, description=None, parameters=None)}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_python(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n",
"/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:441: UserWarning: Pydantic serializer warnings:\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" Failed to get discriminator value for tagged union serialization with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - defaulting to left to right union serialization.\n",
" PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `[TextContentItem(type='te...TRIEVED-CONTEXT ===\\n')]` - serialized value may not be as expected\n",
" return self.__pydantic_serializer__.to_json(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b79a023a8ddd4f1d80c2c737affc3c91",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Batches: 0%| | 0/1 [00:00 Tool:memory Args:{'query': '{\"role\":\"user\",\"content\":\"Plot average yearly inflation as a time series\",\"context\":null}', 'memory_bank_id': 'inflation_data_memory_bank'}\u001b[0m\n",
"\u001b[36mtool_execution> fetched 3079 bytes from memory\u001b[0m\n",
"\u001b[33minference> \u001b[0m\u001b[36m\u001b[0m\u001b[36mimport\u001b[0m\u001b[36m pandas\u001b[0m\u001b[36m as\u001b[0m\u001b[36m pd\u001b[0m\u001b[36m\n",
"\n",
"\u001b[0m\u001b[36m#\u001b[0m\u001b[36m Define\u001b[0m\u001b[36m the\u001b[0m\u001b[36m data\u001b[0m\u001b[36m\n",
"\u001b[0m\u001b[36mdata\u001b[0m\u001b[36m =\u001b[0m\u001b[36m {\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mYear\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m201\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m5\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m201\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m202\u001b[0m\u001b[36m3\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mJan\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mFeb\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m5\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mMar\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m5\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mApr\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m3\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m5\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mMay\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m3\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mJun\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m1\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m5\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mJul\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m6\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m5\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mAug\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m4\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m0\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m6\u001b[0m\u001b[36m.\u001b[0m\u001b[36m3\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m4\u001b[0m\u001b[36m.\u001b[0m\u001b[36m8\u001b[0m\u001b[36m],\n",
"\u001b[0m\u001b[36m \u001b[0m\u001b[36m \"\u001b[0m\u001b[36mSep\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m [\u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m7\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[36m.\u001b[0m\u001b[36m9\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m2\u001b[0m\u001b[36m.\u001b[0m\u001b[36m2\u001b[0m\u001b[36m,\u001b[0m\u001b[36m \u001b[0m\u001b[36m1\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[32mtool_execution> Tool:code_interpreter Args:{'code': 'import pandas as pd\\n\\n# Define the data\\ndata = {\\n \"Year\": [2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\\n \"Jan\": [1.6, 1.6, 2.2, 2.3, 1.8, 2.2, 2.3, 1.4, 6.0, 5.6],\\n \"Feb\": [1.6, 1.7, 2.3, 2.2, 1.8, 2.1, 2.4, 1.3, 6.4, 5.5],\\n \"Mar\": [1.7, 1.8, 2.2, 2.0, 2.1, 2.0, 2.1, 1.6, 6.5, 5.6],\\n \"Apr\": [1.8, 1.8, 2.1, 1.9, 2.1, 2.1, 1.4, 3.0, 6.2, 5.5],\\n \"May\": [2.0, 1.7, 2.2, 1.7, 2.2, 2.0, 1.2, 3.8, 6.0, 5.3],\\n \"Jun\": [1.9, 1.8, 2.2, 1.7, 2.3, 2.1, 1.2, 4.5, 5.9, 4.8],\\n \"Jul\": [1.9, 1.8, 2.2, 1.7, 2.4, 2.2, 1.6, 4.3, 5.9, 4.8],\\n \"Aug\": [1.7, 1.8, 2.3, 1.7, 2.2, 2.4, 1.7, 4.0, 6.3, 4.8],\\n \"Sep\": [1.7, 1.9, 2.2, 1'}\u001b[0m\n",
"\u001b[32mtool_execution> Tool:code_interpreter Response:error\n",
"[stdout]\n",
"[Errno 2] No such file or directory: 'bwrap'\n",
"[/stdout]\n",
"[stderr]\n",
"[Errno 2] No such file or directory: 'bwrap'\n",
"[/stderr]\u001b[0m\n",
"\u001b[33minference> \u001b[0m"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mThe\u001b[0m\u001b[33m error\u001b[0m\u001b[33m message\u001b[0m\u001b[33m indicates\u001b[0m\u001b[33m that\u001b[0m\u001b[33m the\u001b[0m\u001b[33m system\u001b[0m\u001b[33m cannot\u001b[0m\u001b[33m find\u001b[0m\u001b[33m the\u001b[0m\u001b[33m '\u001b[0m\u001b[33mb\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m'\u001b[0m\u001b[33m file\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m is\u001b[0m\u001b[33m required\u001b[0m\u001b[33m for\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m to\u001b[0m\u001b[33m be\u001b[0m\u001b[33m displayed\u001b[0m\u001b[33m.\u001b[0m\u001b[33m This\u001b[0m\u001b[33m issue\u001b[0m\u001b[33m is\u001b[0m\u001b[33m likely\u001b[0m\u001b[33m due\u001b[0m\u001b[33m to\u001b[0m\u001b[33m a\u001b[0m\u001b[33m missing\u001b[0m\u001b[33m or\u001b[0m\u001b[33m incorrect\u001b[0m\u001b[33m installation\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m '\u001b[0m\u001b[33mb\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m'\u001b[0m\u001b[33m package\u001b[0m\u001b[33m.\n",
