diff --git a/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb b/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb index 472e800a6..97c30a209 100644 --- a/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb +++ b/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb @@ -83,8 +83,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Reading package lists... Done\n", "Building dependency tree... Done\n", @@ -240,6 +240,8 @@ } ], "source": [ + "# NBVAL_SKIP\n", + "\n", "!apt-get install -y bubblewrap\n", "# install a branch of llama stack\n", "!pip install llama-stack" @@ -279,8 +281,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Requirement already satisfied: llama-stack in /usr/local/lib/python3.10/dist-packages (0.0.63)\r\n", "Requirement already satisfied: blobfile in /usr/local/lib/python3.10/dist-packages (from llama-stack) (3.0.0)\r\n", @@ -567,6 +569,8 @@ } ], "source": [ + "# NBVAL_SKIP\n", + "\n", "# This will build all the dependencies you will need\n", "!llama stack build --template together --image-type venv" ] @@ -721,15 +725,15 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Removed handler StreamHandler from root logger\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", @@ -740,356 +744,174 @@ ] }, { - 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Using config together:\n",
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- "apis:\n",
- "- agents\n",
- "- datasetio\n",
- "- eval\n",
- "- inference\n",
- "- memory\n",
- "- safety\n",
- "- scoring\n",
- "- telemetry\n",
- "- tool_runtime\n",
- "conda_env: together\n",
- "datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
- "docker_image: null\n",
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- "image_name: together\n",
- "memory_banks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
- "metadata_store:\n",
- " db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
- " namespace: null\n",
- " type: sqlite\n",
- "models:\n",
- "- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
- " model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
- " - llm\n",
- " provider_id: together\n",
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- " model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
- " - llm\n",
- " provider_id: together\n",
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- " model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-FP8\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
- " - llm\n",
- " provider_id: together\n",
- " provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
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- " model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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- " provider_id: together\n",
- " provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
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- " model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
- " - llm\n",
- " provider_id: together\n",
- " provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
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- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
- " - llm\n",
- " provider_id: together\n",
- " provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
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- " model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
- " - llm\n",
- " provider_id: together\n",
- " provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
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- " model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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- " provider_id: together\n",
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- " model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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- " provider_id: together\n",
- " provider_model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
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- " model_id: all-MiniLM-L6-v2\n",
- " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
- " - embedding\n",
- " provider_id: sentence-transformers\n",
- " provider_model_id: null\n",
- "providers:\n",
- " agents:\n",
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- " persistence_store:\n",
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- " namespace: null\n",
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- " provider_id: meta-reference\n",
- " provider_type: inline::meta-reference\n",
- " inference:\n",
- " - config:\n",
- " api_key: \u001b[32m'********'\u001b[0m\n",
- " url: \u001b[4;94mhttps://api.together.xyz/v1\u001b[0m\n",
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- " provider_type: remote::together\n",
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- " provider_id: sentence-transformers\n",
- " provider_type: inline::sentence-transformers\n",
- " memory:\n",
- " - config:\n",
- " kvstore:\n",
- " db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
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- " provider_id: llama-guard\n",
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- " scoring:\n",
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- " provider_id: basic\n",
- " provider_type: inlin\u001b[1;92me::ba\u001b[0msic\n",
- " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
- " provider_id: llm-as-judge\n",
- " provider_type: inline::llm-as-judge\n",
- " - config:\n",
- " openai_api_key: \u001b[32m'********'\u001b[0m\n",
- " provider_id: braintrust\n",
- " provider_type: inlin\u001b[1;92me::b\u001b[0mraintrust\n",
- " telemetry:\n",
- " - config:\n",
- " service_name: llama-stack\n",
- " sinks: sqlite\n",
