mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-07-31 16:01:46 +00:00
tool calling working now
This commit is contained in:
Kai Wu
parent
45e9e3ac4a
commit
bb475d58ae
2 changed files with 61 additions and 55 deletions
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@ -128,23 +128,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 6,
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"id": "64d36476-95d7-49f9-a548-312cf8d8c49e",
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"metadata": {},
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: 'logo.png'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[4], line 17\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(models_response)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Execute the main function\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m main()\n",
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"Cell \u001b[0;32mIn[4], line 9\u001b[0m, in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m client \u001b[38;5;241m=\u001b[39m LlamaStackClient(\n\u001b[1;32m 5\u001b[0m base_url\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttp://\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mHOST\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mPORT\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Process image\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m process_image(client, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogo.png\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Query available models\u001b[39;00m\n\u001b[1;32m 12\u001b[0m models_response \u001b[38;5;241m=\u001b[39m client\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mlist()\n",
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"Cell \u001b[0;32mIn[3], line 29\u001b[0m, in \u001b[0;36mprocess_image\u001b[0;34m(client, image_path, stream)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess_image\u001b[39m(client: LlamaStackClient, image_path: \u001b[38;5;28mstr\u001b[39m, stream: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 21\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m Process an image through the LlamaStack Vision API.\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;124;03m stream (bool): Whether to stream the response\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m data_url \u001b[38;5;241m=\u001b[39m \u001b[43mencode_image_to_data_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 31\u001b[0m message \u001b[38;5;241m=\u001b[39m UserMessage(\n\u001b[1;32m 32\u001b[0m role\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 33\u001b[0m content\u001b[38;5;241m=\u001b[39m[\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 36\u001b[0m ],\n\u001b[1;32m 37\u001b[0m )\n\u001b[1;32m 39\u001b[0m cprint(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUser> Sending image for analysis...\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"Cell \u001b[0;32mIn[3], line 15\u001b[0m, in \u001b[0;36mencode_image_to_data_url\u001b[0;34m(file_path)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mime_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not determine MIME type of the file\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m image_file:\n\u001b[1;32m 16\u001b[0m encoded_string \u001b[38;5;241m=\u001b[39m base64\u001b[38;5;241m.\u001b[39mb64encode(image_file\u001b[38;5;241m.\u001b[39mread())\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmime_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m;base64,\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mencoded_string\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n",
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"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/IPython/core/interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 319\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 322\u001b[0m )\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'logo.png'"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32mUser> Sending image for analysis...\u001b[0m\n",
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"\u001b[36mAssistant> \u001b[0m\u001b[33mThe\u001b[0m\u001b[33m image\u001b[0m\u001b[33m features\u001b[0m\u001b[33m a\u001b[0m\u001b[33m simple\u001b[0m\u001b[33m,\u001b[0m\u001b[33m mon\u001b[0m\u001b[33moch\u001b[0m\u001b[33mromatic\u001b[0m\u001b[33m line\u001b[0m\u001b[33m drawing\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m the\u001b[0m\u001b[33m words\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mLL\u001b[0m\u001b[33mAMA\u001b[0m\u001b[33m STACK\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m written\u001b[0m\u001b[33m above\u001b[0m\u001b[33m it\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m is\u001b[0m\u001b[33m depicted\u001b[0m\u001b[33m in\u001b[0m\u001b[33m a\u001b[0m\u001b[33m cartoon\u001b[0m\u001b[33mish\u001b[0m\u001b[33m style\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m large\u001b[0m\u001b[33m body\u001b[0m\u001b[33m and\u001b[0m\u001b[33m a\u001b[0m\u001b[33m long\u001b[0m\u001b[33m neck\u001b[0m\u001b[33m.\u001b[0m\u001b[33m It\u001b[0m\u001b[33m has\u001b[0m\u001b[33m a\u001b[0m\u001b[33m distinctive\u001b[0m\u001b[33m head\u001b[0m\u001b[33m shape\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m small\u001b[0m\u001b[33m circle\u001b[0m\u001b[33m for\u001b[0m\u001b[33m the\u001b[0m\u001b[33m eye\u001b[0m\u001b[33m and\u001b[0m\u001b[33m a\u001b[0m\u001b[33m curved\u001b[0m\u001b[33m line\u001b[0m\u001b[33m for\u001b[0m\u001b[33m the\u001b[0m\u001b[33m mouth\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m body\u001b[0m\u001b[33m is\u001b[0m\u001b[33m composed\u001b[0m\u001b[33m of\u001b[0m\u001b[33m several\u001b[0m\u001b[33m rounded\u001b[0m\u001b[33m shapes\u001b[0m\u001b[33m,\u001b[0m\u001b[33m giving\u001b[0m\u001b[33m it\u001b[0m\u001b[33m a\u001b[0m\u001b[33m soft\u001b[0m\u001b[33m and\u001b[0m\u001b[33m cudd\u001b[0m\u001b[33mly\u001b[0m\u001b[33m appearance\u001b[0m\u001b[33m.\n",
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"\n",
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"\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m words\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mLL\u001b[0m\u001b[33mAMA\u001b[0m\u001b[33m STACK\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m are\u001b[0m\u001b[33m written\u001b[0m\u001b[33m in\u001b[0m\u001b[33m a\u001b[0m\u001b[33m playful\u001b[0m\u001b[33m,\u001b[0m\u001b[33m handwritten\u001b[0m\u001b[33m font\u001b[0m\u001b[33m above\u001b[0m\u001b[33m the\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m head\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m text\u001b[0m\u001b[33m is\u001b[0m\u001b[33m also\u001b[0m\u001b[33m in\u001b[0m\u001b[33m a\u001b[0m\u001b[33m mon\u001b[0m\u001b[33moch\u001b[0m\u001b[33mromatic\u001b[0m\u001b[33m color\u001b[0m\u001b[33m scheme\u001b[0m\u001b[33m,\u001b[0m\u001b[33m matching\u001b[0m\u001b[33m the\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m outline\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m background\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m image\u001b[0m\u001b[33m is\u001b[0m\u001b[33m a\u001b[0m\u001b[33m solid\u001b[0m\u001b[33m black\u001b[0m\u001b[33m color\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m provides\u001b[0m\u001b[33m a\u001b[0m\u001b[33m clean\u001b[0m\u001b[33m and\u001b[0m\u001b[33m simple\u001b[0m\u001b[33m contrast\u001b[0m\u001b[33m to\u001b[0m\u001b[33m the\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m design\u001b[0m\u001b[33m.\n",
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"\n",
