\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/lib/python3.10/dist-packages/httpcore/_async/http11.py\", line 348, in aclose\n",
@@ -1346,6 +2015,8 @@
" File \"/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py\", line 456, in __exit__\n",
" raise RuntimeError(\n",
"RuntimeError: Attempted to exit cancel scope in a different task than it was entered in\n",
+=======
+>>>>>>> Stashed changes
"/usr/local/lib/python3.10/dist-packages/pydantic/main.py:426: UserWarning: Pydantic serializer warnings:\n",
" PydanticSerializationUnexpectedValue: Expected `str` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n",
" PydanticSerializationUnexpectedValue: PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n",
@@ -1360,7 +2031,11 @@
"data": {
"text/plain": [
"\u001b[1;35mCompletionResponse\u001b[0m\u001b[1m(\u001b[0m\n",
+<<<<<<< Updated upstream
"\u001b[2;32m│ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m' \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"name\": \"Michael Jordan\", \"year_born\": \"1963\", \"year_retired\": \"2003\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
+=======
+ "\u001b[2;32m│ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"name\": \"\", \"year_born\": \"\", \"year_retired\": \"\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n",
+>>>>>>> Stashed changes
"\u001b[2;32m│ \u001b[0m\u001b[33mstop_reason\u001b[0m=\u001b[32m'end_of_turn'\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mlogprobs\u001b[0m=\u001b[3;35mNone\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
@@ -1424,7 +2099,11 @@
"height": 368
},
"id": "sUJKJxvAFCaI",
+<<<<<<< Updated upstream
"outputId": "2cd4f88f-4c2f-41de-f6f6-8e431cba5aaa"
+=======
+ "outputId": "e3bf180c-7305-4bc5-ab4b-541706142b66"
+>>>>>>> Stashed changes
},
"outputs": [
{
@@ -1583,6 +2262,189 @@
"4. Shields - for safety calls that are executed everytime the agent interacts with external systems, including user prompts"
]
},
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2.1. List available tools on the provider"
+ ],
+ "metadata": {
+ "id": "lYDAkMsL9xSk"
+ },
+ "id": "lYDAkMsL9xSk"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from rich.pretty import pprint\n",
+ "for tool in client.tools.list():\n",
+ " pprint(tool)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "MpMXiMCv97X5",
+ "outputId": "8057a7b2-cb56-4857-f1ef-0002dadfc810"
+ },
+ "id": "MpMXiMCv97X5",
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "\u001b[1;35mTool\u001b[0m\u001b[1m(\u001b[0m\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mdescription\u001b[0m=\u001b[32m'Search the web for information'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'web_search'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mparameters\u001b[0m=\u001b[1m[\u001b[0m\n",
+ "\u001b[2;32m│ │ \u001b[0m\u001b[1;35mParameter\u001b[0m\u001b[1m(\u001b[0m\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mdescription\u001b[0m=\u001b[32m'The query to search for'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'query'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mparameter_type\u001b[0m=\u001b[32m'string'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mrequired\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mdefault\u001b[0m=\u001b[3;35mNone\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[33mprovider_id\u001b[0m=\u001b[32m'tavily-search'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'web_search'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtool_host\u001b[0m=\u001b[32m'distribution'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtoolgroup_id\u001b[0m=\u001b[32m'builtin::websearch'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtool_prompt_format\u001b[0m=\u001b[3;35mNone\u001b[0m\n",
+ "\u001b[1m)\u001b[0m\n"
+ ],
+ "text/html": [
+ "Tool(\n",
+ "│ description='Search the web for information',\n",
+ "│ identifier='web_search',\n",
+ "│ parameters=[\n",
+ "│ │ Parameter(\n",
+ "│ │ │ description='The query to search for',\n",
+ "│ │ │ name='query',\n",
+ "│ │ │ parameter_type='string',\n",
+ "│ │ │ required=True,\n",
+ "│ │ │ default=None\n",
+ "│ │ )\n",
+ "│ ],\n",
+ "│ provider_id='tavily-search',\n",
+ "│ provider_resource_id='web_search',\n",
+ "│ tool_host='distribution',\n",
+ "│ toolgroup_id='builtin::websearch',\n",
+ "│ type='tool',\n",
+ "│ metadata=None,\n",
+ "│ tool_prompt_format=None\n",
+ ")\n",
+ "
\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "\u001b[1;35mTool\u001b[0m\u001b[1m(\u001b[0m\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mdescription\u001b[0m=\u001b[32m'Retrieve context from memory'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'query_memory'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mparameters\u001b[0m=\u001b[1m[\u001b[0m\n",
