From 3c5a21228cd5081f5f715876f58e28af958fa90b Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Wed, 14 Feb 2024 15:26:17 -0800 Subject: [PATCH 1/3] Update agentic RAG example --- examples/rag/langgraph_agentic_rag.ipynb | 180 +++++++++++++++++------ 1 file changed, 139 insertions(+), 41 deletions(-) diff --git a/examples/rag/langgraph_agentic_rag.ipynb b/examples/rag/langgraph_agentic_rag.ipynb index ecd65ba90..a572cb46b 100644 --- a/examples/rag/langgraph_agentic_rag.ipynb +++ b/examples/rag/langgraph_agentic_rag.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "id": "e50c9efe-4abe-42fa-b35a-05eeeede9ec6", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "id": "0b97bdd8-d7e3-444d-ac96-5ef4725f9048", "metadata": {}, "outputs": [], @@ -69,7 +69,7 @@ "tool = create_retriever_tool(\n", " retriever,\n", " \"retrieve_blog_posts\",\n", - " \"Search and return information about Lilian Weng blog posts.\",\n", + " \"Search and return information about Lilian Weng blog posts on LLM agents, prompt engineering, and adversarial attacks on LLMs.\",\n", ")\n", "\n", "tools = [tool]\n", @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "id": "0e378706-47d5-425a-8ba0-57b9acffbd0c", "metadata": {}, "outputs": [], @@ -114,8 +114,8 @@ }, { "attachments": { - "f886806c-0aec-4c2a-8027-67339530cb60.png": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAHICAYAAACYkW15AAAMP2lDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnluSkEBCCSAgJfQmCEgJICWEFkB6EWyEJEAoMQaCiB1dVHDtYgEbuiqi2AGxI3YWwd4XRRSUdbFgV96kgK77yvfO9829//3nzH/OnDu3DADqp7hicQ6qAUCuKF8SGxLAGJucwiB1AwTggAYIgMDl5YlZ0dERANrg+e/27ib0hnbNQab1z/7/app8QR4PACQa4jR+Hi8X4kMA4JU8sSQfAKKMN5+aL5Zh2IC2BCYI8UIZzlDgShlOU+B9cp/4WDbEzQCoqHG5kgwAaG2QZxTwMqAGrQ9iJxFfKAJAnQGxb27uZD7EqRDbQB8xxDJ9ZtoPOhl/00wb0uRyM4awYi5yUwkU5olzuNP+z3L8b8vNkQ7GsIJNLVMSGiubM6zb7ezJ4TKsBnGvKC0yCmItiD8I+XJ/iFFKpjQ0QeGPGvLy2LBmQBdiJz43MBxiQ4iDRTmREUo+LV0YzIEYrhC0UJjPiYdYD+KFgrygOKXPZsnkWGUstC5dwmYp+QtciTyuLNZDaXYCS6n/OlPAUepjtKLM+CSIKRBbFAgTIyGmQeyYlx0XrvQZXZTJjhz0kUhjZflbQBwrEIUEKPSxgnRJcKzSvzQ3b3C+2OZMISdSiQ/kZ8aHKuqDNfO48vzhXLA2gYiVMKgjyBsbMTgXviAwSDF3rFsgSohT6nwQ5wfEKsbiFHFOtNIfNxPkhMh4M4hd8wrilGPxxHy4IBX6eLo4PzpekSdelMUNi1bkgy8DEYANAgEDSGFLA5NBFhC29tb3witFTzDgAgnIAALgoGQGRyTJe0TwGAeKwJ8QCUDe0LgAea8AFED+6xCrODqAdHlvgXxENngKcS4IBznwWiofJRqKlgieQEb4j+hc2Hgw3xzYZP3/nh9kvzMsyEQoGelgRIb6oCcxiBhIDCUGE21xA9wX98Yj4NEfNheciXsOzuO7P+EpoZ3wmHCD0EG4M0lYLPkpyzGgA+oHK2uR9mMtcCuo6YYH4D5QHSrjurgBcMBdYRwW7gcju0GWrcxbVhXGT9p/m8EPd0PpR3Yio+RhZH+yzc8jaXY0tyEVWa1/rI8i17SherOHen6Oz/6h+nx4Dv/ZE1uIHcTOY6exi9gxrB4wsJNYA9aCHZfhodX1RL66BqPFyvPJhjrCf8QbvLOySuY51Tj1OH1R9OULCmXvaMCeLJ4mEWZk5jNY8IsgYHBEPMcRDBcnF1cAZN8XxevrTYz8u4Hotnzn5v0BgM/JgYGBo9+5sJMA7PeAj/+R75wNE346VAG4cIQnlRQoOFx2IMC3hDp80vSBMTAHNnA+LsAdeAN/EATCQBSIB8lgIsw+E65zCZgKZoC5oASUgWVgNVgPNoGtYCfYAw6AenAMnAbnwGXQBm6Ae3D1dIEXoA+8A58RBCEhVISO6CMmiCVij7ggTMQXCUIikFgkGUlFMhARIkVmIPOQMmQFsh7ZglQj+5EjyGnkItKO3EEeIT3Ia+QTiqFqqDZqhFqhI1EmykLD0Xh0ApqBTkGL0PnoEnQtWoXuRuvQ0+hl9Abagb5A+zGAqWK6mCnmgDExNhaFpWDpmASbhZVi5VgVVos1wvt8DevAerGPOBGn4wzcAa7gUDwB5+FT8Fn4Ynw9vhOvw5vxa/gjvA//RqASDAn2BC8ChzCWkEGYSighlBO2Ew4TzsJnqYvwjkgk6hKtiR7wWUwmZhGnExcTNxD3Ek8R24mdxH4SiaRPsif5kKJIXFI+qYS0jrSbdJJ0ldRF+qCiqmKi4qISrJKiIlIpVilX2aVyQuWqyjOVz2QNsiXZixxF5pOnkZeSt5EbyVfIXeTPFE2KNcWHEk/JosylrKXUUs5S7lPeqKqqmql6qsaoClXnqK5V3ad6QfWR6kc1LTU7NbbaeDWp2hK1HWqn1O6ovaFSqVZUf2oKNZ+6hFpNPUN9SP1Ao9McaRwanzabVkGro12lvVQnq1uqs9Qnqhepl6sfVL+i3qtB1rDSYGtwNWZpVGgc0bil0a9J13TWjNLM1VysuUvzoma3FknLSitIi681X2ur1hmtTjpGN6ez6Tz6PPo2+ll6lzZR21qbo52lXaa9R7tVu09HS8dVJ1GnUKdC57hOhy6ma6XL0c3RXap7QPem7qdhRsNYwwTDFg2rHXZ12Hu94Xr+egK9Ur29ejf0Pukz9IP0s/WX69frPzDADewMYgymGmw0OGvQO1x7uPdw3vDS4QeG3zVEDe0MYw2nG241bDHsNzI2CjESG60zOmPUa6xr7G+cZbzK+IRxjwndxNdEaLLK5KTJc4YOg8XIYaxlNDP6TA1NQ02lpltMW00/m1mbJZgVm+01e2BOMWeap5uvMm8y77MwsRhjMcOixuKuJdmSaZlpucbyvOV7K2urJKsFVvVW3dZ61hzrIusa6/s2VBs/myk2VTbXbYm2TNts2w22bXaonZtdpl2F3RV71N7dXmi/wb59BGGE5wjRiKoRtxzUHFgOBQ41Do8cdR0jHIsd6x1fjrQYmTJy+cjzI785uTnlOG1zuues5RzmXOzc6Pzaxc6F51Lhcn0UdVTwqNmjGka9crV3FbhudL3tRncb47bArcntq7uHu8S91r3Hw8Ij1aPS4xZTmxnNXMy84EnwDPCc7XnM86OXu1e+1wGvv7wdvLO9d3l3j7YeLRi9bXSnj5kP12eLT4cvwzfVd7Nvh5+pH9evyu+xv7k/33+7/zOWLSuLtZv1MsApQBJwOOA924s9k30qEAsMCSwNbA3SCkoIWh/0MNgsOCO4JrgvxC1kesipUEJoeOjy0FscIw6PU83pC/MImxnWHK4WHhe+PvxxhF2EJKJxDDombMzKMfcjLSNFkfVRIIoTtTLqQbR19JToozHEmOiYipinsc6xM2LPx9HjJsXtinsXHxC/NP5egk2CNKEpUT1xfGJ14vukwKQVSR1jR46dOfZyskGyMLkhhZSSmLI9pX9c0LjV47rGu40vGX9zgvWEwgkXJxpMzJl4fJL6JO6kg6mE1KTUXalfuFHcKm5/GietMq2Px+at4b3g+/NX8XsEPoIVgmfpPukr0rszfDJWZvRk+mWWZ/YK2cL1wldZoVmbst5nR2XvyB7IScrZm6uSm5p7RKQlyhY1TzaeXDi5XWwvLhF3TPGasnpKnyRcsj0PyZuQ15CvDX/kW6Q20l+kjwp8CyoKPkxNnHqwULNQVNgyzW7aomnPioKLfpuOT+dNb5phOmPujEczWTO3zEJmpc1qmm0+e/7srjkhc3bOpczNnvt7sVPxiuK385LmNc43mj9nfucvIb/UlNBKJCW3Fngv2LQQXyhc2Lpo1KJ1i76V8ksvlTmVlZd9WcxbfOlX51/X/jqwJH1J61L3pRuXEZeJlt1c7rd85wrNFUUrOleOWVm3irGqdNXb1ZNWXyx3Ld+0hrJGuqZjbcTahnUW65at+7I+c/2NioCKvZWGlYsq32/gb7i60X9j7SajTWWbPm0Wbr69JWRLXZVVVflW4taCrU+3JW47/xvzt+rtBtvLtn/dIdrRsTN2Z3O1R3X1LsNdS2vQGmlNz+7xu9v2BO5pqHWo3bJXd2/ZPrBPuu/5/tT9Nw+EH2g6yDxYe8jyUOVh+uHSOqRuWl1ffWZ9R0NyQ/uRsCNNjd6Nh486Ht1xzPRYxXGd40tPUE7MPzFwsuhk/ynxqd7TGac7myY13Tsz9sz15pjm1rPhZy+cCz535jzr/MkLPheOXfS6eOQS81L9ZffLdS1uLYd/d/v9cKt7a90VjysNbZ5tje2j209c9bt6+lrgtXPXOdcv34i80X4z4ebtW+Nvddzm3+6+k3Pn1d2Cu5/vzblPuF/6QONB+UPDh1V/2P6xt8O94/ijwEctj+Me3+vkdb54kvfkS9f8p9Sn5c9MnlV3u3Qf6wnuaXs+7nnXC/GLz70lf2r+WfnS5uWhv/z/aukb29f1SvJq4PXiN/pvdrx1fdvUH93/8F3uu8/vSz/of9j5kfnx/KekT88+T/1C+rL2q+3Xxm/h3+4P5A4MiLkSrvxXAIMNTU8H4PUOAKjJANDh/owyTrH/kxui2LPKEfhPWLFHlJs7ALXw/z2mF/7d3AJg3za4/YL66uMBiKYCEO8J0FGjhtrgXk2+r5QZEe4DNkd+TctNA//GFHvOH/L++Qxkqq7g5/O/AFFLfCfKufu9AAAAVmVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAADkoYABwAAABIAAABEoAIABAAAAAEAAAOxoAMABAAAAAEAAAHIAAAAAEFTQ0lJAAAAU2NyZWVuc2hvdJ9yF8cAAAHWaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA2LjAuMCI+CiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjQ1NjwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj45NDU8L2V4aWY6UGl4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpVc2VyQ29tbWVudD5TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KWU789QAAQABJREFUeAHsnQm8VdP7h18qlVIaiEoDmSqzQqaUKcoQypjMM5kjfiQZEikl/xASMmesRCKSocxlTKXB3ExR3f9+Vtax77nnnqlz7z3nnu/7+Zyzp7XXXvvZa++13vW+a631CgIxiQiIgAiIgAiIgAiIgAiIgAiIgAjkAIH1cyCNSqIIiIAIiIAIiIAIiIAIiIAIiIAIOAJSYpURREAEREAEREAEREAEREAEREAEcoaAlNiceVRKqAiIgAiIgAiIgAiIgAiIgAiIgJRY5QEREAEREAEREAEREAEREAEREIGcISAlNmcelRIqAiIgAiIgAiIgAiIgAiIgAiIgJVZ5QAREQAREQAREQAREQAREQAREIGcISInNmUelhIqACIiACIiACIiACIiACIiACEiJVR4QAREQAREQAREQAREQAREQARHIGQJSYnPmUSmhIiACIiACIiACIiACIiACIiACUmKVB0RABERABERABERABERABERABHKGgJTYnHlUSqgIiIAIiIAIiIAIiIAIiIAIiICUWOUBERABERABERABERABERABERCBnCEgJTZnHpUSKgIiIAIiIAIiIAIiIAIiIAIiICVWeUAEREAEREAEREAEREAEREAERCBnCEiJzZlHpYSKgAiIgAiIgAiIgAiIgAiIgAhIiVUeEAEREAEREAEREAEREAEREAERyBkCUmJz5lEpoSIgAiIgAiIgAiIgAiIgAiIgAlJilQdEQAREQAREQAREQAREQAREQARyhoCU2Jx5VEqoCIiACIiACIiACIiACIiACIiAlFjlAREQAREQAREQAREQAREQAREQgZwhICU2Zx6VEioCIiACIiACIiACIiACIiACIiAlVnlABERABERABERABERABERABEQgZwhIic2ZR6WEioAIiIAIiIAIiIAIiIAIiIAISIlVHhABERABERABERABERABERABEcgZAlJic+ZRKaEiIAIiIAIiIAIiIAIiIAIiIAJSYpUHREAEREAEREAEREAEREAEREAEcoaAlNiceVRKqAiIgAiIgAiIgAiIgAiIgAiIgJRY5QEREAEREAEREAEREAEREAEREIGcISAlNmcelRIqAiIgAiIgAiIgAiIgAiIgAiIgJVZ5QAREQAREQAREQAREQAREQAREIGcISInNmUelhIqACIiACIiACIiACIiACIiACEiJVR4QAREQAREQAREQAREQAREQARHIGQJSYnPmUSmhIiACIiACIiACIiACIiACIiACUmKVB0RABERABERABERABERABERABHKGgJTYnHlUSqgIiIAIiIAIiIAIiIAIiIAIiICUWOUBERABERABERABERABERABERCBnCEgJTZnHpUSKgIiIAIiIAIiIAIiIAIiIAIiICVWeUAEREAEREAEREAEREAEREAERCBnCEiJzZlHpYSKgAiIgAiIgAiIgAiIgAiIgAhIiVUeEAEREAEREAEREAEREAEREAERyBkCUmJz5lEpoSIgAiIgAiIgAiIgAiIgAiIgAlJilQdEQAREQAREQAREQAREQAREQARyhoCU2Jx5VEqoCIiACIiACIiACIiACIiACIiAlFjlAREQAREQAREQAREQAREQAREQgZwhICU2Zx6VEioCIiACIiACIiACIiACIiACIiAlVnlABERABERABERABERABERABEQgZwhIic2ZR6WEioAIiIAIiIAIiIAIiIAIiIAISIlVHhABERABERABERABERABERABEcgZAlJic+ZRKaEiIAIiIAIiIAIiIAIiIAIiIAJSYpUHREAEREAEREAEREAEREAEREAEcoaAlNiceVRKqAiIgAiIgAiIgAiIgAiIgAiIgJRY5QEREAEREAEREAEREAEREAEREIGcISAlNmcelRIqAiIgAiIgAiIgAiIgAiIgAiIgJVZ5QAREQAREQAREQAREQAREQAREIGcISInNmUelhIqACIiACIiACIiACIiACIiACEiJVR4QAREQAREQAREQAREQAREQARHIGQJSYnPmUSmhIiACIiACIiACIiACIiACIiACUmKVB0RABERABERABERABERABERABHKGgJTYnHlUSqgIiIAIiIAIiIAIiIAIiIAIiICUWOUBERABERABERABERABERABERCBnCEgJTZnHpUSKgIiUJIECgoKSjJ6xS0C5ZKA3pty+Vh1UyIgAiKQ9QSkxMZ4RKtWrbIvvvjC/f74448YIbRLBFIn8Pfff9t7771nU6ZMsd9//71QBI8++qgdeOCB9vHHHxfaXxIb8+fPd9fiev63ePHipC713XffuXOGDBmSVHgfaNasWZF3infr+++/txUrVvjDZb6cMGGCtWjRwgYPHlzmaVECRCAdArzXvFszZsxI5/SUz0F5PeGEE2yvvfayX375JeXzdYIIFEeAOhhlxPjx412ZuHTp0kjQ0iwrueh9993nyryvv/46koZ0Vv78889CZeCXX35pv/76q6XTCHTyySdHym7K8MmTJ6eTJJ0jAjlPoGLO30EJ3ACKxLHHHutiPuuss+y6664rgaukFuVpp51mn376qX3wwQdWsaIeW2r0yjb08uXL7ZprrrEXXnihUELOP/98u/TSS22DDTZwSu23335rhC1pqVSpkm2zzTbuMhMnTnTXpNKQjKB4ks6ff/45meCRMLxDkyZNimz7lQsvvNAuv/xyW3/91NrTMv0+fP75544DDQykSSICuUaA98hXZqdNm2Z16tRJ+Rb++usv23vvvW3PPfe0e++9N+75VMr99X788UfbdNNN44bXQRFIhgB5im9wdEPvHXfcYV26dCnVspL00kBDmbeuja40LnXu3LkIgu23397uvPNO14ha5GAxOxo3bmw1atSwb775xqUtrOQXc4p2i0C5JCBtKMZjfeutt9xeKgHjxo3LCiWWD3r0Rz1G0rUrCwn83//9n1NgqRjSgopFFovf888/b926dbPNN9+8VFO9ySabRCqoKINYIUtL+vbt6y5F4fvMM884DhTIVE5SkUy/D2eeeaY1bdrUWrdunUoyFFYEsoIAlVgq/5RZvBvvvvuuHXHEESmnbc2aNe78ZMqaatWq2UsvvWR4K+26664pX0sniEA0gQ8//NBZ99lPI2+rVq0ML57evXsbDa7HHXdc9Ck5t33wwQfb/vvvbzQYjR071j766CM744wz7O2333YN2snckC9H77//frv55puTOUVhRKBcEpASG+Ox8mHZcsst7cgjj7QBAwbYDz/84Cq44aC0qr3zzjvOgnTAAQcY7pi01nXq1MmqVq0aCUrFAgsqlYOddtrJ9tlnn8gxrFkozLvvvrstXLjQuZluu+221rZt24i19ZVXXnEWIu+u9dRTT0WO0fJNWEl2E/jkk09cAm+88Uaj1RU57LDDbMmSJVavXj237f9QcN944w3D1Wi//faznXfe2R+KLDn2/vvv27Jly6x58+ZG/qtQoYI7jnXk5ZdftgYNGjiLCjspLKls1q9fv1D+i0QYZ2XlypWuIQelE3fbdbW2oMR72XfffQ3l8fXXXy+kxOK2xf1Rkd5qq63s0EMPjRTuyb4PL774YqTlvEOHDvbbb7/Za6+9Zs2aNXMVCO/N4OPzaZo7d65tttlmfjOyxO2LStScOXMc2/bt2xuNAQjvPo1dtWvXdi5ekZOClWeffdZWr17tKl/rrbeeOxTv/sLnal0EkiVANwWkZ8+eduWVV9qbb74ZU4n96quvDCvtTz/95N6F3XbbzeVn/43w1iZckylrvPDu80NQKvAICgvnhcs9fwxXSco43JwrV67syrpddtnFH3YeHYnKwEhgrZRrAuQVr5zR8Mt33wtl3BZbbGH+G8r+ZMrKZL615F2+7XSVIY/uuOOOrlEGj6XixNfdmjRpknLDJw2lvhzE04/7pD6J+7SvH3DdeHXH4tIVa3+myi7qxXhcwIjvhv8e+GtSzlHeNWrUyJWhXik//PDDbaONNvLBIsvivkWRAMFKMs8vHF7reUgg+HBIQgTmzZtXELyEBTfddFNBUJF264888kgoREHBmDFj3H7C8Qs+PAVBZdytBx+3SNjLLrusUDjCXnHFFZHjQWu5O37JJZcUChcU8gWB8uDCBUpvoWP+miyPP/74SFxayV4Ct956q3uG99xzT0HQmBEzoUFjiQsTWGYLPe/ArapQePJiOA+wfsoppxT8888/LpzPv+edd17kvKBC6s4555xzIvv8Svfu3d2xQMnzuyLLoHAvCBpyCl2Pa3HN66+/PhIumZWTTjrJnRcOGyiMbh9xenniiScKXY9rHXLIIQWBwu6CJPs+8A55Ts8991xk3ccXFLguvj322KPQsV69evmkRJZB9wL3jvv4WPLOBxVzFwb2bLM/cAePnBcUwG5f4EIW2Zfo/iIBtSICKRC49tprXV7jPaZcID/6b4KP5q677iqU131+DhTgAv+N8Puil4MGDfLRFIwePbpIPAsWLIgcZ4XvHL/AmlYk7MCBAyNhkykDI4G1Uq4JBMqWyytBA2/c+0y2rEzmWxsojgWBIaBIHj399NMLAiXZpSOwArvjQWN0JF0PP/yw2zds2LDIvkQrgcU15jlXX321289xL4nqjj4c1+ddDRRMv6vQMpNlF+VY+LsQNOQWhNNMfYHjlPXh8pd9vqz0iYv3LfJhknl+PqyW+UsgtY5oeaDkY11F6BfkrWBYxrwEFQPDoobQz48W71NPPdVZedzOf/84B3dJrK8MRBC8kEYLNK3buMyEBbfS//3vf4ZViE76WKDom4cEFQbX6h1UStw2rXO0gvMbOnSo26e/7CaARR+hT8/RRx8d132XlkfyC1ZDvAGwKGKxRWj9DZRHt05+eeihh2zrrbd2lo5AUXP7M/lH3qN/OJ4CpCNQoF1LbCaugeWH+0SIH+H+ggLdcFPEA4L3gb7ptFJzr0iy7wMDggQNUe6coMC0/v37u5btli1buvhodUe4L6xY3FssCYoGCxRb5w1x1VVXOfevQGFw296NC6suA9wgtD57wcKM+P71ydyfP1dLEUiWAHk0aFg18jbuxLgq0reeft5eKE/uvvtu925RbuE5cMMNN7jDuB7jEUKZQnmGUFb5coZlUKl3+/nDHZJ3hh/XiiW8f/zwCgkqsYZlLWjMc9en/x9Wp7DEKwPD4bRefglg5UOw8iUjicrKRGUJ12CsipkzZ7o8/dhjjzlLInkfb6egQbjYZPg6IV2E1kXw3qMMQvCqQlKpO7oTivnLZNnFJYJGeJe2xx9/3C6++GLneXjBBRcUGZiKsS+Chmfn/XXRRRe51L366quRVCb6FhFQZWUEl1YSEJASGwXIVzxx+WDAHZRKXE1w00QofINWZ1fI4wqCosGAGlS8w+L7GVIBxi20TZs25l9o3LnCQiWePhFUQnxlwSvTVEqoYJAWhHX/23jjjcPRaD1LCdAAQR6iQQOlkH6o9PfBhTxaUIbIL7iVe+XXN3rgYot07NjR5Zd27dq5gaHY5/Mb65kS3zecwadIO67rQSvrOkWPWxi/7bbbzjXC8N7gWo34+wusyG4ADN4H3h/EM0j2fSBczZo13bm4P9GXChdrGpxwb/JuabhH42bNOxVLcLvEFZLKCgU2zzKwaLslDUq4UCH+WYULa5RwhAIdSeb+XED9iUAKBHDLo+GT9wqhrEHCDSoouQjvFuUW7x9lDXnYD6zmyxXC8c74bZbh8g23Yd4ZflWqVCF4EeHb4b8f5557rnOZPPHEEyMNOijOYYlXBobDab38EvDlof9uJ7rTZMrKeGUJbrbeWBB4GriuNuRDlFkaeXAVjhYGQMR4QN6mAdkbF6LDxdumgZZ3lTKF+iUNTtQHvDu+L8uTqTvGu04myy6uw/tOdxz6v/MNIe3UhVE4o4WGMtyyuS/EK/2sJ/oWEUZlJRQkyRBQn9gQJfpY8PGiAuwLWV9I87FDaeDDgHjrEetYYhiAAEXFi29V9K3d7Pcjz/r+rT5seDAZ/wGnn4ak/BBg0CBGJ6bwo0UTBYePP31IwoKy6IXRBxH6pSK+ZTicX7y3gM9vLmCG/ugfiuywww6RGJNtJY+cELXi87ffzf1TMCL+/qhEeAXQh/PH/HYqy/C7ygBSqQwiRd9ABKtWuI8W1mFk0aJF7ntBgU2DFs/49ttvd0oFyi8FPX1lEX8Pmb4/F7n+8paAVxbxEqL88iON480RdFVxXBjXAaGBLCw07JSEhL9H/hvFdagA4/Xg3wV/7fA3zX8jVAZ6Ovmx9A2J/pub6K6TKSvjfWt9+Ua9ziuQXDPcYBNOQ48ePZzV1u9DmfVjK/h9ySypU/o8TnjGbMBq7MW/O8nUHf05sZaeYybKLr4pgeuyq794xd9fkzEhwuNI8Fw8ww033NAF83UYNpL5FvnvQ7zn56+vZX4TkBIbev6MEofQqn322WeHjphTUPnY0UKNeIXUB/KWUr/tP1K4UoVfcI6j8IYl3gAC4XCsU1FJ58MZHY+2S58A1j+smQzuRSsybnoUWAxY4SXes/UFA4MUefHzGPvCwu8PL33DS3hf9DoDj0VLOK/7/JxKXo2Oj23cgRHm3sO9kEq3b83216ARKawsU8GNfodcJMFfMu8DVqd0xTckUPEIV7T9epg71l4UWNypaKFGjjrqqMil07m/yMlaEYFiCOC2i/BOhYVGFBpM8TbwA6vgRh+u/IfDh9e9Ihzel8p6+L3wFjbOp2xFwsfZXtfvCnFIcpuAb1DBpR2lx5c/xd1VvLIymW+tL09pkMT11nvnFHc9lDXKKt+A6b1wigtf3H7vDUEdkroglkkaQ71nnU97MnVHf41Y5Xcmy67hw4e7so3BGOnKVL16ddc1zteZfTpYJnqXk/kWeQap1AXCadB6/hCQEht61t79qk+fPq4fjz8UdLJ3fRTpY+c/tLj78uFgfksKfFy6wkLFmb4OfCh937zw8VTX/UiouFmgCElyhwBKZ926dSMJDhe+HAsrsZFAMVa8exOWFywsxOPzrJ/31ReEtL76gtm74saIMlJw0mrr85gPx/VQtCmo/HQdvi+pD5PuEtdCXLhwrcLFt1atWpG5a+kDTF+lcOt49HV8WpN5H8K8o+NJtE1FAqHyHQxA5VzIijuHkclRYqmUhFv5fXj/jJK5P3+OliIQjwCVX95PvA18dxXCYy3BUoRHEf3wKY/on8o4DbjvF/dueeWSOKm0+8pkvDTEOua9KziGhxINd1T6vdUYzxSJCIQJUHbhuUKXLvpNMwZBut/uZL61vtylwZGGx2gvhXDaWGdcBhqA/LQ2jE/BGCeJlN/oePw2dUMUWsZrQEmknomkUnf076cvb3zcLDNZdvk57vmmoIRSt0h3HI5kvkXJPL/wvWo9fwlIiQ09e6xCCBXs8McTC+zTTz/tOrLTD4I+R/QlYoAL+idS4DP1RlgYQv3BBx90LzrHmZKDViXcJJjzLFErYzgu1uk/wcedPgb0seMDRUsefQ8k2UuAjz1WRQaLwHpHwTV16lSnHJIf6PuarJDvcFllyib61DC/KoUv4ofspxJK/iAvk49RDn0fbBTSBx54wE1r46/JYBIURkwuT15GqaYwRyHDskghTeUYZZF3gql6MiG0EtMPHEWWApx+eVTEedfoEwQz7oPKMH2XunbtGrHYcv147wNu2gykxuAcCG6V3BeD0PAcvKCQU7FHvLWad5UBcBD6LlNJoG8SUz9QwSJ9uETy7jHFAJy8UCniGfvCnYntfWs/YVK5Px+nliIQjwDlEIJLYrhxEyWRCifvEkosDTBsE553iz7cNARhqeWYny6DCrl/B/keUG6Rh+kP5/vDjxgxws0Ny3W9VQpXQ95pLFW8t36gM7pOUOlnLACUYgay4btH2SkRgWgCKK7UcxgIjAZaGj+oK1Hm8f3EpTcZSeZbS0NOMFuEUyJ5B1BQKVcZdBDPG69URl8vGNHfGNyI8pQByfjOpytcFyU2GLHbiJeuJ6nUHb2yR2MwdUuMKtwX02zBLVNl10EHHeTGhrjxxhtdGcqz8QPHkXYGL2Qw1GQkmW9RMs8vmWspTB4QCCrZkoBA0JLlhgcPOqwX4RG4QLpjDKuOBB+LgvBw40yLwrDwgWJZELSMR86fNWtWZOodjvlf0CfAhQkqFG4fw417YShywgUuG36XWzJ8uZ9GwcfDkv2S7CUQFIgFgYJWwHD04ecWfPQLgkIgkvBAcXLHAwt/ZF/Q8uv2BYMFRfbNnj27gHN9XIFSVhAorJHjrJCH/NQ5HA8U3Uh40hGWoGLp8lpQ+YyECU+nEZ7ShzB+SP9Up9gJCi4Xf/jagWIZuaZ/b5YuXVoQKIyRKWv8fQZ9Z8Onunxf3Pvg3yF/rl/699dHxLQE/lisJayRwNOiIKi0FJk2IDxdlo/zySefjMQZuMX53ZFlsvcXOUErIhCHgJ+eY/r06UVCMX0U7yz5Fwkabdx0XNF5nfItLJSFgbIQyceEp7zzEv0tC8cX/U7wPQtPY0U5ydRTXlIpA/05WpZvAkEXG1dvCpdJTK3Wr18/d+PJlpXJfGuD7igFgbGhSHnD9bww3SJ5PFCk/a4Cvu3sI41++rfIwWJWggZSd07QqFMohJ+CL1wPTFR3DEcQdCMo9I6Fp17MVNkVNPIWUDf273rQCFYQWGcj2zBiWkiOh78VpJN9QQNbOMlJfYuSeX6FItVGXhJYj7vOA129RG4Rt0BarnEnZvAKWqyDykSRa+FGhSWIJa3QqVphwxFyLVwbiQsrW3FuYeFztJ4dBBioBPc/Wluj+1CnmkIsgcRHfipOsHzg+kPrbFAguL4qXJftaKH1mbRhefQuhT4MeY08h0s0+Z2Rusl3seLx52RiSX9f0kWa6IMTS0r7fQgqLM6ixP37AZtipSuZfcncXzLxKIwIpEKAdwYLLO8yLpzFlSF8Y/gm4IHBdybsnZTK9QhLPBUqVIj0zU31fIXPTwKUO1hFwx4t6ZBI5ltLHqVso35G3aqky7dk7iPZuiNd2/AmgpN3MY6OPxNlF3UOxqGgXsF3hL7L9IHlx/ckVUn2W5TM80v12gpfPghIiV2H58gLjfshfSVwgcFty7sirkO0OlUEREAEREAEREAEREAEREAERKAYAuoTWwyYeLuZ987PdeXD0VeR/hwSERABERABERABERABERABERCBkiMgJTYNtgwOg1smbieMVszgPAz6kmho8TQupVNEQAREQAREQAREQAREQAREQARCBOROHIKhVREQAREQAREQAREQAREQAREQgewmUHSEl+xOr1InAiIgAiIgAiIgAiIgAiIgAiKQxwSkxObxw9eti4AIiIAIiIAIiIAIiIAIiECuEVCf2H+fGFN5BPNxukm1mXWoT58+Wf0sTzvtNGOia6b2YXL5bBgOPquBZThxuZZfwrdP/k5nOPxwHH49k3H5OLUsSkDve1Em2lOUQC5+l5S3iz7H8rQnF/Ok55/J8i2Tcfn0aVmUgL4nRZmU5z2yxAZPd8GCBXbsscfaZZddZo888oib+8o/9GDibfviiy/s119/9bvcPFnR+yIHU1wJJol2g0ItXLgwpTOZPy2Y5NoYKZkfc/pJSodAvPziU0CBNX/+fJs4caK9++67bl5Gf6ykl88//7zLU++8806RS02YMMFatGhhgwcPLnIs1R1MJ0Vcb7/9dqqnZjT8ySef7O6XwdX4TZ48Oe34eba827F+33zzTbHxfvfdd+7aQ4YMKTbMuhzQ+74u9PLj3HjfpWwtx3gyytvlN3/Gy5P+rlVWehIlv1RZWfKMdYXSJZD3SiwfUFpuqLTygn/++efWr1+/yFO47bbb7PDDD7crrrgism/evHlu36hRoyL70l2hcvHtt986xTiVOF588UX74IMPbN9997Vx48bZ//73v1ROV9g0CSTKL0Q7Z84c1yiy11572amnnmonnniitWrVyq677jo3QXial076NBpEyFNLliwpcg75mwaPKVOmFDnGDt6FXXfdNal0opwTF+9OWUrjxo2dVwJp4L6XLl2adnLuv/9+927zzkf/unXrVmy8zBnNtX/++ediw6zLAb3v60Kv/J+b6LuUreUYT0Z5u3zmz0R5krtWWVm6z15lZeny1tVKnkDeuxNPmjTJZsyYYQcffLDdfPPNxbpZYlGbPXu28RHIFqlXr549+OCDzgL0zDPPWI8ePWyLLbbIluSVy3Qkyi+LFy+2Qw891Cl3HTt2tE6dOhnuVIMGDXIWWRTL2rVrlxmbM88805o2bWqtW7eOmQasIvySkYEDB9rUqVOtffv2yQQvsTB9+/Z1caOA8g5nQi6//PIiz6l69eqZiDrtOPS+p42u3J+Y6LvkAWRjOUbalLf9Eyo/y0R5UmVl6T9rlZWlz1xXLFkCea/EPvnkk47wGWecUawC6x/BU089ZVdeeaXfLLL88ssv7f3337dly5ZZ8+bN7YADDrAKFSoUCofCjJsnfVg5XpzgEvnpp5/amjVrbKeddrJ99tknZlDmqj3rrLPs+uuvtxdeeMEuvPDCmOG0MzMEEuWXYcOGOQUWi+aNN94YuWjbtm3dswwrsInyC66/WBX3228/55b8yy+/OOtgw4YNI/Gywv7XX3/dfvrpJ9t7770LHfMbr7zyikuX3547d65tttlmftP8ceJCyOsVK679PGy66aZG+pE///zTXn75Zbfu/3C1j9W4E+/+Vq9ebc8++6w1atTIpQOXZOZexvq50UYb+ajdkoYa3oMaNWo4z4P69esXOl4SG8ccc4ybA7q4uFeuXOk8IHAxxqUaRrGE+/rkk09s8803t3bt2tmHH35oWCg6dOhQKLje90I4tJEigUTfpXB0pVmOcV3l7TD9/FlPlCdVVv6XF1RWmuuWpLLyvzyhteQI5L0SO2vWLEeqOMuUx3jkkUfaQw89ZJdcconfVWg5YsQIp0iGd+6///42fPjwiDIwduxYO+eccyJB7rzzTtt+++0j234FKxCW1bB06dLF7rjjjvCuyLpXcHHNkZQsgUT5hcHBkOh8UqtWrUIJSya/0L/yo48+coodSiZyyy23uN9JJ53ktmfOnOlcl731FOso+S5aGKiM/kleTjnlFNt99939puFuGM4/11xzTeRYmzZtIkrsokWLijTk0L82WolNdH+rVq1y8eAOP3369Ij19+qrr3YDrKEYIiiL9BsPC+dwn3Xq1AnvLrV10tS1a1f7+OOPI9eMxRyrMNZhL6SbvrNVq1YtpMTqffeEtEyXQKLvko+3NMsxrqm87cnn3zJRnlRZuTZPqKw050GlsjL/vhGZuOP1MxFJLsfxww8/OCtJotF96ddI/z/6n0YL/eCwhCL0TUXZ3Xrrre2tt96y5557zu3/559/IpY5+ka++eabrr8kSkpY3njjDafAYn199NFH7YknnrBddtnFWcaw4sQSbwWif62kZAnEyy9YF1Eqt9xyS4tWWsOpSia/hMNj6aT/NQN5Ib6Fm3XclFFgaeQgT/Xv39/lO46FhX5n7733nhu4LLzfr48ePdr1sfaNKlhP6HPNb+jQoT6Yc/sjHn4MhBZLUrk/XM4YXfull16yiy66yEXnKzdsYA3mPRo/frzRcn/EEUcY59x+++2xLp2xfTQi8f75Hy3lXmCFAksjAFwZDC763fv++++dAouiTbqxXuM1EW5IID69756qlutCIN53KRxvaZZjytth8vm3Hi9Pqqxcmx9UVpqprMy/b0Mm7zivlVhcFFFMN95444RM99hjD6ec0GoWLbgQI/SBxC0Zt8FLL73U7cMlFOFFpQKLQor7L4oOrdTVqlVzx/2fD9+rVy/nRooVzFfup02b5oMVWm644YZuO9URjgtFoo2EBBLlF/ISUrNmzbhxJZNfwhHQ15lBonBBRXHCzZz+RMhrr73mlrguk6eOO+4410fa7Qz90dCBGy59z2IJyhbHcOlFWPe/8PuBezzx8CtOUU/1/mjU2XHHHe38889316by64XrNWnSxA3cBAOssrwzKNElKXfddZcbiIu08WMQKy8o1QjvOI1NuFp7y7gPQyMAwn6U9B122KGIdZ7jet+hIFkXAom+S+G4S7McU94Ok8+v9UR5UmXl2vygsnJtdwNoqKzMr29Epu42r92Jsb5Seff9AONBZV5N+jlicf36668LBWW0YiTsksz8rYi30NBfEQm7cGJlYtTaicGgUV58+BtuuMHvivRlLC6dXnlt0KBB5BytZJ5AovxCn00k7JYbKxXJ5JfweShKXnyfWlxaqQjww3oabgyhokof2bKSVO6Pe/Np940x3JsXWqrpE0vfUvqTl5bgTRFW+MP9h+lPjKCYetltt938qlt6iyvKuZeWLVv61chS73sEhVbSJJDouxSOtrTLMa6tsiz8BPJjPVGeVFm5Nh+orFw7xSU0VFbmx7ch03eZ10osMBmpFZdeRpClr1o8oT8RSiwuhmHxlfDffvstsvuPP/5w675ijish4lsg3Ubw5y1ffttb8fyAN34/SxTeWOIVZCmxsehkdl+i/IJCibL11Vdf2XbbbRfz4snkl/CJ0YOD+WN+4CX6qYbF7w/vS3Ud9/d040nl/ipVqhQ3aT179nTWSvqg0o+XFv4BAwYYU9oUJ4RZV9l2222LHdgp/C779zX6PnwYBsLyQsUuWvz5et+jyWg7FQKJvkvhuEqzHOO6ytth+vmznihPqqy0SANuvLqjzzHRZYzf75cqKz2J/5aqG//HoryuFa1Vldc7Lea+GEUY8W6ZxQRzu6lwUpmmT15YcHdEcDNkwBoEyxGyzTbbuKVXMBmZ2FeyCYuyExav+KAI4DoZ/jF1Syzxg/74a8UKo32ZIZAovxx77LHuQlgfvMtv9JWTyS/R58TaRlHCkwCrn7f8EY4Rc9OVTTbZxJ3q3ZzSiSdT98fIzLgkYq1l7mbcjY4++ujIIFDRafMKobeURh/P1La/v3B/dgZsCouf6oo+xV681dVvs9T7Hqah9XQJJPouheMtrXJMeTtMPf/WE+VJlZVmviyJV3dMJueorFTdOJl8Uh7D5L0lFusO/VwZvObAAw+MtIwV97AZGCM8sA7h6LdKf0T6KjJtDiO1ekX35JNPdlHRGk04+soxJy39Z6kER7ueEh73SQaE4jhzcKKo4HbSu3dvNzhMOG3z58+3hx9+2KX7qKOOCh/SegkQSJRfeH6PPfaYTZkyxeWFww47zFAMGRgI6yF5LZn8kmzScXFnMCfySffu3Q332+iRrVGw/LQ4vmWSvHX33Xe7yxCHVwD33HNP54pM/1T6cpJv8R6gXyiC6zvD4CNeiWNQJvp80/BCf+9M3R9T7eB+z3UYjZhtpuXhOqSJhgKeR7NmzVx6fCMOllreFyyfeFfEmxbLnRjjj3cteqof+gDDgX7HTFNCX3WUfSzWMAjLQQcd5NI5cuRI4x2lQhfu6+vD6n33JLRcFwKJvkvRcZdGOcY1VZZFk8+f7UR5UmWlykreBpWV+fNNKJE7DeYszHsJ5lYtCCrrBZ06dSoILKUFQatWhElQmXfHIjuClaBy7/bdc889kd2zZ88uCFoW3X7iCgZwKghGMo4cZyWoWBd07tw5EqZbt24FwQBObjtQPiJhg6HpC84888xIOOLjF4z2FwkTuJ+4+LkOx8JpiQTSSokQiJdfuGAwT3BBYEF3ecA/O55ToGQWBG66Lk3J5BefnwJX98h9+HwR9I92+zgWTEsTySvBdEsFwdRNbjuw0LswwdROkeM+PeElafESKNoF1157bZHw7Ee4r/C54XXu0Uui+wv6vbp4eB/CQnzBQEmRXYGSWOidCRTHgkChjaQh+h0LRnAuCPoER44ff/zxkbiSWYl3f8TrJRiROHKNwC2uIBiB2G0H3Q18kALSHk5LMM2RCxM0bETCsKL3vRAObaRJIN53qSzKMW5DeTvNh1lOTouXJ7lFlZUFBSorC1RWlpP3vSxuYz0uWiLacQ5F6qe/wWqCMOdjrFGIk7klLERY3LCeFie4fjDARvXq1V1YLEbRfWM5l2Ho6SvBkvh8PzuOYdnx/WsZyfjss89mt6QUCKSSX3jWhPcDMkUnL5n8En1OrG0GQwoqBC6f4KbONRP18Y4Vj99HHEzdQ97DApluXJm6PzjyjvAOcK+ki+1Y/XZx18fijMXWW5j9fWVySRpgVLduXfc+0/8VTtF9X5kiiXTgnXH66ae7eX/vvffeIkkhPr3vRbBoR5IEUvkuJYoymfc22XKMaylvJyJePo+nkidVViauOyaTS1RWqm6cTD4pL2GkxIaeJG6XX3zxhVMGcHXJZmF+UNwnGSHV97fN5vSWx7TlUn4pj/xz4Z5oI6RSgSvxkCFD7Ntvv3X9e+lbn4rofU+FVn6HzbXvkvJ2+c+vuZYny/8Tyb47VFmZfc8kF1IkJTYXnpLSKAIikHMEwt4SPvH0h6f/fSwLsg+jpQiIgAiIgAjkCwGVlfnypDN/n3k/sFPmkSpGERABETA3MBaty7gYMwoloywzcJZEBERABERABERgLQEGkVRZqdyQDgFZYtOhpnNEQAREQAREQAREQAREQAREQATKhEDezxNbJtR1UREQAREQAREQAREQAREQAREQgbQISIlNC5tOEgEREAEREAEREAEREAEREAERKAsCUmLLgrquKQIiIAIiIAIiIAIiIAIiIAIikBYBKbFpYdNJIiACIiACIiACIiACIiACIiACZUFASmxZUNc1RUAEREAEREAEREAEREAEREAE0iIgJTYtbDpJBERABERABERABERABERABESgLAhIiS0L6rqmCIiACIiACIiACIiACIiACIhAWgSkxKaFTSeJgAiIgAiIgAiIgAiIgAiIgAiUBQEpsWVBXdcUAREQAREQAREQAREQAREQARFIi4CU2LSw6SQREAEREAEREAEREAEREAEREIGyICAltiyo65oiIAIiIAIiIAIiIAIiIAIiIAJpEZASmxY2nSQCIiACIiACIiACIiACIiACIlAWBKTElgV1XVMEREAEREAEREAEREAEREAERCAtAlJi08Kmk0RABERABERABERABERABERABMqCgJTYsqCua4qACIiACIiACIiACIiACIiACKRFQEpsWth0kgiIgAiIgAiIgAiIgAiIgAiIQFkQkBJbFtR1TREQAREQAREQAREQAREQAREQgbQISIlNC5tOEgEREAEREAEREAEREAEREAERKAsCUmLLgrquKQIiIAIiIAIiIAIiIAIiIAIikBYBKbFpYdNJIiACIiACIiACIiACIiACIiACZUFASmxZUNc1RUAEREAEREAEREAEREAEREAE0iIgJTYtbDpJBERABERABERABERABERABESgLAhIiS0L6rqmCIiACIiACIiACIiACIiACIhAWgSkxKaFTSeJgAiIgAiIgAiIgAiIgAiIgAiUBQEpsWVBXdcUAREQAREQAREQAREQAREQARFIi4CU2LSw6SQREAEREAEREAEREAEREAEREIGyICAltiyo65oiIAIiIAIiIAIiIAIiIAIiIAJpEZASmxY2nSQCIiACIiACIiACIiACIiACsQl888039sUXX9iaNWuKBJg1a5Y7tmLFiiLHtCM5AlJik+OkUCIgAiIgAiIgAiIgAiIgAiKQFIFHH33UDj/8cBs/fnyh8EuWLLHDDjvMunXrVmi/NlIjICU2NV4KLQIiIAIiIAIiIAIiIAIiIAJxCZx//vnu+KBBg6ygoCASFuV2+fLldtVVV1mVKlUi+7WSGoH1Aqj/UU3tXIUWAREQAREQAREQAREQAREQARGIQeDmm2+2+++/3x566CFr166dU15btWplderUsQkTJlilSpUiZ02ePNk+/fRT536800472T777BM55le+/PJLmzp1qi1cuNBWrlxpNWvWdOFatGjhg+TNsmLe3KluVAREQAREQAREQAREQAREQARKicC5557rlNi7777bKbGPP/64U2T79u1bSIG9/PLL7ZlnnimUqi5dutgdd9zh9mFzvPbaa43zo2XAgAGWj0qsLLHROUHbIiACIiACIiACIiACIiACIpABAnfeeafhUoxFtkePHlavXj3XT7ZixbW2xDfeeMNOP/10w/p6xRVXGPv79etnH3/8sVNssdzOnDnTDjjgAKtWrZrdc8891rRpU2eF/euvv2zjjTe26tWrZyCluRWF+sTm1vNSakVABERABERABERABERABHKEwBlnnOGUz7POOivSF9YrsNwCbsVIr169bL/99rM2bdrYRRdd5PZNmzbNLTfccEO3pA/tP//8Y7Vr13YuyQ0bNsxLBRYYcid2WUJ/IiACIiACIiACIiACIiACIpBZAlhKGeQJ1+Dtt9/eDjnkkEIX+PHHH932DTfcENnPwE/IL7/84pabbbaZnXfeeTZ06FA755xz3L6WLVsainGnTp2sQoUKbl8+/UmJzaenrXsVAREQAREQAREQAREQAREoVQLt27d3SmyHDh1s/fULO8IyOBPSqFEjQ1kNC67EXnr27GmnnHKKvf3228YgUC+++KJdcsklzhJ74IEH+mB5s5QSmzePWjcqAiIgAiIgAiIgAiIgAiKQTQS22247p5DS3/Wmm26Km7QGDRrYCSec4H4HHXSQczseO3asSYmNi00HRUAEREAEREAEREAEREAEREAEMkXg5JNPtgcffNCee+45++ijjwyrLVPwzJs3z3r37m2VK1e27777zgYOHGgosfSL5RgDQiFbb711ppKSU/HIEptTj0uJFQEREAEREAEREAEREAERKC8EcCd+/vnnjTllX3vtNTenrL83puhp0qSJzZkzx1lr/X6WWG67d+9uDByVj6IpdvLxqeueRUAEREAEREAEREAEREAEsorA6tWr7bfffjOWWGOxwnr5888/bdGiRcacsSiwNWrUKNK/1ofNh6WU2Hx4yrpHERABERABERABERABERABESgnBAoPj1VObkq3IQIiIAIiIAIiIAIiIAIiIAIiUD4JSIktn89VdyUCIiACIiACIiACIiACIiAC5ZKAlNhy+Vh1UyIgAiIgAiIgAiIgAiKQOwRuueUW22OPPXInwUppmRLQ6MRlil8XFwEREAEREAEREAEREIH8JLBgwQIbPny4jRgxwlasWGEVKlTITxC665QJSIlNGZlOEAEREAEREAEREAEREAERSJfAhx9+aE8++aQ9/fTTVrFiRVu1apWL6vbbb083Sp2XZwQ0OnGePXDdrgiIgAiIgAiIgAiIgAiUBYE33njDKa5jxoyx+vXrW+fOnW3w4MG27bbb2rJly2zy5MllkSxdMwcJyBKbgw9NSRYBERABERABERABERCBXCEwZcoUe+SRR+zVV1+1Vq1a2c0332xHHXWUtWzZ0nbYYQf7/PPPTVbYXHma2ZFOWWKz4zkoFSIgAiIgAiIgAiIgAiJQrgiMHz/eBg4c6JTUI4880imu7dq1c/fYuHFj23PPPW2TTTax9957z6ZOnVqu7l03U7IENDpxyfJV7CIgAiIgAiIgAiIgAiKQVwRGjhxprVu3tjPPPNOOPfZYe+utt2zQoEHmFdiOHTvaXnvtZRdccIG99NJLdvHFF+cVn3g3u2TJErv77rvjBdGxgIDciZUNREAEREAEREAEREAEREAE1pnAxIkTrVevXrZmzRo7//zzrXv37kXixAKLAjtq1Cg7++yzbaONNnKKbpGAebpj+vTpNmDAAHf3PXr0yFMKiW9bSmxiRgohAiIgAiIgAiIgAiIgAiKQgMDHH3/sFNJLL700ZsiwAvvmm2/auHHj7LLLLrNq1arFDJ+POwsKCvLxtlO+ZymxKSPTCSIgAiIgAiIgAiIgAiIgAtEEilNeCRdWYNlGga1UqZIdc8wxbEqiCKy33npRe7QZJqA+sWEaWhcBERABERABERABERABEcgoARTYXXbZxbkQE/Eff/xhL774op100knWsGHDjF4r1yOTJTa5JyglNjlOCiUCIiACIiACIiACIiACIpAigW222ca22morGz16dORMrLDLly9XX9gIkaIrssQWZRLeIyU2TEPrIiACIiACIiACIiACIiACGSGA9bVmzZo2YcKEQvGh0J544olujthCB7RhssQmlwmkxCbHSaFEQAREQAREQAREQAREQASSJNCmTRtbsWKFffjhh4XOmDFjhk2ZMsUpsYUOaKMQAVliC+EosqGBnYog0Q4REAEREAEREAEREAEREIF0CRx44IE2b948mz17dpEonn32WevataussEXIrN0hS2wxYKJ2yxIbBUSbIiACIiACIiACIiACIiAC6RE48sgj7dtvv7Wvv/46ZgSPP/64HX300TGPaed/BGSJ/Y9FrDUpsbGoaJ8IiIAIiIAIiIAIiIAIiEBKBI4//nj75JNPbNq0aValSpUi5/7yyy92yCGH2F577VXkmHasJeAtsX4pLrEJSImNzUV7RUAEREAEREAEREAEREAEkiSAAvvee+/ZpEmTrE6dOjHP2nTTTW3AgAExj2lnYQKyxBbmEb0lJTaaiLZFQAREQAREQAREQAREQASSJuAV2DFjxlijRo2SPk8BixKQBbYok1h7pMTGoqJ9IiACIiACIiACIiACIiACCQl4BZYBm5o3b54wvAIkR0CW2PicpMTG56OjIiACIiACIiACIiACIiACMQh4Bfbhhx+23XffPUYI7UqVgCyxyRHTFDvJcVIoERABERABERABERABERCBfwkwwjADOA0aNMgOOOCAUuWyevVqW3/99S1srVyzZo3bDu8r1URl+GLl5T4yjCUSnSyxERRaEQEREAEREAEREAEREIHyS2D69Ol2991325IlS9bpJtu1a+cU2D59+hhT6pS2nHvuuXbJJZdELrt06VJr2bKlvfTSS5F9uboiS2xyT05KbHKcFEoEREAEREAEREAEREAEcpIASuvZZ55pHTp0cKMD792mjaHQpiO77rqrff/993bllVdat27d0olinc/p0aOHvfDCCzZz5kwX18iRI61evXp22GGHReJesWKFrVq1KrLtV1ASUXqZ7uevv/7yu7NuKUts/EciJTY+Hx0VAREQAREQAREQAREQgZwlMG7cONtn771tdaC4XXPaada6RXNbEqx37dIlZUV2yy23tN9//91OP/10u/DCC8uMSYsWLaxjx442dOhQW758ud1zzz129dVXW8WKFd32LbfcYijbrVu3tmHDhkXS+c4777g5arHatmrVyvYOuGSrSImN/2TUJzY+Hx0VAREQAREQAREQAREQgZwjgPX18ksvdXO3Xhsor50PaOvuoXvHw+25Nyda3+HD7azAOjtm7FirUaNGwvtr3LixC4P78A033JAwfEkHuDS4t/bt21vlypWtadOmdvDBB7tLTpgwwV588UV7/PHHbdGiRXbqqada586drW7dutavXz8XdtSoUVarVq2YltqSTnei+OVOnIjQ2uOyxCbHSaFEQAREQAREQAREQAREICcIoMB2OfZYW7hggb1wZ/+IAusTj0L75n1DbdsG9a3DoYcmtMh6BRbLJQM5ZYM0a9bMjjvuOHv00UedazMDPSHjx4830vvZZ5/ZnDlzrFq1ajZp0iR3rE3gRj158mTDUouiu8EGG7j92fgnS2z8pyIlNj4fHRUBERABERCBvCewusBs7uLV9v7MpXbnyzPt1IFTrc9zM23Qq9/b9Y9Pt+9/Wp73jARABLKFAAps10C5W7Nypd179VXWYJNNYiatRqDc3Ru44LbaZmvnWozbcSzxCuwhhxzirJuxwpTVviOOOMJdum3btpEkLF682ObPn29z5851v5NPPtm22GILdxyX46eeesq22WYb52aMEpxtIktsck9E7sTJcVIoERABERABEcgrAstWrLYRE2fbrJ+X29dzl9pPv64dAKVy5Yq2cc2q9voHc23Fin8ck9c+WGC7Na9r3Q9oZK23rpVXnHSzIpBNBLwCuyjot4oFFkU1kdwW9G3ted99dvbZZ1v//v2dddOfc/7557vVrl27Oldcvz9blhUqVHCW1nB69tlnH5s4caLtvPPOrt8rAzw1aNDABWEgqCZNmjilFmVx8ODBbpCnjTbaKBxFVqzLEhv/MUiJjc9HR0VABERABEQg7wiM/fgne2DcLPvxXwtrlSqVrOX2m9t2TerY1o3rRHgsW/63/bFkhX3x3c/26dc/2UXTf7P2rTZ3x6/pvI1tVFXVjAgsrYhACRNAgT0+GKwJF9qRfW5KSoH1SbotmLKm4eab2xVXXOF2YaF877337I8//jDmg6UvaTZKLEWve/fuLqm4Pc+YMcOtcy/169d3CjoDU3np2bOnZZsCK0usfzrxl+sFoAInIYkIiIAIiIAIiIAImD3z3jy748mvHIomjWrbtk3q2nZb1rUNA0U2nvy28C/75OsF9kWgzCKb1q5qN52wnW3XMPssHPHuQ8dEIBcJOAtsYC39cfZse/Sm3rZ9YG1MR0ZPm2Z97xlsTz75pDVv3tymTJlie+65ZzpRZcU5DOzEwE9Vq1Z16WHKHdyNGcW4evXqhiU32+SNN95woz9fd911dtZZZ2Vb8rImPWoizZpHoYSIgAiIgAiIQNkSGDFxjg0Z/a01DaytuwaW120Cy2uyUrdWVTtwzy1tj5Zr3fbGTf7ezh08za4/cXtrv+OmyUajcOtIACscU44gVN6ZEiXfZGXQF9TPH8q9Y4GrWbNmucUQUWCDZ78uCiyAjgqmpVnvsssM9+H7778/pxVY7mfjjTdmERGU1zp1kv+uRU4sxRXZF5ODrYGdkuOkUCIgAiIgAiJQrgncN26mU2B3DpTQ4zu0TEmBDYPZqHpl43fswc2tYf1adufo78KHtV7CBLDeHBqMNsuPPo6x5NNPP7VXX3011qFysW92YI30DFi+9tpr5eK+Yt2EV2ALVq+2R3vfmLYFNhz3kbvuYpdccIGzAk6fPj18SOsikDUEpMRmzaNQQkRABESgfBFQa3LuPM+v5y2zh8b8YNtstal12KdZxhKOIlt74+p26cO5WRHGre/coK8grpXffvttxriUdES9evUyFLnXX3895qWYbuTBBx+MeSwbd/bp0yelUXEZeZb757d50M+zvEpEgQ16Bj564w0ZUWAdq3/+sVP23ccObNfOWWQZ5VdSegRUdibHWkpscpwUSgREQASykgD9e77//ns3L97HH3/sRlnMhoTefffd1qJFC3v77bezITlZl4YbbrjBmG9xwIABbvCUsk7gsPGzrVHDWnZQ4A6cadl7l0Y2+ZMFdverP2Q66hKPj/5ya9assZtvvtkOPPBAO/74492clAx2k4uCmzHfjFjy119/uXuNdezvv/82jnuhkr06sPx5Yd1XvP36smXL3GG/9GFZendnvy8c39KlS/1utyS+r7/+2k2Vwjq/sHAu5/zyyy+F0hgOUx7XIwpskD9H9u1rG2V4vtOC4HnfdvZZdtB++9lZZ55pXE9SugRiDVpVuinI7qtJic3u56PUiYAIiECxBJiwvXXr1tYuaC0/M6hkHHXUUdayZUs3B16xJ63DgaFDh9quQX8plOVE8u6777qK6hdffJEoaF4e7927t10W9Dv74IMPnGJ00EEHGYp/WVj7Pp65yN755CdrvtUmVmOjyhl/HptvUt123aGhvfbRAvtn9ZqMx1+SETL35LBhwwyL7O233+4UQNx1UWivv/56mzp1aklePqNxX3PNNW6gnq222qpQulEysXRut9121r59e3v66acj112wYIH973//c1OVcPyMM85wiuLIkSMdAx8QTqNGjXKbO+ywg+0XKD40YjE/J8tXXnnFHfvhhx/s4osvduk4/PDD3TQoHHjppZdcfPTD5BvGEsUUt2f69GI5HjJkiFtn+6kqJ88AAEAASURBVPPPP3fxvfPOO7bXXnu5c1q1auUahtyBcv6HZZRRiKuvZzbi2musekHJvFcosrec1t223ayem3dWimzpZCzfIFQ6V8vdq0iJzd1np5SLgAjkMYEPP/zQTjjhBGOqAObxe+ihhwzrHsL8eCVRCFKp5HpYZRLJwIED3fx7fqqDROHz8fgxxxxjTzzxhD3yyCO2xx57OJdVlCOep6/0lwaXFz76xerUrmYtty65wZf22rGhLV32t42YtKA0binj19h0001dY8MzzzxjNOYw/+SIESOsc+fOrt/puHHjMn7NTEY4f/58545777332tixYyPTjnANFHTyIUoo06owxYq3npI3aSxjOXr0aPsncDP99ddfE35f4LLLLrs45XL33Xe3acGItwguzHxHuCZ9VbFwI3yvGIjpkEMOcelDSeU7hgKMIktjHQ11rPPbfvvt3XlM+9K0aVN766237LPPPnMeKe5AOf7zFtiFwbd4yNVXpzSNTrpYmEd22wb1rWugOEuRTZdi6ufJEhufmUYnjs9HR0VABEQg6whQ4esbuI8h//d//+cqgz6RBxxwgJvE3Rd+hKWCh0WUkUqpUFK59PLzzz+74+xfuHChm05h2223NSwrjOKIfPTRR66C6a2q48ePd33NfBxUfLnen3/+aS+//LLf7ZZUeBs3bhzZl8z1fDxMTo/LLYIrI9YaRhllIvuwUMmmYovb50477VTkeDhsNq7Dmh/9GKnc04/xqquucpbZww47zDp06OCsZCWV9kmf/my7BIM5VapYclNNYOHdvF4Ne23qz3ZG27WjF5fU/ZR0vDwTfihaY8aMcQMkMYDSbrvtZkceeaRhVSefZpMwRyYjsmL9RDp27BjxqCDPHXzwwc6iiacFFmfeKRpUnnrqKevRo4dh5URQ3JMR+qSiVPItmTVrlnMB5v1kMCmuRfy4auN58OOPP0ai7Natm/vuYM3lWKdOndzosn6KlOiRZtu0aeMaFW655Rbbd999nTdKJLIyXsFjBZbJCCwqVaqU1I8GB76rI27qXSoKrE//bUHj2lFXXmW9g8bSO4NuEJKSI1ASjdAll9qyi1lKbNmx15VFQAREIC0CWEOpIOF2hzUjLFglvFAQXhi0oEcrlpdffrlz6SMc/WmvvPJKN5n9888/7091FV7mB9xggw2M/bgPemHahbBgdUHhZT4+4grL4MGDCymxyVzPx0OF2yuxfh8KXViJ5V6wjoWlS2AtuOOOO8K7cmKdeQxRLvjRvw/rHg0GuBlz37iN4+6Zyekhvv9puS1b/rdt2aBWiTOqv2lNe2/qLHv3qz9s7+1qp3U9LNQMsoQyw69GjRpursdq1arZhhtuaH6JQlAaguKEFZ2GIlzDcb296aabDJddflhts0HoB8u77CVaGUTBRHzl2TeCcU50/1Ufh1/SR5VvUiIhbsLhUsxzQ84555xIunh2vuFs/fXXLxQd27E8QK4OLJE03OFujNv3Y4895iy5hU4ug42S9qboFnwb9wis1KUtQ6660tqde55dGnx3GzZsWNqXz7vr+fcw7248yRuWEpskKAUTAREQgWwh4C0XWH7iCQoQCmyjRo2clY/BaHDfu/POO51Fhkq2FxRVKuBUyBlsCGsgSix923r27OmUXvqk4VZIxdxfm0LWVzzr1asXGaSIfnV33XWXj77IMt71igQuZgcWJBRYrK+4QJIO3AuxHqHIeutRMafH3I2ShMKOEoT1x1tHwuvRx9imsu/Dsh69zcX8oDQoDPHW/XHYYg3DdZJpLuCJxYmGiiZNmrhj3rIW82aS2PnKtJ9dqJrVqyQRet2CNKy3kYtg1u8rba19Pb348CTg2ZOf+YUHHEovxsyehcLIQET8tt56a9ewU9xUN5m9cvGx0W8U6z4NI1g5Ubq9kMd4xxmEDY8GBAsniiMu77zz9EPlnceVlPVmzZo57wy6NeAGjKL722+/FTtoFHFibaQhhu8XS6y0PDu+G4kEd2IaL4444gjbZJNNIg0XXJt3YYsttnAKOI1muCtvtNHavJYo3pI6jts24wLgfk1+4OfXUcaj9/lj7I8+zjF//KuvvrIF8+ZZr9NPK6mkJxXvwjmzpcQmRUqBSpKAlNiSpKu4RUAERKAECOD2i9SsWTNu7L6iyhQh3mJLJYhKKRWssBKLOzGDtiCnn366U2IZNAUllgohv+rVq7vjWAJjVTyppHo3ylq14lv24l3PXSSJvwkTJrhQuOGifCMXXXSRSz998NJRYkk/lfHvvvvOVbq5J35U6P0SZdmvh49H7/fnEAYrFJVTfr5SynLlypWRbV95dTcS54/nz/1VqVIl4h4aJ3jcQ/+sLggU7gpWbcOSt1xusdna/PrbkpVx0xTvIEo7v0svvTResLjHYA5rlrF+/jklWvrnSDi/jrv8Tz/95Czpixcvds8d18+yFhqy8JKAGwonDEkrgnWfAZj4TvBeY/nHKorg3rtixYrIeeybMWOG66OK8nvsscc6hRSvkP79+0e8JMIWJN4DL7fddpvrv4+lEqss6cKKGg7vw4b34aaN67ZvtKGhDQ8QujKErcAo42WtwPr0ey8Sv52J5V0BPxrulgTPsMa/zygT8SYbR8+gkaB1i+a2Xf3c7hKQ7P2WdbjwO1DWacnG60uJzcanojSJgAiIQBwCXoFksJZ44i22DELjhT5vKLHzgtb8sGDp8OKVYyqvJSWZuJ6/Pz+gFWn1ro+446YjWPmwYJeloBChGDHHJQ0JuI5jIeOZ4cKHcs5zRMFYV1l/PbPq1TI/InGsdHld5vfF6SuxseJNdR9WdX6ZUnZoEMKKyQ+LOXmIRiPc3vfcc89Uk5eR8PSZ54fV9M0333Rx0rUA993oijEK67XXXuu8GfAmCB/nW8MozIxsjFKOGzBhEAZpwpKKGzyNATTW0JADA4R+w8jRRx/tlvwRH4omVmEaY/y3BgsrPy/0+wwLlla8JDgHpdi7I+PCTbq4Lo1spAHBCk7f2/ImXYLGhuFBv+RrAq+YIQHD0pTx739gH3zxpY249VarkIT1vDTTVt6uFe3WX97uL1P3IyU2UyQVjwiIgAiUEgEGPEKonFJ5pEIeS+gjiHjLLeveauGPsQ/xFdO1W/H/UbLWVVK5HtfCuhUtvgKMNWezzTYrdDgdK2yhCMpwAxfNF1980f28Uo77Jf3/sEhlUurVrBxU/P+zlGUy7ui4Vq5cO7/nkuVlq8RGpyudbRRXXJp5B3Fpbdu2rZsWhj6yYQ+HdOJe13NuDZQMBkdDot+z6O3wtXCBL05QEmP1xUaBRYr7BhUXH4porPiKC+/3R3t4FJcuXO7pEuGF0aXLg9CI9VRgie0SWMCvCayitwYNE6UhWH5RnC/qfqrtx0B+/zZklMa1dQ0RKI6AlNjiyGi/CIiACGQpAQZlwZWPShr9W7FqUJmLFvqtIRMnTnRWIfphehfj8ABQ0ecVt+0rnVTgw4MrFRc+3f1+0BlGf6VFGssQil20MG8lyh6WJAbTyWVBWfWKKyO3IlRYcddkhNaSUsqbbbah/frbMvtrxSqrWqVoHsok05V/r1ViS3AQ5Ewmt0hcn3zyiXvnUF6xNuLCftJJJ7l3kX6Z2SK+kStb0lMW6UAhpz9yeZTmzZuvVWQDZTKYx6zEFVkU2AuCsQa2D0acvqJ3bn9ncyU/yBKb3JMq2RIruTQolAiIgAiIQIoEUFxRYpliBzdGlEqsIbid0t+UaTGYR/aee+4xRhPGJRW3O6atQBlNx9XOD+bEoCX0rd1xxx3diMTnnXees4SiLFPRR5iWB2FaHEYkRtE866yz3L5Ef1iJmS+SQWhOPPFEw/ri55nEffCBBx5wc0bSjw+Xxueee85dz4/ci9tt7969U7YOJUpXSRxHOec+mXqEdHOv9PPjXmioiGc5y2R65ixYZNs2rZvJKIvEtfKfVW7fQTsnHsinyMlltAMrK32vsbji2o2VlXcHV11cuktaeM98hbZ27dox+6KXdBryKX7eQT8PKu+ebwjMNgYospODaZO6BN+Kax962G4971wrKIHuHyiw3W7sjUnfnnpiVLZhKLfp8e98ub3BDN2YlNgMgVQ0IiACIlCaBBjZE4soyhpLP+0NrrWM1otgkXn88ceNaWi8UskALAyKwqBAiO//5pfF7WM/fWv79OljDM5Cxd4PrITCiTsvA7SgYIYFBY0fijNKrL+OXxLWr/sl+y655BI3WA7X4NxBgwY5qxfu0KNGjXJKLO7EjHLMiMuvvfaaGzCGcxEGqckm69jaVP33z+A5KP00LlAhpQ8llj1GkfX9/f4LXXJrrbeubXVrV7U5Py0pcSV24dIVztp7SA4psTTY0DBE/04GDSvtPq68W17OPPNM1z/Vb7Nk7mYUbKz24b6n4TAluY6b/7PPPusGU/OuxSV5vWTjxquBabkQBoPyXiT0Nef7gdB/Fkt6WJjX9YUXXnC7OMc3noXDZMs634mnglHg+XZc++Bwp8iuCUaPzpQ4BbbPzbZF0HBzZzAyeml+lzJ1D7keT7hMzPV7KYn0rxdo+wUlEbHiFAEREAERKD0CKHcopn5U0egrU6Gj0paJwWwoNpjahP64XM/3TY2+Zia2sR6TZvrQMXUG1hFcBcMjnnIdXKUZVZgllc9U++hlIq2pxEFDAunETZgpdMpSBo+bZWM++Mm6H72rVapYcv1jR7z4iW1Zr6r1P7X057csS77rcu3GjRvb6NGj3WBR0fGgVKOsMSASXhHJejpExxNvm3cdl3YGdwoPEOfPIW00ONGIRMNatsjAgQOdBwgKKV4OLf6dU5WBqGj4Y9A03PZZxhIax2iwy2Yl1qcb13amFNsraATre1p3q/FvA6U/ns5yafAd7fa/G6xG0HWFqY0kpUuAQcwYwZs8mIkB/Eo39aV3NVliS4+1riQCIiACJUbAWxqKu4DvZ1rc8VT20zqc6HqpxBcvbFhBjqeAo6D7UZvjxZctx9ZliphM38Npbbewp9+cba+/N9M67Nss09G7+L6Z9bvNm7/YLuzQqETiz7dIfR9q5kT200vBgEYcGnh4R1FAmXPYj9jLMdY5F5f9aCsP4ZctWxZp6GKbc/DioBGMdSQcX8eOHZ33QKx3D4WRxqRwg5NPHxZRrh9vMCl3sdBfdPo45Kc3ogHPp4v9KNY07HmrKvsQrMV4kuAd4vuerz2Su/+uj+y/c2N3mzPHRlzXa52m31kvYNTt8iusIHg+3sMnd+nkdsqj39HcvpvMp77kmlwzn1bFKAIiIAIiIAIikGEC1SpXsLa7bG6ffDnP5v+yLMOxB4rGP6vt3Y9nW8tmdazdDuVjlNiMQ0oxQtzQkeh+uTvssIONHTvWHXv55ZeNbYTpuJhuh770KD2Mds3AaQiKIH3nmROa7gb0xZ41a5brGuBHWj711FPd+cSBoCCyznGmywqPHo4ijAWJgdfo2/104PLqhVGcL7vsMtsmGCQIyy6uyImkuPRxHkoW98N9XnfddZFRmRPFWd6Oe0V27oIF1i0YfAlX4HRkvaBx49oHHjRcNGkgkQtxOhTX/RwabBC/XPcYy2cMUmLL53PVXYmACIiACIhA0gRO3re+6xs7+vXpSZ+TbMCx73xnCxf9aTccnz3upsmmPdvCLQiUlIsvvtj1aydt9Hdn2/d5D6c3VgUYSyijK9etWzfiJsqAVf3793dzwdK/FgVzTmDRw43RD9Q2dOhQ1zf4s88+c5dgkCkU6WeC6V6ihfiZ55W+pwxSdsUVVzgLL+GYnmvGjBlO0WbgOfqGJ5Li0sd5KNfvBQMc3XfffW7k9TFjxiSKrtwe94rsvGCO7HbnnW8zgoaIVOXa/xtmXwbPBwWWQfNwa5WUHQFZYuOzlxIbn4+OioAIiIAIiEC5J7B1/ep2S7cWzmUUpTNT8vqUmfbl1z9ZrxO2t0Z1184pmqm48zEe3IAZidz38WSdH0ppMsKI3oy4i9UVZRJhBHEGP2LwKqyrDGbFcVyBfTeE6tWru3Xv3k/lGkXWHw9fGyWWEZwZpOyMM85wh8KuuwxART9wwqAs43YcT4pLH+cwBzYDpOEezMje3godL77yfAxF9slAAeX5nBL0aU1Fkb12+EM2Lnh23gLLFEX0y0SZlZQugVgNUKWbgty4mpTY3HhOSqUIiIAIiIAIlCiBnZrUtJsDRfbzrxbYvU98YL8tjK9cFJeYpcv+Nn7Pvf6VfT5jvt3UvaXl0ojExd1XNuxHiWQQH5RMhHV+TULz1PoKMApetISVUH+MvqS4AMcTLKipCH1xEZ+WsEXJK77hvrLx4i4ufdwfozcz7Rauywwyh+uxFx+/T4vfzzKcnvD+8rDuFNnAhbtB/fp2wW23J+VafM2wYfZaMFWbV2DhQH9nRoVnDm5ZZMsmZ5TnfJoJolJiM0FRcYiACIiACIhAOSCw17a17cZTmttmtavY/U9+YJOmzUn6rv5c8U/Q9/VHGzzyPfersOZve6bXXlJgkya47gGxcI4fP95ZWRm5GKH/ajxhdOO33nrLRo4cabgrh62jVKLbtGnj+rXST5Z5VHFJXhHMSUq8XlEOr9Onljmsmb/aDwxEHOlKcemjHy6DVF1wwQXOisw6yjgjpyNYZhmAjnSQ7vBIxLhMI0xPxv0mYuQC59AfiuzTwfzZNQJrORbZ4vrIsv+Cfv0Ci+3a0Zo5LyxHHnmkc/nGIpsLiix50zeccB+xGjDC95et6+F7yNY0ZkO6pMRmw1NQGkRABERABEQgSwgctFM9G3HJrnbnebva7B9/tTuHv2MjX/rU3vpots2Y+ZstXLzCVq1aYz//vjzY/tUmf/KjPTn2Sxv48GSb99NCu6FbS/d76OJdbZMalbPkrvIjGcyPjELK3KHMF41ix0BK8QQF8+qrr3Zuo8yDu++++9rUqVMjp5xzzjnO2rn//vs7hRaFkKl1GFSKPq8Irsh77723W2cwJ9yWSctzgSJFv1c/9RdTZEULijJKJINPhX8///yzC1pc+lDYUbJQmknzLrvs4voGh0f+RsEdMmSIS/fgwYMjl8ZyjXJ84oknunNhVt7EzSMb9FleL2B+VDDacLRrMdv0nX3/y+n2VBCuuEGccP+mzzSKLFODZavQRxt3+GGBVRmhgWL33XfP1uQmlS5ZYuNj0jyx8fnoqAiIgAiIgAjkNYFJ03+zKd8ttg+++t3mzF8ak0XrHTez/VvUsWP32Czmce1MjwDzxHo57bTT7MYbb/SbxS5XrVrlrFG407KO4uhda4s96d8DWDGZroa+t2HBwsU0O/Hmog6HZx3XXq6dTEWcAZro2xqWzTff3KZMmRLe5ays0enDKuyV41j3iyv0n3/+GXM+a5R80ufvF6WXUZ0RrLi5ME9sIUAxNpYsWWJdjjrKZnz/vV17+ul26uGH2eCg3+w9Tz7lQjMPLI0XiYRwjG7do0cPCzcUJDqvtI6jxB4V3Cf5BgWWvMNcyuFnGGvap9JKXyrXYWooBmy75ZZb7KSTTkrl1LwKq3li8+px62ZFQAREQAREIDUC+zava/zsiK3sh5+X269L1vY7bLZ5NatdfYPUIlPolAjgluvF92f128UtK1b8r2oXnju1uPDh/QzWFEuIJ9W5oVOZA/aRRx6Jddki+2KlD6XWS6z7RcEtjp23EPvzmaYHJQ2JFZcPl0tLZ5ENLOdnBNbxW4YPdz/Sz7zbDzzwQFIKLOG7du3qXMmvueYaNrNSkSVdzAU8adKkSMMG+3Azx4rM/WKtxarsvQg4nm3iG378MtvSly3p+e9Lly0pUjpEQAREQAREQASykkDTetWMn6R0CDBCrKT0CGDFK4+CIvt00Ef6qcces2cDK1/zFi3cyNENGzZM6XZxv6aR5Morr3TnZaNFFo+Fx4L77N69e+TewtM+YbFl2qcOHToYo25no6hPbHJPRUpscpwUSgREQAREQAREQAREQARylkCXwDWV37oIo2Fj3fYW62xTZOknff3117u+0v4+w9M+0Zf79ttvN6Z9YpqnbBZZYuM/HSmx8fnoqAiIgAiIgAiIgAiIgAiIwL8EGOwJi+yFF15oDRo0cNM8ZQscXMdxfR4xYkShJPmRir2VM5sVxFxIYyG4ZbQhJbaMwOuyIiACIiACIiACIiACIpCLBDp16uQssoxeTd/iww8/PGtuA7dnLLK+HzcjWPfs2dNNt/Tpp5+6dK7LtE9Zc6N5nhBNsZPnGUC3LwIiIAIiIAIiIAIiIAKpEmAqpwcffNANlJQN88j6gbp23nlnYwomL/GmffJhsmkpS2xyT0NT7CTHSaFEQAREQAREQAREQAREQASiCDA9EtMk3XXXXXbMMcdEHf1vc+7cuZbqYFL/nb3ua6lM+7TuV0s/hueff971Oe7Xr59zjU4/pvJ9piyx5fv56u5EQAREQAREQAREQAREoMQItG3b1h5//HG77LLL3DQ2xV2IeU+ffvrp4g6X+H6mfcrmvrAegCyxnkT8pZTY+Hx0VAREQAREQAREQAREQAREIA6Bvffe201t06dPHzcna6yg9Ju9++67bcGCBbEOa9+/BLwSKyDxCUiJjc9HR0VABERABERABERABERABBIQ2GeffWzYsGFOUR0wYECR0Cixm266aVxrbZGTtEMEiiEgJbYYMNotAiIgAutKoG/fvusahc4XAREQAREQgZwhcMghhzglFotrLEWWPrMPPPCAffLJJzlzT6WdUFlikyMuJTY5TgolAiIgAkkRWLx4sRutccstt7TRo0cndY4CiYAIiIAIiEB5IcA8sjTiosgOHjy40G116dLFmjZtag8//HCh/dooSiAX+u8WTXXp7dE8saXHWlcSAREoxwRoVX7ppZfsoYcestWrV7s7veiii8rxHevWREAEREAERCA2gZNPPtn+/PNPp8xWqlTJmE8WYXAlrLH9+/c3lN39998/dgR5vFeW2OQevpTY5DgplAiIgAjEJPDGG