"\n",
"\u001b[0m\u001b[33mTo\u001b[0m\u001b[33m fix\u001b[0m\u001b[33m this\u001b[0m\u001b[33m issue\u001b[0m\u001b[33m,\u001b[0m\u001b[33m you\u001b[0m\u001b[33m can\u001b[0m\u001b[33m try\u001b[0m\u001b[33m reinstall\u001b[0m\u001b[33ming\u001b[0m\u001b[33m the\u001b[0m\u001b[33m '\u001b[0m\u001b[33mb\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m'\u001b[0m\u001b[33m package\u001b[0m\u001b[33m using\u001b[0m\u001b[33m pip\u001b[0m\u001b[33m:\n",
"\n",
"\u001b[0m\u001b[33mpip\u001b[0m\u001b[33m install\u001b[0m\u001b[33m b\u001b[0m\u001b[33mwrap\u001b[0m\u001b[33m\n",
"\n",
"\u001b[0m\u001b[33mIf\u001b[0m\u001b[33m the\u001b[0m\u001b[33m issue\u001b[0m\u001b[33m persists\u001b[0m\u001b[33m,\u001b[0m\u001b[33m you\u001b[0m\u001b[33m can\u001b[0m\u001b[33m try\u001b[0m\u001b[33m to\u001b[0m\u001b[33m display\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m using\u001b[0m\u001b[33m a\u001b[0m\u001b[33m different\u001b[0m\u001b[33m method\u001b[0m\u001b[33m,\u001b[0m\u001b[33m such\u001b[0m\u001b[33m as\u001b[0m\u001b[33m saving\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m to\u001b[0m\u001b[33m a\u001b[0m\u001b[33m file\u001b[0m\u001b[33m:\n",
"\n",
"\u001b[0m\u001b[33mimport\u001b[0m\u001b[33m matplotlib\u001b[0m\u001b[33m.pyplot\u001b[0m\u001b[33m as\u001b[0m\u001b[33m plt\u001b[0m\u001b[33m\n",
"\n",
"\u001b[0m\u001b[33m#\u001b[0m\u001b[33m ...\u001b[0m\u001b[33m (\u001b[0m\u001b[33mrest\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m code\u001b[0m\u001b[33m remains\u001b[0m\u001b[33m the\u001b[0m\u001b[33m same\u001b[0m\u001b[33m)\n",
"\n",
"\u001b[0m\u001b[33mplt\u001b[0m\u001b[33m.savefig\u001b[0m\u001b[33m('\u001b[0m\u001b[33min\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m_rate\u001b[0m\u001b[33m.png\u001b[0m\u001b[33m')\n",
"\n",
"\u001b[0m\u001b[33mThis\u001b[0m\u001b[33m will\u001b[0m\u001b[33m save\u001b[0m\u001b[33m the\u001b[0m\u001b[33m plot\u001b[0m\u001b[33m to\u001b[0m\u001b[33m a\u001b[0m\u001b[33m file\u001b[0m\u001b[33m named\u001b[0m\u001b[33m '\u001b[0m\u001b[33min\u001b[0m\u001b[33mflation\u001b[0m\u001b[33m_rate\u001b[0m\u001b[33m.png\u001b[0m\u001b[33m'\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m current\u001b[0m\u001b[33m working\u001b[0m\u001b[33m directory\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n",
"\u001b[30m\u001b[0m"
]
}
],
"source": [
"agent_config = AgentConfig(\n",
" sampling_params = {\n",
" \"max_tokens\" : 4096,\n",
" \"temperature\": 0.0\n",
" },\n",
" model=model_id,\n",
" instructions=\"You are a helpful assistant\",\n",
" tools=[\n",
" \"brave_search\",\n",
" \"code_interpreter\",\n",
" ],\n",
" tool_choice=\"required\",\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=False,\n",
")\n",
"\n",
"memory_bank_id = \"inflation_data_memory_bank\"\n",
"client.memory_banks.register(\n",
" memory_bank_id=memory_bank_id,\n",
" params={\n",
" \"memory_bank_type\": \"vector\",\n",
" \"embedding_model\": \"all-MiniLM-L6-v2\",\n",
" \"chunk_size_in_tokens\": 512,\n",
" \"overlap_size_in_tokens\": 64,\n",
" },\n",
")\n",
"AugmentConfigWithMemoryTool(agent_config, client)\n",
"codex_agent = Agent(client, agent_config)\n",
"session_id = codex_agent.create_session(\"test-session\")\n",
"\n",
"client.memory.insert(\n",
" bank_id=memory_bank_id,\n",
" documents=[\n",
" Document(\n",
" document_id=\"inflation\",\n",
" content=\"https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv\",\n",
" mime_type=\"text/csv\",\n",
" metadata={},\n",
" )\n",
" ],\n",
")\n",
"\n",
"user_prompts = [\n",
" {\"prompt\": \"Can you describe the data in the context?\", \"tools\": [{\"name\": \"memory\", \"args\": {\"memory_bank_id\": memory_bank_id}}]},\n",
" {\"prompt\": \"Plot average yearly inflation as a time series\", \"tools\": [{\"name\": \"memory\", \"args\": {\"memory_bank_id\": memory_bank_id}}, \"code_interpreter\"]},\n",
"]\n",
"\n",
"for input in user_prompts:\n",
" cprint(f'User> {input[\"prompt\"]}', 'green')\n",
" response = codex_agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": input[\"prompt\"],\n",
" }\n",
" ],\n",
" session_id=session_id,\n",
" tools=input[\"tools\"],\n",
" )\n",
" # for chunk in response:\n",
" # print(chunk)\n",
"\n",
" for log in EventLogger().log(response):\n",
" log.print()\n"
]
},
{
"cell_type": "markdown",
"id": "9GHJHfLmIQQi",
"metadata": {
"id": "9GHJHfLmIQQi"
},
"source": [
"- Now, use the generated response from agent to view the plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "JqBBVLKdIHHq",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"id": "JqBBVLKdIHHq",
"outputId": "4563e803-8385-426b-ec6c-e8b19e2ee6e6"
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Read the CSV file\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Extract the year and inflation rate from the CSV file\u001b[39;00m\n\u001b[1;32m 8\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\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[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
"File \u001b[0;32m~/miniconda3/envs/stack/lib/python3.10/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv'"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Read the CSV file\n",
"df = pd.read_csv('/tmp/tmpco0s0o4_/LOdZoVp1inflation.csv')\n",
"\n",
"# Extract the year and inflation rate from the CSV file\n",
"df['Year'] = pd.to_datetime(df['Year'], format='%Y')\n",
"df = df.rename(columns={'Jan': 'Jan Rate', 'Feb': 'Feb Rate', 'Mar': 'Mar Rate', 'Apr': 'Apr Rate', 'May': 'May Rate', 'Jun': 'Jun Rate', 'Jul': 'Jul Rate', 'Aug': 'Aug Rate', 'Sep': 'Sep Rate', 'Oct': 'Oct Rate', 'Nov': 'Nov Rate', 'Dec': 'Dec Rate'})\n",
"\n",
