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Xi Yan 2025-03-03 16:08:58 -08:00
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docs/notebooks/Llama_Stack_Agent_Workflows.ipynb
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@ -1798,6 +1798,15 @@
" print(\"\\n\")\n" " print(\"\\n\")\n"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.3.1 Monitor Parallelization Internals\n",
"\n",
"Now, let's see how the worker agents processed the tasks. "
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 126, "execution_count": 126,
@ -2223,16 +2232,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### 1.3.1 Monitor Parallelization Internals\n", "## 2. Evaluator-Optimizer Workflow\n",
"\n",
"Now, let's see how the worker agents processed the tasks. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2. Evaluator-Optimizer Workflow\n",
"\n", "\n",
"In the evaluator-optimizer workflow, one LLM call generates a response while another provider evaluation and feedback in a loop. \n", "In the evaluator-optimizer workflow, one LLM call generates a response while another provider evaluation and feedback in a loop. \n",
"\n", "\n",
@ -2399,7 +2399,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"#### 2.1. Monitor Generator-Evaluator Internals\n", "### 2.1. Monitor Generator-Evaluator Internals\n",
"\n", "\n",
"In addition to final output from workflow, we can also look at how the generator and evaluator agents processed the user's request. Note that the `evaluator_agent` PASSED after 1 iteration. " "In addition to final output from workflow, we can also look at how the generator and evaluator agents processed the user's request. Note that the `evaluator_agent` PASSED after 1 iteration. "
] ]