCernEvRNFofi7na3YSmC0BWWoJBr9GLXkdd+nhOGerOBVCZ6kqoC107LmNdj0sch+3Y4m77NqyW841JW98IrUcw3xdAaV7kmNaEyJOqSo33ZBlKL4jjMIdSLdCG9+13B0RwZmglIiIyJG1gNct0D8SwTy+iXrWpY2mVTFPACp9p70J6qn4GfLiyGTm2NJbsNNlHnoPNpupjxPjPWEQoLJdXDjBCpXueCJ05qPTv0oJfm7ftHhU+scr/Acq1IG6EdHk1AdUOndQVW8Uh/coB+nuSjrlFIrSmVqHcEvTDtCEE6jERERF1k8Dq81XjRNYgeFSbuko6PJkuERLDZ8DX+nZFjtfUKo4i7NmvHqCFRPOZ/UdQbhqH2jrrWNmKk1WAUUFt47Yt40xl+DUF9MslxWJb8rJW4nGiZ/Obx8oGPoCvJVbVWR8rW2kab6s5vhK3p1yDlAkrUaNmERER9RTOvA6r/TqrinE9VuP6rCG8DqujWK45Gu1apjKwypOxol7n1Ho91EJPeByq/VqmOtP1UOX4VJ8IqYaI7cfedsS6ca+DKrcjr1hgnARm3W5IaBtyTK+8dqs+W4r3uqJdexYipq6ccDMWHSjG9uA6TFNziYiIegJH3zjAyRhYqbPtKLoG02uX48R7CzFUzSMiIuoJusWQAKJubeejSElhWCUiop6LgZXI6fLXIRj8DEGGVSIi6qEYWImIiIjI0RhYiYiIiMjRGFiJiIiIyNEYWImIiIjI0RhYiYiIiMjRGFiJiIiIyNEYWImIiIjI0Rx9pyvjFqzGLVkjnUXZiu1Y9WlvLHjsPhR14s23evSdriy3MTXdGjUhttuU2m+BGnfbraxru0Wq9Ran9tujxj7uii2zUdDYhlu32pcZ1HOM7dlZtk9EREQxObTCKoOoF/MxEFPUnGga9u7Hqn4D4j6H2llgK+7xBUTY24TLJZvgF4GrZOMaVKjFranYIkJjXxHkxLqXS4pR2OgV22tSS0Xg9AGbtWWR2671eeHLXBZjXRlIvagTIdVYnuVfivl+tRjpmFukb1ebPK7oxy3CZ8GFHBSmqrbBPS+8bskylF4w7duyTC0X6+f20dNw3izzMjHdn49cEZjTr9cWExERUSscGVhlEK0fX4zdk/qoOVF8chDzDwMLPIPUDOoMFYd2ojJjaqgyme2ZikJUoTIUDOMQYbe80YXSsUZV9DYUul2obPgAtaq9umQe8rTHYtvDc0SwC6ApoNqe5aaK6W3IzQAqL6qF/l0oaRFB07Rcbrv85BHVtrneJbZtJwNzFQpHTxXxNp50pPdVD6MJfACf5Visao+L98v0HhIREVF8jgysmZPuizEEwHAWZa9XA2MmduowAGpC0wWgcIgROPWqpuyCr2s2Kp1xnAugMjUHHiOo+dfA7ReBsyWAOjXrqqS6kKUeStlpYkcXzqgwbKWHxlGhcCxVbBGvJaM4gW76I6gUwdszPHoglaEe7qmWbYcdQbkI9+HQTkRERK1JzpOujr2PVRiB1ZNuUDOos8lxmb1Kl2pd9H5ZJTUqnYmQwwpKxfpa938xCk1V1DARhius1VwL2XVvrta6R6GwZacWBnV6tdRKjnGVxz1bBGVbaNSqvznYbBkTa1XrW6yt20uOk411XGo7Maurvl2x1yUiIqKokjCwVmPBtktY8I1xyFRzqHOV+2ajvI8+VlR20deJsGqM12yVCJXujQEUauM55yEvcAZ1cjynbXVtrCvy4Y8WILVxtFUiFJpPmroNqz052rHpofID5Io2+vZHtnqGPuTAGEs6FU0bZ6sxrno4zvKEhyNEI4ckGONQ/X12oVfZ1ojqrT5kwlq5DZPVVfHaQxVqIiIiSkTyBdZjp7Abl7DqeS8Gl8jpYLi9oVo9iTqGGruZUWw5+14OE8hKiz/qU6ONG5Vn55uCoTZMwNqVr59Vn4PNRTNNYVORYXWj7HJfFtl1bzn5aR6ymuMFaX0MrDaUQRtzqgdxPezqVxPQ2lFCqaSNr7UPZdCqq+YxulZadTU1H4W8MgAREVGbJF9gHZmH+tJi0zQOUyAvayUezxmhnkQdJW9IDtDoDZ99r052yrWEMDm2VQa/xVhr7up3jYInNYCSCiMEiucdrUJu5qhQMA2FVdPJVyGmsBoOzNHJ7nt3Qw5WxXqeCpfaOFTXTLwZCrpy0s/yl5eduhwtNAvRKqn2E9KsVHV1dPTtATVYOeEapKTcgZXH1SwiIiLSODOwHqswVU+B3dv0auqCY/pi6kKyimnuetfGocbvSg+Tl5YSYRA74VaVTDkGNhQ+tRApH1ShQFuupi36mf5aIBT/r/QvDS8LhWIjJOuT++JUW9gMj1/Vpo1V8LTh+rHh8av6VADbNWDtY2pttBO64lZXh+KeuyeI/7+Hxo/1OURERKRz9I0DnKxH3ziAOsbOR5EyrQYrqt/BwuFqHhERESXpVQKIupUdeCTlGoZVIiKiGBhYibrcNLwQ/AzBIMMqERFRNAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORojr7TVcPeV+E5fAlTZhRj1Ug1U/rkIKY8X40TqqnJGof6OSNUo+P16Dtd+degl69KNVwobcMtTvVbqC5FSYtqZthucardQtWLctXKdZtu3SpUbJmNAu32rTp5v//V9tudBrbino07UZmaD3/E7Vljb1uSt2B1y3v+R2y3teMWjP1qjejvi3H8UY+biIiIonJohfUsylZ4MR8DMUXNiXDdCPhKi1FvTJ0YVns0Gcp8ARHGNuFyySb4Regq2bgGFWpxayq2iNDXV4Q9se7lkmIUNnrF9prUUhkKvagTQVJbfn++CMdLMd+vL5VhUruHv7aumDw5KPfZ9y22UbETWRk5qm2Iv219+WwsQA4K1RyzWp8Xvky1bsRxCzLEb6yCR70vl0uihHjxnIILYvupqk1EREQJcWRgbdi7H/Xji7F7Uh81h5yi4tBOVGZMDYWxbM9UEfCqUBkKfnGIsFve6ELpWKMyeRsK3S5UNnyAWtn070JJiwh0RtXTNROFGUD5ySNas+5iALl9TCnwehdy1UODDJYlyEfhEDXD0Mq25XpNozfhTU9/rW2X7VluqsbehlyxbqU4Hp0Iu0dliI9XaT6C+b4qFI6eCmtNl4iIiFrjyMCaOek+6xAAcogmNF2ACING4NSrlrKLva7ZVG2M5VwAlak58Bihzr9G635HSwB1olnbLB5njEKevlSvqMru/wtntECbNyRHBGOjKqpXUitNz5eBeIFYVppnHgaga23bMpBecRd94AP4WsSLOr4YvUpn61PZVj2EKxVbxPuUUcxhAERERFcgeU+6+rQanhIvBmtTBXxqNnUOORazV+lSrZvcL6ukoWpjAuSwAhnqxIe2WXavI4Am8+qye10sdzfkwH9/PnJVoIV7ntYdD1943+ZxpLL6C3dx/PG0sbbdFrJr31wplkEcVeI7aAxXWIZS7IR7i1691SvLOdhsH/NKRERECUnOwHrjOOw2jV/1jbmEYobWTlMuAmN5H308p+wmj+iqj6dFBLmNARRqwW4e8gJnRGB0Id1YvdGLXkdd8Mvl8oQprSrrQpZcpoXNXUjXxokWI8u/NFzJlCHyQj5W2U6isoi37URpY3irUOixdf+nmvedjrmjc1T1Vq8EZ3nEa1VLiYiIqG2St8JqkukeiGHqMXWkdKT3Ff/LKDaN59SHCWSlJTAyUxtzKs+eN4U3U2jMTpMJMAebTWf2a135ffuLthwnWoXcUAX1NqyW1VkRgMv9QMXJKj0MG13y8ioGqi2HEMTfdoJkWN0oq7jLrF378nXFqtRqwwX0kK8PF9CvNKC1bcMGiIiIKLpuEFjPouz1apzIGgSPmkMdR44jlZXK0Nn16mSmXMvYTDm2VYazxVhr7up3jYInNYCSCiOoqRCaOUoPje5R2glcBUZXOo6IMBowjZk1n+gk+D9AuazOXi+Oa5asupomjzhOeVkr8VgLlwlsOy5TWLVfCkt/XVUihJqudmC8LtdMvGk+LjlcIFW/rJVW5VVrADVYOeEapKTcgZXH1SwiIiLSOPM6rMcqMHjbadUIM67Halyf1TBszHTsnnSDanUOXofVuA5rDjbLrn3V0snAKiuJ0a5FaizTW5HXQrVeK9V6vVLrsljXOtXIY5Td/5ZQGGfbltcUZjzHfv1XnXn/rV/jVae/fnlFAkuVVqhZfgeGLXkPxTs+wwv5aiYRERE5+8YBTtajAyt1jJ2PImVaDVZUv4OFw9U8IiIi6h5jWImS2w48knINwyoREVEMDKxEXW4aXgh+hmCQYZWIiCgaBlYiIiIicjQGViIiIiJyNAZWIiIiInI0BlYiIiIicjQGViIiIiJyNAZWIiIiInI0BlYiIiIicjRH3+nKuAWrcUtWO98GL4rrVCNrHOrnjFCNjsdbsxq3MY1ze9SorLdmRUYxLs+y3c9f3be/MtoywXKbVNNzan2L4fYHtMeG2LdfjX3cxvbj3rrVfmzGMasmUvNtt4XVRd02ERERxeXQCutZlK3wYj4GYoqaY6UvL4YIqaXF+tSJYbVHk8HMFxBhbxMul2yCX4Suko1rUKEWt6ZiiwirfUXYE+teLilGYaNXbK9JLdVDZy+xMU+GmmEhw64IfOKT19cXky3Qynv4h5aJKRwKj2C+D9is5sc8bhFMCy7koDBVtQ3ueabtLkPpBfNxi21vrIJHvSfacuzEAtPr0sTaNhEREcXlyMDasHc/6scXY/ekPmqOzbH3sapf51ZUSVdxSFY+p4Yqk9meqShEFSr9ejsuEXbLG10oHWuEzNtQ6HahsuED1MqmWL7g4lRcLpqJdG25jX+XHnajVF1bdxtWl8xDnmplD89BLgJoshRkZaitQuHoqdH3H5KO9L7qoRQ4gzq4kB6q1tqWaxLdNhEREdk5MrBmTrov6hAAg6/6NIalXcSCEi8Gq2nBMbWQOlATmi4AhUOMwCgrnl6Ui0d1zbZqYjTnAqhMzYHHCHb+NXoXfktABD7BNRNvxgmjFSerkNvnDOaXzkYvNc1PJCgnqGKLeC0ZxQl01R9BpQjenuEqerpGwZNahYKyrXrwlpVU83Ih8W0TERGRXRKedHUWDeeBE4cvwmMMB5gxALu3vYqyT9RTqMPJsZi9SpfCl7kMflklvWgdOxqXHFYgA6fWRV+MwohKZzR6WK4UATfX6Jr35KDctxhrTetW+peGwmyvLUfUXDsRtCuslWK9+puDzXECszZcQdu2DJ+mdZGOuUWb4M+sgjv0ukzjYxPYNhEREcWWtFcJmDIjDx71GCNvxYLrLqH+jGpThyr3zUZ5H32s6JuedNSJsJrbJ5Te4mvZCffGAAq10DkPeRHd6fEVesLd+nBPRWmqCLvn9Ga2Z7kaQ6rGkV7wRg2t2jha5MMfCpB6gM0ybzsK8/b9fXahl1FR1SrNs+GWwxnkMncABSK46tXfxLZNREREsSVhYL0Bmf2A+rNnVZs6jxqbmVGsBVWdXvnMSktgZOb1LuRqZ+ebwps2TMCFLNWMTd93QkMPNOnwZEamYP0s/RxsNp/BH/gAvhY9iOsVVP0qBlo7FEqttDGwxlAGOba2JVxBlcFWVp3Lj4p1r2DbREREZJWUFVbPiAE4cfh9+FRbOwnr0wHwxBn3Su0jb0gO0OgNjx1VYS3XMjZTrzj2KrV21+tjPQMoqTBVJo9WITdzVMTln6KR+64U+wud2R9134o8gcsfMI23NYVV08lXGjl2NlSZVdXZVP3SU/IEsGjHpp98Nsq0HfOwhib4GkSjb39kJ7ztGqyccA1SUu7AyuNqFhEREWmceR3WYxUYvO20aoSZr8dqXKNVNwDeUtMQgU7A67Aa1ySNEgC1wCoridGudWos01vyMlThau0RzJfjQ1UrJOa1Vs37tm434jqrctys+TqpBvv1VDX6tppGhy+LFXGNV9t6rS0Pi9y2oWb5HRi25D0U7/gML+SrmUREROTsGwc4WY8OrNQxdj6KlGk1WFH9DhYOV/OIiIgoeU+6Iuo+duCRlGsYVomIiGJgYCXqctPwQvAzBIMMq0RERNEwsBIRERGRozGwEhEREZGjMbASERERkaMxsBIRERGRozGwEhEREZGjMbASERERkaMxsBIRERGRozk6sMrbrw4u8WLBMTVDcxZlK7zafOv0Kso+UU+hLiVvU9qrdLaa1oTv/Z8QeXtWY93ZuMfXpOaHVWyRyxZjrelOqBbyNqxy/bKtqFWzdLG3rW8zcprvV09Q4u7b2K82RX+OsR/7dnXytq1xtk9ERNRDOTSw6qF0PgZiipoTdgOKFhWjvtQ0zRgg5vdGZs++W6wz+NfA7Xdhc8kmXBbT5owqFEQEx1hkYPOizr1MW/fy/flie0tN4U4PnJV98pGr5kQS26jYiayMHNU2xN923iz9eEOTWJ4LF9Kv15e3um/xunttrILnfmMbyzHXpZYZxHMKLuSgMFW1bWp9XpT0FctVm4iIiHSODKwNe/ejfnwxdk/qo+bE56s+jWFjboVHtamriFB4tAq57qnIU3PyxoqA11IFXyIVQ/8ulLSIwOZJ19uumSjMAMpPHtGaFVt2IV0EwtVx7galhT7ko3CImmFoZdt2tcerUJkxNRQ64+9bvu4ASu+PElJDROD1VaFw9FSoI7AKbMUCEZ5Lx45SM4iIiMjgyMCaOek+rBqpGq355CC8dQNQPOkGNYO6TgBNLS54hhuRTIS0jTtRKeefU7PiqG0WqTZjVCjsyqEFBY3iwYUzWoU2b1a8QCgYoS9vJrLVLENr27Y6gnItPN6m2q3sO/ABfOJ147hpKIStqlyxxYvyjGKsdqsZFnpVGO7i+K+PiIioh0r6k658vmqA1VWHkd3vMrh5AY8cFgDUNUeORY1Jdq+L0OduyIFfds23BFCnFsVTcSiB0JfAtmt9u0S4DFdXW3UuIEJ5FXwoVsMBlqEUO+Heoqq3IkiXN+Zg86xwALaQ1V/kY5VR/SUiIiKL5A6sWnW1N+5ys7ramewnKFlPjAqgZONSNI3Wx3Kudjeh6QKQlZZgGGv0otdRF/wy+BXNRLYMg6kuZKnFMWnjQ1sJfQltW1ZXAygcEiNcxpJq3nc65o7OUdVbvXqa5ZkXqu5ayaECgahVYSIiItIldWCV1dUTWbegiCdbdSr7CUpvhoKaC+mpQK57manrWx8mED55KbbsNFnSzMFmGSb1WXpXft/+rYa5ipNVQMtOuI0g7Qu35YlViW5bq66K8FkYtes+hutdsavA2nABoNxnBPylKDHactiA/wOUayHfWO4Nt40KLRERUQ+XvIFVVVcXeEaoGdT10uHJdKHS7w1dlkkPgDnwmLvXY112yj0KhahCQSioJV7tjDjL35OjVT1lNVULzwltW80b3cZqp2sUPKlVIoQalWZ18lnmKGS7ZuJN83HJ4QIi1Bd6xGMZnt3zTMvkVCyO04VSebUB8xCC4ytxe8o1SJmwEjVqFhERUU/hzMB6rEJdW/Ugdovm7m36tVbD12M9i7LXWV11omzPcvhFQDQqhtpYUVNVM77bsFoGNtl1r6qNcgysUa0NXd9