本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
使用聊天完成 API 進行推論
OpenAI 聊天完成 API 會使用 Amazon Bedrock 模型產生對話回應。您可以在 bedrock-mantle和 bedrock-runtime端點上使用聊天完成 API。我們建議您盡可能使用bedrock-mantle端點。如需完整的 API 詳細資訊，請參閱OpenAI聊天完成文件。
| 端點 |
基本 URL |
身分驗證 |
bedrock-mantle (建議) |
https://bedrock-mantle.{region}.api.aws/v1/chat/completions |
Amazon Bedrock API 金鑰或 AWS 登入資料 |
bedrock-runtime |
https://bedrock-runtime.{region}.amazonaws.com/v1/chat/completions |
AWS 登入資料 (SigV4) 或 Amazon Bedrock API 金鑰 |
每個端點都有自己的每個模型字符配額。如需套用至每個端點流量之配額的詳細資訊，請參閱 bedrock-mantle 端點的配額和 bedrock-runtime 端點的配額。
使用 bedrock-mantle 端點完成聊天
bedrock-mantle 端點支援 Amazon Bedrock API 金鑰身分驗證和 OpenAI SDK。
列出可用的模型
若要列出bedrock-mantle端點上可用的模型，請選擇您偏好方法的索引標籤，然後遵循下列步驟：
- 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"
建立聊天完成
選擇您偏好方法的索引標籤，然後遵循下列步驟：
- OpenAI SDK (Python)
-
使用環境變數設定OpenAI用戶端：
# 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 提出 POST 請求/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!"}
]
}'
串流
若要逐步接收回應，請選擇您偏好方法的標籤，然後遵循下列步驟：
- 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 提出 POST 請求/v1/chat/completions，並將 stream設定為 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
}'
使用底ock-runtime 端點完成聊天
bedrock-runtime 端點支援 AWS SigV4 身分驗證和 Amazon Bedrock API 金鑰身分驗證。
列出可用的模型
若要列出bedrock-runtime端點上可用的模型，請選擇您偏好方法的索引標籤，然後遵循下列步驟：
- 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"
建立聊天完成
選擇您偏好方法的索引標籤，然後遵循下列步驟：
- OpenAI SDK (Python)
-
設定OpenAI用戶端以指向bedrock-runtime端點：
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:$AWS_SECRET_ACCESS_KEY" \
-d '{
"model": "us.anthropic.claude-sonnet-4-6",
"messages": [{"role": "user", "content": "Hello"}]
}'
如需端點支援之模型、區域和進階功能的詳細資訊bedrock-runtime，請參閱 聊天完成 API （舊版參考）。
在聊天完成時包含防護機制
若要在模型輸入和回應中包含保護措施，請在執行模型調用時套用防護機制，方法是在請求內文中包含下列額外參數做為欄位：
如需 Amazon Bedrock 防護機制中這些參數的詳細資訊，請參閱 測試您的防護機制。
若要查看在 OpenAI 聊天完成時使用防護機制的範例，請選擇您偏好方法的索引標籤，然後遵循以下步驟：
- OpenAI SDK (Python)
-
import openai
from openai import OpenAIError
# Endpoint for Amazon Bedrock Runtime
bedrock_endpoint = "https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"
# Model ID
model_id = "openai.gpt-oss-20b-1:0"
# Replace with actual values
bedrock_api_key = "$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);