adaptive rag mistral

Details

File: third_party/langchain/adaptive_rag_mistral.ipynb
Type: Jupyter Notebook
Use Cases: Adaptive RAG, RAG
Integrations: Langchain
Content

Notebook content (JSON format):
{
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  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "5afcaed0-3d55-4e1f-95d3-c32c751c29d8",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Adaptive RAG with Langchain\n",
    "\n",
    "Adaptive RAG is a strategy for RAG that uses [query analysis](https://blog.langchain.dev/query-construction/) to route questions to various RAG approaches based on their complexity.\n",
    "\n",
    "We will borrow some ideas from the [paper](https://arxiv.org/abs/2403.14403), shown in red (below):\n",
    "\n",
    "* Perform query analysis to route questions\n",
    "\n",
    "We will also build on some ideas from the Corrective RAG [paper](https://arxiv.org/pdf/2401.15884.pdf), shown in blue (below), and Self-RAG [paper](https://arxiv.org/abs/2310.11511), shown in red:\n",
    "\n",
    "* Route between our index (vectorstore) and web-search\n",
    "* Evaluate retrieved documents for relevance to the user question\n",
    "* Evaluate LLM generations for faithfulness to the documents (e.g., ensure no hallucinations)\n",
    "* Evaluate LLM generations for usefulness to the question (e.g., does it answer the question)\n",
    "\n",
    "We implement these ideas from scratch using Mistral and [LangGraph](https://python.langchain.com/docs/langgraph):\n",
    "\n",
    "* We use a graph to represent the control flow\n",
    "* The graph state includes information (question, documents, etc) that we want to pass between nodes \n",
    "* Each graph node modifies the state \n",
    "* Each graph edge decides which node to visit next\n",
    " \n",
    "![Screenshot 2024-04-10 at 11.41.57 AM.png](attachment:7723caff-911c-4186-b757-f5fae6ed8b4c.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85501ca-eb89-4795-aeab-cdab050ead6b",
   "metadata": {},
   "source": [
    "# Environment  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f4a6ae8-d381-4d4a-8850-4b2a8fd99277",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install --quiet -U langchain langchain_community tiktoken langchain-mistralai scikit-learn langgraph tavily-python bs4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcf19ab-0518-4983-837b-dd593c178dfb",
   "metadata": {},
   "source": [
    "Ensure your [Mistral API key](https://console.mistral.ai/) is set.\n",
    "\n",
    "For search, we use [Tavily](https://tavily.com/), which is a search engine optimized for LLMs and RAG. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d2c774b-9493-4979-afe6-aebbcf01ff3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, getpass\n",
    "\n",
    "def _set_env(var: str):\n",
    "    if not os.environ.get(var):\n",
    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
    "\n",
    "_set_env(\"MISTRAL_API_KEY\")\n",
    "_set_env(\"TAVILY_API_KEY\")\n",
    "os.environ['TOKENIZERS_PARALLELISM'] = 'true'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018c9e9f-8199-4f33-b5b4-7adfb66d6219",
   "metadata": {},
   "source": [
    "Optionally, you can use [LangSmith](https://docs.smith.langchain.com/) for tracing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08edba00-988a-478b-96fc-ae0199cbef49",
   "metadata": {},
   "outputs": [],
   "source": [
    "_set_env(\"LANGCHAIN_API_KEY\")\n",
    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "os.environ[\"LANGCHAIN_PROJECT\"] = \"mistral-cookbook\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac1c2cd-81fb-40eb-8ba1-e9197800cba6",
   "metadata": {},
   "source": [
    "## Index\n",
    " \n",
    "Let's index 3 blog posts with [Mistral embeddings](https://docs.mistral.ai/guides/embeddings/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b224e5ba-50ca-495a-a7fa-0f75a080e03c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n",
      "/Users/rlm/Desktop/Code/mistral-cookbook/mistral-env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_community.document_loaders import WebBaseLoader\n",
    "from langchain_community.vectorstores import SKLearnVectorStore\n",
    "from langchain_mistralai import MistralAIEmbeddings\n",
    "\n",
    "urls = [\n",
    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
    "]\n",
    "\n",
    "# Load documents\n",
    "docs = [WebBaseLoader(url).load() for url in urls]\n",
    "docs_list = [item for sublist in docs for item in sublist]\n",
    "\n",
    "# Split documents\n",
    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
    "    chunk_size=1000, chunk_overlap=200\n",
    ")\n",
    "doc_splits = text_splitter.split_documents(docs_list)\n",
    "\n",
    "# Add to vectorDB\n",
    "vectorstore = SKLearnVectorStore.from_documents(\n",
    "    documents=doc_splits,\n",
    "    embedding=MistralAIEmbeddings(),\n",
    ")\n",
    "\n",
    "# Create retriever\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "attachments": {
    "c1f293ca-cda8-4211-a133-2649771e411a.png": {
     "image/png": 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l3WelZ/kCUKNLA161rM7bLalFlDedrVkXKaVBLVuYlevL9sgoffgdFxteZeDq5dLsLcsOuGyE7+G3VzETCCBwzAQsw8eyKq18pllR5cu3K7LEMpms9GjX1FttwauqSlrpZ4OtDwvz/dNwsHYtaF3+WXlXuxd0M7ucBfrj7e+2upNyxqBEb7r8hAXHLxntGxdqe3ZZwL38doczHxke4nRJeM9F3eXJmwd4VW3cXnHcr9bxvs9I85ixKsfbNpgm2vp9zk9ZmhnQ9NyCItnhN15XwEpmEEAAAQQQQAABBBA4TgXqx1fRjlNcTgsBBBBAAAEEECgvEBcdLm1aRjnjcNm60b0TZMqywAeV5fepyXy0Zi9YUMoe3mbqQ073QfcA7f5wybo82aVdW2Xs8AW6khKaVFplqo7vYt18WbHMjqhwX0ZEpRuzEAEEjrrAaUNaycffbpXNaQXym9eXye3ndZUoDUx9tjBD3vw6xWvPKL9xqC5/fI5E6efDuSe0kT7tYyWpeaRs09/12WtzZU7pOID22dGhNHhi4/ddfEo7eXdqqpMRdss/Fspd53WReB2b6wsdn+//vijrwnRc/+q7ML1iVDv5z5dl7fIaeBgTE2Ztk7Z6DpZpFhPVWANwB2SLBtD+9uFar9aWlXSd2K1N2ZhVT3+0VsI1YJjcKlqK9TMzQ7t87Krr4/Xz2bqCzM4vcuoqKvZ1rViix3C7hYzT8bcqG5fMAkz7NDvMLTk675ZMzZ5Ny/ONbdZUs8GaartrU8b2S5THQ1Y7n/NPTFgjic0ipU+HWFmh3c/+39SU2lTJPggggAACCCCAAAIIBLVA7f7POqhPmcYjgAACCCCAAALHVuCike3ko5lbJS4m3HnAKsvqpj3NNDMrd0ehWAZXXkGxU+mp/Vs6Aa7NmbslW9dZSW5d9qDXWVD649XJKd7sDeM6edNMIIBA/RC485yuMnnudtmzr0S+XZzpvMq37IKT20lPvwwuy56ygPdT2nVfVeX2i7sHrLphXEf5ZmmW2L4rtdvSW/6+IGC9BcT+dFXvKjNB3Y0TmkbIkJ7NZcFqX+DcXV7bdwvU/+291dXubt0JXlpJ9um5Q9vIC59ukF362WjnZeN1+Zc/Xt1bbJs5a/Pk1y8HrrOuHy96YKaz+fjhbeQvV/b239WZvu/NFVWe5+9fLfuQ7981Xl65bXCF/WuyIFYDY5eP7SBvf71Z9hXul9s1+EhBIJgF9hbvlQlLP5SDB8syTUNDQqVV00RpG5sk3Vt2k8YhPLYK5mtM2xFAAAEEEDjSAvyfwpEWpn4EEEAAAQQQQKCcwLWndhB71XVpqRkNToBrZ5Fszd7jjH/Tu/RBd4qOr5NX2n1VcquKGVz5+uD4q3m+cXJG9E/QzICIum4e9SGAwGEKWObQxPtHysMTVss0zdryLxbYuf3CbnLR8CT/xXLeyLYyfUmm89kQsEJnWidEyS1nJcs5Q9oErLJM0wm/O1H+9PZKmaGBLjcj1DZqrwHyv/6kr/RoGxOwjzvTWDOj/MtPxnT0Aj+h5db5b1eT6czSIH1V257Qp4Vmm3VzMrHKb2OZaS/fMVTu13Nak7Kz/GrZ5jduYYWVfgv8nsP7La355AG/Cspb+ddSldWd53Z1MtdeneQbx9H2sS4lbzw7WV79bJNzrWKj+We+vyXT9VcgoyBTJi6eUGUDm0TEyA0n3SxjuoyuchtWIIAAAggggEDDFuD/fBv29efsEUAAAQQQQOA4ErCux+zBbfaufbJVA1rtNJDVKdEXzNqUsdvJ+rDT7dSyYgbX29+lOt1e2fpbzuhsbxQEEKiHApbF8+g1feWgBpm2a7ejezWTp1V8pFg3pZUVG5/KXnuL9ot1m5e/p0SiI0OdLv4s6FNVsXV2HCuZOwtln2aBJelxGtvgWuWKdWc655lx5Zb6Zk/q0bzKdZXuUM1C60Zw5lNjne4C92hQvkS7F7R2Nm8a5gS1LLOsupKsn4dv3DVU9hUdkFT9EoDFmmyP5rFhYtlmVk7u3aJW7f3nrYOc/Q/lx/2X9xJ7VVYsS6yyTDHb9kbNsLWXdStrxbqX3LG7SF783wbffMuKX2JwVvADgXosEB4WKU3Co6Vkf7EU7PNlnO4pLJDnpz+tXTCHyinJo+px62kaAggggAACCBwrAQJcx0qe4yKAAAIIIIAAAnUskNQi0qkxSzO4dmi21gk6Do9lYthD3yUbyzIWOpbL4LKHxG9PSXX2TW7bVHr5dW9Wx02kOgQQqCOBRhqZaaMBp5oWC0K1ax6l0Zya7lG2XX3K6LQAmzteWFkLD20qMjxEuidVnoF2aDUd2639Hf6u3S+65Xg4N/dceG84Ar8//T7p38YXVN9/YL98tOJ/8t+5/+cA/OObv8uIjifSXWHDuR04UwQQQAABBGosQICrxlRsiAACCCCAAALHs8D69N0ydVlmlafYQwM/be3hsF+pzT5+u9f5ZFJp+7Zu3+3U3bm171v81g3Zqk1lAa625R6Kfzo/3RnPxXa65cxOzr78QAABBBCofwJbtSvFvPwi2a2Ze/l7i2Vz1l6ZquOxbdia7zQ2TsdiHD+odf1rOC1C4BAELGPrR/0ukuyCLPlq5WdOVtfmvFTp0qKzU8unqz6Xaeum6DiAoRIZFqFjdrWWPq37yKhOIyQsNKzCkV6a/Yrk7M7xlp/d62zpoeN7zUiZJV+v+UryNWOsW2JPuWn49dI0oqm3nU2szlwj09ZPl607tsiufTtld9FuCQ+NkJjIpnrMvnL9sGsCtrcZ63rxw6UfyYacDZKnx20X30H6JfWXi/teoG0O8bZflbFaPlr+sTcfo8e+Y9QvZGPOJvlyzWRZuHW+tIhOkDN7nSWndj7F244JBBBAAAEEECgTIMBVZsEUAggggAACCDRggTnLs8VeVZWf69g25cfNqs0+VdVfF8ut+zAr+zUjy0rnVr4MhY4a6NqWucdZFhsT5mR0OTOlP16bnOJM2YPRMX0T/VcxjQACCCBQjwTuenmJbCn9EkP5ZtlYXH/VbiUr60ay/LbMIxAMAiOTRzoBLmvrxtwUL8C1NG2JpGSt905hpSyVaasny7/CX5I/nPFH6d2ql7fOJmZt+M7r9tDmE2MSZcq6qTJLA1duydiZJsu13leveNVdJM/PfEHr/dKb95/I2pUuUdqtYvny7aYZ8tz0p+TAgQPeqh0a5Fq+dZF8tfoLefKCJyUm3Pf/Z2n56bIgZba3nU1cMegy+c3Hd3vLcvOzZN32VdqdaiMZ3flkbzkTCCCAAAIIIOATIMDFnYAAAggggAACCNRAoDYPDN19rCuxmpSQ0BpuWEVlbcplmCW39o21ldwqWmaJL3jXttzYLHPX5cn2bN84Ltec1klq2tYqmsBiBBBAAIEjKNAkMvCf8NYFbTv9rB/cNU5uHZ/sdEt7BA9P1QgcVQEbJ88tYaFl4wz2TxqgwaUoKSwpku270mSLZjxZ2Ve0R/46+UF56fKXvSCSLT9/wEWSt2eHTNNsLdtmc16KrE5fLt1a95KerXrLFA1i2XhfFohakr5MBrTpJ3NS53nBrZCQEOnVpr+0jWvnHPeAdqFYoJlcPRJ7WPVeyS/MDwhuDUseIQmagbUsfalszUnRMVIz5PV5/5HbRv7M2adz82Q5q98Fkpmf4QW6Xp/3hjTWLLRxPc+UrN2ZsjBljrPth0s/IMDlSTOBAAIIIIBAmUCjg1rKZplCAAEEEEAAAQQQaEgCNz63QJZv2CH2zf+pj46WcH2nIIAAAgjUT4GcgiLNDDkoEWHWNVsIn9n18zLRqhoKpORtll99eKez9f1nP+SNweXu/uQ3T2uW1TfO7D81aNVKM68qKyUHSjRw9IZ8vvx/zuqbR/1czuxxRoVN7/z4LifQZCsGdhgmfzz9D842c7fMl8cmP+RMXz/ip3Kudgn4mtb36dIPnWX3nHGfnNB+qDNd3Y9X5rwqXyz/xNnkzrG/llOSR3mb3/Pp72S9dklo5b3rP9Bs+rKAnQXV/vLZH511Fkx7/IKnJLl5J2f+5+//XCy7LFyzxf57zTvOMn4ggAACCCCAQJkATzDKLJhCAAEEEEAAAQQalMA2HcvFgltWLjqlHQ9KG9TV52QRQCAYBVrEhEvL2AiJjWrMZ3YwXkDaXKXAgYMH5IB+/3pfyT5nDCr/4Fbzpi2rDG7Zfvs1o+rHg68UCw5Z2ZizscrjuCt+PPgqd1J6aSaWZXPZKyG6hbO8S0Jnb/1bC94U63qwoKjAW1bZxBodr8tKZHgTHQ9sZMAmY7qN9eYzNDOrqtJdM8rc4JZtc3rP8U67+ugYXhQEEEAAAQQQqCgQ2L9BxfUsQQABBBBAAAEEEDhOBZLio+Sbx8c4ZxeumQAUBBBAAAEEEEDgWAg8+PmfqjzsH067L2DdKs2EenvR27I+c60UFe9z1lm3fu64Vzv37QzYvvyMBcI6t0j2FjeNaCqPnvOIN28TozqNkJc0UGVdGlr3gn+f+jdnvQXbTtLg1aUDLhHbz79k7druzJbsL5b7v7zff5UUFO725tN3bpekpm28ef+Jge0G+s/KRX0vcF4BC5lBAAEEEEAAAU+AAJdHwQQCCCCAAAIIINCwBGy8rchwAlsN66pztggggAACCASHQO+2/eW2UbcFZG+ty14v92l3f+WLBZVqWpqEx/zgpiGNQpyxvN5Z9J5MW/u1E+iynXLzs2TSso/l8xX/kzvH/NoJhLmVWVeJVqwtK7ctdRdXeA+pZsDT5k18GWQVdmIBAggggAACCFQqQICrUhYWIoAAAggggAACCCCAAAIIIIAAAggcDYEfDblSu+ZLdsamahXTSpJiW0vjkMBHVgfloDz6tS/TyjK2btGxtnol9pTmTeIld2+e3DPxN7KnsPpuBO1cIsOjanRKMRoIu2n4Dc4rLT9dFmxdKDM3zpB121c52WL/+PbZgABXy6atZEvOJqfuX4y+S7tM1G8SVVI6tyjr/rD86iZhNWtb+f2YRwABBBBAoKEKBP7fQkNV4LwRQAABBBBAAAEEEEAAgSMgsDx1l6Rk7pbGoY3kzEGta3UEq+N/89JlXP+WMrxb81rVwU4I1IVAXdzPddEO6jj+BPq06iP92/St9sR27cuXHbtznG1O7322jOvq62bZFjTVYFRNglvVHqCaldalYFKvc+Q8fd318d1OIMu6R9xbvFeiSoNSHTVA5wa4snZnyeUDLq2mxspXNdLMMQoCCCCAAAII1FyAAFfNrdgSAQQQQAABBBBAAAEEjjOBopIDUrz/oNj37JtEhNb52b07Y6tMnpuuWQm1C3BZ+255Zr7sP3BQJn63VSY9OEoSmkbUeTuPlwrd6xmmAcXwxvXnQbHdY9Y2K03CQ6WaHsrq9aU43Pu5Xp8cjav3AtE6JpZbFqbOk8w+50lidIKk5m2RR6eUjaGVmZ8pqzPXSLu4thIeGi55e3c4uxWXFDnvJftLJKMg05mO1XG03ACVW7e9r81aJ2n5aTpWVpLERsbqlxQa6zhaBTJ/6wIviGVjefnve/mgS2XGuqlONe/Nf0s2aTbXsA7DpF+bfhKqgat0HaPLjtchvr2zjWWdFWt3hrl7cp15+5Gny6xt1kViSz03CgIIIIAAAghUL0CAq3of1iKAAAIIIIAAAggggMBxLHDPG8tk1tJsidTg1jePnVrvznRv0X4nuOU2bNfuEgJcLkYl72fdP0MKdhfLCX1ayHM3D6xki2Oz6KM52+TJ99Y4B3/n3pMkObHsQf2xaRFHRSD4BKzLwp6a5bU6fblk7EyTn71zU8BJ9G8/RJZuWSCp2RvkD5/cI2N6jpcTNMD02OSHArazLLCfv3uLs2xUt7Fy9yl3BKy3mc9WfyHfrZ1SYbn/gosHX+E/q8GwNvLj4dfJW3Ned5bP2zRL7OVfTu4+Tu46+XZn0X2T/uCch//6f814wZt9/er/SFMNiFEQQAABBBBAoGqB+vOVtqrbyBoEEEAAAQQQQAABBBBAoEEKNGsSJr+6rIe01YDI9WclS+fW0Q3SoaYnfUAzpSgIIBB8AqEhNcug/d24e6Rf+8EBJxjbJE6uGHa1DGk/NGB5zWYq/8zYWZr1VVkdzaKby+V6vCsHXFZh9cV9L5RHzn9C2jbvUGGdLcjRrgtrWg4crLxtNd2f7RBAAAEEEGgIAmRwNYSrzDkigAACCCCAAAIIIIBA0ApcNqKd2IuCAAIIHE8CneI7ygc3fnxIp2QZTX8+40/O2FeZBVkSHREtCU1aOHUUlhTKyE4nSZh2JxgRGuF0K9hIO6A91GNYZfef8UfJL8zXgFSuFO0vcroMjImIkWbaXaF/t4SVNb57y27y7EXPykH9z9q4u2i3s1kLbaft75Z//uif7iTvCCCAAAIIIFBLAQJctYRjNwQQQAABBBBAwF9gU+YeWZG603+RMz2kS7y0iY+ssPxwFnyzIlvy9xZLq7hIGdY1/nCqqtf7puftkwUb8pw2njGwVb0aT6dewwV54/5veqosTdkhQ7s2l7yCIvl6YYYkJ0XLfZf2lPdnbZPP522XvsnN5LoxHQOymd6duVU+LR3rKlLHOGqbECVDOsfJaQMSK9w7U5ZmymcLtztSSzf4fm/3Fe6XX72+tILeQ1f1kSitz7/YWEpvfZsqyzbvko1pBRIeFiqDusbJYDte/0RnvC3/7d3p7PxCmaDnMHNFjuzZVyI92sfKHed2qfAZsU7rfHHyRnc37z2icag8fHUfb95/4omP18r2HfvkdD3fTi2j5aO5aTJvda6YxdDu8fLzM7vodMUOPBZu3CETZm6TVfr5FRneWM45obVccmJbeWLiWtm1p1guH9leTuh2+J8z+XtL5N9TUmR5yi5Jy9kjHVtFy6AucXKtXsfyY2W9qtut3LJLOiVGy+1nd/E/TXnqk3WyLWev9GjbVG45PdlZ95JarVUzK+ZqZen6HRWuZ1LzKPnV+d2c9fbD36xr66bywextMmdVjkRHNZaB2jarv6lO+5ff/2eFPuze7zifOai1t8ruid+/udyZP2dIaxnbL1Hs78Lzn613lm3evsfb9i/vrJLmsWHevE1cP7aT9O1Q9uA7YOUhzKTp5+an89Nl8YYdkptfLPl77MF8I2kWEy7NohvL87cMqjD+15G+n93mz12XJ5/MS5c1W/OlqHi/dNNreMagRLVs5W7ivM9aneNci+ba5qH6N+4/U1MlTMdT+9WF3SRD7/HXvt4sTSJD5Rq9d0b18gU2bMdlm3fK4x+udX7/7J5qERshAzs3k3H9W9KtZ4Dw8TFjQaaO8YFZUhGNI8RedVUsmHY4XQRacK1VTGJdNYd6EEAAAQQQQKASgcD/W69kAxYhgAACCCCAAAL1UaBYu6Gyh3JWmugDXH1+d0zL1GWZ8vInGyq04WZ9eH3TaZ0qLD+cBY9OWC25Owqltz5Mf+2OIYdTVb3ed+HGPHnwzZVOG+1Bd108/K3XJ0zjHIGZGsBdog/C56/KFQs6WdmmgYKfZu6VzaVBDJvflr1XXr297P6fuy5X1mrAyS2LdLijTzVw88SENfL0TwfIwOQ4d5Ws0ODJjMUVu4mqbFnx5QclytvT9xD9l68skV0FxX5LxWnbx99ulUl9E+SZGwdoUCFgtTPz8PtrZOaSsuPaeXyzKEMm3HeSdmdVdpSMnYWVti/UKq0iwDV1cabzuRAWEiLz1qx1xqFyW7BBAwrTdP3H940ICL598P02efzd1e5mzvvzH+VL9q5C+WxWmjPfq13Tww5wrdlWILc+t8ALPlnF2XmFskADcB9rwO/VO4dKaw3Yu+W75dmyatNOWRm3q0KAa9qiTMnM3ScZuYVegOsbHUPNztG/2L1T/nomxEcEBLhcs/DQUHn6o3WyY1eRV4XdS19owPT1Xw4LuDZTF/gCo9ERjcU/wFWif5Pc49nnlQW4ctTRXeZVrBMrNahYvlgQ/3A/4yYvzpA/vu4LspWv38wsSFT+b6UFhY70/Wxt+dvEdTJhWmpAs9Kz9sq3el9+NShTHr+2n7duTVq+5/b57HQpLv1bf89rSyVvZ5E3Jt1vNYg548mx3u+aBTn9PwOsQrtef/9grdx+cXe5chRZkB4yEwgggAACCCCAwHEiQIDrOLmQnAYCCCCAAAINTeCjOdvkyff0CbaWd+49SZJ1fJpjWVo1i5A2LX0PqPfrg057mEhBAIHaC1iA4s5LussUfQC+XLNRLLhlY1G9M32LE/CyAMgBHZ7EDSSd0K25BrsbS6FmhmzVB+duwMMyeu5+aYn87/6RXjbO8O7NxQISVqZogMmCLRY8+tGp7QMa3Di0kTSJKMvesqD6HS8s9gI1FjA5QTNIsjTgvFCDNfu1QXM0OPPPLzbIbWcFZh7ZOgtunTIwUbq0iXYy0bZrkM6WP/O/9fLEdWUP+DvoZ8nFo8vaMmNZVo0/UyxgYHUmNo+Uru1itD05zrx9Jk3UrK6LNTvLyk7NznpSg39uOX9UW2mt2abTl2ZpIGKLu7hO3u95faln1rFNjPTTrJrZK7Mdd7N/6L3Vmlk0sNbHumRkW9mc5cuQel/vDzv/5nERcvrgwMygDi0r/zthQUbb55wRbaVdi0iZqtdpXeouJ4j5mGYEPXvTgFq1LalFlFw+1pdhskIDZnYfWxk/vI3ERQdmcB1ucGuXZsj5B7e6aTZY746xEqOZTo00qrVbfw/872Vrx9G6ny0jyw1uWZDtrBPbSIj+vk3V7EwLFH+jQcupgzOdoKC1y7+M1t+XFs3C5d0pqc79Yvdpwd79TuDKrpll+rl27TRr02yLSw46wcWV+hlhwTHb7hkNLvfWYO2ATs38q2caAQQQQAABBBBAIMgFKvZREeQnRPMRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSObwEyuI7v68vZIYAAAggggMBREjh3aBuxlxUbZ+ecP844SkfmMAgcnwKW6XHVye2d7AvLfImLDZfLRrSTrTr+kmVzWFbGvqL9XlbK5SPb6XhRZRbWjemzk9bLezp+j2VxfbUkw8teGq7ZXvaysiV7j5MZEhYWIr/0G5+prKayKRsbyh3jaayOs/TQj3t7Xf7ZmHFX/22u9NHxwW7Q8ZQqK9eemaxjYXV2Vt2k4zud8utpvvNLCRy/r0NCE7nnou5eFfl7tP256d58dRPm8tvLe8olJ/kytZZo3bc8M9/ZZbFmtLgZXG9968t0shW/u7KXXDQ8ydnmxnGd5PZXFstcHSesLsp3K3PEuqKzMkDHAnvpZ4OdbvIse+iyx2Y76+bpNjZeVW0zcd1ztWN8ol0r2jXq2jbmB6+nbW/FzJ6+daCM6Okbz+k6vX5XPznXyQK0jLws7WqwpY7ndKglSTPi3HvqvVlbvQyu69W4tudaVRtW+3XRaNl//vdPVfscrfv5mYm+ccisHR/9aYRnede5XeX0e791sqz+9WVKpRlcN2oXv3p5nN952/9n+vtjXRG6XUVm7rRsad/YZf6/17atlW81U/A3Ly9xpt/U8f0G+GVKOgv5gQACCCCAAAIIIBDUAmRwBfXlo/EIIIAAAggggAACCByfAlHatZoVdyir6Ejfd/Mau30S2jp3pbNl2Q8LWFhXoT8b39kLQK3WcaAOtyxc5+tizroz/MOlPb26rd42GsyYdP8opzu78l3Buce9TLvSc4udx5BeviDbTr+xn9z1h/N+1uDW3u7WJVtkaTeL6TllXaeu0LGXrFgg8fxhvuC8u9O1Yzq6k4f9viy1LHh3+zldvGsWrse9aXyyV/8q7WruWJXYmDAvuGVtsFvs+tPLDGwMsfpeOrcu635x8vzt8vZ3WyRjR9n1rqz9R+t+3pzu8zupX4IX3LL2ROn4mSfpmHVWtpV2MenM+P1o2qSxd8/Y4qZRYQG/d36bBkwe1KDYHu3m9JTeCdJFuya0stYvCBiwMTMIIIAAAggggAACQStABlfQXjoajgACCCCAQMMTsG/4P/+Z75vgm7f7xlsxhb+8s0qaxwaOZ3K9fgPfHZfDlcrXMUr+rRkYy1N2SVrOHunYKloGdYkTe5hrD1srK7XZp7J6jtQyy8748Ps0WbguV1o3j5IBXZqJZWDYg8PKin2b/fMFGbJGHyY3Dg2Rnh2ayiUnJsmA5LiAzbfm7pWn/7fOWfbTMzpL96QYb/0qfUj4r683OfO3n9NVOpWOa7NMH5i/Pm2zs/zRn/STCZqxMFOPl5qh1q2j5dqxHWVY13ivHv+Jj3VsoClLMmWjfjO/fWK0XKhjtFAQOBSBJZt2yAtfbJJV+jth43dZseCNBbus5OUXOe+H8yM1c7eze0sd38rGNipfIsMr/xxxt0toGpgFFKsP6+u62BhX5QNsFuAykwP21L+0uMGuJB2/0AJ2/sXGMqqrsqU0e8vq69HWF2hw6+7Z1pd5Y/ObNZPuWJVW+tlZvvRoV9a2bfp5WN+L3VvDNVhkGWcFu4vl7x+sdV4xOtbXcB0n7ubTNWtM/+b5l6NxP1v2m1tW6Thkt76wyJ113reU/k7Z/Vmiv6v+AeyADWswY1mUL0/eJN/qOHJmYMX/3i7Q/wegIIAAAggggAACCBxfAgS4jq/rydkggAACCCBwXAvk6IOyGYuzKpzjyo2+rAr/FWcMbBUQ4LJv4N/63AKvezHbNjuvUBaszpWPZ22TV+8cKq3jIv2rkNrsE1DBEZ4p1i6+7tCHhe7D/MzcfbJ0fZ5MmpMu/9bzsYwS//LghNXy6cxt/ovEvln/pW5/y3ldnMCYu9KCAa716QMSAwJcafqw111nXci5AS7rOs5d/mqbTfLa574gmNVpbbNuyB64to+cOagsu8TW/f4/K7zupmzersuiNblyxgkEucyD8sMCKzVge8vfF1TY0H5H6rIUFfnqqypzrLpj+T9or267w10XHVWzf+Lt1e4drURo14zliwUG66rsLiwLKlhQ3b+E+wUELdumJsU/SFeT7WuyTWXnG12a9Wb75+/1BUuqq8uCM8e6PHVDf/ng+23y5pTNzmeutccCPVM0o8tel43tIL/y64bzaNzPe4vLfgd3aKbiol25VTKF1OYXq7Q2u3+uemxOwN94W+UGuKs8KCsQQAABBBBAAAEEglqgZv/6CepTpPEIIIAAAgggcLwIJLWIksv1AZ2VFfpNcBuXx8r44W0kTr+l7l/KZ2/d8/pS78GXZTj069xMZmt2kQVT7PXQe6vl+VsG+lchtdknoIIjPLMu1del19kjkqSDZlxMW5otazR7JXdHoTz8/mp57uay85m+ItsLbtmD9lMHt5LCkv1eQOrlTzbI6N4tpWubwG/41/YU3vgyxfnm/Al9WkjK9t3eGDxPvL82IMA1Y1WOF9yyLJMf6dgx1r6JGnScPLdmYw7Vto3sd3wIWFLSb/691DkZC1Tcc0VPsW75bMykLA3UXv/UPC+bo7ozLtYH8T+UQdJGf882aAZjpnb190PbVnes+rAuoVmE81mxqzTT5Ui1qb2azSmtPEvHS2rl90WCdL/MKNuuJmVn/g8Hm6yeHQU1266qY+bsLMv4K/9lAdvHLxnOqcK+gFFdaeR1timyc7fVXdalYHX7Hco6y37yjUXXTnILimTO2lz5RjO6pi3McKqx8ehsvDL3SwlH435u55cd1zwuQu44v2ulp2SxrXKJhJVuV9XCJz5e6/2Nv/ncLjKiR3PppNnAFmC9/+2VzpdZqtqX5QgggAACCCCAAALBK0CAK3ivHS1HAAEEEECgwQkkaUbSL0u/ff6edn/nBriuH6ddL2k3W1WV7zRzKL20m6wB3ePlpZ8Ndsb0KNLsjssem+2ss+wi6wLRrac2+1R1/CO5/OlbB3pjx1w3ppPcoFlqltE2d4Wes3bX5D6YfXbiOq8ZL9811Mtu+3Jxhvzp9eUeoHGqAABAAElEQVTOumcnrXfGD/I2PIyJZrHh8sHvT/K6SrvmmflO8M2yCXbow9246HCn9pe+2Ogd5T+/HS4dNIhp5abTOslZ98+oUWDCq4CJBimwY0+RE6S2k7/4lHZy3tCyzL9Y7UbQ7aqsKpzW8b57zjI9LOB6ah/fmECVbd9Zg+MW4LJtX5+aovdpcmWbBcWytvq7tla/KLA9e69k7iyURA14uWWZBsrrqliQwS3WFal1eeqWiZo96paOpV2d2nxMaRaaBevz9PMivvTzYqMGy38oKy9eP3v27Ctxzs26mG1ag4y2FO0atVjHbAsLLeuq8aulvqCQtcc/SGNBVGvDYs2W9S/fazCpuuKfIfz5ogwZWK5b2Or2rc265jHhYmOx2evZFpHy1lebnWo2bi/wAlxH4362oJUFtuxa2qud3nf9OjarzSlVu89s/d210lG707W/H26x7jq36N92CgIIIIAAAggggMDxKRDYR8TxeY6cFQIIIIAAAgg0cIFlqWUPa28/p4s3YL2Nu3XT+LIH1Ku0mzO31GYfd9+j9R4bE+YFt+yY9g34G/we7K3Vh7Zu2Vb6gK9XcjMvuGXrxmtXjnH6QNjKmtKMMGfmMH+MGZjoBbesqjOHtPJqTNeMObds0q4jrQzSb9u7wS2bt2tzgWamURD4IYGYyLLszRmarbJ9xz5nFwuG3ODXbWG6dpNp48RZ0MO/dPPLWnz6o7Uya7UvOJyqXW7O0yCGBVjccuNpHd1JeeXTjfKn/2pmyIY8ydBj2vY2bcHxwy12DmkaoHZfuwt92UgWWHOX2bsF6WtbzvfrAvS2lxZLStYeJ+tn6rJMeeKDNbWttsJ+FmBxu2f892eb5L8ztspqDRL+U4Pb1m2eFfsMGta1ubdvso7Z55bfv7FCZmvw6I3pqXKzBvDdkpdfKMv1M6u8QRe/8QLvfXO5s40F8Ox+sGtbWbGA2IPvrRIbx223dnU3WQP/bkDIxrDq6xeQSSoNxFm3q0/pOIUL9QsFj+l98/xHZV8i2JSxWzbo8fyzvLr43WeTZqXJxHnpzj1jX0Sw+9L8D6fYWFcfztbxGLU9qTqemc3bu53Le9O2eFVbZqNbjtb9fKv+3XXLbf9cJA/r/WXjQWbrNbTxzey6+P+eudseynuz0iDoVnW3cSLN3uq/980VXneNhXpt7Xfaf1ywQzkG2yKAAAIIIIAAAgjUPwEyuOrfNaFFCCCAAAIIIFDHAltKs7es2h5tmwbU3rNtrDe/WR8GuqU2+7j7Hq33Fs0Cx9iy4/p3Mbil9Hz8H+b1aB94/raPPRBeoOOi2PgodVVO7hWYBdOsSdn/drpjoljGhJuN0b1dTIVDt2tRdVZehY1Z0GAFLOtmQLd4WbIuTyyQe8GfZwZYWDeZltFoXXre9PR8OX9UW/nDj3p625yrGV8vfLpBdmmXdpbNdPeLi711NvHHq3uLbWMluVW0XHdWsrxeOr6cjV9nL/9iGT4z/jbGf9EhT//ooe+9343yO1/0QNn5lR87r/y21c2P6NlCkvXzcNO2fNmswfDL//p9dZvXel2MZtH9/IKu8lxpAOiZ9ysGz+65tEdA93SXj2on1p2eFRuPz15WLNjkttm6lr1Ru5985MZ+MrZforPefvzszM7y7eJMZ96uu738y6QHR0lC07Igj62z7lEru5a27jbtUs8N0Nn8j7Wb3IffWmmT8q620V5WRvRPkFnaTayVmUuynNe0x071Av2WgexuY597bh3ODvpj3NDW8vDVfdzZQ353Am3vrKp2v75d4vQLDmXZU0frfr5gWBuZvHC7zF+V64wZOfG7rWIv//KX6/o6X7jwX3Yo02cMTpSX9F62vy/X/W1uwK4n9UuQ75dlO79Ttz2/UFprd5gT7xsRsA0zCCCAAAIIIIAAAsEpQAZXcF43Wo0AAggggAAChyBgY3C4pXFo4P/+hIeXzdsg9W6pzT7uvkfrPUofypYv+qV1r7iZDXuLys4rMqzsfN0NI/wMbFyhHyo12cYeav9QKdhXNkZOZFjF7SPCNCWN0mAFQkoH5HHfXQj/YIO77HF9OD6sdwt31nm3rKCfntdFRvUODLYGbKQzli348h1DpYeO21VZsQwT//Kz8Z3l2V8Mch6S+y93py14YcFbtzT26/bOXea+h1azzt2muveDfilC7vhFNg5T+VLe0F3/+p1D5RTNtvQvlhl6x8XdvUXNmvgyPL0FtZi4enQHeej6vtIksizQbdVY13X/vH1wQIDKlluXgBa4ssCTW+x6/vXavpLQrPr2dNbsryduGSAJ8YFBLLcey+QrXzproO8n4zsFBLIsUPmnn/SWi4YHZpJasObaM5MDqrBu8f5waU+xfaorD17VV07TQFZl93CaZgAeTsnR8eaqKtYuG6vxWR1n0jJ9/cvRup//8dNBTrDYzRj2b4NNWyZbZcXaW/73pLJ7/PqxneTSMR0CbO28Rw9KlDvP61ZZ1SxDAAEEEEAAAQQQOA4EAv+FcRycEKeAAAIIIIAAAg1DoJGUPaXb6XQhVnW2T3v9tvacUpasnfukVVxZ5lO638Nr284ttdnH3beR3xPE4v2170LMre9Q3jek7/Y2757ky9ZqUzrGkK1Iy6n4EDE9x9dloD18ruzBod8zdKfu7DrK9HLH1bFKd+jYXBQETODlXwwOgLjq5PZiL7f84qwuYi//YmO6Pa8P7y1IbQ/Kbdwld0ypfUUHZFz/lhLROFQiNMAbVi7IbfXY2Htv6Nh0tq1162b3vH3CNI8Nq5DtY9sP79bcyQCxYO8Wzfraq0F0C57Ha3DIsoP8PgLk/st7OS/br3z5y5W9xV7lS20ywCbdP6p8Nd7853+ufF2kBrefuK6f2HlYF34tNIjUQsdu+mKRr+tAq6BDy7LPRa/CWkycPqCV2GvnnmKxzxAbH9DGR6qqWFbWmEcTtUvGvc41c69n7/axGkA8IBYUt+Ck/7hZbl2naFDzFPWwbu+2a6aXBZTsZZ/9VQXfb7P76swusk2PF67X0j2eW6f/+881S+yWM5Kd7iJj9BxsvCsrH98/wjmO3Wvheq+V/zy1Y/9Vs7T2X9VbUrVLwuKSg3JAb7ZYzUxr5TcGmv+xajptvyMXD28rafo3bV/xfq1XJFo/05vrPRkbFRZwT5av80jfz+7xLBPSXhYATtduOAv0XojQ61j+XrBglb3ckqB/yuY8M86dlUGd4wLmbYX9zv36gm5y57ld9TNgr3P+nUq7k7Ssrk//Msq5X9xr41XGBAIIIIAAAggggEBQCxDgCurLR+MRQAABBBBouAKt/YJUny/KkIHJcVVidEosG8/l47lp8tMzOnvbTvTrXqxj6cMwW1mbfdxKmzUpGxNoq1/3iO76I/n+8pcbvep7lXZHaA+ALXhl48x8r2MUWWaXPRi2YmMHWRdlVlq1KHuQHR1R9r+J8zfsEBtHxy2z1wR2+eUur827ZYtY13DfLMmUey/pEVDFkpSyMdECVjCDQBUCFjDp4jd+k21mQZzI8MqzecpXY9t29xvDqfz68vMWwLDgWLAXOw/3vO3z4cXPyj5HOmu3jHVZ7PPR/zOyurotaNFWs7n8S6wGL2taLIjuH0j/of3seJY9VpNiZv7jBto+5bs+rKoeC7ZZ94B1Xez+tQy22pajdT/b3yTHzu9vTm3bXH4/p+6EwN9J8/Yfe6z8PswjgAACCCCAAAIIBK9Azf91ELznSMsRQAABBBBA4DgU6NKm7CHepFlpYt/qt291h+mDrOxdhdJUH6K639624MzTOu6LfYv7359tkljtcmuQdkc2dXmWTJnvy1SwbpOGdW3uSdVmH3dne0hoXWvt02ySr7X+Yd3jZbC2LWtnoSzTcYDsm/ZugMndpzbv7lhW9r4pY488/b+1snazLyhk47lYJoZbLj6lnbw5OcUZg+TG5xbIL/Wb7vYt+gfeXuFuIleeWpYl094v2Pf592liD7ktcPDujC0BY9rY+bTTh5T+WXFehTWYGK/dfU2YluqM//XAu6vkF2d3lj2aRbNwQ558OnNbDWpgEwQQqI2AZamtSy/QrLcSJ/Nt195iWatjcX2iv+8WdLZi3bsRGKiNLvsggAACCCCAAAIIIIDA0RAgwHU0lDkGAggggAACCNS5QJJ2bzWif4LMWuobOP7ht1YGHMMCPA9rV1BWrFuon1/QVZ77aJ0z/4wGu8qXey7tIRqX8kpt9vF21olzT0qS96dvcRY98vYq/1VySq+Ew/qWvVvZmpSdMvyuKe6s925jz9yn48H4lxvHdZKPNWBUoF0BWhDs1mcX+K+WtpqFcr52HeUW+xb82CGtZeqC7U5g8NkP17qrvOW24IWJ6+UjrXfifSO89YcycdNpHeXj77Y6gbfP9MG6vdxiQcL9fuOiuct5RwCBwxewrvF+8vicKiuyz5Hf+o3FVeWGrEAAAQQQQAABBBBAAAEEjpFA9aPgHqNGcVgEEEAAAQQQQKAmAg9e1VdO00CWdT9UvqTl7A1YdPXoDvLQ9X2drvr8VzSPi5B/3j5YbLyX8qU2+7h12BhBJ/Rp4c5679bWfO0q8HBKJafrVGfdEN58bmeZ+MeRFca2sa7bPtIg1LDegW2y9owf3kbe+e3wCmO0/EGDZMP7JgQ0ddTAlnLz6Z0Clrkz/tfBxiPyL/7r/Jfb2Env3nuidGwT47/YmX/sxv4By5hBAIG6E6jsdzJMuy7trdmmP9MvBNjnSE273Ku7Vh3dmizT1xyaNuF7n0dXnqMhgAACCCCAAAIIIFA3Ao0OaqmbqqgFAQQQQAABBBA4NgLWRV9q1h7NAjqoA8sflNjoMGnVLKLSwJe1cKcObJ+9q6jCwPbVtb42+1h9e4v2y5bsvWL/xxWv400larsOt9gYOXkFRXoeJVJYvF8iwkJ1zLAmNe720LzSdewtfZP2Ot6MjTtTXdmjWVQ2Vleb+ChnPCPbP1ePb90sRjQOlfCwkIDst+rqqm6dHWerBibbJ0RJVHio077cgkKJiQhzjlvdvqxDAIFDF7Df61DN1owMa6yfIyFimZsUBBBAAAEEEEAAAQQQQCBYBAhwBcuVop0IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKOAH0xcCMggAACCCCAAAIIIIAAAggggAACCDRIgY9W/E/mpMz2zr1vm35yYd/zJCY8sPtkbwMmEEAAAQQQQKDeCJDBVW8uBQ1BAAEEEEAAAQQQQAABBBBAAAEEEDgaAhtyNsqjXz+s3S5nO4drn9hJtmSmONPxMQly+yl3ygANdlEQQAABBBBAoP4KEOCqv9eGliGAAAIIIIAAAggggAACCCCAAAII1LHArM3fyz++fU72Fe2Rtokd5bQh43QM12aya/dO+X7l97J28yrniBcNulyuHnxlHR+d6hBAAAEEEECgrgQIcNWVJPUggAACCCCAAAIIIIAAAggggAACCNRrgclrp8hL3z0nEeFRMqz3cBnUdVCF9m7J2iJfzftSdu/Jl4Edhsndo++ky8IKSixAAAEEEEDg2AsQ4Dr214AWIIAAAggggAACCCCAAAIIIIAAAggcYQE3uBUbHSdnnXSOJMYlVnnEwuJCzeaaJcvWLZa2zTvKw+f8lSBXlVqsQAABBBBA4NgIEOA6Nu4cFQEEEEAAAQQQQAABBBBAAAEEEEDgKAm4wa3OSd3ktGGnS0RYRI2ObNlck2Z9IqGNQuUvZz8kXVp0rtF+bIQAAggggAACR16AANeRN+YICCCAAAIIIIBAtQKrVvnGeejVq1e127krSw6I7Cs5KCGNGkmTMHcp7wgggAACCCCAAAKVCbjBre4de8n4YWdWtkm1yyyba8L092Tfvj3ywFkEuarFYiUCCByyQObuLFmevsLZb1TyCAkPDT/kOtgBgYYqQICroV55zhsBBBBAAAEE6oXAv/71L1mwYIGER0bLOWedIVFRUZKelSNv/fddadqyo2xP3yZREWHSsmULOXX0aBk/9mRZtClPvpu7WNI2rpOElvFy5RVXSvfWTbTbnEb14pxoBAIIIIAAAgggUF8EDje45Z6HG+Tau3cPmVwuCu9HTWDulvmyaNtiSd+VJmGhYdIloYv0Tuwj/dv0qbQNRfuLpORAiTTS/6LCoirdJlgWHpSDsrd4r9NcC/w0DmkcLE2vcTunbZguz09/xtn+kfOfkO4tu9V436o2zCjIlPlbF8jqjNVSUFgg3RK7Sd9WffWe6VvVLoe8vD5fm+Ppd+CQL0wD24EAVwO74JwuAggggAACCNQfgRUrVsjNN98srZJ7ybb0LMnatlG7vzko4TouhP5bVNp36S1y8KAcOHBAsrZvkcaNwyQtZZ3ENIuX6KbxEhvfQjK2bpKS4iIJaRwqRQX5GgxrLPHNmkpSUpKM1oDYsGHD5MQTT6w/J01LEEAAAQQQQACBoyRQV8Ett7kEuVwJ3o+WwL6SfXLPp7+TrTkplR6yf/sh8qtT764wPtxDXz8sizbPlfCwSPnvNe9Uum+wLEzLT5fb3/uZ09xrT7pJzu99brA0vcbtrOsA1+zUufLklEedf0eWb8SIrqfK3afcob2BhJRfdcjz9fnaHE+/A4d8YRrYDsdfyLuBXUBOFwEEEEAAAQTqr0B+0UFZumazfP7lV5KVnStF2q1gYUG2dIwPk3bt2snWtO2yR7e54NYHJK5l6x88EQtuvXD/rbJ26TzpN3yM3PX469JI/2GyIztD0jevl8K9uyU0tLGsXzhdtm5Jlbcmfi2v/neiDOjdVR576M/SuvUPH+MHG8EGCCCAAAIIIIBAEAjUdXDLTtnG7Tpj2Hj58JsJ8qfP7iOTKwjug2Bv4j9mvuAFt0JCQqRzy+5OBtPajJVO8GLplgXy0vevyK9G3x3sp0r760hgqXZ1+MRXD3u1tWvRSeKi4mV1+nIp2V8ss9ZP13UHuWc8ISaCXYAAV7BfQdqPAAIIIIAAAvVOYFv+QdmUd0C25RTIBy+/ImkafNqZkyERUU0kNq6llOTulblz50oj/UZlSJNYiYlrXqNzSOrUTUaeebls3bhGRo6/xAlu2Y5xCa2cl1tJv5PGOpPFhftk9aLv5b9/u1veeecdueuuu9xNeEcAAQQQQAABBI5bgSMR3HKxEuMS5eLRlxLkckF4P6ICczfNcuq34NYbV7/ldTeYX5gvf5v+lHY9ly93jLrtiLaByoNL4N3FZRl7vzrtHhnR8STnBHbu2yV3fXSH7NqzQ4Nc38gtJ94kTSOaBtfJ0VoEKhEgwFUJCosQQAABBBBAAIHDEfhq8TZZt3y+TPv4P5Kzfav0P3GsnHz2pTL8tAu9al/8w/Uya8okOeOymzXrKtRb/kMTo866RD554xkZff6Pf2hTCYuIlCEjx8qCj7tKfn7+D27PBggggAACCCCAQLALTF73tbz03fPSvWMvGT/szCNyOm6Q692v3yKT64gIU6kJWPeElnFjZXDH4V5wy+YtMPHA+Ptt0iuzNn8v0zVwYWXN9pXOe1HxPnlYu6orX345+i6JbBzpLH5p9iuSszvH2+TsXmdLDx0DakbKLPl6zVeSr4GRbok95abh13sBkfeWvi/rs9ZL22bt5NqhV3v72sSrc1+XjPztktwiWa4ceHnAOpsp1nOauPITWZO5Rrbkpkq4dsPeq1Uf6aPjiY3qNMLrOu+RqY9pb+02/tYer45Plk+U5ZqJ5F+GdRgmp3cb5y16fPqTjtuI5BFyaudTvOV23Cd0nZUx2k3fSR193bh/sWayLNy60NvO2nFer3NlwbaF8vXar2Vj9nppF9dBfjLkaums5+Rf7Bp9sPQjWZ25StJ2bJOEmJY6Plo3uXLQZZ6V//Y2/dW6KTJLA5epuSmSFNdWxnU/rfwmtZ7P3pMjq9OWOfsP7jTcC27ZgmaRsXLbyXfKw18+4Kz/ZNUkuWrgFc70oZrV5trYOGpPf/t353hXDbpSsvZkyzd6v65Ru9axbWRwuyFyYd/znXHjnI30h3W1OHXdVL0nQjXj7C5n/Dl33ZT102TO5jnO/fK7sb91Ftf2d8Ctk/fgFCDAFZzXjVYjgAACCCCAQD0WeO/Fh6V1u87SpkNnOfcnt0nvoSdXaO35N/1eFs7+TkZqwKpRSM0DXClrlkl0bJyE1DAolp+VKuvXr5dBgwbJM88+J+2Se0ivrp2kT6/uFdrEAgQQQAABBBBAIJgFJq35Qv4948UjGtxyfSzINXbo6TJ1/ldOkOuly1+uMA6Suy3vCNRGIKJxhLfbSg1aWNZWdRk3azXgtCBltrePO1HZspKTS9zVMmvDd1KgQSy3JMYkyhQNKvi6svMtzdiZJsvTlsirV7zqLJi3eZ5s1ADV+ujmFQJcs1NmSm6+ji9ckFUhwLVa93nkq78GHM8q3KaBrq9XfS7TOwyV+077gwYtGsn8Td/7Du730+q1l39pEh4dEOCao+djJSqsSUCAq+RAiedjgTk3wLU0fZm33PZL37lNYiNi5fnpT9usU+yYv7HuIK98VRKatHCWbc5LdX73/e12aKBwfcZqmbL6C3ng7IekZ2KP0hp8bxZIcttnS2z7lduWyqhuYwK2q+3M+uwN3q7j/IJ+7sJBbQdI49AwJwC4NnOtu9hrU03NanNtijTA6N6LHeM7yoeL3vWOb77mMGfzbHnorAedbjht5cbcjd4++0beGhDgWpWxylvnVlTb3wF3f96DU4AAV3BeN1qNAAIIIIAAAvVUYPPmzRIZFqa9mh+UlkmdKg1uWdOTkrtLHw18PX/frXLKuVfK6ZdcL000cPVDJTY+QfaXlMiu3CyJbd6y2s1nfj5BJr72tBTpOF+bd4VIxMFQWZu3Qd54f5Kceepw+fn1V1W7PysRQAABBBBAAIFgEfhkzSR5fcYr0jGp8xHL3Cpv0adTX2eRBbl+N+n38ug5jxDkKo/EfK0FGmkuS992g2T51kWyp7BAbnn3ZrnuxBvltK5jJbSSL8gNTBogFsSx8v3GGU7wxLo2HN/nvIA2NA5pLFGNo7xl5w+4SPK027ppmq21r2iPbM5LccZr6ta6l/Rs1VuDNV86x7dgzBINBg1o08/b91AmivYXyYOaPWTHsBIX3UIGtBssuZp1tGLbYmdMsSWp8+XNhW/LNUN+LOf0u1AO6L+p8vflywwNuFnp2qqnZpMFBo2GaubP4ZSTk0dK8ybNZZkG8LbmpEi2BuY+W/mZ075Tuo7R7K753jhon6yYJNcPu8Y53N+mPeEF6jprm3po29I1ELg4dZ4TQHr6m6fkxUtf9DKS5m1d4AWSwrWr+jN7n+tcxylrvtTzm3Y4p+Dtm7W7LPjXNaGLt9ydCNHxm1tr1pidZ+7ubHfxIb8f7rX5eMkEMYPxmi0YpgG3yRrctEDhuu2r5HP1OK/XOYfcJtuhtr8DtToYO9UbAQJc9eZS0BAEEEAAAQQQCHaBXbt2yeq162XdkhmS1G2wXHnnn6s9pTsfe02mazeG33zytuTn5cjVv3yo2u1tZftufcT+obpx5SIZOOoMZ/u8zHTZU7BLYprFS7MWic6yf/7pVlm3dK6MvfBaycveLulp2mVG60YSFh6pr3D5vzfelNNHj5RunTs62/MDAQQQQAABBBAIVoF3l02Q9+a+JfHNEo5acMu18g9y/X7SvfLIOQ8T5HJxeD9sgTtPuUP+/MX9ToaTdTf48nf/kDfmvPb/7J0HeBRVF4Y/IL2RBBISQiCE3nvvvSOgFEF/UbEAomIXkKKigAUVKSqKCgoC0ov03ntvgQQIJYWQ3gjw33OXO5ndbEIS0nMOz2Zm7tz6zibszjfnHAwUAlBPIQ6Qp5OyuqVrixv8teXh7chbOCYEKYtiVhjW+EVVxez26Zp9ZbkSeMiTpq4I+/dJp7GyvHqpapi60fA95Xp4YKYFriUn/tXErSYVWuG9NqNleDkaJFgIM+8uH41KQiTqX7ufHPelxkPl9mbULU3gauHbEr2FMJSVRp5c9Jp36E8p/BDnO2I+Pz79I+yEF9iguv0x+A9DqMXA8Oty6CNCdLx517DfWoQYfKtVch60v0UOrH+PLEJoZBBO3TqD2p4GIXzR0YXatL/pOx2lHT3l8YA6T+PlhS9JEVGrkMmdO9HJoSadbYqb7cXZ1gWBCMDdmDCz59NTmBXXZs6An2XYRBqvZ/UeeHXRy1IYXHZ8yRMIXJn7HUjPmrlO3iXAAlfevTY8MybABJgAE2ACTCCfEejYsROCwiLRuFFDXAy4gqvnT8K5Rac0V9G2z/OgV8Sd4DTrqZP+506gZqM2+Gf2ZCz9eaoQtiKkR1fSvUR0Hfgaeg19CxsXz0XglfP4YsF22Do4yaY3/S8iKvyOCIdYFH6njuDY7o0Y+dZozJo+DRUrVlTd85YJMAEmwASYABNgAvmKwFc7vsZ+v91S3OrfdgCsLa1zfP4kcgWGBOLi1XMYI0SuL1jkyvFrUFAHdBVixHd9vpdeTSvFjX8y8oD6Y98vWHlyGSaL95qHY6ksX/6Q+smRHqoJ7yTy5iIrKbyuMmunbxtyZ9HDem+0GK6JW9Sfu70b5orwh/qwjJkdJyvada/RU4pb1BfNqXnFNiLkYrDIweUruz8ffF4bpr8QqPTWQXjYkcBFdk0IYkrgCgy7Ksuqe9XWxC0qsBIiZIeqXbBaCIBPatGJMVoX5KlnzmyE5xRZYlKCudM5UtZQ5FqjnGDKaL+l8JbbLvKhRQpvQvJETG3+qg1vmYAiYP6drs7ylgkwASbABJgAE2ACTOCxBC5cuID33nsPFeq1xoCOT2PvhqVwK/UAS36agjqPEbhU58rzSh2b2+7b8K/o80u4lPRAtbrNUKl2I1ha26Ks8Opy90r2xDqweQVadR+kiVvUF4VEVFa5ThNUqdcMi2d9ikmfTcbot0ahfv366jRvmQATYAJMgAkwASaQLwgoccvS0gq9m/fOFXFLgerSqKvcJZHrc5FjiMIVsjGBrCBAXloUsq+rEEEWH1+MHRc3y3B+FDJw9PK38MuguVnqNUgClG+J8trUKe9XVryfb4vcVmSuDm6aeKQNInbyirhFc6pXuq5+anhXeJvp7UbkTe1w9t7Z2r7pzq1H9UiwSRI5qMh8XA0imb6up5OH/jDT+y62ySHv4+7FiTxkyaEoVafRIuQjmb2NoyrK8a2nk8F7TT9wefGe2/6ogDz6lIebvg7vMwFzBIqaK+QyJsAEmAATYAJMgAkwgfQT2Ln3AIKjk9CwU39UrNUQA0aMQ4seg5EQF4MDm1emvyOTmg8fPsS9hHhZ+uB+kvDYmoK2vZ7DJz+vwf/en4JmXZ5Gw7bdjcStB/fvI+DiadRrmbbnWIUa9eBbtR5CIuPx999/g8ZiYwJMgAkwASbABJhAfiHw1Y5vpOcWiVv92vSHk735cFw5uR4SuSqXqybzyHy787ucHJrHKgQE3O1LCs+nEfht8B+aRxWF01t2KvPfN8xhs7NyMFec7rIHDx6YrZuYlCjLiyB3bkfff3jf7LzMFbrYuZgr1sr03k8U0tH0pSpSHjWymEd5x2jfnJBnJfJQZYW5OxrC1VNfd+LMhyC8G2sodxF5xx5nGWH2uL705y2LpfS5sbdKFuOi46P11c3uJz1I//U02wEXFhgCKd9NBWZpvBAmwASYQEoCSUlJiIuLg6Nj7j2pknJWXMIEmEB+JnDpzgP8tXw9ylaugwo1DV5QNnYOqNeqCw5tW4PFIpSgd8XqKO1TKUPLPLrzPyyb+xXa9ByMTgNextnDu1FEJAXu/eLbafZzM+ASGrbuBo+yKZMKq4Z+p4+IMIa/IPTWdSQKAe3qpXOIjX8Ts36coarwlgkwASbABJgAE2ACeZbAxotbhLi1C0rccndOvqmb25NWnlx7Lm2XU3mnddqf3XJ7vjx+/iNAHlUTOo/Hc38+Kyd/OdQv1UUk3U/EfSEEFCtaLNU6pidsdEKD6bn0HEfHR5qt5iZCKV6/44870UEZmlNRnSAW+cj7yOwAKQqNH+ALE6Hv0mt2Zjyf9G3LOHvj2NWDsmhAwyEopROW9PV8XQzeWvpwfBHxEfoqWbrvJkI9Kjt47RDK1PRSh3IblRCFoAiD91lJ4UmX0jLGLPPXJuXId2LvaoV6oU4VPjB5IDM8Lrm+qmNum5nfAXP9cFneJZA7knne5cEzYwI5SuDevXv46quvMH78+Aw9Ob948WK8+eabCAkJydB8/fz8cOvWrQy1KWiV+/XrhyZNmiA2NragLY3XwwSYQC4ROBPyANf8zqFMBUNcev00hn4wFRVrNMA37w7Bb1+8g+0r5mPb8j+xZ91iREeYf6KO2s/4eBgWzpiISjUbolVPQ0Lj6Mi7sLGz13dvdr9MhaoY/mnqYTIOb1+LHz5+EUmJiWjcvjf6vPQu6rTpjQ1bd2Ls1DmITDD+UmN2EC5kAkyACTABJsAEmEAuESBx66ddM/KkuKWQKE8uErnYk0tR4W1GCdANffpnzkJj72jFlma8f9weiRfkTXUo8IhWN6t27K0N30siYsIQoRO0rt29roXiMx3L26WcLKI5LTn1r+npVI9L6DyNdl/emWo9dcLiEY+zj3J+qfLjN4+r3cduledVahXLu/pop44FHkVb3zZmX2VdvLV6Do9yTh2+ul8rUzvngpJzeqmyzGxrlKoGtf61p1fLXFb6flaeWaMdtvJtpe2rNhllltFrowY8b2a9+/z3qNNwtjF45NpbJX//PRN0RjtPou0lM31oFcROdv8O6Mfi/dwlUGyisNydAo/OBAovgXXr1oF+BQMDA/H666+nCwQJVAMHDgTle3n++edRvHj6wjCQ51KDBg0wd+5cKfB4eyf/JztlyhR8+eWX8PDwgK+v4emSdE3GpFJAQADs7OxQrFj6nwwy6SLbDz/77DPExMSgadOmKFfO8OEq2wflAZgAE9AI7Nu3Dzdu3ECZMmW0svy+s2XLFhzcvQ2vfPJDiqUUs7BEo3Y9YSuEqWt+Z3Hu2F6cP7YPkXdD0bhDb1iIkDqmdmLvZpw6uA2N2vbEwDc+0eqEhwbhuDjX8ZmXTJtk6PjPb8agQatuGPrhNBlOkTzLajZpI24S2WD9sgU4cOQ0+vfukqE+uTITYAJMgAkwASZQsAhMnz4dBw4cgJOTE9zczD3lnzvrVeKWS/GS6ClybuUlzy1TIhW9KsLBzhE7T2/FjejbaFauqWkVPmYCaRI4cP0QPlj5Hm5FBcOiqAjCJaLdJQiPrIPXD2Pegd8QGWfwSGpdqS1qetQw6itU5Oc6Kjx4yC6GXkTp4mWE8GGBiIRI+IddhY347G9jYY1E0R+JZTGJMdgqxOMY4eFjZWGDpj7NZFkxEUHCnIDmF3oZfsEXZP+Xwi7D1a4E9l7dh5lCfL4n+iSj6BPVSlWHkxB2yIOsjHMZbDi3Tp47e/MUbkYHw8HaAUVFvYiECATcvYar4uVVvLSso35Q2zVn18h+YxOikSREP+ozSYQcvB0ZhCDRT0kRvlHZziu7ECVEtzgRFjAqKQ5Wxayx/PQKrDi+RHu43N7WEeWE4Fbc1kmEmg+Raz124zgC7lyW3bSs0Abx9xOkpxlxMrUyxb2w6dJmJIg8V2HRoTh26wSi7sWCBBkSJQPDb+Bm5G2467ykQkXIQL/gi6JNPG7HhKKKe2Xcjb+L/dcO4t9ji7S5dazSGSXsS5gOma5jYhUhriVdm3gxt+O3TqKMuPb3HtzDGsF+2dFFsh87wf3NVm+Ka2QIoZhRZkoAzMi1SUhKwMpTy+X4wYKNjY097CzsQDnfFp9Yiv1Xdstz9X2aoPUj8S0uMQ47/bbL8gshF+EsxM6Au1fxnQgBGyaum7JqnjVhb20nrnXy9+v0/g6oPnibfwkUEfkWzD8KkH/XxDNnAvmGwPLly/H222/jqaeewg8/pLwxam4h169fR8uWLWFvb4+zZ8+aq2K27Ny5c+ja1ZD01tPTE7t374aFhfiAJKx69epS9KE+jxw5Alvb5Li3ZjszU/jHH39ITzQSyFasWJFu4c1MV9lapNY6e/ZsdO/ePVvH4s6ZABMwJhAcHIxGjRrJQh8fH9SoUQPVqlVD5cqV0bx583wbOnTEG2/jbMBNvP3138YLfnTkd+ow9v63FOsWzsEXC7bBt3q9FPV2r/1HeoD5VK2N9X/Nxp4NS/H5n1tS1HuzV22MmDQHVes3T3EuPQUxwgvs/QHNMXXhLji6JH8JVG3/mPYhQm5fR6/O7fDh28NVMW+ZgBEBelDk5ZdfhouLi/xsQZ9LSpTI3Jdwo44L8MGd6ET8teMaLt2Mxr2kh2hYyRnDOpYvwCvmpeVVAsv238TWk8F48OAhfDzsMahlGZQtaZdXp8vzyiUC+/fvlw9VquG9vLzQuXNntG7dWj4oSA815oYtP7MKC/b/JnNcta3bDtaWKW8658a8Hjem300/bDm0EY2EYMDhCh9HK/vP9+zZE6dOnZLfS3r37i3f2/Swb160ZUKU+evA72lOzcnOGbOemQ1bk5B69+7fw7BFLyO1cIEj27yN9hXbSrFs6sbPUx2jZaX2GN36zRTnb0cFYeTi11KUk3Di6lASgXcCtHPvdfxIE3j/OroQy479o50z3SFPon+GLjEtxrrz/+HXPXNSlFMBMZj37O/auU2XtmLOzpT32OqVa6yFFVSVf39uPoYueF4dptgWt3fFb4N+S1FOBeR1NX7dGPF/qvmcY85CpPp10K9aWwoPOGzhS2Y93KyE4Ej51Mi+7P0VKrtlLLy9NojYoXHeW/UeQoX4Z85IUHqvw8doUtbw3ZzqZITZgv8tNHq/pffakKffS3/9T07JxsoO8bq8ZPp5fvfMj/AWohzZg4cP8PKilxBpJrxkA5+mOBKQ7A3XrmoXkaMu+Ttsen8H9GPzfv4kwCEK8+d141kXEAI7dxpcq9UN3/Qsi7wfyNq2bSu36f1BApcy8gI7dMjwJE9kZKQUt+gc3bC6fNnwtIqqm95taGiorHrlyhWMHDkS9+/fT2/THKtHXmy0RrKZM2dKrzkS/erXr4927dqBvLvYmAATyD4C+mdqyONz7dq1+Prrr/Hqq6+iffv2GDNmDLZt25Z9E8iGnnfs2IG9u7fjXtQdrPnjO22EB4/+BgZd98f8b8cg4OIp2Dk44eTeLVod/c7ejctwfPdGWdS869OoWrcZfp/6vr6K3K9evyW2r/orRbkqeHA/CfOFh9aqedNVkdH2tpgPPaWX2pew59+djFrCm2vx4oXYtWuXUVs+YAKKAP3u0ucR8kSnkMn0/+j//vc//PLLLzhz5oyqxttHBK6FxqLX+N34a9NVHDxzB8cuhGG32LIxgdwgcNw/HIfO3sGR82H4d/t19P98Hw753c2NqfCYeZgAfWajB5CUkff9vHnz8OKLL6Jhw4by+96CBQtkJBJVJ7u3FOaPxK3qPnVA4f/yi7hFXCqWroiWdVqDwxVm97skff2TuEVG90Q++eQT+T2EPs8sXbo0w2kg0jdi5mtVLlkZVYVnCgkSpkZlJD5N6/21kdig6pHX1ec9v0B54SVkzm4Lr8L0mXm/CA+RT4uEKxJmlJHQ9E779+GiCymozqntkPrP4pNun6KkUylVZLRNEsJc0oMkozI66CbEi4GNnjMaT1UyFT86CS596xnCvKs6Xq5lMUKIHyoUnyp/km21UlXx08BfQcKZuWtkKi5S7rQfnpmJ0rqwhTQ+HX/Q8eMnmYpRWxpnRr8ZaOzbMsV6PYQX3dTe3xiJW9T4SZhl5NqoiXav0Uu+t9UxbUkQ/KrPdE3cojLy7pvU9TO4OXnSoTS6hs82/h9qeNZQRWa3Wfc7YLZ7LsxDBNiDKw9dDJ5K4SKQkJCgfWnYu3cv6Km49NgLL7yA7du34/vvv0efPn3S00TWmTZtmhR1VINhw4bJD3ObN2+WT2Gr8iVLlqBx48bqMN3buLg4/Pnnn6D2ly5dwp49e3I9BBkJWrNmzZKebjdv3sSJEyfSXA89lUg359iYABPIPgIXL17EsmXL8N9//8Hf39/sQFWqVJFPUnbq1Al16tQxWycvFB48eBBffPEF6G8NCXQ/zVuA/iMniFALdti87HfUbtoeW5b/jlJevhgwYix+m/IuXN08UatpO3iLfF1lK9fUljH1zQGo2ag1ejz/hizbtXYRVvz2jcjB1QivT5ql1bt+6Qy+eKMfRk+bj8p1zP+tnjt5NOKiozBo1Hi4lS6rtaWdHz56GYkizMOwj7+Fs1vqT6r++fXHCL10CDN/nIFatWoZ9cEHTODq1auYPHkyNmzYYBYGhUSm3196VaxY0Wyd/Fq46UQQzt+IltMf0dVXhNsxhHVJaz2jfjkuhS2q4+lmiybVSqBaGUf0aWwcficx6QHmbg4QoXAeoqqXAzrVKYU1h2/BPzhWjkPj5SfbJ4S8w5cNwskL7cqJcDz3sWh3oFxCi6olUN/XOVeWU9g57xLi1q5zoTh3LRIXr0bKa+BR0hYrx6XPM3je1gDhDXAfHs426N88fd9fcuVC86BPTCAsLAz0XXHjxo3YtGmT2f7Ik6tHjx7yRQ8MZpeRuEXiUCmX0niqdZ98JW7pmZwJOI2thzehffUuGNks2ctAX4f3s58APajz999/y6g2pqPRe7pVq1Zo06aN9FjUp3YwrZuTx5SLK1KEFrwba/h/1cXOBcVFiD4VJu5xc6HQcDcjbxlC4ImPLi62LuKVNf8PUzg+CvFHoepKijCFZNGJIoygyJFkJYQIChknwyuamSTlUboZdUuE0ouXdSjsoKtY2+PWFRwTgtiEWBle0U6EpStl7242jCL1HxQTDHtLe8mLpnBXhHUk0UTNjeadVUYeSmGxYTKsoZNtccmj6KMQgKZjxInwgeQF5+nkIUJF2ghPpYcil1kE7IQnnrWZkIim7TNyTCEoKRQlcXrcep+UWVrXRu/B9UyDZ/Fs3YFSzKRQjq5CHHWwckhzWdQ+KjEKpR1Li2tYBPS+jhUcLUXoTWsRhpJCcKb23snO34E0J80nc4QAC1w5gpkHYQIpCZBIRWJVzZo1pRdDyhopSyIiIlC7dm15gsQaZ+f0fyBRwpjqlUIJUTjC4cOHY/369apYClSZEbi0DsQO3exV4Q/15endJ+8vevpFxQJObzvTeuQhR3nKzBmFY+zQoQPq1aunhUjj8ErmSHEZE8geAvR3gkQuukFOLxL9zRkJz7169QKFD8lLRkI+eatSXoqXhr2KYh41QWFnt674AzGR4Wje+WlUqt0IRcTfMsrBFXzjKtYtmAlXd0+Zf+vQtjVCoGqC7kNG4srZY1g8ZzI++O4fVKhRX1vmucO7sW3lfLTpNRg1GrfRyn/57C3Ex8Zg1JdztTLTnW/eHYJ7iYn4aIYhvEdifBy2rZiPJT99iZGfzkG9Vl1Mm6Q4nvPu0/Dx9sScOeZDgaRowAWFjsDRo0exatUq+bpzx7xHEv1f27FjRyl25aUcLpm9WK/8eBQnH3m77JveQXy5fnxPncbtRGT0PTjYW2LL5NapNggTYQy7jTN4Trau646vhtbCgGkHcFWENSQhbe+37VNtmxdPjPvrLDYduiWntmJCC1wNicVbs47J42E9fPFKp/K5Mm3mnIz9+emHNJGL3l/pEWybv7NVirDlPB2w+MMmyZ3xXoEmQPmfV69ejTVr1qT6gBI9lKTErqzMtarELWsrWwzu9BwcbNO+AZrXL8Tes3tx5OwBPNfiRfSt+lRen26Bnt/p06elgEsirj7ijVo03ZMgoUu9r3MrNKeaD2+ZQH4nYE7gyu9r4vnnDQIpfVzzxrx4FkygwBMgDwYy+rCUXlNCFD1RlBFxi8LykaCmN7oRNXfuXCNxi87TTecnNSVuUV+U64vCIJLR8dixY2Xc9nHjxqUIkXXv3j0ZrozyePn4+GDEiBFmva5u374NyvlFAl1a5urqqp2uVKmSFBRVwUcffYQZM2bgpZdeQrNmzTh3iALDWyaQQwTo7wTFv6ffQwpLOGHCBPm7aDo8feEcNWqU9OiiuuQ5khdsy5YtMh9iKW/hUVGhA+Js3NH12dcw7Z+9mLn+LIaM/gyNO/SW4hbNNzjQH0d3rUdrIVY9985k9H99DIJE2bS3B0gRa/CoiUbiFrUp4emNM4d3oWwl49ALHZ4eistnjyDQ7xxVM2tPDR2NkJsBWP379/K8lY0tTh/agWFjvpXiVsSdYLPt9IXvjJ8qvXFVSFv9Od5nAkSAQhNOnDhRPtk/adIkGbbKlAz9rnz88ccyFPAHH3wg31OmdfLTsZVlEW266RG3qHJMrOGzVS3f4lpbczs2lslPEFtZGL6mWVsatkXTO5i5jnOpzNoq+aumlViHzaO10HSsdWvN6ekx52Ti9SomPywXGXcv+UQae+q9SNeUrfAQIO/69957T/69nz59uvRcN109PYBJnu3kvfv+++/LULamdTJ6vPz0Sum5Re16N+mb78UtWkfz6s1lDrEFe+Zhe8BOKmLLJQL0sPE777wjH7qjaDT0cGypUqW02VBIb/qeQu99el/T+/txUWG0xrzDBJgAE2ACOUaAP5XmGGoeiAkkE7h79y5WrlwpC7p375584jF75EZP9tRTGXvSi0QmZXQDStnnn3+udrVtSEiItp/RHYrVrg85RsmJhwwZooVApPEoVjvlAJs/X3gSiHCGykhwo5CLdANbGYUOIK+NqVOnqiK5pZw948ePxyuvvGJUrg4o/wfddCOPN8qPQ+un8Bqffvqp9mUstfwzqg/eMgEmkHMEKEQric2LFi2SfxtJ0KIbKXqjJ4fpd588uuhmuYqdr6+Tk/v0NyUwMBDdhrwhwj2Yj0uvnw/l5IqNiYbf6SNCeLqGlj0GYtJvG/DV4n34/M8taNN7iKweFx0J8u5a8+cMjH2+Peq16AxHl5L6ruBbvR4o15ajiyEEiNHJRwcVazXEs6MmYdO/v2LTkt9k6bvf/IWmnfrK/Fy71v5jrplRmat3JbRt2xa//WZob3SSD5iAjgB5QA8dOhT//vuvfAClf//+cHAwfsI+KioK//zzDwYPHiw/GyxevFiEy0zU9ZI/dq2tDCJUejxdTFckIg+maXrBQI2jxJhixZKFtTQ7yUMnlThHU6J16EUt/bmcnjJzNk88tRBKprUtH4mvamt6no8LNgFLS0v069dP5uMiD97XXnstRVj62NhYkctzMQYNGiQfMCQP98zY5TtXsODAPNm0WZXW8NAJD5npLy+1aVu3HeztHPHzrtkIin78Q0d5ae4FdS7kqUX3K0jQ+u6770D3adSDu7Rm+tz/008/yfsT9Jln4cKFoDCebEyACTABJpD7BCxyfwo8AyZQ+AhQeAcyCo9HnkrpMXKZV08L0Q3ejJgS06jNgAED5NPT5BWhrFq1aiJnjA2OHTuG69evq+IMb3/44Qd8++23WLFihVybra2t7IM8rkico8TEeqMbxAMHDpRF9GGRQgQoK1u2LFxcXOSaKY8WPUlFHyTJjh8/LreenslJJmXBox/kHUeC2bp162SOEP254sUNT0+nFkpJXzcr9umDsF7Iy4o+uY/sJ0ACKAm2ZLSllypTx3TOtExfT99WX0776py59qp//VbVU+1M+1N9qnqmbfXt1DlVV39Olak6dM60jM5Rmb6dKlPtaEuW3rb69vp+6SYK9aH6oXPx8fHy7wn9TSFPVvr7RcJYThp5xfr5+WHK9z8j3sE4h05q8yhewh3uXuWw+o/vEC1CGDbt2Af9h4+BQ/FkT9NzR/Zg/cLZIO8qYtJOiF6D3/40RZexor2I4Qr/c8dRt2Xq/x80bNsdkWEhOLhtNQ7vWIs6zTogSQgKpw5uh3OJ5KdTUwzwqCBKaA9dunTBlClTUquS5eV0M2zfvn1amFoKVUsvFbaWtvQi05epeqZ1Uys3195cv2oM1Y86VnXlRPiHWQIkUtONH3qoJzxcvGd1Rg+e0IsSu9etW1e+x8qXL6+rkXd3rS0MApfyYsnITO/fN/ztTK2Nhc5LSwlAymPM4pGooNoGRyRg6vIL8rBMCVuM7lVJnZLb3efuYPmBG3K/RwMPtK/ljsy0oTxaS/cFyn7a1nRDr4bGn71+33YVp65GyPNv9ayIsiXt5D790HtskUeatW4Nan1aZZMdynX203p/1KngjLHPVE1XKEiTLlI9ZM7JaJJ0D2jQ37n0mIWFod7jrmF6+qI69DmZvCMKo6n/WxR79X+MvlztEx+1b25ret60L9Pz6emD6uj7Sa0PippB9ehvvYrcQXXJKIoIvcijiz6zqe/BhrNp/1xxepWsYC3y0dStViftyvnsrLWlNTo16oIVO5Zi+o7vMKXHF9m2AvodW7p0qbxGxYoV064pXTP1omutzqlrrs6Z26r3Bp2jdvpjfXv6PEvpByiSi3rRMUVueVy5/vuB+i5AWzJ1rOroy9S51OpSG/XS11Vlqk/aWltbS0Y0V3opIxGMXhQVRhl5f5l7gFid5y0TYAKQubLobwSZnVXyZ0ZmwwSelAALXE9KkNszgUwQUJ5YzzzzTLpb04dSMgrppUSa9DS+efOmluOrb9++oLjRr7/+uow1rdqTWz7daCKB6+LFi6o4w1u66UtGnhUk3tGNaLJr165Jjwvap/CKNA8aUx9iUJ+4mPJj0XopVwfVo6f+pk2bJtvR2in3DVmLFi3kVv+D8vgo8c4cJ/oATkYfsHPCXnzxxSdimhNz5DGYQH4lQDdSSAyZOXOmzIeVU+ugMelviHOFBrgdbRDz1NjxMVEIC74FR2dXI8+rclVq4a0v58n8W9ERYVj5+3TY/G6PXkPfkk0fii/si2Z+CjfPskL86is8ugbDztEgyKu+aXsz4BK+/2goKtduIsUtErt+m/I+6rXshBbdB+iryv32/V5A9YYtZf6tw9vXihsC91G9QSv0fG5kirpUkHQvERaWVvJc/D3Im1EkUOSUqf+D1E2JnBqXx8kdAvQ5gby92wpPQRLE1P/fuTOb9I1q8yjsXno9qsT9Mc3u6cQErTCVHSUOKU8uvdcRNXEvbi1+nx9i78lQ2UP5Uvbo09gguFOOqY9+PYl7SQ9gY11MCkSZbVOvvDPGzz8tc4jtPh6CmmWLo7y74YbEgUthmL3ST45fuZwTvEsY36jQCyCWwgNNH7JQrU82NvlBnm6T5p+V879+OwatqpdE2xrGnqwmTTJ9qOZRGDkTtPs6t8J7jxFgFWT1XlS/C6o8M1v6XpPbHtmZmXdWtVE31FV/+hvoqqygbEnQOHnyZIaWExIdJOvX8a1v5EmToU7ycGVvN294upXBpdtnpRdXKQf3bJktiS6mD5pky0CFvFP6PMPGBJhA2gTsLO2w5EVDupa0a/JZJpAxAgbZNGNtuDYTYAJPQCAgIEBLYEpxnNNrKrRDRkIaUt8UClAZhQska9CggRS5aJ9yU7Vv3x7e3t50+ERfMpWgRGskM/WSorFmz54tQxGSiEXng4KC5I3iK1euyDb0g8KTkccWPdlBH8ipLoln5JGlN9MQZnSO8oqRoEbWunVrudX/oC9XZKZfICmsYXZ8waa5szEBJpC9BMz9LcjOEcnThG5UXws1iPpqrA3//IKx/2uPr98djM9e7421838UoQSTn/Ys5V0elWo3kjmwGrbuLnJy/Yezh3bJ5hdOHEB4yG089eJoEfZwuBS3zhzaiRkfD5PhCNUYpX0qYfS0+Xhj8i+yyM7JGRF3Q3AvMUFVkdvjuzfKMIckWHmUrYBn35yIcXNWYczM5RgwYiyonamdObgDv37xjlacINZoa++QYw8E0MCUz6lWrVraHHin8BCgMKT5wdTNfVOPKnNzp78TM9b5aSJCTSECPc5U6EMlDimPMZWTS9/+8yE1YWdjeF5x2qLzCAo3PFj00Z+npThEdb98sRZcHQyiNR1ntA2JGNNfrUtNpb07cSu6ewAAQABJREFU94RcT0zCfSGinZJlFKpu+rDa5FhqZPqQhHRCrYn2za2HysnIkc3RwdJwIH6WdEyev1b4hDvM2QCwgW/y/wVfLbuIWHFdH2cqNGFa1/BxfajzFLqUrXAReOstw4M96Vl1TQ/D54EaZWqkp3q+rGNZ1PD3zT4bPRnoIRK27CdAeb3ZmAATYAJMIHcIsAdX7nDnUQsxAQo1qEyfwFSVmdtSXiwlFpUrV85cFbNlJNpQeD8yEpcaNWqk1SN3ehKAKDQQxZZu2LChPEdCEz3hRaG/MmrkcUW2d+9euQ0NNTxVTAeUn4MStzo6OspzLVu2xIYNG2RYQgpXobe4uDjtkHJ4ULhC4hYdHS3LqS/iQe0pBjyFRCCjnF3k6aXMVMSicgqhQaZ/io08MShfCIloly9fztInBClcI32RozCNKlwDzVe9lHu2OlZbVZfmalqmjumcqqfKaGuuT9N6GWmr+qQtmRrrSfrUt1V96svUGKltqa5qp+qYtqfzpmVU93FtZcf8I9cI0O8geXcePXrUKKef6YTq1KmD5s2by3w+9DciJ+38+fNwEB5aZ4Q4Va9VFzn06QPbsW/jv2jVbSDqtOiEnav/xtYVf+LqxVMY8dlPKaZXuW4TnDywDbv/W4Iq9ZoiIS4GRYWHqXfF6lrdzUvnIS42SpQbf1zzKOur1aGdmKhwFBPhHJWRULbu79moXr+F5o1F56h/q2KG0LGqrn5bo3EbzJv2gczR1VsIbWTBYZGwssr6m8v6cfX7lHeRXvQUrOnvtv53V/+7rd/PSBsaV99Wv2/ajzqmrb4e7ev7oX36v4f+XzHdmpbpw/OouuS5Rvv0on3VRpWrrTqvr2u6r+pSH2o/I+1UXdM5qHL9Nq19yrNF/6/TltZMHgvmrGLFiuaK81xZvfLFcSssHp6uNmnOjULsTfjjjCZulfdyxODWhoeJ0mrYsZEHwmPuobq3QQxrVMkFMQlJqOiZ8oEZe+Gd9fUrtTFixlE5znvzTqFvMy+cuGjwuuzbugyaVzXO1ZeZNjXLOuGFruXxx3/+uBEci9kbruBqUCxi4w3e8JOFiFbS0TrFsqqUdkSTmiVhb234G2ZnZSGPqWIZE28v08azR9bHv/tuoLYQBWn8rDbmbCDavrY7lor32IlLd7HtaJB8DX+qIoa2S/37Rvu67vC7FYMmlQ2fqZ/k2vz1118yRKHyZFJ/H/TH5vZpTHPlqkydp799qky/Vef1Zfr9x52nuumpo+/TdJ/a0/zU32c6r47VvB93rP72mqtvrkyNQdvcMoomkl7rU6s31p9dg4s3LqK+S730Nss39YLDg3Et6Ar61h0AByuHbJv3999/L/OhHTx4UPsMQ59f6GUuvKA6p7b02UeFL1SfgzLSNtsWlgMdX716FYcOHQKxo31To/czPWjXuHFjjBkzxvQ0HzMBJsAEmEAOETC+Y5JDg/IwTKAwE6CcTMo+++wzUDJTDw8PUL4qEl0oFBTlrKAX5aDq2rUrKMygsm+++Ube0C1durQMVUhPPlIbeikRjNqQ2ESijTLyitIbfTjVh/ijJ+aVp9TOnTvlDUZ9/fTslyxpCB9DYhR94bpxw5D7gdpSolaas7LatWtLgYpuZDs5Gd+4oA/h5FFG3mr0gVKJgtQnGeXtIuGOBK4JEybIsId0M5S8t/S2fv16ozXSOWJNpv+AevjwYSlukXCmTyQrK2bBD1oPGxNgAmkToDCpFJpUeauaq00CNYUzIgFEL9ibq5udZfT3qUqtBlg9/wf4Vq8nb579+e0YVKheH32GvSeHLl+tDijsIIUrNGeV6zRB1bpNce7YPvy36Gf0ECED7Z1csH3FArTt85xsEnTDH81EuMLHma29I5xcDH9/qe6JfVtEnq+7IsyhwWv3ce3158tWrIltK+ejVY9BcHH3xKnTZ+QDCvo6ObHftGnTnBgmW8ag/0ey4/+SbJlsNnZKD52oV2rD0OcVyivarVs3GUI5tXp5qbxrPQ/Q63F2V4hUKvyblwjp93LncsKTKqUIZNrPp88mi9x0rl/T0vJlWk8dN6jggv7tymLJtmu4eDUSU8WLzNPNFu/1qayqGW0z0+b1Lr7YfjIEV29GY/6GAK2/3i290CaV8IHNqriCXsrshCD3w7A66jDNrY+bHd7tXSnNOk9ykjkb6FE+sqEdyuFnEc7ynH+ELAwKN/YINuX8Zo+sE6PLlCmD0aNHmw7Bx9lMQAlnSvBKzzF9D6P6/v7++O+//+T3sODg4FRnSg8pUtQQCltPDzYqwS0j/7+T6NOtek8pctX0qQkrnWdnqgPnkxMJ9xKw7cgWeLmWBQl52W3kXcQeRumnTLnCKa3Eli1bzDZq0qSJ/OxCn1/U/QWzFbmQCTABJsAEcoQAC1w5gpkHYQLJBPTeSiTImIoyyTUNe56enli4cKFWvHXrVtArLaOk7XSjWOXEIu+GHj16pNVE3oxr164d1qxZA8qHRTeQM2p6jzR6yotEO7L2IgSiabhAErjIKN+G8h6jtVIb8iKjZNP6hNMkvpG3FhnlEKOnqEiY+uOPP+RLnhA/6OY3fVmeM2eODM84fvx4I+8Dd3dDbHMSxCjhLjH64YcfZHPVv+qLt0yACWQ/AQo9Sn+vUvsCSTOgmyP0N6lXr15SvM/+WaU9AnmiVPB0Fg8ZtMSkYd1QsnQ5OJcohdcm/GjUsIh4MpZC/N24ckF6WZGopbd+r36IqW8OwIHNK7BpyVw07/IMVsz7Rgpc+zYuk33WaJwy1Kq+j7CgmzKvV60m7bTiA1tWoVq95lKg0grN7FBYQ/+zx0HeZMqGvj8Fn77WA3vF+CS67d29ExUqVFCnecsE0iRAOTKVqKXyqekb2NjYyM8DlI+THvDJiFe6vp/8sN9ReMY42Vrgt01XpSg0bt5pWL5SLFtySb3dqyJ2nw7BrRCDBzyF3/txeD2QeJGaZbQNdfX9K3Xw9Gd7NeHOo6QtPuhbJbUhClx5RpkRgIy2yWnOl0WOs9Fzjstr5epsjVe7+RqJkgXuIvKCJAHlmZMRHJR/lB5CWrZsmfTGNdeWHnak73z0972tCIuXmYggpv2S+LPdbyv+2PobXu3xOh4Wyz3vM9O5PcnxlsObEREdgc96TM5W760nmWNha0vfSehFD9HSZ31TI09zSjFBLxJv2ZgAE2ACTCDvEGCBK+9cC55JISFAT7B98MEHmDlzpiZApbX0W7duSU8oEnIorCAdp8fIC+vTTz/F1KlTMWXKlHQ9Td6lSxcpcK1atQpjx47N8NNI5J5PIpWXl5ec4ttvvw1ra2v07ZvSA4E40BN89DSfMspr8+OPP0o+9NQUGfVH4tzLL78MJU5Rri8S/X766SesXLkSdEON6tDNb3oanJLW0xOF9EWMPNX0pn9yTS+gkQj4xhtv6KvyPhNgAtlEICEhQd4kIWGLvDTNGQnm6ksk3STJS0ZParqVcMXunctRwskOFkUeYsCoSUZTvH3tCjb+8zMunzsmQsAUg5dPZfhUqQ0rG+MQgR/+sBh/fz8B/uePC4HraQReOY/vPxwqxKU3YGVtI/J4zUStJm3g7uUDWwcnGcqQhLPb1y7D0soa+zevhGNxVxl+kCZAgheFO6xQo77RfMwdWFhYYuaE1/DxjH9lni6q4+jqhoZte+LE3s1i3LY4cnA/3hjxurnmXMYENAJK1KKtqdENTrrhqUQt/cMwpnUL0jHlvSJPr2ZVSqDzmJ1yaasO3swWgYuErFa13LB46zU5DuWvKlU87RCKmWnj6WKDMh72UrCjgap4O8KymPHnLDmBAvojM8wy0yYnOa88lBwl4rtX6qKKV/aFSSugb4sCv6zVq1dLUSu1ByzpQQX6+05/5+n7XVbnHyYvro87jsH4deOwZMdi9GrVW+Tze7w3bF6+MJsOb0Jg8HUpblUo4ZuXp1rg50bRZCjqC4laKo+3ftEUFYe+j3Ts2BEdOnTQn+J9JpAmgeCYEJy+dUbWaVm+uQgTn3Mh39OcWAZOXgy5hM2XtqC5T3PULW14QD0DzXO1anBMqOB/Ws6hkXcDOFobUqVkdFIR8ZFYeWY1its6oXuVruJzb3JagIz2xfWzlwALXNnLl3tnAmYJjBw5EsOHDweFK6QQg/SEED1JR18IKI4z5TuJiIiQIg3lnaIvC2TkdRQUFCTDF5KIQ0b1qR09FR0bGwvK10W5r0jIodB/L7zwgqyXnh/04Y08zCgkIHlATZw4MT3NtDoUkmnHjh1SYKJCOjYNjagq03op7j55em3btk0WW4ocMhQm8Ndff5XrJy4qr5dqp7bEiPo21z95gU2fPl1VNdqSYPb+++/jq6++kuW+vr4y5OPgwYOz/AuZ0cB8wASYgCRAv5skTFOIG1Ojv2P0BVIJW1l9k8R0vMwek5coeZ5u374dgwZ3x6a9p+BVPjkU2MUTB/Drl+/gvsh91Lh9b3murMitZSpuqfEHv5Usjr0+fgYmvdoDu9f9g7rNO+Lo7g3YvGye+FtpIbzAIkQYQ2cp3EfeDYWjcwn5GjB8rOoKcTFRwvPLQ+T+Oo3mXZ/Rys3t3BW5ukjkeiDCDtGzAJSSg7blq9bG5TNHRU6xZfJvND+lao4elz3OW4ue4KeHTuil8l8WRmrF7SxBHlUUrjA+wRBqOas5nL4WqYlb1Hd4ZCKmLb+Asc9UTXWozLT5Y/s1TdyijnccC8bW+sFoX8vgHZ/qYAXkRGaYZaZNTnJOvJf8oFnpx+SVKyCXkZeRTgIU5WLQoEE4efJkihY+Pj5oKx4+ougftM1uIxFoZOtR+GbzVKzcuQJPte6Tb0WujYc34ELAWbzb8UOwuJXd75zU+6cHchYsWKDlDtfXpM8s9H2E7sFQJBqVQ1xfh/fzPoGVZ1cjPNYQelfN1tXeBWWKl0GlkhWy3XPyzO0zmLnjOzl0meJeqOyWfWGX1frMbRPvJyLpQZLw6rfIkMhG7cau+VA+kL7l3H/4ZfA8uNq6mBsiT5adDUrmP7nXVFR1z1zEgVl7Z+Ow/z65Rno4v2/Np3JtvQ/xEHH3DNEaSDCla8qWTIBpJLPgPSaQowRI4CGvodSMQjyYCwtFTz6n9fRzWn2mNpYqJ2GIPujRq27duqo4Q1vy2KJXekzlKFGJjknsUkZeWtll5KlFoQzJ6AsaGxNgAjlDgPIJUj4+UyNBi57+pa0+V59pvbx0TAJXZGQkLty4iyZdB8FSeFuRkTA0c9yrMtxgX5GPy5yoFSPyY1G+LXNmJwSs597+HDPHvyZDFL467nuRTysC9xLiEHLruhDLqogch0mIjgiDh7cvnITHld68fKugiLiZHhV+B+eO7EG1Bi30p432I8JChJeYLU7u34rSjwS6TUt+xa61i1C+Sh1cOL4fXTt0Qo0aNYza8QEToBtDI0aMSAGCwvcoUYtye7IZEyiWDd5OsUI0G/2zIcScjchx5SVyV10OjMKq3TfQurobWlUvYTwJcZSZNhduRGPWikuyr3KlHRASFo/Y+CSM/+MMao0vDjen9H32SzGZNAoCQmJFfhrbPOEllhlmmWmT05wtLYpqV+ABPeXAxgQeESBhSy9u0UOI9P2JbvjnhKhleiGal2sGCFFo5s4ZWL7jX/Rt83S+E7k2CHHrohC3Xms1CnI9povk4xwjYPoZhh60JUGLcmpRvm+2/E/g74N/Iun+PbMLoXtxvWo/jcH1BhZ4keDlhS+Jz33RqC28mCZ0/sQsD3OFCUkJRtGWouOj85XAZW5NmSkLj72rNQvT7WuFObhzK+o2Ri0eLkd8odkw9BY5KtmSCbDAlcyC95gAExAESFij0II5aUoQo5vFOWUsbOUUaR6HCSQTIPGKwonSzXG6OTJgwADpbZofvTvoi1ExK1v4X7uF1oMayUXeT7qHnz97E/VadsHAN8x/gVg650vplTVQeF3VadEpGY5ur1bTduj8zMviS8V92Ng7wkF4apF5V0qf0NSwbXecP7oPG0SIRPLyKlPB2Isj6V4i5kwciZqN2qCFyPu1YfFcrJk/A9Y2dijlXQHewtssSYhoRXEf7741Ujcz3mUCBgKm+bXIC5pC99ATz2ypE7DIBoFrwsKziIw23MD59H81UaucE3pP3IN7SQ/w8W8nsXJiC5QQ4RL1ltE28YkP8OZPx2QX5I028/W68A+KxaiZR+U4o+eexPzRjaQHqH6cJ9l///dT2Hk8GCTArJnUAs72xmt4kr4z0zajzGiMjLbJDc4UQlGZeuBMHfO2cBOgaCD0mS0sLAzPP/+8/PuuvrPlFhkShUp1LyXCFY7FvzuW4uk2z+QbkUsvbnWuzKHucus9pMalh3BOnTolc3x37twZTZo0KdTe5opLQdzSdzYn4XlE3i9RceFStCFPnJXHlyAsNgxvC8G5INuDh8me2hlZJ4X0e6nFa1h7ejVa+LZCWRfvjDQvMHVfbfYqZu+dAycbJ/Sr1afArKsgLoQFroJ4VXlNTCCfEVC5tW7cuJHPZs7TZQJMIKMEZs2aldEmebI+eaDSjV57IUApo7xbFuIJ0Na9nlVF2nbHqr+wfuEcxIqE4nWbdxI5rypq58zt9Hv1Q3PF6Spr2qkvQm8FIlF4fc2fPhYDR4yDb/V6su2xXRuw6vfvQF92mnbuAxs7B3R/biSu+53F3ZBbiLgTgsALR3F4xzqsWr9Rho1N16BcqVARGD16tFwveZQ/9dRTHOL3MVe/qPhbQSEKQ8JTJq1/TNM0T68+fEuKQFSpTT13tKlRUtaf/GItfPDLCSk+vfvrSfz+VkOtn8y0mfTPORn2kDr5YFBV6a1FHlt9W5fB8p2BuCRCJP608Qpe7+KrjfMkOwKVti4S6nadu4NeDT2fpMsnapsZZplpk9OcCcrtuwkaGyuL5EgKWiHvFGoCefEzG4X1+7T7ZHyylkSuJULk6p/nRS4lbvWpOwAsbuWNX6k1a9bkjYnwLLKdQI+afTC00f+0cQ5cO4Tvtn+DxHvx2HVxC7pW6Zzp8HVapwV0p0fVbqBXYTb6P+frXtMKM4J8s3YWuPLNpeKJMoGCS0Dl2bp165bMO6YEr4K7Yl4ZE2ACBYGAt5enELTiRN6rSNjaOwlBKR73EhPgXto4/OyKX7/Bvk3LUKNha/zvvS/TtXR6kn71799j3cJZmL3hgvCMKIJYkYMrLOgmigkRrVQZHxQVeblSs57/G4X9m5Zj74Z/MfWtASLBFmSuLcrj5e5VVuTZqoPFsybDs1wlWBV9iKL3ohB2KwCJsVGIDg3GX/P/QK3K5VPrnsuZAJTIxSgeT8Bd5Da6ERwrhaBTVyOEl9WTh2G+E52IL/8+Jwe3s7HAhEHVtYmQ0NWliSc2HLiFc/4RWLg7EM+2LIPMtNl5NhRbj9yWfdep7II+jUtr47z7VGXsOR2KYBGucN56f3SsXQoVPe2185ndIaei1nXdNQ+uVtVShlnMbN8ZbZcZZplpkxuc/cV7cv+ZUImEPOXsRIhLNiaQHwjQDcfPekzGuLVj8PfmBejRrBfcnfNmLkAlbtXwroPnGwzOD3h5jnmYwH8XNuJo4FFcDfOHjaUtfFx98VTNXvB1zbrP7JsubcFe/724FhaA0s5e6FC5I2p71sKcvT9JMsObvw4XW+c8TCntqTUp2wij272HqRs/lxVP3T4FOyt7LDiyQB53FOtt7J38YJDq7ZTIqbX6zGp5+Fz9IUYeTeaYqXbmtmvOrcc2wblokWLiOlqjlKMHanjUQEuf5iIss2WKJhsubsaR64dRxtkbPat3x3qRE+uwOE4Q+Zh8RD4xEvDc7ZPD1v99fBEC7gTIfuITY+X2vJj/F1umGPXt7uCOYU1e0soCwq7i72MLtWO1Q/me3mv7jjo0u70jvOFWnF4l3psBuBV5AyXsS6JaqRqoX6Yeaom1ZYVFJ0ZjyYlluBhyAcGRt+EleFTzrI5navY1y02NmShCVS49tRwHAvYjJiFKMutcuRPqlq6tqmjbrX7bsf/qfu1Yv9NH5N+qXqqavkjbD4oOxrKTy3H5zmXcjbmDMi5lUUv030+0KVokORy01kDspJfZl1unilzZlH/LcC2pj9WnV+L0rdP67tBIvLc7VSq8HsJFBCQOtm30luADJsAEcoMAhTWihPUfffQRhg8fnhtT4DGZABNgAhkisHjxYixZsgRO7t4o16AL7ByL4/evPsDLH09H+Wp1ZF/7hMA0f/o4DB41ES17pB3TPyEuBif2bhFCViROHdiOyPAQKWJ9/OO/2CjCCG5b8acU0EhEs7C0QtteQ9Br6FtpznnPusXYunIBKCxhtMj9VbtxG5Qt7YYiSfG4HXgVwSKvl4fwwqG8hySiUQ6CIUOGoHHjxmn2yyeZABNIP4EFO65hxnJD/ipqRd6fLWq74auhnKcsLYq3w+NRwtE6T+TgSmue+e3cjHWXsXDzVelVqObeu6UXxj5jHM5WneMtE8irBC7fuYJP1o2RXun9hCdXXhO5lLjlZO+MGf1+hIOVQ15FyfPK4wTihJAxccMk+AWdNztTCiWXFZ4204Rn04HLu1KM0U/kqlp27B9Z/mXvr1DZrVKKOnmtYODv/WUOrl61+xl5cNE8Kedk/9/6yik3qdAK77R+C0P+fFbWLy1C8c3oNyPFcqZs/QqH/PfI8t+fmw8K4UeWGrOWldph96Vtso4pMxKajgixxdRsrOwwVuTJMhVRpu/8XvblZOeMiu5VcDTggFFTCsP4Y/85KCUEK7K3V4zG9Tv+RnXMHTjbl8Cvg37VTh0KPIIpGz7TjtUO9b/kxWXqMMWWhNdfRRg/Cv1ozv7X7GU8Vb2XuVPpLrsi1vOJCE+rBDt9Q1rHl72mCpGvpFa8/coOzNg2XR43LN8Mh/33aefUztMNBmFw3UHqUG5/3DML285vNCpTB6mtY6f/bszY/q3Z9Zd0KoVvnvomxd//jDB7+tf0hUZsJcLfFvSQm+pamNum/uivudpcxgSYABPIJgIU4ujrr78GhQtggSubIHO3TIAJZCmB3r1749ChQ7h//z5O7VoJd9+6wpPLEcWKJT8Fv2npb6hUq1Ga4lZifCyWzpmCY3s2SgGLvKqcS7ijQ7+hqN+qC07t24qdaxaibe/n0KxzXxFW0B7LfvkKO9cuRFREGAa/NcnsukgUO31wO1zdPEROr6Ho2aElvIsXER+whXvEI6PcGuQ9GxcXB0dHR1SpUkWd4i0TYAJZRODZVt64eDNaelRRlxSu8KrIYcWWNgEPZ5u0K/DZTBEICI4xErfqVXHF273y/s3KTC2WGxVoAuTJ9fPAuXhl0TAsE+EK85LIpcQtK+GdMa7T+BQ3Nwv0heHFZTmBv44u1MQtupnf2KcpEpISsUt4AZGo8Pu+X9CsXFO4ilxTmTUSN5S4ZWVpg67Ve4oHcophy4UNWHFiSWa7zaPtkv08LItawEK8WlVqL4SNDbh59zquRwSK70xljOZ+QvAhK1PCRxO30mKmxC2jTh4d1C5dB7bCA4+u4e3Im5oYReLN5I2f4aeBP5v9mxEZGy7FLbo+dcrUx6kbx6XgQ++B3w7Ow8ftDSHuu1TrhhsRN+RoG4TXGZ0vbu+K5iKXlt68iid75VO5l1NpdK7RQ6ty+NpBhEWFaMfmdvzDAvDL7lnaqfLulVFe/G2+EuqHgBA/Wf7nvl9R3sVXeAJm3pNr2tZpmrhFImQV4Ul17PoRhAtvKXrNEsLUxM7jtXnod0jcKiXW2qpiG8QIxorJv0cWoWW5FkbeeI3LNpbekap9dEK0DGWpjk23UcIjTC9uNSrfHCWF0Hbq1kkECi+60Mgg/H5oPt5oMVxrmlFmPUTurweUQy4+SgidW2U/FUtVRSUhduqtYZkG+sNCt88CV6G75LxgJpA3CZDANXv2bDg5OeXNCfKsmAATYAImBGxsbDBu3Di8++67eBATg3NH9qD7kBHw8jV82Lxy9hisbe3RvHM/k5bGh39/PxEn9m1Gv2Hvo1XPZxF1NxSOLslPoO1cuwge3r7oMuhVreGgURPQouszMvxguco10aJbf+0c7WxfMR/Hdm8UoQhr45WRb6O+T3EUt04WtlRlV1dXTqqtYPCWCWQTAfLY+vTZ6nirV0VcEkJX0v2HKOtul02jcbdMIG0CH/WrgktNvWQlXw97sJCYNi8+m7cJkFfU5z2+EOEKP5YiV6s6bVDDp2auTTrhXgKW7liMsPBQkLj1eY8vQUIcGxPILIFYEZZsvQj9RkbeILOeniWFJzruLMLpjV39oRQwVp1encJTieqk1xYJEU3ZN32no7SjIfflgDpP4+WFLyFW3OgvKLbjSrKXWlUhFJD1rtFLCly0v/bsOrzeLPl7F3kPUc4uslYV2sgt/cgss55CgAK9HlnSgyQhgvwprzOJXLtFiEjKDWbOSED5QIRYpLB31O7ZPwbI638p+IJWvZuu7bYLm6QwVE6EsRzW+EWtjrmd0k6eeK3pK9qp6IQY7I3arh2b25kphCVlr7Z6A13Ee1LZkcBjmLLpM5AHYC0RSjCzdlCEYwyJvCWbVy1dC593+xRFxL/E+4l4c9lb8typ60fNCpPUyMu1LL7r871gZvguTKEg6feG7O9ji/BR+/flPv2g8JT6EJU3o26lKXAtOr5Y8qe2b7V/D63Lt6RdaR+u+UgK0yScDhfvJxKMyTLK7KXGQ2U7mosSuFr4tkRvIUKzJRMwHwgy+TzvMQEmwARyhEDZsmWxe/duzJs3L0fG40GYABNgAllBgEL7zZ07F7WqVxFhAy1w7eJZFLOwlF3T03IWIuQfhQdMy8pWqoG3p/4hxS2qpxe3wkODcEp4YT03+vMUXXiLdk+9+C4Wz54s82mpCsd3bxLeXYtQpXZjfDpxPNpVcTYrbqn6vGUCTCBnCJRwsELTyq5oKXJKlS1hmzOD8ihMwISAm5M1mlctIV8sbpnA4cN8SYAEJBKSyLYe3oQzAcZ5SXJqUcbilg2LWzkFvoCPczXsmrbCXjX7aDfJqbCq8OCws3aQ56+HX9fqZWYnUORfIqvuVVsTt+iY8i91qNqFdvOlPXj4QIYkJDGIBIKFJxZjlgj5R0ah95TXS1nnMlIIofIdwkuG2inbq8vJ1FrnBZUVzGic+w/uY0j9Z+V8aMwrIvxqatZBhKFTOZ3I86ymVz1ZNSouPLUm2Vp+VXhqkZGIpBe3qKyByL+14PmFeLbuQClIUVlm7GJIcpjv5xs8p/VF780BQjxT5hd6We0abXvJPFjJD3rS7w15dJEFpMHaqJNUDi48EhYpvGRLnxZGtdoJr0BlQTHBahc5wUwbrBDtsAdXIbrYvFQmkNcJkCcBGxNgAkwgPxKw96wEl5AkBAUmfyHxKOOD+Nhokfsq7S8c7fu9kOqSSeByL10OziVLma3Tqf9Lsnz/pmV4s1dtEdqwFALOn0SFGvUwZeKH8HBI/jBvtgMuZAJMgAkwASbABJhAPidAItfkHlMwdu1HUuSi5eS0J9eGQ/9Jzy1rK1t81n0ye27l8/dUXpl+oAhhp2y73zYcuLpPHcotCTdkwVG35TYzP6iPpPv3ZFMfV98UXXg6eaQoyy8Fa0+tAL3M2YvCq8ZNl7eJhJA5O2dIb60DIjwfhX0kOxCwV27Jg07leXoSZudELrW/j/0Nv+CLmmeYRTFLzRMoIj5CjmfuRz0R3lBvDo8ETn1ZTu3fFaIaPdBJVqWUeQ8tawvrJ57OTd3vgG+J8kb96Y9vRiT/rugruT/KTaYv83YthyBR/25MqL44w/shkYbfO/r9mbBhglF78oBTdivithSOc4qZGrcwbVngKkxXm9fKBJgAE2ACTIAJZAuBlh17ITTBUnpSzf92DNr0GgLyzCrp4Y0i4unAzFqJUqVxN+QWwkNuw1nk0jJnJHLRy//cCfz8+ZtCDHPDyy8MYXHLHCwuYwJMgAkwASbABAokARK5JnX/HBPWjctxkYtybl29eUWEJbRhcatAvrtyb1GJ9xO0wf2FIJIdRnmJlJkTJKyE+FKQjISqkS1HiZxQNY2W1da3NX7ePVOKNmvOrJECV3RitMzLRRVbiPPKMsvskvB4GidC15maEhhNy/XH5HFGXlt5xeKS4rSpFBV+Vdll8feSx7EoavxetLKw0oaN09XTCsWOtfD0MrWHDx+aFmXqWAnMdP3O3jiZah8qPGJOMUt1IgX4RN75zSjAkHlpTIAJMAEmwASYQMEm0KyCE5wGPQP6sPzvz1OwY9XfqFynCVyFKHUnKBDxMVGwsXfMMAQKV+hepjzWzP8Rz73zeZrtLxzbj6T4aGzYsBHVKpRNsy6fZAJMgAkwASbABJhAQSNQpWRljOn6Cb7477McE7lI3LoYcBbsuVXQ3k15Yz3exZM/09ct20jkgErO8aOfoattCf1hhvaL2yTnQU/LeyhDneaRyvXKNUZHkReKcjaVsHOFlwhNZ2tpPky0pRDyWlXqgB0ib9X5W6cRLjypjt04rq2kjS48YWaYPcRDTNlsCKdKHluvthyBau5V4WrngrC4u/hw5fvZlussMg2vMG2BGdzxcEiOMBIQ5p/B1umv7iHywZ14VP1O7B0jr7vgqBCtI8/ihrxxWkEaO4F3r8mz7k/onejmWArXRY42spFt3hZhJs0Lfb6PcjE+CbOiSH5oNjI+So7JP5IJsMCVzIL3mAATYAJMgAkwASaQaQI13Iti0siBaFatNI6fOIfzly7i+tUAxMfFYs6kUahUqxEq120CRycXFLEoJr9ohdy8JmLpF4WbVznYOTjBVrxMrX6rriLs4AkE37gKd1HPnJ05uAMbF/6Ib6Z9weKWOUBcxgSYABNgAkyACRQKArXda2Fct4n4fP3EbBe5lLjFnluF4q2VK4ssL3IbKTt76xTea/tOqgKNqme6fSAewAsMD0RZF2/TU9qxgxC5ouMjcZjyTTV/XSunHQqplx4jD5rIhCiUMhMSLj3ts6NOmeJl0LRs43R3/VSNXlLgogbrL2yAf+gV2Zb4lHNJvhZUmFFmJEqEx9yR/XWq3h0dKraT+/TD0cohW8St4rbOiBceegEhfiBvNAcxTlYZ5QJzsnNGZGw4/MR75LzIR0X5rbLayjh7aV1uurQZg+sO0o43X9ys7Xs5JdfTCs3snA06J8MT0imfR8KTmWrpKirnWl4TuEJiQjCwTv802z0JMxJole2+vBPPibxtbMkEik0UlnzIe0yACTABJsAEmAATYAKZJWApHqyqVqEc2rVogP69OqNqZV9YCzErMVZ8Ydy5ATvXLMT+zcuxa80iHNu1HnvX/YPzh7fjyJZl2L9+EZJiwlGiTEVY29prU/D2rQobOwdUrNVQK9Pv3PY7iTmfvIIRr7+Cl156SX+K95kAE2ACTIAJMAEmUOgIuNu7oWrpGth7ZTf8Ai+ihLMbXB2Tbw5mBRAlbnkK0WBsp3GccysroHIfKQhQyMCrkTdwQ3ic3Be5snb678ZdIURZibBrFJ4tODoE54LPo0xx8zf3KYTasH9exqqTy7H/+iF0rdolxRhUEBoXJnNCJdyLx22Rl6iKe2Uxzl3sF7mo/j22SEapoHodq3RGCfuU3mL+YQEYseQ1rDm9CnEP7qGuSa4oamtqRwKPYdSS4VhM/RcripoeNUyrZPp46fElePDwgcgNVQ11vYzzVqXVqbNtcewO2IMowTgw/DoCw67KftpU7oCG3g2MmmaUmUXRYlh6fLHsI0oIgY3KNYG9lR2u3b2Oif9NQIwoI7MQ19zH1Qc2IuQpXef9Vw/gmuBbpEgRDKg3UNZRP/YJQfK6mKO5c1Tn5O3TuCnETbIrd6+itFNpPBRORqHRd+B354o4TvZ6ChbXnUSwmMQY+Tpw7QBuR9yQ175t5fZaOYX8KybWQkaeq0evHZb72/224qF4cJPes8T+Tqx4T4VeRnRCtNn3jGyUjh/kdbfy1HI5j3PCs87O1lGGalx3/j9sOrtO9kBC26sipxpxIAsQaz0YIMRaYe2FB5+bQ0kQ890in9q3W6fJ+VHIx486jhGiX/L3btlA9yNK8Fh/Zq0sqeNdH1XdjAW8ckKAVufP3DwF//BrSLifCAdrR8QnxeOK8O6KF79TxcX7SllmmRHzNWfX4J7oP1YwTRIegU5CeE16eB+3I4MQFB2Mkrqccmq8wrJlD67CcqV5nUyACTABJsAEmECOE2jRpCHopTc/Pz9cvHgRjRo1gpubmzx19OhRLFmyBMUQgd0LvsTgER/BxrkUIuLFaeHVVb91V30X2n7omR34YfybeH7Is3jnnXe0ct5hAkyACTABJsAEmEBhJlDTrTrGdpuAyesnYfOhDXBq4wR3Z/csQaLELS9xc/OLHl9kqVdElkyQOylQBEY0fw1nbpyQHlah4kb2SiHe0Etvfzy/wOz78LzI20UeNmTXhNhAofBcbV30TeX+oLoDsPncf6BcQjuFVwy9MmK7/PfI3FXUZpsI8Te04fOPbX47+rZWx1OEocsr1rNmb/y8a6bGjebVpkKbFNNLixl5dSY+oC9yyUb5s6qKvF8U/jAo4iaGLxqWfFLs1RYC2snrR+R1Grv6Q7QTYuQbLYYb1cnowZB6g3HYf59sRn3TS29zB/8OF+HlRUZiY2q5wEb886rWbGCj5zCg9jPyuFuVLth6cSuuCO+tBw8eYPHhv+RLqyx2apaph0ldJuiLMrRvZ2mHwY2HYsH+32S73/f+kqL9q8LrUOW5Mj1pLucZ1Rna7JUn9jYsLd63Q5oMxV8HfpfDHvLfC3rprZUQR99uNUorehJmzzZ8Dr/umSP7WnbsH9BLGYl88579XR0Wum1yAMdCt3ReMBNgAkyACTABJsAEcp5AxYoV0b17d03cohnUr18fAwcORFBQEBxtLfHtmNdxcNlMON85jA4iKmFz72Ko71kMNdzE040iFGLJhABsm/sJpn08HK++8go++eSTnF8Ij8gEmAATYAJMgAkwgTxMgESuaX2+hYtDCSzbsQTB4cFPPFslbjWv2JbFrSemyR2khwCFlZs76Ff0rN0PJJyYs6Ao8+/taiJkHN34JitbsoJZcYvOOQqPkx+emYnSJmEM6bhbraeoijTyGDFnrcu3FPmHDLeY21XpZK5KijLyOlHmW6K82s3SrfI0ykin7YSYpdZC7Yrbu4pcWcaeO1SeFrMPOn5MVVLYRx0+RC3hCaQ3uj6DhGjUwNv4oUhVp2gRg7eUOtZv0zpH9Sgs5Yedx8HZjNcdnQ+KSr4GdJweI+8svU3rNQUvNBsGG+GNZs4i4gwCq7lz6S3rW6M3Rnf4IMUYdG0m9piMZuWaGnWVGhe6ruWFd+I3/b5Hj6rdjNqYO4hNiNWKS9il9Fykk/1q9sGXvb8CPfBgzu6I0IWmlllm3YToSQKjub8DSsg2HauwHBcRydAfFpbF8jqZABNgAkyACTABJpCXCYSHh2PVqlVYuXIlAgICYG1tjcjISNjb28PW1hbOzs6IiIiQZWXLlsUHH3yAZs2a5eUl8dyYABNgAkyACTABJpCrBCjs1odrPkRYdCj6tnkm055cenHr3TZv5+qaePDCS4ByXYWIXE7xYutg7QB3BzcZsi01IpSDi/IDpTc3FvV/Q3gYUe4jGwsbTN/5A3Zf2iq7X/LSMuEpY95XgtrFibBs5jzEzM3ty61TNe+iJS8tT9UDx1zb7C47I/I0jV9jEKmGNn8Fvar1SHNIWvttIRZ5OnlIZsQ8Ij4Cdpa2MmSfaWOqT+El7a3tUfKRcJKQlIBYUW5ZzALWxaxhIbZFxL+ssggRdjFEjEmiH11DCttH3lFZabSGYPFei0uMkyE0KW8UCYFZaRRqMCz2rnzf2wq+5uyhCN9HoRHDxTVIuJcACtXpJn5P9HmszLUzLdsk3vdzxPufbNbAnx/7O0Tj0nWlMI9kJIoVT0UUlhXEj8wyI84kwCWKkIV21nYoZe8u3juWqttCt2WBq9Bdcl4wE2ACTIAJMAEmkB8IxMTE4NChQ3BxccHJkydx/vx5lChRAqVKlZIeX9WqVcsPy+A5MgEmwASYABNgAkwg1wmQyPXNjuk4ee0I2jfshBo+NTM0JyVuvSZCTXUWIafYmEBhIEA30Ucufk2Gn8vqEGijV74rw/G5OrrhlwEpw87lFl8K4zhq6UjEJ8YKkckS85//S+bCyq358Li5QyD2XixG/TsK4UJMJs+vJS8uy52J8KjpIsA5uNKFiSsxASbABJgAE2ACTCBnCZDXVtu2beWgdeqkPzlyzs6SR2MCTIAJMAEmwASYQN4nQGHeJnT6RIhc32Hr4U3yqf76lRqka+IsbqULE1fKxwTIo+hm5C3pQUT75H10MeQStl/YqOXWGpyOvFoZQRAmPF3IfEr4ZqRZlte9J/KOnQ+5iMCIG9jhtw2Xbp/TxnhR5GmyKmalHfNOwSVA3l7kuUjejhdETrGdl7ZJkZNW3ESE4GTL2wRY4Mrb14dnxwSYABNgAkyACTABJsAEmAATYAJMgAkwASaQBQQotGBIdBD2nNiJsMi76NigY6q9UmirHSe242LAWbDnVqqY+EQBIHA26Dy+2DAp1ZXUKdsQHSu1T/V8Zk5Ei5B5ZL65LHCRsDdx7dgUS3i5xevoWqVzinIuKJgEFh5dhM3n1qdYXH2fJninzegU5VyQtwiwwJW3rgfPhgkwASbABJgAE2ACTIAJMAEmwASYABNgAkwgmwiM6zQWY9aOwTn/Uwi5G4wezXrAyb640Wgkbi3bsRSh4cFoXrEthyU0osMHBY2AnVXKXEx2Ir9XBffK6FylE5qXy9qcv5QTqbpXbVCuqnql6+YqTv3aac3VPGuib80+qFaqaq7OiwfPWQK2ut8BCptZS7wv63rVQUufFnkqP1zOUsk/o3EOrvxzrXimTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJPSIBycpHIdSPsGqytbNGwWiOokIXBQtRav38tIqPD0bfuADzXYPATjsbNmUDeJkCCU1hcOKxFOD4rkXfK2sJa3NQvmrcnnUWzI5EtNPYOStq5Fpo1ZxG6AtUN/Z+QkJSI4jZOsCjK/kD57eKywJXfrhjPlwkwASbABJgAE2ACTIAJMAEmwASYABNgAkzgiQlQTq69fttlP072LqjhXQv7zu+EjXia/4UmL7Pn1hMT5g6YABNgAkyACWQvARa4spcv984EmAATYAJMgAkwASbABJgAE2ACTIAJMAEmkEcJbLy4BX8c+BXxibFS2GolQhKS15aDlUMenTFPiwkwASbABJgAE1AEWOBSJHjLBJgAE2ACTIAJMAEmwASYABNgAkyACTABJlAoCZy4dQp1PGsVyrXzopkAE2ACTIAJ5FcCLHDl1yvH82YCTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJhAISVQODIGFtKLy8tmAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYQEEkwAJXQbyqvCYmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACTIAJFGACLHAV4IvLS2MCTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJhAQSTAAldBvKq8JibABJgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkUYAIscBXgi8tLYwJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEmEBBJMACV0G8qrwmJsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACRRgAixwFeCLy0tjAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYQEEkwAJXQbyqvCYmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACTIAJFGACLHAV4IvLS2MCTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJhAQSTAAldBvKq8JibABJgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkUYAIscBXgi8tLYwJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACTABJsAEmEBBJMACV0G8qrwmJsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACRRgAixwFeCLy0tjAkyACTABJsAECg6B9u3bo1y5cmjYsGHBWRSvhAkwASbABJgAE2ACTIAJMAEmwASYABNgApkkwAJXJsFxMybABJgAE2ACTIAJ5BSBhIQE+Pv7y+FCQkLg5+eXU0PzOEyACTABJsAEmAATYAJMgAkwASbABJgAE8iTBFjgypOXhSfFBJgAE2ACTIAJMIFkAvv27cODBw+0ghkzZmj7vMMEmAATYAJMgAkwASbw5ATIS5685Rs3bvzknXEPTIAJMAEmwASYQI4QYIErRzDzIEyACTABJsAEmAATyDyBAwcOGDXeuXMnIiIijMr4gAkwASbABJgAE2ACTCBzBIKDg0Fe8mS0HxgYmLmOuBUTYAJMgAkwASaQowRY4MpR3DwYE2ACTIAJMAEmwAQyTmDv3r1GjcLCwrBs2TKjMj5gAkyACTABJsAEmAATyByBTZs2aQ0fPnyIWbNmace8wwSYABNgAkyACeRdAixw5d1rwzNjAkyACTABJsAEmAAOHjyI48ePpyCxfPnyFGVcwASYABNgAkyACTABJpBxAnqBi1rv378/451wCybABJgAE2ACTCDHCbDAlePIeUAmwASYABNgAkyACaSfwJYtW8xWPnHiBLZv3272HBcyASbABJgAE2ACTIAJpI/AhQsXsG3bNqPKly9fTlFmVIEPmAATYAJMgAkwgTxBgAWuPHEZeBJMgAkwASbABJgAEzBPYPPmzfKEhYVFigqpiV8pKnIBE2ACTIAJMAEmwASYgFkCGzduNFu+du1as+VcyASYABNgAkyACeQdAixw5Z1rwTNhAkyACTABJsAEmIARAXqa2M/PT5a5ublp59T+f//9h6ioKK2cd5gAE2ACTIAJMAEmwAQyRkA9TGTaigSuwMBA02I+ZgJMgAkwASbABPIQARa48tDF4KkwASbABJgAE2ACTEBPYP369dqhh4eHtl+1alW5HxwcDH0drQLvMAEmwASYABNgAkyACTyWwJ49e8zmOqWGsbGxYC+uxyLkCkyACTABJsAEcpUAC1y5ip8HZwJMgAkwASbABJiAeQKhoaHYsGGDdlIvcFWuXFkrJy8uNibABJgAE2ACTIAJMIGME3jc56h169ZlvFNuwQSYABNgAkyACeQYARa4cgw1D8QEmAATYAJMgAkwgfQTIM+s8PBw2aBZs2ZwdXXVGlesWBE+Pj7ymPJwnTt3TjvHO0yACTABJsAEmAATYAKPJxATE4PU8m9ZWlrKDo4fP44jR448vjOuwQSYABNgAkyACeQKARa4cgU7D8oEmAATYAJMgAkwgbQJ6L23WrdujWLFimkN7t+/j7Zt22rH+rpaIe8wASbABJgAE2ACTIAJpEqAvLdu374tzzdt2tSoXsOGDbXjrVv/3959wFdVZYsfXyQhhBQCIQktQOi9Kb2ogIAMgmIdyzi2sY3OHyzD+GYcn2/mOdZnmRnbFB0dFcuo2BBUQEDpIL2E3kIqkEYS2n+vE87JuclND+Hm3t+ez+Xu0/bZ+7uv7++f5dp7nlOnggACCCCAAAK+JUCAy7fmg94ggAACCCCAAALWXhCLFi1yJDTAFRIS4hwfP35cRo8e7RyXtTm6cwMVBBBAAAEEEEAAAQ8Bd/bW2LFjPa6df/75zjEBLoeCCgIIIIAAAj4nQIDL56aEDiGAAAIIIIBAoAu4A1YDBgyQ3r17e83gspcpXL9+vSxdujTQ2Rg/AggggAACCCBQKYHdu3eLvf9WUFCQjBs3zuO58847T2JjY61zmzZtkiVLlnhc5wABBBBAAAEEfEOAAJdvzAO9QAABBBBAAAEEHAF3gGvixInWeXcG14kTJ6xz7mUKdS8uCgIIIIAAAggggEDFAgsWLHBu0uBWhw4dnGOtREREyMUXX+ycI4vLoaCCAAIIIICATwkQ4PKp6aAzCCCAAAIIIBDoAgsXLpTNmzdbDOHh4TJp0iSr7g5w6R5cWlim0GLgDwQQQAABBBBAoEoC8+fPd+53B7Kck6bizuoiwOWWoY4AAggggIDvCBDg8p25oCcIIIAAAggggIDMnj3bUdDgVkJCgnUcHBzsnNc9uLRoBlfnzp2t+s6dO2Xjxo1WnT8QQAABBBBAAAEEvAvo8oR2BpcuQ+gOZLmf0MCX/e9Z27dvl++++859mToCCCCAAAII+IAAAS4fmAS6gAACCCCAAAIIqEBqaqq88847DoadveWc8FK55JJLnLMEuBwKKggggAACCCCAgFcBO7ilF6+44gpp1qyZ1/v0pPvfswhwlcnEBQQQQAABBM6ZAAGuc0bPixFAAAEEEEAAAU8Bd3BL/6th9xKEnncWH/3kJz9xDj766COnTgUBBBBAAAEEEECgtIB7r9Nbb7219A2uM+5/z1qyZInrClUEEEAAAQQQ8AUBAly+MAv0AQEEEEAAAQQQMAI9evRwHB588EGnXl6lV69ezj5d5d3HNQQQQAABBBBAINAFNNt90aJFFsOVV14prVq1KpdE/z3L/g+ONm3aJGvWrCn3fi4igAACCCCAQN0KhNTt63gbAggggAACCCCAQFkCEyZMkJkzZ1qX3cGusu63z7/00kui/1XxsGHD7FN8I4AAAggggAACCJQQmDt3rnPmqquucurlVfTfz+bPn2/dossbDhgwoLzbuYYAAggggAACdShABlcdYvMqBBBAAAEEEECgIgENUlUnUFWdZyrqC9cRQAABBBBAAAF/EkhKSrKGo/ucDh8+vFJD0wBX165dK3UvNyGAAAIIIIBA3QqQwVW33rwNAQQQQAABBBBAAAEEEEAAAQQQQOAcCGjW+9KlS2Xo0KGVfntMTIzocxkZGVV6rtIv4EYEEEAAAQQQqLYAAa5q0/EgAggggAACCCCAAAIIIIAAAggggEB9EqhKcMseV5cuXUQ/FAQQQAABBBDwLQGWKPSt+aA3CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACFQgQ4KoAiMsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAK+JUCAy7fmg94ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghUIECAqwIgLiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPiWAAEu35oPeoMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFCBAAGuCoC4jAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4FsCBLh8az7oDQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQAUCBLgqAOIyAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAbwkQ4PKt+aA3CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACFQgQ4KoAiMsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAK+JUCAy7fmg94ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghUIECAqwIgLiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPiWAAEu35oPeoMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFCBAAGuCoC4jAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4FsCBLh8az7oDQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQAUCBLgqAOIyAggggAACCCCAAAIIIIAAAggggAACCFB9NH8AAEAASURBVCCAAAIIIICAbwkQ4PKt+aA3CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACFQgQ4KoAiMsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAK+JUCAy7fmg94ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghUIECAqwIgLiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPiWAAEu35oPeoMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFCBAAGuCoC4jAACCCCAAAII+ILAqVOnfKEb9AEBBBBAAAEEEEAAAQQQQAABBBDwCQECXD4xDXQCAQQQQAABBBAoX+DEiRPl38BVBBBAAAEEEEAAAQQQQAABBBBAIIAECHAF0GQzVAQQQAABBBCovwLHjx+vv52n5wgggAACCCCAAAIIIIAAAggggEAtCxDgqmVQmkMAAQQQQAABBM6GAAGus6FKmwgggAACCCCAAAIIIIAAAgggUF8FCHDV15mj3wgggAACCCAQUAIsURhQ081gEUAAAQQQQAABBBBAAAEEEECgAgECXBUAcRkBBBBAAAEEEPAFgcLCQqcbISEhTp0KAggggAACCCCAAAIIIIAAAgggEIgCBLgCcdYZMwIIIIAAAgjUOwF3BlejRo3qXf/pMAIIIIAAAggggAACCCCAAAIIIFCbAgS4alOTthBAAAEEEEAAgbMk4A5whYWFnaW30CwCCCCAAAIIIIAAAggggAACCCBQPwQIcNWPeaKXCCCAAAIIIBDgAu4lCsngCvAfA8NHAAEEEEAAAQQQQAABBBBAAAEhwMWPAAEEEEAAAQQQqAcCZHDVg0miiwgggAACCCCAAAIIIIAAAgggUGcCBLjqjJoXIYAAAggggAAC1RdwB7jI4Kq+I08igAACCCCAAAIIIIAAAggggIB/CBDg8o95ZBQIIIAAAggg4OcC7iUK2YPLzyeb4SGAAAIIIIAAAggggAACCCCAQIUCBLgqJOIGBBBAAAEEEEDg3Av07NnT6QQZXA4FFQQQQAABBBBAAAEEEEAAAQQQCFCBkAAdN8NGAAEEEEAAAQTqlcAjjzwikZGRkpSUJD169KhXfaezCCCAAAIIIIAAAggggAACCCCAQG0LEOCqbVHaQwABBBBAAAEEzpLA9OnTz1LLNIsAAggggAACCCCAAAIIIIAAAgjULwGWKKxf80VvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGAFyDAFfA/AQAQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfolQICrfs0XvUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEAl6AAFfA/wQAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTqlwABrvo1X/QWAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAh4AQJcAf8TAAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqF8CBLjq13zRWwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAg4AUIcAX8TwAABBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKB+CRDgql/zRW8RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYAXIMAV8D8BABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB+iVAgKt+zRe9RQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQCXoAAV8D/BABAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOqXAAGu+jVf9BYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCHiBkIAXAAABBBBAAAEEEKihwIa9WfLpimQZ2zdOhnSJqWFr/vm4Gu1OzZWQ4AZyyYCW/jlIRoUAAggggAACCCCAAAIIIIAAAnUmQICrzqh5EQIIIIAAArUncM8990hGRoZMnTpVJk2aJFFRUbXXeAUt5RWclH/O2y2nT1dwo7l8/QVtpXlkaMU3+sAdhSdOyfGTp6WhCcCEhlQ+yV2fu+P5lXLy1GmZtWi/fPGHkRIb1cgHRlS1LujYP11+UDbuy5aDGcckLrqRdE+IkuHdYqRDi4iqNebl7vcW75e5y5MlOIgAlxceTiGAAAIIIIAAAggggAACCCCAQBUFCHBVEYzbEUAAAQQQONcCS5culS+++MLqhtZnzJghEydOlClTpsiYMWMkLCzsrHbxYGa+vDVnd6XeMaF/i3oT4Jr46GLJyT0ug3s1lz//on+lxqc3HSs8aQW37Aeyck/UuwDXtgM5cuefV0le/gl7GNa3BqReNLWbJiTK3Zd0EhOboiCAAAIIIIAAAggggAACCCCAAAI+IUCAyyemgU4ggAACCCBQeYFWrVrJhAkTZM6cOc5Ds2fPFv2Eh4fL5ZdfbgW6Ro8eLSEhZ/f/qQ9rFCyR4WW/o0k515zO+0jllMlgqk6JDm8oD1zTTWYu2Cfjz28hHVvWPNupOv2o7jOaiffLl1c7wa2GJnutf9dmkplVKDv2Z1vNvmkCmonx4TLp/FbVfQ3PIYAAAggggAACCCCAAAIIIIAAArUqUPbfSNXqa2gMAQQQQAABBGpLoH379vLaa6/JmjVr5LPPPrM+qampVvN5eXnyzjvvWJ82bdrIxRdfbAW7Lrrootp6vUc7z/yinwzq3MzjXCAeXDM8QfRTH8vGfVmSlXPc6nrX9k3kremDnGEkHcyRaX/7Ucae14LglqNCBQEEEEAAAQQQQAABBBBAAAEEfEGAAJcvzAJ9QAABBBBAoBoCAwYMEP3cd999VpBr/vz5snDhQjlxomiZuQMHDsi//vUv65OYmCjjx4+XW2+9VTQDrK7Lgo3p8tmKgxLcoIH88YZeHntcfbYyWRZsSJMgaSBP39zH6dr6PUfljfl7rOMnftZHPvhhv3y/KV32puRJe5Ml9fMx7csMrum+WG8v3Cvr92TJThOkCW0YLAM6N5XzOjaVi/vGW/tAacOvzt0p28x1LfbyfOu2H5EH3lhnnbP/aB3TWB6Y0sU+FA38vGKeLVkahQTL4zf2Knna43ihGcPsVSmy1QSWQoKDpHu7KLlyaGvp16Gpx32619kj7260zt01vqMcyDwmc9akyoZdRyS+WZhMMNlitRFU25ee57x36rDWTl0rXVpHyhePjvQ45z6orLP7Ga2nZxeY+Twg32/MsNy7tW0iv7q0k7Qy4/JWlicdNr+fZNlqMsoKj5+ULm2iZPyAeBnXr4XH7T9syZD/LD0gMWbft4Em8PrWvL2iGWkPXN5FUo7ky+vf7JHwsGC5aXR7GdmjufOs/tae+mib9bvQ/deaN2kk/TtGy9i+cfVuuUlnUFQQQAABBBBAAAEEEEAAAQQQ8HMBAlx+PsEMDwEEEEDA/wWaNWsmN910k/U5ePCgFeTSQJd+srOLlpjbvXu3lfWlmV8DBw6UkSNHyqRJk6Rr1651ArT1QJYs/jHNelfeVd1MgCvUee+Pu44615yTprI/45hz/h+tdsnrs3c5l1PNPmArNmXIYz/vJZcMaOmc14oGK+7/21onK8m+uMcEpT5ZuF++6B0rz9/Wz9pP6rt16c4yfPZ9+SawZPfVPhfbrJFHgCvlaEGpe/TeYN2kqpwA1x8+2CKff3/Abtb63pOcI3OWJcsdkzvJbWMTnWsFJ4r70aFFhMe+Zzr+DTuOyOZ92fLotT2cZ6pTadm0OKj0zY+pctng1k4AsLz2quJcsp3HP9wq368t+j3otQOpefLdmhT54HfDpI0JJrrLM7OS5IP5e92nJDntmCw0ff16QKo89fPioOjWg9nOvMxemizHTaBTy4zX18nho4XOXmm/NkHMxc+OcfYU0yDnNhMMdZd5qw7JC//ZJvdd0VWuG1k/s/Pc46GOAAIIIIAAAggggAACCCCAgL8JEODytxllPAggUO8Epk6dKqtXr653/fa1DjcwmUFa9Nv+2H20j73dY5/z9qz7Ofs+b9/u+9zt2HX7u6z79PxpsxGS+7rWy3qu5PmyjhMTE60AV1aWWYLOfOzMrpUrV4p+nn/+eWnevLnExMRIbGysDBkyRKZPn26919f+0D2gNHg0uFdz2X0o1wpwaB+f/nCbR4BLM4p+9fKPTjaWBqYGm0ydtCMFsnpLphXgWLYhXV76aofcO7GTXDmijexJK8pg+tDsoXXy1GmJadpIxpkl+dylXVy4+1DaxTWWKy5s65xbvD5NNOhUXtEsNju4pWO5yLzDHcR67bMdcmHPOOncqvQeXu98vcdqultitDQMbmAFt/TEl0sOyi0XJ0q75p5BofL6UfJar3ZNTFZTiGW2yhhd/cRSmXF1NxnSJabkrc5xVZ2dB01FjTW4dUH/eOlkxjp7xSE5lH7MOv/8p9s9svg0I8sObmkm1sShrSTI2M1bnWIFML8zGW3zzkuVMX3i3a+w6hea9ptHh8p73+6V9MMFMmVkG8k5dlI0cKV92GQy6HqbsWtJiG0sE4a0MgGx05KRVSCbTNBVg2N63/MmGNczIUr6GXsKAggggAACCCCAAAIIIIAAAgj4jgABLt+ZC3qCAAIBKPDdd98R3KqledcAkRb7u5aa9ftmMjIyRD9JSUmyZMmSKge4NItHgwAlSy+z5FxU49r714zoJqHyn4eHSXijYOtVNz2/UrbuPio5ucflSG6hNI0oygj7x7e7neDWmPNbmuUQezrZSMmH8+XGZ5ZLrw7RcuuYRKudK4e1cbr+2Q8HrWc7t4mU+13LETo3uCrtYsNlxtTi7LfsvBPydWay647S1RdNJpJdXps20AmuzPkxRX7/xgbr0otfbJcXb+9n3+Z8q/EHjwx3AlmvzNnpZLRt2nvUOe88UIWKLsn33J39ZPqra63xazbVr/66RtrEh8tDV3aTYd1KB7qq6lyyOz+/pIPcc0lH6/Tt4zrIBQ/Ot35HG8ycusvzs7Y7hx//frjEmaUDtUy7tLOM+6+FVhDq7yb46S3AdZsJ/OlPUwNcWu4279NMLQ1waUk9qgHJogCXBvNKBvR0KcmHXltr3fvvBXuln2v5TOskfyCAAAIIIIAAAggggAACCCCAwDkVqL2/eTqnw+DlCCCAQP0U8NVsmfqpSa9rKjBt2rQqN/GqyTryVv78y/NkcJdm3i5V69xok41jB7e0gUvM/lMa4NKSbLJz7ADX6qQj1jnNkPrt1d2d4Jae1P2ddD+psNAg6566/kMDR1p6mACbnTmkxxP6t5D/a7JNjmQVyta9nsvk6XUtbc2eY+4srQlmWUZ7ycYDFWSOFbVQ/p/9zf5fnz46Qh5+a4O19KPerf2d9vIaK2vu6Z/39XCrqfM1JnPOLiFmrs7vESPLzX5cR42Bu+jyjVqG9Yl1glt63Dg0WIaZpSZ1mcIDZzLw9Ly7RIWHSJYJPNolqnFDj9+Dfb7kt8bKjxWelAt6xkonk7m1w+z7tc18KAgggAACCCCAAAIIIIAAAggg4FsCBLh8az7oDQIIBJiA7n+kWTOUmguUXN6v5JJ/+oaS99jn7O/ylgW0n9V7tdjH9re3cyX7UFb7Je/z1pZ9zv4uq62S1/Py8qxlCo8ePSq5ubl6uVRp3Lix9OtnMnhqcXnC0JCiJRZLvayaJ0b1iPV4MtoEL+ziziDbm1o0xriYMIkMK8r2su/T73MV3Eozy97ZpVvbKLvqfHdqHSmrsjKtIJdz0lUZ1rO568jkHbnGX1tZi5px95c7+lt7mL08e6focoVaNPB09ytr5PVfne/0oabOsVFFmVh2g01M8KlkcZttNvtj3WWCbe6y78xc655pJ0yqlgbKqls0u++1ubtk4bo0KytQ27H2UzvTYM6x4kBZdd/BcwgggAACCCCAAAIIIIAAAgggULsCxX87VLvt0hoCCCCAQCUEZs6cWYm7uAWBqgmkp6fLJ598Ip9//rls3brV68Pjxo0T/YwaNUpat27t9Z7KnHz6jn4yuHPpJeyqEkg6YfY9qqh4C1Z5e6aw8JR1+swWZt5uOSfnNCPILmENS2eQNXJllXkL1tTmco92P8r67tM+Wl66a4C1nN8vX1pt7XW1aecRWW0+53Vsaj1WE2d34KisPuj5Y8eL5lLrmt22xgQAyypBNZjwPBMgu/7JZc7SlvY73IFT+xzfCCCAAAIIIIAAAggggAACCCDgOwIEuHxnLugJAggggAACNRJYu3atzJo1ywpu6b5aJUuXLl1k6tSpcs0110hcXFzJy9U61qXiqhLM0peUDGdlZBdnN1WrE66HWsU2tpaUS83Ir3ZWz5Gc464Wa6faqlljp6GDpm8lS3JGkUF4WEiNMpFKtluT464mq+yR63s6+1Bt2pflBLhqw7miviXEFJvFNG0kv5rS2esjGtuqQfKWPP3JNie49YtLO8lws+dYYnyE5BackEff2eRksnl9OScRQAABBBBAAAEEEEAAAQQQQOCcCRDgOmf0vBiB2hHYlpYk+48ekKCgILmo4wW10yitIIBAvRKYPXu2FdT66quvvPZ70qRJYn+83lAHJyPDipegW73jiIztG2+9VbOVNu4s2kurNrrRsVWkFeDS7Js35u2W2y/uUOlmmzUJtQId28xyeNlmSbrazJpqGNxANHiVl39ClmxIl8ITpyQ0pCiTK+VIvuw6ULTHU4vmxUGdSne8hjeqVVlZVe79vUJdmWc1ca5sdzVopYGtzCMF1ifB2Gh2WW2XpZuLgsHtTUDv9osTneZ1z7d9Z/ZNc05SQQABBBBAAAEEEEAAAQQQQAABnxEgwOUzU1H3Hck7nifL9q6QLSlb5GDWAWnfLFG6t+guQ9oOkobBxX8RWdOeFZ4sNP8V/QnzX6SHSGhwaE2bq7XnT5scgmPHj1ntab+0f/WxfLH5S1mcNJ8AV32cPPqMQA0E7GUINWNr3bp1pVrSZQcvv/xyueyyy6R79+6lrtf1iY4tIpxXPv9JkpVdpXtHvfHNXid7Rm9Ysf2wdGsTJU3MflDVKbdd3F6+XpFsPfq3z3fK3rRjctngVqLBkYKTpyXNBJPyCk7JqBJ7WukDug/WgTMBjf/69wa5c0JHiY9uJLr/0iHz3PDuxftg6bGJCzklt6Ao60uDRQfNfk52iY0KdQJZV1yQIP+eu1uOm+DWbX9eJfdf1kWOmz499s5G+3a57qK2Tr2uKk+ZDKYfTNBtonEa2jVG2posuJz8k7I8KVNe+3yH043ebZs49Zo4O41UonLXpE7y+NubrDvvfWmNTDB9HNmjufQ0+5gVmCUM95j56mHqzSKq/+8X0eZZDaLtP5Qrm/dnS3fz+8vIKZD/m7VdUjOL5rLALGOov83E+HCJa+K5f1glhsEtCCCAAAIIIIAAAggggAACCCBwFgSq97dHZ6EjNFm3Agezk+XhT2eYv8DKcl686cA6mb3hU2nZNEEen/S4RIcV/0WWc1M1Kre9e6v5y8Qc6dv2fHl0/CPVaOHsPJKcfUjue/9uq/GfD7tdpvS89Oy8iFYRQACBWhbYtm2b3HPPPZKUlFSq5UGDBllBLQ1uRUVFlbp+rk4M7tJMmpoMKd1LSYMGv39jg9OVkf3jZPGPadbxvX9ZLVNGtpHfXlW9oFwHE0i7eWIHeWP2Lqu9OcuSRT/u0tBkTi1+ZrT7lFW/+5KOsvDHVKu+fGOG6MddvvjDSImNKgpuXPXHJVagyn3drk997Hu7KndM7iS3jU20jvX7k+8PSE7ucdEssbteXOXcp5U2JngyZWArj3N1cbAnJc+ak399tUv0460M6R1rgkrF/15QE2dv7Zd17rJBrWTu6kOycnOm5Jsg06xF+62P+/7/ubm3TOjfwn2qSvXx58XLqyaDToOTNz+z3OPZYX1iZcn6dGuu9bfZ0gT/Zv1uuMc9HCCAAAIIIIAAAggggAACCCCAwLkRKL3L+bnpB2+tQwHN3Jr+n185wa2YqDjp326QRJ4JaB06sl+mfzLN5De5/tP0GvTv1OniTeJr0AyPIoAAAgicEcjMzPQIbnXs2FHuuOMOefPNN+XDDz+Un/3sZ3UW3Aqp5OZHugTey/ecJ63iipfg00DTXVM6mX2dmnmdW/eyeSHBnv/K4r5W8uG7TebVi78cYAUjSl7TY82g0sypkqVjywh5+o5+EtvMe4ZO8plsnpLPlXesWWp20SXvPjbBkUElssd0LBOGtJKZvx4iup+UXYLN0rN2KTles+JhrZXxA+JFl+fzVsJMn2/9SQd55uY+pS5X1TmknE4Hl3Ptr3cOkEdu7GkFSEt1wpxIdmXMua+rZcl2vf1ebxmTKFePbuexTKP+Ni80Lv9vchd3k9QRQAABBBBAAAEEEEAAAQQQQMCHBBqYv3gp/psXH+oYXTl7Ah+u/1jeXf4v6wUX95godw+/06qfMj+Fx+b+j2zYv8Y6fmjcf8nQdoNr3JEb3rpe8gvzfC6DS7PY/CGD67mFLzhLFH5wy0c1ni8aQACB+iHw3HPPWR0dNWqUDBw4sH50+kwvM3MKJcss+9cuNlw0PpZfeEpyzBJ/jUKCpZHZ56mhCWa5Az01GZzu8bUv/ZgcKzBL5Zp2m0U2tLKwKmr/cG6hHDpcYAU9NLjUommY+Q9BgmvSFY9nNVso+cwyh21jGtfaeD1eUsUDDfodNnNzJOe4Wfa2gbRsVvkxV9e5il20ApPqlpNnfi8Ng6WV6aMGDmuj6PiTDx+zlp5MjAu3mtR50t+r7pemv0/dh0x/sxQEEEAAAQQQ8D+B9u3bO4N67733ZOjQoc4xFQQQQAABBBDwTQECXL45L2e1V7e8e7Nk5R2RsNBweftn73i8S/ekuvHN66xzHeO7ydOTn7TqS/cul3lJ88xf6gTLAxdO89ij69vt82XZnmXmWpD8Zsyvrfvf+XGm7M7YbdVX7V5qfYc2DJM+bfpbdfuP+Mh4uX3IrfahzNn2jazat1ISmraVy3pNli+2fCmr9q6U/OP50sX05/LeUySxWfG/dOqDTy14Vk6cPC7DOwyXizpe4LR13Jx72lzTMrrzRTKsfdG/nP5p3pOicd1jJpNNl2XUollsHZp3sur2H4NMVtu4LmPtw2p/p+SkyjxjtPnQZjmaf8RkzmVb+2VFNmoiTUzW3H9P+L00MP8rWRbs/M5YrJa9h/eY8R+TLnHdpFer3jIycZhENYpybncHuF796T9k9uavZKUxLDDPJMZ2kpsH3STxEXHO/e6K9u2jdR/Ljowdcjg3QxKatZM+rfvKFb0vs+bTvneVCXrO2TrH9Dda+rbuIx+bZ0LNPm23DblN0vPS5YMfP5Rw83u6vM/lMijhfPsxa++1hz6bYf6CONjscRYskabfXeK6mH3eBktijOc8Og9RQQABBBBAAAEEEEAAAQQQQKCOBQhw1TE4r0MAAQQQQKAWBNiDqxYQ61MTBScKrOCW9nlEp+JgkD2Gxg0bS882fa3Az96MnfZp2Zm5U+xAVf6IuzwCXJtTNjvX7AeW714m+zJ22YfWd6EJUtlt2BeaRjT3CHBtOrTRuicpfIscMntkLduxyL5VdOnE702g6KGL/0sGty3OVrDvadww3CPAdeLUCed9baITnADXyl1LnDbtSmZ2mujHXcJDI2oc4Fq4a7G8MO8Zd7NOXd8XYoJEJYNbh48dkT9+/UfZnbbduVcr6VkpsmTHQvlgzUx57vLnve6R9vIPr8hqY2+XlKMHZcWu7+UvV78iLUww0V20b39e8H9y6lTxEpJHTJBLM/i+3vKVPHvZsxIZWrRk1a7DuxzLRSbQqQFFLU9++4TJwjjstPGUmb/3bv6PCY4VBewy8jJlb/oO92tlzZ7l8v7Kt2VIp1Hy0EX3lxq/x80cIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgRYAAlxcUfz6lAQe7aCaNt9KxeWcrwKVBDN0/SzOzqlommKUPDxw9YD02Z+NnVgAkOiJGhncc5dFUm+jWHsf2gWaYaeCqd8IAk1HUR3ak75TlOxdb7bxggjJv3vCWlRVk31+V70kmy+iU2V8s22RSLTbBGi2dW3S3MsTc7Qx0ZSK5z1e2nlOY4xHcameyqdRcA2ca1NIMsrCQsFLNvbDoRSe4pVlvA0wmmZb1JvCUV5AjR3Mz5b/nPCbPmQCUu2igSoNbg0wmW1uTAbdwxwIrKKbn/7n8dXl4zAzn9uyCbI/glj4TGxEr65PXyX6TeafBtDdWvCX3jrjbecauDDb3Nm3cTL5c/4loQGx09wmi+7rpfOm7tqdvl65nflv62xnVdawUniyUY2aZyu2pW60xaFt6//smY+zaflfbTfONAAIIIIAAAggggAACCCCAAAIIIIAAAggggEClBAhwVYrJf25KNwEJu0Sb5ea8labhTZ3TmSY7Jza8uXNc2crEbuOdW+dv/drag6t9TAe5ffAtzvmKKpf0niy/MEvg2eV1k4H0+bqPrLYW7lokoztdZF+q0vetg2+27tc9uOwA14iOI2VKz0ur1E5FN+9wZbCN7zVJ7hz6i4oekU0mG269WZZQS2yTFh5ZVJqR9tsvfyeZeRny0JgHvbY1dcC1cuN5RUtM/rT/NfLTf11tBZ2STGDJXWb++L51Xs/9P9PWBR1GOpdnfP4b2Z6yReZvmSN3D7ujVCDx6r5XmQDhKSvApQ/97PwbZFfmbifbLsP0T6QoeBpngmbTRt3ntK2VtNx0ufeDu60ssM/XzyLA5aHDAQIIIIAAAggggAACCCCAAAIIIIAAAggggEBlBKqemlOZVrnHZwVyTVaRXXQ5Qm8l3HU+x2QMnatyRZ+pHq++qm/x8bYSy/d53OgjB+1MFpVdFm//Tj7d9IUV3LHPefvekLLROX3XiHucJQL1ZEhQiDz+k/+Vl696WVpHtXLuc1cmmcw5u+i+V73bDLAOs82yh+6y9UzAS/dhG5k4wn1JRncZ4xyn5KY6dbsS0agoA805Nm1UNssv/0S+NA+PkYlmfzUtmpGm+75REEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoigAZXFXR8oN7dWk5u2QXZNlVj++s/KPOsQYjzlUp+e6oRlGiS/bpXl6p2SnnqluVfm+zxk2lX7uBsnbvSiuQ868lfxP9hDeKlH4J58k1JsOqXdMEj/b2Zu51jnvEd3PqdkWDVuUVfae7RJp3eStpWYes07oM5aNzHvW4Jacg1zlOPnqozGCac1M5FQ1ezdr0mczd/JVokM3e70v3HrNLtgm6lhVste/hGwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABtwABLrdGANTjImOdUaabvZy8lQzXeQ0qVVROnDpZ0S1Vvh4U5D25MOxMgMudiVZW4ydP136/ynpXWed/O/Zhmb11rsxa/5FkZqdZt2nW0pIdC63PxN5T5PYhtzqPa4aTXSqbFeXcX4aZfd39rcsdatEA16YD69yXPOpBDRp4HFf14PFv/+S1fX0vBQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB6goQ4KquXD19LsaVwbVi7zKz79SkUiNZtW+FdU4zjbyVU6dPe5w+YvbpqkxxZ4ZV5n5v9+TkF2WdxZn9uEoXz35l5nkuy1fy/iApDqJl5WeXvFwrx5pxdalZNlA/R03f1xxcI8v2rJDlOxdb7c/e8Klc0mOCJDRpYx0nRCfIGllu1Xdn7pGucUV7WdVKZ1yNxEW1kH1n9gj75YXTJCjIeyCrY/OOrqeqVl24a7ET3BrUYbhMNAadmneQ0+b38/nmL+XDVe9WrUHuRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgjEDx3/BDEhACmhXUtVUva6yauZOaW5RVZA9+U8pmJ9NocOJw+7REhEY49Y2ufaJOmuytpJQtzjVvlegzy+btNvtm5bj2APN2r31Ol7Lbc3ivfWh9bzR9s5e4axnV0rlmL3e36dAG55xWfjz4o8dxyQP3EoiLTUbV2S7RYU3koo4XyozRD8rkflc6r9t3eJ9Tbx/T3qn/c/k/nXptV9rHdHCaTDO/Ae2Xt4/2ubpl1b5VzqP3myBav1Z9rD3FNCswPSfDuUYFAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGqChDgqqqYH9yvez/Z5YGPp8vyfSutQNeCnQvlsdm/ty/J1a4gTLvots7515f9UzQ757udi+T+Tx+Q/MI859q65A2lglhtXUGbZxc8J9vSkiQ9L0P2msDOqv1rnGdLVp777jlZsX+V1V5S+nZ5Zt6Tzi2jO1/k1OObFAW7dAnAvy9/XTYc2iSvLv2bvGX6aZf9R/dZAbPTUpzl1dDsA2VnqaVlJcvbq9+V3Yf3WH3babKbtqRutR+v1ndm3mGZs+1rqz8HTfsZeZmi32qnmVt2aebKqhvVYYREngkqJR3aLNNnPWDNT0pOquhH7dTdXmLQbqOq39cOuNp55P2Vb8sT856Sb7fPN7+DdKufaqjzU5MSHRbtPD5/x3dWn+09uRaYZRvtsvbgetmfdcDMTPHc2Nf4RgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAmwBLFHpT8fNzA1r3k4EdhsnKXUtE94N6cu4fS414ct8rpKVZxs4u/Vr3lSbhTSXLLPungaQX5j1jX5LzE4fKqt1LrePHvvydjO4+Qe4dcbdz/YYB11vv0hPrTFaPftzl79e/Ic3OZHm5z6dlp8gTc/7gPmXVdbm71k1aOeen9Jkqryx80TqevX6W6EfLgPaDZc2eouX+Vu9eJvr5903vSuOGja3r+sd1A2+Uf3z/inX80Zr3RD920fG+ft0b9mGVvzeYTLfXFv213Oe6tOwh3eK7OveEBIXIdJPh9Yczgca96Tu8zk+HK/4s7ZoVBx2dBipZaR3VSm4YcrO8vewN64kVu34Q/bjLqK5jZdqo+9ynqlQfaYJ1X6z/xHpGHdwWHcyY95igpWbk2XNXcm6q9DJuRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAgoATK4Amq6iwf78JgZcs3AG5wMJvuKBnV0T6abB91kn7K+dWnDxy75g8S5Aku6NOB1g2+SXmeWPPR4wHWggZgZ438nTSOau84WV1NMIKtkCQoKkl9dOF3CQsOdS3puXM+fyK9HP+Sc08q4LmNk6oBrPc61iWkn95ggm718ocdF18FEE4y7dtCNEtowzHW2qKrBvJqUI8fKfl77dWG3cfL78Y9IA/M/d+lvgol/veZVaylJHbO3kuna9yyoQbC3W6xz5V27ovfl8qcpT4taeSsZJZavtO/R30JwsOc7da+xkkX3D7v3omml5rBzi+7y6zEPmbkJLfkIxwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIVEqgwWlTKnUnN/mtgO6LlZl7WOKj4iQspHSgp+TAj+ZnSXZhtrSOai1BDRpIwYkCyTt+TBoGh0ij4EYmcBFSKmhjt6HPpuWkiQZENFASFxkr4Q2Lg1jPLXxBFifNFw3sfHDLR9Zjumxe/vF8SYhuY73Pbqvkt+4HlpKbKhENI8TeO+qwCTLpe0JNQCnUBFS8BWLsdnQ/sryCPCk8WWgCf+HSIiLejKmhfbla32qTkp0qBScLRP9RaxzaWJqapfsiG0WWaVTyRWlm/NkF2dbzuhdaXERsueMo+XxljnV5wFQzL7mFudbtzcObO4aVeb68e7Tt9NwMyTHZgm2bJohmqWmpytyU1z7XEEAAAQQQQAABBBBAAAEEEKipQPv27Z0m3nvvPRk6dKhzTAUBBBBAAAEEfFOAJQp9c17qtFeRoZGin8oWDR7ZASR9plFII+tTmedLPluZZ+JNQKcyRYNXuvSeu3hb+tB93V2Pj4gTiXCfqXldbWqylKD2QANa+jmbRbPIWkTGn5VXaNvexlCVuTkrHaNRBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgXor4H39s3o7HDqOAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDg7wIEuPx9hhkfAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOBnAgS4/GxC6/twwkPDrf23QiuxF1h9Hyv9RwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgeoJsAdX9dx46iwJ3Dn0F6IfCgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQlgAZXGXJcB4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMAnBQhw+eS00CkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGyBAhwlSXDeQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZ8UIMDlk9NCpxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMoSCCnrAucRqC8CqblpsiF5o9XdkR2GS2hwaH3putPPbWlJ8k3StzI8cbj0b93XOV8fKqm56cZ/g9XVQW3Pl6hGUdXq9tH8LJm18TOJbtxEftLtEmkY3LBa7fAQAggggAACCCCAAAIIIIAAAggggAACCCCAgP8LEODy/zk+6yOctekzOZJ31OM9MRHNJCE6QbrEdpLI0EiPa7V9sPHQRvnrd89bzSZEt5GucV1q+xWVaq/wZKGcOHVCQoJCqhRk0+d++/kMOXXqlHy7+Sv52/WvS0zjZpV659m6Ke94nizbu0K2pGyRg1kHpH2zROneorsMaTuoVOBpU0qx//9OflK6x3erVrde+uFlWblrifWsWkztfVm12qmNh07LaTl2/JjVlAZMdU4pCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAr4jwN/a+s5c1NuevLP8TTlx8rjX/gcFBcnkvlfK9QOu9fsgwW3v3ip5BTnS12QxPTr+Ea8e3k4WnCiwglv2tZz8nHMa4DqYnSwPfzpDckxGlV02HVgnszd8Ki2bJsjjkx6X6LAm9qVa+z6Sd9hpK9NVd07WYSU5+5Dc9/7d1ht/Pux2mdLz0jp8O69CAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqEiAPbgqEuJ6pQU0mNU0orlER8SI1rVoJs6sHz+Qv3z/cqXbqa83njp9qlpd1yX9bh1xp7SIbi1XmEBgu2Ztq9VObTykmVvT//MrJ7gVExUn/dsNksgzAa1DR/bL9E+mmfym07XxOo827hh2h3SI7yr92g2UK/pc7nGNAwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE3AJkcLk1qNdIYFLvy+XmQTc5begSd88veFYKj+fLom3fyiXdxld7+TqnUT+tTOo+UfRzrsuXW+Y42XgX95godw+/0+rSqdOn5bG5/yMb9q+Ro7mZ1vKFQ9sNrtXudmreUZ6Z/FSttkljCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAv4pQICrjHn9autcWb1/tezJ3CVhDRtLYkxHuaz3ZOkY06GMJ6p++uukb+WHXT/I3szd0rppGxnb9WLp26qPvPLDq1Zjdw+/S5o1blr1hn3kiSEm82f66Aflybl/tHq0/tB6CQ+NkH+v+rd1fLEZ7+C2A0v1dr3ZU+uzjZ9Z52887waPjCZvZqUacJ34fPNsmW+cgxoEm3lsJC2iWkqvlr1kZOLwUntJ6WNztn0jq/atlISmbeXSnj+R2WZPrJXmuMDsx5Ro9hPTAF58RJzzhnd+nCm7M3Zbx/mFedb3FtP/x799wrlHK/GR8XL7kFudc7sz98g7a951ju2K7vf04EX324devzPyMuUTs1zgHvO7STb7YzWPiJUeLXrJeQkDpI8ZW03KFxtmWY+HhYY7wS09EdSggfxmzK/lxjevs67/Z+1/xFuAq9AsVfnh+o9l2e6lkluQbZmN7zpO+rfuaz3n/mPe9gWydM9S9ymnfrnZf6tnix7OsbuSkpMqH637WHZk7JDDuRmS0Kyd9DHtX2GeCWrgPSm1smZ/mveknDbBvGMmk80unxmTDckb7EPre5D5bY/rMtbjHAcIIIAAAggggAACCCCAAAIIIIAAAggggAACdSdAgKuE9TETyPjvOY/J9pQtHlf2myDG4qR51lJytZFp85TJbFq2Y5HzjiPmL+p1nyNdom6VCQ5oSTN7V9XnAJeOYWDC+fpllV0mqDPVZHmt3bfKyhJKPnrAa4Dri01fOgb3jfyl/biUZTayy2jnnpKVdQfXyu607c7pTbJO5m+ZK38PfVV+a/bJKhlE2WSCU+qfFL5F9h3ZK6t3L3OeTTl6UFbs+l7+cvUr0sIErLQsN9f3Zexy7tGKZqzZc2hf0KUb3QGutLz0UvfovdbSjuUEuDTw+o8fXvHYsyszO02SDm2WT9d+KDcNu00u6znZfm2VvnUvsKy8I9YzIzpdUOrZxibQ27NNX+t3ujdjZ6nreuKLzV/Iyl1LnGtqpr/zK8//qVzf/6fOea1sStnk1UCv9WrVq9Tc6PmFuxbLnxf8n8f49Z8dzSz7estX8uxlz0pkaKTe6pSqmLn7bjegvvpxFw3UEuByi1BHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTqVoAAVwnvt1e/6wS3NCgxOHGoFJwolEUmC0j3k3pjyd9kWPuhEtO4WYknK3+4Yv8qJ7gV2jBMLul5qQQHBcu3W+fIJ2s/qHxD9eLO4r2aGgaFSIj5jOoyxgSZ5sjBw/tk39H90jY6wWMka42PloTmiaL7U2kpz2xx0nzrHm9/9G3dTzQwo3N4KOugE4zSbKv/nfsHefXa10oFRLQdDfRocEvnp1/CebL+wI+iz+hv4J/LX5eHx8ywXjfBLON3wATqtMwxWWd6XfcgG95xlHXO/qON2V/LXdo0aS3je01yTq3cu7xUEMW5eKayy2Rs/W3xS85p3a+qg1nWb2f6dieI9+aSf0iHZh1NJmAv577KVjTLyS5d4rrYVY/vjs07WwGuEyZTS/ccK5kxpQEi3UtsVOcLJdd42Sb/WTVTRrYf4ZGNN9gscajZkXbJKcixlrK0j0t+Z5uMMHdwa1CH4RJrstfWJ68TDUCnZ6XIGyvekntH3O08WlWzSWbvr1Nmf7Hs/GwroK0NdW7RXbrEd3Pa1Io7cOtxgQMEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBOhEgwOVizjPLks02S79piW3SQl668iUr8KTH481yer/9bIYVwPh0w2cee03p9aqUmSaIZpdnpz4nraNaWYfX9LtSbnv3Vskzf9HvL+W7ncVZat1NoEDLlF6TrQCX1jVb665hd2jVKjtNNpRmQGkZ1elC61v/qK7ZpSYAJfo5U06cOmGCIG9a86wBq8VmiUjdG8xb0QDKr80SixrE0eeu+9c11vwnpW51bp/oenb+1q+tIFh7s4zl7YNvce7xVmndpJXcOfQXzqWcglz5IXuBc+yt8tfvi4Nbd4y6VyaY36RdVpkMpie+/oOVAdinVU/7dJW+000mlF2iw6Ltqsd30/DiJTMzjx2W2PDmHtfbxLST5y9/wVrSUC/oUpD6z42Wd9bMNMscPmTV9Q9dntK9ROXB7ORyA1wzf3zf8tdn/9+YB+WCDiO1apUZn//GCkxr4PRu83vSgLGWqprdOvhm6znti2ZsahnRcaRMMUFoCgIIIIAAAggggAACCCCAAAIIIIAAAggggIDvCHjfsMZ3+lenPdmTudd532SzlJ79l+R6srvJ4AhvVLT02b4j+5z7qlPZb5bq06LLvdnBLT3W/ZfGdp+g1XpZNKPnlNm/SINBGiB4d+378tLCF6yx6NJ7dtZLu6YJooEQLd+ZIII+Z5cfXHsyXeDKgqoNM33PyVMn5YbzritaCtC8dGcZS+1pf8Z2HetkKGnmWe82A6xuZh8rWsbPOqjDP/aYTC0taucObum5883+W//+2btyXf9rpYH5X3VKbmFxYFWz3ryVcNd5zbgqWSZb+2AVv1//udGMLi27y7Eu2Y63461nAou6P9jIxBEet4w2WYF2SclNtatyts2cF1FBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqFMBMrhc3PvNEnZ2WbB9vizbU7yXkJ7XwI2W1OxD1nd1/tA2dHk3LYkxHUs10apJy1Ln6suJL9Z/IvrxVm4xWTVxZjk5u2gg5JWFf7aytZaZ5fl02Ucty3b/YH1rBl38mftrYrbZ7KX2zpp3ZHvqNiczLCS4oZMJdDT/qPU+b38MMMsbukvkmQCn+1xd1Q+boJouf6ilWwvvGVqNQhrVqDtNXctuZhdkeW0ry+XVPDym1D3xZ/Ymc19oG9NedC+uw7np7tNVrqdlFf1zp//8PDrnUY/nNQPOLslHD1mB47ows9/JNwIIIIAAAggggAACCCCAAAIIIIAAAggggEDdChDgcnkXnixwjnaZgMjZKLovkV28BSRCTfDFn4oGqn458j6zJ1Rvj2Fd1PECeW3xX62gzecbP7cCXDkmg0j35dIywly3S3XNkkzG0+/M0nUlix1gLHnefawZZ5q15Svl2IljTleCqpmh5TRQRiUusjgAmZ5bvB+X+/YM13l7fzT39UYmC7FkOW2y+mqj2AFmnb9NB9aV2WRQg6IMsrowK7MTXEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBA4qwK+8zf4Z3WYlWu8bXTRsnl6d/92g8weUMV7/LhbiGnsue+Q+1pF9eiwJs4t5WUPOTfVo8qA9oPlYrMvlC6Rp9k9bczSdGUtddfQBPJGdRkr35l9q7Ykb5AjJjNozYEfndFe6FqesDpmp+W0PPHNn6z2NGPrjpH3SI/47hIT3kx076gZsx46a3udubOcnAHVsNIysoXTwu7MXU69NisxrgyuFXuXmX2nJpVqftW+FdY5e7nOUjd4ObH/cNHSn/E1zE6Mi2oh+8webVp+eeE0s8xk8VKI7td2bF6UGVkTsyAJcprMys926lQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEfEOAAJdrHjqc2RdKT21KXi8PXnR/mQEa12MeVd2Dav+R/dKuWVuP8+6DSBPkysnPkpW639Twu9yXRJfUq0w5dvyYZBVkSwsvS8JV5vmzcU9CdIIMbTe40k1f1muyFeDSB2ZvnSO70ndaz6pP+2bFwUY9WVUzDUocyc2w2hvX8ycytvNoq65/RIVGnpXgVnTjppJvMvR2p20XzUaLNO+prRLUIEiahDeVrLwjst38RraY/ah0f6vaLPqOrq16ybbkjVaGVGpumlkmMs55xaaUzZKZnWYdD04c7pwvr6LP6PKEWhLPBJ7Ku7+8a+1jOjgBrjTTt2v7XV3e7db+adU1cy+/uHjHQrnR7NtGQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPAdgeI0Bd/p0znriS65NqTTKOv9hcfzZdon0+TNVW9bQaejJiC11wSulpr9osoquoTabTNvkekf3SfTZz1Q1m0ysvOF1jUNVryw6C+iewUdzE6Wr5O+lflb55b5nH1hV+ZuuenfN8g9790hb6x8yz5d7veq/Wvkyn9cbn1mrn2/3Hvr6qIGsVqfCQTO3fSlrN23ynr1sI6lM+eqahYRGu4MY/XeFZJ6Zv+nvWYJxBmfzXCupWanWsEiDUjVtOheU3Z5dsFzsi0tSdLzMkTfqf7uov1JyUl1PseOFy1dqftsuc8fP7Nfmz57jSvI8sgXD8t7az8QXYYxzbSlv019h76zJuWa/tc4jz/w8XRZvm+lsUuTBTsXymOzf+9cu7rflU7dXbGXI8w2wddvzT529jO65OON59/gvrXK9WsHFAe03l/5tjwx7ynrHWqZkZcpGw5tsqzdDVfXTDMM7Sy1tKxkeXv1u7L78B5rPneaLDINMFIQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEzp0AGVwl7O8ZfqdsPLDWyrBKz0qRWT9+YH3ct/3rZ//2mp2zxezbpUErLXvTd1hL4bmXfbPb+KkJInyz+SvRvYQWbvvG+tjXKvO9aNf31t5Veu98s8TfzQN/VuFjh3IOOfe0imrl1M915dLeU+S1RX913LQ/F3YqCgC6+1aeWWjDMCk8le++3do/q7vZ90uXP9QMortn3u5xvW/b82WdCajpPP3WBLxGd58g94642+Oeqh7cMOB6WblrifWYtq0fd/n79W9IM5PlpeW+D+625t993a5r4NIu1w66Ua7pe5V1OLHbBJm3bZ7sNMEVDYRpkEc/7tI7YYA8NuFR96kq1Qe07icDOwyzxpFXkCNPzv1jqecn971CWprlAr0Vb3ue6X03D/tFjbMNW5vf7Q1Dbpa3l71hvXrFrh9EP+4yqutYmTbqPudUTcyuG3ij/OP7V6y2PlrznujHLpoZ9vp1b9iHfCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgjUsQAZXCXAdVm5v//0H3Kp+Ut8DZx4Kykm68db6WGWjNO/+NbSLraTeAtu6TXNFHvxqr862Ut6TotmM03sc1nRgfmziWu/LuekqVzQYaTZf6ho6kZ3G+e+VGb9kAnW2aVj8w52tVa/g4OCq9zeaBPMsseiD0dHxJi9skovvVee2a8vftjre38zdob0aXuexzWdn5+aoNH5bQd6nLcPghqUPYbyrunzuizljPG/k6YR3vdoS8kungP7fRV9nzp9yuOWpyY/IT8fdruEuTLU3DccNdmANS0Pj5kh1wy8wclgsttTO9376uZBN9mnrO+yXHReO8R3lWeveEEmdZ/o8Yy3g7yCoiw2vdY83LvhFb0vlz9NeVrauJYTdbeVYbLNSpbqmk00QU8NMHr7vwN2ILvkuzhGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqBuBBmZJsdN186r6+Rbd6yrN7OWUb74jG0VKfGSclR1U1mh0Dy7dH6iye2Np+wdMhlFC0zYSFhImzy18URYnzbOa/+DWj6x9hLy9S587diK/zCBayWf+NO9JJ7vog1s/Nu02KHnLOTveaPZp+v3nRUGqm4f/Qib3mFRuX3Tsh0ywqFWTlpaZmh/NPyrhDRtLo5BGpZ7V+1Nz0iSiUYTEngmcFJwokDxzvmFwiDQKbiQh5ruB+V9tFV3SMs28U4N+urdVXGSs6V/xsom18R4dgy4feKzwmISGhJqgUIwVPK2Ntu02dOnGzNzDEh8VZ1nb593fp+W05JhsryNmDgqOF4gu1Rln/jlx72Plvr+s+tfmd/+K+f1reena1yr8Z0jfq/OaW5hrPaNBsegygsLWDeaP6pqpswbgCk8WmsBfuLSIiDe/nYZ2szX63r9/v9x7772yZo3nMpY1apSHEUAAAQQQOMcCDcr5d83avKb/X5nabM9mq+02a9qet/8vW3XarM4zalJXz/nSfJ7NMfvbfJZlxXza/xfF87ssr7LO1+U/g+W9KxDmc8mSohVZ1OG9996ToUOHapWCAAIIIIAAAj4swBKFFUxOYxM0adc0oYK7ii9r4KiywS19StvvbLK9tOhfov+wY4FV12wZDYyUVfQ5/VS