+ "\u001b[2;32m│ │ \u001b[0m\u001b[1;35mParameter\u001b[0m\u001b[1m(\u001b[0m\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mdescription\u001b[0m=\u001b[32m'The input messages to search for'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'messages'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mparameter_type\u001b[0m=\u001b[32m'array'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mrequired\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mdefault\u001b[0m=\u001b[3;35mNone\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[33mprovider_id\u001b[0m=\u001b[32m'memory-runtime'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'query_memory'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtool_host\u001b[0m=\u001b[32m'distribution'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtoolgroup_id\u001b[0m=\u001b[32m'builtin::memory'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtool_prompt_format\u001b[0m=\u001b[3;35mNone\u001b[0m\n",
+ "\u001b[1m)\u001b[0m\n"
+ ],
+ "text/html": [
+ "Tool(\n",
+ "│ description='Retrieve context from memory',\n",
+ "│ identifier='query_memory',\n",
+ "│ parameters=[\n",
+ "│ │ Parameter(\n",
+ "│ │ │ description='The input messages to search for',\n",
+ "│ │ │ name='messages',\n",
+ "│ │ │ parameter_type='array',\n",
+ "│ │ │ required=True,\n",
+ "│ │ │ default=None\n",
+ "│ │ )\n",
+ "│ ],\n",
+ "│ provider_id='memory-runtime',\n",
+ "│ provider_resource_id='query_memory',\n",
+ "│ tool_host='distribution',\n",
+ "│ toolgroup_id='builtin::memory',\n",
+ "│ type='tool',\n",
+ "│ metadata=None,\n",
+ "│ tool_prompt_format=None\n",
+ ")\n",
+ "
\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "\u001b[1;35mTool\u001b[0m\u001b[1m(\u001b[0m\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mdescription\u001b[0m=\u001b[32m'Execute code'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'code_interpreter'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mparameters\u001b[0m=\u001b[1m[\u001b[0m\n",
+ "\u001b[2;32m│ │ \u001b[0m\u001b[1;35mParameter\u001b[0m\u001b[1m(\u001b[0m\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mdescription\u001b[0m=\u001b[32m'The code to execute'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mname\u001b[0m=\u001b[32m'code'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mparameter_type\u001b[0m=\u001b[32m'string'\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mrequired\u001b[0m=\u001b[3;92mTrue\u001b[0m,\n",
+ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mdefault\u001b[0m=\u001b[3;35mNone\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[33mprovider_id\u001b[0m=\u001b[32m'code-interpreter'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'code_interpreter'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtool_host\u001b[0m=\u001b[32m'distribution'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtoolgroup_id\u001b[0m=\u001b[32m'builtin::code_interpreter'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool'\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
+ "\u001b[2;32m│ \u001b[0m\u001b[33mtool_prompt_format\u001b[0m=\u001b[3;35mNone\u001b[0m\n",
+ "\u001b[1m)\u001b[0m\n"
+ ],
+ "text/html": [
+ "Tool(\n",
+ "│ description='Execute code',\n",
+ "│ identifier='code_interpreter',\n",
+ "│ parameters=[\n",
+ "│ │ Parameter(\n",
+ "│ │ │ description='The code to execute',\n",
+ "│ │ │ name='code',\n",
+ "│ │ │ parameter_type='string',\n",
+ "│ │ │ required=True,\n",
+ "│ │ │ default=None\n",
+ "│ │ )\n",
+ "│ ],\n",
+ "│ provider_id='code-interpreter',\n",
+ "│ provider_resource_id='code_interpreter',\n",
+ "│ tool_host='distribution',\n",
+ "│ toolgroup_id='builtin::code_interpreter',\n",
+ "│ type='tool',\n",
+ "│ metadata=None,\n",
+ "│ tool_prompt_format=None\n",
+ ")\n",
+ "
\n"
+ ]
+ },
+ "metadata": {}
+ }
+ ]
+ },
{
"cell_type": "markdown",
"id": "fN5jaAaax2Aq",
@@ -1590,20 +2452,25 @@
"id": "fN5jaAaax2Aq"
},
"source": [
- "### 2.1. RAG Agent\n",
+ "### 2.2. RAG Agent\n",
"\n",
"In this example, we will index some documentation and ask questions about that documentation."
]
},
{
"cell_type": "code",
+<<<<<<< Updated upstream
"execution_count": 11,
+=======
+ "execution_count": 7,
+>>>>>>> Stashed changes
"id": "GvLWltzZCNkg",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 351,
"referenced_widgets": [
+<<<<<<< Updated upstream
"b3aeb4406e0b4147821cee93d430fd27",
"c73ed62cc02149cf8658d38a1544ac7c",
"42bc7503d5b846e2a01d59c4182a1ab6",
@@ -1663,6 +2530,67 @@
},
"id": "GvLWltzZCNkg",
"outputId": "fb9d4f77-9e93-4ef7-a5d7-97184bad324e"
+=======
+ "d90ae0a531b941b4841e1ad7407fd4fc",
+ "51d8b257a8954557b3a057438f9c7c4e",
+ "7ab5d9edbdb649e2b44dcb646889792c",
+ "e07333c41e8f4f95ae1253ff3af96232",
+ "df158a470e1a4aec80f74c8d524a391b",
+ "116223f0414d47eb9a63a60751c474e9",
+ "ff47333fbc684a9f8bf916ce791254b6",