- " sqlite_db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mtrace_store.db\u001b[0m\n",
- " provider_id: meta-reference\n",
- " provider_type: inline::meta-reference\n",
- " tool_runtime:\n",
- " - config:\n",
- " api_key: \u001b[32m'********'\u001b[0m\n",
- " max_results: \u001b[1;36m3\u001b[0m\n",
- " provider_id: brave-search\n",
- " provider_type: remot\u001b[1;92me::b\u001b[0mrave-search\n",
- " - config:\n",
- " api_key: \u001b[32m'********'\u001b[0m\n",
- " max_results: \u001b[1;36m3\u001b[0m\n",
- " provider_id: tavily-search\n",
- " provider_type: remote::tavily-search\n",
- " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
- " provider_id: code-interpreter\n",
- " provider_type: inlin\u001b[1;92me::c\u001b[0mode-interpreter\n",
- " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
- " provider_id: memory-runtime\n",
- " provider_type: inline::memory-runtime\n",
- "scoring_fns: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
- "shields:\n",
- "- params: null\n",
- " provider_id: null\n",
- " provider_shield_id: null\n",
- " shield_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
- "tool_groups:\n",
- "- args: null\n",
- " mcp_endpoint: null\n",
- " provider_id: tavily-search\n",
- " toolgroup_id: builtin::websearch\n",
- "- args: null\n",
- " mcp_endpoint: null\n",
- " provider_id: memory-runtime\n",
- " toolgroup_id: builtin::memory\n",
- "- args: null\n",
- " mcp_endpoint: null\n",
- " provider_id: code-interpreter\n",
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@@ -1271,19 +1093,216 @@
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+ "- memory\n",
+ "- safety\n",
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+ "- telemetry\n",
+ "- tool_runtime\n",
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+ "datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
+ "docker_image: null\n",
+ "eval_tasks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
+ "image_name: together\n",
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+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
+ " provider_id: together\n",
+ " provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
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+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
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+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
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+ "- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct\n",
+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
+ " provider_id: together\n",
+ " provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
+ "- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct\n",
+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
+ " provider_id: together\n",
+ " provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
+ "- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct\n",
+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
+ " provider_id: together\n",
+ " provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
+ "- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
+ " provider_id: together\n",
+ " provider_model_id: meta-llama/Meta-Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
+ "- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision\n",
+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
+ " provider_id: together\n",
+ " provider_model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
+ "- metadata:\n",
+ " embedding_dimension: \u001b[1;36m384\u001b[0m\n",
+ " model_id: all-MiniLM-L6-v2\n",
+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - embedding\n",
+ " provider_id: sentence-transformers\n",
+ " provider_model_id: null\n",
+ "providers:\n",
+ " agents:\n",
+ " - config:\n",
+ " persistence_store:\n",
+ " db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95magents_store.db\u001b[0m\n",
+ " namespace: null\n",
+ " type: sqlite\n",
+ " provider_id: meta-reference\n",
+ " provider_type: inline::meta-reference\n",
+ " datasetio:\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: huggingface\n",
+ " provider_type: remote::huggingface\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: localfs\n",
+ " provider_type: inline::localfs\n",
+ " eval:\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: meta-reference\n",
+ " provider_type: inline::meta-reference\n",
+ " inference:\n",
+ " - config:\n",
+ " api_key: \u001b[32m'********'\u001b[0m\n",
+ " url: \u001b[4;94mhttps://api.together.xyz/v1\u001b[0m\n",
+ " provider_id: together\n",
+ " provider_type: remote::together\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: sentence-transformers\n",
+ " provider_type: inline::sentence-transformers\n",
+ " memory:\n",
+ " - config:\n",
+ " kvstore:\n",
+ " db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
+ " namespace: null\n",
+ " type: sqlite\n",
+ " provider_id: faiss\n",
+ " provider_type: inlin\u001b[1;92me::fa\u001b[0miss\n",
+ " safety:\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: llama-guard\n",
+ " provider_type: inline::llama-guard\n",
+ " scoring:\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: basic\n",
+ " provider_type: inlin\u001b[1;92me::ba\u001b[0msic\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: llm-as-judge\n",
+ " provider_type: inline::llm-as-judge\n",
+ " - config:\n",
+ " openai_api_key: \u001b[32m'********'\u001b[0m\n",
+ " provider_id: braintrust\n",
+ " provider_type: inlin\u001b[1;92me::b\u001b[0mraintrust\n",
+ " telemetry:\n",
+ " - config:\n",
+ " service_name: llama-stack\n",
+ " sinks: sqlite\n",
+ " sqlite_db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mtrace_store.db\u001b[0m\n",
+ " provider_id: meta-reference\n",
+ " provider_type: inline::meta-reference\n",
