Modelle von OpenAI

Das folgende Python-Skript ruft die API zum Erstellen von Chat-Vervollständigungen mit dem OpenAI-Python SDK auf:

from openai import OpenAI

client = OpenAI(
    base_url="https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1", 
    api_key="$AWS_BEARER_TOKEN_BEDROCK" # Replace with actual API key
)

completion = client.chat.completions.create(
    model="openai.gpt-oss-20b-1:0",
    messages=[
        {
            "role": "developer",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Hello!"
        }
    ]
)

print(completion.choices[0].message)

Sie können den folgenden Befehl in einem Terminal ausführen, um die API zum Erstellen von Chat-Vervollständigungen mit curl aufzurufen:

curl -X POST https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1/chat/completions \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer $AWS_BEARER_TOKEN_BEDROCK" \
   -d '{
    "model": "openai.gpt-oss-20b-1:0",
    "messages": [
        {
            "role": "developer",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Hello!"
        }
    ]
}'

import boto3
import json

# Initialize the Bedrock Runtime client
client = boto3.client('bedrock-runtime')

# Model ID
model_id = 'openai.gpt-oss-20b-1:0'

# Create the request body
native_request = {
  "model": model_id, # You can omit this field
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "assistant", 
      "content": "Hello! How can I help you today?"
    },
    {
      "role": "user",
      "content": "What is the weather like today?"
    }
  ],
  "max_completion_tokens": 150,
  "temperature": 0.7,
  "top_p": 0.9,
  "stream": False # You can omit this field
}

# Make the InvokeModel request
response = client.invoke_model(
    modelId=model_id,
    body=json.dumps(native_request)
)

# Parse and print the message for each choice in the chat completion
response_body = json.loads(response['body'].read().decode('utf-8'))

for choice in response_body['choices']:
    print(choice['message']['content'])

{
  "model": "openai.gpt-oss-20b-1:0",
  "messages": [
    {
      "role": "developer",
      "content": "You are a helpful assistant."
    },
    {
      "role": "assistant", 
      "content": "Hello! How can I help you today?"
    },
    {
      "role": "user",
      "content": "What is the weather like today?"
    }
  ],
  "max_completion_tokens": 150,
  "temperature": 0.7
}

{
    "messages": [
        {
            "role": "user", 
            "content": [
                {
                    "text": "Hello! How can I help you today?"
                }
            ]
        },
        {
            "role": "user",
            "content": [
                {
                    "text": "What is the weather like today?"
                }
            ]
        }
    ],
    "system": [
        {
            "text": "You are a helpful assistant."
        }
    ],
    "inferenceConfig": {
        "maxTokens": 150,
        "temperature": 0.7
    }
}

# Use the Conversation API to send a text message to Anthropic Claude.

import boto3
from botocore.exceptions import ClientError

# Initialize the Bedrock Runtime client
client = boto3.client("bedrock-runtime")

# Set the model ID
model_id = "openai.gpt-oss-20b-1:0"

# Set up messages and system message
messages = [
    {
        "role": "assistant", 
        "content": [
            {
                "text": "Hello! How can I help you today?"
            }
        ]
    },
    {
        "role": "user",
        "content": [
            {
                "text": "What is the weather like today?"
            }
        ]
    }
]

system = [
    {
        "text": "You are a helpful assistant."
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = client.converse(
        modelId=model_id,
        messages=messages,
        system=system,
        inferenceConfig={
            "maxTokens": 150, 
            "temperature": 0.7, 
            "topP": 0.9
        },
    )

    # Extract and print the response text.
    for content_block in response["output"]["message"]["content"]:
        print(content_block)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

import boto3
from botocore.exceptions import ClientError
import json

# Initiate the Amazon Bedrock Runtime client
bedrock_runtime = boto3.client("bedrock-runtime")

# Model ID
model_id = "openai.gpt-oss-20b-1:0"

# Replace with actual values from your guardrail
guardrail_id = "GR12345"
guardrail_version = "DRAFT"

# Create the request body
native_request = {
  "model": model_id, # You can omit this field
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "assistant", 
      "content": "Hello! How can I help you today?"
    },
    {
      "role": "user",
      "content": "What is the weather like today?"
    }
  ],
  "max_completion_tokens": 150,
  "temperature": 0.7,
  "top_p": 0.9,
  "stream": False # You can omit this field
}

try:
    response = bedrock_runtime.invoke_model(
        modelId=model_id,
        body=json.dumps(native_request),
        guardrailIdentifier=guardrail_id,
        guardrailVersion=guardrail_version,
        trace='ENABLED',
    )
    response_body = json.loads(response.get('body').read())
    print("Received response from InvokeModel API (Request Id: {})".format(response['ResponseMetadata']['RequestId']))
    print(json.dumps(response_body, indent=2))

except ClientError as err:
    print("RequestId = " + err.response['ResponseMetadata']['RequestId'])
    raise err

import openai
from openai import OpenAIError

# Endpoint for Amazon Bedrock Runtime
bedrock_endpoint = "https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"

# Model ID
model_id = "openai.gpt-oss-20b-1:0"

# Replace with actual values
bedrock_api_key = "$AWS_BEARER_TOKEN_BEDROCK"
guardrail_id = "GR12345"
guardrail_version = "DRAFT"

client = openai.OpenAI(
    api_key=bedrock_api_key,
    base_url=bedrock_endpoint,
)

try:
    response = client.chat.completions.create(
        model=model_id,
        # Specify guardrail information in the header
        extra_headers={
            "X-Amzn-Bedrock-GuardrailIdentifier": guardrail_id,
            "X-Amzn-Bedrock-GuardrailVersion": guardrail_version,
            "X-Amzn-Bedrock-Trace": "ENABLED",
        },
        # Additional guardrail information can be specified in the body
        extra_body={
            "amazon-bedrock-guardrailConfig": {
                "tagSuffix": "xyz"  # Used for input tagging
            }
        },
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "assistant", 
                "content": "Hello! How can I help you today?"
            },
            {
                "role": "user",
                "content": "What is the weather like today?"
            }
        ]
    )

    request_id = response._request_id
    print(f"Request ID: {request_id}")
    print(response)
    
except OpenAIError as e:
    print(f"An error occurred: {e}")
    if hasattr(e, 'response') and e.response is not None:
        request_id = e.response.headers.get("x-request-id")
        print(f"Request ID: {request_id}")

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.http.HttpResponseFor;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;

// Endpoint for Amazon Bedrock Runtime
String bedrockEndpoint = "http://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"

// Model ID
String modelId = "openai.gpt-oss-20b-1:0"

// Replace with actual values
String bedrockApiKey = "$AWS_BEARER_TOKEN_BEDROCK"
String guardrailId = "GR12345"
String guardrailVersion = "DRAFT"

OpenAIClient client = OpenAIOkHttpClient.builder()
        .apiKey(bedrockApiKey)
        .baseUrl(bedrockEndpoint)
        .build()

ChatCompletionCreateParams request = ChatCompletionCreateParams.builder()
        .addUserMessage("What is the temperature in Seattle?")
        .model(modelId)
        // Specify additional headers for the guardrail
        .putAdditionalHeader("X-Amzn-Bedrock-GuardrailIdentifier", guardrailId)
        .putAdditionalHeader("X-Amzn-Bedrock-GuardrailVersion", guardrailVersion)
        // Specify additional body parameters for the guardrail
        .putAdditionalBodyProperty(
                "amazon-bedrock-guardrailConfig",
                JsonValue.from(Map.of("tagSuffix", JsonValue.of("xyz"))) // Allows input tagging
        )
        .build();
        
HttpResponseFor<ChatCompletion> rawChatCompletionResponse =
        client.chat().completions().withRawResponse().create(request);

final ChatCompletion chatCompletion = rawChatCompletionResponse.parse();

System.out.println(chatCompletion);