26F5TWmJMoMKXgluZxw7L43P/YPUtxAQMJnStOCNNx90hJtF6Rv/Q8dhL/zknXRW9X/f7chcNhHkLhrnvqUldAy0VBVtq0r4+q/1vG13532Z13qcZjfopr2hgUDPs9FPdkmf2IJtp9rvToplflflnSN9bmfvcfaquWXxEnEiEu6Xaq2/fvp3gVu1x0hICCCCAgI8IePsLfLtr5V2z7+EbAQQQQAABBBBAAAEEEEAAgYoECHBVJHQWrmtG0cGsZCuDSOuafbQtLUkWbJ3r7K11fSX21apK1zJNpouWxOYdq/JYrd973Ow7tiVtm+w/ekC+2z5fkg5tdt5xi9mnKTQ41Dmm4r8Cmu2lmYua7bjV7CkMGgpPAAASo0lEQVS2MGm+5BcWLVE4xCzBGUjl/vvvD6ThMlYEEEAAAQQQQAABBBBAwCcFwsLCJD/fc39vn+wonUIAAQQQQAABR4AAl0NRd5VNKVvk8TmPlfnCfu0GysVdxpR5vToXcsySeVo6nuMAlwb2/vuL35Yawm0j7pJLuo0vdZ4T/inw7uqZ8s3m2aUGd17iELn/wumlzvvzia5du4p7KQwda1nLk5R1vrxnyrpm/9fztdlmWe/S81pq+13ltWm/yx6n1YEzf9jX3Ofsel1eq8132eOszTbVpLbbq26bdj/scdrzVd32zsZzdh/dfbPrVb1mj7Oqz52NcZ2NNu1x2eO0nc7Gu8pr0+6H+/12vTav2eOsTpvVeaa8MVf3WnX7YXvyjQACCCCAQH0QmDZtmnzzzTcSERHB8oT1YcLoIwIIIIAAAkaAANc5+BmEh5beiync7O/VKb6rjO82Toa3H1arvdI9kXq26Su6V9WA1v1rte2qNuYeu465R6veMrX35dKjRfeqNsX99VigseufAV02s4/5XfZv009GJo7wqSU064J45syZdfEa3oEAAggggAACCCCAAAIIIFCBAPtuVQDEZQQQQAABBHxMoIH5r0pP+1if/L47GnDKPHZEGpnl+ELNvlO6L1B5+235E4gG2dLzMiQ2PCZgxuxP81dbY8kpzJGCE4XWPmUhQcTZa8uVdhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUARIMAVKDPNOBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABPxEI8pNxMAwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAESDAFSATzTARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAX8RIMDlLzPJOBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBABEgwBUgE80wEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF/ESDA5S8zyTgQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQARIMAVIBPNMBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABfxEgwOUvM8k4EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAESDAFSATzTARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAX8RIMDlLzPJOBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBABEgwBUgE80wEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF/ESDA5S8zyTgQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQARIMAVIBPNMBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABfxEgwOUvM8k4EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAESDAFSATzTARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAX8RIMDlLzPJOBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBABEgwBUgE80wEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF/ESDA5S8zyTgQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQARIMAVIBPNMBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABfxEgwOUvM8k4EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAESDAFSATzTARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAX8RIMDlLzPJOBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBABEgwBUgE80wEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF/ESDA5S8zyTgQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQARIMAVIBPNMBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABfxEgwOUvM8k4EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAESDAFSATzTARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAX8RIMDlLzPJOBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBABEgwBUgE80wEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF/ESDA5S8zyTgQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQARIMAVIBPNMBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABfxEgwOUvM8k4EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAESDAFSATzTARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAX8RIMDlLzPJOBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBABEgwBUgE80wEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF/ESDA5S8zyTgQQAABBBBAAAEEEEA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    }
   },
   "cell_type": "markdown",
   "id": "0f52b427-750c-40f8-8893-e9caab3afd8d",
   "metadata": {},
   "source": [
    "## LLMs\n",
    "\n",
    "We can use Mistral function calling [to produce structured outputs](https://python.langchain.com/docs/modules/model_io/chat/structured_output/#mistral) at specific nodes.\n",
    "\n",
    "It will use a flow like this:\n",
    "\n",
    "![Screenshot 2024-04-10 at 10.51.13 AM.png](attachment:c1f293ca-cda8-4211-a133-2649771e411a.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6d7f5ae-aac9-4f08-a264-0c3c4495c50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Set LLM\n",
    "from langchain_mistralai import ChatMistralAI\n",
    "mistral_model = \"mistral-large-latest\" # \"open-mixtral-8x22b\" \n",
    "llm = ChatMistralAI(model=mistral_model, temperature=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4dec9d98-f3dc-4b7f-abc0-9d01c754f2be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasource='websearch'\n",
      "datasource='vectorstore'\n"
     ]
    }
   ],
   "source": [
    "### Router\n",
    "\n",
    "from typing import Literal\n",
    "from pydantic import BaseModel, Field\n",
    "from langchain_core.messages import HumanMessage, SystemMessage\n",
    "\n",
    "# Data model\n",
    "class RouteQuery(BaseModel):\n",
    "    \"\"\" Route a user query to the most relevant datasource. \"\"\"\n",
    "\n",
    "    datasource: Literal[\"vectorstore\", \"websearch\"] = Field(\n",
    "        ...,\n",
    "        description=\"Given a user question choose to route it to web search or a vectorstore.\",\n",
    "    )\n",
    "\n",
    "# LLM with structured output\n",
    "structured_llm_router = llm.with_structured_output(RouteQuery)\n",
    "\n",
    "# Prompt \n",
    "router_instructions = \"\"\"You are an expert at routing a user question to a vectorstore or web search.\n",
    "\n",
    "The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.\n",
    "                                    \n",
    "Use the vectorstore for questions on these topics. For all else, use web-search.\"\"\"\n",
    "\n",
    "# Test router\n",
    "print(structured_llm_router.invoke([SystemMessage(content=router_instructions)] + [HumanMessage(content=\"Who will the Bears draft first in the NFL draft?\")]))\n",
    "print(structured_llm_router.invoke([SystemMessage(content=router_instructions)] + [HumanMessage(content=\"What are the types of agent memory?\")]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "856801cb-f42a-44e7-956f-47845e3664ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l9/bpjxdmfx7lvd1fbdjn38y5dh0000gn/T/ipykernel_77692/4241217079.py:27: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
      "  docs = retriever.get_relevant_documents(question)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binary_score='yes'\n"
     ]
    }
   ],
   "source": [
    "### Retrieval Grader \n",
    "\n",
    "from langchain_mistralai import ChatMistralAI\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "# Data model\n",
    "class GradeDocuments(BaseModel):\n",
    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
    "\n",
    "    binary_score: str = Field(description=\"Documents are relevant to the question, 'yes' or 'no'\")\n",
    "\n",
    "# LLM with structured output\n",
    "structured_llm_doc_grader = llm.with_structured_output(GradeDocuments)\n",
    "\n",
    "# Doc grader instructions \n",
    "doc_grader_instructions = \"\"\"You are a grader assessing relevance of a retrieved document to a user question.\n",
    "\n",
    "If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant.\n",
    "\n",
    "Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\"\n",
    "\n",
    "# Grader prompt\n",
    "doc_grader_prompt = \"Here is the retrieved document: \\n\\n {document} \\n\\n Here is the user question: \\n\\n {question}\"\n",
    "\n",
    "# Test\n",
    "question = \"agent memory\"\n",
    "docs = retriever.get_relevant_documents(question)\n",
    "doc_txt = docs[1].page_content\n",
    "doc_grader_prompt_formatted = doc_grader_prompt.format(document=doc_txt, question=question)\n",
    "print(structured_llm_doc_grader.invoke([SystemMessage(content=doc_grader_instructions)] + [HumanMessage(content=doc_grader_prompt_formatted)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2272333e-50b2-42ab-b472-e1055a3b94a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "content='The types of agent memory are short-term memory and long-term memory. Short-term memory involves in-context learning, while long-term memory enables the agent to retain and recall information over extended periods using an external vector store and fast retrieval.' additional_kwargs={} response_metadata={'token_usage': {'prompt_tokens': 2876, 'total_tokens': 2928, 'completion_tokens': 52}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'} id='run-989afcac-73d9-4560-8575-753933d8f42a-0' usage_metadata={'input_tokens': 2876, 'output_tokens': 52, 'total_tokens': 2928}\n"
     ]
    }
   ],
   "source": [
    "### Generate\n",
    "\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "# Prompt\n",
    "rag_prompt = \"\"\"You are an assistant for question-answering tasks. \n",
    "\n",
    "Use the following pieces of retrieved context to answer the question. \n",
    "\n",
    "If you don't know the answer, just say that you don't know. \n",
    "\n",
    "Use three sentences maximum and keep the answer concise.\n",
    "\n",
    "Question: {question} \n",
    "\n",
    "Context: {context} \n",
    "\n",
    "Answer:\"\"\"\n",
    "\n",
    "# LLM\n",
    "llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
    "\n",
    "# Post-processing\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "# Test\n",
    "question = \"The types of agent memory\"\n",
    "docs = retriever.get_relevant_documents(question)\n",
    "docs_txt = format_docs(docs)\n",
    "rag_prompt_formatted = rag_prompt.format(context=docs_txt, question=question)\n",
    "generation = llm.invoke([HumanMessage(content=rag_prompt_formatted)])\n",
    "print(generation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0c08d14-77a0-4eed-b882-2d636abb22a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes The student's answer is grounded in the facts provided. It accurately describes the types of agent memory as short-term and long-term, with short-term memory involving in-context learning and long-term memory enabling the agent to retain and recall information over extended periods using an external vector store and fast retrieval. There is no hallucinated information in the student's answer.\n"
     ]
    }
   ],
   "source": [
    "### Hallucination Grader \n",
    "\n",
    "# Data model\n",
    "class GradeHallucinations(BaseModel):\n",
    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
    "\n",
    "    binary_score: str = Field(description=\"Answer is grounded in the facts, 'yes' or 'no'\")\n",
    "    explanation: str = Field(description=\"Explain the reasoning for the score\")\n",
    "\n",
    "# LLM with function call \n",
    "structured_llm_hallucination_grader = llm.with_structured_output(GradeHallucinations)\n",
    "\n",
    "# Hallucination grader instructions \n",
    "hallucination_grader_instructions = \"\"\"You are a teacher grading a quiz. \n",
    "\n",
    "You will be given FACTS and a STUDENT ANSWER. \n",
    "\n",
    "Here is the grade criteria to follow:\n",
    "\n",
    "(1) Ensure the STUDENT ANSWER is grounded in the FACTS. \n",
    "\n",
    "(2) Ensure the STUDENT ANSWER does not contain \"hallucinated\" information outside the scope of the FACTS.\n",
    "\n",
    "Score:\n",
    "\n",
    "A score of 1 means that the student's answer meets all of the criteria. This is the highest (best) score. \n",
    "\n",
    "A score of 0 means that the student's answer does not meet all of the criteria. This is the lowest possible score you can give.\n",
    "\n",
    "Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. \n",
    "\n",
    "Avoid simply stating the correct answer at the outset.\"\"\"\n",
    "\n",
    "# Grader prompt\n",
    "hallucination_grader_prompt = \"FACTS: \\n\\n {documents} \\n\\n STUDENT ANSWER: {generation}\"\n",
    "\n",
    "# Test using documents and generation from above \n",
    "hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(documents=docs_txt, generation=generation)\n",
    "score = structured_llm_hallucination_grader.invoke([SystemMessage(content=hallucination_grader_instructions)] + [HumanMessage(content=hallucination_grader_prompt_formatted)])\n",
    "print(score.binary_score, score.explanation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ded99680-437a-4c9d-b860-619c88949d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes The student's answer is concise and relevant to the question. It provides a clear explanation of the types of agent memory, including short-term and long-term memory, and describes their functions. This directly addresses the question.\n"
     ]
    }
   ],
   "source": [
    "### Answer Grader \n",
    "\n",
    "# Data model\n",
    "class GradeAnswer(BaseModel):\n",
    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
    "\n",
    "    binary_score: str = Field(description=\"Answer addresses the question, 'yes' or 'no'\")\n",
    "    explanation: str = Field(description=\"Explain the reasoning for the score\")\n",
    "\n",
    "# LLM with function call \n",
    "llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
    "structured_llm_answer_grader = llm.with_structured_output(GradeAnswer)\n",
    "\n",
    "# Answer grader instructions \n",
    "answer_grader_instructions = \"\"\"You are a teacher grading a quiz. \n",
    "\n",
    "You will be given a QUESTION and a STUDENT ANSWER. \n",
    "\n",
    "Here is the grade criteria to follow:\n",
    "\n",
    "(1) Ensure the STUDENT ANSWER is concise and relevant to the QUESTION\n",
    "\n",
    "(2) Ensure the STUDENT ANSWER helps to answer the QUESTION\n",
    "\n",
    "Score:\n",
    "\n",
    "A score of 1 means that the student's answer meets all of the criteria. This is the highest (best) score. \n",
    "\n",
    "A score of 0 means that the student's answer does not meet all of the criteria. This is the lowest possible score you can give.\n",
    "\n",
    "Explain your reasoning in a step-by-step manner to ensure your reasoning and conclusion are correct. \n",
    "\n",
    "Avoid simply stating the correct answer at the outset.\"\"\"\n",
    "\n",
    "# Grader prompt\n",
    "answer_grader_prompt = \"QUESTION: \\n\\n {question} \\n\\n STUDENT ANSWER: {generation}\"\n",
    "\n",
    "# Test using question and generation from above \n",
    "answer_grader_prompt_formatted = answer_grader_prompt.format(question=question, generation=generation)\n",
    "score = structured_llm_answer_grader.invoke([SystemMessage(content=answer_grader_instructions)] + [HumanMessage(content=answer_grader_prompt_formatted)])\n",
    "print(score.binary_score, score.explanation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07c0b31-b919-4498-869f-9673125c2473",
   "metadata": {},
   "source": [
    "## Web Search Tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01d829bb-1074-4976-b650-ead41dcb9788",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "web_search_tool = TavilySearchResults(k=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efbbff0e-8843-45bb-b2ff-137bef707ef4",
   "metadata": {},
   "source": [
    "# Graph \n",
    "\n",
    "We build the above workflow as a graph using [LangGraph](https://langchain-ai.github.io/langgraph/).\n",
    "\n",
    "### Graph state\n",
    "\n",
    "The graph `state` schema contains keys that we want to:\n",
    "\n",
    "* Pass to each node in our graph\n",
    "* Optionally, modify in each node of our graph \n",
    "\n",
    "See conceptual docs [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e723fcdb-06e6-402d-912e-899795b78408",
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "from typing_extensions import TypedDict\n",
    "from typing import List, Annotated\n",
    "\n",
    "class GraphState(TypedDict):\n",
    "    \"\"\"\n",
    "    Graph state is a dictionary that contains information we want to propagate to, and modify in, each graph node.\n",
    "    \"\"\"\n",
    "    question : str # User question\n",
    "    generation : str # LLM generation\n",
    "    web_search : str # Binary decision to run web search\n",
    "    max_retries : int # Max number of retries for answer generation \n",
    "    answers : int # Number of answers generated\n",
    "    loop_step: Annotated[int, operator.add] \n",
    "    documents : List[str] # List of retrieved documents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2d6c0d-42e8-4399-9751-e315be16607a",
   "metadata": {},
   "source": [
    "Each node in our graph is simply a function that:\n",
    "\n",
    "(1) Take `state` as an input\n",
    "\n",
    "(2) Modifies `state` \n",
    "\n",
    "(3) Write the modified `state` to the state schema (dict)\n",
    "\n",
    "See conceptual docs [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#nodes).\n",
    "\n",
    "Each edge routes between nodes in the graph.\n",
    "\n",
    "See conceptual docs [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#edges)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b76b5ec3-0720-443d-85b1-c0e79659ca0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema import Document\n",
    "from langgraph.graph import END\n",
    "\n",
    "### Nodes\n",
    "def retrieve(state):\n",
    "    \"\"\"\n",
    "    Retrieve documents from vectorstore\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
    "    \"\"\"\n",
    "    print(\"---RETRIEVE---\")\n",
    "    question = state[\"question\"]\n",
    "\n",
    "    # Write retrieved documents to documents key in state\n",
    "    documents = retriever.invoke(question)\n",
    "    return {\"documents\": documents}\n",
    "\n",
    "def generate(state):\n",
    "    \"\"\"\n",
    "    Generate answer using RAG on retrieved documents\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): New key added to state, generation, that contains LLM generation\n",
    "    \"\"\"\n",
    "    print(\"---GENERATE---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    loop_step = state.get(\"loop_step\", 0)\n",
    "    \n",
    "    # RAG generation\n",
    "    docs_txt = format_docs(documents)\n",
    "    rag_prompt_formatted = rag_prompt.format(context=docs_txt, question=question)\n",
    "    generation = llm.invoke([HumanMessage(content=rag_prompt_formatted)])\n",
    "    return {\"generation\": generation, \"loop_step\": loop_step+1}\n",
    "\n",
    "def grade_documents(state):\n",
    "    \"\"\"\n",
    "    Determines whether the retrieved documents are relevant to the question\n",
    "    If any document is not relevant, we will set a flag to run web search\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Filtered out irrelevant documents and updated web_search state\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    \n",
    "    # Score each doc\n",
    "    filtered_docs = []\n",
    "    web_search = \"No\" \n",
    "    for d in documents:\n",
    "        doc_grader_prompt_formatted = doc_grader_prompt.format(document=d.page_content, question=question)\n",
    "        score = structured_llm_doc_grader.invoke([SystemMessage(content=doc_grader_instructions)] + [HumanMessage(content=doc_grader_prompt_formatted)])\n",
    "        grade = score.binary_score\n",
    "        # Document relevant\n",
    "        if grade.lower() == \"yes\":\n",
    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
    "            filtered_docs.append(d)\n",
    "        # Document not relevant\n",
    "        else:\n",
    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
    "            # We do not include the document in filtered_docs\n",
    "            # We set a flag to indicate that we want to run web search\n",
    "            web_search = \"Yes\"\n",
    "            continue\n",
    "    return {\"documents\": filtered_docs, \"web_search\": web_search}\n",
    "    \n",
    "def web_search(state):\n",
    "    \"\"\"\n",
    "    Web search based based on the question\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        state (dict): Appended web results to documents\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---WEB SEARCH---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state.get(\"documents\", [])\n",
    "\n",
    "    # Web search\n",
    "    docs = web_search_tool.invoke({\"query\": question})\n",
    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
    "    web_results = Document(page_content=web_results)\n",
    "    documents.append(web_results)\n",
    "    return {\"documents\": documents}\n",
    "\n",
    "### Edges\n",
    "\n",
    "def route_question(state):\n",
    "    \"\"\"\n",
    "    Route question to web search or RAG \n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---ROUTE QUESTION---\")\n",
    "    source = structured_llm_router.invoke([SystemMessage(content=router_instructions)] + [HumanMessage(content=state[\"question\"])]) \n",
    "    if source.datasource == 'websearch':\n",
    "        print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n",
    "        return \"websearch\"\n",
    "    elif source.datasource == 'vectorstore':\n",
    "        print(\"---ROUTE QUESTION TO RAG---\")\n",
    "        return \"vectorstore\"\n",
    "\n",
    "def decide_to_generate(state):\n",
    "    \"\"\"\n",
    "    Determines whether to generate an answer, or add web search\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Binary decision for next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
    "    question = state[\"question\"]\n",
    "    web_search = state[\"web_search\"]\n",
    "    filtered_documents = state[\"documents\"]\n",
    "\n",
    "    if web_search == \"Yes\":\n",
    "        # All documents have been filtered check_relevance\n",
    "        # We will re-generate a new query\n",
    "        print(\"---DECISION: NOT ALL DOCUMENTS ARE RELEVANT TO QUESTION, INCLUDE WEB SEARCH---\")\n",
    "        return \"websearch\"\n",
    "    else:\n",
    "        # We have relevant documents, so generate answer\n",
    "        print(\"---DECISION: GENERATE---\")\n",
    "        return \"generate\"\n",
    "\n",
    "def grade_generation_v_documents_and_question(state):\n",
    "    \"\"\"\n",
    "    Determines whether the generation is grounded in the document and answers question\n",
    "\n",
    "    Args:\n",
    "        state (dict): The current graph state\n",
    "\n",
    "    Returns:\n",
    "        str: Decision for next node to call\n",
    "    \"\"\"\n",
    "\n",
    "    print(\"---CHECK HALLUCINATIONS---\")\n",
    "    question = state[\"question\"]\n",
    "    documents = state[\"documents\"]\n",
    "    generation = state[\"generation\"]\n",
    "    max_retries = state.get(\"max_retries\", 3) # Default to 3 if not provided\n",
    "\n",
    "    hallucination_grader_prompt_formatted = hallucination_grader_prompt.format(documents=format_docs(documents), generation=generation.content)\n",
    "    score = structured_llm_hallucination_grader.invoke([SystemMessage(content=hallucination_grader_instructions)] + [HumanMessage(content=hallucination_grader_prompt_formatted)])\n",
    "    grade = score.binary_score\n",
    "\n",
    "    # Check hallucination\n",
    "    if grade == \"yes\":\n",
    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
    "        # Check question-answering\n",
    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
    "        # Test using question and generation from above \n",
    "        answer_grader_prompt_formatted = answer_grader_prompt.format(question=question, generation=generation.content)\n",
    "        score = structured_llm_answer_grader.invoke([SystemMessage(content=answer_grader_instructions)] + [HumanMessage(content=answer_grader_prompt_formatted)])\n",
    "        grade = score.binary_score\n",
    "        if grade == \"yes\":\n",
    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
    "            return \"useful\"\n",
    "        elif state[\"loop_step\"] <= max_retries:\n",
    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
    "            return \"not useful\"\n",
    "        else:\n",
    "            print(\"---DECISION: MAX RETRIES REACHED---\")\n",
    "            return \"max retries\"  \n",
    "    elif state[\"loop_step\"] <= max_retries:\n",
    "        print(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
    "        return \"not supported\"\n",
    "    else:\n",
    "        print(\"---DECISION: MAX RETRIES REACHED---\")\n",
    "        return \"max retries\"  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab01f36-5628-49ab-bfd3-84bb6f1a1b0f",
   "metadata": {},
   "source": [
    "## Build Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "67854e07-9293-4c3c-bf9a-bc9a605570ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from langgraph.graph import StateGraph\n",
    "from IPython.display import Image, display\n",
    "\n",
    "workflow = StateGraph(GraphState)\n",
    "\n",
    "# Define the nodes\n",
    "workflow.add_node(\"websearch\", web_search) # web search\n",
    "workflow.add_node(\"retrieve\", retrieve) # retrieve\n",
    "workflow.add_node(\"grade_documents\", grade_documents) # grade documents\n",
    "workflow.add_node(\"generate\", generate) # generatae\n",
    "\n",
    "# Build graph\n",
    "workflow.set_conditional_entry_point(\n",
    "    route_question,\n",
    "    {\n",
    "        \"websearch\": \"websearch\",\n",
    "        \"vectorstore\": \"retrieve\",\n",
    "    },\n",
    ")\n",
    "workflow.add_edge(\"websearch\", \"generate\")\n",
    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
    "workflow.add_conditional_edges(\n",
    "    \"grade_documents\",\n",
    "    decide_to_generate,\n",
    "    {\n",
    "        \"websearch\": \"websearch\",\n",
    "        \"generate\": \"generate\",\n",
    "    },\n",
    ")\n",
    "workflow.add_conditional_edges(\n",
    "    \"generate\",\n",
    "    grade_generation_v_documents_and_question,\n",
    "    {\n",
    "        \"not supported\": \"generate\",\n",
    "        \"useful\": END,\n",
    "        \"not useful\": \"websearch\",\n",
    "        \"max retries\": END,\n",
    "    },\n",
    ")\n",
    "\n",
    "# Compile\n",
    "graph = workflow.compile()\n",
    "display(Image(graph.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29acc541-d726-4b75-84d1-a215845fe88a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---ROUTE QUESTION---\n",
      "---ROUTE QUESTION TO RAG---\n",
      "{'question': 'What are the types of agent memory?', 'max_retries': 3, 'loop_step': 0}\n",
      "---RETRIEVE---\n",
      "{'question': 'What are the types of agent memory?', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={'id': '3fb33621-a314-49b6-8bf5-4b48db735350', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Planning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\nAnother quite distinct approach, LLM+P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.\\nSelf-Reflection#\\nSelf-reflection is a vital aspect that allows autonomous agents to improve iteratively by refining past action decisions and correcting previous mistakes. It plays a crucial role in real-world tasks where trial and error are inevitable.\\nReAct (Yao et al. 2023) integrates reasoning and acting within LLM by extending the action space to be a combination of task-specific discrete actions and the language space. The former enables LLM to interact with the environment (e.g. use Wikipedia search API), while the latter prompting LLM to generate reasoning traces in natural language.\\nThe ReAct prompt template incorporates explicit steps for LLM to think, roughly formatted as:\\nThought: ...\\nAction: ...\\nObservation: ...\\n... (Repeated many times)'), Document(metadata={'id': '98922f8f-fb32-4301-9a35-e2aa9524bc2b', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Long-Term Memory (LTM): Long-term memory can store information for a remarkably long time, ranging from a few days to decades, with an essentially unlimited storage capacity. There are two subtypes of LTM:\\n\\nExplicit / declarative memory: This is memory of facts and events, and refers to those memories that can be consciously recalled, including episodic memory (events and experiences) and semantic memory (facts and concepts).\\nImplicit / procedural memory: This type of memory is unconscious and involves skills and routines that are performed automatically, like riding a bike or typing on a keyboard.\\n\\n\\n\\n\\nFig. 8. Categorization of human memory.\\nWe can roughly consider the following mappings:\\n\\nSensory memory as learning embedding representations for raw inputs, including text, image or other modalities;\\nShort-term memory as in-context learning. It is short and finite, as it is restricted by the finite context window length of Transformer.\\nLong-term memory as the external vector store that the agent can attend to at query time, accessible via fast retrieval.\\n\\nMaximum Inner Product Search (MIPS)#\\nThe external memory can alleviate the restriction of finite attention span.  A standard practice is to save the embedding representation of information into a vector store database that can support fast maximum inner-product search (MIPS). To optimize the retrieval speed, the common choice is the approximate nearest neighbors (ANN)\\u200b algorithm to return approximately top k nearest neighbors to trade off a little accuracy lost for a huge speedup.\\nA couple common choices of ANN algorithms for fast MIPS:\\n\\nLSH (Locality-Sensitive Hashing): It introduces a hashing function such that similar input items are mapped to the same buckets with high probability, where the number of buckets is much smaller than the number of inputs.\\nANNOY (Approximate Nearest Neighbors Oh Yeah): The core data structure are random projection trees, a set of binary trees where each non-leaf node represents a hyperplane splitting the input space into half and each leaf stores one data point. Trees are built independently and at random, so to some extent, it mimics a hashing function. ANNOY search happens in all the trees to iteratively search through the half that is closest to the query and then aggregates the results. The idea is quite related to KD tree but a lot more scalable.\\nHNSW (Hierarchical Navigable Small World): It is inspired by the idea of small world networks where most nodes can be reached by any other nodes within a small number of steps; e.g. “six degrees of separation” feature of social networks. HNSW builds hierarchical layers of these small-world graphs, where the bottom layers contain the actual data points. The layers in the middle create shortcuts to speed up search. When performing a search, HNSW starts from a random node in the top layer and navigates towards the target. When it can’t get any closer, it moves down to the next layer, until it reaches the bottom layer. Each move in the upper layers can potentially cover a large distance in the data space, and each move in the lower layers refines the search quality.\\nFAISS (Facebook AI Similarity Search): It operates on the assumption that in high dimensional space, distances between nodes follow a Gaussian distribution and thus there should exist clustering of data points. FAISS applies vector quantization by partitioning the vector space into clusters and then refining the quantization within clusters. Search first looks for cluster candidates with coarse quantization and then further looks into each cluster with finer quantization.\\nScaNN (Scalable Nearest Neighbors): The main innovation in ScaNN is anisotropic vector quantization. It quantizes a data point $x_i$ to $\\\\tilde{x}_i$ such that the inner product $\\\\langle q, x_i \\\\rangle$ is as similar to the original distance of $\\\\angle q, \\\\tilde{x}_i$ as possible, instead of picking the closet quantization centroid points.\\n\\n\\nFig. 9. Comparison of MIPS algorithms, measured in recall@10. (Image source: Google Blog, 2020)\\nCheck more MIPS algorithms and performance comparison in ann-benchmarks.com.\\nComponent Three: Tool Use#\\nTool use is a remarkable and distinguishing characteristic of human beings. We create, modify and utilize external objects to do things that go beyond our physical and cognitive limits. Equipping LLMs with external tools can significantly extend the model capabilities.'), Document(metadata={'id': '003f1a14-32fe-4d98-a2ef-74f94cb1d09e', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content=\"LLM Powered Autonomous Agents | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\\n\\n\\n\\n\\nemojisearch.app\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n      LLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\\n\\n\\nComponent Three: Tool Use\\n\\nCase Studies\\n\\nScientific Discovery Agent\\n\\nGenerative Agents Simulation\\n\\nProof-of-Concept Examples\\n\\n\\nChallenges\\n\\nCitation\\n\\nReferences\\n\\n\\n\\n\\n\\nBuilding agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview#\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\"), Document(metadata={'id': 'a5fdc850-5181-4684-a4ae-df57417e212a', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Finite context length: The restricted context capacity limits the inclusion of historical information, detailed instructions, API call context, and responses. The design of the system has to work with this limited communication bandwidth, while mechanisms like self-reflection to learn from past mistakes would benefit a lot from long or infinite context windows. Although vector stores and retrieval can provide access to a larger knowledge pool, their representation power is not as powerful as full attention.\\n\\n\\nChallenges in long-term planning and task decomposition: Planning over a lengthy history and effectively exploring the solution space remain challenging. LLMs struggle to adjust plans when faced with unexpected errors, making them less robust compared to humans who learn from trial and error.\\n\\n\\nReliability of natural language interface: Current agent system relies on natural language as an interface between LLMs and external components such as memory and tools. However, the reliability of model outputs is questionable, as LLMs may make formatting errors and occasionally exhibit rebellious behavior (e.g. refuse to follow an instruction). Consequently, much of the agent demo code focuses on parsing model output.\\n\\n\\nCitation#\\nCited as:\\n\\nWeng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.')]}\n",
      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT RELEVANT---\n",
      "---GRADE: DOCUMENT NOT RELEVANT---\n",
      "---ASSESS GRADED DOCUMENTS---\n",
      "---DECISION: NOT ALL DOCUMENTS ARE RELEVANT TO QUESTION, INCLUDE WEB SEARCH---\n",
      "{'question': 'What are the types of agent memory?', 'web_search': 'Yes', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={'id': '3fb33621-a314-49b6-8bf5-4b48db735350', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Planning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\nAnother quite distinct approach, LLM+P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.\\nSelf-Reflection#\\nSelf-reflection is a vital aspect that allows autonomous agents to improve iteratively by refining past action decisions and correcting previous mistakes. It plays a crucial role in real-world tasks where trial and error are inevitable.\\nReAct (Yao et al. 2023) integrates reasoning and acting within LLM by extending the action space to be a combination of task-specific discrete actions and the language space. The former enables LLM to interact with the environment (e.g. use Wikipedia search API), while the latter prompting LLM to generate reasoning traces in natural language.\\nThe ReAct prompt template incorporates explicit steps for LLM to think, roughly formatted as:\\nThought: ...\\nAction: ...\\nObservation: ...\\n... (Repeated many times)'), Document(metadata={'id': '98922f8f-fb32-4301-9a35-e2aa9524bc2b', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Long-Term Memory (LTM): Long-term memory can store information for a remarkably long time, ranging from a few days to decades, with an essentially unlimited storage capacity. There are two subtypes of LTM:\\n\\nExplicit / declarative memory: This is memory of facts and events, and refers to those memories that can be consciously recalled, including episodic memory (events and experiences) and semantic memory (facts and concepts).\\nImplicit / procedural memory: This type of memory is unconscious and involves skills and routines that are performed automatically, like riding a bike or typing on a keyboard.\\n\\n\\n\\n\\nFig. 8. Categorization of human memory.\\nWe can roughly consider the following mappings:\\n\\nSensory memory as learning embedding representations for raw inputs, including text, image or other modalities;\\nShort-term memory as in-context learning. It is short and finite, as it is restricted by the finite context window length of Transformer.\\nLong-term memory as the external vector store that the agent can attend to at query time, accessible via fast retrieval.\\n\\nMaximum Inner Product Search (MIPS)#\\nThe external memory can alleviate the restriction of finite attention span.  A standard practice is to save the embedding representation of information into a vector store database that can support fast maximum inner-product search (MIPS). To optimize the retrieval speed, the common choice is the approximate nearest neighbors (ANN)\\u200b algorithm to return approximately top k nearest neighbors to trade off a little accuracy lost for a huge speedup.\\nA couple common choices of ANN algorithms for fast MIPS:\\n\\nLSH (Locality-Sensitive Hashing): It introduces a hashing function such that similar input items are mapped to the same buckets with high probability, where the number of buckets is much smaller than the number of inputs.\\nANNOY (Approximate Nearest Neighbors Oh Yeah): The core data structure are random projection trees, a set of binary trees where each non-leaf node represents a hyperplane splitting the input space into half and each leaf stores one data point. Trees are built independently and at random, so to some extent, it mimics a hashing function. ANNOY search happens in all the trees to iteratively search through the half that is closest to the query and then aggregates the results. The idea is quite related to KD tree but a lot more scalable.\\nHNSW (Hierarchical Navigable Small World): It is inspired by the idea of small world networks where most nodes can be reached by any other nodes within a small number of steps; e.g. “six degrees of separation” feature of social networks. HNSW builds hierarchical layers of these small-world graphs, where the bottom layers contain the actual data points. The layers in the middle create shortcuts to speed up search. When performing a search, HNSW starts from a random node in the top layer and navigates towards the target. When it can’t get any closer, it moves down to the next layer, until it reaches the bottom layer. Each move in the upper layers can potentially cover a large distance in the data space, and each move in the lower layers refines the search quality.\\nFAISS (Facebook AI Similarity Search): It operates on the assumption that in high dimensional space, distances between nodes follow a Gaussian distribution and thus there should exist clustering of data points. FAISS applies vector quantization by partitioning the vector space into clusters and then refining the quantization within clusters. Search first looks for cluster candidates with coarse quantization and then further looks into each cluster with finer quantization.\\nScaNN (Scalable Nearest Neighbors): The main innovation in ScaNN is anisotropic vector quantization. It quantizes a data point $x_i$ to $\\\\tilde{x}_i$ such that the inner product $\\\\langle q, x_i \\\\rangle$ is as similar to the original distance of $\\\\angle q, \\\\tilde{x}_i$ as possible, instead of picking the closet quantization centroid points.\\n\\n\\nFig. 9. Comparison of MIPS algorithms, measured in recall@10. (Image source: Google Blog, 2020)\\nCheck more MIPS algorithms and performance comparison in ann-benchmarks.com.\\nComponent Three: Tool Use#\\nTool use is a remarkable and distinguishing characteristic of human beings. We create, modify and utilize external objects to do things that go beyond our physical and cognitive limits. Equipping LLMs with external tools can significantly extend the model capabilities.'), Document(metadata={'id': '003f1a14-32fe-4d98-a2ef-74f94cb1d09e', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content=\"LLM Powered Autonomous Agents | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\\n\\n\\n\\n\\nemojisearch.app\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n      LLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\\n\\n\\nComponent Three: Tool Use\\n\\nCase Studies\\n\\nScientific Discovery Agent\\n\\nGenerative Agents Simulation\\n\\nProof-of-Concept Examples\\n\\n\\nChallenges\\n\\nCitation\\n\\nReferences\\n\\n\\n\\n\\n\\nBuilding agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview#\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\")]}\n",
      "---WEB SEARCH---\n",
      "{'question': 'What are the types of agent memory?', 'web_search': 'Yes', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={'id': '3fb33621-a314-49b6-8bf5-4b48db735350', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Planning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\nAnother quite distinct approach, LLM+P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.\\nSelf-Reflection#\\nSelf-reflection is a vital aspect that allows autonomous agents to improve iteratively by refining past action decisions and correcting previous mistakes. It plays a crucial role in real-world tasks where trial and error are inevitable.\\nReAct (Yao et al. 2023) integrates reasoning and acting within LLM by extending the action space to be a combination of task-specific discrete actions and the language space. The former enables LLM to interact with the environment (e.g. use Wikipedia search API), while the latter prompting LLM to generate reasoning traces in natural language.\\nThe ReAct prompt template incorporates explicit steps for LLM to think, roughly formatted as:\\nThought: ...\\nAction: ...\\nObservation: ...\\n... (Repeated many times)'), Document(metadata={'id': '98922f8f-fb32-4301-9a35-e2aa9524bc2b', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Long-Term Memory (LTM): Long-term memory can store information for a remarkably long time, ranging from a few days to decades, with an essentially unlimited storage capacity. There are two subtypes of LTM:\\n\\nExplicit / declarative memory: This is memory of facts and events, and refers to those memories that can be consciously recalled, including episodic memory (events and experiences) and semantic memory (facts and concepts).\\nImplicit / procedural memory: This type of memory is unconscious and involves skills and routines that are performed automatically, like riding a bike or typing on a keyboard.\\n\\n\\n\\n\\nFig. 8. Categorization of human memory.\\nWe can roughly consider the following mappings:\\n\\nSensory memory as learning embedding representations for raw inputs, including text, image or other modalities;\\nShort-term memory as in-context learning. It is short and finite, as it is restricted by the finite context window length of Transformer.\\nLong-term memory as the external vector store that the agent can attend to at query time, accessible via fast retrieval.\\n\\nMaximum Inner Product Search (MIPS)#\\nThe external memory can alleviate the restriction of finite attention span.  A standard practice is to save the embedding representation of information into a vector store database that can support fast maximum inner-product search (MIPS). To optimize the retrieval speed, the common choice is the approximate nearest neighbors (ANN)\\u200b algorithm to return approximately top k nearest neighbors to trade off a little accuracy lost for a huge speedup.\\nA couple common choices of ANN algorithms for fast MIPS:\\n\\nLSH (Locality-Sensitive Hashing): It introduces a hashing function such that similar input items are mapped to the same buckets with high probability, where the number of buckets is much smaller than the number of inputs.\\nANNOY (Approximate Nearest Neighbors Oh Yeah): The core data structure are random projection trees, a set of binary trees where each non-leaf node represents a hyperplane splitting the input space into half and each leaf stores one data point. Trees are built independently and at random, so to some extent, it mimics a hashing function. ANNOY search happens in all the trees to iteratively search through the half that is closest to the query and then aggregates the results. The idea is quite related to KD tree but a lot more scalable.\\nHNSW (Hierarchical Navigable Small World): It is inspired by the idea of small world networks where most nodes can be reached by any other nodes within a small number of steps; e.g. “six degrees of separation” feature of social networks. HNSW builds hierarchical layers of these small-world graphs, where the bottom layers contain the actual data points. The layers in the middle create shortcuts to speed up search. When performing a search, HNSW starts from a random node in the top layer and navigates towards the target. When it can’t get any closer, it moves down to the next layer, until it reaches the bottom layer. Each move in the upper layers can potentially cover a large distance in the data space, and each move in the lower layers refines the search quality.\\nFAISS (Facebook AI Similarity Search): It operates on the assumption that in high dimensional space, distances between nodes follow a Gaussian distribution and thus there should exist clustering of data points. FAISS applies vector quantization by partitioning the vector space into clusters and then refining the quantization within clusters. Search first looks for cluster candidates with coarse quantization and then further looks into each cluster with finer quantization.\\nScaNN (Scalable Nearest Neighbors): The main innovation in ScaNN is anisotropic vector quantization. It quantizes a data point $x_i$ to $\\\\tilde{x}_i$ such that the inner product $\\\\langle q, x_i \\\\rangle$ is as similar to the original distance of $\\\\angle q, \\\\tilde{x}_i$ as possible, instead of picking the closet quantization centroid points.\\n\\n\\nFig. 9. Comparison of MIPS algorithms, measured in recall@10. (Image source: Google Blog, 2020)\\nCheck more MIPS algorithms and performance comparison in ann-benchmarks.com.\\nComponent Three: Tool Use#\\nTool use is a remarkable and distinguishing characteristic of human beings. We create, modify and utilize external objects to do things that go beyond our physical and cognitive limits. Equipping LLMs with external tools can significantly extend the model capabilities.'), Document(metadata={'id': '003f1a14-32fe-4d98-a2ef-74f94cb1d09e', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content=\"LLM Powered Autonomous Agents | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\\n\\n\\n\\n\\nemojisearch.app\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n      LLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\\n\\n\\nComponent Three: Tool Use\\n\\nCase Studies\\n\\nScientific Discovery Agent\\n\\nGenerative Agents Simulation\\n\\nProof-of-Concept Examples\\n\\n\\nChallenges\\n\\nCitation\\n\\nReferences\\n\\n\\n\\n\\n\\nBuilding agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview#\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\"), Document(metadata={}, page_content='2.3 Memory. The agent\\'s memory has two categories: short-term and long-term memories. Short-term Memory: In context memory allows the agent to utilize the short-term memory of the large language model to operate the original problem from the beginning. This capability allows the agent to hold temporarily the information and process it when it ...\\nAgents can be categorized into five types: Simple Reflex agents, Model-based Reflex agents, Goal-based agents, Utility-based agents, and Learning agents [1]. As AI advanced, the term \"agent\" is used to depict entities exhibiting intelligent behavior and possessing ... If the agent has been armed with the memory module, memory recollection ...\\nLTM type 3: Procedural memory: This memory represents the agent\\'s procedures for thinking, acting, decision-making, etc. Unlike episodic or semantic memory that may be initially empty or even absent, procedural memory must be initialized by the designer with proper code to bootstrap the agent. It is of two types\\nAgent Types 12 Table-driven agents use a percept sequence/action table in memory to ﬁnd the next action. They are implemented by a (large) lookup table. Simple reﬂex agents are based on condition-action rules, implemented with an appropriate production system. They are stateless devices which do not have memory of past world states. Agents ...\\nSign up for Latest SuperAGI Updates\\n\"*\" indicates required fields\\nSuperAGI builds infrastructure components, tools, frameworks and models to enable opensource AGI\\ncommunity@superagi.com\\nFor Developers\\nDocs\\nGitHub\\nReleases\\nRoadmap\\nAPIs\\nCommunity\\nSupport Forum\\nMarketplace\\nSocial Mentions\\nReddit\\nCollectibles\\nResources\\nBlog\\nUse Cases\\nAGI Research Lab\\nTutorials\\nImportant Links\\nSuperAGI Cloud\\nApp Spotlight\\nSuperCoder\\nArchitecture\\n Check it out✨\\nFeatures\\nAction Console\\nResource Manager\\nTrajectory Fine-Tuning\\n\\u200c\\nMultiple Vector DBs\\nMulti-LLM Support\\nAgent Workflows\\nMarketplace\\nAgent Templates\\nDiscord\\nGitHub\\nTwitter\\nReddit\\nYoutube\\nTowards AGI (part 1): Agents with Memory\\nFebruary 6, 2024\\n7 mins read\\nAgents are an emerging class of artificial intelligence (AI) systems that use large language models (LLMs) to interact with the world. In a professional setup like this, the agent is responsible for extracting tasks from conversations and passing them to an employee and once the employee completes the task the agent will convey the output to the user. Then use the solutions of those basic tasks as in-context examples to solve the current task.\\nConclusion & Next Steps\\nIn this blog, we saw that design choices for Memory depend on the end use case. This analogy is better captured in the following table:\\nDeep dive into various types of Agent Memory\\nChoosing the right Memory design in Production\\nSince agents are powered by LLMs, they are inherently probabilistic.')]}\n",
      "---GENERATE---\n",
      "---CHECK HALLUCINATIONS---\n",
      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
      "{'question': 'What are the types of agent memory?', 'generation': AIMessage(content='The types of agent memory are short-term memory and long-term memory. Short-term memory involves in-context learning, while long-term memory enables the agent to retain and recall information over extended periods using an external vector store.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 3261, 'total_tokens': 3309, 'completion_tokens': 48}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-aab36245-6c61-465c-a070-a70bd67795e0-0', usage_metadata={'input_tokens': 3261, 'output_tokens': 48, 'total_tokens': 3309}), 'web_search': 'Yes', 'max_retries': 3, 'loop_step': 1, 'documents': [Document(metadata={'id': '3fb33621-a314-49b6-8bf5-4b48db735350', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Planning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\\n\\n\\n\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\nAnother quite distinct approach, LLM+P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.\\nSelf-Reflection#\\nSelf-reflection is a vital aspect that allows autonomous agents to improve iteratively by refining past action decisions and correcting previous mistakes. It plays a crucial role in real-world tasks where trial and error are inevitable.\\nReAct (Yao et al. 2023) integrates reasoning and acting within LLM by extending the action space to be a combination of task-specific discrete actions and the language space. The former enables LLM to interact with the environment (e.g. use Wikipedia search API), while the latter prompting LLM to generate reasoning traces in natural language.\\nThe ReAct prompt template incorporates explicit steps for LLM to think, roughly formatted as:\\nThought: ...\\nAction: ...\\nObservation: ...\\n... (Repeated many times)'), Document(metadata={'id': '98922f8f-fb32-4301-9a35-e2aa9524bc2b', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Long-Term Memory (LTM): Long-term memory can store information for a remarkably long time, ranging from a few days to decades, with an essentially unlimited storage capacity. There are two subtypes of LTM:\\n\\nExplicit / declarative memory: This is memory of facts and events, and refers to those memories that can be consciously recalled, including episodic memory (events and experiences) and semantic memory (facts and concepts).\\nImplicit / procedural memory: This type of memory is unconscious and involves skills and routines that are performed automatically, like riding a bike or typing on a keyboard.\\n\\n\\n\\n\\nFig. 8. Categorization of human memory.\\nWe can roughly consider the following mappings:\\n\\nSensory memory as learning embedding representations for raw inputs, including text, image or other modalities;\\nShort-term memory as in-context learning. It is short and finite, as it is restricted by the finite context window length of Transformer.\\nLong-term memory as the external vector store that the agent can attend to at query time, accessible via fast retrieval.\\n\\nMaximum Inner Product Search (MIPS)#\\nThe external memory can alleviate the restriction of finite attention span.  A standard practice is to save the embedding representation of information into a vector store database that can support fast maximum inner-product search (MIPS). To optimize the retrieval speed, the common choice is the approximate nearest neighbors (ANN)\\u200b algorithm to return approximately top k nearest neighbors to trade off a little accuracy lost for a huge speedup.\\nA couple common choices of ANN algorithms for fast MIPS:\\n\\nLSH (Locality-Sensitive Hashing): It introduces a hashing function such that similar input items are mapped to the same buckets with high probability, where the number of buckets is much smaller than the number of inputs.\\nANNOY (Approximate Nearest Neighbors Oh Yeah): The core data structure are random projection trees, a set of binary trees where each non-leaf node represents a hyperplane splitting the input space into half and each leaf stores one data point. Trees are built independently and at random, so to some extent, it mimics a hashing function. ANNOY search happens in all the trees to iteratively search through the half that is closest to the query and then aggregates the results. The idea is quite related to KD tree but a lot more scalable.\\nHNSW (Hierarchical Navigable Small World): It is inspired by the idea of small world networks where most nodes can be reached by any other nodes within a small number of steps; e.g. “six degrees of separation” feature of social networks. HNSW builds hierarchical layers of these small-world graphs, where the bottom layers contain the actual data points. The layers in the middle create shortcuts to speed up search. When performing a search, HNSW starts from a random node in the top layer and navigates towards the target. When it can’t get any closer, it moves down to the next layer, until it reaches the bottom layer. Each move in the upper layers can potentially cover a large distance in the data space, and each move in the lower layers refines the search quality.\\nFAISS (Facebook AI Similarity Search): It operates on the assumption that in high dimensional space, distances between nodes follow a Gaussian distribution and thus there should exist clustering of data points. FAISS applies vector quantization by partitioning the vector space into clusters and then refining the quantization within clusters. Search first looks for cluster candidates with coarse quantization and then further looks into each cluster with finer quantization.\\nScaNN (Scalable Nearest Neighbors): The main innovation in ScaNN is anisotropic vector quantization. It quantizes a data point $x_i$ to $\\\\tilde{x}_i$ such that the inner product $\\\\langle q, x_i \\\\rangle$ is as similar to the original distance of $\\\\angle q, \\\\tilde{x}_i$ as possible, instead of picking the closet quantization centroid points.\\n\\n\\nFig. 9. Comparison of MIPS algorithms, measured in recall@10. (Image source: Google Blog, 2020)\\nCheck more MIPS algorithms and performance comparison in ann-benchmarks.com.\\nComponent Three: Tool Use#\\nTool use is a remarkable and distinguishing characteristic of human beings. We create, modify and utilize external objects to do things that go beyond our physical and cognitive limits. Equipping LLMs with external tools can significantly extend the model capabilities.'), Document(metadata={'id': '003f1a14-32fe-4d98-a2ef-74f94cb1d09e', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content=\"LLM Powered Autonomous Agents | Lil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLil'Log\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPosts\\n\\n\\n\\n\\nArchive\\n\\n\\n\\n\\nSearch\\n\\n\\n\\n\\nTags\\n\\n\\n\\n\\nFAQ\\n\\n\\n\\n\\nemojisearch.app\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n      LLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\\n\\n\\nComponent Three: Tool Use\\n\\nCase Studies\\n\\nScientific Discovery Agent\\n\\nGenerative Agents Simulation\\n\\nProof-of-Concept Examples\\n\\n\\nChallenges\\n\\nCitation\\n\\nReferences\\n\\n\\n\\n\\n\\nBuilding agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview#\\nIn a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\n\\nThe agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.\"), Document(metadata={}, page_content='2.3 Memory. The agent\\'s memory has two categories: short-term and long-term memories. Short-term Memory: In context memory allows the agent to utilize the short-term memory of the large language model to operate the original problem from the beginning. This capability allows the agent to hold temporarily the information and process it when it ...\\nAgents can be categorized into five types: Simple Reflex agents, Model-based Reflex agents, Goal-based agents, Utility-based agents, and Learning agents [1]. As AI advanced, the term \"agent\" is used to depict entities exhibiting intelligent behavior and possessing ... If the agent has been armed with the memory module, memory recollection ...\\nLTM type 3: Procedural memory: This memory represents the agent\\'s procedures for thinking, acting, decision-making, etc. Unlike episodic or semantic memory that may be initially empty or even absent, procedural memory must be initialized by the designer with proper code to bootstrap the agent. It is of two types\\nAgent Types 12 Table-driven agents use a percept sequence/action table in memory to ﬁnd the next action. They are implemented by a (large) lookup table. Simple reﬂex agents are based on condition-action rules, implemented with an appropriate production system. They are stateless devices which do not have memory of past world states. Agents ...\\nSign up for Latest SuperAGI Updates\\n\"*\" indicates required fields\\nSuperAGI builds infrastructure components, tools, frameworks and models to enable opensource AGI\\ncommunity@superagi.com\\nFor Developers\\nDocs\\nGitHub\\nReleases\\nRoadmap\\nAPIs\\nCommunity\\nSupport Forum\\nMarketplace\\nSocial Mentions\\nReddit\\nCollectibles\\nResources\\nBlog\\nUse Cases\\nAGI Research Lab\\nTutorials\\nImportant Links\\nSuperAGI Cloud\\nApp Spotlight\\nSuperCoder\\nArchitecture\\n Check it out✨\\nFeatures\\nAction Console\\nResource Manager\\nTrajectory Fine-Tuning\\n\\u200c\\nMultiple Vector DBs\\nMulti-LLM Support\\nAgent Workflows\\nMarketplace\\nAgent Templates\\nDiscord\\nGitHub\\nTwitter\\nReddit\\nYoutube\\nTowards AGI (part 1): Agents with Memory\\nFebruary 6, 2024\\n7 mins read\\nAgents are an emerging class of artificial intelligence (AI) systems that use large language models (LLMs) to interact with the world. In a professional setup like this, the agent is responsible for extracting tasks from conversations and passing them to an employee and once the employee completes the task the agent will convey the output to the user. Then use the solutions of those basic tasks as in-context examples to solve the current task.\\nConclusion & Next Steps\\nIn this blog, we saw that design choices for Memory depend on the end use case. This analogy is better captured in the following table:\\nDeep dive into various types of Agent Memory\\nChoosing the right Memory design in Production\\nSince agents are powered by LLMs, they are inherently probabilistic.')]}\n"
     ]
    }
   ],
   "source": [
    "graph_input = {\"question\": \"What are the types of agent memory?\", \"max_retries\": 3}\n",
    "for event in graph.stream(graph_input, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11fddd00-58bf-4910-bf36-be9e5bfba778",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/4644990a-6a6c-49b7-8e4e-9b171908a0a5/r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "69a985dd-03c6-45af-a67b-b15746a2cb5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---ROUTE QUESTION---\n",
      "---ROUTE QUESTION TO WEB SEARCH---\n",
      "{'question': 'What is the recent Mistral multi-modal model?', 'max_retries': 3, 'loop_step': 0}\n",
      "---WEB SEARCH---\n",