2+4URlff/11N+F748aN7Z9//jEssd26dYt5jnaWDwJ//fWXrVmzxj33eHf0448/2ptvvumCML1Eu3btIsE//PBDmzFjhttu1apVobkNI4GyfGXq1Kn27bff2vHHH19iKY3HsMQuGhUx03OMGjXK7a1QoYKddNJJUSG0KQIiECZw9tln2/Lly41RiVFkTz/9dHf4xBNPdH1nscZKiQ0TK7wuS2xhHtFbcieOJqJtERABEUiCwEcffWRnnHGGK5TnzZtnl156qe2+++62/vrr22+//WbnnXdeErEoSC4SmDlzprMkbLfddta8eXM74ogjjDxQnCxbtsymT5/urPReCfJhf/nlF3fstttusw8++MDvLvUlLf9YR9Lpp8aUGTfddJNT6Esq4fEYJromo6Uy/ce6Ch4WPMcxY8bYtddeu67R6XwRyAsClI09evSw3r1724gRI9w916lTx31vJkyYYC+++GJecEjlJmWJTY6WlNjkOCmUCIiACDgCc+bMcQoqFf7KlSvbE088YWPHjnWV//fff986depk++23n+21114iVg4JYI3DioC1Hdfxt956y1q0aGFVqlRxbuQoWxwLy/bbb28oqWELrD/OaJ0cq1+/vt9VZInFF2WXuBNVbrAMo2yxRLCCRMuKFSts1apVkd3EyTk0zCxatMite5f4ePH582688UZ79913XQNOJNLQSnQawukjmI+HdY6lypDzihPi+/rrr23u3LmF7iscPjp9/hiMVq5c6TetatWq7ll17949sk8rIiACawnMnz/fzj//fFcm/vHHH4WweEX2+uuvd9PwcPDUU091YR555JFCYbXxHwFZYv9jEWtNSmwsKtonAiIgAlEE3n77bVdA77vvvs4tihbke++919q0aeMKY5QMWpQpkLt27Rp1tjbLCwHyAXMc3nzzzU4pbdKkid16662GZeHjjz92Cm2zZs0MNzostusiVAqx8mLxxdUYZXn27Nlxo7z66qudKzuWURpasBRfccUV7hzv1rfrrrta69atnTsfBxgpdKuttnJhqFjiCs8PiRffa6+95sJtu+22Lp3uhNDfDz/8YBdffLFLA8r6xIkT3VHYXHLJJZGQDPzCHJNIJhl++umnLn2TJk2yIUOGRO7r888/d9fi+VDphtEJJ5zglHh3IPgbPny4MV3ILrvsYv369TMaLyQiIALFE6Ah7oADDrAnn3zSLXnHGdzQN+p5RRYvhkcffdQN7nTkkUe69w5vDokIpEpASmyqxBReBEQgrwhQCKNInHLKKU5RYfCmQYMGRSr97KdyjlWOghgFpn379nnFKJ9uFiWyWrVq1rJlyyK3veOOO9qUKVNcxQ0LII0c6yIjR4607777zl599VVn6afv6RZbbBE3Slz2cHN/6qmnjArigw8+6PLlkiVLzLvu4Vp71113uQFXcH2n77Z3Ix46dKih/H322WfuOvHiw7KM0tmrV6+YaeLaS5cuNfqNH3rooU7xJ2Dnzp1dgw8WZuSVV14xRixFMskQpZ97QWE/88wz3TrbWMaR+++/337++Wc3IFvt2rVdYwT7aTzgvnGB5BmiAJelqzdpkohALhA47rjj3PfvnnvucZ4ZKLJMuUNDle92w3t13XXXuTKT9xLhGyApSkCW2KJMwnukxIZpaF0EREAEQgQogPlRsWZgHvrWse6FgV2wzH311VeG+xRWWEZblJR/At5dN3ynKGW45NLwQd/odbUu0Mca6ynuuii0xM+AQvFkww03dK7NhKGBpW3btk4JZkTQ8ePHGwOPoaDiFo8yjpUSt/iNN97YRVu9enW3XrNmTbcdLz4GakH545ywwIYfyne9evVs8uTJLt0M/sQATd6tmmvTKMDAVlhqkUwyrFixorsX7g9XYO6RH/tJ37hx44xKN+80Fmie3e+//+5cxLGs41EBP7wt/MBc4fvUugiIQGwCdKlhjli62vC+Dxw40CmzuBPzTtFwxneNxiG8m3i/KEclawkk6jYiTmsJSIlVThABERCBGARQSlEiKFxxHfXulT4o7ofvvPOOYR2jgsyAFZtttpkdddRRPoiW5ZBAo0aNnGLpLZfhWzz33HON0TaZAxHlLVpoVcdCG0tQrML9VAlD5Q/r6cEHH+waS3Bv/fXXX2OdXmTfTjvtZFyPeH2fXeYwxspI/1B+TIERbdn1rn/REcaKLzqM36YCxg+FEJdirkVfW6bYQJnmfUFBRMllVG8so7gkI+vC0F8/ekmDQiruwHCj33BYwhaR8Ho4jNZFQAQKE+DdxupKgxWDHT733HOujKT7zR577OEahv03iGOSwgT0rSnMI3pLSmw0EW2LgAiIQEAACxOWrGjlFThUwLEuMahP3bp1jT52uGiiwGK5kpRfAlgNNt98c1cxw9KKUkhewIKIqyr9UMN9ojnuBUUN91sUOwYbCiuMKJq4IhPeT7nDOgofbsHe7e7LL7/00cVcoixivcXSiBIZVoxRgrHA7rzzznbWWWe5/E3/WITKEhZH7mnWrFnO9Q+FO158DMDENXAZ5l5YZx/WYn4o4exjSQW2ezAgklfu8Vh4/vnnXaUWZghpXheGLpIYf7gTM6LwF1984dyH4YNi26FDB/fe0hBFH1garbDAYkUiDNZvrNc8X/r6edlmm23cKoNZwZN7lIiACBRPgAbeCy64wH3/GEMACywDIfK+UXbyXZBL8X/8vCVWSux/TGKuBaAkIiACIiACSRI49thjCwJrXEFQIY6c0bNnz4KgL2zB999/H9mnlfJL4Jtvvinw+YC8EFgUCtgX9IsuCCwPLn8EFTW35LiXYECogkAhjewPlFl/qCBQiAoCJdMdCwYTcvuDAZkiYYknGISoILAoRs6JteLjIDy/QFmOBOPcYcOGFQR91CLxBv3UIscDr4MCru3PDVx9I2ny+8LxXXTRRZGw/jj7vPz0008FcPBxkjYvgXId2R9Ow7ow9HFHLwNFs+Cwww6LpPXZZ591QYKBnQouvPBCtz9osCqYNm1a5NSga4C7d55n0H+4CHd/Hvft44ucrBUREIGEBAKltWDPPfeMvJe8S3yDJAUFwawHjkvQ0CcccQisx7GY2q12ioAIiIAIFCKAxYh+cwyagysUghW2Y8eObmCaO+64o1B4bZRvAn4KFvqWemEfFkXcZrHOsozux4pbLxZWjoWF8zhWo0YNdw7bDMjEkn24BmdKsLD6vqLhOL31lSmDwvcVDpPqOulfuHCh0c82mXtYF4bx0kYasMDCMmzhwHWY+40WWFBFKi7NWGuJR94X0eS0LQLJE6BLAVZa3kMss0FjUvInl9OQTN0XNI67QSTxxJHEJpC5EjF2/NorAiIgAuWCAO6PFK4M3uQVWG4MVyiE+WEl+UUA5SZawQlvo6jGEj9oUvQxFKxatWpFdrPtB1yK7MzQSnHxonBTkcykcB+pxLkuDOOlO8w2HC6WAsvx6MaH8DmsZ0rJj45X2yKQTwQOPPBA172CWQAY+E0iAskSkBKbLCmFEwERyFsC9HWlL+N9991nbYORFcPC6MQotfSjk4iACIiACIiACKROgHnWGRdAYs4DBA5hjxFxKUpAAzsVZaI9IiACIhAhQOswCizzajIQTFiYw5ORV3EnloiACIiACIiACKRPIOgjm/7J5ehM39PTL8vRrWX0VmSJzShORSYCIlCeCOAizJyaffv2daPORt8byi0uhcx5JxEBERABERABERCBTBGQJTY+SVli4/PRUREQgTwlgAsxCixz3DGfZixhQBemKpGIgAiIgAiIgAiIQCYIyAKbHEVZYpPjpFAiIAJ5RKBz587Ohfjyyy+Pq6Qefvjhxk8iAiIgAiIgAiIgApkkIEtsfJqyxMbno6MiIAJ5RqBLly42depUO/vss+3iiy/Os7vX7YqACIiACIiACJQlAVlik6MvJTY5TgolAiKQBwROOeUUe//99+3444+3Xr165cEd6xZFQAREQAREQASykYAssfGfipTY+Hx0VAREIE8IXHTRRcZ0ObgH33777Xly17pNERABERABERCBbCIgS2xyT0N9YpPjlHaou1761ub+9pfN/fUvW/Drn7ZRtUq2Rb1q1njTDW23rTa2Q3aul3bcOlEE8p3AkiVLbPr06dawYUP3S5fH9ddfb8xRx1yv9957b7rRJDxv9erVtv766xea+23NmjVuWy2uCfEpgAiIgAiIgAjkDQHVC+I/aimx8fmkdfTLH5fYlG/+sGcmzbM/Fq2IxLFh1UpWsVIlW7R8jX0yaa69EPxuq/qV7bfjptZ+p01sv+Z1I2G1IgIiEJ/A8OHDbcCAAYYiW6NGDTvjjDOsR48e8U+KcbR///42YsQIp8A++uijMUJkbte5555rVatWtUGDBrlIly5danvssYfddtttxny0EhEQAREQAREQAREQgcQEpMQmZpR0iF+XrLR+o7+3t6ctsE3qVreW29a3xpvXDKyvla1G9Q0KWV9+mLfQZs1bbHPmL7Sx7893v2tO3N6Oal0/6espoAjkIwGU1ptuusnGjRljnQ891A7adx+bt3ChDbr/AXvvvffs/vvvd0ptMmyGDRtm99xzjx188MHuvGTOWZcwKNmHHXaYU7a33HJLGzlypNWrV8/t8/GuWLHCKlas6H5+H0vci5YtW2Z//fWXbbTRRk4ZDh/XugiIgAiIgAiIQPkhIEts/GcpJTY+n6SPPh8oonc9841VrlLJDthrK9tzp4Zxz23aoJbxM2tivy38yz74fK7d+vgMW75itZ203xZxz9VBEchXAiiwXYPRgxf/8YeN6H2jbd+kiUOxe+BOfED/O+yC2/u5408+9VRCRfaxxx6zvn37OgsoimxpSIsWLaxjx442dOhQu/HGG50CfddddzmFdfny5TZw4ECn2FapUsWw2jJCMvLOO+/YFVdcYQsWLHDbderUsWnTprl1/YmACIiACIiACJQfAuoTm9yzlBKbHKe4oe4dO9MeGfuD7d2qie23W+O4YWMdrFurqh2239ZWp9aGNnj0t7b4r3/s/EO2jBVU+0QgbwmgwHY59lirv3FNe+SanlajWrVCLGpssIE9ev111nPIEDsUy+r//Z+12GmnQmH8xgMPPGB9+vQxptO54447/O5SWV566aXWvn17q1y5sjVt2tRZgbnwhAkTXL/cxx9/3BYtWmSnnnqqMV9t3bp1rV+/fi7sqFGjrFatWrZq1apSSasuIgIiIAIiIAIiUDYEZImNz12jE8fnk/DoFQ9/Zq9++LOdfNQuaSmw4QvssUMDO6HjTvZc0Jd23Ke/hA9pXQTymoBXYNesXGn3XnVVEQU2DOe2Cy6w1ttta11POMHGPPNM+JBbx+qKAtu1a9dSV2BJQLNmzey4444z+t9eeeWVbqAn9o8fP94aN25sn332mc2ZM8eqBUr6pEmTOGRt2rSxyZMn2y233OIU3Q0ChV0iAiIgAiIgAiJQ/gjIEpvcM5USmxynmKEefWuOTfrkV2u7x1a2xWY1YoZJdWej+jVt9x22sOGv/5jqqQovAuWSQFiBHXlT76Tu8bYLL7QDW7eycy+/3EY9+EChcxjICQsn1s2yEj+IU9u2bSNJWLx4sc2fP9/mzp3rfieffLJtscXargVXX321PRW4SG+zzTZGP16UYIkIiIAIiIAIiED5JSBLbPxnK3fi+HyKPcogTqMmzrFWO9a3Zo1qFxsunQO7tahvH34214a8NtsuODh19+R0rqlzRCAbCUQrsNEuxPHSjCKLXH1TH/v6+5l2Q2DFZDTjTp06uWW8c0v6WIUKFZylNXydffbZxyZOnGg777yztWrVyhjgqUGD/2/vTOCtmto/vsxDA0WlKA0oSYjMVIZQMoZM5TXLPLzKWBkyhV6S9zUmFSEJGVJKKVTGUoQSGRq8kSTD6/7Xd2Wd/777nr3POfeee+++9/7W53Pv3mevtdde67tvp/3bz7OeZ0vXZP78+aaxXf+LqOUN7aBBgwyRjQnwpCICIiACIiACIlB5CHhLrERs/D2ViI3nE1mLFfb3PwvM7js2imxT3Ir11l3b7GKF7OjJX5mjdqtrtqy9UXG70nkiUGEJlETA+kkjZFfYgEmP2CBOK6zou7OMAjj560dt0/3HdNppp7nmpN+ZO3eu2yfacoMGDZzl9Ycffkh117t3bwnYFA3tiIAIiIAIiIAIVDUCErHFuONzbB7YUW8sMgft3dTUrLFBMXrIfMrOzbcwb7//lXnAWmP7dWuR+QS1EIFKRMAL2K+//toMu/GG2DWwmaaNkD3l+j7mmeefNw3q1zeXX311plNKvZ41rnPmzCl0nfVsDumzzjrL/RDYicBP5JSlTJ8+3eBuTOqd6tWrGyy5KiIgAiIgAiIgApWPgLfEVr6Z5XdGWhNbDJ7zvltpo4P+ZRrWz68bcXAo1autbzavXc188MWPwcNlto8FSEUEyoNAWMD6NDrFHQsuyKylbW7dce+xEYufshF+k1423XTTlIBlrIhX0upssskmErBJv3kanwiIgAiIgAiIQKkTkIgtBuJFP/xqNt5oPUNqnNIsdTarbpYs+8W6Lf9Vmpcp0vfbb79tunXrZo488kiXt/KDDz4o0ibfBz755BPnQokb5eLFi/PdvfoLEfjmm29SvD///PNQbfl99AKWPLBYYEsqYP1MgkL2nzZIUtgK6ttpKwIiIAIiIAIiULEJ8Cw5e/Zss2rVqtRE8HAKH0tVJmzHW2LTLT1K2FDLdTgSscXA/+XiX0xtm9O1tEuDOtXNXwXGfL3s19K+VKH+99xzTzNz5kzTunVrQz5NxCx5LW+66SYzceJE8/vvvxdqn48PhxxyiDn00EPdD9FXw4UvHlKjjB49OlxVJp+///57c5/NP/rrr2V7LzJN7nnrIjt06FD3E1wzyT3yx4fb9aDhctttt6V4kys1CYWovOSBJY3OmDsH5E3A+rkFhexZZ5xhEMwqIiACIiACIiAClYvAscceazp37uxS2fmZkcaOYzxPqlQOAhKxxbiPX36/ylphqxXjzNxO2areJu6Er5f9/5uk3Hoofus6deq4XJqsxbv22mtdEJkHH3zQEHyGKKqXXXaZeemll8z//ve/4l8kdOZzzz1nFi5caK677rpCNRdeeKH74pk3b55ZtmxZobp8feCtF196UVZnrNOkZCF/Z5LKggUL3AsHmCG0feG+YG18+eWXzdVp1oASPAjWAwcO9KeU6/aRRx4xh9mXGC0a1Heuv7lEIc5l4PT7vBXIbZtvZ06waXYkZHOhp7YiIAIiIAIiUHEIPProo3l9Ti2rmcsSmx1pBXbKjlOhVl9//4upuUnpp7aovcmG7roLlvxi2ps6hcZQVh8ILOODzcyaNctMnjzZ/YwaNcrws/322xtyXmKpbd68ed6H9YuNLIu1kRyZe+yxR6p/RNraa69tcLXgH/tff/2VWitIHYFvOHfjjTd2bVIn2h3ar1y5MhXdlc+cg/UZdxP2KT54Dp8PP/xws9dee5l69eq5uuAvrLME4WE8vnAOn7GIMsb111/fV2XchsfHCfTzxx9/mA033DA1Lo5ffPHFBgvsmDFj+Jgq3Ldbb73VvPrqq2batGmp40ncueKKK8zTTz/t1qz6tDilPU6u09umqTnOptsZOWKE2fTvVDalfV31LwIiIAIiIAIiUDYEvvvuOzN16lSz//77p70gz1tvvPGGs87yHLfbbruZXXbZJW3bsjzIuFQyE5CIzcyoSItmW9Uw3y0pfVfE5StWu2s3+DvFDlazd955x4kYBBYiKbgtyb7vK1Mf9W1015NOOsmJ1k8//dStqxwwYICz6CEycUEmzyX5K3FLLml5//33XRdt2rQp1NWOO+5o7rzzTnPYYYeZF1980fT6e53jt99+68TmCSecYEaOHGmaNm1qsDrSHiH4HwL7WEGMRXXbbbd17tK4mOAqTenRo0fqOlgqEYjBa8N/iy22cG0QwuQdxeWa6/Ts2dOlQqGyffv27svw2WefdflAb7zxRmfpTXWeZidqfOQHxQqOJbiatSQeYy2IWFcR6BW99OvXLyVgCb5UlgUhe+r115uzzj7LjLTBntauUbMsL69riYAIiIAIiIAIlBKB/fbbz3mkPf7442lFLELxAvscwDNksFx++eXmoosuCh4qt32tiY1HLxEbzydtbfNGNcxL0741v6z6w1TbeL20bfJx8Mef14jY7a1oRsxdcsklZqeddnLWwnz0n88+sDx6Ky39NmrUyEyZMqXYl+Dt2S233GIIQEThS4XSvXt3Jw7dh79/pXtjxXgmTJhgrrrqKidmEbFvvvmmQXDff//9pkWLFm4fMUufXe1aTMQ3daQ/8V8ctWvXduy/+OIL1yZ4Xfp/4oknzJNWAHF/sCgiqkmBgtWUwAKvvPKKeeaZZ5zIx105rkSNDxGLuD766KMNQZiuueYa96YwU39x1ypO3dixYw3uy0TKJR2M//Gfg1u/7zlGXQ83Ysr9vXuVKI1OVP+Zjt9qXdUPOPc889LIp8zhZ56ZqbnqRUAEREAEREAEKgABnlFYAofBAwNHuGDAQMDyvMpz1X9tQEkMGrRn7WyzZs3Cp5TZ53TPtWV28Qp0IYnYYtys7RpYEWvPW7T4J9O8yebF6CG7U1as/M1sUnMD02jzjc2sd741BOPBfTffxVth/TZojeVYuuPBNn/++adbX0lOT6yXtWrVKiI0cx0zVkbW3rIAHzdf9imbb54d71NOOcVss8027u3bpEmT3LkvvPCC+2Lq1KmT+zx48GC35Zd390WAkt7EF0QYQjYYNMnXIWI7duzoLL9Ya7k/uO5yjILo5H7xGWstbsc+76fvI7iNG9/y5cvdS4GPP/7Y8UUcl6WIxcrMGmjEKazYeqHqt/yH4ff9lpcJ/H1gZWaLuGfLTzXrutPArr2uYa3LW9pteRSuu/sOLc2zr75iOp14olnbjkVFBERABERABESg4hM47rjjnChlyVKDBg0KTQg3Ysq5557rAl2yT6aMxx57zLkgl6eI9QYAv2VsKkUJSMQWZZLxSLN6ax50v7Ppb0pTxP686nez7VZrXBx5K8RPUgrihLdYrLkcP368y2Hpowtj0SxpIR8mUXMRkHyhpIug699UIfDChfMpwS8AhDcuwHEFkZVLYS0uxY8leD0vhnkJkE2JGh/zI3ozYnjXXXc18+fPd6LQ9+n792Pxx9kGxxM8nut+UPDnem5U+wIrbCc/McJ0v76Pmfvll3mPRhx13eDxb5YuNdM/nmOO6XiwBGwQjPZFQAREQAREoIITYAkcz088R+ItFywYXijBZ1YMErT1XoDB9mW5758py/KaFfFa2T1dV8SZleKYt6m/RsR+OPdbs/KX/KebYejLlq8y785aZHZomCzL0IwZM8zNN9/s1sSyDhXhhNsvQrZ3796FvgxK8RY4CyciGpddAj9R0llLg2MgABVv3oYNG2ZwV8aV2KfMQezhRszbui+toOILDKG+evVq168XylzD7x900EFu3rhRs2aVQh/FLVHjI+owQarOP/98gxWZfcQ4ri8ULN+bbbaZc+dm3FjDfdluu+3cLoENmG8mRv68stiuZS26+9p70r3L4ab3vYPMCjuvsi49b73NXfKMM88q60vreiIgAiIgAiIgAqVMAM88nn3wJgsWH1fEP9NR55+RfF2wfXns58sQUR5jL4trSsQWg3Lt6uubbgc2skmUfzcz53xXjB4yn/L2R9+YOrU2MD0PKz+ffD/Kzz77zGCJI6AQa0f5jKBCEP7rX/9ykXtxHy3LgvsH18f6yzphhB1pf+IKAhPh/fDDD7ugUyz6f/fdd1OnnHPOOYaUQu3atXNiFEFI2h/ezOGSQkFE7rPPPm6fiMx8OTIWAjgReIvASxRca8OFLyO+IFmbEfxZvHixaxo1PlySydWLaGbMRM7DxfrSSy9NXYL7QR5b+hhko+76wnpaxDHBuDjXu8/4+vLerlOrtulnoygTCOxUa5Ety3KV5fSJfWFxh71+q0Dk67Icg64lAiIgAiIgAiJQegR4ZsMiG47TwpIzil9yhuHCPyM1adKk9AaURc+yxGYByTZZy4JSHOfsWBVq9fOvf5oeA2ea5T//Yc44dldTvdr6hepL8mHBN8vNky98ZO49v43ZfdtaJemq2OeuWrXKEMiHn4kTJ5qtttrKuTPj0oxozHfZeuutU13+4x//MH379k19jtphXSV/vrjTso9w9K61Uef441gxSVcTftvGlxhpdqjzgtSfE7VlvSfXzuaNGQGa/Bem748vV/LQBku68WEV9uI43Xxxhea+eVfqYH+IfMbn54vo9RH5sOK+9957weZlvk++1uPsGuKWDbcyt9hogaVdELDjZ8w0D/79QqO0r6f+RUAEREAEREAEyoZAy5YtXVpG8sRS8JbzWSjwuNt9992dx533niO1zk8//eSMNDwTEd+E58DyKmTS6N+/vxkyZIjp0KFDeQ0j8deViC3BLXph5nfmpmFzzN67NTbtdvt/EVaCLk3BXwXm2fFzTIstNzTXHNu8JF0V+1wi7RJ2fNGiReaoo45y/4BYj+kFULE7jjkR664viLC6dev6j9qWAgHcqf36YNbikiKovAtC9ngbwXn7LRuUmpBdy+bPveq+weYd+zf+sl3PXbPmmjXn5T13XV8EREAEREAERCA/BBCxe+21l/O8o0e84Hy6RDJGtG3b1l2IpVZkv+CZiNKqVSuXOtEvxXIHy+HXv//9b7dUTyI2Hn7Z+oDGj6XC1XbZrb55Y/YyM2Xml+Ynmw7niA4lE52/rv7TvDhprl2n+Zvpts8aN4fygFLHRmwlGi1WVx+cqLTHQc5WlbIjgPU3aQVBeZd1Tz/euqxbn+i8C9l1bH7fqwb+y4yza5jJFSwBm7S/AI1HBERABERABEpOYM6cOYU6wboajBfiK3E1xhMODzxe6LO0KUklGw+/JI23rMeiNbElJH5+p2amRZNNzceffm+Gv/iR+WnFmtyuuXa7/KfVZvRrsw1eyfeft7NptkX5BXTCdfjkk08uMwGbKyu1r7wEeHv6xNChZtw70w0uv/kq69rQ+ghY3Ij69OljuI6KCIiACIiACIiACGCwSZKA9Ss9JWLj/zYlYuP5ZKxtUndj88iFbUynvRuYrxYtNyNfmW3ezTHY09z5S82YCbPNtvU3NvedvZOpt2n5+eFnnLAaiEApE9jRrk254/rrzLMTJ+VFyK5lg231vutuJ2AvueSSVJCuUp6GuhcBERABERABERCBYhPwYrbYHVTyE+VOnIcbvM7aa5k+x29vGm2+kfn3819Yd8V55gObfmfn7RuYXVumd9tc/duf5sN5i82sT78zS22+2eM7NDSXH7kmHUoehqQuRKBCE+h04knmtv8uN71uv93MWfClefyGfqbm35Gfc53YNQ8/4gQsicsbNWqU6+lqLwIiIAIiIAIiIAJlRsCLV1li45FLxMbzyan2Hwc0NttsUd28+ckPZursH5yYJZfsJjU3MtU2Wt+sv946ZqVNy/PzL785q21ta3E9YJe65qAdW5hdmm6a07XUWAQqO4FuNoLy3AULzBDrAkz6neII2fteedUJWNIUkQ6JNEzkyw2mJ6rsHDU/ERABERABERABEahsBCRi83xH92u5ueHHHGPMhwt+MpM+Xmo+XrjCbGQdtzdeZ11Tr846pnrDmqZVx63Mga0VgTfP+NVdJSPQb8AA87NNHTTK5uvNVciOnjHD3GPD6pPb+M4773Rkli5d6vL58kFCtpL9sWg6IiACIiACIlAJCMgSm91NlIjNjlOxWu3UZBPDj4oIiEDxCRCxeK111zWExc9WyN7y+ONmyHNjzAArgo877rjUxRGuDWyQpyuvvNIdk5BNodGOCIiACIiACIiACFQYAgrsVGFulQYqAlWXAJbUBx54wHy7bJnN83qfWfHLL5EwiGr8mo1u/PLLLxcSsP6EE044wQwePNhZZO+++25/WFsREAEREAEREAERKHcCssRmdwskYrPjpFYiIALlTOCQQw4xU996y3zz0wrTo2+/tELWCdgZM81T1mobl0aHHMiDrNgdOHCgS2xezlPT5UVABERABERABERABHIgIBGbAyw1FQERKF8CNWvWNE/ZQE8FG2xgjrqyl5n75ZepASFgScvz0MMPG3IdZypdunRxa2WTIGT/97//Gf/mlXH/9ddfmYavehEQAREQAREQgUpIwD8PKDpx/M2ViI3no1oREIGEEXBC9qmnTM1atcxRl19helv34lP79HECljWwe+65Z9YjJuhT//79y9Ui+/7775umTZs6d2kGPnXqVLObzZWrIgIiIAIiIAIiIAIikJ6ARGx6LjoqAiKQYAJeyJ5++ulm+qfzzFobbGhGjhyZdg1spmmcfPLJpo8VweVtkX300UcNFtl05ddff5V1Nh0YHRMBERABERCBSkpAltj4G6voxPF8VCsCIpBQAghZxCc/JS2I4d9++83ceuut5vfffze9evUqaZc5n7/RRhuZKVOmmPXWWy917sqVK92a3YceeshZa3v27FksoZ7qUDsiIAIiIAIiIAKJJuDdiRM9yAQMTpbYBNwEDUEERKD8CZx33nnmkksucZGL+/btW+YD+sc//mGGDx9e6LoTJkwwTzzxhHnyySedeL3iiisMwlZFBERABERABESgchOQJTb+/krExvNRrQiIQBUiQN5YhCyuvWVtjT3yyCPNuHHjzKJFi1LEEbEdO3Y0e+21lznjjDPc8WnTpqXqtSMCIiACIiACIlC5CMgSm939lDtxdpzUSgREoIoQQMhSWCPLWtR77rmnTGa+ySabGHLYDh06tND1fKRi/5+a3swWwqMPIiACIiACIlApCej/+/jbKktsPB/VioAIVEEC3iI7ZswYc/bZZ8cSuPvuu2Prc6k86aScIvGmAABAAElEQVSTzOzZs1OnHHTQQWb8+PFm8uTJ5sEHH3TH995771S9dkRABERABERABCoXAf/SunLNKv+zkYjNP1P1KAIiUAkIeCH76quvmlNPPTVyRu+884657bbbIuuzqahWrZprtvPOO5vtt98+dcqBBx5oTjnlFHPuueeaZ5991lmHfdtUI+2IgAiIgAiIgAhUOgKyxMbf0rWs2i+Ib6JaERABEai6BLC04lrMulQCLIXLK6+8Yi688EIzevRo06pVq3B1Xj4TMZmoxfoPLS841YkIiIAIiIAIJJaAf+7gmYNnD5X0BMrdEktexKjciOmHnPnon3/+ae6//37z1VdfpW2Mbmfd2bfffpu2XgdFQAREwBPAIktU4Lfeest069bNH05tDz30UNO8eXODmC2tsv7660vAlhZc9SsCIiACIiACCSSgF9fxN6VcRewXX3zhch82bdrUtGnTxvCwuGTJkvgRZ1H7/fffu3yPrCNLVwiUct1115n58+enqy7xMUTyscceaz744IMS95WkDm688UYzYsSIJA1JYxGBMiGApbV3796RQrZLly4usnCZDEYXEQEREAEREAERqLQEvJOsRGz8LS5XEeuH9txzzxlyNGLpIFeij8ZJ/erVqw2W1XQFFzuihwYLVt369eubGTNmGIKkBAv9Ll++PHio0P4vv/xS6LP/8Mcff5hly5aZH3/8MaPVmD88xjBz5sxU+7Clmfn89ttvvvust+HxMZ8gK/bD18qFnx+7HxB9+X9I7H/66acu/Qf74etwTnh8vh9tRaAyEOA76pprrkkrZEmPw3fExIkTK8NUNQcREAEREAEREAERSDSBRIjYbbfd1px11llu3RmROd977z0niPr37+8stLvvvrt54IEHUiC/++47c/311xuCoLRo0cLlT/Ri9pBDDnHW3bZt25qxY8emzvn444/Nbrvt5s7BqhIsCxYsMBdddJFp2bKl6dy5s5k0aVKq+tprrzXbbLON2XXXXc1OO+3kgqukKtPsPPTQQ6ZZs2aupkePHilLs2/6yCOPmH333dfssssu5vbbbzcI8UwlanzDhw83TZo0cW7Rq1atMu3atTM333yz6w5BmSu/YcOGGaKh+tK+fXu3BvDDDz9085gyZYq57777UnOaNWuWaxo1Pt+PtiJQWQgQqbhv375FhOwWW2xhjj/+eDNkyJDKMlXNQwREQAREQAREoBwIeAOSLLHx8BMhYv0QEXaUhQsXmtdff908//zzzn31rrvucuIMSwflscceM9OmTXNbrLhYSpcuXerqRo0aZd5//30TjuCJQGPd2htvvOG2rvHfvx5++GHz888/mwkTJhjWt910002uBpH2+OOPm3//+9/Osop78OGHHx48tch+9+7dU27ErMtFAH700UeuHWtw+/XrZy655BIzePBgJwinT59epI/wgajxEbUUoXn55Zc7wbrBBhuYK6+80p1eHH7+H034+jvssIObBy8TzjzzTLfPvHwU1ajxhfvRZxGoDATwFuE7As+RE088MTUlRCz/nt9+++3UMe2IgAiIgAiIgAiIgAjkn0CiROw666zjxCfutq+99prZeuutnQAkQBOiFEsgLrNPPfWUQSxibUX4EqSpUaNGjs4mm2xiateuXYQUaTK6du1qGjdu7M71DejvpZdeMvXq1XPCmDF89tln5uuvvzZYVyhYTP/zn/8Y1tputNFG/tS0W4Tkpptu6uqqV6/u9hkTBQG92WabmRNOOMGJT/I9ZnI/jBsfb2juuOMOJyoR24jmDTfc0F2ruPzcyaFf6667rpsHc2P+zI8fjseNL9SNPopAhSSwaNGiIuMm5Q6eDrxM83lkWduPkOWljooIiIAIiEDZEPjpp59SS59K84rvvvtu2gj1XJPnVrz4iCrLcrJsC8YDBRrNllbVaeeNSrLExt/zRIlYrHu4wTZs2NDwpYTlkgdIfrA6cpxCpM5c1l/6PwYEKqVmzZpuyy/qfvjhB4NLLNdh3es555zjrlGnTh2DezNr4XBhxkqLOMy2YCEOFv4YWaMaLJn+QOPGRz/MyQvX4NrhkvJjzStcgmXttdcu4v6caXzB87UvAhWRwDHHHGP22Wcf9yKLl1u+nHzyyYZgZ7wg++c//+kOI2LHjRvnXiz5dtqKgAiIgAjknwAGCJZAtW7d2uAxxhKr0ixPP/20ueGGGwrFIuF6POvhqXbZZZe559YVK1ZkPQwMAaUZaDTrgWTZkGe+yhi4NMvpq1nCCKybhPEQMAgXYNyEsWbwZcAaVt5qse4Viyvib8sttzQIKf4B+basVeULg/MQcYg3X3ARRohhCT3wwAPdelbacy1fEIEHHHCAs7yyxeWY9bVYZhG0BILiXL4gsZp++eWX/tTILcIUKytfeKyPJb8jVt3999/fiW9cm/mMFeeCCy6I7IeKuPHxZULqjx133NEFs+rZs6ezKmMtZd1trvxY+0vEZoJiseVFAS7ccMXqyn0ZOXKkOeKIIwwCH0szFvIofrETU6UIVBACL774olvWgIX1zjvvdC+zOnXq5LZ4hPAQ0qdPH1OjRg23Vh/Ry7991tCriIAIiIAI5J8AzyUISl4c4hnD81k4YCbPjTy78BMuPN+El52F2/jPPGvxPd+3b1/Tq1cv9xzq69jyzMSz5ty5c83GG28crIrcpz+eV4NGlWDjuPHxzBs8j74oPB9TMEJ4ow37jB+hzbMo26BHIefCLXzMdWR/8TzsOdEP/QUDl9LOX4t9+mdeMOf/xGAd9eFCXBjOwRgTbMu4uB7P08wrHY+4+xu+TkX9nMnQVVHnlbdx2z+Sciuff/55gXUDdj/WLbjABlwqsNZXNx77h11ggzkV2EBNqTbffPONq7NuvQX2y6vArslM1dk/8IJgf75fttY9uMB+uRTYLzrXni3nWvfkVH+33HJLAWOgvRWA7rhdX5vqn+P2wbXAfnm4uky/7Bdqqj/Otet83SlWfLv+ub5d61vAPDMV5ptufPRFPzBbuXKl69e+0XPdFYef/SIpOP30092cTzvtNDdfxm4Dbbk+rVt36hjH4UOJGp+r1C8RqCQErDdGwa233pr63rEvcwqeeeYZNzsb0M39u+HftF024PY/+eSTSjJzTUMEREAEkkWAZz6eQ6z1tcjAeB6yQS7ddzXPdXY5WKqNfUHvnjU5l2c6ntUyFZsD3F2Lc/zzYfCcAQMGFNgc4sFDsfvWwy/1fGg9/Vzf/nnUpp4s8Mfo0wrkVF+jR48usC9JXXvmZb2AXB3tbbBTt8//U4yT517+D2J/jz32cCz88zTPchT/PEqbq6++2j1Hcpx9G2w19X+d75tnctqGfziHYqP3F6qzS//WVMT8HjRokDuHZ1nO575SrIHGHbcviFNztvFfXF3c/XUNKsEvu1TQzT94/yvBtPI+Bd50JL5Ya2iBjb5bZJyILmspzEoI+pN9P/bNkz+U2to3TK4/+vXFWnPdMX+eP57N1r4pdOfyDy5YOB68RrAubj/d+OLa+7pc+fm52rdcacf53//+t8BaqQvsmzJ/Cbct7vgKdaIPIpBwAvPmzXP/yVsvC/efjA3uVPDyyy+7l278585/xNZCW2CDuCV8JhqeCIiACFRcAnb9qfsOtgEnC6xHX2oiNiioE27W686JVL6XbfBPV8/3My/pEXn33HNPgfW0S50XtYNRwFpanRgOiliEJ8YXjnlDDJ95RoorNue4E712GVvBwIED3Ry8iKUOoWqX1xVY7zq3T1/0yTz+9a9/FXCejdVSgACkBEUsRg3aBUWsF+HPPvusq+Ozb8c+opo5sE9hDPRhvQULxowZ4/YRxzwT+nHY7B8FPFvyLEixAUxdO+viXbBkyRLXzj9LugYRv3jGxkDFy1/G4F8MI2b5PxRxi9i2S/lc/9bKWxB3fyMuU+EOc3+5B9bqXeHGXpYDTtSa2CjzMkGEgq4Ovh3uCgRKwkUi2+L7YV1tuOCyQH9B1xNcZjnmzwufE/cZ1wjO9a4Yvi3Hg9fwxzNt040v0znU58rPz5VATunGWatWLeeiHXZzKO74spmD2ohAUgiQEoxUVvY/fLccwD5QuHX0RCs+7rjjXERz3KlwP85m+UFS5qVxiIAIiEBFIkCmhxEjRrilXwcffLD7Tmb8cYEtowJ5xs2bZ0wChvI8GCwsC7PCywUhpZ59fnh2iitxgUap4/8R1vmSphHXXVyVmRPPkiwba2wDlBKH4fzzz4+7TKqOZXkU0kxSrMhwy814Pl28eLEhYFXdunVdhg7XwP5q37692WuvvQxLZyjW09HNi+dJSjhwaXECodIPS/ZY+sa8ebbk/1UKbtk+3gvu4oyHe8eze9T9dSdWkl/cI5XMBCqEiM08DbUQAREQgbIlwBpyHiT4T5e1UqT58uvgWXfPWnzyRquIgAiIgAiUDgGC7pGxgtgcpC6k8BLRWhqLBAZFGCAIefEYDuRZnNHxfwBrclu0aOGELPv8xK2L9eLEr/8Mrm2NGwPtrXXSxShJ1873ay2l6aqLHPMxY8j+AQvixRB/xhcEJSVsrPD11pvQ77ptcQKhImCti7NLTQdDRLq1ehfql9gSjAGDCrFpELZR97fQiZXkQxT/SjK9Ek+j6Gr3EnepDkRABESg6hBArJI7lh/rVmzIXW3XNbn/aEl9xZvzBg0aVB0gmqkIiIAIlDIB64LqLIhYFwk2hCfYVltt5a6KNRTrXrrAoMUJRGmXhLngRwg/xBtCGGtr2DKbzZQRJVGBRpnDYYcd5qzLePw88sgjznqKxZQozBQbH8WliUQoY5WkbrvttjPW7dfYNbCpFECkhERYRhUCdVL4/+vII4/Mej6MP13gUtggSnMJhMoYEeZYlLFk8xIYEYsQhwV1BHiCN+P0noFR9zdqrhXxuH8pURHHXpZjrtCWWKLT8UWVa16ufAKOyxuWz+vk0hdfAuQd4ycccp4UIb7u9ddfz6VbtRUBEchAgAcQckrjWmyDabg3yLhCqYiACIiACOSPAMIGyysur4gaIux691q75tVce+21xq55dSKQegQTxQbnc4KQl4uI3BNOOCHjoGygI9OmTRuXG5x0i+xzLFgQXdmWM844wzVlXHYNaKElZ3Z9r7PqEuXeril186AxIo752pgMzuq85557uiwd1CFCEbVYNb2wJgWcL96ax9Yvb2Pu9Ie78n777eeyT0yYMMGdkm4uvg8akIZy+vTppl27dk7Q8kKB58n21uUXNrggN2nSxNjAVH4Iabd2vasbOwKdMdh1xW48l156qenSpYsZMmSIefvtt12fZMzwJe7++jaVZRvkXlnmlM95rGXVfoV0vOZtGA+JFN788PDIG7Z8F7sQ3oVP5+1XumIXwBu7yNzlk033Dz/dOaV9jJDodkG8sRGRXRoftr4QAt4ukjfvvPOO4U2fjTbnqzJuyYlJyqCTTjopY1s1EAEREAEREAEREIHSJODdT9PFOeG6pErEaupjffixYOHDchi08Pm6stryrMa4mEN4/KSP8WtCw+PB6smje9gV2ffHlv68y3L4/PBnb/EkJU62