+ "e7066ddf191e437faa1d84e44a5410ee",
+ "4355f8bc9c434b78b82a3a219f3a1b77",
+ "61399af883fd466aa13c35ae788e2cc3",
+ "64c5428d11134056ba1c84e7290d105a",
+ "6762003e4d5c49349aabd82e944fbf8b",
+ "9259f686497140db8424dd5958a740ce",
+ "5be4d9bfe4ca41ddb451527796e365ab",
+ "e1c4cb8a3240434eb807e0122ef5f33a",
+ "49e457648bc145c7ba4ce448121c380f",
+ "d76185a6ff05423a98f9927c3d02feea",
+ "96cbf379d9bf41baa5e38db62d4b1e3d",
+ "fd8f04c37df94cdaa77aa78efd3f3c73",
+ "a3c076e149104d0f8c50eae37dcc3def",
+ "1d9fd701a7ea467ab2f3d17e0e978f8c",
+ "bc09309977b34483bee47453aa2c7a62",
+ "77ba019b3e414410b330971b0e62c9cc",
+ "75f2ed98417e4f5b8653057b66c5def7",
+ "44b5ece960d746438c720e0e0a9aa22d",
+ "98a85a047d034c018151b9356cb6d641",
+ "6fb7d255e5e540b6b12235954ef1ec8d",
+ "a9496e2bf3794874af30212172dc62b9",
+ "3413c41edf514291a1495d2478e01eac",
+ "25959703abf84ef5a70094644a7a1691",
+ "ffa86fa04e1040d5a7f6900e924abf61",
+ "a885e06b118f4895b3f3881a618c6426",
+ "9a0a2851ad7f4a53ac931bff7150eb27",
+ "f5390948e7fc480f86e2f60843b0418e",
+ "171e8a4cabac4b2ca64cd30970c8f21c",
+ "c9756fc8f7c94174a265c878606dc376",
+ "d5e18738455149f4928aacc48f0967e4",
+ "ca932a302deb429982db22944598f503",
+ "96c5c997e80b4579a33e54f3542da86a",
+ "88dc1cc48508416fbdce6c05ad25f06d",
+ "9ff97d20245e4217aab115b1bb62d897",
+ "40d203e27ddc4a929b27d0928434f23a",
+ "e18157e06e464fe8b3ee412b7f1b6c9b",
+ "2ca57033b1854af586ec96a77c96dd2c",
+ "4d9cb8791fde4b85983f5a83fe30975c",
+ "e51fda70dd054b40b867cb59694419db",
+ "96c295f6ae6e4a449b6f8acad2943993",
+ "9eca8287cc5d4c96a99b70d2870eb830",
+ "b0ec76eb983c4ccba08df2cfe8f65f2a",
+ "703eea437c04465eb2095b37ac30bb56",
+ "df2e980119a6471297c65348a5152066",
+ "c8e1576d9e1e4674ba789e68bd5fba92",
+ "1cd91700b19d456db5d0cf107dc7c1c0",
+ "337df483bf7d4d8a81445137d7c66d13",
+ "575b6200c7a246dba15f94930b7f4487"
+ ]
+ },
+ "id": "GvLWltzZCNkg",
+ "outputId": "ed86ad29-52b7-41c1-ca18-ba8e29beea8d"
+>>>>>>> Stashed changes
},
"outputs": [
{
@@ -1674,7 +2602,11 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
+<<<<<<< Updated upstream
"model_id": "b3aeb4406e0b4147821cee93d430fd27"
+=======
+ "model_id": "d90ae0a531b941b4841e1ad7407fd4fc"
+>>>>>>> Stashed changes
}
},
"metadata": {}
@@ -1688,7 +2620,11 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
+<<<<<<< Updated upstream
"model_id": "daca91b8e4c643daa33d15f8e347fd8e"
+=======
+ "model_id": "6762003e4d5c49349aabd82e944fbf8b"
+>>>>>>> Stashed changes
}
},
"metadata": {}
@@ -1702,7 +2638,11 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
+<<<<<<< Updated upstream
"model_id": "1c8d9ea02b594336b674b784a72399db"
+=======
+ "model_id": "77ba019b3e414410b330971b0e62c9cc"
+>>>>>>> Stashed changes
}
},
"metadata": {}
@@ -1716,7 +2656,11 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
+<<<<<<< Updated upstream
"model_id": "4985be31c38a404a810401f7e4a8631f"
+=======
+ "model_id": "f5390948e7fc480f86e2f60843b0418e"
+>>>>>>> Stashed changes
}
},
"metadata": {}
@@ -1737,7 +2681,11 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
+<<<<<<< Updated upstream
"model_id": "8cdb6600d3ad4fd9ae7c843e9b5973f3"
+=======
+ "model_id": "4d9cb8791fde4b85983f5a83fe30975c"
+>>>>>>> Stashed changes
}
},
"metadata": {}
@@ -1748,6 +2696,7 @@
"text": [
"tool_execution> Tool:query_memory Args:{}\n",
"tool_execution> fetched 10848 bytes from memory\n",
+<<<<<<< Updated upstream
"inference> Here are the top 5 topics explained:\n",
"\n",
"• Fine-tuning Llama3 with a custom chat dataset \n",
@@ -1755,6 +2704,15 @@
"• Template changes from Llama2 to Llama3 \n",
"• Using a prompt template \n",
"• Fine-tuning with a custom chat dataset\n"
+=======
+ "inference> Here are the top 5 topics explained in the provided context:\n",
+ "\n",
+ "• Fine-tuning Llama3 with chat data\n",
+ "• Tokenizing prompt templates and special tokens\n",
+ "• Template changes from Llama2 to Llama3\n",
+ "• Fine-tuning on a custom chat dataset\n",
+ "• Using prompt templates for specific tasks and inference behavior\n"
+>>>>>>> Stashed changes
]
}
],
@@ -1824,7 +2782,7 @@
"id": "i2o0gDhrv2og"
},
"source": [
- "### 2.2. Search agent\n",
+ "### 2.3. Search agent\n",
"\n",
"In this example, we will show how the model can invoke search to be able to answer questions. We will first have to set the API key of the search tool.\n",
"\n",
@@ -1835,14 +2793,22 @@
},
{
"cell_type": "code",
+<<<<<<< Updated upstream
"execution_count": 12,
+=======
+ "execution_count": 8,
+>>>>>>> Stashed changes
"id": "WS8Gu5b0APHs",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WS8Gu5b0APHs",
+<<<<<<< Updated upstream
"outputId": "d49d4e5e-dc51-4110-9c0a-b0d8a97faaf1"
+=======
+ "outputId": "3d335908-3eef-4a4a-846b-ff79151d8d87"
+>>>>>>> Stashed changes
},
"outputs": [
{
@@ -1854,7 +2820,11 @@
"User> Which teams played in the NBA western conference finals of 2024\n",
"inference> brave_search.call(query=\"NBA Western Conference Finals 2024 teams\")\n",
"tool_execution> Tool:brave_search Args:{'query': 'NBA Western Conference Finals 2024 teams'}\n",
+<<<<<<< Updated upstream
"tool_execution> Tool:brave_search Response:{\"query\": \"NBA Western Conference Finals 2024 teams\", \"top_k\": [{\"title\": \"2024 NBA Western Conference Finals - Basketball-Reference.com\", \"url\": \"https://www.basketball-reference.com/playoffs/2024-nba-western-conference-finals-mavericks-vs-timberwolves.html\", \"content\": \"2024 NBA Western Conference Finals Mavericks vs. Timberwolves League Champion: Boston Celtics. Finals MVP: Jaylen Brown (20.8 / 5.4 / 5.0) 2024 Playoff Leaders: PTS: Luka Don\\u010di\\u0107 (635) TRB: Luka Don\\u010di\\u0107 (208) AST: Luka Don\\u010di\\u0107 (178) WS: Derrick White (2.9) More playoffs info\", \"score\": 0.9310187, \"raw_content\": null}, {\"title\": \"NBA Western Conference Finals 2024: Dates, schedule and more - Sportskeeda\", \"url\": \"https://www.sportskeeda.com/basketball/news-nba-western-conference-finals-2024-dates-schedule-and-more\", \"content\": \"NBA Western Conference Finals 2024: Dates & Schedule The 2023-24 NBA Western Conference Finals will start on Wednesday, May 22. The Mavericks will face the team that wins in Game 7 between the\", \"score\": 0.8914433, \"raw_content\": null}, {\"title\": \"2024 Playoffs: West Finals | Timberwolves (3) vs. Mavericks (5) - NBA.com\", \"url\": \"https://www.nba.com/playoffs/2024/west-final\", \"content\": \"The Dallas Mavericks and Minnesota Timberwolves have advanced to the 2024 Western Conference Finals during the NBA playoffs.\", \"score\": 0.8884594, \"raw_content\": null}, {\"title\": \"NBA Conference Finals Schedule: Full List of Games & Results\", \"url\": \"https://www.si.com/nba/nba-conference-finals-schedule-full-list-of-games-results\", \"content\": \"The 2024 NBA conference finals matchups are set. Here's the schedule for all the games. ... Western Conference First Round (1) Oklahoma City Thunder def. (8) New Orleans Pelicans in 4 games\", \"score\": 0.85008353, \"raw_content\": null}, {\"title\": \"2024 NBA Western Conference playoff bracket - Basketnews.com\", \"url\": \"https://basketnews.com/news-204687-2024-nba-western-conference-playoff-bracket.html\", \"content\": \"In the 2024 NBA Western Conference playoffs, the Oklahoma City Thunder clinched the No. 1 seed. Every team from the Western Conference played their final game of the regular season, and two playoff pairs have been confirmed. The Los Angeles Lakers beat the New Orleans Pelicans, 110-106, in the Play-In Tournament to secure the 7th seed to set up a first-round matchup with the Denver Nuggets. Meanwhile, the Sacramento Kings will host the Golden State Warriors in the second Western Conference NBA Play-In Tournament game. The winners secure the No. 8 seed in the NBA playoffs for its conference. EuroLeague Play-In: Baskonia-Virtus game schedule announced\", \"score\": 0.84767824, \"raw_content\": null}]}\n",
+=======
+ "tool_execution> Tool:brave_search Response:{\"query\": \"NBA Western Conference Finals 2024 teams\", \"top_k\": [{\"title\": \"2024 NBA Western Conference Finals - Basketball-Reference.com\", \"url\": \"https://www.basketball-reference.com/playoffs/2024-nba-western-conference-finals-mavericks-vs-timberwolves.html\", \"content\": \"2024 NBA Western Conference Finals Mavericks vs. Timberwolves League Champion: Boston Celtics. Finals MVP: Jaylen Brown (20.8 / 5.4 / 5.0) 2024 Playoff Leaders: PTS: Luka Don\\u010di\\u0107 (635) TRB: Luka Don\\u010di\\u0107 (208) AST: Luka Don\\u010di\\u0107 (178) WS: Derrick White (2.9) More playoffs info\", \"score\": 0.9310187, \"raw_content\": null}, {\"title\": \"NBA Western Conference Finals 2024: Dates, schedule and more - Sportskeeda\", \"url\": \"https://www.sportskeeda.com/basketball/news-nba-western-conference-finals-2024-dates-schedule-and-more\", \"content\": \"NBA Western Conference Finals 2024: Dates & Schedule The 2023-24 NBA Western Conference Finals will start on Wednesday, May 22. The Mavericks will face the team that wins in Game 7 between the\", \"score\": 0.8914433, \"raw_content\": null}, {\"title\": \"2024 Playoffs: West Finals | Timberwolves (3) vs. Mavericks (5) - NBA.com\", \"url\": \"https://www.nba.com/playoffs/2024/west-final\", \"content\": \"The Dallas Mavericks and Minnesota Timberwolves have advanced to the 2024 Western Conference Finals during the NBA playoffs.\", \"score\": 0.8884594, \"raw_content\": null}, {\"title\": \"NBA Conference Finals Schedule: Full List of Games & Results\", \"url\": \"https://www.si.com/nba/nba-conference-finals-schedule-full-list-of-games-results\", \"content\": \"The 2024 NBA conference finals matchups are set. Here's the schedule for all the games. ... Western Conference First Round (1) Oklahoma City Thunder def. (8) New Orleans Pelicans in 4 games\", \"score\": 0.850382, \"raw_content\": null}, {\"title\": \"2024 Playoffs: West Final | News - NBA.com\", \"url\": \"https://www.nba.com/playoffs/2024/west-final/news\", \"content\": \"Stellar Doncic named West Finals MVP. Luka Doncic dominates the Wolves, raising the Magic Johnson Western Conference Finals MVP award after a 4-1 series win.\", \"score\": 0.75181264, \"raw_content\": null}]}\n",
+>>>>>>> Stashed changes
"inference> The teams that played in the NBA Western Conference Finals of 2024 were the Dallas Mavericks and the Minnesota Timberwolves.\n"
]
}
@@ -1897,7 +2867,7 @@
"id": "yRzRwu8qxyl0"
},
"source": [
- "### 2.3. Code Execution Agent\n",
+ "### 2.4. Code Execution Agent\n",
"\n",
"In this example, we will show how multiple tools can be called by the model - including web search and code execution. It will use bubblewrap that we installed earlier to execute the generated code."
]
@@ -2705,7 +3675,11 @@
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
+<<<<<<< Updated upstream
"b3aeb4406e0b4147821cee93d430fd27": {
+=======
+ "5efa1befafc4442fa4c2ebe64415f78b": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
@@ -2720,6 +3694,7 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
+<<<<<<< Updated upstream
"IPY_MODEL_c73ed62cc02149cf8658d38a1544ac7c",
"IPY_MODEL_42bc7503d5b846e2a01d59c4182a1ab6",
"IPY_MODEL_35fc33cc83ad4d32a6dd9bd66a59d687"
@@ -2728,6 +3703,16 @@
}
},
"c73ed62cc02149cf8658d38a1544ac7c": {
+=======
+ "IPY_MODEL_d6ac7a1194a74402a55ce01455c5a17a",
+ "IPY_MODEL_cd072e192c9547a7b7a6d9f6648dd587",
+ "IPY_MODEL_dbed4fdaf53f49bd94d1b763b6406c7e"
+ ],
+ "layout": "IPY_MODEL_5fa6c89f4ff146c782d7ad5332724648"
+ }
+ },
+ "d6ac7a1194a74402a55ce01455c5a17a": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -2742,6 +3727,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_e67c00f0d96447fdbb5ea73f9fd4cc7b",
"placeholder": "",
"style": "IPY_MODEL_b144748fa2904815b3ce08d3a601dfc1",
@@ -2749,6 +3735,15 @@
}
},
"42bc7503d5b846e2a01d59c4182a1ab6": {
+=======
+ "layout": "IPY_MODEL_49c4514aaed146c3b633c1dc329351a7",
+ "placeholder": "",
+ "style": "IPY_MODEL_c47b7222cd0444049129c6a78386d23b",
+ "value": "modules.json: 100%"
+ }
+ },
+ "cd072e192c9547a7b7a6d9f6648dd587": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
@@ -2764,6 +3759,7 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_01674d9ff69b4de49093a3039fc0895f",
"max": 1,
"min": 0,
@@ -2773,6 +3769,17 @@
}
},
"35fc33cc83ad4d32a6dd9bd66a59d687": {
+=======
+ "layout": "IPY_MODEL_400b59adf711468ea2db52e4ff62bc11",
+ "max": 349,
+ "min": 0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_76395834b32b47bea30c695022156c07",
+ "value": 349
+ }
+ },
+ "dbed4fdaf53f49bd94d1b763b6406c7e": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -2787,6 +3794,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_f43d865f12b74852b4d0df47ab0fdd84",
"placeholder": "",
"style": "IPY_MODEL_ef56dad1627c4d32a59167786ae88e28",
@@ -2794,6 +3802,15 @@
}
},
"75d8b7c10f9640438ffdc253a939be4a": {
+=======
+ "layout": "IPY_MODEL_fee442a2d1ab4c23bab7309706974605",
+ "placeholder": "",
+ "style": "IPY_MODEL_e5d068844cc849a3ab98a0882bf64b68",
+ "value": " 349/349 [00:00<00:00, 22.8kB/s]"
+ }
+ },
+ "5fa6c89f4ff146c782d7ad5332724648": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -2845,7 +3862,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"e67c00f0d96447fdbb5ea73f9fd4cc7b": {
+=======
+ "49c4514aaed146c3b633c1dc329351a7": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -2897,7 +3918,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"b144748fa2904815b3ce08d3a601dfc1": {
+=======
+ "c47b7222cd0444049129c6a78386d23b": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -2912,7 +3937,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"01674d9ff69b4de49093a3039fc0895f": {
+=======
+ "400b59adf711468ea2db52e4ff62bc11": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -2964,7 +3993,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"fe8902b35dc446ed9c3c897835d1b92e": {
+=======
+ "76395834b32b47bea30c695022156c07": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
@@ -2980,7 +4013,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"f43d865f12b74852b4d0df47ab0fdd84": {
+=======
+ "fee442a2d1ab4c23bab7309706974605": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3032,7 +4069,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"ef56dad1627c4d32a59167786ae88e28": {
+=======
+ "e5d068844cc849a3ab98a0882bf64b68": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -3047,7 +4088,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"daca91b8e4c643daa33d15f8e347fd8e": {
+=======
+ "5add3cee30b24697ae31767e1bf662e2": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
@@ -3062,6 +4107,7 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
+<<<<<<< Updated upstream
"IPY_MODEL_75595e2bdeb84866b50a772cd27ba320",
"IPY_MODEL_e9b6488fb75e47f88d669638b2d35f40",
"IPY_MODEL_6a73b8be819a4ce9b3232b22f6a2fc52"
@@ -3070,6 +4116,16 @@
}
},
"75595e2bdeb84866b50a772cd27ba320": {
+=======
+ "IPY_MODEL_8472d5dab9a04356ac2cc253081b33c5",
+ "IPY_MODEL_6f5110129dd441dc936eaa6fc6fdda7f",
+ "IPY_MODEL_18274644ad86446a84ee59cfd6dda073"
+ ],
+ "layout": "IPY_MODEL_f4ad6e849ff24071a2d6194186f52549"
+ }
+ },
+ "8472d5dab9a04356ac2cc253081b33c5": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -3084,6 +4140,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_bdb868b9d70c4a9f8ff6dcdd1acd47b7",
"placeholder": "",
"style": "IPY_MODEL_295c83e70a8546cb983e8e323374ca43",
@@ -3091,6 +4148,15 @@
}
},
"e9b6488fb75e47f88d669638b2d35f40": {
+=======
+ "layout": "IPY_MODEL_fac666b62f4a4d70aa406e7fe7114158",
+ "placeholder": "",
+ "style": "IPY_MODEL_37ffa3e9bf5b4f1faa22083a3b80c057",
+ "value": "config_sentence_transformers.json: 100%"
+ }
+ },
+ "6f5110129dd441dc936eaa6fc6fdda7f": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
@@ -3106,6 +4172,7 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_9389e809bcf544b4a1a054b1d9880217",
"max": 1,
"min": 0,
@@ -3115,6 +4182,17 @@
}
},
"6a73b8be819a4ce9b3232b22f6a2fc52": {
+=======
+ "layout": "IPY_MODEL_6092412a1fa4445aa72314a707374ea2",
+ "max": 116,
+ "min": 0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_33a6b4aa70584faca6319b3dc1cfd13e",
+ "value": 116
+ }
+ },
+ "18274644ad86446a84ee59cfd6dda073": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -3129,6 +4207,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_18ef9d1481bc476fb268980f2ce6790d",
"placeholder": "",
"style": "IPY_MODEL_7e402295522f46fe926483315611d189",
@@ -3136,6 +4215,15 @@
}
},
"b6d3c11cf77344f599700dfc65fabfe0": {
+=======
+ "layout": "IPY_MODEL_2c29f1d881784db59b0113bd7bccbe75",
+ "placeholder": "",
+ "style": "IPY_MODEL_1d18e7a686374756b80b46ab5546f8ed",
+ "value": " 116/116 [00:00<00:00, 4.40kB/s]"
+ }
+ },
+ "f4ad6e849ff24071a2d6194186f52549": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3187,7 +4275,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"bdb868b9d70c4a9f8ff6dcdd1acd47b7": {
+=======
+ "fac666b62f4a4d70aa406e7fe7114158": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3239,7 +4331,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"295c83e70a8546cb983e8e323374ca43": {
+=======
+ "37ffa3e9bf5b4f1faa22083a3b80c057": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -3254,7 +4350,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"9389e809bcf544b4a1a054b1d9880217": {
+=======
+ "6092412a1fa4445aa72314a707374ea2": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3306,7 +4406,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"3a9188e2305b4e2686cd6fc679922862": {
+=======
+ "33a6b4aa70584faca6319b3dc1cfd13e": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
@@ -3322,7 +4426,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"18ef9d1481bc476fb268980f2ce6790d": {
+=======
+ "2c29f1d881784db59b0113bd7bccbe75": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3374,7 +4482,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"7e402295522f46fe926483315611d189": {
+=======
+ "1d18e7a686374756b80b46ab5546f8ed": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -3389,7 +4501,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"1c8d9ea02b594336b674b784a72399db": {
+=======
+ "f7bee04278ce4ad9bb0b4f62f54b7455": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
@@ -3404,6 +4520,7 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
+<<<<<<< Updated upstream
"IPY_MODEL_a55f599757aa402b9d8d3f1472670852",
"IPY_MODEL_5ffdaa756a0446648c300b2885977cb8",
"IPY_MODEL_296746e96a97439ea2f8436154fae754"
@@ -3412,6 +4529,16 @@
}
},
"a55f599757aa402b9d8d3f1472670852": {
+=======
+ "IPY_MODEL_4477705a42c94351af63639d30876689",
+ "IPY_MODEL_a1bc1de6665f4a5297c7b051d9441bec",
+ "IPY_MODEL_5d70890d42b542cca897588a5a27e56c"
+ ],
+ "layout": "IPY_MODEL_d2bd17846c9a402ca7c16e9c2b330e67"
+ }
+ },
+ "4477705a42c94351af63639d30876689": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -3426,6 +4553,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_33e3c68f72eb47debd306faee58ccd3f",
"placeholder": "",
"style": "IPY_MODEL_e882c8b99b08479a9705cf3242cf04bb",
@@ -3433,6 +4561,15 @@
}
},
"5ffdaa756a0446648c300b2885977cb8": {
+=======
+ "layout": "IPY_MODEL_912680aea42543c2b59d64aab34ef849",
+ "placeholder": "",
+ "style": "IPY_MODEL_03d068ceb20b434c85bb631659347556",
+ "value": "README.md: 100%"
+ }
+ },
+ "a1bc1de6665f4a5297c7b051d9441bec": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
@@ -3448,6 +4585,7 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_23a0123ce90e45658533d28a4cd47c11",