      "{'question': 'What is the recent Mistral multi-modal model?', 'max_retries': 3, 'loop_step': 0, 'documents': [Document(metadata={}, page_content='Mistral releases Pixtral 12B, its first multimodal model French AI startup Mistral has released its first model that can process images as well as text. Built on one of Mistral’s text models, Nemo 12B, the new model can answer questions about an arbitrary number of images of an arbitrary size given either URLs or images encoded using base64, the binary-to-text encoding scheme. Similar to other multimodal models such as Anthropic’s Claude family and OpenAI’s GPT-4o, Pixtral 12B should — at least in theory — be able to perform tasks like captioning images and counting the number of objects in a photo. Available via a torrent link on GitHub and AI and machine learning development platform Hugging Face, Pixtral 12B can be downloaded, fine-tuned and used under an Apache 2.0 license without restrictions.\\nMistral releases ‘Pixtral 12B,’ its first multimodal AI model It\\'s not clear what image data the French AI startup firm used to develop the Pixtral 12B. French AI startup Mistral has released its first multimodal model, the Pixtral 12B, which can handle both text and images, according to\\xa0Techcrunch. The model uses 12 billion parameters and is based on Mistral’s Nemo 12B text model. Pixtral 12B can answer questions about images via URLs or images encoded with base64 such as how many copies of a certain object are visible. Most generative AI (genAI) models have been partially trained on copyrighted material, which has led to lawsuits from copyright owners. It is unclear what image data Mistral used to develop the Pixtral 12B.\\nMistral launches its first multimodal AI model called Pixtral 12B Home Mistral launches its first multimodal AI model called Pixtral 12B Mistral launches its first multimodal AI model called Pixtral 12B French AI startup Mistral has released its first ever multimodal model called Pixtral 12B, competing with the likes of OpenAI and Anthropic. Most generative AI models, such as those from Mistral, use extensive amounts of public data from the web, which is often under copyright. The ReadWrite Editorial policy involves closely monitoring the tech industry for major developments, new product launches, AI breakthroughs, video game releases and other newsworthy events. Mistral launches its first multimodal AI model called Pixtral 12B AI  AR / VR  Cryptocurrency  Gaming  Smartphone  Gambling  Wearables  Web\\nPixtral 12B is here: Mistral’s new multimodal AI can analyze images without any limits Today, the French AI startup taking on the likes of OpenAI and Anthropic released Pixtral 12B, its first ever multimodal model with both language and vision processing capabilities baked in. While the official details of the new model, including the data it was trained upon, remain under wraps, the core idea appears that Pixtral 12B will allow users to analyze images while combining text prompts with them. Since its launch last year, Mistral has not only built a strong pipeline of models taking on leading AI labs like OpenAI but also partnered with industry giants such as Microsoft, AWS and Snowflake to expand the reach of its technology.\\nPixtral 12B - the first-ever multimodal Mistral model. Pixtral is trained to understand both natural images and documents, achieving 52.5% on the MMMU reasoning benchmark, surpassing a number of larger models. Unlike previous open-source models, Pixtral does not compromise on text benchmark performance to excel in multimodal tasks. Pixtral even outperforms or matches the performance of much larger models like LLaVa OneVision 72B on multimodal benchmarks.All prompts will be open-sourced. Pixtral particularly excels at both multimodal and text-only instruction following as compared to other open multimodal models. Final architecture: Pixtral has two components: the Vision Encoder, which tokenizes images, and a Multimodal Transformer Decoder, which predicts the next text token given a sequence of text and images. The model is trained to predict the next text token on interleaved image and text data. \"model\": \"pixtral-12b-2409\",')]}\n",
      "---GENERATE---\n",
      "---CHECK HALLUCINATIONS---\n",
      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
      "{'question': 'What is the recent Mistral multi-modal model?', 'generation': AIMessage(content=\"The recent Mistral multi-modal model is Pixtral 12B. It is Mistral's first model that can process both images and text. Pixtral 12B is built on Mistral’s text model, Nemo 12B, and can answer questions about images given URLs or base64 encoded images.\", additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 1138, 'total_tokens': 1213, 'completion_tokens': 75}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-bf0bfe48-4119-49de-9d4c-75c617de3da6-0', usage_metadata={'input_tokens': 1138, 'output_tokens': 75, 'total_tokens': 1213}), 'max_retries': 3, 'loop_step': 1, 'documents': [Document(metadata={}, page_content='Mistral releases Pixtral 12B, its first multimodal model French AI startup Mistral has released its first model that can process images as well as text. Built on one of Mistral’s text models, Nemo 12B, the new model can answer questions about an arbitrary number of images of an arbitrary size given either URLs or images encoded using base64, the binary-to-text encoding scheme. Similar to other multimodal models such as Anthropic’s Claude family and OpenAI’s GPT-4o, Pixtral 12B should — at least in theory — be able to perform tasks like captioning images and counting the number of objects in a photo. Available via a torrent link on GitHub and AI and machine learning development platform Hugging Face, Pixtral 12B can be downloaded, fine-tuned and used under an Apache 2.0 license without restrictions.\\nMistral releases ‘Pixtral 12B,’ its first multimodal AI model It\\'s not clear what image data the French AI startup firm used to develop the Pixtral 12B. French AI startup Mistral has released its first multimodal model, the Pixtral 12B, which can handle both text and images, according to\\xa0Techcrunch. The model uses 12 billion parameters and is based on Mistral’s Nemo 12B text model. Pixtral 12B can answer questions about images via URLs or images encoded with base64 such as how many copies of a certain object are visible. Most generative AI (genAI) models have been partially trained on copyrighted material, which has led to lawsuits from copyright owners. It is unclear what image data Mistral used to develop the Pixtral 12B.\\nMistral launches its first multimodal AI model called Pixtral 12B Home Mistral launches its first multimodal AI model called Pixtral 12B Mistral launches its first multimodal AI model called Pixtral 12B French AI startup Mistral has released its first ever multimodal model called Pixtral 12B, competing with the likes of OpenAI and Anthropic. Most generative AI models, such as those from Mistral, use extensive amounts of public data from the web, which is often under copyright. The ReadWrite Editorial policy involves closely monitoring the tech industry for major developments, new product launches, AI breakthroughs, video game releases and other newsworthy events. Mistral launches its first multimodal AI model called Pixtral 12B AI  AR / VR  Cryptocurrency  Gaming  Smartphone  Gambling  Wearables  Web\\nPixtral 12B is here: Mistral’s new multimodal AI can analyze images without any limits Today, the French AI startup taking on the likes of OpenAI and Anthropic released Pixtral 12B, its first ever multimodal model with both language and vision processing capabilities baked in. While the official details of the new model, including the data it was trained upon, remain under wraps, the core idea appears that Pixtral 12B will allow users to analyze images while combining text prompts with them. Since its launch last year, Mistral has not only built a strong pipeline of models taking on leading AI labs like OpenAI but also partnered with industry giants such as Microsoft, AWS and Snowflake to expand the reach of its technology.\\nPixtral 12B - the first-ever multimodal Mistral model. Pixtral is trained to understand both natural images and documents, achieving 52.5% on the MMMU reasoning benchmark, surpassing a number of larger models. Unlike previous open-source models, Pixtral does not compromise on text benchmark performance to excel in multimodal tasks. Pixtral even outperforms or matches the performance of much larger models like LLaVa OneVision 72B on multimodal benchmarks.All prompts will be open-sourced. Pixtral particularly excels at both multimodal and text-only instruction following as compared to other open multimodal models. Final architecture: Pixtral has two components: the Vision Encoder, which tokenizes images, and a Multimodal Transformer Decoder, which predicts the next text token given a sequence of text and images. The model is trained to predict the next text token on interleaved image and text data. \"model\": \"pixtral-12b-2409\",')]}\n"
     ]
    }
   ],
   "source": [
    "graph_input = {\"question\": \"What is the recent Mistral multi-modal model?\", \"max_retries\": 3}\n",
    "for event in graph.stream(graph_input, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebf41097-fc4c-4072-95b3-e7e07731ada1",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/a90f2ab9-d684-4b9e-a55a-574767c1147c/r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1106e45f",
   "metadata": {},
   "source": [
    "Try another question, see that it will default to max_retries = 3 if not provided. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b9fdf28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---ROUTE QUESTION---\n",
      "---ROUTE QUESTION TO WEB SEARCH---\n",
      "{'question': 'Who is the top candidate in the 2025 NFL draft?', 'loop_step': 0}\n",
      "---WEB SEARCH---\n",
      "{'question': 'Who is the top candidate in the 2025 NFL draft?', 'loop_step': 0, 'documents': [Document(metadata={}, page_content=\"NFL Draft 2025: Big board of top 50 players overall. 1. James Pearce Jr., EDGE, Tennessee (6-5, 243 pounds) Pearce is a naturally explosive and super athletic pass rusher who can become more ...\\nRanking top 10 candidates to go No. 1 overall in 2025 NFL Draft: Shedeur Sanders, Carson Beck best QB options We have no idea who could be the top pick next year, let's rank the candidates\\n2025 NFL Draft Big Board Top-300. September 21, 2024 1:00 PM EST. 2025 NFL Draft Big Board: 1-100 Below. Players Ranked Regardless of Position. Drafttek.com's Big Board of 2025's top NFL draft prospects is compiled by our internal staff of talent evaluators. The NFL draft prospect rankings are adjusted regularly during the NCAA Football season.\\nHere's how the initial top 50 sit on my 2025 NFL Draft big board. 1 ... If Williams puts together a big season, he's a decent candidate for No. 1 overall in the 2025 NFL Draft. 8\\nThe dynamic 6-foot-2, 223-pound quarterback now appears on track to potentially secure the top spot in the 2025 NFL Draft. Exhibiting a newfound boldness in his throws, Ward has been pinpointing passes directly to his receivers with remarkable precision. He's consistently racked up at least 304 passing yards and three touchdowns in each game ...\")]}\n",
      "---GENERATE---\n",
      "---CHECK HALLUCINATIONS---\n",
      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\n",
      "{'question': 'Who is the top candidate in the 2025 NFL draft?', 'generation': AIMessage(content='James Pearce Jr., EDGE, Tennessee, is the top candidate in the 2025 NFL draft according to the big board of top 50 players overall. However, other candidates like Shedeur Sanders, Carson Beck, and Williams are also in consideration for the top spot. The dynamic quarterback Ward has also been exhibiting impressive skills, making him a potential candidate for the number one overall pick.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 475, 'total_tokens': 562, 'completion_tokens': 87}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-7a8cacd5-5879-4a61-b193-25c7dd50860e-0', usage_metadata={'input_tokens': 475, 'output_tokens': 87, 'total_tokens': 562}), 'loop_step': 1, 'documents': [Document(metadata={}, page_content=\"NFL Draft 2025: Big board of top 50 players overall. 1. James Pearce Jr., EDGE, Tennessee (6-5, 243 pounds) Pearce is a naturally explosive and super athletic pass rusher who can become more ...\\nRanking top 10 candidates to go No. 1 overall in 2025 NFL Draft: Shedeur Sanders, Carson Beck best QB options We have no idea who could be the top pick next year, let's rank the candidates\\n2025 NFL Draft Big Board Top-300. September 21, 2024 1:00 PM EST. 2025 NFL Draft Big Board: 1-100 Below. Players Ranked Regardless of Position. Drafttek.com's Big Board of 2025's top NFL draft prospects is compiled by our internal staff of talent evaluators. The NFL draft prospect rankings are adjusted regularly during the NCAA Football season.\\nHere's how the initial top 50 sit on my 2025 NFL Draft big board. 1 ... If Williams puts together a big season, he's a decent candidate for No. 1 overall in the 2025 NFL Draft. 8\\nThe dynamic 6-foot-2, 223-pound quarterback now appears on track to potentially secure the top spot in the 2025 NFL Draft. Exhibiting a newfound boldness in his throws, Ward has been pinpointing passes directly to his receivers with remarkable precision. He's consistently racked up at least 304 passing yards and three touchdowns in each game ...\")]}\n",
      "---WEB SEARCH---\n",
      "{'question': 'Who is the top candidate in the 2025 NFL draft?', 'generation': AIMessage(content='James Pearce Jr., EDGE, Tennessee, is the top candidate in the 2025 NFL draft according to the big board of top 50 players overall. However, other candidates like Shedeur Sanders, Carson Beck, and Williams are also in consideration for the top spot. The dynamic quarterback Ward has also been exhibiting impressive skills, making him a potential candidate for the number one overall pick.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 475, 'total_tokens': 562, 'completion_tokens': 87}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-7a8cacd5-5879-4a61-b193-25c7dd50860e-0', usage_metadata={'input_tokens': 475, 'output_tokens': 87, 'total_tokens': 562}), 'loop_step': 1, 'documents': [Document(metadata={}, page_content=\"NFL Draft 2025: Big board of top 50 players overall. 1. James Pearce Jr., EDGE, Tennessee (6-5, 243 pounds) Pearce is a naturally explosive and super athletic pass rusher who can become more ...\\nRanking top 10 candidates to go No. 1 overall in 2025 NFL Draft: Shedeur Sanders, Carson Beck best QB options We have no idea who could be the top pick next year, let's rank the candidates\\n2025 NFL Draft Big Board Top-300. September 21, 2024 1:00 PM EST. 2025 NFL Draft Big Board: 1-100 Below. Players Ranked Regardless of Position. Drafttek.com's Big Board of 2025's top NFL draft prospects is compiled by our internal staff of talent evaluators. The NFL draft prospect rankings are adjusted regularly during the NCAA Football season.\\nHere's how the initial top 50 sit on my 2025 NFL Draft big board. 1 ... If Williams puts together a big season, he's a decent candidate for No. 1 overall in the 2025 NFL Draft. 8\\nThe dynamic 6-foot-2, 223-pound quarterback now appears on track to potentially secure the top spot in the 2025 NFL Draft. Exhibiting a newfound boldness in his throws, Ward has been pinpointing passes directly to his receivers with remarkable precision. He's consistently racked up at least 304 passing yards and three touchdowns in each game ...\"), Document(metadata={}, page_content='NFL Draft 2025: Big board of top 50 players overall. 1. James Pearce Jr., EDGE, Tennessee (6-5, 243 pounds) Pearce is a naturally explosive and super athletic pass rusher who can become more ...\\n2025 NFL Draft Big Board Top-300. September 21, 2024 1:00 PM EST. 2025 NFL Draft Big Board: 1-100 Below. Players Ranked Regardless of Position. Drafttek.com\\'s Big Board of 2025\\'s top NFL draft prospects is compiled by our internal staff of talent evaluators. The NFL draft prospect rankings are adjusted regularly during the NCAA Football season.\\nThe dynamic 6-foot-2, 223-pound quarterback now appears on track to potentially secure the top spot in the 2025 NFL Draft. Exhibiting a newfound boldness in his throws, Ward has been pinpointing passes directly to his receivers with remarkable precision. He\\'s consistently racked up at least 304 passing yards and three touchdowns in each game ...\\nMel Kiper Jr. ranks QBs from 2025 NFL draft class (3:05) Mel Kiper Jr. ranks Carson Beck, Shedeur Sanders and Quinn Ewers in the 2025 NFL draft class, as well as breakout candidate Cam Ward.\\n6) Luther Burden III, WR, Missouri. The top prospect on PFN\\'s initial 2025 NFL Draft big board is Missouri\\'s Luther Burden III.At 5\\'11\", 208 pounds, with speed, agility, run-after-catch (RAC) physicality, route-running nuance, body control, and spider-like hands, Burden can be a prospect in the same tier as Ja\\'Marr Chase.')]}\n",
      "---GENERATE---\n",
      "---CHECK HALLUCINATIONS---\n",
      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
      "---GRADE GENERATION vs QUESTION---\n",
      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
      "{'question': 'Who is the top candidate in the 2025 NFL draft?', 'generation': AIMessage(content='The top candidate in the 2025 NFL draft is James Pearce Jr., EDGE, Tennessee. He is a naturally explosive and super athletic pass rusher. However, the dynamic quarterback Cam Ward is also a strong candidate for the top spot.', additional_kwargs={}, response_metadata={'token_usage': {'prompt_tokens': 909, 'total_tokens': 964, 'completion_tokens': 55}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-14c6be3f-044a-4e4b-91a1-39178b4c7634-0', usage_metadata={'input_tokens': 909, 'output_tokens': 55, 'total_tokens': 964}), 'loop_step': 3, 'documents': [Document(metadata={}, page_content=\"NFL Draft 2025: Big board of top 50 players overall. 1. James Pearce Jr., EDGE, Tennessee (6-5, 243 pounds) Pearce is a naturally explosive and super athletic pass rusher who can become more ...\\nRanking top 10 candidates to go No. 1 overall in 2025 NFL Draft: Shedeur Sanders, Carson Beck best QB options We have no idea who could be the top pick next year, let's rank the candidates\\n2025 NFL Draft Big Board Top-300. September 21, 2024 1:00 PM EST. 2025 NFL Draft Big Board: 1-100 Below. Players Ranked Regardless of Position. Drafttek.com's Big Board of 2025's top NFL draft prospects is compiled by our internal staff of talent evaluators. The NFL draft prospect rankings are adjusted regularly during the NCAA Football season.\\nHere's how the initial top 50 sit on my 2025 NFL Draft big board. 1 ... If Williams puts together a big season, he's a decent candidate for No. 1 overall in the 2025 NFL Draft. 8\\nThe dynamic 6-foot-2, 223-pound quarterback now appears on track to potentially secure the top spot in the 2025 NFL Draft. Exhibiting a newfound boldness in his throws, Ward has been pinpointing passes directly to his receivers with remarkable precision. He's consistently racked up at least 304 passing yards and three touchdowns in each game ...\"), Document(metadata={}, page_content='NFL Draft 2025: Big board of top 50 players overall. 1. James Pearce Jr., EDGE, Tennessee (6-5, 243 pounds) Pearce is a naturally explosive and super athletic pass rusher who can become more ...\\n2025 NFL Draft Big Board Top-300. September 21, 2024 1:00 PM EST. 2025 NFL Draft Big Board: 1-100 Below. Players Ranked Regardless of Position. Drafttek.com\\'s Big Board of 2025\\'s top NFL draft prospects is compiled by our internal staff of talent evaluators. The NFL draft prospect rankings are adjusted regularly during the NCAA Football season.\\nThe dynamic 6-foot-2, 223-pound quarterback now appears on track to potentially secure the top spot in the 2025 NFL Draft. Exhibiting a newfound boldness in his throws, Ward has been pinpointing passes directly to his receivers with remarkable precision. He\\'s consistently racked up at least 304 passing yards and three touchdowns in each game ...\\nMel Kiper Jr. ranks QBs from 2025 NFL draft class (3:05) Mel Kiper Jr. ranks Carson Beck, Shedeur Sanders and Quinn Ewers in the 2025 NFL draft class, as well as breakout candidate Cam Ward.\\n6) Luther Burden III, WR, Missouri. The top prospect on PFN\\'s initial 2025 NFL Draft big board is Missouri\\'s Luther Burden III.At 5\\'11\", 208 pounds, with speed, agility, run-after-catch (RAC) physicality, route-running nuance, body control, and spider-like hands, Burden can be a prospect in the same tier as Ja\\'Marr Chase.')]}\n"
     ]
    }
   ],
   "source": [
    "graph_input = {\"question\": \"Who is the top candidate in the 2025 NFL draft?\"}\n",
    "for event in graph.stream(graph_input, stream_mode=\"values\"):\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c435676a",
   "metadata": {},
   "source": [
    "Trace: \n",
    "\n",
    "https://smith.langchain.com/public/74d0c510-e139-48be-8c38-f1511e0c8c7a/r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482bd427",
   "metadata": {},
   "source": []
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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