Bes3bBmjF8beYo2gDvOO65e5+tg2GKfYz/Z5Our+xl2vItTxsuX+++93nl2Ie5X0BMrdnZh/bLxpiPrHmn7Y0Xm5+Ifl/+HyJUXhH4Pf5zPX9P/o+OwLXySc7//xsc+6CnLWsk+hL8bLFwh9shYuXd4w2vLFELyOP4fx8SWUyxcG/YXHxzG+NGDn3Sw4xvj5B8BCeXLRBgtvvqhDkGKVzbYwf/L58p+BZ+E5+z7C8/XHtRUBERABERABERCBfBMIi79w/z4QUfg4z3K44pZn8c+a6eYQ90wc9ezo+/PbbOcWfE7N9hye/8L8sAJ7S3C2/dAuONfwc2WmfqLub6bzVF85CGTv/1BK87W5qEzz5s3NUUcd5dxcs70M7nr45QcX0LMeoGnTpi7ACv3Y3FaG6HUUmz/VnHvuuaZVq1amZcuWxuaecsf5hXsIbXGvYHE5rh4IXfqyuZqMDXXu9vnMGgXKuHHj3DHGbvNFumP+F24PuKpwHZt+w7lHUPfCCy84NxbcVxgHW8RsphI1Pt5YsdAd6yhjnjx5cqauilXv529DwJv77rsvxWLWrFmuv6j5FutiOkkEREAEREAEREAEREAEqiiBCuokW+Z3q9xFrM15ZWx+KIOPO0Lzyy+/jIXw5ptvmosuusiZ2LEMss8PbiHhmx78jNUUyyRpL+69917na+/XSOBei8WSLUFZcFUm0qjNe+X6RnAi5Pg55ZRT3PhwXSYCqc05VmS8Dz74oAtbjmhlsbp3RWY8CD7WLRDRFBHImt5MJWp8vLFiXQKCGjF95ZVXpqykmfrMpR6hzNwJBMB6Dc8Cqy4lar65XENtRUAEREAEREAEREAEREAE1hDQmtj4v4RydydmQfjUqVOdNRTXBBaHn3766ZGj9nm5iF6GCGVhPCVTXi7atG+/Ju8VFkybkNrlvSI3FuHZsdj68OIEPvIFtwz888MuC1F5w7xYRlD6PF/k/CIIgS/du3d37r877rijsQmp/eG0W/qLGh/zQMiz8B23Yiy2rHlFdOez4KrM/P26kiCLuPmGXU3yOSb1JQIiIAIiIAIiIALZEmAJ1cSJE11zIhmXRhyVbMeidoUJjB492vB8T6AulTW5fMUhM4FytcRilSQCGQKMIEOsCwjnngpPIZu8XN4CizU1WKLyXnFd1nOmK6ybYNF5vgprD/z6VfrOpkSNj3/0iGXWIDRs2NB1FeQX9waHOr+2NZsx0Ibx+iAK2Z6jdiIgAiIgAiIgAiJQ3gR41pwzZ4559NFHU6loymtMxCUZMWJETpcn0OhVV12V0zlJaZxpvrfddpsZNmxYUoabmHHEPccnZpDlOJDsVFQpDZB1rY0aNTLnnXeeC6G9YsUKZ7EkrHhxCn1RXnrpJRdRGNdjkiJHCVTaIsyOPfZY50qMu/GyZcucyy91FBJAY93E7RdR7C2qfBmyz5pWhCP7HKM/n+eL9DvBPF9resztd9z4GBNvrbBce6sn1lgstBRyh1GwdJPQ2o+dY7gf4w5N0m+suUHxS326gjsxeTBnz57t3KXhmu/5pruujomACIiACIiACIiAJ4Cxghfx/mV8uuc8DBD+eYjzfGDLTBbYcF9cwxtH6AcPNH4ovNinvR+HO2h/+fHxORz7hLY8dy1atKjQHPy56bacEww0Gh4T5xDLxY8rXR/pjhHdl8J5fo7B+VEXnhvHwow4RuFZkudo+vXnsY2aL9einiV2AwYMWNNJ4Df3j/RJweLHxzZqHMH2FXHf3wuJ2Pi7V64i9tBDDzVYR1lzSdAlAiSRY/Hxxx+PH/XftQioYMHKedlll7mgTbgLIyYRerzdCbflPP/HgXtv586dDbmpEK0dOnRw+bloQ86rrl27ukBNJNUmsi8lLm9YVJ4vfz3Xwd+/0h0L1rMfNT6O8yIAQcoXAGG4eSHgAy41btzYMSUlDjm4WHvsC58R/e3btzcdO3Y0uHVnKuQiI6Q7rBC0iH5K1Hwz9ad6ERABERABERABEciVAJ5oBNskxghWPAJp8mziDRf9+/d3z288q2SbShDDB8u/6ItgmXgLIlJZ+vXEE0+khkgOVtIrUogJQnvakJvWG2GiAnkSU4RxRwXKTF0ktBMXaBQDCpZOApOScvLpp58OnV30I0YNnsF32mknF3CUIKQ8f1MuuOACl6aRfZbtce3PP/+cj87wQRwaz5tnbF+YP96SPEfTL5bjTPO9+eabXf+0Zz9YMAKxZJBn29tvvz3lCRgXqDV4vvarAAGr9su92Dcpbgz2DU6B/cIosG9XSjQm+/bN9ZFrf7S3b5DcGMIDoM7+oy9gm22xb8WybZpVu6jxeX7MO9347BdcgW8TvpB9W1Zg33KFD8d+tuuRCzgvfJ/yPd/YQahSBERABERABESgShLgWfHFF18ssC/jC6yoKfjiiy/cvl3zWvD8888X7LHHHgXW26yAz7SxnnQpTjfccEOBzYyR+ux3rGAqsNZA1xf1/fr1c1VWnBV069bN7fOMSH9WhLrP1spaYI0ABdZIUMD5zzzzjDtug4S6djaYaIF1YS6wVmA3Lp7RbCDSAmscKWAc7POT7tnNdfT3L565GFunTp1S5/DMR+Fa9G8DlBbYDBLuuowrrlhXZseIsVnvRXeOzUvqTrHGkAKbscPtW+uvq7PxW9xnG8y0wAZiLeDzPffcU2BFsztuA6G6dvS1ZMmSAp4TraB384qbL8+mPFtbY0gBffsCUzjblwepe+iZ2xcIro75jhkzxu1bL0R/aqXY2pcSbl5wVYkmUNiUWU6i3afJYa0oAZOysU7GDZUARPSRa3+0xy2XMYQLdUQaZpttCea+yvacuHZR4/P8mHe68WGh9m3C/ZPoO12OsnC74Ges55wXvk/5nm/wmtoXAREQAREQAREQAQjwnObzm2IZxVo4fvx4F1DztddeM1tvvbXLMMFSKtph+YwrWBppS4wRLLI847A0jYIXGhkssF5ieaS/Pffc09VZgeaO4ZnGsxFeccGCxxxuzD6QJ89o4UCZfE737Bbsh/EEA436PmgzYcIE51HH8jIsyBTGG1fggRWbseG1aAVjXHNXh/suTOrVq+f6J0MGwUkJmEVQJgoWUyy6WHAZb6b58mzKs3X4+RHPQZ7HSUWJxyApNe0LCXcNfnGM+VpR7455S3GqQQXfCT9fV/DplNrwEyFiS2126lgEREAEREAEREAERKDSEmjWrJmbGwFCEUS4FLN+lDWn/JAa0Qe/pCECwa/X9FD8ulVEGecgSFlKRmFZWf369Z1IRsSxRAtxhoBFCJLqEFdexC3ux77w2YvT8JI2Pgfb+nPitpzDOt90BYFJsTYrt80kgqzlNzU2TmC+weL7IROILxxDyBNLBUasez3nnHOcIaROnTouXgpL2ojNgqsyLxN8yXW+jD881+Cc/HiDx/y1KsPW86+s88vXPcrerJivK6ofERABERABERABERABESgmAeuGaggGSkG0Ihi9NY91lFhMd955Z5c6ETG05ZZbpq5EHBHWayLGEJJYcQmESR8ER8Lyimj1whABxprR4cOHuzWe1oXV9YW1kXGcf/75TjyzFpX+gsIvddHQDmt1R44c6eKWIACxAHP9uMJa01tuucXNjVgyjAtr5UEHHeTW6E6ePNmNjz6wXMaVdu3aOYspQh1By9pVb9WEBXP85JNPUlGcmSvrXQmKheWVLRzhhWUWQYuoZ00uY8Nq+uWXX6aGEDVfzoEz9wivQEQyHPbff3/Hlpg2WHmxLLNWV0UEggRkiQ3S0L4IiIAIiIAIiIAIiECiCRBkkwCeFCylo0aNSo3Xrtl0QZbsmk1DQE5ELSLMl3SBLRFOBG/CBRYxRzChu+++25/i0kEi9HC7JQgRBVdcBC8ikj45Z+bMmS5IaDoLWvBYVKDM1AXT7EQFGkU4Ym0mQCrifODAgRkFMS7Edt2ws5hefPHFTsj7SzI23HyxMiOuKbhsU+DOfHv27OleEuDuS3n99ddNe+viyxjh16RJE2PXEbs6fkXNd5999nHnjBs3ztj1xG6foF0NGjRwwaoIysX4LrnkEhdQlL4Q7+ESZBuuq4ifZYnN7q6tZUGt8T3Irn2FaEVIbiLGEakuGz//8poUayjq1q3rvvjKawy6rgiIgAiIgAiIgAhURgJYCIkXgjgNFyy4HA/HBcGaioWVdajpBFO4H6yIPpYKz5/sZ3Me/WCJpC2ZH7IVYlwDKzTneHdl+mLcXDvbfjgHSyoMyA6C+EQI++Lr2MKINbC+YD1l7DDyYyBKMulwEMDpeHNurvPF7RuZ4q/hr1/Zt0Sbfuihh9waZCzbKukJFH2dkb5doo7yB01u1w8++CDtuHjjxtsiXCuSXIYMGVJooXqSx8rYMiWrTvr4NT4REAEREAEREIGqQ4AASFGCCgEWFrCQ4RhrLrMVorgxI/D4QTBnex7X4jqMIxfhiaBLF2iUcefSD9ePYhOso01QwFLHHHFlDopLrLYci+sz1/ly3eA1uHZVKN6+mOv9rApsgnMsVxHLTeIti19gz5uvcOENF2+dfPHn4LLBG7bg+bThM2sZZsyY4Rbf+/N8O38+b5bCJXx9zvFrImjrzw2eFx5fsC5qn7d/9JWuMK7gNYNteMsWHHd4PIzX9+v3eTNG8dtgf+H5BvvzQQ58e/qLSlZNG87lHBtavdAY/fnaioAIiIAIiIAIiIAIJI8A+XFZQ6ySDAL+WT4Zo0nuKMpVxBYnWTXmdR+JrkePHs6Pn0X5vuDDz+e2bduasWPH+sPuM8dxM6aOSHI2T5arZ3F/uuTNZ599tvPF950MGjTI4L9PQQDmmkwb8YrLRuvWrVOL1n3fiMyoZNVEerM5u9wXDOMmhDpilgXvrE3whfUITz75pPtIOHcWxuOGwFoJtp5H1HyLm5ybcPSEOm/VqpVj6xn5cWkrAiIgAiIgAiIgAiKQTAKnnnpqKm1QMkdYNUclS2z8fS9XEdulSxczePBgl2cKUUg0s9mzZxuboNotErcJq53QvOuuu8zNN99sbJJpQ84t70ZsEzO7iGo2GXBqlizu5/xwlLdJNlIdi/Aff/xxgxglmhyR5ig2GbWzIpJri7DgN910kzt+zDHHGMbgrZ+IwOOPP97VsYg93fhcZcQvxkBYcpug29gk2qkocjTn2gQVQITyRuyKK65IWU8fe+wxF5mNrU1q7SLJ2cTdKatrxOUM4yfQAOKS4Abvvfeeaxo1X978zJ8/3y3mJ9fZrFmzDGNGABPQgOhyNiG12+czPCnkBWMRP3m9uBfBsOqugX6JgAiIgAiIgAiIgAiIgAhkJOAtsX6b8YQq2qBcRWxxklWz3oA1DhT879lnPYEv7LNWIFxYAM96AayghB4nt9WAAQOc625U8mZCiFNICr1w4UIzd+5cFyyKY8VJpk0/9ImVlK0XgfQXlawa1+KnnnrKiXcsyIjSoUOHZhWwijDprD8gDDoh3L17dNR8GQcl1+Tc8CT8OZZphH26NSZretZvERABERABERABERABERCBTARkiY0nlJg8sd5FmGTVFFxvfbJqPoeTVXOM3Fa5FtxxKYhdfhB2PnkzQpfikzezOJ3w4Yg+8mIhOhGElGzG5xoGfrG21+cx4zACM1j8Wlj/5sX/8SIKw+tXg+ex7+cRPh7+TN9R86UtFmy/iD4cnIDPrMsNl169epkOHTo4sU84dCzcWHJVREAEREAEREAEREAEREAEsifgdUD2Z1TNluUqYhFmxUlWjbjD+kdiacQvFl2SIXPTEZe+EGgIwVajRg3nLowA44dE1N5aS+QzrKLpkjfTz9FHH+1yXX322WcuIrLvO1Mybd8uuGWNKq7KrD1AEOKSi3WVEpWsmnZEYsaVmDW9JLuGGfsknsb9lyBWbOGJy3UwEFbw+uxnmm+4ffBzVLJqrt24cWPTsGFDdw9w14Y93FVEQARKTuDzzz93yeDT9cR3GTn1ilvom7QKfNexzEJFBERABERABESg/Al4Y1b5jySZIyhXEUsaHFxjKSRIxh3VJ1Q+zSarppCsGjdeyltvvZV6WMNaetlll5l27dq5Olx1scx6F2AOXnXVVe4Hd1y/lpW+WBM6Z84cdx6/GMejjz7qkjcjesktS38UhBshw1mrS1AoXzKNz7cLbklIPX36dJeLiwjKuBX7EkxWXa9evULJqnHvJQrypZdemrLIMg/Ghvjt2rWrmzdrX3GRRmBTgn/8Qatq1HyD7f24gsdIVv3yyy+nXKpJBM66W9bwws2X3r17S8B6GNqKQB4IIDJ5kZaunHjiie47LF1dNsf4bqHvxYsXZ9NcbURABERABERABEqRgCyx2cFdy4JKn+slu/PLpFVUsmpcaKnDRTccyKk4A8OdN5y8OZt+osYXdS6BonBVRnTjuhsUiliKo5JVY2HF0ozbM2188f2RZDqXnFrFnW84WbUfF3NhnTJjUBEBEcgfAV5Wsbzi6quvLtIpHhl77rlnkePZHuAFXefOnQ3R3m+44YZsT1M7ERABERABERCBUiBARhI8MMeNG5daxlgKl6nwXZarJTZbej6QU7g9Ygkrab4K1sri9Bc1vqhx+UTQQSHq28YFRUIkphuf74+gV7mU4s43vJY3aly5jEVtRUAE4gnw8orYAFEFMYqHCV4dRHD/+OOPXZqtcO4/Xna9+uqrZt68eS7yeN26daO61HEREAEREAEREIEyJlAB7ItlTCT95SqEiE0/dB0VAREQARHwBMaPH2+8i/+zzz7rDt95550uN/W9997rPiNgCVZHGjJf/JIM/1lbERABERABERCB8icQ9NQs/9EkbwTlmmIneTg0IhEQARFIJgECupHnOvhDILdwIXYAwdWeeeYZt76ftFc+gB55phGw5I3mOO5KBLVTEQEREAEREAERSBYBidj4+yFLbDwf1YqACIhAIggQffzaa68tNBaCuW2++eaFjmFp7dKliztG4LWBAwe6COa4Gb/xxhvuOEHidtppJ7dPdOIbb7yxUB/6IAIiIAIiIAIiIAJJJiARm8Xd+f77782oUaPM6aef7gIyZXFK2iZYPggCRSGQSrr1rWlPLMWDRCRlfdxJJ52Uyg9bipdT1yIgAsUkwPcFVthgIdVWuOyyyy6pQ+TCpuBGTFm0aJHbBiOjk7ZLRQREQAREQAREIBkE/JpYWWLj74fcieP5uNq3337b3H777earr77KonV0kwULFpiZM2ea6667ziCMk1CYE+MhUrKKCIhAcgkQ9G2HHXYo9OODugVHTaC1qOKDv2HV9SVdgDlfp60IiIAIiIAIiIAIJJFAhRaxwQcx4PLmgrQ7/FDC9e5gxC/O/fnnn82SJUsMKWt8oa/DDz/c5Xdt3ry5P+yuwTkrV650x/yWD5xDHelygn1dfPHFpk+fPqk+gjt+zP4Y6W/4yabQLngdzvHns03HAdHKfFVEQASqDoHGjRu7yfIyzRfciVVEQAREQAREQASSQQANQZElNv5+VEgRO3/+fNOzZ0/TsmVLc+KJJzrrJtMcPXq0wb3ukEMOMbfddpurx22X3Kpx5c033zR77bWXYX1Z27ZtzT777OOa//DDD66/Zs2amd13372Q9RR3vP33399ZRUh7gYVk7Nix7rz27dubf/7zn2bbbbc1LVq0MAMGDIi7vBPOjHvq1KmpdqeddpqzkKYOROwMHTrUEF2U61xzzTUpwYp19dxzz3VzglNQPOMaTW5J5vvggw9G9KzDIiACSSLw3Xffmcsvv7zIz1NPPZX1MI877jjX9sILL3TfF3wvKDds1vjUUAREQAREQAREICEEKqSIRXixlvOFF14wtWvXNrfccovDSTCTwYMHm88++8yJuYkTJxpyJwbTSaTjjqtwkyZNXNCTjz76yLz22muuGX1zLlE+0xWCprD+DDFItM/33nvPNcPKyXVff/11FzCF9BbprKG+T/I0Iryffvppd4i2BGA5+OCDfZO0Wx5qEasEe3nppZfM5MmTDYKcggWWta4PP/yw4fpDhgxJifA77rjDnHXWWWbKlCkpq3XaC+igCIhAogjwXRT+8d9v/o2t3zJwckFT/DFexvkgTsOGDXPfOeedd55ro18iIAIiIAIiIALlT0CW2OzuQYUTsV6cYVFo3bq16dGjh7PEYjVlbVe1atXczE8++WRnRSV3Iu3iyt57722mTZtm+vfv79JOsPaMwoMfQnbTTTdNe/p2221natWqZXAzrlOnTiFBeNRRRxksuARMosyYMSNtH/4g7bAkE/jpnXfecYf33HNPX512O2nSJBccCkH/7rvvGsTwhAkTUm2xCGNh7tSpkzuG2+DChQsN4vfUU0916Te6deuWaq8dERCBZBLge4x/u+l+/Es8litQ7z1JmAkeHRw77LDDUhPr3r27wZuF7yRetp155plmzpw5pm/fvqk22hEBERABERABERCBJBOIjgCS5FFnMTYEJAWX3kylV69epkOHDs4y+cADD5jhw4ebV155JdNpsfWI22DxgZO8ZQQxHiz77ruvqV+/vnnxxRfNvHnzzJFHHmk23HDDYJMi+6xpRbwTnIl+iTIanK8fg7fC0IFfe+uDuUQJ9CIX0wEREIFKQ2CdddZxL738hPzLP/9ZWxEQAREQAREQgfIhIEtsdtwrnIhFrGFVGDFihBNsjzzyiHPlJf0EbrgrVqxwM2cdLA9mmYQgjbFKEPCkYcOGLiDToEGDXNAjhB59Ll++3PWJYCS6pxeH7mDELywnrFXFWkrZY4893JZzGSuuv1h5//zzT7P11lu79DZYj3HxwxqLm3CmgmsghTQaiF7GVr169djTWHuLWCZVB1YaXI5VREAEREAEREAEREAEREAEkkPAi9nkjChZI6lw7sTgw/0N4cea1FWrVrk1oRy/9dZbDQFLKG3atHG5Xd2HDL9wTUYQ4nqLgO3du7epUaOGee6551w/PhgKbrlBV72ghdNbWP2lyMuIcGW9LWvQatas6avM+eefb+677z6DGzPX86Vr165m7ty5zt2XoFGZys477+zWABNpdL/99nNz8O7E4fHQlx8vgVywNDPnkqYNyjRG1YuACIiACIiACIiACIiACGRHwItX/9ye3VlVr9VaFtSaOM4VcO6rV6/OytKaaWpYQ7Hckl8RSyaudiUprGW99NJLncjmDzBd3kbcixHgWFF9YRxEWyaacL9+/fzhrLZYjHFRRnxnU2hLCiDW/+Ji7N2LszlXbURABERABERABERABERABPJPgGWOTz75pPPmJPCsSnoCFc6dODiNbFyFg+2j9hGZuPjmu8QJQ+q8gF26dKm55557XIoejl1wwQU5DyXXNW1Yaj2/dFbbnAegE0RABERABERABERABERABPJCQJbYeIwVWsTGT638as844wyXtzXbEWAMR0iSA/Lwww9Pidtsz1c7ERABERABERABERABERCBik+gAjvJlil8idhSwE0O1lwKqXFydR/OpX+1FQEREAEREAEREAEREAERqDgEZImNv1cVMrBT/JRUKwIiIAIiIAIiIAIiIAIiIAIVj4AssdndM4nY7DiplQiIgAiIgAiIgAiIgAiIgAiUCQFZYuMxS8TG81GtCIiACIiACIiACIiACIiACIhAgghIxCboZmgoIiACIiACIiACIiACIiACIiBLbPzfgERsPB/VioAIiIAIiIAIiIAIiIAIiECZEPBrYiVi43FLxMbzUa0IiIAIiIAIiIAIiIAIiIAIiECCCEjEJuhmaCgiIAIiIAIiIAIiIAIiIAJVl4Assdnde4nY7DiplQiIgAiIgAiIgAiIgAiIgAiIQAIISMQm4CZoCCIgAiIgAiIgAiIgAiIgAiIgS2x2fwMSsdlxUisREAEREAEREAEREAEREAEREIEEEJCILeObsGjRItOzZ88yvqouJwIiIAIiIAIiIAIiIAIikHQC3hKb9HGW9/jWLe8BVLXrd+zY0axataqqTVvzFQEREAEREAEREAEREAEREIG8EJAlNi8Ys+vkxx9/NL/88kt2jdVKBERABERABERABERABESgShHwlljliY2/7RKx8XzyWvv666+bDTfc0PDHOXbs2Lz2rc5EQAREQAREQAREQAREQAREoCoQkIgtw7v8xBNPmC233NKsu+66ErFlyF2XEgEREAEREAEREAEREIGKQECW2OzukkRsdpxK3Oqzzz4z06dPN3Xq1DHrrbeeE7Hz588vcb/qQAREQAREQAREQAREQAREoHIQ8CK2csym9GYhEVt6bAv1/Pzzz5tddtnFidgNNtjAVKtWzUyYMKFQG32oegR++ukn515e2jN/9913zZNPPpn2Mn/++aeZNGmSufvuu83MmTPTtsnm4KhRo8ynn36aTVO1EQEREAEREAEREAERiCGgNbExcGyVRGw8n7zU/vbbb+bZZ581BxxwgHMlXmeddcwRRxwhEZsXuhWzk5deeskcdNBBpnXr1maHHXYww4cPL9WJPP300+aGG24wf/31V6Hr/PHHH2b33Xc3l112mfn222/NihUrCtWn+3DjjTeaESNGFKm64447zAcffFDkuA6IgAiIgAiIgAiIgAhkR0CW2Ow4ScRmx6lErV599VVDflhELK7ErIlFwLz11lvm448/LlHfOrniEcDyiaDs1KmTs3z27du3iLhcvXq1oV26kkuEa74I//e//xmuMXXqVLP22oX/yc+YMcP88MMP5s033zSIUP5G4wp9YW3l75l9fsKFlza///57+LCb46+//lrkeNQB+oYB5/htsC2CPKo/ji9ZssSsXLmykKUbHj///LOrC55LuyjeXGf58uXu0unmm8v9CI5f+yIgAiIgAiIgAiIQJuAtsH4brtfnNQQKP9GKSqkQQCDstddeplWrVilLLCK2efPmssaWCvFkd4rA++6778wWW2zh3MuPP/54c+qpp7pBI4j69+9v2rRp4yykDzzwQGoyCxYsMBdddJFp2bKl6dy5s3MBTlVG7IwbN840bdrU/a1h/Q8XXqTsvffeZuONNw5XFfn84Ycfur6mTJli7rvvPrdP37NmzUq1feWVV8x2221ndt55Z4N7sS9Dhw417dq1My1atDDXXHNNxlRTCGX63nfffc2uu+5qDj/8cHfu119/7bqM6g9rMvPkOm3btnVW7oULF7pzgv8Oqdtnn33cccQr1vBmzZqZM844w0yePNkP271k2m233dx8YL/jjju6lwE0KM79SHWsHREQAREQAREQARFIQ0CW2DRQ0hySiE0DJd+HeHj2Fi6ssLgTU7p06WLGjx+f78upv4QTQDBeeuml5qqrrjJnnXWWmTdvXmrEpGFi/TTuunfddZe5+eabzbJly1z9ww8/7KyIrKU+9NBDzU033ZQ6L2qHv7v333/fCcdgG/4mEWXPPfecs6yyz4+3OAbb+n2EHkIW9+MzzzzT7fN5++23902chfONN94wPXr0MAMHDnTHEezXXXedufbaaw1u1IhErp9N6devnxO855xzjms+Z84c9wIgqr9hw4aZzz//3F0H12bWAjds2NCde/vtt5smTZoYxvfRRx+Z1157zR3n3yNB1xD8vFi68sorUxZm+uMY5yByecng/3Mpzv3IZs5qIwIiIAIiIAIiIAKyxMb/DUjExvMpcS0P699884058MADXV+4E3sRi3WJh3L/MF3ii6mDCkPgkksucUL1xx9/NAcffLDBgknhb2Hrrbd2Iuurr75yAcCwfOLSigCsV6+emTZtmvsbIuK1t0xGTZy/t9q1a5vq1asXaoIVGCsn16KefX4IOhZVeAGz6aabujYbbbSR2+czx3058sgjTePGjU2HDh0M48dll6BRm222mVm8eLETlXXr1s3aAwGLLgVrKAUBGdcf7RCafa37NAKU6/t/b1icYYelmxcF66+/vusTF2Esv2PGjHHux4juuXPnujqWAnTt2tXNqXv37u4Yv4p7P1IdaEcEREAEREAEREAE0hDwL8vTVOlQgIBEbABGaexidcKNEisOhQdq/1CNVQhr7AsvvFAal1afCSeAO+tTTz3lrPSDBw92oyVaMS6xrDnl55RTTnGWRL7QWLuKCyvHEb9YJ70Qy3Wq22yzjcGNGbdbhCz7/GTjVsy62nRrXhnDJpts4oYSfHvIGlTGjqhl7LgH485b3BLXH5ZnrNkdO3Z0Fl+E+dKlS92levXq5Xjj7oyb9nHHHeeOjx492llfEfreakvAK/+fiP/3WrNmzdSQ830/Uh1rRwREQAREQAREQAQsgeCzlIAUJfD/JpSidTqSBwJY0Y4++uhUT0FLLAcRsbhmXnzxxSmhm2qsnUpJAMs8bq5YDbECIgq32morN1dEF5ZGLJAIPQI8bbnllq4NAg3LK1tcXLEyYpmNKwQsItASwg9hhpjE2hq2zMb1Ea7DnXjkyJFu7Sl5j+mLlFFRhfYUBC6W2nxcP6o/XgBgJeY6jRo1cm7CBE9r3769IS8zVmKEKiJ00KBBjgu8WbN++umnO2s3fWONZf0rHhREFkd445btC8K2OPfDn6+tCIiACIiACIiACKQj4F+ip6vTsf8nIEvs/7PI+x7unrgLI0x8Ca6J5RgPwjxUyxrrCVX+LUISyyvCib8NROb555/vJn7aaae5taP33HOPE7nUf//9967u1ltvdVGte/bs6UTuCSeckBHW1Vdf7YJE4UKLMCNgFMeCJRyxOFiXbh+BiFWS4FIIVFxuKbygCRfeIiLImS85aPfbbz93TrY5kv1bSLZeKMf1R65buPIC4Oyzz3YBoXwAJyyvjJd6BGzv3r1NjRo1DG7CBLjixQBu3eRzPu+881zAKgI9UbgPDz30kNv37tPFuR+uA/0SAREQAREQAREQgQwE/DNQhmZVtnotq/YLquzsS3niBLaZOHGiW2vnL8VDNsGcxo4d6w+5NXq009rYFJIqseNdcqNcgnEZxmqJZTFYfMoXLJteUAXry2qfIFAIYARttl+0rFdl/IjHfJRwf/RNrlu2jCvIhyjEuGtzDOuxdxP241i1apVzp+alAnXBc6lD6GKtZYkALti+JOV++PFoKwIiIAIiIAIiUHEJYKxAJ+ABRtwSlfQE5E6cnktejvLQy9q8cAk+HFOHJeg///mPe0jGSqRSNQhEiVc/e4ImpSsIRwIllXepVatWzkPw1tScT4w4IdwfbKK48e8ujptfDxwMbsX62nfeecetRcbijPU5KGAZVlLuRwQiHRYBERABERABEaiABGRnjL9pcieO51Oi2rffftvsscceRfoIW4BYD4mrI6lBVERABJJDAEsvP97V2AfgSs4INRIREAEREAEREIHKRMCL12y93CrT3HOZiyyxudDKse2TTz6ZSg0SPDUsYqkbMGCA+e9//xtspn0RyAsB1uAuWbIk1de2225byFU2VaGdIgQIvMZPLgV3ZIJI+dKgQYNU1GZ/TFsREAEREAEREAEREIHiE5CILT67jGdGuQZHvVmR33tGpJWiAZa9UaNGuQi6PiVNaU6MND4EIfKFyMibb765/2hYK0o+Y9ZeEHjJ52RNNSiDHcZEILRu3bqVwdWyuwRrlnkRReHF08knn5w6kSjRrGOn4ElBgDZfFi5caA499FD/0b2g8ul8Uge1IwIiIAIiIAIiIAJpCMgSmwZKmkNyJ04DpbQPkepEpeoSILjQdddd59LdlBUFUswgrvgJClj+FonYe9lll7n8tARFKo1Cmpqrrroqsuunn37a3HDDDS4gU2SjMq4g/RHRxV9++eUiEZ1JXUTdo48+mhK6fnjkofWs69ev7w9rKwIiIAIiIAIiIAIikCcCErF5AplLN1i+VEQAAlhl/Rs3PiOcEEhRLzpoS85X3IPJE1vSMmPGDCemscTecccdzqLINRiHL+z7MRKJ1/8w9nQFC2ZwbJxP/lbytbIf7M9fq2/fvmbq1KkuSFK4z/B1/Dm0g0WuJTw+zod5+N8lUaGxYJP2KFy23357Vxe0wIbb6LMIiIAIiIAIiIAI5ErAP3NFeW7m2l9lbS8RWw53NkqglMNQdMlyJIBAatmypROOs2bNciPBpXeHHXYw22yzjctzGlxbidDERb1Vq1YuD6rPf1qSKRBBe++993apZXw/w4YNc/lo/WesuN6tFgvyueee68bA2Pv06eObuTy0119/vcsL26JFC0OOVcRs06ZNnUD+8MMP3T6fH3nkEXfeuHHj3DECJx1xxBGpvthh7oSZ5zonnniiyzPLcXIqH3TQQYY8ubBgm42YJU9uuvEhXmHerFkzN2ZS6KiIgAiIgAiIgAiIgAgkl4BEbDncG4nYcoCewEuyBhoRWbduXTNy5Eg3wtatWxuiWj/33HPOYhmMhnv77bebJk2amDfeeMN89NFHJcorjCC+6KKL3HU+/fRTt89ncr/6N4DpkGGFJdXMww8/bO69914zZMgQ8/3337umjz32mJk2bZphy/j5O1+6dKkbK30jOBGy/JxyyinuHCyZCPdrrrmmyOUefPBBs3jxYidaYXXLLbe4NowPgXvIIYeYV155xfACYNKkSUXODx+IGh/rXadPn24Q1IjpK6+8spAlOtyPPouACIiACIiACIhAaRHwz2GyxMYTloiN51MqtRKxpYK1wnWKBZHItfvuu6+ZO3euGz+Wy5kzZzoRSP5R1or6gsUUkdi/f3/z/PPPm0x5Zv156bZbbLGFuy45TxGIjIGfYI7UdOdxDMssFuFOnTq5JqSGQtwSQIqcx23btjW77LKLGTp0qGnUqJGLzItr7nrrredyuJLH1V+HY1y/evXqhS7nxTIBkRD2PXr0cFyItOwL18Ktd8cdd3RBofzxdNu48eHejJAfM2aMcyvGYuvvR7q+dEwEREAEREAEREAERKB8CSg6cTnwl4gtB+gJvKSPTBx804arLms2SeuCuAqWXr16mQ4dOpgpU6aYBx54wAwfPtxZIoNtst3HXZmfefPmOUF5/PHHpz2VMQSFI41q1arl2gbHzQFEdXj9qmtofyHIV69e7T+WeFutWrVUmiD6zqZEjW/06NHmzjvvdALczy34bzQ8z+C1qAvfp2C99kVABERABERABESgOATinj+K019lOye7p7/KNutynk84gEw5D0eXTwgBrIW42h577LFunacfFkGRKLjQNm7c2Jx00klu/SjWwmzWgvp+st0ibrkWQZ+eeeYZJ0yXLVtWJPBRsD+EJOPGZRd3Y9rThy+77rqrs27i9ouLsRfGBFRin3kgHNnnGP0ddthhZsSIEYb0O6yhJfXPZptt5rvMaRs3PsaEZfn0009P9Y811v87JdowhcBTX331VWrsHMP9GHfoBQsWOGtuUPxSryICIiACIiACIiACuRDw7sS5nFMV20rElsNd14NuOUCvAJdEaLEe86abbjIERvIutj7fMK61pMPh86BBg0zv3r1NjRo1SjwzrhssXIPASV27dnWWXtayDhgwwK09DbflPP+mEPfezp07m0svvdQgWrEar1q1ynXdpk0b1x+BmhCjPm/t1VdfbajDRRrhyD7HKGeeeabB3fmYY45x/Vx77bXuuL+e+/D3L38MwR/+YZ0vJWp8HGdtMoKUNba4Qp933nluvpzHiwOCTvHygDy6rEn2hc+4TLdv39507NjRfPPNN75KWxEQAREQAREQAREoNgH/bFPsDir5iWtZtV9QyeeYqOndfffd5qGHHnLpRhI1MA0mMQSwAGKVxf2VNbJsCT7EcXLMrrvuuk7gciybcv/996dEI+2xbAZzxUb1wbVZy/rbb7+563PdbIofZ82aNd062OA51JGLlrps+8MNecMNNwx2k3YfZgS+ChfEPyLcl6jxIbg33njjyPniKs1/KLQJF+4LrLhXrK9F0PrCtRmDigiIgAiIgAiIgAhkIsBL/Ndee83Mnj07L8aKTNerqPXZPZVW1NklcNw8aPMQrSICUQSC4g5h5AvHi+NOSwApLKu+EFgpm+Kv7YMwZXMObeLGSR2BnHIp2QhY+sNKvHDhwoxdR43Pi9Oo+bION6r49c3UI6THjx+fakr0aRUREAEREAEREAERyIWALLHxtCRi4/nkvRYRK3fivGNVhzEEEI25CseY7lSVgQDW2G233TZDK1WLgAiIgAiIgAiIQFECcpItyiTdkcKL4dK10LG8EkDEEs1Uf6B5xarOREAEREAEREAEREAERKDSEJAlNv5WSsTG88l7LSKWImts3tGqQxEQAREQAREQAREQARGo0AS8octvK/RkSnHwErGlCDdd1z6npERsOjo6JgIiIAIiIAIiIAIiIAIiIEts/N+ARGw8n7zX+rcqCu6Ud7TqUAREQAREQAREQAREQAQqNAGvFSRi42+jdnG0ygAACmJJREFURGw8n7zXyp0470jVoQiIgAiIgAiIgAiIgAiIQBUiIBFbxjdb7sRlDFyXEwEREAEREAEREAEREIEKQkCW2OxulERsdpzy1sr/YWpNbN6QqiMREAEREAEREAEREAEREIEqREAitoxvtncn1prYMgavy4mACIiACIiACIiACIhAwgl4g5fWxMbfKInYeD55r5U7cd6RqkMREAEREAEREAEREAEREIEqREAitoxvtrfEyp24jMHrciIgAiIgAiIgAiIgAiJQQQjIEht/oyRi4/nkvda7CEjE5h2tOhQBERABERABERABERABEagCBCRiy/gme3dirYktY/C6nAiIgAiIgAiIgAiIgAgknIA3eMkSG3+jJGLj+eS9Vu7EeUeqDkVABERABERABERABERABKoQAYnYMr7Z3hIrd+IyBq/LiYAIiIAIiIAIiIAIiEDCCcgSm90NkojNjlPeWvk/TInYvCFVRyIgAiIgAiIgAiIgAiJQKQh4rVApJlOKk5CILUW46br27sRaE5uOjo6JgAiIgAiIgAiIgAiIgAhoTWz834BEbDyfvNd6EStLbN7RqkMREAEREAEREAEREAERqNAEZInN7vZJxGbHKW+tJGLzhlIdiYAIiIAIiIAIiIAIiIAIVEECErFlfNO9iJU7cRmD1+VEQAREQAREQAREQAREIOEEZInN7gZJxGbHKW+tvIiVO3HekKojERABERABERABERABEahUBLQmNv52SsTG88l7rVLs5B2pOhQBERABERABERABERCBSkFAltjsbqNEbHac8tbK/2HKEps3pOpIBERABERABERABERABCoVAVli42+nRGw8n7zXendirYnNO1p1KAIiIAIiIAIiIAIiIAIVmoA3eEnExt9Gidh4PnmvxZ24ZcuWRpbYvKNVhyIgAiIgAiIgAiIgAiIgAlWAwLpVYI6JmuLSpUvNr7/+aiZNmmSmTZvmxubftOSy5S1NLu25ULr2+ejHA07Xf9R1w+eEP+faF+3zMZd89VPS+fjz2RaHhT+vIjHxY47bUkepbEzWzEq/RUAEREAEREAEqjqBn376ySHwzzpVnUfU/CVio8iU0vE+ffqYJ554wowdO7aUrqBuRUAEREAEREAEREAEREAEKiqBzp07V9Shl9m417KWmoIyu5ouJAIiIAIiIAIiUISA/6843dZ7iHBSuvpsjvsLFvd8f162/WQaU9ycMp3r6+P68OPNtM12PmXRT3A+fo65boN9ZBpzVH3SmOTKINw+H0xglY9+iss2OKfi9hG83/mYS3kxCbLwc6psTLp3724kYv1djd5KxEazUY0IiIAIiIAIiIAIiIAIiIAIiEDCCCiwU8JuiIYjAiIgAiIgAiIgAiIgAiIgAiIQTUAiNpqNakRABERABERABERABERABERABBJGQCI2YTdEwxEBERABERABERABERABERABEYgmIBEbzUY1IiACIiACIiACIiACIiACIiACCSMgEZuwG6LhiIAIiIAIiIAIiIAIiIAIiIAIRBOQiI1moxoREAEREAEREAEREAEREAEREIGEEZCITdgN0XBEQAREQAREQAREQAREQAREQASiCUjERrNRjQiIgAiIgAiIgAiIgAiIgAiIQMIISMQm7IZoOCIgAiIgAiIgAiIgAiIgAiIgAtEEJGKj2ahGBERABERABERABERABERABEQgYQQkYhN2QzQcERABERABERABERABERABERCBaAISsdFsVCMCIiACIiACIiACIiACIiACIpAwAhKxCbshGo4IiIAIiIAIiIAIiIAIiIAIiEA0AYnYaDaqEQEREAEREAEREAEREAEREAERSBgBidiE3RANRwREQAREQAREQAREQAREQAREIJqARGw0G9WIgAiIgAiIgAiIgAiIgAiIgAgkjIBEbMJuiIYjAiIgAiIgAiIgAiIgAiIgAiIQTUAiNpqNakRABERABERABERABERABERABBJGQCI2YTdEwxEBERABERABERABERABERABEYgmIBEbzUY1IiACIiACIiACIiACIiACIiACCSMgEZuwG6LhiIAIiIAIiIAIiIAIiIAIiIAIRBOQiI1moxoREAEREAEREAEREAEREAEREIGEEZCITdgN0XBEQAREQAREQAREQAREQAREQASiCUjERrNRjQiIgAiIgAiIgAiIgAiIgAiIQMIISMQm7IZoOCIgAiIgAiIgAiIgAiIgAiIgAtEEJGKj2ahGBERABERABERABERABERABEQgYQQkYhN2QzQcERABERABERABERABERABERCBaAISsdFsVCMCIiACIiACIiACIiACIiACIpAwAhKxCbshGo4IiIAIiIAIiIAIiIAIiIAIiEA0AYnYaDaqEQEREAEREAEREAEREAEREAERSBgBidiE3RANRwREQAREQAREQAREQAREQAREIJqARGw0G9WIgAiIgAiIgAiIgAiIgAiIgAgkjIBEbMJuiIYjAiIgAiIgAiIgAiIgAiIgAiIQTUAiNpqNakRABERABERABERABERABERABBJGQCI2YTdEwxEBERABERABERABERABERABEYgmIBEbzUY1IiACIiACIiACIiACIiACIiACCSMgEZuwG6LhiIAIiIAIiIAIiIAIiIAIiIAIRBOQiI1moxoREAEREAEREAEREAEREAEREIGEEZCITdgN0XBEQAREQAREQAREQAREQAREQASiCUjERrNRjQiIgAiIgAiIgAiIgAiIgAiIQMIISMQm7IZoOCIgAiIgAiIgAiIgAiIgAiIgAtEEJGKj2ahGBERABERABERABERABERABEQgYQQkYhN2QzQcERABERABERABERABERABERCBaAISsdFsVCMCIiACIiACIiACIiACIiACIpAwAhKxCbshGo4IiIAIiIAIiIAIiIAIiIAIiEA0AYnYaDaqEQEREAEREAEREAEREAEREAERSBgBidiE3RANRwREQAREQAREQAREQAREQAREIJqARGw0G9WIgAiIgAiIgAiIgAiIgAiIgAgkjIBEbMJuiIYjAiIgAiIgAiIgAiIgAiIgAiIQTUAiNpqNakRABERABERABERABERABERABBJGQCI2YTdEwxEBERABERABERABERABERABEYgmIBEbzUY1IiACIiACIiACIiACIiACIiACCSMgEZuwG6LhiIAIiIAIiIAIiIAIiIAIiIAIRBOQiI1moxoREAEREAEREAEREAEREAEREIGEEZCITdgN0XBEQAREQAREQAREQAREQAREQASiCUjERrNRjQiIgAiIgAiIgAiIgAiIgAiIQMIISMQm7IZoOCIgAiIgAiIgAiIgAiIgAiIgAtEEJGKj2ahGBERABERABERABERABERABEQgYQQkYhN2QzQcERABERABERABERABERABERCBaAISsdFsVCMCIiACIiACIiACIiACIiACIpAwAhKxCbshGo4IiIAIiIAIiIAIiIAIiIAIiEA0AYnYaDaqEQEREAEREAEREAEREAEREAERSBiB/wPpG/8ocYPdfAAAAABJRU5ErkJggg==" + "a9af19ff-8cee-4521-9e94-b4bb09128528.png": { + "image/png": "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" } }, "cell_type": "markdown", @@ -134,12 +134,12 @@ "\n", "We can lay out an agentic RAG graph like this:\n", "\n", - "![Screenshot 2024-02-02 at 1.36.50 PM.png](attachment:f886806c-0aec-4c2a-8027-67339530cb60.png)" + "![Screenshot 2024-02-14 at 3.17.29 PM.png](attachment:a9af19ff-8cee-4521-9e94-b4bb09128528.png)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "id": "278d1d83-dda6-4de4-bf8b-be9965c227fa", "metadata": {}, "outputs": [], @@ -277,7 +277,7 @@ "\n", "\n", "# Define the function to execute tools\n", - "def call_tool(state):\n", + "def retrieve(state):\n", " \"\"\"\n", " Executes a tool based on the last message's function call.\n", "\n", @@ -310,7 +310,39 @@ " function_message = FunctionMessage(content=str(response), name=action.tool)\n", "\n", " # We return a list, because this will get added to the existing list\n", - " return {\"messages\": [function_message]}" + " return {\"messages\": [function_message]}\n", + "\n", + "# Rewrite query\n", + "def rewrite(state):\n", + " \n", + " \"\"\"\n", + " Transform the query to produce a better question.\n", + " \n", + " Args:\n", + " state (messages): The current state of the agent, including all messages.\n", + " \n", + " Returns:\n", + " dict: The updated state with the new function message added to the list of messages.\n", + " \"\"\"\n", + " \n", + " print(\"---TRANSFORM QUERY---\")\n", + " # we know the first message involves a user question\n", + " question = messages[0]\n", + "\n", + " msg = HumanMessage(\n", + " content=f\"\"\" \\n \n", + " Look at the input and try to reason about the underlying semantic intent / meaning. \\n \n", + " Here is the initial question:\n", + " \\n ------- \\n\n", + " {question} \n", + " \\n ------- \\n\n", + " Formulate an improved question: \"\"\",\n", + " )\n", + "\n", + " # Grader\n", + " model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n", + " response = model.invoke(msg)\n", + " return {\"messages\": [response]}" ] }, { @@ -328,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "id": "8718a37f-83c2-4f16-9850-e61e0f49c3d4", "metadata": {}, "outputs": [], @@ -340,12 +372,13 @@ "\n", "# Define the nodes we will cycle between\n", "workflow.add_node(\"agent\", call_model) # agent\n", - "workflow.add_node(\"action\", call_tool) # retrieval" + "workflow.add_node(\"retrieve\", retrieve) # retrieval\n", + "workflow.add_node(\"rewrite\", rewrite) # retrieval" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "id": "b2158218-b21f-491b-853c-876c1afe9ba6", "metadata": {}, "outputs": [], @@ -360,22 +393,24 @@ " should_retrieve,\n", " {\n", " # Call tool node\n", - " \"continue\": \"action\",\n", + " \"continue\": \"retrieve\",\n", " \"end\": END,\n", " },\n", ")\n", "\n", "# Edges taken after the `action` node is called.\n", "workflow.add_conditional_edges(\n", - " \"action\",\n", + " \"retrieve\",\n", " # Assess agent decision\n", " check_relevance,\n", " {\n", " # Call agent node\n", " \"yes\": \"agent\",\n", - " \"no\": END, # placeholder\n", + " \"no\": \"rewrite\", \n", " },\n", ")\n", + "workflow.add_edge(\"agent\", END)\n", + "workflow.add_edge(\"rewrite\", \"agent\")\n", "\n", "# Compile\n", "app = workflow.compile()" @@ -383,7 +418,62 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, + "id": "90d09305-5302-4730-8173-57de80162145", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---CALL AGENT---\n", + "---DECIDE TO RETRIEVE---\n", + "---DECISION: RETRIEVE---\n", + "---EXECUTE RETRIEVAL---\n", + "---CHECK RELEVANCE---\n", + "---DECISION: DOCS RELEVANT---\n", + "---CALL AGENT---\n", + "---DECIDE TO RETRIEVE---\n", + "---DECISION: DO NOT RETRIEVE / DONE---\n" + ] + }, + { + "ename": "InvalidUpdateError", + "evalue": "Invalid update for channel __end__: LastValue can only receive one value per step.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:736\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 736\u001b[0m \u001b[43mchannels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mchan\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/channels/last_value.py:47\u001b[0m, in \u001b[0;36mLastValue.update\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(values) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLastValue can only receive one value per step.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", + "\u001b[0;31mInvalidUpdateError\u001b[0m: LastValue can only receive one value per step.", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[30], line 9\u001b[0m\n\u001b[1;32m 1\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\n\u001b[1;32m 3\u001b[0m HumanMessage(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6\u001b[0m ]\n\u001b[1;32m 7\u001b[0m }\n\u001b[0;32m----> 9\u001b[0m \u001b[43mapp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:569\u001b[0m, in \u001b[0;36mPregel.invoke\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 560\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 561\u001b[0m \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 566\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 567\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]:\n\u001b[1;32m 568\u001b[0m latest: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 569\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream(\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 571\u001b[0m config,\n\u001b[1;32m 572\u001b[0m output_keys\u001b[38;5;241m=\u001b[39moutput_keys \u001b[38;5;28;01mif\u001b[39;00m output_keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput,\n\u001b[1;32m 573\u001b[0m input_keys\u001b[38;5;241m=\u001b[39minput_keys,\n\u001b[1;32m 574\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 575\u001b[0m ):\n\u001b[1;32m 576\u001b[0m latest \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m latest\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:605\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 597\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 598\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 604\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 605\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform_stream_with_config(\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform,\n\u001b[1;32m 608\u001b[0m config,\n\u001b[1;32m 609\u001b[0m output_keys\u001b[38;5;241m=\u001b[39moutput_keys,\n\u001b[1;32m 610\u001b[0m input_keys\u001b[38;5;241m=\u001b[39minput_keys,\n\u001b[1;32m 611\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 612\u001b[0m ):\n\u001b[1;32m 613\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langchain_core/runnables/base.py:1497\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1497\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:350\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys)\u001b[0m\n\u001b[1;32m 347\u001b[0m _interrupt_or_proceed(done, inflight, step)\n\u001b[1;32m 349\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m \u001b[43m_apply_writes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpending_writes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m 352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdebug:\n\u001b[1;32m 355\u001b[0m print_checkpoint(step, channels)\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:738\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 736\u001b[0m channels[chan]\u001b[38;5;241m.\u001b[39mupdate(vals)\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 738\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\n\u001b[1;32m 739\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid update for channel \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mchan\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 740\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 741\u001b[0m checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchannel_versions\u001b[39m\u001b[38;5;124m\"\u001b[39m][chan] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 742\u001b[0m updated_channels\u001b[38;5;241m.\u001b[39madd(chan)\n", + "\u001b[0;31mInvalidUpdateError\u001b[0m: Invalid update for channel __end__: LastValue can only receive one value per step." + ] + } + ], + "source": [ + "inputs = {\n", + " \"messages\": [\n", + " HumanMessage(\n", + " content=\"What does Lilian Weng say about the types of agent memory?\"\n", + " )\n", + " ]\n", + "}\n", + "\n", + "app.invoke(inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "id": "7649f05a-cb67-490d-b24a-74d41895139a", "metadata": {}, "outputs": [ @@ -394,31 +484,49 @@ "---CALL AGENT---\n", "\"Output from node 'agent':\"\n", "'---'\n", - "{ 'messages': [ AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory Lilian Weng\"}', 'name': 'retrieve_blog_posts'}})]}\n", + "{ 'messages': [ AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory\"}', 'name': 'retrieve_blog_posts'}})]}\n", "'\\n---\\n'\n", "---DECIDE TO RETRIEVE---\n", "---DECISION: RETRIEVE---\n", - "---EXECUTE RETRIEVAL---\n", - "\"Output from node 'action':\"\n", + "\"Output from node '__end__':\"\n", "'---'\n", - "{ 'messages': [ FunctionMessage(content='Citation#\\nCited as:\\n\\nWeng, Lilian. (Jun 2023). LLM-powered Autonomous Agents\". Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.\\n\\nLLM 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\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nWeng, Lilian. (Mar 2023). Prompt Engineering. Lil’Log. https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/.', name='retrieve_blog_posts')]}\n", + "{ 'messages': [ HumanMessage(content='What does Lilian Weng say about the types of agent memory?'),\n", + " AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory\"}', 'name': 'retrieve_blog_posts'}})]}\n", + "'\\n---\\n'\n", + "---EXECUTE RETRIEVAL---\n", + "\"Output from node 'retrieve':\"\n", + "'---'\n", + "{ 'messages': [ FunctionMessage(content='Table 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\\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\\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\\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', name='retrieve_blog_posts')]}\n", "'\\n---\\n'\n", "---CHECK RELEVANCE---\n", "---DECISION: DOCS RELEVANT---\n", "---CALL AGENT---\n", "\"Output from node 'agent':\"\n", "'---'\n", - "{ 'messages': [ AIMessage(content='Lilian Weng\\'s blog post titled \"LLM-powered Autonomous Agents\" discusses the concept of agent memory but does not provide a detailed list of the types of agent memory directly in the provided excerpt. For more detailed information on the types of agent memory, it would be necessary to refer directly to the blog post itself. You can find the post [here](https://lilianweng.github.io/posts/2023-06-23-agent/).')]}\n", + "{ 'messages': [ AIMessage(content='Lilian Weng discusses the concept of memory within agent systems, highlighting its importance but does not provide specific details on the types of agent memory in the provided excerpt. The discussion on memory is part of a broader overview of agent systems, which also includes planning and tool use. In the context of planning, agents are capable of breaking down large tasks into smaller, manageable subgoals (task decomposition) and engaging in self-reflection and refinement based on past actions to improve future outcomes.\\n\\nWhile the excerpt mentions a section titled \"Types of Memory,\" specific details or descriptions of these types are not provided in the provided content. Additionally, there\\'s a mention of Maximum Inner Product Search (MIPS) in the context of memory, suggesting it might be a technique or tool related to how agents access or utilize their memory, but again, specific details are not given.\\n\\nFor a more detailed understanding of the types of agent memory Lilian Weng discusses, it would be necessary to access the full content of her blog post or publication.')]}\n", "'\\n---\\n'\n", "---DECIDE TO RETRIEVE---\n", - "---DECISION: DO NOT RETRIEVE / DONE---\n", - "\"Output from node '__end__':\"\n", - "'---'\n", - "{ 'messages': [ HumanMessage(content=\"What are the types of agent memory based on Lilian Weng's blog post?\"),\n", - " AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory Lilian Weng\"}', 'name': 'retrieve_blog_posts'}}),\n", - " FunctionMessage(content='Citation#\\nCited as:\\n\\nWeng, Lilian. (Jun 2023). LLM-powered Autonomous Agents\". Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.\\n\\nLLM 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\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nWeng, Lilian. (Mar 2023). Prompt Engineering. Lil’Log. https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/.', name='retrieve_blog_posts'),\n", - " AIMessage(content='Lilian Weng\\'s blog post titled \"LLM-powered Autonomous Agents\" discusses the concept of agent memory but does not provide a detailed list of the types of agent memory directly in the provided excerpt. For more detailed information on the types of agent memory, it would be necessary to refer directly to the blog post itself. You can find the post [here](https://lilianweng.github.io/posts/2023-06-23-agent/).')]}\n", - "'\\n---\\n'\n" + "---DECISION: DO NOT RETRIEVE / DONE---\n" + ] + }, + { + "ename": "InvalidUpdateError", + "evalue": "Invalid update for channel __end__: LastValue can only receive one value per step.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:736\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 736\u001b[0m \u001b[43mchannels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mchan\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/channels/last_value.py:47\u001b[0m, in \u001b[0;36mLastValue.update\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(values) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLastValue can only receive one value per step.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", + "\u001b[0;31mInvalidUpdateError\u001b[0m: LastValue can only receive one value per step.", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 12\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain_core\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmessages\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HumanMessage\n\u001b[1;32m 5\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\n\u001b[1;32m 7\u001b[0m HumanMessage(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10\u001b[0m ]\n\u001b[1;32m 11\u001b[0m }\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m app\u001b[38;5;241m.\u001b[39mstream(inputs):\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m output\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 14\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOutput from node \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:605\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 597\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 598\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 604\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 605\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform_stream_with_config(\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform,\n\u001b[1;32m 608\u001b[0m config,\n\u001b[1;32m 609\u001b[0m output_keys\u001b[38;5;241m=\u001b[39moutput_keys,\n\u001b[1;32m 610\u001b[0m input_keys\u001b[38;5;241m=\u001b[39minput_keys,\n\u001b[1;32m 611\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 612\u001b[0m ):\n\u001b[1;32m 613\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langchain_core/runnables/base.py:1497\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1497\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:350\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys)\u001b[0m\n\u001b[1;32m 347\u001b[0m _interrupt_or_proceed(done, inflight, step)\n\u001b[1;32m 349\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m \u001b[43m_apply_writes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpending_writes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m 352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdebug:\n\u001b[1;32m 355\u001b[0m print_checkpoint(step, channels)\n", + "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:738\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 736\u001b[0m channels[chan]\u001b[38;5;241m.\u001b[39mupdate(vals)\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 738\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\n\u001b[1;32m 739\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid update for channel \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mchan\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 740\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 741\u001b[0m checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchannel_versions\u001b[39m\u001b[38;5;124m\"\u001b[39m][chan] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 742\u001b[0m updated_channels\u001b[38;5;241m.