"# Calculate the average yearly inflation rate\n",
"df['Yearly Inflation'] = df[['Jan Rate', 'Feb Rate', 'Mar Rate', 'Apr Rate', 'May Rate', 'Jun Rate', 'Jul Rate', 'Aug Rate', 'Sep Rate', 'Oct Rate', 'Nov Rate', 'Dec Rate']].mean(axis=1)\n",
"\n",
"# Plot the average yearly inflation rate as a time series\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(df['Year'], df['Yearly Inflation'], marker='o')\n",
"plt.title('Average Yearly Inflation Rate')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Inflation Rate (%)')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "FJ85DUhgBZd7",
"metadata": {
"id": "FJ85DUhgBZd7"
},
"source": [
"## 3. Llama Stack Agent Evaluations\n"
]
},
{
"cell_type": "markdown",
"id": "ydeBDpDT5VHd",
"metadata": {
"id": "ydeBDpDT5VHd"
},
"source": [
"#### 3.1. Online Evaluation Dataset Collection Using Telemetry\n",
"\n",
"- Llama Stack offers built-in telemetry to collect traces and data about your agentic application.\n",
"- In this example, we will show how to build an Agent with Llama Stack, and query the agent's traces into an online dataset that can be used for evaluation. "
]
},
{
"cell_type": "markdown",
"id": "_JueJAKyJR5m",
"metadata": {
"id": "_JueJAKyJR5m"
},
"source": [
"##### 🚧 Patches 🚧\n",
"- The following cells are temporary patches to get `telemetry` working."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "klPkK1t7CzIY",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "klPkK1t7CzIY",
"outputId": "ab0c1490-7fa6-446c-8e35-7b42f57e8a04"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found existing installation: llama_stack 0.0.61\n",
"Uninstalling llama_stack-0.0.61:\n",
" Would remove:\n",
" /usr/local/bin/install-wheel-from-presigned\n",
" /usr/local/bin/llama\n",
" /usr/local/lib/python3.10/dist-packages/llama_stack-0.0.61.dist-info/*\n",
" /usr/local/lib/python3.10/dist-packages/llama_stack/*\n",
"Proceed (Y/n)? Y\n",
" Successfully uninstalled llama_stack-0.0.61\n",
"Collecting git+https://github.com/meta-llama/llama-stack.git@main\n",
" Cloning https://github.com/meta-llama/llama-stack.git (to revision main) to /tmp/pip-req-build-oryyzdm1\n",
" Running command git clone --filter=blob:none --quiet https://github.com/meta-llama/llama-stack.git /tmp/pip-req-build-oryyzdm1\n",
" Resolved https://github.com/meta-llama/llama-stack.git to commit 53b3a1e345c46d7d37c1af3d675092a4cbfe85f9\n",
" Running command git submodule update --init --recursive -q\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: blobfile in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (3.0.0)\n",
"Requirement already satisfied: fire in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.7.0)\n",
"Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.28.1)\n",
"Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.26.5)\n",
"Requirement already satisfied: llama-models>=0.0.61 in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (0.0.61)\n",
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"Requirement already satisfied: prompt-toolkit in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (3.0.48)\n",
"Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (1.0.1)\n",
"Requirement already satisfied: pydantic>=2 in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (2.10.3)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (2.32.3)\n",
"Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (13.9.4)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from llama_stack==0.0.61) (75.1.0)\n",
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"Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from llama-models>=0.0.61->llama_stack==0.0.61) (10.4.0)\n",
"Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (3.7.1)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (8.1.7)\n",
"Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.9.0)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (2.2.2)\n",
"Requirement already satisfied: pyaml in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (24.12.1)\n",
"Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.3.1)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (4.66.6)\n",
"Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from llama-stack-client>=0.0.61->llama_stack==0.0.61) (4.12.2)\n",
"Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx->llama_stack==0.0.61) (2024.8.30)\n",
"Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->llama_stack==0.0.61) (1.0.7)\n",
"Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx->llama_stack==0.0.61) (3.10)\n",
"Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx->llama_stack==0.0.61) (0.14.0)\n",
"Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2->llama_stack==0.0.61) (0.7.0)\n",
"Requirement already satisfied: pydantic-core==2.27.1 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2->llama_stack==0.0.61) (2.27.1)\n",
"Requirement already satisfied: pycryptodomex>=3.8 in /usr/local/lib/python3.10/dist-packages (from blobfile->llama_stack==0.0.61) (3.21.0)\n",
"Requirement already satisfied: urllib3<3,>=1.25.3 in /usr/local/lib/python3.10/dist-packages (from blobfile->llama_stack==0.0.61) (2.2.3)\n",
"Requirement already satisfied: lxml>=4.9 in /usr/local/lib/python3.10/dist-packages (from blobfile->llama_stack==0.0.61) (5.3.0)\n",
"Requirement already satisfied: filelock>=3.0 in /usr/local/lib/python3.10/dist-packages (from blobfile->llama_stack==0.0.61) (3.16.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->llama_stack==0.0.61) (2024.9.0)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->llama_stack==0.0.61) (24.2)\n",
"Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit->llama_stack==0.0.61) (0.2.13)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->llama_stack==0.0.61) (3.4.0)\n",
"Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->llama_stack==0.0.61) (3.0.0)\n",
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->llama_stack==0.0.61) (2.18.0)\n",
"Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.2.2)\n",
"Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->llama_stack==0.0.61) (0.1.2)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->llama-models>=0.0.61->llama_stack==0.0.61) (3.0.2)\n",
"Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.26.4)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-stack-client>=0.0.61->llama_stack==0.0.61) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-stack-client>=0.0.61->llama_stack==0.0.61) (2024.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-stack-client>=0.0.61->llama_stack==0.0.61) (2024.2)\n",
"Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken->llama-models>=0.0.61->llama_stack==0.0.61) (2024.9.11)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->llama-stack-client>=0.0.61->llama_stack==0.0.61) (1.17.0)\n",
"Building wheels for collected packages: llama_stack\n",
" Building wheel for llama_stack (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for llama_stack: filename=llama_stack-0.0.61-py3-none-any.whl size=464145 sha256=da71747aceef9aec43553f66c43095486d1a920e47bb0e47e2729a8e4328fff6\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-jquw5j7f/wheels/74/e4/3b/079983408fa9323c1f2807e404ee78b468c74bec381eb70d4f\n",
"Successfully built llama_stack\n",
"Installing collected packages: llama_stack\n",
"Successfully installed llama_stack-0.0.61\n"
]
},
{
"data": {
"application/vnd.colab-display-data+json": {
"id": "7701cb0c982f4250a46721fededf9647",
"pip_warning": {
"packages": [
"llama_stack"
]
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# need to install on latest main\n",
"!pip uninstall llama-stack\n",
"!pip install git+https://github.com/meta-llama/llama-stack.git@main"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9jJ75JlnETTH",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9jJ75JlnETTH",
"outputId": "76bd3912-f814-428c-88e1-c1113af77856"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed handler StreamHandler from root logger\n"
]
}
],
"source": [
"# disable logging for clean server logs\n",
"import logging\n",
"def remove_root_handlers():\n",
" root_logger = logging.getLogger()\n",
" for handler in root_logger.handlers[:]:\n",
" root_logger.removeHandler(handler)\n",
" print(f\"Removed handler {handler.__class__.__name__} from root logger\")\n",
"\n",
"\n",
"remove_root_handlers()"
]
},
{
"cell_type": "markdown",
"id": "_t_tcWq0JcJ4",
"metadata": {
"id": "_t_tcWq0JcJ4"
},
"source": [
"##### 3.1.1. Building a Search Agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4iCO59kP20Zs",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4iCO59kP20Zs",
"outputId": "f6179de6-054d-4452-a893-8d9b64c5a0d1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inference> Let me check the latest sports news.\n",
"inference> bravy_search.call(query=\"Bill Cosby South Park episode\")\n",
"CustomTool> Unknown tool `bravy_search` was called.\n",
"inference> brave_search.call(query=\"Andrew Tate kickboxing name\")\n",
"tool_execution> Tool:brave_search Args:{'query': 'Andrew Tate kickboxing name'}\n",
"tool_execution> Tool:brave_search Response:{\"query\": \"Andrew Tate kickboxing name\", \"top_k\": [{\"title\": \"Andrew Tate kickboxing record: How many championships ... - FirstSportz\", \"url\": \"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\", \"content\": \"Andrew Tate's Kickboxing career. During his kickboxing career, he used the nickname \\\"King Cobra,\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\", \"score\": 0.9996244, \"raw_content\": null}, {\"title\": \"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\", \"url\": \"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\", \"content\": \"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\", \"score\": 0.99909246, \"raw_content\": null}, {\"title\": \"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\", \"url\": \"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\", \"content\": \"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\", \"score\": 0.9976586, \"raw_content\": null}, {\"title\": \"About Andrew Tate: A Journey from Champion to Controversy\", \"url\": \"https://reachmorpheus.com/andrew-tate/\", \"content\": \"Andrew Tate's kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\", \"score\": 0.99701905, \"raw_content\": null}, {\"title\": \"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\", \"url\": \"https://www.nextbiography.com/andrew-tate/\", \"content\": \"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\", \"score\": 0.99368566, \"raw_content\": null}]}\n",
"shield_call> No Violation\n",
"inference> Andrew Tate's kickboxing name is \"King Cobra.\"\n"
]
}
],
"source": [
"from llama_stack_client.lib.agents.agent import Agent\n",
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from google.colab import userdata\n",
"\n",
"agent_config = AgentConfig(\n",
" model=\"meta-llama/Llama-3.1-405B-Instruct\",\n",
" instructions=\"You are a helpful assistant. Use search tool to answer the questions. \",\n",
" tools=(\n",
" [\n",
" {\n",
" \"type\": \"brave_search\",\n",
" \"engine\": \"tavily\",\n",
" \"api_key\": userdata.get(\"TAVILY_SEARCH_API_KEY\")\n",
" }\n",
" ]\n",
" ),\n",
" input_shields=[],\n",
" output_shields=[],\n",
" enable_session_persistence=False,\n",
")\n",
"agent = Agent(client, agent_config)\n",
