system message

aws/aws-model-list.py

@@ -0,0 +1,16 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+import boto3
+
+# Initialize the Bedrock client (note the service_name is 'bedrock', not 'bedrock-runtime')
+client = boto3.client(service_name="bedrock", region_name="us-west-2")
+
+# List available foundation models
+response = client.list_foundation_models()
+
+# Print model IDs and their details
+for model in response['modelSummaries']:
+    print(f"Model ID: {model['modelId']}")
+    print(f"Provider: {model.get('providerName', 'N/A')}")
+    print("-" * 50)

aws/bedrock1.py

@@ -15,7 +15,7 @@
 from traceloop.sdk import Traceloop
 from traceloop.sdk.decorators import task, workflow
 
-Traceloop.init(app_name="joke_generation_service")
+# Traceloop.init(app_name="joke_generation_service")
 bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name="us-west-2")
 
 

aws/bedrock2.py

@@ -1,63 +1,73 @@
-'''
-Put following parameter to a .env file
-
-TRACELOOP_API_KEY=
-AWS_ACCESS_KEY_ID=
-AWS_SECRET_ACCESS_KEY=
-'''
-
+# Use the native inference API to send a text message to Anthropic Claude.
 from dotenv import load_dotenv
 load_dotenv()
 
 import boto3
 import json
 
-from traceloop.sdk import Traceloop
-from traceloop.sdk.decorators import task, workflow
+from botocore.exceptions import ClientError
 
-Traceloop.init(app_name="joke_generation_service")
-bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name="us-west-2")
+# Create a Bedrock Runtime client in the AWS Region of your choice.
+client = boto3.client("bedrock-runtime", region_name="us-east-1")
 
+# Set the model ID, e.g., Claude 3 Haiku.
+model_id = "anthropic.claude-v2"
 
-@task(name="gyliu_joke_creation")
-def create_joke():
+# Define the prompt for the model.
+# prompt = "Describe the purpose of a 'hello world' program in one line."
 
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful assistant that provides weather forecasts."
-        },
+# Prepare the messages
+'''
+messages = [
+    {
+        "role": "system",
+        "content": [{"type": "text", "text": "You are a helpful assistant that provides information about AWS services."}],
+    },
+    {
+        "role": "user",
+        "content": [{"type": "text", "text": "Tell me about AWS Bedrock"}],
+    }
+]
+
+native_request = {
+    "anthropic_version": "bedrock-2023-05-31",
+    "max_tokens": 512,
+    "temperature": 0.5,
+    "messages": messages,
+}
+'''
+
+# Format the request payload using the model's native structure.
+native_request = {
+    "system": "You are a helpful assistant that provides information about AWS services.",
+    "anthropic_version": "bedrock-2023-05-31",
+    "max_tokens": 512,
+    "temperature": 0.5,
+    "messages": [
+
         {
             "role": "user",
-            "content": "What's the weather like today in New York City?"
+            "content": [{"type": "text", "text": "Tell me about AWS Bedrock"}],
         }
-    ]
-
-    # Prepare the payload
-    payload = {
-        "messages": messages,
-        "max_tokens_to_sample": 150
-    }
-
-    body = json.dumps(payload)
+
+    ],
+}
 
-    response = bedrock_runtime.invoke_model(
-        body=body,
-        modelId="amazon.titan-text-express-v1",
-        accept="application/json", 
-        contentType="application/json"
-    )
 
-    response_body = json.loads(response.get('body').read())
-    outputText = response_body.get('results')[0].get('outputText')
+# Convert the native request to JSON.
+request = json.dumps(native_request)
 
-    text = outputText[outputText.index('\n')+1:]
-    about_lambda = text.strip()
-    return about_lambda
+try:
+    # Invoke the model with the request.
+    response = client.invoke_model(modelId=model_id, body=request)
 
-@workflow(name="gyliu_joke_generator")
-def joke_workflow():
-    print(create_joke())
+except (ClientError, Exception) as e:
+    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
+    exit(1)
 
+# Decode the response body.
+model_response = json.loads(response["body"].read())
 
-joke_workflow()
+# Extract and print the response text.
+response_text = model_response["content"][0]["text"]
+print(response_text)