\u001b[39madd(chan)\n", + "\u001b[0;31mInvalidUpdateError\u001b[0m: Invalid update for channel __end__: LastValue can only receive one value per step." ] } ], @@ -430,7 +538,7 @@ "inputs = {\n", " \"messages\": [\n", " HumanMessage(\n", - " content=\"What are the types of agent memory based on Lilian Weng's blog post?\"\n", + " content=\"What does Lilian Weng say about the types of agent memory?\"\n", " )\n", " ]\n", "}\n", @@ -442,16 +550,6 @@ " pprint.pprint(\"\\n---\\n\")" ] }, - { - "cell_type": "markdown", - "id": "93781e8c-dd25-4754-9c26-e5faac57e715", - "metadata": {}, - "source": [ - "Trace:\n", - "\n", - "https://smith.langchain.com/public/6f45c61b-69a0-4b35-bab9-679a8840a2d6/r" - ] - }, { "cell_type": "code", "execution_count": null, From 8b10dc7f3b8b5858a41d8e2dd3527166ad537ea1 Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Wed, 14 Feb 2024 16:23:35 -0800 Subject: [PATCH 2/3] Update agentic RAG --- examples/rag/langgraph_agentic_rag.ipynb | 324 +++++++++++------------ 1 file changed, 149 insertions(+), 175 deletions(-) diff --git a/examples/rag/langgraph_agentic_rag.ipynb b/examples/rag/langgraph_agentic_rag.ipynb index a572cb46b..20b1e9132 100644 --- a/examples/rag/langgraph_agentic_rag.ipynb +++ b/examples/rag/langgraph_agentic_rag.ipynb @@ -17,14 +17,20 @@ "source": [ "# LangGraph Retrieval Agent\n", "\n", - "We can implement [Retrieval Agents](https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents) in [LangGraph](https://python.langchain.com/docs/langgraph).\n", + "[Retrieval Agents](https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents) are useful when we want to make decisions about whether to retrieve from an index.\n", "\n", - "## Retriever" + "To implement a retrieval agent, we simple need to give an LLM access to a retrier tool.\n", + "\n", + "We can incorperate this into [LangGraph](https://python.langchain.com/docs/langgraph).\n", + "\n", + "## Retriever\n", + "\n", + "First, we index 3 blog posts." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "e50c9efe-4abe-42fa-b35a-05eeeede9ec6", "metadata": {}, "outputs": [], @@ -57,9 +63,17 @@ "retriever = vectorstore.as_retriever()" ] }, + { + "cell_type": "markdown", + "id": "225d2277-45b2-4ae8-a7d6-62b07fb4a002", + "metadata": {}, + "source": [ + "Then we create a retriever tool." + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "id": "0b97bdd8-d7e3-444d-ac96-5ef4725f9048", "metadata": {}, "outputs": [], @@ -97,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "id": "0e378706-47d5-425a-8ba0-57b9acffbd0c", "metadata": {}, "outputs": [], @@ -114,8 +128,8 @@ }, { "attachments": { - "a9af19ff-8cee-4521-9e94-b4bb09128528.png": { - "image/png": "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" + "7ad1a116-28d7-473f-8cff-5f2efd0bf118.png": { + "image/png": "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" } }, "cell_type": "markdown", @@ -124,22 +138,18 @@ "source": [ "## Nodes and Edges\n", "\n", - "Each node will - \n", - "\n", - "1/ Either be a function or a runnable.\n", - "\n", - "2/ Modify the `state`.\n", - "\n", - "The edges choose which node to call next.\n", - "\n", "We can lay out an agentic RAG graph like this:\n", "\n", - "![Screenshot 2024-02-14 at 3.17.29 PM.png](attachment:a9af19ff-8cee-4521-9e94-b4bb09128528.png)" + "* The state is a set of messages\n", + "* Each node will update (append to) state\n", + "* Conditional edges decide which node to visit next\n", + "\n", + "![Screenshot 2024-02-14 at 3.43.58 PM.png](attachment:7ad1a116-28d7-473f-8cff-5f2efd0bf118.png)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 38, "id": "278d1d83-dda6-4de4-bf8b-be9965c227fa", "metadata": {}, "outputs": [], @@ -148,13 +158,17 @@ "import operator\n", "from typing import Annotated, Sequence, TypedDict\n", "\n", + "from langchain import hub\n", "from langchain.output_parsers import PydanticOutputParser\n", "from langchain.prompts import PromptTemplate\n", "from langchain.tools.render import format_tool_to_openai_function\n", + "from langchain_core.utils.function_calling import convert_to_openai_tool\n", "from langchain_core.messages import BaseMessage, FunctionMessage\n", + "from langchain.output_parsers.openai_tools import PydanticToolsParser\n", "from langchain_core.pydantic_v1 import BaseModel, Field\n", "from langchain_openai import ChatOpenAI\n", "from langgraph.prebuilt import ToolInvocation\n", + "from langchain_core.output_parsers import StrOutputParser\n", "\n", "### Edges\n", "\n", @@ -167,14 +181,16 @@ " present, the process continues to retrieve information. Otherwise, it ends the process.\n", "\n", " Args:\n", - " state (messages): The current state of the agent, including all messages.\n", + " state (messages): The current state\n", "\n", " Returns:\n", - " str: A decision to either \"continue\" the retrieval process or \"end\" it.\n", + " str: A decision to either \"continue\" the retrieval process or \"end\" it\n", " \"\"\"\n", + " \n", " print(\"---DECIDE TO RETRIEVE---\")\n", " messages = state[\"messages\"]\n", " last_message = messages[-1]\n", + " \n", " # If there is no function call, then we finish\n", " if \"function_call\" not in last_message.additional_kwargs:\n", " print(\"---DECISION: DO NOT RETRIEVE / DONE---\")\n", @@ -185,61 +201,67 @@ " return \"continue\"\n", "\n", "\n", - "def check_relevance(state):\n", + "def grade_documents(state):\n", " \"\"\"\n", - " Determines whether the Agent should continue based on the relevance of retrieved documents.\n", - "\n", - " This function checks if the last message in the conversation is of type FunctionMessage, indicating\n", - " that document retrieval has been performed. It then evaluates the relevance of these documents to the user's\n", - " initial question using a predefined model and output parser. If the documents are relevant, the conversation\n", - " is considered complete. Otherwise, the retrieval process is continued.\n", + " Determines whether the retrieved documents are relevant to the question.\n", "\n", " Args:\n", - " state messages: The current state of the conversation, including all messages.\n", + " state (messages): The current state\n", "\n", " Returns:\n", - " str: A directive to either \"end\" the conversation if relevant documents are found, or \"continue\" the retrieval process.\n", + " str: A decision for whether the documents are relevant or not\n", " \"\"\"\n", "\n", " print(\"---CHECK RELEVANCE---\")\n", "\n", - " # Output\n", - " class FunctionOutput(BaseModel):\n", + " # Data model\n", + " class grade(BaseModel):\n", + " \"\"\"Binary score for relevance check.\"\"\"\n", + "\n", " binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n", "\n", - " # Create an instance of the PydanticOutputParser\n", - " parser = PydanticOutputParser(pydantic_object=FunctionOutput)\n", + " # LLM\n", + " model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n", "\n", - " # Get the format instructions from the output parser\n", - " format_instructions = parser.get_format_instructions()\n", + " # Tool\n", + " grade_tool_oai = convert_to_openai_tool(grade)\n", "\n", - " # Create a prompt template with format instructions and the query\n", - " prompt = PromptTemplate(\n", - " template=\"\"\"You are a grader assessing relevance of retrieved docs to a user question. \\n \n", - " Here are the retrieved docs:\n", - " \\n ------- \\n\n", - " {context} \n", - " \\n ------- \\n\n", - " Here is the user question: {question}\n", - " If the docs contain keyword(s) in the user question, then score them as relevant. \\n\n", - " Give a binary score 'yes' or 'no' score to indicate whether the docs are relevant to the question. \\n \n", - " Output format instructions: \\n {format_instructions}\"\"\",\n", - " input_variables=[\"question\"],\n", - " partial_variables={\"format_instructions\": format_instructions},\n", + " # LLM with tool and enforce invocation\n", + " llm_with_tool = model.bind(\n", + " tools=[convert_to_openai_tool(grade_tool_oai)],\n", + " tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n", " )\n", "\n", - " model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\")\n", + " # Parser\n", + " parser_tool = PydanticToolsParser(tools=[grade])\n", "\n", - " chain = prompt | model | parser\n", + " # Prompt\n", + " prompt = PromptTemplate(\n", + " template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n", + " Here is the retrieved document: \\n\\n {context} \\n\\n\n", + " Here is the user question: {question} \\n\n", + " If the document contains keyword(s) or semantic meaning related to the user 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", + " input_variables=[\"context\", \"question\"],\n", + " )\n", + "\n", + " # Chain\n", + " chain = prompt | llm_with_tool | parser_tool\n", "\n", " messages = state[\"messages\"]\n", " last_message = messages[-1]\n", - " score = chain.invoke(\n", - " {\"question\": messages[0].content, \"context\": last_message.content}\n", - " )\n", "\n", - " # If relevant\n", - " if score.binary_score == \"yes\":\n", + " question = messages[0].content\n", + " docs = last_message.content\n", + " \n", + " score = chain.invoke(\n", + " {\"question\": question, \n", + " \"context\": docs}\n", + " )\n", + " \n", + " grade = score[0].binary_score\n", + "\n", + " if grade == \"yes\":\n", " print(\"---DECISION: DOCS RELEVANT---\")\n", " return \"yes\"\n", "\n", @@ -252,19 +274,16 @@ "### Nodes\n", "\n", "\n", - "# Define the function that calls the model\n", - "def call_model(state):\n", + "def agent(state):\n", " \"\"\"\n", - " Invokes the agent model to generate a response based on the current state.\n", - "\n", - " This function calls the agent model to generate a response to the current conversation state.\n", - " The response is added to the state's messages.\n", + " Invokes the agent model to generate a response based on the current state. Given\n", + " the question, it will decide to retrieve using the retriever tool, or simply end.\n", "\n", " Args:\n", - " state (messages): The current state of the agent, including all messages.\n", + " state (messages): The current state\n", "\n", " Returns:\n", - " dict: The updated state with the new message added to the list of messages.\n", + " dict: The updated state with the agent response apended to messages\n", " \"\"\"\n", " print(\"---CALL AGENT---\")\n", " messages = state[\"messages\"]\n", @@ -275,21 +294,15 @@ " # We return a list, because this will get added to the existing list\n", " return {\"messages\": [response]}\n", "\n", - "\n", - "# Define the function to execute tools\n", "def retrieve(state):\n", " \"\"\"\n", - " Executes a tool based on the last message's function call.\n", - "\n", - " This function is responsible for executing a tool invocation based on the function call\n", - " specified in the last message. The result from the tool execution is added to the conversation\n", - " state as a new message.\n", + " Uses tool to execute retrieval.\n", "\n", " Args:\n", - " state (messages): The current state of the agent, including all messages.\n", + " state (messages): The current state\n", "\n", " Returns:\n", - " dict: The updated state with the new function message added to the list of messages.\n", + " dict: The updated state with retrieved docs\n", " \"\"\"\n", " print(\"---EXECUTE RETRIEVAL---\")\n", " messages = state[\"messages\"]\n", @@ -305,29 +318,25 @@ " )\n", " # We call the tool_executor and get back a response\n", " response = tool_executor.invoke(action)\n", - " # print(type(response))\n", - " # We use the response to create a FunctionMessage\n", " function_message = FunctionMessage(content=str(response), name=action.tool)\n", "\n", " # We return a list, because this will get added to the existing list\n", " return {\"messages\": [function_message]}\n", "\n", - "# Rewrite query\n", "def rewrite(state):\n", - " \n", " \"\"\"\n", " Transform the query to produce a better question.\n", " \n", " Args:\n", - " state (messages): The current state of the agent, including all messages.\n", + " state (messages): The current state\n", " \n", " Returns:\n", - " dict: The updated state with the new function message added to the list of messages.\n", + " dict: The updated state with re-phrased question\n", " \"\"\"\n", " \n", " print(\"---TRANSFORM QUERY---\")\n", - " # we know the first message involves a user question\n", - " question = messages[0]\n", + " messages = state[\"messages\"]\n", + " question = messages[0].content\n", "\n", " msg = HumanMessage(\n", " content=f\"\"\" \\n \n", @@ -342,6 +351,41 @@ " # Grader\n", " model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n", " response = model.invoke(msg)\n", + " return {\"messages\": [response]}\n", + "\n", + "def generate(state):\n", + " \"\"\"\n", + " Generate answer\n", + "\n", + " Args:\n", + " state (messages): The current state\n", + "\n", + " Returns:\n", + " dict: The updated state with re-phrased question\n", + " \"\"\"\n", + " print(\"---GENERATE---\")\n", + " messages = state[\"messages\"]\n", + " question = messages[0].content\n", + " last_message = messages[-1]\n", + "\n", + " question = messages[0].content\n", + " docs = last_message.content\n", + "\n", + " # Prompt\n", + " prompt = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + " # LLM\n", + " llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n", + "\n", + " # Post-processing\n", + " def format_docs(docs):\n", + " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", + "\n", + " # Chain\n", + " rag_chain = prompt | llm | StrOutputParser()\n", + "\n", + " # Run\n", + " response = rag_chain.invoke({\"context\": docs, \"question\": question})\n", " return {\"messages\": [response]}" ] }, @@ -360,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 39, "id": "8718a37f-83c2-4f16-9850-e61e0f49c3d4", "metadata": {}, "outputs": [], @@ -371,14 +415,15 @@ "workflow = StateGraph(AgentState)\n", "\n", "# Define the nodes we will cycle between\n", - "workflow.add_node(\"agent\", call_model) # agent\n", + "workflow.add_node(\"agent\", agent) # agent\n", "workflow.add_node(\"retrieve\", retrieve) # retrieval\n", - "workflow.add_node(\"rewrite\", rewrite) # retrieval" + "workflow.add_node(\"rewrite\", rewrite) # retrieval\n", + "workflow.add_node(\"generate\", generate) # retrieval" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 40, "id": "b2158218-b21f-491b-853c-876c1afe9ba6", "metadata": {}, "outputs": [], @@ -402,14 +447,13 @@ "workflow.add_conditional_edges(\n", " \"retrieve\",\n", " # Assess agent decision\n", - " check_relevance,\n", + " grade_documents,\n", " {\n", - " # Call agent node\n", - " \"yes\": \"agent\",\n", + " \"yes\": \"generate\",\n", " \"no\": \"rewrite\", \n", " },\n", ")\n", - "workflow.add_edge(\"agent\", END)\n", + "workflow.add_edge(\"generate\", END)\n", "workflow.add_edge(\"rewrite\", \"agent\")\n", "\n", "# Compile\n", @@ -418,62 +462,7 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "90d09305-5302-4730-8173-57de80162145", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---CALL AGENT---\n", - "---DECIDE TO RETRIEVE---\n", - "---DECISION: RETRIEVE---\n", - "---EXECUTE RETRIEVAL---\n", - "---CHECK RELEVANCE---\n", - "---DECISION: DOCS RELEVANT---\n", - "---CALL AGENT---\n", - "---DECIDE TO RETRIEVE---\n", - "---DECISION: DO NOT RETRIEVE / DONE---\n" - ] - }, - { - "ename": "InvalidUpdateError", - "evalue": "Invalid update for channel __end__: LastValue can only receive one value per step.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:736\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 736\u001b[0m \u001b[43mchannels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mchan\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/channels/last_value.py:47\u001b[0m, in \u001b[0;36mLastValue.update\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(values) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLastValue can only receive one value per step.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", - "\u001b[0;31mInvalidUpdateError\u001b[0m: LastValue can only receive one value per step.", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[30], line 9\u001b[0m\n\u001b[1;32m 1\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\n\u001b[1;32m 3\u001b[0m HumanMessage(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6\u001b[0m ]\n\u001b[1;32m 7\u001b[0m }\n\u001b[0;32m----> 9\u001b[0m \u001b[43mapp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:569\u001b[0m, in \u001b[0;36mPregel.invoke\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 560\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 561\u001b[0m \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 566\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 567\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]:\n\u001b[1;32m 568\u001b[0m latest: Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 569\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream(\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 571\u001b[0m config,\n\u001b[1;32m 572\u001b[0m output_keys\u001b[38;5;241m=\u001b[39moutput_keys \u001b[38;5;28;01mif\u001b[39;00m output_keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput,\n\u001b[1;32m 573\u001b[0m input_keys\u001b[38;5;241m=\u001b[39minput_keys,\n\u001b[1;32m 574\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 575\u001b[0m ):\n\u001b[1;32m 576\u001b[0m latest \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m latest\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:605\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 597\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 598\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 604\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 605\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform_stream_with_config(\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform,\n\u001b[1;32m 608\u001b[0m config,\n\u001b[1;32m 609\u001b[0m output_keys\u001b[38;5;241m=\u001b[39moutput_keys,\n\u001b[1;32m 610\u001b[0m input_keys\u001b[38;5;241m=\u001b[39minput_keys,\n\u001b[1;32m 611\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 612\u001b[0m ):\n\u001b[1;32m 613\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langchain_core/runnables/base.py:1497\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1497\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:350\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys)\u001b[0m\n\u001b[1;32m 347\u001b[0m _interrupt_or_proceed(done, inflight, step)\n\u001b[1;32m 349\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m \u001b[43m_apply_writes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpending_writes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m 352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdebug:\n\u001b[1;32m 355\u001b[0m print_checkpoint(step, channels)\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:738\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 736\u001b[0m channels[chan]\u001b[38;5;241m.\u001b[39mupdate(vals)\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 738\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\n\u001b[1;32m 739\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid update for channel \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mchan\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 740\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 741\u001b[0m checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchannel_versions\u001b[39m\u001b[38;5;124m\"\u001b[39m][chan] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 742\u001b[0m updated_channels\u001b[38;5;241m.\u001b[39madd(chan)\n", - "\u001b[0;31mInvalidUpdateError\u001b[0m: Invalid update for channel __end__: LastValue can only receive one value per step." - ] - } - ], - "source": [ - "inputs = {\n", - " \"messages\": [\n", - " HumanMessage(\n", - " content=\"What does Lilian Weng say about the types of agent memory?\"\n", - " )\n", - " ]\n", - "}\n", - "\n", - "app.invoke(inputs)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, + "execution_count": 41, "id": "7649f05a-cb67-490d-b24a-74d41895139a", "metadata": {}, "outputs": [ @@ -488,51 +477,36 @@ "'\\n---\\n'\n", "---DECIDE TO RETRIEVE---\n", "---DECISION: RETRIEVE---\n", - "\"Output from node '__end__':\"\n", - "'---'\n", - "{ 'messages': [ HumanMessage(content='What does Lilian Weng say about the types of agent memory?'),\n", - " AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory\"}', 'name': 'retrieve_blog_posts'}})]}\n", - "'\\n---\\n'\n", "---EXECUTE RETRIEVAL---\n", "\"Output from node 'retrieve':\"\n", "'---'\n", - "{ 'messages': [ FunctionMessage(content='Table 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\\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\\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\\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', name='retrieve_blog_posts')]}\n", + "{ 'messages': [ FunctionMessage(content='Table 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\\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\\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 design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.', name='retrieve_blog_posts')]}\n", "'\\n---\\n'\n", "---CHECK RELEVANCE---\n", "---DECISION: DOCS RELEVANT---\n", - "---CALL AGENT---\n", - "\"Output from node 'agent':\"\n", + "---GENERATE---\n", + "\"Output from node 'generate':\"\n", "'---'\n", - "{ 'messages': [ AIMessage(content='Lilian Weng discusses the concept of memory within agent systems, highlighting its importance but does not provide specific details on the types of agent memory in the provided excerpt. The discussion on memory is part of a broader overview of agent systems, which also includes planning and tool use. In the context of planning, agents are capable of breaking down large tasks into smaller, manageable subgoals (task decomposition) and engaging in self-reflection and refinement based on past actions to improve future outcomes.\\n\\nWhile the excerpt mentions a section titled \"Types of Memory,\" specific details or descriptions of these types are not provided in the provided content. Additionally, there\\'s a mention of Maximum Inner Product Search (MIPS) in the context of memory, suggesting it might be a technique or tool related to how agents access or utilize their memory, but again, specific details are not given.\\n\\nFor a more detailed understanding of the types of agent memory Lilian Weng discusses, it would be necessary to access the full content of her blog post or publication.')]}\n", + "{ 'messages': [ 'Lilian Weng mentions two types of agent memory: short-term '\n", + " 'memory and long-term memory. Short-term memory is used for '\n", + " 'in-context learning, while long-term memory allows the agent '\n", + " 'to retain and recall information over extended periods.']}\n", "'\\n---\\n'\n", - "---DECIDE TO RETRIEVE---\n", - "---DECISION: DO NOT RETRIEVE / DONE---\n" - ] - }, - { - "ename": "InvalidUpdateError", - "evalue": "Invalid update for channel __end__: LastValue can only receive one value per step.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:736\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 735\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 736\u001b[0m \u001b[43mchannels\u001b[49m\u001b[43m[\u001b[49m\u001b[43mchan\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/channels/last_value.py:47\u001b[0m, in \u001b[0;36mLastValue.update\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(values) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLastValue can only receive one value per step.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", - "\u001b[0;31mInvalidUpdateError\u001b[0m: LastValue can only receive one value per step.", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mInvalidUpdateError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 12\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain_core\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmessages\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HumanMessage\n\u001b[1;32m 5\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\n\u001b[1;32m 7\u001b[0m HumanMessage(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10\u001b[0m ]\n\u001b[1;32m 11\u001b[0m }\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m app\u001b[38;5;241m.\u001b[39mstream(inputs):\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m output\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 14\u001b[0m pprint\u001b[38;5;241m.\u001b[39mpprint(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOutput from node \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:605\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m 597\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 598\u001b[0m \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 604\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 605\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform_stream_with_config(\n\u001b[1;32m 606\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transform,\n\u001b[1;32m 608\u001b[0m config,\n\u001b[1;32m 609\u001b[0m output_keys\u001b[38;5;241m=\u001b[39moutput_keys,\n\u001b[1;32m 610\u001b[0m input_keys\u001b[38;5;241m=\u001b[39minput_keys,\n\u001b[1;32m 611\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 612\u001b[0m ):\n\u001b[1;32m 613\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langchain_core/runnables/base.py:1497\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1497\u001b[0m chunk: Output \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:350\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys)\u001b[0m\n\u001b[1;32m 347\u001b[0m _interrupt_or_proceed(done, inflight, step)\n\u001b[1;32m 349\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[0;32m--> 350\u001b[0m \u001b[43m_apply_writes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpending_writes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m 352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdebug:\n\u001b[1;32m 355\u001b[0m print_checkpoint(step, channels)\n", - "File \u001b[0;32m~/miniforge3/envs/llama2/lib/python3.9/site-packages/langgraph/pregel/__init__.py:738\u001b[0m, in \u001b[0;36m_apply_writes\u001b[0;34m(checkpoint, channels, pending_writes, config, for_step)\u001b[0m\n\u001b[1;32m 736\u001b[0m channels[chan]\u001b[38;5;241m.\u001b[39mupdate(vals)\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidUpdateError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 738\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidUpdateError(\n\u001b[1;32m 739\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid update for channel \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mchan\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 740\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 741\u001b[0m checkpoint[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchannel_versions\u001b[39m\u001b[38;5;124m\"\u001b[39m][chan] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 742\u001b[0m updated_channels\u001b[38;5;241m.\u001b[39madd(chan)\n", - "\u001b[0;31mInvalidUpdateError\u001b[0m: Invalid update for channel __end__: LastValue can only receive one value per step." + "\"Output from node '__end__':\"\n", + "'---'\n", + "{ 'messages': [ HumanMessage(content='What does Lilian Weng say about the types of agent memory?'),\n", + " AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory\"}', 'name': 'retrieve_blog_posts'}}),\n", + " FunctionMessage(content='Table 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\\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\\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 design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.', name='retrieve_blog_posts'),\n", + " 'Lilian Weng mentions two types of agent memory: short-term '\n", + " 'memory and long-term memory. Short-term memory is used for '\n", + " 'in-context learning, while long-term memory allows the agent '\n", + " 'to retain and recall information over extended periods.']}\n", + "'\\n---\\n'\n" ] } ], "source": [ "import pprint\n", - "\n", "from langchain_core.messages import HumanMessage\n", "\n", "inputs = {\n", From 1253c9d05bc818a374f47f96ab12c9101983c9e7 Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Thu, 15 Feb 2024 16:02:21 -0800 Subject: [PATCH 3/3] Local self-RAG --- examples/rag/langgraph_crag_mistral.ipynb | 82 +- examples/rag/langgraph_self_rag.ipynb | 2 +- .../langgraph_self_rag_mistral_nomic.ipynb | 712 ++++++++++++++++++ 3 files changed, 767 insertions(+), 29 deletions(-) create mode 100644 examples/rag/langgraph_self_rag_mistral_nomic.ipynb diff --git a/examples/rag/langgraph_crag_mistral.ipynb b/examples/rag/langgraph_crag_mistral.ipynb index 5c84f59ab..6a4743b8a 100644 --- a/examples/rag/langgraph_crag_mistral.ipynb +++ b/examples/rag/langgraph_crag_mistral.ipynb @@ -48,13 +48,12 @@ "If you want to run this locally (e.g., on your laptop), use [Ollama](https://ollama.ai/library/mistral/tags):\n", "\n", "* Download [Ollama app](https://ollama.ai/).\n", - "* Download a `Mistral` model e.g., `ollama pull mistral:7b-instruct`, from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n", - "* Download LLaMA2 `ollama pull llama2:latest` to use Ollama embeddings.\n", + "* Download a `Mistral` model e.g., `ollama pull mistral:instruct`, from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n", "* Set flags indicating we will run locally and the Mistral model downloaded:\n", " \n", "```\n", "run_local = \"Yes\"\n", - "local_llm = \"mistral:7b-instruct\"\n", + "local_llm = \"mistral:instruct\"\n", "```\n", "\n", "### Tracing \n", @@ -70,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "id": "abc064ab-7de1-4d03-a987-cd3078438d61", "metadata": {}, "outputs": [], @@ -84,14 +83,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "9f644869-436e-4bf6-a267-b2465c7b5aef", "metadata": {}, "outputs": [], "source": [ "# Flags for running locally\n", "\n", - "run_local = \"No\"\n", + "run_local = \"Yes\"\n", "local_llm = \"mistral:instruct\"" ] }, @@ -113,10 +112,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "254ae533-79e0-42f4-b200-1ec9160e1d3d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bert_load_from_file: gguf version = 2\n", + "bert_load_from_file: gguf alignment = 32\n", + "bert_load_from_file: gguf data offset = 695552\n", + "bert_load_from_file: model name = BERT\n", + "bert_load_from_file: model architecture = bert\n", + "bert_load_from_file: model file type = 1\n", + "bert_load_from_file: bert tokenizer vocab = 30522\n" + ] + } + ], "source": [ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", @@ -187,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e", "metadata": {}, "outputs": [], @@ -224,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d", "metadata": {}, "outputs": [], @@ -484,12 +497,12 @@ "source": [ "## Build Graph\n", "\n", - "The just follows the flow we outlined in the figure above." + "This just follows the flow we outlined in the figure above." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06", "metadata": {}, "outputs": [], @@ -540,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a", "metadata": {}, "outputs": [ @@ -552,32 +565,45 @@ "\"Node 'retrieve':\"\n", "'\\n---\\n'\n", "---CHECK RELEVANCE---\n", - "---GRADE: DOCUMENT NOT RELEVANT---\n", + "---GRADE: DOCUMENT RELEVANT---\n", "---GRADE: DOCUMENT RELEVANT---\n", "---GRADE: DOCUMENT RELEVANT---\n", "---GRADE: DOCUMENT RELEVANT---\n", "\"Node 'grade_documents':\"\n", "'\\n---\\n'\n", "---DECIDE TO GENERATE---\n", - "---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\n", - "---TRANSFORM QUERY---\n", - "\"Node 'transform_query':\"\n", - "'\\n---\\n'\n", - "---WEB SEARCH---\n", - "\"Node 'web_search':\"\n", - "'\\n---\\n'\n", + "---DECISION: GENERATE---\n", "---GENERATE---\n", "\"Node 'generate':\"\n", "'\\n---\\n'\n", "\"Node '__end__':\"\n", "'\\n---\\n'\n", - "('In agent-based systems, episodic memory can be likened to a long-term memory '\n", - " \"module that records agents' experiences in natural language, with retrieval \"\n", - " 'based on relevance, recency, and importance. Semantic memory is similar to '\n", - " 'an external vector store that provides agents with the ability to retain and '\n", - " 'recall information over extended periods. Procedural memory can be seen as '\n", - " 'the reflection mechanism that synthesizes memories into higher-level '\n", - " \"inferences, guiding the agent's future behavior.\")\n" + "(' In an LLM (large language model)-powered autonomous agent system, LLM '\n", + " 'functions as the agent’s brain, complemented by several key components: '\n", + " 'planning and memory.\\n'\n", + " '\\n'\n", + " 'Planning involves breaking down large tasks into smaller subgoals for '\n", + " 'efficient handling of complex tasks and self-criticism and refinement to '\n", + " 'improve results.\\n'\n", + " '\\n'\n", + " 'Memory includes short-term memory, which utilizes in-context learning, and '\n", + " 'long-term memory, providing the agent with the capability to retain and '\n", + " 'recall information over extended periods using an external vector store and '\n", + " 'fast retrieval. The agent also learns to call external APIs for missing '\n", + " 'information.\\n'\n", + " '\\n'\n", + " 'Types of Memory:\\n'\n", + " '1. Sensory Memory: retains impressions of sensory information for a few '\n", + " 'seconds.\\n'\n", + " '2. Short-Term Memory (STM) or Working Memory: stores information needed for '\n", + " 'complex cognitive tasks and lasts for 20-30 seconds.\\n'\n", + " '3. Long-Term Memory (LTM): stores information for a remarkably long time, '\n", + " 'with two subtypes: explicit/declarative memory and implicit/procedural '\n", + " 'memory.\\n'\n", + " '\\n'\n", + " 'The agent uses LLM as its core controller, which can be extended beyond '\n", + " 'generating well-written copies, stories, essays, and programs to a powerful '\n", + " 'general problem solver.')\n" ] } ], diff --git a/examples/rag/langgraph_self_rag.ipynb b/examples/rag/langgraph_self_rag.ipynb index b4b1766f3..c05d5f2b6 100644 --- a/examples/rag/langgraph_self_rag.ipynb +++ b/examples/rag/langgraph_self_rag.ipynb @@ -223,7 +223,7 @@ " return {\"keys\": {\"documents\": documents, \"question\": question}}\n", "\n", "\n", - "def generate(state):\n", + "def generate(state):a\n", " \"\"\"\n", " Generate answer\n", "\n", diff --git a/examples/rag/langgraph_self_rag_mistral_nomic.ipynb b/examples/rag/langgraph_self_rag_mistral_nomic.ipynb new file mode 100644 index 000000000..f26ef00fb --- /dev/null +++ b/examples/rag/langgraph_self_rag_mistral_nomic.ipynb @@ -0,0 +1,712 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "a1908e8c-5970-4d9d-bc2b-d5fee7aa3baa", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -U llama-cpp-python langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph" + ] + }, + { + "attachments": { + "e3e60bc2-6033-4d66-af6d-1dfafce5cb9f.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "848ba742-7443-4123-8115-061da9823309", + "metadata": {}, + "source": [ + "# Self RAG\n", + "\n", + "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n", + "\n", + "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n", + "\n", + "Here we show how to implement self-reflective RAG using `Nomic`, `Mistral`, and `LangGraph`.\n", + "\n", + "We'll focus on ideas from one paper, `Self RAG` [here](https://arxiv.org/abs/2310.11511).\n", + "\n", + "This can run fully locally (e.g., on a laptop).\n", + "\n", + "![Screenshot 2024-02-15 at 3.33.46 PM.png](attachment:e3e60bc2-6033-4d66-af6d-1dfafce5cb9f.png)\n", + "\n", + "### Embeddings\n", + "\n", + "We'll use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n", + "\n", + "Simply: \n", + "\n", + "(1) Clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp):\n", + "\n", + "```\n", + "git clone https://github.com/ggerganov/llama.cpp\n", + "```\n", + "\n", + "(2) Download GGUF weights for Nomic's embedding model(s), allowing them to be run locally: \n", + "\n", + "* https://huggingface.co/nomic-ai/nomic-embed-text-v1-GGUF\n", + "* https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF\n", + "\n", + "(3) Add to `llama.cpp/model` directory.\n", + "\n", + "(4) Build llama.cpp:\n", + "```\n", + "cd llama.cpp\n", + "make\n", + "```\n", + "\n", + "### LLM\n", + "\n", + "(1) Download [Ollama app](https://ollama.ai/).\n", + "\n", + "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n", + "```\n", + "ollama pull mistral:instruct\n", + "```\n", + "\n", + "(3) Set flags indicating we will run locally and the Mistral model downloaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d35ebcdc-8510-4ce5-a0e9-8db714944c68", + "metadata": {}, + "outputs": [], + "source": [ + "! ollama pull mistral:instruct" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "32d50725-04c1-487c-89cc-a119ec708c17", + "metadata": {}, + "outputs": [], + "source": [ + "# Ollama model name\n", + "local_llm = \"mistral:instruct\"\n", + "\n", + "# Local embedding model paths (downloaded above)O\n", + "embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.Q4_K_S.gguf\"\n", + "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.f16.gguf\"\n", + "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.5.f16.gguf\"\n", + "# embd_model_path = \"/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.5.Q4_K_S.gguf\"" + ] + }, + { + "cell_type": "markdown", + "id": "ba68a46d-b617-4fdc-9113-fabdcf736feb", + "metadata": {}, + "source": [ + "## Indexing\n", + "\n", + "First, let's index a popular blog post on agents. \n", + "\n", + "For local, we can use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n", + "\n", + "We'll use the [llama.cpp](https://github.com/ggerganov/llama.cpp) integration.\n", + "\n", + "We'll use a local vectorstore, [Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3bb9060-ad74-4470-9991-2ba167b6b8d8", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_community.document_loaders import WebBaseLoader\n", + "from langchain_community.vectorstores import Chroma\n", + "from langchain_community.embeddings import LlamaCppEmbeddings\n", + "from langchain_nomic.embeddings import NomicEmbeddings\n", + "\n", + "# Load\n", + "url = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n", + "loader = WebBaseLoader(url)\n", + "docs = loader.load()\n", + "\n", + "# Split\n", + "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n", + " chunk_size=500, chunk_overlap=100\n", + ")\n", + "all_splits = text_splitter.split_documents(docs)\n", + "\n", + "# Embed and index\n", + "embedding = LlamaCppEmbeddings(model_path=embd_model_path, n_batch=512)\n", + "\n", + "# Index\n", + "vectorstore = Chroma.from_documents(\n", + " documents=all_splits,\n", + " collection_name=\"rag-chroma\",\n", + " embedding=embedding,\n", + ")\n", + "retriever = vectorstore.as_retriever()" + ] + }, + { + "cell_type": "markdown", + "id": "3d3339b9-5f30-4d54-bfc9-9091c6035955", + "metadata": {}, + "source": [ + "## State\n", + "\n", + "Every node in our graph will modify `state`, which is dict that contains values (`question`, `documents`, etc) relevant to RAG." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "90fb1dc6-c482-483a-8441-39965c401beb", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Dict, TypedDict\n", + "\n", + "from langchain_core.messages import BaseMessage\n", + "\n", + "\n", + "class GraphState(TypedDict):\n", + " \"\"\"\n", + " Represents the state of our graph.\n", + "\n", + " Attributes:\n", + " keys: A dictionary where each key is a string.\n", + " \"\"\"\n", + "\n", + " keys: Dict[str, any]" + ] + }, + { + "cell_type": "markdown", + "id": "b89b2f21-b6b3-42db-a826-ff9d9aaec443", + "metadata": {}, + "source": [ + "### Nodes and Edges\n", + "\n", + "Every node in the graph we laid out above is a function.\n", + "\n", + "Each node will modify the state in some way.\n", + "\n", + "Each edge will choose which node to call next." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "5324ea49-5745-47b5-a0a5-bf58c8babe46", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import operator\n", + "from typing import Annotated, Sequence, TypedDict\n", + "\n", + "from langchain import hub\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain_community.vectorstores import Chroma\n", + "from langchain_core.messages import BaseMessage, FunctionMessage\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.pydantic_v1 import BaseModel, Field\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_community.chat_models import ChatOllama\n", + "from langchain_core.output_parsers import JsonOutputParser\n", + "\n", + "### Nodes ###\n", + "\n", + "\n", + "def retrieve(state):\n", + " \"\"\"\n", + " Retrieve documents\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", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " documents = retriever.get_relevant_documents(question)\n", + " return {\"keys\": {\"documents\": documents, \"question\": question}}\n", + "\n", + "\n", + "def generate(state):\n", + " \"\"\"\n", + " Generate answer\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", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " documents = state_dict[\"documents\"]\n", + "\n", + " # Prompt\n", + " prompt = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + " # LLM\n", + " llm = ChatOllama(model=local_llm, 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", + " # Chain\n", + " rag_chain = prompt | llm | StrOutputParser()\n", + "\n", + " # Run\n", + " generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n", + " return {\n", + " \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n", + " }\n", + "\n", + "\n", + "def grade_documents(state):\n", + " \"\"\"\n", + " Determines whether the retrieved documents are relevant to the question.\n", + "\n", + " Args:\n", + " state (dict): The current graph state\n", + "\n", + " Returns:\n", + " state (dict): Updates documents key with relevant documents\n", + " \"\"\"\n", + "\n", + " print(\"---CHECK RELEVANCE---\")\n", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " documents = state_dict[\"documents\"]\n", + "\n", + " # LLM\n", + " llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", + "\n", + " # Prompt\n", + " prompt = PromptTemplate(\n", + " template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n", + " Here is the retrieved document: \\n\\n {context} \\n\\n\n", + " Here is the user question: {question} \\n\n", + " If the document contains keywords related to the user question, grade it as relevant. \\n\n", + " It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n", + " Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n", + " Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n", + " input_variables=[\"question\",\"context\"],\n", + " )\n", + "\n", + " # Chain\n", + " chain = prompt | llm | JsonOutputParser()\n", + "\n", + " # Score\n", + " filtered_docs = []\n", + " for d in documents:\n", + " score = chain.invoke(\n", + " {\n", + " \"question\": question,\n", + " \"context\": d.page_content,\n", + " }\n", + " )\n", + " grade = score[\"score\"]\n", + " if grade == \"yes\":\n", + " print(\"---GRADE: DOCUMENT RELEVANT---\")\n", + " filtered_docs.append(d)\n", + " else:\n", + " print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n", + " continue\n", + "\n", + " return {\"keys\": {\"documents\": filtered_docs, \"question\": question}}\n", + "\n", + "\n", + "def transform_query(state):\n", + " \"\"\"\n", + " Transform the query to produce a better question.\n", + "\n", + " Args:\n", + " state (dict): The current graph state\n", + "\n", + " Returns:\n", + " state (dict): Updates question key with a re-phrased question\n", + " \"\"\"\n", + "\n", + " print(\"---TRANSFORM QUERY---\")\n", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " documents = state_dict[\"documents\"]\n", + "\n", + " # LLM\n", + " llm = ChatOllama(model=local_llm, temperature=0)\n", + " \n", + " # Create a prompt template with format instructions and the query\n", + " prompt = PromptTemplate(\n", + " template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n", + " Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n", + " Here is the initial question:\n", + " \\n ------- \\n\n", + " {question} \n", + " \\n ------- \\n\n", + " Formulate an improved question:\"\"\",\n", + " input_variables=[\"question\"],\n", + " )\n", + "\n", + " # Chain\n", + " chain = prompt | llm | StrOutputParser()\n", + " better_question = chain.invoke({\"question\": question})\n", + "\n", + " return {\"keys\": {\"documents\": documents, \"question\": better_question}}\n", + "\n", + "\n", + "def prepare_for_final_grade(state):\n", + " \"\"\"\n", + " Passthrough state for final grade.\n", + "\n", + " Args:\n", + " state (dict): The current graph state\n", + "\n", + " Returns:\n", + " state (dict): The current graph state\n", + " \"\"\"\n", + "\n", + " print(\"---FINAL GRADE---\")\n", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " documents = state_dict[\"documents\"]\n", + " generation = state_dict[\"generation\"]\n", + "\n", + " return {\n", + " \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n", + " }\n", + "\n", + "\n", + "### Edges ###\n", + "\n", + "\n", + "def decide_to_generate(state):\n", + " \"\"\"\n", + " Determines whether to generate an answer, or re-generate a question.\n", + "\n", + " Args:\n", + " state (dict): The current state of the agent, including all keys.\n", + "\n", + " Returns:\n", + " str: Next node to call\n", + " \"\"\"\n", + "\n", + " print(\"---DECIDE TO GENERATE---\")\n", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " filtered_documents = state_dict[\"documents\"]\n", + "\n", + " if not filtered_documents:\n", + " # All documents have been filtered check_relevance\n", + " # We will re-generate a new query\n", + " print(\"---DECISION: TRANSFORM QUERY---\")\n", + " return \"transform_query\"\n", + " else:\n", + " # We have relevant documents, so generate answer\n", + " print(\"---DECISION: GENERATE---\")\n", + " return \"generate\"\n", + "\n", + "\n", + "def grade_generation_v_documents(state):\n", + " \"\"\"\n", + " Determines whether the generation is grounded in the document.\n", + "\n", + " Args:\n", + " state (dict): The current state of the agent, including all keys.\n", + "\n", + " Returns:\n", + " str: Binary decision\n", + " \"\"\"\n", + "\n", + " print(\"---GRADE GENERATION vs DOCUMENTS---\")\n", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " documents = state_dict[\"documents\"]\n", + " generation = state_dict[\"generation\"]\n", + "\n", + " # LLM\n", + " llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", + "\n", + " # Prompt\n", + " prompt = PromptTemplate(\n", + " template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n", + " Here are the facts:\n", + " \\n ------- \\n\n", + " {documents} \n", + " \\n ------- \\n\n", + " Here is the answer: {generation}\n", + " Give a binary score 'yes' or 'no' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n", + " Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n", + " input_variables=[\"generation\", \"documents\"],\n", + " )\n", + "\n", + " # Chain\n", + " chain = prompt | llm | JsonOutputParser()\n", + " score = chain.invoke({\"generation\": generation, \"documents\": documents})\n", + " grade = score[\"score\"]\n", + "\n", + " if grade == \"yes\":\n", + " print(\"---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\")\n", + " return \"supported\"\n", + " else:\n", + " print(\"---DECISION: NOT SUPPORTED, GENERATE AGAIN---\")\n", + " return \"not supported\"\n", + "\n", + "\n", + "def grade_generation_v_question(state):\n", + " \"\"\"\n", + " Determines whether the generation addresses the question.\n", + "\n", + " Args:\n", + " state (dict): The current state of the agent, including all keys.\n", + "\n", + " Returns:\n", + " str: Binary decision\n", + " \"\"\"\n", + "\n", + " print(\"---GRADE GENERATION vs QUESTION---\")\n", + " state_dict = state[\"keys\"]\n", + " question = state_dict[\"question\"]\n", + " documents = state_dict[\"documents\"]\n", + " generation = state_dict[\"generation\"]\n", + "\n", + " llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n", + "\n", + " # Prompt\n", + " prompt = PromptTemplate(\n", + " template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n", + " Here is the answer:\n", + " \\n ------- \\n\n", + " {generation} \n", + " \\n ------- \\n\n", + " Here is the question: {question}\n", + " Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question. \\n\n", + " Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n", + " input_variables=[\"generation\", \"question\"],\n", + " )\n", + "\n", + " # Prompt\n", + " chain = prompt | llm | JsonOutputParser()\n", + " score = chain.invoke({\"generation\": generation, \"question\": question})\n", + " grade = score[\"score\"]\n", + "\n", + " if grade == \"yes\":\n", + " print(\"---DECISION: USEFUL---\")\n", + " return \"useful\"\n", + " else:\n", + " print(\"---DECISION: NOT USEFUL---\")\n", + " return \"not useful\"" + ] + }, + { + "cell_type": "markdown", + "id": "bdf3826c-b668-4f0b-bf83-81c40baaaf02", + "metadata": {}, + "source": [ + "## Build Graph\n", + "\n", + "This just follows the flow we outlined in the figure above." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5605dee4-b2df-46ae-a640-cc2ed90c21a6", + "metadata": {}, + "outputs": [], + "source": [ + "import pprint\n", + "\n", + "from langgraph.graph import END, StateGraph\n", + "\n", + "workflow = StateGraph(GraphState)\n", + "\n", + "# Define the nodes\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", + "workflow.add_node(\"transform_query\", transform_query) # transform_query\n", + "workflow.add_node(\"prepare_for_final_grade\", prepare_for_final_grade) # passthrough\n", + "\n", + "# Build graph\n", + "workflow.set_entry_point(\"retrieve\")\n", + "workflow.add_edge(\"retrieve\", \"grade_documents\")\n", + "workflow.add_conditional_edges(\n", + " \"grade_documents\",\n", + " decide_to_generate,\n", + " {\n", + " \"transform_query\": \"transform_query\",\n", + " \"generate\": \"generate\",\n", + " },\n", + ")\n", + "workflow.add_edge(\"transform_query\", \"retrieve\")\n", + "workflow.add_conditional_edges(\n", + " \"generate\",\n", + " grade_generation_v_documents,\n", + " {\n", + " \"supported\": \"prepare_for_final_grade\",\n", + " \"not supported\": \"generate\",\n", + " },\n", + ")\n", + "workflow.add_conditional_edges(\n", + " \"prepare_for_final_grade\",\n", + " grade_generation_v_question,\n", + " {\n", + " \"useful\": END,\n", + " \"not useful\": \"transform_query\",\n", + " },\n", + ")\n", + "\n", + "# Compile\n", + "app = workflow.compile()" + ] + }, + { + "cell_type": "markdown", + "id": "105ae1b5-6963-4186-bb83-6d6cb96d095f", + "metadata": {}, + "source": [ + "## Run\n", + "\n", + "Trace for below run: https://smith.langchain.com/public/928651fd-85b3-49ff-b481-bd28417645e5/r" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "26a64f7d-0c14-4e31-a67f-63021dee626e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "llama_print_timings: load time = 149.49 ms\n", + "llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)\n", + "llama_print_timings: prompt eval time = 17.39 ms / 12 tokens ( 1.45 ms per token, 690.01 tokens per second)\n", + "llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)\n", + "llama_print_timings: total time = 17.39 ms / 13 tokens\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---RETRIEVE---\n", + "\"Node 'retrieve':\"\n", + "'\\n---\\n'\n", + "---CHECK RELEVANCE---\n", + "---GRADE: DOCUMENT RELEVANT---\n", + "---GRADE: DOCUMENT RELEVANT---\n", + "---GRADE: DOCUMENT RELEVANT---\n", + "---GRADE: DOCUMENT RELEVANT---\n", + "\"Node 'grade_documents':\"\n", + "'\\n---\\n'\n", + "---DECIDE TO GENERATE---\n", + "---DECISION: GENERATE---\n", + "---GENERATE---\n", + "\"Node 'generate':\"\n", + "'\\n---\\n'\n", + "---GRADE GENERATION vs DOCUMENTS---\n", + "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n", + "---FINAL GRADE---\n", + "\"Node 'prepare_for_final_grade':\"\n", + "'\\n---\\n'\n", + "---GRADE GENERATION vs QUESTION---\n", + "---DECISION: USEFUL---\n", + "\"Node '__end__':\"\n", + "'\\n---\\n'\n", + "(' In a LLM (large language model)-powered autonomous agent system, LLM '\n", + " 'functions as the agent’s brain, complemented by several key components. One '\n", + " 'of these components is memory. Memory can be defined as the processes used '\n", + " 'to acquire, store, retain, and later retrieve information. There are several '\n", + " 'types of memory in human brains:\\n'\n", + " '\\n'\n", + " '1. Sensory Memory: This is the earliest stage of memory, providing the '\n", + " 'ability to retain impressions of sensory information (visual, auditory, etc) '\n", + " 'after the original stimuli have ended. Sensory memory typically only lasts '\n", + " 'for up to a few seconds. Subcategories include iconic memory (visual), '\n", + " 'echoic memory (auditory), and haptic memory (touch).\\n'\n", + " '2. Short-Term Memory (STM) or Working Memory: It stores information that we '\n", + " 'are currently aware of and needed to carry out complex cognitive tasks such '\n", + " 'as learning and reasoning. Short-term memory is believed to have the '\n", + " 'capacity of about 7 items (Miller 1956) and lasts for 20-30 seconds.\\n'\n", + " '3. Long-Term Memory (LTM): Long-term memory can store information for a '\n", + " 'remarkably long time, ranging from a few days to decades, with an '\n", + " 'essentially unlimited storage capacity. There are two subtypes of LTM:\\n'\n", + " ' * Explicit / declarative memory: This is memory of facts and events, and '\n", + " 'refers to those memories that can be consciously recalled, including '\n", + " 'episodic memory (events and experiences) and semantic memory (facts and '\n", + " 'concepts).\\n'\n", + " ' * Implicit / procedural memory: This type of memory is unconscious and '\n", + " 'involves skills and routines that are performed automatically, like riding a '\n", + " 'bike or typing on a keyboard.\\n'\n", + " '\\n'\n", + " 'We can roughly consider the following mappings in an LLM-powered agent '\n", + " 'system:\\n'\n", + " '\\n'\n", + " '* Sensory Memory: Input data from sensors\\n'\n", + " '* Short-Term Memory: Active processing of information, temporary storage for '\n", + " 'complex tasks\\n'\n", + " '* Long-Term Memory: Stored knowledge and experiences that can be accessed '\n", + " 'and used to learn new tasks or make decisions.')\n" + ] + } + ], + "source": [ + "# Run\n", + "inputs = {\"keys\": {\"question\": \"Explain how the different types of agent memory work?\"}}\n", + "for output in app.stream(inputs):\n", + " for key, value in output.items():\n", + " # Node\n", + " pprint.pprint(f\"Node '{key}':\")\n", + " # Optional: print full state at each node\n", + " # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n", + " pprint.pprint(\"\\n---\\n\")\n", + "\n", + "# Final generation\n", + "pprint.pprint(value['keys']['generation'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d40a430-be4a-4d9c-98f9-46c5eb3066e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}