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"\u001b[0m\u001b[33mOverall\u001b[0m\u001b[33m,\u001b[0m\u001b[33m the\u001b[0m\u001b[33m image\u001b[0m\u001b[33m appears\u001b[0m\u001b[33m to\u001b[0m\u001b[33m be\u001b[0m\u001b[33m a\u001b[0m\u001b[33m logo\u001b[0m\u001b[33m or\u001b[0m\u001b[33m icon\u001b[0m\u001b[33m for\u001b[0m\u001b[33m a\u001b[0m\u001b[33m brand\u001b[0m\u001b[33m or\u001b[0m\u001b[33m product\u001b[0m\u001b[33m called\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mL\u001b[0m\u001b[33mlama\u001b[0m\u001b[33m Stack\u001b[0m\u001b[33m.\"\u001b[0m\u001b[33m The\u001b[0m\u001b[33m use\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m cartoon\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m and\u001b[0m\u001b[33m a\u001b[0m\u001b[33m playful\u001b[0m\u001b[33m font\u001b[0m\u001b[33m suggests\u001b[0m\u001b[33m a\u001b[0m\u001b[33m l\u001b[0m\u001b[33migh\u001b[0m\u001b[33mthe\u001b[0m\u001b[33mart\u001b[0m\u001b[33med\u001b[0m\u001b[33m and\u001b[0m\u001b[33m humorous\u001b[0m\u001b[33m tone\u001b[0m\u001b[33m,\u001b[0m\u001b[33m while\u001b[0m\u001b[33m the\u001b[0m\u001b[33m mon\u001b[0m\u001b[33moch\u001b[0m\u001b[33mromatic\u001b[0m\u001b[33m color\u001b[0m\u001b[33m scheme\u001b[0m\u001b[33m gives\u001b[0m\u001b[33m the\u001b[0m\u001b[33m image\u001b[0m\u001b[33m a\u001b[0m\u001b[33m clean\u001b[0m\u001b[33m and\u001b[0m\u001b[33m modern\u001b[0m\u001b[33m feel\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n"
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]
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}
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],
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@ -157,12 +154,9 @@
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" )\n",
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"\n",
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" # Process image\n",
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" await process_image(client, \"logo.png\")\n",
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" await process_image(client, \"../_static/llama-stack-logo.png\")\n",
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"\n",
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" # Query available models\n",
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" models_response = client.models.list()\n",
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" print(\"\\nAvailable Models:\")\n",
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" print(models_response)\n",
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" \n",
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"\n",
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"# Execute the main function\n",
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"await main()"
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"Query: What are the latest developments in quantum computing?\n",
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"--------------------------------------------------\n",
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"\u001b[30m\u001b[0m\u001b[33minference> \u001b[0m\u001b[33mF\u001b[0m\u001b[33mIND\u001b[0m\u001b[33mINGS\u001b[0m\u001b[33m:\n",
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"\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m latest\u001b[0m\u001b[33m developments\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m include\u001b[0m\u001b[33m advancements\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m processors\u001b[0m\u001b[33m,\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m algorithms\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Researchers\u001b[0m\u001b[33m have\u001b[0m\u001b[33m made\u001b[0m\u001b[33m progress\u001b[0m\u001b[33m in\u001b[0m\u001b[33m building\u001b[0m\u001b[33m larger\u001b[0m\u001b[33m and\u001b[0m\u001b[33m more\u001b[0m\u001b[33m stable\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m processors\u001b[0m\u001b[33m,\u001b[0m\u001b[33m such\u001b[0m\u001b[33m as\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m's\u001b[0m\u001b[33m \u001b[0m\u001b[33m53\u001b[0m\u001b[33m-q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m and\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m's\u001b[0m\u001b[33m \u001b[0m\u001b[33m127\u001b[0m\u001b[33m-q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m (\u001b[0m\u001b[33m1\u001b[0m\u001b[33m).\u001b[0m\u001b[33m New\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m algorithms\u001b[0m\u001b[33m,\u001b[0m\u001b[33m like\u001b[0m\u001b[33m the\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Approx\u001b[0m\u001b[33mimate\u001b[0m\u001b[33m Optimization\u001b[0m\u001b[33m Algorithm\u001b[0m\u001b[33m (\u001b[0m\u001b[33mQ\u001b[0m\u001b[33mAO\u001b[0m\u001b[33mA\u001b[0m\u001b[33m),\u001b[0m\u001b[33m have\u001b[0m\u001b[33m been\u001b[0m\u001b[33m developed\u001b[0m\u001b[33m to\u001b[0m\u001b[33m solve\u001b[0m\u001b[33m complex\u001b[0m\u001b[33m optimization\u001b[0m\u001b[33m problems\u001b[0m\u001b[33m (\u001b[0m\u001b[33m2\u001b[0m\u001b[33m).\u001b[0m\u001b[33m Additionally\u001b[0m\u001b[33m,\u001b[0m\u001b[33m researchers\u001b[0m\u001b[33m are\u001b[0m\u001b[33m exploring\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m techniques\u001b[0m\u001b[33m to\u001b[0m\u001b[33m mitigate\u001b[0m\u001b[33m errors\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computations\u001b[0m\u001b[33m (\u001b[0m\u001b[33m3\u001b[0m\u001b[33m).\n",
|
||||
"\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m latest\u001b[0m\u001b[33m developments\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m include\u001b[0m\u001b[33m the\u001b[0m\u001b[33m introduction\u001b[0m\u001b[33m of\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m processors\u001b[0m\u001b[33m with\u001b[0m\u001b[33m increasing\u001b[0m\u001b[33m numbers\u001b[0m\u001b[33m of\u001b[0m\u001b[33m q\u001b[0m\u001b[33mubits\u001b[0m\u001b[33m,\u001b[0m\u001b[33m advancements\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m the\u001b[0m\u001b[33m development\u001b[0m\u001b[33m of\u001b[0m\u001b[33m new\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m algorithms\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Companies\u001b[0m\u001b[33m such\u001b[0m\u001b[33m as\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m Microsoft\u001b[0m\u001b[33m are\u001b[0m\u001b[33m leading\u001b[0m\u001b[33m the\u001b[0m\u001b[33m charge\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m research\u001b[0m\u001b[33m and\u001b[0m\u001b[33m development\u001b[0m\u001b[33m.\u001b[0m\u001b[33m For\u001b[0m\u001b[33m example\u001b[0m\u001b[33m,\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m's\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Experience\u001b[0m\u001b[33m cloud\u001b[0m\u001b[33m-based\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computer\u001b[0m\u001b[33m has\u001b[0m\u001b[33m \u001b[0m\u001b[33m127\u001b[0m\u001b[33m q\u001b[0m\u001b[33mubits\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mwww\u001b[0m\u001b[33m.ibm\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m/\u001b[0m\u001b[33mquant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m-com\u001b[0m\u001b[33mput\u001b[0m\u001b[33ming\u001b[0m\u001b[33m/\u001b[0m\u001b[33m),\u001b[0m\u001b[33m while\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m's\u001b[0m\u001b[33m S\u001b[0m\u001b[33myc\u001b[0m\u001b[33mam\u001b[0m\u001b[33more\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m has\u001b[0m\u001b[33m \u001b[0m\u001b[33m53\u001b[0m\u001b[33m q\u001b[0m\u001b[33mubits\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33ms\u001b[0m\u001b[33mcai\u001b[0m\u001b[33m.st\u001b[0m\u001b[33manford\u001b[0m\u001b[33m.edu\u001b[0m\u001b[33m/~\u001b[0m\u001b[33mmw\u001b[0m\u001b[33mj\u001b[0m\u001b[33m/s\u001b[0m\u001b[33myc\u001b[0m\u001b[33mam\u001b[0m\u001b[33more\u001b[0m\u001b[33m.html\u001b[0m\u001b[33m).\n",