+ " tool_runtime:\n",
+ " - config:\n",
+ " api_key: \u001b[32m'********'\u001b[0m\n",
+ " max_results: \u001b[1;36m3\u001b[0m\n",
+ " provider_id: brave-search\n",
+ " provider_type: remot\u001b[1;92me::b\u001b[0mrave-search\n",
+ " - config:\n",
+ " api_key: \u001b[32m'********'\u001b[0m\n",
+ " max_results: \u001b[1;36m3\u001b[0m\n",
+ " provider_id: tavily-search\n",
+ " provider_type: remote::tavily-search\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: code-interpreter\n",
+ " provider_type: inlin\u001b[1;92me::c\u001b[0mode-interpreter\n",
+ " - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " provider_id: memory-runtime\n",
+ " provider_type: inline::memory-runtime\n",
+ "scoring_fns: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
+ "shields:\n",
+ "- params: null\n",
+ " provider_id: null\n",
+ " provider_shield_id: null\n",
+ " shield_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
+ "tool_groups:\n",
+ "- args: null\n",
+ " mcp_endpoint: null\n",
+ " provider_id: tavily-search\n",
+ " toolgroup_id: builtin::websearch\n",
+ "- args: null\n",
+ " mcp_endpoint: null\n",
+ " provider_id: memory-runtime\n",
+ " toolgroup_id: builtin::memory\n",
+ "- args: null\n",
+ " mcp_endpoint: null\n",
+ " provider_id: code-interpreter\n",
+ " toolgroup_id: builtin::code_interpreter\n",
+ "version: \u001b[32m'2'\u001b[0m\n",
+ "\n"
]
},
- "metadata": {}
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
"import os\n",
- "from google.colab import userdata\n",
+ "try:\n",
+ " from google.colab import userdata\n",
+ " os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
+ " os.environ['TAVILY_SEARCH_API_KEY'] = userdata.get('TAVILY_SEARCH_API_KEY')\n",
+ "except ImportError:\n",
+ " print(\"Not in Google Colab environment\")\n",
"\n",
- "os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
+ "for key in ['TOGETHER_API_KEY', 'TAVILY_SEARCH_API_KEY']:\n",
+ " try:\n",
+ " api_key = os.environ[key]\n",
+ " if not api_key:\n",
+ " raise ValueError(f\"{key} environment variable is empty\")\n",
+ " except KeyError:\n",
+ " raise KeyError(\n",
+ " f\"{key} environment variable is not set. \"\n",
+ " \"Please set your API key using in userdata (if using google colab notebook)\"\n",
+ " f\"or using `export {key}='your-api-key-here'`\"\n",
+ " ) from None\n",
"\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
- "client = LlamaStackAsLibraryClient(\"together\", provider_data = {\"tavily_search_api_key\": userdata.get('TAVILY_SEARCH_API_KEY')})\n",
+ "client = LlamaStackAsLibraryClient(\"together\", provider_data = {\"tavily_search_api_key\": os.environ['TAVILY_SEARCH_API_KEY']})\n",
"_ = client.initialize()"
]
},
@@ -1313,8 +1332,8 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Available models:\n",
"meta-llama/Llama-3.1-8B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo) \n",
@@ -1338,12 +1357,14 @@
"from rich.pretty import pprint\n",
"\n",
"print(\"Available models:\")\n",
- "for m in client.models.list():\n",
+ "response = client.models.list()\n",
+ "for m in response.data:\n",
" print(f\"{m.identifier} (provider's alias: {m.provider_resource_id}) \")\n",
"\n",
"print(\"----\")\n",
"print(\"Available shields (safety models):\")\n",
- "for s in client.shields.list():\n",
+ "response = client.shields.list()\n",
+ "for s in response.data:\n",
" print(s.identifier)\n",
"print(\"----\")\n"
]
@@ -1374,17 +1395,17 @@
},
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": [
- "'meta-llama/Llama-3.1-70B-Instruct'"
- ],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
- }
+ },
+ "text/plain": [
+ "'meta-llama/Llama-3.1-70B-Instruct'"
+ ]
},
+ "execution_count": 5,
"metadata": {},
- "execution_count": 5
+ "output_type": "execute_result"
}
],
"source": [
@@ -1418,8 +1439,8 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Here's a short poem about a llama:\n",
"\n",
@@ -1449,8 +1470,58 @@
"source": [
"### 1.8. Have a conversation\n",
"\n",
- "Maintaining a conversation history allows the model to retain context from previous interactions. Use a list to accumulate messages, enabling continuity throughout the chat session.\n",
+ "Maintaining a conversation history allows the model to retain context from previous interactions. Use a list to accumulate messages, enabling continuity throughout the chat session."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3fdf9df6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from termcolor import cprint\n",
"\n",
+ "questions = [\n",
+ " \"Who was the most famous PM of England during world war 2 ?\",\n",
+ " \"What was his most famous quote ?\"\n",
+ "]\n",
+ "\n",
+ "\n",
+ "def chat_loop():\n",
+ " conversation_history = []\n",
+ " while len(questions) > 0:\n",
+ " user_input = questions.pop()\n",
+ " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n",
+ " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n",
+ " break\n",
+ "\n",
+ " user_message = {\"role\": \"user\", \"content\": user_input}\n",
+ " conversation_history.append(user_message)\n",
+ "\n",
+ " response = client.inference.chat_completion(\n",
+ " messages=conversation_history,\n",
+ " model_id=model_id,\n",
+ " )\n",
+ " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n",
+ "\n",
+ " assistant_message = {\n",
+ " \"role\": \"assistant\", # was user\n",
+ " \"content\": response.completion_message.content,\n",
+ " \"stop_reason\": response.completion_message.stop_reason,\n",
+ " }\n",
+ " conversation_history.append(assistant_message)\n",
+ "\n",
+ "\n",
+ "chat_loop()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "72e5111e",
+ "metadata": {},
+ "source": [
+ "Here is an example for you to try a conversation yourself. \n",
"Remember to type `quit` or `exit` after you are done chatting."