Vncil7yPRa5bG37aknRgVo7oaf9/pmFtUjCz/UrXtpfBlLjNVntvB8Htwz3jx/wNNDKxERNTjpAQF9ThhZ86cQf/+PTspvvCCF488Uqxanev48V9j+PCbVYt6ih1F12B67XKceG8hhqp5REREPUHSXyWAqNvb+ShSUhhWiYio52JgJXK6/HUIBj9DkGGViIh6KAZWIiIiInI0BlYiIiIicjQGViIiIiJyNAZWIiIiInI0BlYiIiIicjQGViIiIiJyNAZWIiIiInI0R9/pqmHvq/AcvoQpM4qxaqSaqfg2eFFcpxpCtOd0JN7pKjZ5G1O337hfag42l8xDnmq17gjml3pRrlq57mi3OG3C2rKlKGmR99xfjrkuNTuwFfdot07Vyfv1m2+9Gn/b1mWa1HzLbWXjva6KLbNR0KgamvCxRS7TRR5fjNdFRETUwzm0wnoWZSu8mI+BmKLmmMkgW4xxqC8t1qcZA7B7WwV8ajl1If8aEepcIsxtwmUxbc6oQkHZVtSqxfHJwOZFnQiSct3L9+eL7S3FfL9arNT6vCjpm4NC1daJdSuq4Llf3+9lTw7KRcDU7/UvJbJtGULV+nIyhdVEXpcMoKF1S8KBM2+Web6YxL5zRaBNv15fboj+uoiIiMiRgbVh737Ujy/G7kl91Byr+uZLGJaWplpC/z4Yph5SVxKh8GgVct1TQ5XHvLEinLVUwRcKjnH4d6GkRQQ2o+rpmonCDKD85BG9LQW2YoEImaVjR6kZhnTMLTJVJd2jRPALoOmcaiey7Ziu8nXZ1B6vQmXGVGsFNebrIiIiIkcG1sxJ98Xt3veMGIATh7djwTHZOouy16txImsQPNpS6joiILa44Blu6mbXuuhNwTGO2maR/jJGhUKh7ILXutIvnFGVTFlF3SnCaHGbu8tb33Y8V/e6rI6gXAumt6m2dOWvi4iIqCdIzpOuRuahvnQcsM2LwSXbsW/odNTPGaEWUteT3e+z0avUK/66kN3nQF1zk1qWAP8ase5suBty4Jfd5y0BaMOVZZUU+VgVMaY1UsUWL8pT81FoGSMqxNq2pgoFYplc3qvUPJzAEP91lfuMdWfjHl/011vr24Vye3W1Da+LiIioJ0rOwHqsQgTVjzD4MTmGdRwGH96OwSsOokEtpo4lTyIygllkOAugZONSNI3Wx2uudjeh6QKQlZZgGGv0otdRF/xyrKccQ3ougMpUF7JkVdMXQGmeaVxpDHr1NAebzWNQpZjblm7DamOMqZw8LvE6zKE1/uuyjlMtRpZ/aZTQKqurARQOMVdXE39dREREPVUSBtazKDtwGsPGTESRdqGCEVglQuuUT6vh1YYIUEezn0QUPtPehfRU/ez78Nnvene6/QSjaLLTZNnRGjS1rvy+/ZHt/wDlWmg0grI8o1+1t4THoepn8gOl91uvTBB326ptoY2BNbT1dd2G3Az10ESrrtqrvgm+LiIiop4sOSuswonmZvVIOHYKu9Ebg/urNnWRdHgyXaj0e0OVST2k5cBj7gKXl5+S4cx+9QAtJFahIBTUTBVJ9zxLSJZVzELt0lHi8Sy9YhkOq1EuCRVv21HoQwqM407wdRn8a1DQaB7zKqn9jbZVUhN4XZrjK3F7yjVImbASNWoWERFRT+HM67DKLv9tp1UjLHyt1WosKDkoQqqhNxY8dp+quHYOXoc1Nj04qmRnu5apxrhearRlsovcdD3UyGuVGuTzdiE9FE6t64VY9hFn23Jsq69KNYSMYmtgFGK/Lvu+rddolbRrsV6I9nrt7K/LUIOVE27GogPF2B5ch2lqLhERUU/gzMCaBBhYqbPtKLoG02uX48R7CzFUzSMiIuoJknZIAFGPsfNRpKQwrBIRUc/FwErkdPnrEAx+hiDDKhER9VAMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaAysRERERORoDKxERERE5GgMrERERETkaI6+01XD3lfhOXzJdEvWMN8GL4rrVOO6EfAtGodM1ewMvNNVbJZbmEa5TWl81tuc5rqX4U1P+J788bZtXaYL3X7VfutVg7oFq3br1EY1z8R+a1j9efJe/9Zbp0aub32OZbn9lrSt3BbWvu3Yt6slIiLqpmRgbatAICD++1kHTmeC3uXrgv9nT2Vw/j+uC87/pXV5/Z6fBDOXVwbrVXvfK+uCma/8yvKcjp7WrVsXdX5nTNXV1VHnO2I6tir4pWWrgrtUe9fmWcEveX8arDE/J+Z0KrjGOyt4975Tevv0T4N3L5sVnHdMLW9l2zX7FobXbXWy7cs+afteGFxz2ph3KDhPHss++3x9kscSOk77JI978yHV1vcbbtunth4XJ06cOHHi1P0nRw4JaNi7H/Xji7F7Uh81x6waXll1HR+uqHo8IzCs7hR8qk1dpQlrj1Yh1z01VPXMG5uP3JYq+KyFz+j8u1DSkoNCo6LqmonCDKD85BHRuMpt2wU+gM+8L5va41WozJhqqpDuQvr9m7B6uN5uE/c8U8U0HZ5MsdELZ1Cr5lilI72vehiNqz+y1EMiIqKewpGBNXPSfRFDAKx6Y3B/9VC6sQ8G4xIaPlFt6iIBNLW44BluhMAjmL9xJyrl/HNqVhy1zSJ5ZoyydPFrXeFauLu6bdtVHNopgmQ4/FodQbkfKB0b7pbPm2UdAtBxjqCy0fw6bfwfoDw1B55OORYiIiJnSMKTrkbAk3UJq3zVqi3Hsx7EbvWYnKAJa8tmo1epF/BswuYMoK65SS1LgBzTWTob7oYc+O+XVdQAjOHKrW270r9UW1ebtsjKbBSBrShvjFNd9e1Cuam6mqhyn9qvmO7xxXi9Yt8L/AEUjjaNYRVkONfX9UbZt/GaxeSriliXiIiou0vKqwR45ozDlLqDGFzi1SbfCNFGb2R2/HlgJMiTgIxgFhnOAijZuBRNozfhcok8OagJTReArLQYFUO7Ri96HXXBL9a9LE9MOhdAZapLdYPH33a2Z7k2X5+WofSC2FaU0Cqrq5WmSq6VrK6KQDkkXF1NRN4sY79yKkaWCM6RoVWvCsO9LOKkKfOx+/vsQq+yraYhA+mYW2RsexnSj8YJxERERN1Qkl7WagRWlRajXk2r+l9E/XVyWAB1Bms422Q6i9+F9FT9zP5wINO78tOvV804stNkWTEHm01n0GvDBPr2F+22bluNFbXTqqsuS3e/mVZdTc1H4VWdhX8bcjPUwxB19YOMYstVD6LJHp5jqyqb6a+r8uKVDNwlIiJKTkkaWE0+OYgpz/8Gd32jcy9rRdGoMOX3Yq3KU3oAtI25FKHxHlmdtVQRBfcoFKIKBaGqqLnameC2DUbXu61SqldXY3X3q/1dbZe7f4126SvLeFsVVs2Xq4rliirAOx9FSso1SCnaoWYQERF1HylBQT1OWIdfh/VYBQZvO60aYcb1WI3rs+oGwFuaB49qdRZehzU2y/VQ7dcclWRglSdMRVtmhDvVsl9zNPa25TjPpShp0RpC5LVS9eudBiLnK9r1Ti9EOybbfkOMfViPOZHrw0rGa4tYbgm29tcV6zqsO/BIygx4xy/HifcWYqiaS0RE1B04M7AmAQZWcpYarJxwMxZlb0OwbJqaR0RE1D0k/5AAoh6uZvkdSElhWCUiou6LgZUoyQ1d/A6Cwc8YVomIqNtiYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR3Psna58G7worlON60bAt2gcMlVT6urbs/boO11ptzitUo0ot0CNy3ar0Yj761tvc5rrXoY3PcY9+a9mXcG4JaxqRrttbLzbymq3bm1UjYh14+3bfutWIcq+LduPeG1EREQ9mAysbRUIBMR/P+u46Zc7g5mv/Eq1zwS9y9eZ2mr5P+4M7lPtfa+I5csrg/XG8k6Y1q1bF3V+Z0zV1dVR53fKdPqnwbuXLQyuOa23a/YtDH5p2argLvvzYky7Ns8KfmnzIdU+FJy3bFbw7n2nVPtUcI3X1Nb2NSs475i+rtxX+LmtrKuWG+vq7fBxRz5fHZv3p8Ea1TZP2us0LbO+jvjHre873nukrx/eHidOnDhx4sTJPDlzSMDIPNTPGaEaN8AztDdw/iIatPZZlB04jWFjbg1VVD2eERj26W/g+0TNoA5TcWgnKjOmhiqq2Z6pKEQVKv16O67AVpQ3ulA61qgc3oZCtwuVDR+gVjb9u1DSkoNCozLpmonCDKD85BGtme1Zbqpa3oZcsazyoqqG2tdV2zbWReAM6uBCeqgSnI70vuqhJI/tQmTVU3cE5f4ACkeHl+WNzUdu4weokI1WjrtVcv2+rKgSERHFkoRjWJtR/2lv3OW+QbWrseD5apzAJdSfUbOogzSh6QJQOMQIVrKLXu/qrmtu0mfFcy6AytQceIzQ6F+jd7+3BESYBGqbxeOMUcjTl2rd81oX+YUzeqBtTaoLWeqhlJ0mdmSs6xoFT2oVCsq2qnC8RmzbBc9wPWTWHhehWwRYX9ls9CrVp3t85tckwu716qHk6i/2FUCTOOSrPe6Kk1XI7XMG89V+5TQ/kT8AiIiIegjnB9ZPDmL+4UuYMt46hlWrtK7wYnDJQWBGMbwiqdSfPauWUUeT4y17lS6FL3MZ/LJKalQ6EyHHkspg5gM2lxSjUAW/EDlGVix3N+TAf38+clWgtVCBM1StdY9CYctOlIeC3hHMD42zldIxt2gT/JlVcIf2HR57WyePv1E8P28TLpeISewX/qUqOMpqbgAlh8IV04ottjGpUtzjFmFZ7lebFmNt6PXqfwRUiuCeK/crJ08OykXoDT+HiIioZ3N4YNWrpxgzHatGqlmaS1j1/HbUjy9GfWmxWHYWDeeBwTcYVVfqSOW+2Sjvs0wLV7KLXoa93D6hvvb4RKh0bwygUAtn85Bn76pv9KLXURf8crnsnteqstbKqRZ4RRgt9JhP9roNq7WgZ4TCD5Ar2ujbX3Xjy2qwCJMXp2rH7XcHtABprmTmuovD21Pd+kblOG+WCNby2FTorBwig3aixy2OTXu9avK4ULLRGkgLPeK9UI/hnorSVBHiz6k2ERFRD+fgwCrCaslB7M4ah92TzEE0DYOvA4ZZQqw+TGBwf9WkDqLGfWYUW87clxXCrDSjHcf1LuRqVxUwhTNTsNO68JGDzaZxpFp3eyh0CupMf7iXYbVbzTO454VDoQjDWWLdUJBW40w3q3GicjysrAyXH9WHCGSJ58WvEltD5+rrRdBuy3GbyWqwemi8pwkNqSAiIuqhHBpYw2E1fPKVQT8J68Th/ShTJ1k17P0Iu68bCE/HXmmLhLwhOVo1MVSZVEEw1xIe9Wqmtetb0MaRBlBSocaRyucdrUJu5ig92GlBrgoFW4yud3WykzFm1hRWLZerikK7PFVDDlZZnmceetAEX0M4VGYPF6+hcVf4eOVJWKYxrhbacVTBk6cCamvHbaMNJzCN5ZXvaaV4H7UTuKRo7+nxlbg95RqkTFiJGjWLiIiop3DkdVit11gNmzJDdv/rjy3PiXKd1o7G67Aa40NzsFl27auWzrhearRrtBrL9FbEtVJt1ywt9GwKVVIt1ykNMfZh3W6065harrEq2Z9jeV3WY7euG+01xz5u63aFVo8t2vZrsHLCzVh0oBjbg+swTc0lIiLqCRwZWJNBjw6s1CV2FF2D6bXLceK9hRiq5hEREfUESXhZK6IeZuejSElhWCUiop6LgZXI6fLXIRj8DEGGVSIi6qEYWImIiIjI0RhYiYiIiMjRGFiJiIiIyNEYWImIiIjI0RhYiYiIiMjRGFiJiIiIyNEYWImIiIjI0Rx7pyvfBi+K61Qjxq1X9ef0xoLH7kNRJ994i7dmjX4L09a1fgtV/V79O1EZbZmg36I1cr8Rt14VLLdINRjHb96+sU+9BaTmw180E9mqKbV++1Qhyraj31I2xrERERFRBGdWWI9VoBjjUF9aLKbpWIBqeDZUq4VSNRaUeOFLG4Fhag51EhnsfAERFjfhcskm+EXgKtm4BhVqcWsqtoiw2leEObHu5ZJiFDZ6xfaa1FI9FPYSG/NkqBkW8n79s1HZJx+5ao5drnuZ2rY+RQZCsQ1x/IUZtoTtmok3Tett7rsT7i1H1EJBBFG33yVCqlqeUYWCsq2oVYt10bedNyu8XW26Xx6/C+nXqycQERFRXM4MrCPzUD9nhGrcAM/Q3sD5i2hQc3wbPsLgx4qxitWpTldxSFY+p4Yqm9meqShEFSr9ejsuEXbLZWV0rFE1vQ2FbhcqGz7Qg59YvuDiVFwumol0bblVxZZdSBdBefVwNeMKVGzxolwcf2EfNSOGrD7m0NmEtUerRBieGqqo5o0VobOlCiKfhiS67drj4v0yvYdEREQUX1KOYfXM6fwhACQ1oekCUDjECJyye1+ENPGorjlcJY3pXACVqTnwGEFNq1qKxNcSgDb6Q1Y5owwBMOTNasvQgyjE/goac7A5zj50TfA1BEyvM4CmFhc8w40YfQTzteEDYv45NSvhbR9BuQj34dBORERErXF+YP3kIOYfvoQp4yPHsFLXkeMye5UuhS9zGfyySnrROnY0LjmsoFSs7wM2y2EBMvi1YfV4Kv1LxXHJYxOTuUtfVUkLPVHGnRrk+FNt3aUoQT4KIyr4MqDL5V7xV5McFmAE9QS2rdT6dmlVWFZXiYiIEufwwFqNBc9XA2OmY9VINYu6XLlvNsr76GNF3/Sko06E1VxLF3ocLTvh3hhAoTaeUwS8wBnUyfGc7RDgsj3LTWNFl6H0gjcUWmt9Xm3sbNyTnNzzwuuPDsBdah6bG0DJxqVoGq0vX+3Wq81ZaemJbVsjq6vmyi0RERElwsGBVZ5YdRC7s8Zh96Qb1DzqWulI7yv+l1GsBVVdOLi16nqXdrJR6f2mSqQ2TMCFLNVsP+nwZBopWO/iR6MIsKr6qg1F0NqLsTZaddc9ylT5FYE6VT+hKxxK9WEC6dcnvm2tupoarXJLRERE8Tg0sIbDavjkK3KCvCE5Whibb5xk5d+FkpYcEeZUW2N0ndvCoGsUPKkBlFQYZ9frXem5maMsl49qF/IErlA1Mx1zi4zKqz7JYQzapadKoo+L1cOlMd5WD7+Vfm/o9YSXJ7ptVV0dbb1UVlgNVk64Bikpd2DlcTWLiIiINI68DmvD3lfhOXxJtcKmzCjWhgZEX96512PldViN67BGux6pDKzyWqvRrtFqLNNbsmoZrtbKy1bpJ3FZqGuaRrvOavg6sNbthuerpo22LXlFAuMkKctrElq7DmuU5YaIbQvatVgvxF5Hqll+B4YteQ/FOz7DC/lqJhERETn3xgFO16MDK3WMnY8iZVoNVlS/g4VXcekuIiKi7iYpL2tF1L3swCMp1zCsEhERxcDAStTlpuGF4GcIBhlWiYiIomFgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHY2AlIiIiIkdjYCUiIiIiR2NgJSIiIiJHc+ydrnwbvCiuU43rRsC3aBwyVRM4i7IV27HqU9XMGof6OSNUo3PwTlexWW+hGu3WrfFYb89qvXVra9