|
||||
"\n",
|
||||
"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m is\u001b[0m\u001b[33m also\u001b[0m\u001b[33m a\u001b[0m\u001b[33m major\u001b[0m\u001b[33m area\u001b[0m\u001b[33m of\u001b[0m\u001b[33m research\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m scientists\u001b[0m\u001b[33m exploring\u001b[0m\u001b[33m various\u001b[0m\u001b[33m methods\u001b[0m\u001b[33m to\u001b[0m\u001b[33m mitigate\u001b[0m\u001b[33m the\u001b[0m\u001b[33m effects\u001b[0m\u001b[33m of\u001b[0m\u001b[33m deco\u001b[0m\u001b[33mherence\u001b[0m\u001b[33m and\u001b[0m\u001b[33m errors\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computations\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mar\u001b[0m\u001b[33mxiv\u001b[0m\u001b[33m.org\u001b[0m\u001b[33m/\u001b[0m\u001b[33mabs\u001b[0m\u001b[33m/\u001b[0m\u001b[33m210\u001b[0m\u001b[33m4\u001b[0m\u001b[33m.\u001b[0m\u001b[33m045\u001b[0m\u001b[33m93\u001b[0m\u001b[33m).\u001b[0m\u001b[33m New\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m algorithms\u001b[0m\u001b[33m,\u001b[0m\u001b[33m such\u001b[0m\u001b[33m as\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m simulation\u001b[0m\u001b[33m and\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m machine\u001b[0m\u001b[33m learning\u001b[0m\u001b[33m,\u001b[0m\u001b[33m are\u001b[0m\u001b[33m also\u001b[0m\u001b[33m being\u001b[0m\u001b[33m developed\u001b[0m\u001b[33m to\u001b[0m\u001b[33m take\u001b[0m\u001b[33m advantage\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m unique\u001b[0m\u001b[33m properties\u001b[0m\u001b[33m of\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mar\u001b[0m\u001b[33mxiv\u001b[0m\u001b[33m.org\u001b[0m\u001b[33m/\u001b[0m\u001b[33mabs\u001b[0m\u001b[33m/\u001b[0m\u001b[33m210\u001b[0m\u001b[33m2\u001b[0m\u001b[33m.\u001b[0m\u001b[33m081\u001b[0m\u001b[33m83\u001b[0m\u001b[33m).\n",
|
||||
"\n",
|
||||
"\u001b[0m\u001b[33mS\u001b[0m\u001b[33mOURCES\u001b[0m\u001b[33m:\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m:\u001b[0m\u001b[33m a\u001b[0m\u001b[33m very\u001b[0m\u001b[33m short\u001b[0m\u001b[33m introduction\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Samuel\u001b[0m\u001b[33m J\u001b[0m\u001b[33m.\u001b[0m\u001b[33m L\u001b[0m\u001b[33momon\u001b[0m\u001b[33maco\u001b[0m\u001b[33m (\u001b[0m\u001b[33mURL\u001b[0m\u001b[33m)\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Approx\u001b[0m\u001b[33mimate\u001b[0m\u001b[33m Optimization\u001b[0m\u001b[33m Algorithm\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Edward\u001b[0m\u001b[33m Far\u001b[0m\u001b[33mhi\u001b[0m\u001b[33m et\u001b[0m\u001b[33m al\u001b[0m\u001b[33m.\u001b[0m\u001b[33m (\u001b[0m\u001b[33mURL\u001b[0m\u001b[33m)\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m for\u001b[0m\u001b[33m beginners\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Robert\u001b[0m\u001b[33m A\u001b[0m\u001b[33m.\u001b[0m\u001b[33m H\u001b[0m\u001b[33mulet\u001b[0m\u001b[33m (\u001b[0m\u001b[33mURL\u001b[0m\u001b[33m)\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mGoogle\u001b[0m\u001b[33m's\u001b[0m\u001b[33m \u001b[0m\u001b[33m53\u001b[0m\u001b[33m-q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m AI\u001b[0m\u001b[33m Lab\u001b[0m\u001b[33m (\u001b[0m\u001b[33mURL\u001b[0m\u001b[33m)\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mIBM\u001b[0m\u001b[33m's\u001b[0m\u001b[33m \u001b[0m\u001b[33m127\u001b[0m\u001b[33m-q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m (\u001b[0m\u001b[33mURL\u001b[0m\u001b[33m)\u001b[0m\u001b[97m\u001b[0m\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Experience\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mwww\u001b[0m\u001b[33m.ibm\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m/\u001b[0m\u001b[33mquant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m-com\u001b[0m\u001b[33mput\u001b[0m\u001b[33ming\u001b[0m\u001b[33m/)\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m's\u001b[0m\u001b[33m S\u001b[0m\u001b[33myc\u001b[0m\u001b[33mam\u001b[0m\u001b[33more\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33ms\u001b[0m\u001b[33mcai\u001b[0m\u001b[33m.st\u001b[0m\u001b[33manford\u001b[0m\u001b[33m.edu\u001b[0m\u001b[33m/~\u001b[0m\u001b[33mmw\u001b[0m\u001b[33mj\u001b[0m\u001b[33m/s\u001b[0m\u001b[33myc\u001b[0m\u001b[33mam\u001b[0m\u001b[33more\u001b[0m\u001b[33m.html\u001b[0m\u001b[33m)\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m with\u001b[0m\u001b[33m encoded\u001b[0m\u001b[33m cat\u001b[0m\u001b[33m codes\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mar\u001b[0m\u001b[33mxiv\u001b[0m\u001b[33m.org\u001b[0m\u001b[33m/\u001b[0m\u001b[33mabs\u001b[0m\u001b[33m/\u001b[0m\u001b[33m210\u001b[0m\u001b[33m4\u001b[0m\u001b[33m.\u001b[0m\u001b[33m045\u001b[0m\u001b[33m93\u001b[0m\u001b[33m)\n",
|
||||
"\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m simulation\u001b[0m\u001b[33m of\u001b[0m\u001b[33m many\u001b[0m\u001b[33m-body\u001b[0m\u001b[33m systems\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mar\u001b[0m\u001b[33mxiv\u001b[0m\u001b[33m.org\u001b[0m\u001b[33m/\u001b[0m\u001b[33mabs\u001b[0m\u001b[33m/\u001b[0m\u001b[33m210\u001b[0m\u001b[33m2\u001b[0m\u001b[33m.\u001b[0m\u001b[33m081\u001b[0m\u001b[33m83\u001b[0m\u001b[33m)\u001b[0m\u001b[97m\u001b[0m\n",
|
||||
"\u001b[30m\u001b[0m"
|
||||
]
|
||||
}
|
||||
|
|
@ -129,12 +130,12 @@
|
|||
" engine=\"brave\",\n",
|
||||
" api_key=\"dummy_value\"#os.getenv(\"BRAVE_SEARCH_API_KEY\"),\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" models_response = client.models.list()\n",
|
||||
" for model in models_response:\n",
|
||||
" if model.identifier.endswith(\"Instruct\"):\n",
|
||||
" model_name = model.llama_model\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"\n",
|
||||
" return await create_tool_agent(\n",
|
||||
" client=client,\n",
|
||||
|
|
@ -194,7 +195,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -211,25 +212,24 @@
|
|||
"\u001b[0m\u001b[33m \u001b[0m\u001b[33m \"\u001b[0m\u001b[33mlocation\u001b[0m\u001b[33m\":\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mSan\u001b[0m\u001b[33m Francisco\u001b[0m\u001b[33m\"\n",
|
||||
"\u001b[0m\u001b[33m \u001b[0m\u001b[33m }\n",
|
||||
"\u001b[0m\u001b[33m}\u001b[0m\u001b[97m\u001b[0m\n",
|
||||
"tool_calls starting: ToolCall(arguments={'location': 'San Francisco'}, call_id='420a73c9-1f6b-4f98-8fef-a7b9d2feb753', tool_name='get_weather')\n",
|
||||
"Using Location: San Francisco\n",
|
||||
"\u001b[32mCustomTool> {\"temperature\": 72.5, \"conditions\": \"partly cloudy\", \"humidity\": 65.0}\u001b[0m\n",
|
||||
"\n",
|
||||
"Query: Tell me the weather in Tokyo tomorrow\n",
|
||||
"--------------------------------------------------\n",
|
||||
"\u001b[30m\u001b[0m\u001b[33minference> \u001b[0m\u001b[36m\u001b[0m\u001b[36m{\"\u001b[0m\u001b[36mtype\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mfunction\u001b[0m\u001b[36m\",\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mname\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mget\u001b[0m\u001b[36m_weather\u001b[0m\u001b[36m\",\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mparameters\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m {\"\u001b[0m\u001b[36mlocation\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mTok\u001b[0m\u001b[36myo\u001b[0m\u001b[36m\",\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mdate\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mtom\u001b[0m\u001b[36morrow\u001b[0m\u001b[36m\"}}\u001b[0m\u001b[97m\u001b[0m\n",