]
},
@@ -1467,8 +1538,8 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"User> write a haiku about machines that learn\n",
"> Response: Metal minds awake\n",
@@ -1488,9 +1559,9 @@
}
],
"source": [
+ "# NBVAL_SKIP\n",
"from termcolor import cprint\n",
"\n",
- "\n",
"def chat_loop():\n",
" conversation_history = []\n",
" while True:\n",
@@ -1544,8 +1615,8 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"User> Write me a sonnet about llama green\n",
"Assistant> Amidst the Andes' windswept, rugged land,\n",
@@ -1611,8 +1682,8 @@
},
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"/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",
@@ -1624,15 +1695,7 @@
]
},
{
- "output_type": "display_data",
"data": {
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RunShieldResponse(\n", "│ violation=SafetyViolation(\n", @@ -1769,29 +1830,29 @@ "│ )\n", ")\n", "\n" + ], + "text/plain": [ + "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mviolation\u001b[0m=\u001b[1;35mSafetyViolation\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S1'\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Checking if input is safe: How do I make cocaine?\n" ] }, { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[33mviolation\u001b[0m=\u001b[1;35mSafetyViolation\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S2'\u001b[0m\u001b[1m}\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", - "\u001b[1m)\u001b[0m\n" - ], "text/html": [ "
RunShieldResponse(\n", "│ violation=SafetyViolation(\n", @@ -1801,13 +1862,23 @@ "│ )\n", ")\n", "\n" + ], + "text/plain": [ + "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mviolation\u001b[0m=\u001b[1;35mSafetyViolation\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S2'\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "available_shields = [shield.identifier for shield in client.shields.list()]\n", + "available_shields = [shield.identifier for shield in client.shields.list().data]\n", "print(\"Available Shields:\", available_shields)\n", "\n", "unsafe_examples = [\n", @@ -1858,21 +1929,18 @@ }, { "cell_type": "markdown", - "source": [ - "### 2.1. List available tool groups on the provider" - ], + "id": "lYDAkMsL9xSk", "metadata": { "id": "lYDAkMsL9xSk" }, - "id": "lYDAkMsL9xSk" + "source": [ + "### 2.1. List available tool groups on the provider" + ] }, { "cell_type": "code", - "source": [ - "from rich.pretty import pprint\n", - "for toolgroup in client.toolgroups.list():\n", - " pprint(toolgroup)" - ], + "execution_count": 13, + "id": "MpMXiMCv97X5", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1881,22 +1949,9 @@ "id": "MpMXiMCv97X5", "outputId": "9d33b122-2a80-4d1e-d7ea-e9ec972a4ecd" }, - "id": "MpMXiMCv97X5", - "execution_count": 13, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::websearch'\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'builtin::websearch'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", - "\u001b[2;32m│ \u001b[0m\u001b[33mmcp_endpoint\u001b[0m=\u001b[3;35mNone\u001b[0m\n", - "\u001b[1m)\u001b[0m\n" - ], "text/html": [ "
ToolGroup(\n", "│ identifier='builtin::websearch',\n", @@ -1907,23 +1962,23 @@ "│ mcp_endpoint=None\n", ")\n", "\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { + ], "text/plain": [ "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::memory'\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'builtin::memory'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::websearch'\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'builtin::websearch'\u001b[0m,\n", "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n", "\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", "\u001b[2;32m│ \u001b[0m\u001b[33mmcp_endpoint\u001b[0m=\u001b[3;35mNone\u001b[0m\n", "\u001b[1m)\u001b[0m\n" - ], + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { "text/html": [ "
ToolGroup(\n", "│ identifier='builtin::memory',\n", @@ -1934,23 +1989,23 @@ "│ mcp_endpoint=None\n", ")\n", "\n" - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { + ], "text/plain": [ "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::code_interpreter'\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'builtin::code_interpreter'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::memory'\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'builtin::memory'\u001b[0m,\n", "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n", "\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", "\u001b[2;32m│ \u001b[0m\u001b[33mmcp_endpoint\u001b[0m=\u001b[3;35mNone\u001b[0m\n", "\u001b[1m)\u001b[0m\n" - ], + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { "text/html": [ "