u23Z5V3dLVIrAV92zciUqtYb19ayLHrd1itTHKbV8t2xXst2613Po1cn19u6ohFHo2YbVbNQzGPuLcFpaIiKg7cmaF9VgFiiFCaGmxmKZjAarh2VCtFgINe/dj39Dpavk4TKk7iCl7z6ql1KVEMHP7XSLsbcJlMW3OqEJB2VbUqsXxycDpRZ0IqXLdy/fni+0txXy/WtzKtiu2iLDaV4RUbXkxChu9uMfXpJYKMjRurILnfn39yyWm0Gjbtt8dsB23DNKzUdknH7lqTphYZtnuMpRiJxYY+5ZB0xcQIdXYNlCycQ0q9KVaUC4Q33h9XTF5clDuCy/XifemYieyMnJUm4iIqOdwZmAdmWeqmN4Az9DewPmLaFBzMifdh92TblCtEfBkASeam1Wbuo4IVUerkOueGqpM5o0VAa+lCiKvtc6/CyUtOSg0KqqumSjMAMpPHhGNVrYtQmG5rHyONSqqt6HQ7UJlwwcqdMr1ZWi0VUY1kdvO9kxFoem4K7bsQroInKuj3YkqcAZ1cCE9tN10pPdVD4WKQztRmTE1tF9t26hCpQridRcDyO1jOqjrXRGhuNbnRQnyUThEzSAiIupBOIaV2lEATS0ueIYbXfiy8ii7ycX8c2pWHLXNIh1mjAqFRq3yKLvJL5wRobOVbZ8LoDI1Bx5LxVRsryUgwqQQ+AA+sT6OL0av0tn6ZKv8ZqWFhx7Ibvv01PBx582KFnQV1yh4Uk3VXrFvOWxAP9YmNF2ACJpGkBbhuEwf8lDXrFdg84bkiPBqVJLF8goZcMPvgwzjC8Sy0jwOAyAiop7J+YH1k4OYf/gSpow3j2E1kcMH6npjgadzx7BSPDKUyVDoBTyy6z4czhIiu+5FoHQ35MB/v6yiqtCpaWXbsvtdhlEfsFkOC5CBVlZJZaBFFXyhrne92969RVZv0+HJdKH8aDjAahVNYyxsq9Ixt2gT/JlVcIf2HRlw5TjVXqVL4ctcBr+s/l5U5Vv3PHE8xYAvvNw89lZWaOEujh2YiYiIujmHB9ZqLHi+GhgzHatGqllmIsxO2XYaU2bch6KOPQeMTPTgFZ4s40RFQCzZuBRNo/XxmKvdeoXRWr2Mo9GLXkdd8MtQKU8s0iqnLmRpC1vZdosIoBsDKNQC6Tzk2bvqU/OxKnQClwiZo3NU9VZ20xfrAVa9pgWYilKx3/Tr9WfHp4do98Wp2nFp41/FNkJjb4VyEUbL++hjc+VJZJZhAFpA14ccyOCa5V8arv7Kau0F83ETERH1PA4OrCKslhzE7qxxpvGqJjKsxguz1GHyZumB0ZjCZ/HLbnT9zP7wGe56V34iwS87TQa4HGw2nQGvDRPo21+0W9m2Nu5Tnn1vOrPfHHblckul1k6vkoZfk4ihctuJVDXV2NvNqiqa7VmuVVD1iq0az5pRbHqfzEFbhF1t/KxRQb0Nq2VlWITvchF4K05W6UFcBWntSgOqbQ7ERERE3ZlDA2s4rEa9XJUprEYNs9RF9K71Sr8Xa1Vvd61vF8rNY0slo9veNoYU7lHayUgFWje9dESEtoAa/9nKtrVxpAGUVBjbVEEwc5QeftU40/JQNdi23EK/tBY8bbkclxp6oGmCr8EI2voYVVk5Dl/tQA+4uabLVoWGB0j+D1AuA7oI4vY/DuQVBLRLZonHlste7XwUKSnXIKVoh5pBRETUfTjyOqwNe1+F5/Al1QqbMqNYq6ZartEa0hsLHuu8oQG8DmtsluuZRrtmqAysMa8nqodF4zqs9uuRxt+27JoPX4fVfg1X+7Yty2W3fJzrpFqv0WoIPy9iuf0asJbt26/xaj2uaPsPkduRQyYi3rcdeCRlBrzjl+PEewsxVM0lIiLqDhx74wCnY2AlZ6nBygk3Y1H2NgTLpql5RERE3QMva0WU5GqW34GUFIZVIiLqvhhYiZLc0MXvIBj8jGGViIi6LQZWIiIiInI0BlYiIiIicjQGViIiIiJyNAZWIiIiInI0BlYiIiIicjQGViIiIiJyNAZWIiIiInI0x97pynL71etGwLdoHDJVE8cqMHjbadUQssahfs4I1egcvNNVbNbblNpvQ9qaOLdPtd161X7704ots1HQqBqC/bauIcZtUuPePtV+e9T4+9YYt5xt5bgMMY+PiIiILJxZYRWBtBgihJYWi2k6FqAang3VaqEwMk8tU8vPH8SUvWfVQupSIvS5/S4RUjfhspg2Z1ShoGwratXi+GQo9KJOhFS57uX788X2lmK+X19a6/PCl6mWlRSjsNGLe3xN+kIhb5a+T2PdOhGc19pv/y8DsS+Awgzbjfpl2BTzS+/X1/eLIFmycQ0q1OLW9i1Dei/xZE+GmmFiOS51bLkiEKdfr55AREREcTkzsMpAGqqY3gDP0N7A+YtoUHOsbkBmP/WQupgInEerkOueGqqo5o0V4aylCiILts6/CyUtOSg0KqqumSJYAuUnj2jNbM9yU7X1NuSKZZUXY2zY1R9Z6qFZxRYvyjOmorCPmqFUHJKV0amhimq2RzwHVahUYTnuvkXYXXBxKi4XzYTxjHhqj4vtmvZFRERE8XWDMazV8NX1xl3uG1Sbuk4ATS0ueIYbse0I5ssucjn/nJoVR22zCIAZo0JhV1Ytta70C2cSrNCa+D9AeWoOPOZQ6F8jtpeDzfaufBG0my4AhUOM+XqlVw5LqGsOV1FjEsH6zYhtxnIE5SIEl45N9PlERETk/MD6yUHMP3wJU8abxrAKDXtfxeASr5gOYnfWLSjq2CG11CYy8M1Gr1Iv4JHDAhIMfgY5lrR0NtwNOfDL7vOWAIzhzCFa+HTZgp+xXzH5qlA4eiay1RKj+lvoiT+eVo437VW6VOv+97td0Su4UfedmFrfLq3Cy+oqERFR4hweWKux4PlqYMx0rBqpZimZk+4LjWP1pX2EwSsOxhgyQO1ND3XhyTyWU1ZZSzYuRdNofbzmardevcxKS6SzXGj0otdRF/xyrGeRCJznAqhMdVm797XxpjJ8mk+KktIxt8gYK7oM6UfDxybHoJb0LY57klO5bzbK++jjVGX3f50Iq7l9bMky5r4TIaurAVMll4iIiBLh4MAqwqpWPR2H3ZPid/dnugdi2KcXUa/a1LHsJxGFx3a6kJ6qn9kfDob6MIFETjDKTpMJMAebZVDVZ+nDBPr2D1dK1Zn4sOwjmnR4Mo0KaRN8DeL/MgyrkK1dxUBryxOz0pHeVzwto9hyRYKIoJ3wvqPTqqup+Si8gnWJiIh6MocG1nBYTeRyVT5fNU5kDYJHtamrqJDo94bOztdDmm0sqQx+Mjjarx7gHqWd6FSwRT/JKqIiaQqM4WAZi3ldc+VVn2R3v3ZpqhK9Upo3JEcLsMYVCYwTwHKNcNmmfUejjscyTMGsBisnXIOUlDuw8riaRURERBpHXodVjk/1HL6kWmFTZhRrQwMilvM6rI5iuQ5raj78poqpRoW/ymjLRLAzX4fVfK3S6NczNa6XKsevmq6TKsS7zql2jPLM/pjXYbVePzb+vq3HHGK6Hqu2/oVorzesZvkdGLbkPRTv+Awv5KuZRERE5NwbBzgdAyu1u52PImVaDVZUv4OFw9U8IiIi6g6XtSJKdjvwSMo1DKtEREQxMLASdblpeCH4GYJBhlUiIqJoGFiJiIiIyNEYWImIiIjI0RhYiYiIiMjRGFiJiIiIyNEYWImIiIjI0RhYiYiIiMjRGFiJiIiIyNEce6cr3wYviutU47oR8C0ah0zVDDuLshXbserT3ljw2H0o6sSbb/FOV7FZbs1qu8Vp66y3Oc213bs//rbtt0g1L7ffutW4rapqGreLVc1ot5S1357VeuvXeMcd5datUW9LS0RERFHJwNpWgUBA/Pezjpt+uTOY+cqvVPtM0Lt8nakdnur3/ETM3xmc/48/CXrPWJd19LRu3bqo8ztjqq6ujjrfEdOxVcEvLVsV3KXauzbPCn7J+9Ngjfk5MadTwTXeWcG7953S26d/Grx72azgvGNqedxt6+uGnmss33wo1LZMtm3ZJ/u6NfsWWrdlWb+V4w4eCs6Lsy9OnDhx4sSJU/zJmUMCRuahfs4I1bgBnqG9gfMX0aDmaD45iPmHgQWeQWoGdb0mrD1ahVz31FDVM29sPnJbquAziqLx+HehpCUHhUZl0jUThRlA+ckjotHatgNoanEh/XptkSarj1E+jeJ6F3LVw2js69ZdDCDXPM+8ftzjJiIioquVpGNYz6Ls9WpgzMROHQZArdFDo2e4qStc62YX88+pWXHUNovkmTEqFEhl97/WBX/hDGpb3fZtyM0IoGTjGlTIZmArFvgDKBxym2xFqD1ehUrTvqya4Guwrps3JAeV/qWY75ctEZ4rxL7V+vGPm4iIiK6W8wOrVkm9hCnjTWNYj72PVRiB1ZNuUDPIWeR40dnoVeoFPJuwOQOoa25SyxLgXyPWnQ13Qw7898sqagDGcOZ4286btQmXPUCBWLfXxip47jePMZXkWFK5rti2CJ6lY21hVu23V+lSlCAfheZ13fNwuaQY8OnLfZnLcHlW9PWjH3eVflzatBhrE6k4ExERkQD8/+3lMs0z1zWgAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now try to query from Azure AI Search!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await search_memory_examples(kernel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have laid the foundation which will allow us to store an arbitrary amount of data in an external Vector Store above and beyond what could fit in memory at the expense of a little more latency."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}