"max": 1,
"min": 0,
@@ -3457,6 +4595,17 @@
}
},
"296746e96a97439ea2f8436154fae754": {
+=======
+ "layout": "IPY_MODEL_93daa507e8ed4138bcbfd82eec2490a3",
+ "max": 10659,
+ "min": 0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_1cfc7f7670be42c982eb6ac8d43dbc1e",
+ "value": 10659
+ }
+ },
+ "5d70890d42b542cca897588a5a27e56c": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -3471,6 +4620,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_97e4cc905970452280a7f07bd785350e",
"placeholder": "",
"style": "IPY_MODEL_064a15a903064adc921694d0c1509443",
@@ -3478,6 +4628,15 @@
}
},
"120605108be744e9ba38a9fb2b2d6e77": {
+=======
+ "layout": "IPY_MODEL_60aad78c138a4391997adcd305a35f03",
+ "placeholder": "",
+ "style": "IPY_MODEL_70daaacc22e34e2a911e842a7705dd32",
+ "value": " 10.7k/10.7k [00:00<00:00, 842kB/s]"
+ }
+ },
+ "d2bd17846c9a402ca7c16e9c2b330e67": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3529,7 +4688,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"33e3c68f72eb47debd306faee58ccd3f": {
+=======
+ "912680aea42543c2b59d64aab34ef849": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3581,7 +4744,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"e882c8b99b08479a9705cf3242cf04bb": {
+=======
+ "03d068ceb20b434c85bb631659347556": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -3596,7 +4763,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"23a0123ce90e45658533d28a4cd47c11": {
+=======
+ "93daa507e8ed4138bcbfd82eec2490a3": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3648,7 +4819,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"6bdf5bda589043d5b42350da193d96ba": {
+=======
+ "1cfc7f7670be42c982eb6ac8d43dbc1e": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
@@ -3664,7 +4839,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"97e4cc905970452280a7f07bd785350e": {
+=======
+ "60aad78c138a4391997adcd305a35f03": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3716,7 +4895,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"064a15a903064adc921694d0c1509443": {
+=======
+ "70daaacc22e34e2a911e842a7705dd32": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -3731,7 +4914,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"4985be31c38a404a810401f7e4a8631f": {
+=======
+ "c470be11fb3147bcb09e1dccb6d88937": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
@@ -3746,6 +4933,7 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
+<<<<<<< Updated upstream
"IPY_MODEL_6f71b8be7ce84193a3f58ce42db1800d",
"IPY_MODEL_ff53a253d2064f42a1d4c6bc5e44a0fd",
"IPY_MODEL_8141d0ab228b4092b569dfe57e6c6218"
@@ -3754,6 +4942,16 @@
}
},
"6f71b8be7ce84193a3f58ce42db1800d": {
+=======
+ "IPY_MODEL_13a497ffcca044059e9498db6f587e19",
+ "IPY_MODEL_4d457db80cb84a4eb798de7dd9a8e844",
+ "IPY_MODEL_81cf9f25de254d7f85683fb4f77a16c1"
+ ],
+ "layout": "IPY_MODEL_f69a8c16ac844412959f04b82d2a7a7a"
+ }
+ },
+ "13a497ffcca044059e9498db6f587e19": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -3768,6 +4966,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_d7252839fbeb4f33b889782d6bcc2b26",
"placeholder": "",
"style": "IPY_MODEL_e76f5bc57e494303b067cea56b75798d",
@@ -3775,6 +4974,15 @@
}
},
"ff53a253d2064f42a1d4c6bc5e44a0fd": {
+=======
+ "layout": "IPY_MODEL_7d97adf6e937438d9ab29f61893f36cf",
+ "placeholder": "",
+ "style": "IPY_MODEL_516b23bf77ca4badadd60005d9275148",
+ "value": "sentence_bert_config.json: 100%"
+ }
+ },
+ "4d457db80cb84a4eb798de7dd9a8e844": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
@@ -3790,6 +4998,7 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_c65d049fd24f4a1b9028ea930b51907f",
"max": 1,
"min": 0,
@@ -3799,6 +5008,17 @@
}
},
"8141d0ab228b4092b569dfe57e6c6218": {
+=======
+ "layout": "IPY_MODEL_723e47c99a2a45eb8e0ad7d73a60e251",
+ "max": 53,
+ "min": 0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_f25c7e613ad04f728f92a8f0bbe057e2",
+ "value": 53
+ }
+ },
+ "81cf9f25de254d7f85683fb4f77a16c1": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -3813,6 +5033,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_fb8599946e43413fb39ea598f3293a69",
"placeholder": "",
"style": "IPY_MODEL_32b5d09cd9d04cfb895837c8eb641e9c",
@@ -3820,6 +5041,15 @@
}
},
"f513cd873a074ef2900fd22d093476ca": {
+=======
+ "layout": "IPY_MODEL_0a09259387304e4f978298aeab0b4655",
+ "placeholder": "",
+ "style": "IPY_MODEL_e42fe5a7f70249578818b46a5c9c8483",
+ "value": " 53.0/53.0 [00:00<00:00, 3.00kB/s]"
+ }
+ },
+ "f69a8c16ac844412959f04b82d2a7a7a": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3871,7 +5101,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"d7252839fbeb4f33b889782d6bcc2b26": {
+=======
+ "7d97adf6e937438d9ab29f61893f36cf": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3923,7 +5157,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"e76f5bc57e494303b067cea56b75798d": {
+=======
+ "516b23bf77ca4badadd60005d9275148": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -3938,7 +5176,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"c65d049fd24f4a1b9028ea930b51907f": {
+=======
+ "723e47c99a2a45eb8e0ad7d73a60e251": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -3990,7 +5232,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"387f6669523244e590e925d7447a552d": {
+=======
+ "f25c7e613ad04f728f92a8f0bbe057e2": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
@@ -4006,7 +5252,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"fb8599946e43413fb39ea598f3293a69": {
+=======
+ "0a09259387304e4f978298aeab0b4655": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -4058,7 +5308,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"32b5d09cd9d04cfb895837c8eb641e9c": {
+=======
+ "e42fe5a7f70249578818b46a5c9c8483": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -4073,7 +5327,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"8cdb6600d3ad4fd9ae7c843e9b5973f3": {
+=======
+ "a9280bb243b6426c87e8295bf74f44e9": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
@@ -4088,6 +5346,7 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
+<<<<<<< Updated upstream
"IPY_MODEL_4c738f46093d48f7be1db7843b4f7106",
"IPY_MODEL_404b1ec6b9324fa593cb7d1ed83695e8",
"IPY_MODEL_b79197026e4a45918a9c6e005157bfa9"
@@ -4096,6 +5355,16 @@
}
},
"4c738f46093d48f7be1db7843b4f7106": {
+=======
+ "IPY_MODEL_845dccd7809d4105a8494d238796d203",
+ "IPY_MODEL_f70b158281904d009017191e8fb7f867",
+ "IPY_MODEL_2785f993e7a442d5a6961b2206f10696"
+ ],
+ "layout": "IPY_MODEL_0aa12df801754c778dd6f433d2c72192"
+ }
+ },
+ "845dccd7809d4105a8494d238796d203": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -4110,6 +5379,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_478e655904bc4ebfa5b95e32c7d1be34",
"placeholder": "",
"style": "IPY_MODEL_be9be7f039dc479ca7f8f6eef36e7afd",
@@ -4117,6 +5387,15 @@
}
},
"404b1ec6b9324fa593cb7d1ed83695e8": {
+=======
+ "layout": "IPY_MODEL_9ad1925e1bdd42e4b1b25fd75c5c97cb",
+ "placeholder": "",
+ "style": "IPY_MODEL_d615cfe64ba4424480fd0b524a93739e",
+ "value": "config.json: 100%"
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+ "f70b158281904d009017191e8fb7f867": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
@@ -4132,6 +5411,7 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_3ca1fdf67c58426aacd9c07fed7cefd3",
"max": 1,
"min": 0,
@@ -4141,6 +5421,17 @@
}
},
"b79197026e4a45918a9c6e005157bfa9": {
+=======
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+ "max": 612,
+ "min": 0,
+ "orientation": "horizontal",
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+ "2785f993e7a442d5a6961b2206f10696": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -4155,6 +5446,7 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
+<<<<<<< Updated upstream
"layout": "IPY_MODEL_e2503d7cb2a24394890ec803ac9958d8",
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@@ -4162,6 +5454,15 @@
}
},
"b686f1677bfa448c954b3e6c682ba44a": {
+=======
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+ "0aa12df801754c778dd6f433d2c72192": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -4213,7 +5514,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"478e655904bc4ebfa5b95e32c7d1be34": {
+=======
+ "9ad1925e1bdd42e4b1b25fd75c5c97cb": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -4265,7 +5570,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"be9be7f039dc479ca7f8f6eef36e7afd": {
+=======
+ "d615cfe64ba4424480fd0b524a93739e": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -4280,7 +5589,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"3ca1fdf67c58426aacd9c07fed7cefd3": {
+=======
+ "a5676481e43b42828c42c892c609c0f7": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -4332,7 +5645,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"059d58ad66344f81bcd43c98778ead66": {
+=======
+ "415cc6dd095c4b4d9851520604e72b60": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
@@ -4348,7 +5665,11 @@
"description_width": ""
}
},
+<<<<<<< Updated upstream
"e2503d7cb2a24394890ec803ac9958d8": {
+=======
+ "ccb4343892c142a6be6ae701dc57549f": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -4400,7 +5721,11 @@
"width": null
}
},
+<<<<<<< Updated upstream
"9909c392be2c430e94a5a52b498fc1e7": {
+=======
+ "1d8a85e7bc0442eb86180fb836052311": {
+>>>>>>> Stashed changes
"model_module": "@jupyter-widgets/controls",
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"model_module_version": "1.5.0",
@@ -4414,6 +5739,3771 @@
"_view_name": "StyleView",
"description_width": ""
}
+<<<<<<< Updated upstream
+=======
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+>>>>>>> Stashed changes
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