updates to tooluse and switch to converse api

Author: CloudsOfChange

AmazonBedrock/boto3/00_Tutorial_How-To.ipynb renamed to AmazonBedrock/00_Tutorial_How-To.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "## How to get started\n",
     "\n",
-    "1. Clone this repository to your local machine.\n",
+    "1. If you are attending an instructor lead workshop or deployed the workshop infrastructure using the provided [CloudFormation Template](https://raw.githubusercontent.com/aws-samples/prompt-engineering-with-anthropic-claude-v-3/main/cloudformation/workshop-v1-final-cfn.yml) you can proceed to step 2, otherwise you will need to download the workshop [GitHub Repository](https://github.com/aws-samples/prompt-engineering-with-anthropic-claude-v-3) to your local machine.\n",
     "\n",
     "2. Install the required dependencies by running the following command:\n",
     " "
@@ -31,36 +31,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "%pip install -qU pip\n",
-    "%pip install -qr ../requirements.txt"
+    "%pip install -qUr requirements.txt --force-reinstall"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "3. Restart the kernel after installing dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# restart kernel\n",
-    "from IPython.core.display import HTML\n",
-    "HTML(\"<script>Jupyter.notebook.kernel.restart()</script>\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "4. Run the notebook cells in order, following the instructions provided."
+    "3. Run the notebook cells in order, following the instructions provided."
    ]
   },
   {
@@ -77,8 +61,8 @@
     "\n",
     "- When you reach the bottom of a tutorial page, navigate to the next numbered file in the folder, or to the next numbered folder if you're finished with the content within that chapter file.\n",
     "\n",
-    "### The Anthropic SDK & the Messages API\n",
-    "We will be using the [Anthropic python SDK](https://docs.anthropic.com/claude/reference/client-sdks) and the [Messages API](https://docs.anthropic.com/claude/reference/messages_post) throughout this tutorial. \n",
+    "### The Boto3 SDK & the Converse API\n",
+    "We will be using the [Amazon Boto3 SDK](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime.html) and the [Converse API](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime/client/converse.html) throughout this tutorial. \n",
     "\n",
     "Below is an example of what running a prompt will look like in this tutorial. First, we create `get_completion`, which is a helper function that sends a prompt to Claude and returns Claude's generated response. Run that cell now."
    ]
@@ -97,13 +81,30 @@
    "outputs": [],
    "source": [
     "import boto3\n",
-    "session = boto3.Session() # create a boto3 session to dynamically get and set the region name\n",
-    "AWS_REGION = session.region_name\n",
-    "print(\"AWS Region:\", AWS_REGION)\n",
-    "MODEL_NAME = \"anthropic.claude-3-haiku-20240307-v1:0\"\n",
+    "import json\n",
+    "from datetime import datetime\n",
+    "from botocore.exceptions import ClientError\n",
     "\n",
-    "%store MODEL_NAME\n",
-    "%store AWS_REGION"
+    "session = boto3.Session()\n",
+    "region = session.region_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#modelId = 'anthropic.claude-3-sonnet-20240229-v1:0'\n",
+    "modelId = 'anthropic.claude-3-haiku-20240307-v1:0'\n",
+    "\n",
+    "%store modelId\n",
+    "%store region\n",
+    "\n",
+    "print(f'Using modelId: {modelId}')\n",
+    "print('Using region: ', region)\n",
+    "\n",
+    "bedrock_client = boto3.client(service_name = 'bedrock-runtime', region_name = region,)"
    ]
   },
   {
@@ -116,29 +117,46 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "import boto3\n",
-    "import json\n",
+    "def get_completion(prompt, system_prompt=None):\n",
+    "    # Define the inference configuration\n",
+    "    inference_config = {\n",
+    "        \"temperature\": 0.0,  # Set the temperature for generating diverse responses\n",
+    "        \"maxTokens\": 200  # Set the maximum number of tokens to generate\n",
+    "    }\n",
+    "    # Define additional model fields\n",
+    "    additional_model_fields = {\n",
+    "        \"top_p\": 1,  # Set the top_p value for nucleus sampling\n",
+    "    }\n",
+    "    # Create the converse method parameters\n",
+    "    converse_api_params = {\n",
+    "        \"modelId\": modelId,  # Specify the model ID to use\n",
+    "        \"messages\": [{\"role\": \"user\", \"content\": [{\"text\": prompt}]}],  # Provide the user's prompt\n",
+    "        \"inferenceConfig\": inference_config,  # Pass the inference configuration\n",
+    "        \"additionalModelRequestFields\": additional_model_fields  # Pass additional model fields\n",
+    "    }\n",
+    "    # Check if system_text is provided\n",
+    "    if system_prompt:\n",
+    "        # If system_text is provided, add the system parameter to the converse_params dictionary\n",
+    "        converse_api_params[\"system\"] = [{\"text\": system_prompt}]\n",
+    "\n",
+    "    # Send a request to the Bedrock client to generate a response\n",
+    "    try:\n",
+    "        response = bedrock_client.converse(**converse_api_params)\n",
     "\n",
-    "bedrock = boto3.client('bedrock-runtime',region_name=AWS_REGION)\n",
-    "\n",
-    "def get_completion(prompt):\n",
-    "    body = json.dumps(\n",
-    "        {\n",
-    "            \"anthropic_version\": '',\n",
-    "            \"max_tokens\": 2000,\n",
-    "            \"messages\": [{\"role\": \"user\", \"content\": prompt}],\n",
-    "            \"temperature\": 0.0,\n",
-    "            \"top_p\": 1,\n",
-    "            \"system\": ''\n",
-    "        }\n",
-    "    )\n",
-    "    response = bedrock.invoke_model(body=body, modelId=MODEL_NAME)\n",
-    "    response_body = json.loads(response.get('body').read())\n",
-    "\n",
-    "    return response_body.get('content')[0].get('text')"
+    "        # Extract the generated text content from the response\n",
+    "        text_content = response['output']['message']['content'][0]['text']\n",
+    "\n",
+    "        # Return the generated text content\n",
+    "        return text_content\n",
+    "\n",
+    "    except ClientError as err:\n",
+    "        message = err.response['Error']['Message']\n",
+    "        print(f\"A client error occured: {message}\")"
    ]
   },
   {
@@ -153,7 +171,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# Prompt\n",
@@ -167,15 +187,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `MODEL_NAME` and `AWS_REGION` variables defined earlier will be used throughout the tutorial. Just make sure to run the cells for each tutorial page from top to bottom."
+    "The `modelId` and `region` variables defined earlier will be used throughout the tutorial. Just make sure to run the cells for each tutorial page from top to bottom."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "py310",
+   "display_name": "conda_tensorflow2_p310",
    "language": "python",
-   "name": "python3"
+   "name": "conda_tensorflow2_p310"
   },
   "language_info": {
    "codemirror_mode": {
@@ -187,9 +207,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }