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Expand Up @@ -23,7 +23,7 @@
" 5. [Cross-Regional Inference in Amazon Bedrock](#ch25)\n",
" 6. [Multi-turn conversations](#ch26)\n",
" 7. [Streaming Responses with the ConverseStream API](#ch27)\n",
"3. [Code Generation wiht Foundational Models](#ch3)\n",
"3. [Code Generation with Foundational Models](#ch3)\n",
"4. [Function Calling with the Converse API](#ch4)\n",
" 1. [4.1 Function Calling Flow](#ch41)\n",
" 2. [Example: Implementation of Weather Function Calling with Bedrock's Converse API](#ch42)\n",
Expand All @@ -38,7 +38,7 @@
"In this notebook, you will:\n",
"\n",
"1. Learn the basics of the Amazon Bedrock **Invoke API**\n",
"2. Explore the more powerful **Converse API** and it's features like multi-turn conversation, streaming, or function calling\n",
"2. Explore the more powerful **Converse API** and its features like multi-turn conversation, streaming, or function calling\n",
"3. Apply these APIs across various foundation models\n",
"4. Compare results across different state-of-the-art models"
]
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"outputs": [],
"source": [
"code_generation_prompt = \"\"\"\n",
"Create a Python function called get_weather that accepts a location as parameter. \\\n",
"Create a Python function called get_weather that accepts a location as a parameter. \\\n",
"The function should return a dictionary containing weather data (condition, temperature, and humidity) for predefined cities.\\\n",
"Use a mock data structure instead of actual API calls. Include New York, San Francisco, Miami, and Seattle as default cities.\\\n",
"The return statement should look like the following: return weather_data.get(location, {\"condition\": \"Unknown\", \"temperature\": 0, \"humidity\": 0}).\n",
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