"user_prompts = [\n",
" \"Which teams played in the NBA western conference finals of 2024\",\n",
" \"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\n",
" \"What is the British-American kickboxer Andrew Tate's kickboxing name?\",\n",
"]\n",
"\n",
"session_id = agent.create_session(\"test-session\")\n",
"\n",
"for prompt in user_prompts:\n",
" response = agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": prompt,\n",
" }\n",
" ],\n",
" session_id=session_id,\n",
" )\n",
"\n",
" for log in EventLogger().log(response):\n",
" log.print()"
]
},
{
"cell_type": "markdown",
"id": "ekOS2kM4P0LM",
"metadata": {
"id": "ekOS2kM4P0LM"
},
"source": [
"##### 3.1.2 Query Telemetry"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "agkWgToGAsuA",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 760
},
"id": "agkWgToGAsuA",
"outputId": "647cd5d2-7610-4fd6-ef66-c3f2f782a1b0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Getting traces for session_id=ac651ce8-2281-47f2-8814-ef947c066e40\n"
]
},
{
"data": {
"text/html": [
"[\n",
"│ {\n",
"│ │ 'input': [\n",
"│ │ │ '{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'\n",
"│ │ ],\n",
"│ │ 'output': 'content: Let me check the latest sports news. tool_calls: []'\n",
"│ },\n",
"│ {\n",
"│ │ 'input': [\n",
"│ │ │ '{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}',\n",
"│ │ │ '{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'\n",
"│ │ ],\n",
"│ │ 'output': \"content: tool_calls: [ToolCall(call_id='19bd3554-e670-4856-89d0-c63f5b016245', tool_name='bravy_search', arguments={'query': 'Bill Cosby South Park episode'})]\"\n",
"│ },\n",
"│ {\n",
"│ │ 'input': [\n",
"│ │ │ '{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}',\n",
"│ │ │ '{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}',\n",
"│ │ │ '{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":{\"query\":\"Bill Cosby South Park episode\"}}]}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'\n",
"│ │ ],\n",
"│ │ 'output': \"content: tool_calls: [ToolCall(call_id='526045a7-5f51-40fb-ba97-5ad29610e511', tool_name=<BuiltinTool.brave_search: 'brave_search'>, arguments={'query': 'Andrew Tate kickboxing name'})]\"\n",
"│ },\n",
"│ {\n",
"│ │ 'input': '{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":{\"query\":\"Andrew Tate kickboxing name\"}}]}',\n",
"│ │ 'output': '{\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"{\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": [{\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null}]}\"}'\n",
"│ },\n",
"│ {\n",
"│ │ 'input': [\n",
"│ │ │ '{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}',\n",
"│ │ │ '{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}',\n",
"│ │ │ '{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":{\"query\":\"Bill Cosby South Park episode\"}}]}',\n",
"│ │ │ '{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}',\n",
"│ │ │ '{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":{\"query\":\"Andrew Tate kickboxing name\"}}]}',\n",
"│ │ │ '{\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"{\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": [{\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null}]}\"}'\n",
"│ │ ],\n",
"│ │ 'output': 'content: Andrew Tate\\'s kickboxing name is \"King Cobra.\" tool_calls: []'\n",
"│ }\n",
"]\n",
"\n"
],
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"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
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"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Bill Cosby South Park episode\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":\u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"query\":\"Andrew Tate kickboxing name\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": \u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? MMA, kickboxing record and controversies of fighter ...\\\\\", \\\\\"url\\\\\": \\\\\"https://www.sportingnews.com/us/kickboxing/news/andrew-tate-mma-kickboxing-record-controversies/u50waalc9cfz7krjg9wnyb7p\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate kickboxing record After launching his career as a 20-year-old in 2007, Tate built a formidable kickboxing record that included 76 wins across 85 fights in more than 13 years in the ring.\\\\\", \\\\\"score\\\\\": 0.9976586, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"About Andrew Tate: A Journey from Champion to Controversy\\\\\", \\\\\"url\\\\\": \\\\\"https://reachmorpheus.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s kickboxing career, beginning in 2005, is a tale of determination and skill. He quickly made a name for himself in the sport, rising through the ranks with his unique fighting style and strategic approach, honed by his chess-playing background.\\\\\", \\\\\"score\\\\\": 0.99701905, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate Bio, Wiki, Net Worth, Age, Family, MMA Career - Next Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.nextbiography.com/andrew-tate/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate Age. Andrew Tate is 36 years old as of 2023, born on December 1, 1986, in Washington, DC. By his mid-thirties, Andrew Tate has become an esteemed figure in the world of kickboxing, showcasing remarkable expertise and experience in the sport. Early Life of Andrew Tate. Andrew Tate was born on 01 December 1986 to an African-American\\\\\", \\\\\"score\\\\\": 0.99368566, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m: \u001b[32m'content: Andrew Tate\\'s kickboxing name is \"King Cobra.\" tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(f\"Getting traces for session_id={session_id}\")\n",
"import json\n",
"from rich.pretty import pprint\n",
"\n",
"agent_logs = []\n",
"\n",
"for span in client.telemetry.query_spans(\n",
" attribute_filters=[\n",
" {\"key\": \"session_id\", \"op\": \"eq\", \"value\": session_id},\n",
" ],\n",
" attributes_to_return=[\"input\", \"output\"]\n",
" ):\n",
" if span.attributes[\"output\"] != \"no shields\":\n",
" agent_logs.append(span.attributes)\n",
"\n",
"pprint(agent_logs)"
]
},
{
"cell_type": "markdown",
"id": "QF30H7ufP2RE",
"metadata": {
"id": "QF30H7ufP2RE"
},
"source": [
"##### 3.1.3 Post-Process Telemetry Results & Evaluate\n",
"\n",
"- Now, we want to run evaluation to assert that our search agent succesfully calls brave_search from online traces.\n",
"- We will first post-process the agent's telemetry logs and run evaluation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "sy4Xaff_Avuu",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 411
},
"id": "sy4Xaff_Avuu",
"outputId": "cb68bae7-b21d-415d-8e71-612bd383c793"
},
"outputs": [
{
"data": {
"text/html": [
"[\n",
"│ {\n",
"│ │ 'input_query': '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}',\n",
"│ │ 'generated_answer': 'content: Let me check the latest sports news. tool_calls: []',\n",
"│ │ 'expected_answer': 'brave_search'\n",
"│ },\n",
"│ {\n",
"│ │ 'input_query': '{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}',\n",
"│ │ 'generated_answer': \"content: tool_calls: [ToolCall(call_id='19bd3554-e670-4856-89d0-c63f5b016245', tool_name='bravy_search', arguments={'query': 'Bill Cosby South Park episode'})]\",\n",
"│ │ 'expected_answer': 'brave_search'\n",
"│ },\n",
"│ {\n",
"│ │ 'input_query': '{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}',\n",
"│ │ 'generated_answer': \"content: tool_calls: [ToolCall(call_id='526045a7-5f51-40fb-ba97-5ad29610e511', tool_name=<BuiltinTool.brave_search: 'brave_search'>, arguments={'query': 'Andrew Tate kickboxing name'})]\",\n",
"│ │ 'expected_answer': 'brave_search'\n",
"│ }\n",
"]\n",
"\n"
],
"text/plain": [
"\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'content: Let me check the latest sports news. tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? Give me the number and title.\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='19bd3554-e670-4856-89d0-c63f5b016245', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m='bravy_search', \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Bill Cosby South Park episode'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"content: tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32mToolCall\u001b[0m\u001b[32m(\u001b[0m\u001b[32mcall_id\u001b[0m\u001b[32m='526045a7-5f51-40fb-ba97-5ad29610e511', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m=\u001b[0m\u001b[32m<\u001b[0m\u001b[32mBuiltinTool.brave_search:\u001b[0m\u001b[32m 'brave_search'\u001b[0m\u001b[32m>\u001b[0m\u001b[32m, \u001b[0m\u001b[32marguments\u001b[0m\u001b[32m=\u001b[0m\u001b[32m{\u001b[0m\u001b[32m'query': 'Andrew Tate kickboxing name'\u001b[0m\u001b[32m}\u001b[0m\u001b[32m)\u001b[0m\u001b[32m]\u001b[0m\u001b[32m\"\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"ScoringScoreResponse(\n",
"│ results={\n",
"│ │ 'basic::subset_of': ScoringResult(\n",
"│ │ │ aggregated_results={'accuracy': {'accuracy': 0.3333333333333333, 'num_correct': 1.0, 'num_total': 3}},\n",
"│ │ │ score_rows=[{'score': 0.0}, {'score': 0.0}, {'score': 1.0}]\n",
"│ │ )\n",
"│ }\n",
")\n",
"\n"
],
"text/plain": [
"\u001b[1;35mScoringScoreResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mresults\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'basic::subset_of'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m0.3333333333333333\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m3\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# post-process telemetry spance and prepare data for eval\n",
"# in this case, we want to assert that all user prompts is followed by a tool call\n",
"import ast\n",
"import json\n",
"\n",
"eval_rows = []\n",
"\n",
"for log in agent_logs:\n",
" last_msg = log['input'][-1]\n",
" if \"\\\"role\\\":\\\"user\\\"\" in last_msg:\n",
" eval_rows.append(\n",
" {\n",
" \"input_query\": last_msg,\n",
" \"generated_answer\": log[\"output\"],\n",
" # check if generated_answer uses tools brave_search\n",
" \"expected_answer\": \"brave_search\",\n",
" },\n",
" )\n",
"\n",
"pprint(eval_rows)\n",
"scoring_params = {\n",
" \"basic::subset_of\": None,\n",
"}\n",
"scoring_response = client.scoring.score(input_rows=eval_rows, scoring_functions=scoring_params)\n",
"pprint(scoring_response)"
]
},
{
"cell_type": "markdown",
"id": "IKbzhxcw5e_c",
"metadata": {
"id": "IKbzhxcw5e_c"
},
"source": [
"#### 3.2. Agentic Application Dataset Scoring\n",
"- Llama Stack offers a library of scoring functions and the `/scoring` API, allowing you to run evaluations on your pre-annotated AI application datasets.\n",
"\n",
"- In this example, we will work with an example RAG dataset you have built previously, label with an annotation, and use LLM-As-Judge with custom judge prompt for scoring. Please checkout our [Llama Stack Playground](https://llama-stack.readthedocs.io/en/latest/playground/index.html) for an interactive interface to upload datasets and run scorings."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "xG4Y84VQBb0g",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"id": "xG4Y84VQBb0g",
"outputId": "f61cebdf-f614-440c-d170-f1e873b542ef"
},
"outputs": [
{
"data": {
"text/html": [
"ScoringScoreResponse(\n",
"│ results={\n",
"│ │ 'llm-as-judge::base': ScoringResult(\n",
"│ │ │ aggregated_results={},\n",
"│ │ │ score_rows=[\n",
"│ │ │ │ {\n",
"│ │ │ │ │ 'score': 'B',\n",
"│ │ │ │ │ 'judge_feedback': 'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE provides more detailed information about the top 5 topics related to LoRA, while the EXPECTED_RESPONSE only mentions \"LoRA\". The GENERATED_RESPONSE expands on the topic, but does not conflict with the EXPECTED_RESPONSE.'\n",
"│ │ │ │ }\n",
"│ │ │ ]\n",
"│ │ ),\n",
"│ │ 'basic::subset_of': ScoringResult(\n",
"│ │ │ aggregated_results={'accuracy': 1.0, 'num_correct': 1.0, 'num_total': 1.0},\n",
"│ │ │ score_rows=[{'score': 1.0}]\n",
"│ │ )\n",
"│ }\n",
")\n",
"\n"
],
"text/plain": [
"\u001b[1;35mScoringScoreResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mresults\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'llm-as-judge::base'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE provides more detailed information about the top 5 topics related to LoRA, while the EXPECTED_RESPONSE only mentions \"LoRA\". The GENERATED_RESPONSE expands on the topic, but does not conflict with the EXPECTED_RESPONSE.'\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m,\n",
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],
"source": [
"import rich\n",
"from rich.pretty import pprint\n",
"\n",
"judge_model_id = \"meta-llama/Llama-3.1-405B-Instruct-FP8\"\n",
"\n",
"JUDGE_PROMPT = \"\"\"\n",
"Given a QUESTION and GENERATED_RESPONSE and EXPECTED_RESPONSE.\n",
"\n",
"Compare the factual content of the GENERATED_RESPONSE with the EXPECTED_RESPONSE. Ignore any differences in style, grammar, or punctuation.\n",
" The GENERATED_RESPONSE may either be a subset or superset of the EXPECTED_RESPONSE, or it may conflict with it. Determine which case applies. Answer the question by selecting one of the following options:\n",
" (A) The GENERATED_RESPONSE is a subset of the EXPECTED_RESPONSE and is fully consistent with it.\n",
" (B) The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it.\n",
" (C) The GENERATED_RESPONSE contains all the same details as the EXPECTED_RESPONSE.\n",
" (D) There is a disagreement between the GENERATED_RESPONSE and the EXPECTED_RESPONSE.\n",
" (E) The answers differ, but these differences don't matter from the perspective of factuality.\n",
"\n",
"Give your answer in the format \"Answer: One of ABCDE, Explanation: \".\n",
"\n",
"Your actual task:\n",
"\n",
"QUESTION: {input_query}\n",
"GENERATED_RESPONSE: {generated_answer}\n",
"EXPECTED_RESPONSE: {expected_answer}\n",
"\"\"\"\n",
"\n",
"input_query = \"What are the top 5 topics that were explained? Only list succinct bullet points.\"\n",
"generated_answer = \"\"\"\n",
"Here are the top 5 topics that were explained in the documentation for Torchtune:\n",
"\n",
"* What is LoRA and how does it work?\n",
"* Fine-tuning with LoRA: memory savings and parameter-efficient finetuning\n",
"* Running a LoRA finetune with Torchtune: overview and recipe\n",
"* Experimenting with different LoRA configurations: rank, alpha, and attention modules\n",
"* LoRA finetuning\n",
"\"\"\"\n",
"expected_answer = \"\"\"LoRA\"\"\"\n",
"\n",
"rows = [\n",
" {\n",
" \"input_query\": input_query,\n",
" \"generated_answer\": generated_answer,\n",
" \"expected_answer\": expected_answer,\n",
" },\n",
"]\n",
"\n",
"scoring_params = {\n",
" \"llm-as-judge::base\": {\n",
" \"judge_model\": judge_model_id,\n",
" \"prompt_template\": JUDGE_PROMPT,\n",
" \"type\": \"llm_as_judge\",\n",
" \"judge_score_regexes\": [\"Answer: (A|B|C|D|E)\"],\n",
" },\n",
" \"basic::subset_of\": None,\n",
"}\n",
"\n",
"response = client.scoring.score(input_rows=rows, scoring_functions=scoring_params)\n",
"pprint(response)"
]
},
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![\"drawing\"](\"https://llama-stack.readthedocs.io/en/latest/_images/llama-stack.png\")
\n",
"\n",
"[Llama Stack](https://github.com/meta-llama/llama-stack) defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.\n",
"\n",
"Read more about the project: https://llama-stack.readthedocs.io/en/latest/index.html\n",
"\n",
"In this guide, we will showcase how you can build LLM-powered agentic applications using Llama Stack.\n"
]
},
{
"cell_type": "markdown",
"id": "4CV1Q19BDMVw",
"metadata": {
"id": "4CV1Q19BDMVw"
},
"source": [
"## 1. Getting started with Llama Stack"
]
},
{
"cell_type": "markdown",
"id": "K4AvfUAJZOeS",
"metadata": {
"id": "K4AvfUAJZOeS"
},
"source": [
"### 1.1. Create TogetherAI account\n",
"\n",
"\n",
"In order to run inference for the llama models, you will need to use an inference provider. Llama stack supports a number of inference [providers](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/inference).\n",
"\n",
"\n",
"In this showcase, we will use [together.ai](https://www.together.ai/) as the inference provider. So, you would first get an API key from Together if you dont have one already.\n",
"\n",
"Steps [here](https://docs.google.com/document/d/1Vg998IjRW_uujAPnHdQ9jQWvtmkZFt74FldW2MblxPY/edit?usp=sharing).\n",
"\n",
"You can also use Fireworks.ai or even Ollama if you would like to.\n",
"\n",
"\n",
"\n",
"> **Note:** Set the API Key in the Secrets of this notebook\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "oDUB7M_qe-Gs",
"metadata": {
"id": "oDUB7M_qe-Gs"
},
"source": [
"### 1.2. Install Llama Stack\n",
"\n",
"We will now start with installing the [llama-stack pypi package](https://pypi.org/project/llama-stack).\n",
"\n",
"In addition, we will install [bubblewrap](https://github.com/containers/bubblewrap), a low level light-weight container framework that runs in the user namespace. We will use it to execute code generated by Llama in one of the examples."