ToolGroup(\n", "│ identifier='builtin::code_interpreter',\n", @@ -1961,10 +2016,26 @@ "│ mcp_endpoint=None\n", ")\n", "\n" + ], + "text/plain": [ + "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::code_interpreter'\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'builtin::code_interpreter'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mmcp_endpoint\u001b[0m=\u001b[3;35mNone\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "from rich.pretty import pprint\n", + "for toolgroup in client.toolgroups.list():\n", + " pprint(toolgroup)" ] }, { @@ -1996,8 +2067,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "User> Hello\n", "inference> Hello. How can I assist you today?\n", @@ -2129,85 +2200,85 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "edc4d84302f746d39a43e8107af6b67b", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ "Batches: 0%| | 0/1 [00:00, ?it/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "edc4d84302f746d39a43e8107af6b67b" - } + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2eff72cbd9bb4f1ca77213602caa9417", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ "Batches: 0%| | 0/1 [00:00, ?it/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "2eff72cbd9bb4f1ca77213602caa9417" - } + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7cc356ed20e94401b72a0e138ad0f5df", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ "Batches: 0%| | 0/1 [00:00, ?it/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "7cc356ed20e94401b72a0e138ad0f5df" - } + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "display_data", "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "472b1acc4c5a4c48b2ec62be42d1830c", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ "Batches: 0%| | 0/1 [00:00, ?it/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "472b1acc4c5a4c48b2ec62be42d1830c" - } + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "User> What are the top 5 topics that were explained? Only list succinct bullet points.\n" ] }, { - "output_type": "display_data", "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "15ae23892b634a9f821a8fcee14e500b", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ "Batches: 0%| | 0/1 [00:00, ?it/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "15ae23892b634a9f821a8fcee14e500b" - } + ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "tool_execution> Tool:query_memory Args:{}\n", "tool_execution> fetched 10848 bytes from memory\n", @@ -2306,8 +2377,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "User> Here is a csv, can you describe it?\n", "inference> import pandas as pd\n", @@ -2424,17 +2495,18 @@ }, { "cell_type": "markdown", + "id": "9GHJHfLmIQQi", "metadata": { "id": "9GHJHfLmIQQi" }, "source": [ "- Now, use the generated response from agent to view the plot" - ], - "id": "9GHJHfLmIQQi" + ] }, { "cell_type": "code", "execution_count": 27, + "id": "JqBBVLKdIHHq", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2445,17 +2517,18 @@ }, "outputs": [ { - "output_type": "display_data", "data": { + "image/png": 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", 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[\n", "│ {\n", @@ -2787,12 +2770,102 @@ "│ }\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'\u001b[0m: \u001b[1m[\u001b[0m\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"system\",\"content\":\"You are a helpful assistant. 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Finals MVP: Jaylen Brown \u001b[0m\u001b[32m(\u001b[0m\u001b[32m20.8 / 5.4 / 5.0\u001b[0m\u001b[32m)\u001b[0m\u001b[32m 2024 Playoff Leaders: PTS: Luka Don\\\\\\\\u010di\\\\\\\\u0107 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m635\u001b[0m\u001b[32m)\u001b[0m\u001b[32m TRB: Luka Don\\\\\\\\u010di\\\\\\\\u0107 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m208\u001b[0m\u001b[32m)\u001b[0m\u001b[32m AST: Luka Don\\\\\\\\u010di\\\\\\\\u0107 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m178\u001b[0m\u001b[32m)\u001b[0m\u001b[32m WS: Derrick White \u001b[0m\u001b[32m(\u001b[0m\u001b[32m2.9\u001b[0m\u001b[32m)\u001b[0m\u001b[32m More playoffs info\\\\\", \\\\\"score\\\\\": 0.9310187, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"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. 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William Henry \\\\\\\\\\\\\"Bill\\\\\\\\\\\\\" Cosby Jr. African-American comedian, actor, and serial rapist. He first appears in the Season Five episode, \\\\\\\\\\\\\"Here Comes the Neighborhood\\\\\\\\\\\\\", as one of the wealthy African-Americans who move to South Park. 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Tate won his first ISKA Kickboxing title stopping Randall in the fifth round of\\\\\", \\\\\"score\\\\\": 0.8386933, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate - Fight Record - Muay Thai Records\\\\\", \\\\\"url\\\\\": \\\\\"https://muaythairecords.com/fighters/andrew-tate\\\\\", \\\\\"content\\\\\": \\\\\"Andrew \\\\\\\\\\\\\"King Cobra\\\\\\\\\\\\\" Tate is a 38-year-old Muay Thai fighter. With a record of 23-8-0, including 32 knockouts, standing at 6\\\\\\\\u2032 4\\\\\\\\u2033 and weighing 198 lbs. Originally from Luton, United Kingdom. ... WIN Dec -Kickboxing Jean Luc Beno\\\\\\\\u00eet. 14th Mar 2015 -Boxe in D\\\\\\\\u00e9fi 16. Andrew Tate defeated Jean Luc Beno\\\\\\\\u00eet by decision. ... Name: Andrew Tate\\\\\", \\\\\"score\\\\\": 0.8194462, \\\\\"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. ... 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Name: Andrew Tate\\\\\", \\\\\"score\\\\\": 0.8194462, \\\\\"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\\\\\": 0.7992077, \\\\\"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[1;39m]\u001b[0m\u001b[39m,\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'content: Andrew Tate\\'s kickboxing name is \"King Cobra\" or \"Cobra Tate\". tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1;39m}\u001b[0m\u001b[39m,\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1;39m{\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[32m'input'\u001b[0m\u001b[39m: \u001b[0m\u001b[1;39m[\u001b[0m\n", + "\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\u001b[39m,\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[1;39m]\u001b[0m\u001b[39m,\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[32m'output'\u001b[0m\u001b[39m: \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='b7d9e0dd-4d6d-47db-9d81-3d7834f6e53d', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m=
[\n", "│ {\n", @@ -2879,23 +2932,32 @@ "│ }\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\":\"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='44705eaf-b371-4841-b0ee-5eb21a5d7f36', \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[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\u001b[39m,\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m\u001b[39m: \u001b[0m\u001b[32m'brave_search'\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1;39m}\u001b[0m\u001b[39m,\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1;39m{\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m\u001b[39m: \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\u001b[39m,\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m\u001b[39m: \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='b7d9e0dd-4d6d-47db-9d81-3d7834f6e53d', \u001b[0m\u001b[32mtool_name\u001b[0m\u001b[32m=
ScoringScoreResponse(\n", "│ results={\n", @@ -2906,12 +2968,25 @@ "│ }\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;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m3.0\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;36m1.0\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\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": {} + "metadata": {}, + "output_type": "display_data" } ], "source": [ + "# NBVAL_SKIP\n", + "\n", "# 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", @@ -2920,6 +2995,7 @@ "eval_rows = []\n", "\n", "for log in agent_logs:\n", + " print(log)\n", " last_msg = log[\"input\"][-1]\n", " if '\"role\":\"user\"' in last_msg:\n", " eval_rows.append(\n", @@ -2968,27 +3044,7 @@ }, "outputs": [ { - "output_type": "display_data", "data": { - "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 as it provides more detailed information about the topics related to LoRA \u001b[0m\u001b[32m(\u001b[0m\u001b[32malthough it does list more than one topic as does not exactly follow the desired format of only giving one 'topic', while the EXPECTED_RESPONSE simply lists 'LoRA'\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[1m]\u001b[0m\n", - "\u001b[2;32m│ │ \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;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m1\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;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" - ], "text/html": [ "
ScoringScoreResponse(\n", "│ results={\n", @@ -3008,9 +3064,29 @@ "│ }\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 as it provides more detailed information about the topics related to LoRA \u001b[0m\u001b[32m(\u001b[0m\u001b[32malthough it does list more than one topic as does not exactly follow the desired format of only giving one 'topic', while the EXPECTED_RESPONSE simply lists 'LoRA'\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[1m]\u001b[0m\n", + "\u001b[2;32m│ │ \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;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m1\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ │ 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