|
||||
"tool_calls starting: ToolCall(arguments={'location': 'Tokyo', 'date': 'tomorrow'}, call_id='9f94b5f1-fa5a-492f-a81f-dcfaafc970f3', tool_name='get_weather')\n",
|
||||
"Using Location: Tokyo\n",
|
||||
"Using date: tomorrow\n"
|
||||
"\u001b[32mCustomTool> {\"temperature\": 90.1, \"conditions\": \"sunny\", \"humidity\": 40.0}\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import TypedDict, Optional, Dict, Any\n",
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"import json\n",
|
||||
"from llama_stack_client.types.tool_param_definition_param import ToolParamDefinitionParam\n",
|
||||
"from llama_stack_client.types import CompletionMessage\n",
|
||||
"class WeatherTool:\n",
|
||||
"from llama_stack_client.types import CompletionMessage,ToolResponseMessage\n",
|
||||
"from llama_stack_client.lib.agents.custom_tool import CustomTool\n",
|
||||
"\n",
|
||||
"class WeatherTool(CustomTool):\n",
|
||||
" \"\"\"Example custom tool for weather information.\"\"\"\n",
|
||||
"\n",
|
||||
" def get_name(self) -> str:\n",
|
||||
|
|
@ -251,31 +251,43 @@
|
|||
" required=False\n",
|
||||
" )\n",
|
||||
" }\n",
|
||||
" async def run(self,messages):\n",
|
||||
" for message in messages:\n",
|
||||
" print(\"tool_calls starting: \", message.tool_calls[0])\n",
|
||||
" return_dict = message.tool_calls[0].arguments\n",
|
||||
" location = return_dict.get(\"location\", None)\n",
|
||||
" date = return_dict.get(\"date\", None)\n",
|
||||
" print(\"Using Location:\",location)\n",
|
||||
" if date:\n",
|
||||
" print(\"Using date:\",date)\n",
|
||||
" return await self.run_impl(location,date)\n",
|
||||
" \n",
|
||||
" async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:\n",
|
||||
" assert len(messages) == 1, \"Expected single message\"\n",
|
||||
"\n",
|
||||
" message = messages[0]\n",
|
||||
"\n",
|
||||
" tool_call = message.tool_calls[0]\n",
|
||||
" # location = tool_call.arguments.get(\"location\", None)\n",
|
||||
" # date = tool_call.arguments.get(\"date\", None)\n",
|
||||
" try:\n",
|
||||
" response = await self.run_impl(**tool_call.arguments)\n",
|
||||
" response_str = json.dumps(response, ensure_ascii=False)\n",
|
||||
" except Exception as e:\n",
|
||||
" response_str = f\"Error when running tool: {e}\"\n",
|
||||
"\n",
|
||||
" message = ToolResponseMessage(\n",
|
||||
" call_id=tool_call.call_id,\n",
|
||||
" tool_name=tool_call.tool_name,\n",
|
||||
" content=response_str,\n",
|
||||
" role=\"ipython\",\n",
|
||||
" )\n",
|
||||
" return [message]\n",
|
||||
"\n",
|
||||
" async def run_impl(self, location: str, date: Optional[str] = None) -> Dict[str, Any]:\n",
|
||||
" \"\"\"Simulate getting weather data (replace with actual API call).\"\"\"\n",
|
||||
" # Mock implementation\n",
|
||||
" return [CompletionMessage(\n",
|
||||
" content=\"\"\"{\n",
|
||||
" if date:\n",
|
||||
" return {\n",
|
||||
" \"temperature\": 90.1,\n",
|
||||
" \"conditions\": \"sunny\",\n",
|
||||
" \"humidity\": 40.0\n",
|
||||
" }\n",
|
||||
" return {\n",
|
||||
" \"temperature\": 72.5,\n",
|
||||
" \"conditions\": \"partly cloudy\",\n",
|
||||
" \"humidity\": 65.0\n",
|
||||
" }\"\"\",\n",
|
||||
" role='assistant',\n",
|
||||
" stop_reason='end_of_message',\n",
|
||||
" tool_calls=[],\n",
|
||||
" )],\n",
|
||||
" \n",
|
||||
" }\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"async def create_weather_agent(client: LlamaStackClient) -> Agent:\n",
|
||||
" \"\"\"Create an agent with weather tool capability.\"\"\"\n",
|
||||
|
|
@ -350,7 +362,7 @@
|
|||
" messages=[{\"role\": \"user\", \"content\": query}],\n",
|
||||
" session_id=session_id,\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" async for log in EventLogger().log(response):\n",
|
||||
" log.print()\n",
|
||||
"\n",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue