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使用聊天完成 API 进行推理
OpenAI聊天完成 API 使用 Amazon Bedrock 模型生成对话回复。您可以在bedrock-mantle和bedrock-runtime端点上使用聊天完成 API。我们建议尽可能使用终bedrock-mantle端节点。如需了解完整的 API 详情，请参阅OpenAI聊天完成文档。
| Endpoint |
基本网址 |
身份验证 |
bedrock-mantle（推荐） |
https://bedrock-mantle.{region}.api.aws/v1/chat/completions |
亚马逊 Bedrock API 密钥或证书 AWS |
bedrock-runtime |
https://bedrock-runtime.{region}.amazonaws.com/v1/chat/completions |
AWS 凭证 (Sigv4) 或 Amazon Bedrock API 密钥 |
每个端点都有自己的每个型号的令牌配额。有关应用于每个终端节点上流量的配额的详细信息，请参阅基岩地幔端点的配额和基底运行时端点的配额。
使用基岩地幔端点完成聊天
该bedrock-mantle终端节点支持 Amazon Bedrock API 密钥身份验证和OpenAI软件开发工具包。
列出可用型号
要列出bedrock-mantle端点上可用的模型，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# List all available models using the OpenAI SDK
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
models = client.models.list()
for model in models.data:
print(model.id)
- HTTP request
-
# List all available models
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
curl -X GET $OPENAI_BASE_URL/models \
-H "Authorization: Bearer $OPENAI_API_KEY"
创建聊天补全
选择与您的首选方法对应的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
使用环境变量配置OpenAI客户端：
# Create a chat completion using the OpenAI SDK
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
completion = client.chat.completions.create(
model="openai.gpt-oss-120b",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
print(completion.choices[0].message)
- HTTP request
-
向以下地址发出 POST 请求/v1/chat/completions：
# Create a chat completion
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
curl -X POST $OPENAI_BASE_URL/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "openai.gpt-oss-120b",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
}'
流式传输
要逐步接收回复，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Stream chat completion responses incrementally using the OpenAI SDK
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
stream = client.chat.completions.create(
model="openai.gpt-oss-120b",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="")
- HTTP request
-
向发出 POST 请求/v1/chat/completions，stream设置为true：
# Stream chat completion responses incrementally
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
curl -X POST $OPENAI_BASE_URL/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "openai.gpt-oss-120b",
"messages": [
{"role": "user", "content": "Tell me a story"}
],
"stream": true
}'
使用基底运行时端点完成聊天
bedrock-runtime终端节点支持 AWS Sigv4 身份验证和 Amazon Bedrock API 密钥身份验证。
列出可用型号
要列出bedrock-runtime端点上可用的模型，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
from openai import OpenAI
import os
client = OpenAI(
base_url="https://bedrock-runtime.us-east-1.amazonaws.com/v1",
api_key=os.environ.get("AWS_BEARER_TOKEN_BEDROCK")
)
models = client.models.list()
for model in models.data:
print(model.id)
- HTTP request
-
curl -X GET "https://bedrock-runtime.us-east-1.amazonaws.com/v1/models" \
-H "Authorization: Bearer $AWS_BEARER_TOKEN_BEDROCK"
创建聊天补全
选择与您的首选方法对应的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
将OpenAI客户端配置为指向bedrock-runtime终端：
from openai import OpenAI
import os
client = OpenAI(
base_url="https://bedrock-runtime.us-east-1.amazonaws.com/v1",
api_key=os.environ.get("AWS_BEARER_TOKEN_BEDROCK")
)
response = client.chat.completions.create(
model="us.anthropic.claude-sonnet-4-6",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)
- HTTP request (API key)
-
curl -X POST "https://bedrock-runtime.us-east-1.amazonaws.com/v1/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AWS_BEARER_TOKEN_BEDROCK" \
-d '{
"model": "us.anthropic.claude-sonnet-4-6",
"messages": [{"role": "user", "content": "Hello"}]
}'
- HTTP request (SigV4)
-
curl -X POST "https://bedrock-runtime.us-east-1.amazonaws.com/v1/chat/completions" \
-H "Content-Type: application/json" \
--aws-sigv4 "aws:amz:us-east-1:bedrock" \
--user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
-d '{
"model": "us.anthropic.claude-sonnet-4-6",
"messages": [{"role": "user", "content": "Hello"}]
}'
有关bedrock-runtime终端节点支持的型号、地区和高级功能的更多详细信息，请参阅聊天完成 API（旧版参考）。
在聊天补全中添加护栏
要在模型输入和响应中加入保护措施，请在运行模型调用时通过在请求正文中添加以下额外参数作为字段来应用护栏：
有关 Amazon Bedrock 护栏中这些参数的更多信息，请参阅测试护栏。
要查看在 OpenAI 聊天补全中使用护栏的示例，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
import openai
from openai import OpenAIError
# Endpoint for Amazon Bedrock Runtime
bedrock_endpoint = "https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"
# Model ID
model_id = "openai.gpt-oss-20b-1:0"
# Replace with actual values
bedrock_api_key = "$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}")
- OpenAI SDK (Java)
-
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);