mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-31 13:10:01 +00:00
pre-commit fixes
This commit is contained in:
Chantal D Gama Rose
parent
967dd0aa08
commit
7e211f8553
314 changed files with 5574 additions and 11369 deletions
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@ -141,7 +141,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 18,
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"id": "E1UFuJC570Tk",
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"metadata": {
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"colab": {
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@ -326,54 +326,108 @@
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" type: sqlite\n",
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"models:\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-FP8\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
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"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
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" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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@ -473,6 +527,9 @@
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" - config: <span style=\"font-weight: bold\">{}</span>\n",
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" provider_id: model-context-protocol\n",
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" provider_type: remote::model-context-protocol\n",
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" - config: <span style=\"font-weight: bold\">{}</span>\n",
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" provider_id: wolfram-alpha\n",
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" provider_type: remote::wolfram-alpha\n",
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" vector_io:\n",
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" - config:\n",
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" kvstore:\n",
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@ -504,6 +561,10 @@
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" mcp_endpoint: null\n",
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" provider_id: code-interpreter\n",
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" toolgroup_id: builtin::code_interpreter\n",
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"- args: null\n",
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" mcp_endpoint: null\n",
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" provider_id: wolfram-alpha\n",
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" toolgroup_id: builtin::wolfram_alpha\n",
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"vector_dbs: <span style=\"font-weight: bold\">[]</span>\n",
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"version: <span style=\"color: #008000; text-decoration-color: #008000\">'2'</span>\n",
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"\n",
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@ -530,54 +591,108 @@
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" type: sqlite\n",
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"models:\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" 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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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-FP8\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" 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",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct\n",
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" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
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" - llm\n",
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" provider_id: together\n",
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" provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
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"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
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" model_id: meta-llama/Meta-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-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-Turbo\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: \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",
|
||||
|
|
@ -677,6 +792,9 @@
|
|||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||||
" provider_id: model-context-protocol\n",
|
||||
" provider_type: remote::model-context-protocol\n",
|
||||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||||
" provider_id: wolfram-alpha\n",
|
||||
" provider_type: remote::wolfram-alpha\n",
|
||||
" vector_io:\n",
|
||||
" - config:\n",
|
||||
" kvstore:\n",
|
||||
|
|
@ -708,6 +826,10 @@
|
|||
" mcp_endpoint: null\n",
|
||||
" provider_id: code-interpreter\n",
|
||||
" toolgroup_id: builtin::code_interpreter\n",
|
||||
"- args: null\n",
|
||||
" mcp_endpoint: null\n",
|
||||
" provider_id: wolfram-alpha\n",
|
||||
" toolgroup_id: builtin::wolfram_alpha\n",
|
||||
"vector_dbs: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
|
||||
"version: \u001b[32m'2'\u001b[0m\n",
|
||||
"\n"
|
||||
|
|
@ -1145,7 +1267,6 @@
|
|||
}
|
||||
],
|
||||
"source": [
|
||||
"# NBVAL_SKIP\n",
|
||||
"from pydantic import BaseModel\n",
|
||||
"\n",
|
||||
"\n",
|
||||
|
|
@ -1157,7 +1278,7 @@
|
|||
"\n",
|
||||
"user_input = \"Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003. Extract this information into JSON for me. \"\n",
|
||||
"response = client.inference.completion(\n",
|
||||
" model_id=model_id,\n",
|
||||
" model_id=\"meta-llama/Llama-3.1-8B-Instruct\",\n",
|
||||
" content=user_input,\n",
|
||||
" stream=False,\n",
|
||||
" sampling_params={\n",
|
||||
|
|
@ -1513,18 +1634,14 @@
|
|||
"source": [
|
||||
"from llama_stack_client.lib.agents.agent import Agent\n",
|
||||
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
|
||||
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
|
||||
"from termcolor import cprint\n",
|
||||
"\n",
|
||||
"agent_config = AgentConfig(\n",
|
||||
"agent = Agent(\n",
|
||||
" client, \n",
|
||||
" model=model_id,\n",
|
||||
" instructions=\"You are a helpful assistant\",\n",
|
||||
" toolgroups=[\"builtin::websearch\"],\n",
|
||||
" input_shields=[],\n",
|
||||
" output_shields=[],\n",
|
||||
" enable_session_persistence=False,\n",
|
||||
" instructions=\"You are a helpful assistant. Use websearch tool to help answer questions.\",\n",
|
||||
" tools=[\"builtin::websearch\"],\n",
|
||||
")\n",
|
||||
"agent = Agent(client, agent_config)\n",
|
||||
"user_prompts = [\n",
|
||||
" \"Hello\",\n",
|
||||
" \"Which teams played in the NBA western conference finals of 2024\",\n",
|
||||
|
|
@ -1693,7 +1810,6 @@
|
|||
"import uuid\n",
|
||||
"from llama_stack_client.lib.agents.agent import Agent\n",
|
||||
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
|
||||
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
|
||||
"from termcolor import cprint\n",
|
||||
"from llama_stack_client.types import Document\n",
|
||||
"\n",
|
||||
|
|
@ -1719,11 +1835,11 @@
|
|||
" vector_db_id=vector_db_id,\n",
|
||||
" chunk_size_in_tokens=512,\n",
|
||||
")\n",
|
||||
"agent_config = AgentConfig(\n",
|
||||
"rag_agent = Agent(\n",
|
||||
" client, \n",
|
||||
" model=model_id,\n",
|
||||
" instructions=\"You are a helpful assistant\",\n",
|
||||
" enable_session_persistence=False,\n",
|
||||
" toolgroups = [\n",
|
||||
" tools = [\n",
|
||||
" {\n",
|
||||
" \"name\": \"builtin::rag/knowledge_search\",\n",
|
||||
" \"args\" : {\n",
|
||||
|
|
@ -1732,7 +1848,6 @@
|
|||
" }\n",
|
||||
" ],\n",
|
||||
")\n",
|
||||
"rag_agent = Agent(client, agent_config)\n",
|
||||
"session_id = rag_agent.create_session(\"test-session\")\n",
|
||||
"user_prompts = [\n",
|
||||
" \"What are the top 5 topics that were explained? Only list succinct bullet points.\",\n",
|
||||
|
|
@ -1856,23 +1971,19 @@
|
|||
"source": [
|
||||
"from llama_stack_client.types.agents.turn_create_params import Document\n",
|
||||
"\n",
|
||||
"agent_config = AgentConfig(\n",
|
||||
"codex_agent = Agent(\n",
|
||||
" client, \n",
|
||||
" model=\"meta-llama/Llama-3.1-8B-Instruct\",\n",
|
||||
" instructions=\"You are a helpful assistant\",\n",
|
||||
" tools=[\n",
|
||||
" \"builtin::code_interpreter\",\n",
|
||||
" \"builtin::websearch\"\n",
|
||||
" ],\n",
|
||||
" sampling_params = {\n",
|
||||
" \"max_tokens\" : 4096,\n",
|
||||
" \"temperature\": 0.0\n",
|
||||
" },\n",
|
||||
" model=\"meta-llama/Llama-3.1-8B-Instruct\",\n",
|
||||
" instructions=\"You are a helpful assistant\",\n",
|
||||
" toolgroups=[\n",
|
||||
" \"builtin::code_interpreter\",\n",
|
||||
" \"builtin::websearch\"\n",
|
||||
" ],\n",
|
||||
" tool_choice=\"auto\",\n",
|
||||
" input_shields=[],\n",
|
||||
" output_shields=[],\n",
|
||||
" enable_session_persistence=False,\n",
|
||||
")\n",
|
||||
"codex_agent = Agent(client, agent_config)\n",
|
||||
"session_id = codex_agent.create_session(\"test-session\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
|
|
@ -2782,18 +2893,14 @@
|
|||
"# NBVAL_SKIP\n",
|
||||
"from llama_stack_client.lib.agents.agent import Agent\n",
|
||||
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
|
||||
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
|
||||
"from termcolor import cprint\n",
|
||||
"\n",
|
||||
"agent_config = AgentConfig(\n",
|
||||
"agent = Agent(\n",
|
||||
" client, \n",
|
||||
" model=model_id,\n",
|
||||
" instructions=\"You are a helpful assistant\",\n",
|
||||
" toolgroups=[\"mcp::filesystem\"],\n",
|
||||
" input_shields=[],\n",
|
||||
" output_shields=[],\n",
|
||||
" enable_session_persistence=False,\n",
|
||||
" tools=[\"mcp::filesystem\"],\n",
|
||||
")\n",
|
||||
"agent = Agent(client, agent_config)\n",
|
||||
"user_prompts = [\n",
|
||||
" \"Hello\",\n",
|
||||
" \"list all the files /content\",\n",
|
||||
|
|
@ -2888,17 +2995,13 @@
|
|||
"source": [
|
||||
"from llama_stack_client.lib.agents.agent import Agent\n",
|
||||
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
|
||||
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
|
||||
"\n",
|
||||
"agent_config = AgentConfig(\n",
|
||||
"agent = Agent(\n",
|
||||
" client, \n",
|
||||
" model=\"meta-llama/Llama-3.3-70B-Instruct\",\n",
|
||||
" instructions=\"You are a helpful assistant. Use search tool to answer the questions. \",\n",
|
||||
" toolgroups=[\"builtin::websearch\"],\n",
|
||||
" input_shields=[],\n",
|
||||
" output_shields=[],\n",
|
||||
" enable_session_persistence=False,\n",
|
||||
" tools=[\"builtin::websearch\"],\n",
|
||||
")\n",
|
||||
"agent = Agent(client, agent_config)\n",
|
||||
"user_prompts = [\n",
|
||||
" \"Which teams played in the NBA western conference finals of 2024. Search the web for the answer.\",\n",
|
||||
" \"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title. Search the web for the answer.\",\n",
|
||||
|
|
@ -4098,7 +4201,7 @@
|
|||
"source": [
|
||||
"## 4. Image Understanding with Llama 3.2\n",
|
||||
"\n",
|
||||
"Below is a complete example of using Together's Llama Stack 0.1 server at https://llama-stack.together.ai to ask Llama 3.2 questions about an image."
|
||||
"Below is a complete example of to ask Llama 3.2 questions about an image."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -4106,14 +4209,12 @@
|
|||
"id": "82e381ec",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 4.1 Setup and helpers\n",
|
||||
"\n",
|
||||
"Below we install the Llama Stack client 0.1, download the example image, define two image helpers, and set Llama Stack Together server URL and Llama 3.2 model name.\n"
|
||||
"### 4.1 Setup and helpers\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"id": "44e05e16",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -4123,7 +4224,7 @@
|
|||
"text": [
|
||||
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
|
||||
" Dload Upload Total Spent Left Speed\n",
|
||||
"100 275k 100 275k 0 0 780k 0 --:--:-- --:--:-- --:--:-- 780k\n"
|
||||
"100 275k 100 275k 0 0 905k 0 --:--:-- --:--:-- --:--:-- 906k\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -4133,32 +4234,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "469750f7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# NBVAL_SKIP\n",
|
||||
"from PIL import Image\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"def display_image(path):\n",
|
||||
" img = Image.open(path)\n",
|
||||
" plt.imshow(img)\n",
|
||||
" plt.axis('off')\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"display_image(\"Llama_Repo.jpeg\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 20,
|
||||
"id": "a2c1e1c2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import base64\n",
|
||||
"vision_model_id = \"meta-llama/Llama-3.2-11B-Vision-Instruct\"\n",
|
||||
"\n",
|
||||
"def encode_image(image_path):\n",
|
||||
" with open(image_path, \"rb\") as image_file:\n",
|
||||
|
|
@ -4167,19 +4249,6 @@
|
|||
" return base64_url"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c565f99e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from llama_stack_client import LlamaStackClient\n",
|
||||
"\n",
|
||||
"LLAMA_STACK_API_TOGETHER_URL=\"https://llama-stack.together.ai\"\n",
|
||||
"LLAMA32_11B_INSTRUCT = \"meta-llama/Llama-3.2-11B-Vision-Instruct\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7737cd41",
|
||||
|
|
@ -4192,55 +4261,44 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 21,
|
||||
"id": "d7914894",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"There are three llamas in the image. The llama in the middle is purple, the llama on the left is white, and the llama on the right is also white, but it is wearing a blue party hat. Therefore, there are two different colors of llama in the image: purple and white.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from llama_stack_client.lib.inference.event_logger import EventLogger\n",
|
||||
"\n",
|
||||
"async def run_main(image_path: str, prompt):\n",
|
||||
" client = LlamaStackClient(\n",
|
||||
" base_url=LLAMA_STACK_API_TOGETHER_URL,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" message = {\n",
|
||||
" \"role\": \"user\",\n",
|
||||
" \"content\": [\n",
|
||||
" {\n",
|
||||
" \"type\": \"image\",\n",
|
||||
" \"image\": {\n",
|
||||
" \"url\": {\n",
|
||||
" \"uri\": encode_image(image_path)\n",
|
||||
" }\n",
|
||||
"response = client.inference.chat_completion(\n",
|
||||
" messages=[\n",
|
||||
" {\n",
|
||||
" \"role\": \"user\",\n",
|
||||
" \"content\": [\n",
|
||||
" {\n",
|
||||
" \"type\": \"image\",\n",
|
||||
" \"image\": {\n",
|
||||
" \"url\": {\n",
|
||||
" \"uri\": encode_image(\"Llama_Repo.jpeg\")\n",
|
||||
" }\n",
|
||||
" }\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"type\": \"text\",\n",
|
||||
" \"text\": \"How many different colors are those llamas? What are those colors?\",\n",
|
||||
" }\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"type\": \"text\",\n",
|
||||
" \"text\": prompt,\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
" ],\n",
|
||||
" model_id=vision_model_id,\n",
|
||||
" stream=False,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
" response = client.inference.chat_completion(\n",
|
||||
" messages=[message],\n",
|
||||
" model_id=LLAMA32_11B_INSTRUCT,\n",
|
||||
" stream=False,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" print(response.completion_message.content.lower().strip())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4ee09b97",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"await run_main(\"Llama_Repo.jpeg\",\n",
|
||||
" \"How many different colors are those llamas?\\\n",
|
||||
" What are those colors?\")"
|
||||
"print(response.completion_message.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -4255,68 +4313,60 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 19,
|
||||
"id": "f9a83275",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[33minference> \u001b[0m\u001b[33mThere\u001b[0m\u001b[33m are\u001b[0m\u001b[33m three\u001b[0m\u001b[33m different\u001b[0m\u001b[33m colors\u001b[0m\u001b[33m of\u001b[0m\u001b[33m ll\u001b[0m\u001b[33mamas\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m image\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m first\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m on\u001b[0m\u001b[33m the\u001b[0m\u001b[33m left\u001b[0m\u001b[33m is\u001b[0m\u001b[33m white\u001b[0m\u001b[33m,\u001b[0m\u001b[33m the\u001b[0m\u001b[33m second\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m middle\u001b[0m\u001b[33m is\u001b[0m\u001b[33m purple\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m the\u001b[0m\u001b[33m third\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m on\u001b[0m\u001b[33m the\u001b[0m\u001b[33m right\u001b[0m\u001b[33m is\u001b[0m\u001b[33m white\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m blue\u001b[0m\u001b[33m party\u001b[0m\u001b[33m hat\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n",
|
||||
"\u001b[30m\u001b[0m"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from llama_stack_client.lib.agents.agent import Agent\n",
|
||||
"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
|
||||
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
|
||||
"agent = Agent(\n",
|
||||
" client, \n",
|
||||
" model=vision_model_id,\n",
|
||||
" instructions=\"You are a helpful assistant\",\n",
|
||||
")\n",
|
||||
"session_id = agent.create_session(\"test-session\")\n",
|
||||
"\n",
|
||||
"async def run_main(image_path, prompt):\n",
|
||||
" base64_image = encode_image(image_path)\n",
|
||||
"\n",
|
||||
" client = LlamaStackClient(\n",
|
||||
" base_url=LLAMA_STACK_API_TOGETHER_URL,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" agent_config = AgentConfig(\n",
|
||||
" model=LLAMA32_11B_INSTRUCT,\n",
|
||||
" instructions=\"You are a helpful assistant\",\n",
|
||||
" enable_session_persistence=False,\n",
|
||||
" toolgroups=[],\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" agent = Agent(client, agent_config)\n",
|
||||
" session_id = agent.create_session(\"test-session\")\n",
|
||||
"\n",
|
||||
" response = agent.create_turn(\n",
|
||||
" messages=[{\n",
|
||||
" \"role\": \"user\",\n",
|
||||
" \"content\": [\n",
|
||||
" {\n",
|
||||
" \"type\": \"image\",\n",
|
||||
" \"image\": {\n",
|
||||
" \"url\": {\n",
|
||||
" \"uri\": encode_image(image_path)\n",
|
||||
" }\n",
|
||||
" }\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"type\": \"text\",\n",
|
||||
" \"text\": prompt,\n",
|
||||
"response = agent.create_turn(\n",
|
||||
" messages=[{\n",
|
||||
" \"role\": \"user\",\n",
|
||||
" \"content\": [\n",
|
||||
" {\n",
|
||||
" \"type\": \"image\",\n",
|
||||
" \"image\": {\n",
|
||||
" \"url\": {\n",
|
||||
" \"uri\": encode_image(\"Llama_Repo.jpeg\")\n",
|
||||
" }\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
" }],\n",
|
||||
" session_id=session_id,\n",
|
||||
" )\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"type\": \"text\",\n",
|
||||
" \"text\": \"How many different colors are those llamas? What are those colors?\",\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
" }],\n",
|
||||
" session_id=session_id,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
" for log in EventLogger().log(response):\n",
|
||||
" log.print()"
|
||||
"for log in EventLogger().log(response):\n",
|
||||
" log.print()\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "15d0098b",
|
||||
"id": "f3352379",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"await run_main(\"Llama_Repo.jpeg\",\n",
|
||||
" \"How many different colors are those llamas?\\\n",
|
||||
" What are those colors?\")"
|
||||
]
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue