diff --git a/01_Text_generation/01_text_and_code_generation_w_bedrock.ipynb b/01_Text_generation/01_text_and_code_generation_w_bedrock.ipynb index 2449a263..2aec684e 100644 --- a/01_Text_generation/01_text_and_code_generation_w_bedrock.ipynb +++ b/01_Text_generation/01_text_and_code_generation_w_bedrock.ipynb @@ -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", @@ -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" ] @@ -661,7 +661,7 @@ "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",