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@@ -5,7 +5,7 @@
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"id": "de56ee05-3b71-43c9-8cbf-6ad9b3233f38",
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"metadata": {},
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"source": [
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- "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/dlai/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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+ "<a href=\"https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks/AI_Agents_in_LangGraph_L1_Build_an_Agent_from_Scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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@@ -35,7 +35,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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- "import os \n",
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+ "import os\n",
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"from groq import Groq\n",
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"\n",
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"os.environ['GROQ_API_KEY'] = 'your_groq_api_key' # get a free key at https://console.groq.com/keys"
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@@ -48,7 +48,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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- "# a quick sanity test of calling Llama 3 70b on Groq \n",
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+ "# a quick sanity test of calling Llama 3 70b on Groq\n",
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"# see https://console.groq.com/docs/text-chat for more info\n",
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"client = Groq()\n",
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"chat_completion = client.chat.completions.create(\n",
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@@ -75,7 +75,7 @@
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"source": [
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"client = Groq()\n",
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"model = \"llama3-8b-8192\" # this model works with the prompt below only for the first simpler example; you'll see how to modify the prompt to make it work for a more complicated question\n",
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- "#model = \"llama3-70b-8192\" # this model works with the prompt below for both example questions \n",
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+ "#model = \"llama3-70b-8192\" # this model works with the prompt below for both example questions\n",
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"\n",
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"class Agent:\n",
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" def __init__(self, system=\"\"):\n",
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@@ -95,8 +95,7 @@
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" model=model,\n",
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" temperature=0,\n",
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" messages=self.messages)\n",
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- " return completion.choices[0].message.content\n",
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- " "
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+ " return completion.choices[0].message.content\n"
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]
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},
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{
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@@ -151,7 +150,7 @@
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" return eval(what)\n",
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"\n",
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"def average_dog_weight(name):\n",
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- " if name in \"Scottish Terrier\": \n",
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+ " if name in \"Scottish Terrier\":\n",
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" return(\"Scottish Terriers average 20 lbs\")\n",
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" elif name in \"Border Collie\":\n",
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" return(\"a Border Collies average weight is 37 lbs\")\n",
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@@ -423,7 +422,7 @@
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"\n",
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" # key to make the agent process fully automated:\n",
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" # programtically call the external func with arguments, with the info returned by LLM\n",
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- " observation = known_actions[action](action_input) \n",
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+ " observation = known_actions[action](action_input)\n",
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"\n",
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" print(\"Observation:\", observation)\n",
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" next_prompt = \"Observation: {}\".format(observation)\n",
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Sanyam Bhutani