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "J2kGed0R5PSf",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "J2kGed0R5PSf",
"outputId": "7d543c6f-623d-4911-b9a7-4ed24d5b82f2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading package lists... Done\n",
"Building dependency tree... Done\n",
"Reading state information... Done\n",
"bubblewrap is already the newest version (0.6.1-1ubuntu0.1).\n",
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]
}
],
"source": [
"!apt-get install -y bubblewrap\n",
"!pip install -U llama-stack"
]
},
{
"cell_type": "markdown",
"id": "414301dc",
"metadata": {
"id": "414301dc"
},
"source": [
"### 1.3. Configure Llama Stack for Together\n",
"\n",
"\n",
"Llama Stack is architected as a collection of lego blocks which can be assembled as needed.\n",
"\n",
"\n",
"Typically, llama stack is available as a server with an endpoint that you can hit. We call this endpoint a [Distribution](https://llama-stack.readthedocs.io/en/latest/concepts/index.html#distributions). Partners like Together and Fireworks offer their own Llama Stack Distribution endpoints.\n",
"\n",
"In this showcase, we are going to use llama stack inline as a library. So, given a particular set of providers, we must first package up the right set of dependencies. We have a template to use Together as an inference provider and [faiss](https://ai.meta.com/tools/faiss/) for memory/RAG.\n",
"\n",
"We will run `llama stack build` to deploy all dependencies."
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "HaepEZXCDgif",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "HaepEZXCDgif",
"outputId": "9c268d26-7444-4741-f14d-3911eea8e4eb"
},
"outputs": [
{
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"torch --index-url https://download.pytorch.org/whl/cpu\n",
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"\u001b[32mBuild Successful!\u001b[0m\n"
]
}
],
"source": [
"# This will build all the dependencies you will need\n",
"!llama stack build --template together --image-type venv"
]
},
{
"cell_type": "markdown",
"id": "25b97dfe",
"metadata": {
"id": "25b97dfe"
},
"source": [
"### 1.4. Initialize Llama Stack\n",
"\n",
"Now that all dependencies have been installed, we can initialize llama stack. We will first set the `TOGETHER_API_KEY` environment variable\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "E1UFuJC570Tk",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"collapsed": true,
"id": "E1UFuJC570Tk",
"outputId": "bac7c9ec-ad49-4040-af43-8869f0afe5ac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWarning: `bwrap` is not available. Code interpreter tool will not work correctly.\u001b[0m\n"
]
},
{
"data": {
"text/html": [
"![\"drawing\"](\"https://github.com/meta-llama/llama-stack/blob/main/docs/resources/agentic-system.png?raw=true\")
\n",
"\n",
"\n",
"Agents are characterized by having access to\n",
"\n",
"1. Memory - for RAG\n",
"2. Tool calling - ability to call tools like search and code execution\n",
"3. Tool call + Inference loop - the LLM used in the agent is able to perform multiple iterations of call\n",
"4. Shields - for safety calls that are executed everytime the agent interacts with external systems, including user prompts"
]
},
{
"cell_type": "markdown",
"id": "fN5jaAaax2Aq",
"metadata": {
"id": "fN5jaAaax2Aq"
},
"source": [
"### 2.1. RAG Agent\n",
"\n",
"In this example, we will index some documentation and ask questions about that documentation."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "GvLWltzZCNkg",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 541,
"referenced_widgets": [
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},
"id": "GvLWltzZCNkg",
"outputId": "26689a4a-6a3a-4d8e-e469-6642e5b39b69"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70f3521ef9a84bf49cca07ff08e23d3c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Batches: 0%| | 0/1 [00:00 What are the top 5 topics that were explained? Only list succinct bullet points.\u001b[0m\n",
"tools_for_turn: [AgentToolWithArgs(name='memory', args={'memory_bank_id': 'memory_bank_1d984362-ef6c-468e-b5eb-a12b0d782783'})]\n",
"tools_for_turn_set: {'memory'}\n",
"tool_name: memory\n",
"\u001b[30m\u001b[0mtool_def: identifier='memory' provider_resource_id='memory' provider_id='memory-runtime' type='tool' tool_group='memory_group' tool_host=