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Mistral AI 大型 (24.07) 参数和推理 - Amazon Bedrock
支持的模型请求和响应示例

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Mistral AI 大型 (24.07) 参数和推理

您可以使用 Mistral AI 聊天完成 API 创建对话应用程序。您还可以将 Amazon Bedrock Converse API 与该模型结合使用。您可以使用工具进行函数调用。

提示

您可以将Mistral AI聊天完成 API 与基本推理操作（InvokeModel或 InvokeModelWithResponseStream）配合使用。但是，我们建议您使用 Converse API 在应用程序中实施消息传递。Converse API 提供了一组统一的参数，适用于所有支持消息的模型。有关更多信息，请参阅 使用匡威 API 进行推理。

Mistral AI 模型在 Apache 2.0 许可下可用。有关使用 Mistral AI 模型的更多信息，请参阅 Mistral AI 文档。

支持的模型

您可以将以下 Mistral AI 模型与本页上的代码示例一起使用...

  • Mistral Large 2 (24.07)

您需要获取想要使用的模型的模型 ID。要获取模型 ID，请参阅 Amazon Bedrock 中支持的根基模型。

请求和响应示例

Request

Mistral AI Large（24.07）调用模型示例。

import boto3 
import json

bedrock = session.client('bedrock-runtime', 'us-west-2') 
response = bedrock.invoke_model( 
        modelId='mistral.mistral-large-2407-v1:0', 
        body=json.dumps({
            'messages': [ 
                { 
                    'role': 'user', 
                    'content': 'which llm are you?' 
                } 
             ], 
         }) 
       ) 

print(json.dumps(json.loads(response['body']), indent=4))
Converse

Mistral AI Large（24.07）converse 示例。

import boto3 
import json

bedrock = session.client('bedrock-runtime', 'us-west-2')
response = bedrock.converse( 
    modelId='mistral.mistral-large-2407-v1:0', 
    messages=[ 
        { 
            'role': 'user', 
            'content': [ 
                { 
                    'text': 'which llm are you?' 
                } 
             ] 
          } 
     ] 
  ) 

print(json.dumps(json.loads(response['body']), indent=4))
invoke_model_with_response_stream

Mistral AI Large（24.07）invoke_model_with_response_stream 示例。

import boto3 
import json

bedrock = session.client('bedrock-runtime', 'us-west-2')
response = bedrock.invoke_model_with_response_stream(
    "body": json.dumps({
        "messages": [{"role": "user", "content": "What is the best French cheese?"}],
        }),
        "modelId":"mistral.mistral-large-2407-v1:0"
)

stream = response.get('body')
if stream:
        for event in stream:
            chunk=event.get('chunk')
            if chunk:
                chunk_obj=json.loads(chunk.get('bytes').decode())
                print(chunk_obj)
converse_stream

Mistral AI Large（24.07）converse_stream 示例。

import boto3 
import json

bedrock = session.client('bedrock-runtime', 'us-west-2')
mistral_params = {
    "messages": [{
            "role": "user","content": [{"text": "What is the best French cheese? "}]
         }],
            "modelId":"mistral.mistral-large-2407-v1:0",
        }
    response = bedrock.converse_stream(**mistral_params)
    stream = response.get('stream')
    if stream:
        for event in stream:

            if 'messageStart' in event:
                print(f"\nRole: {event['messageStart']['role']}")

            if 'contentBlockDelta' in event:
                print(event['contentBlockDelta']['delta']['text'], end="")

            if 'messageStop' in event:
                print(f"\nStop reason: {event['messageStop']['stopReason']}")

            if 'metadata' in event:
                metadata = event['metadata']
                if 'usage' in metadata:
                    print("\nToken usage ... ")
                    print(f"Input tokens: {metadata['usage']['inputTokens']}")
                    print(
                        f":Output tokens: {metadata['usage']['outputTokens']}")
                    print(f":Total tokens: {metadata['usage']['totalTokens']}")
                if 'metrics' in event['metadata']:
                    print(
                        f"Latency: {metadata['metrics']['latencyMs']} milliseconds")
JSON Output

Mistral AI Large（24.07）JSON 输出示例。

import boto3 
import json

bedrock = session.client('bedrock-runtime', 'us-west-2')
mistral_params = {
        "body": json.dumps({
            "messages": [{"role": "user", "content": "What is the best French meal? Return the name and the ingredients in short JSON object."}]
        }),
        "modelId":"mistral.mistral-large-2407-v1:0",
    }
response = bedrock.invoke_model(**mistral_params)

body = response.get('body').read().decode('utf-8')
print(json.loads(body))
Tooling

Mistral AI Large（24.07）工具示例。

data = {
    'transaction_id': ['T1001', 'T1002', 'T1003', 'T1004', 'T1005'],
    'customer_id': ['C001', 'C002', 'C003', 'C002', 'C001'],
    'payment_amount': [125.50, 89.99, 120.00, 54.30, 210.20],
    'payment_date': ['2021-10-05', '2021-10-06', '2021-10-07', '2021-10-05', '2021-10-08'],
    'payment_status': ['Paid', 'Unpaid', 'Paid', 'Paid', 'Pending']
}

# Create DataFrame
df = pd.DataFrame(data)


def retrieve_payment_status(df: data, transaction_id: str) -> str:
    if transaction_id in df.transaction_id.values: 
        return json.dumps({'status': df[df.transaction_id == transaction_id].payment_status.item()})
    return json.dumps({'error': 'transaction id not found.'})

def retrieve_payment_date(df: data, transaction_id: str) -> str:
    if transaction_id in df.transaction_id.values: 
        return json.dumps({'date': df[df.transaction_id == transaction_id].payment_date.item()})
    return json.dumps({'error': 'transaction id not found.'})

tools = [
    {
        "type": "function",
        "function": {
            "name": "retrieve_payment_status",
            "description": "Get payment status of a transaction",
            "parameters": {
                "type": "object",
                "properties": {
                    "transaction_id": {
                        "type": "string",
                        "description": "The transaction id.",
                    }
                },
                "required": ["transaction_id"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "retrieve_payment_date",
            "description": "Get payment date of a transaction",
            "parameters": {
                "type": "object",
                "properties": {
                    "transaction_id": {
                        "type": "string",
                        "description": "The transaction id.",
                    }
                },
                "required": ["transaction_id"],
            },
        },
    }
]

names_to_functions = {
    'retrieve_payment_status': functools.partial(retrieve_payment_status, df=df),
    'retrieve_payment_date': functools.partial(retrieve_payment_date, df=df)
}



test_tool_input = "What's the status of my transaction T1001?"
message = [{"role": "user", "content": test_tool_input}]


def invoke_bedrock_mistral_tool():
   
    mistral_params = {
        "body": json.dumps({
            "messages": message,
            "tools": tools           
        }),
        "modelId":"mistral.mistral-large-2407-v1:0",
    }
    response = bedrock.invoke_model(**mistral_params)
    body = response.get('body').read().decode('utf-8')
    body = json.loads(body)
    choices = body.get("choices")
    message.append(choices[0].get("message"))

    tool_call = choices[0].get("message").get("tool_calls")[0]
    function_name = tool_call.get("function").get("name")
    function_params = json.loads(tool_call.get("function").get("arguments"))
    print("\nfunction_name: ", function_name, "\nfunction_params: ", function_params)
    function_result = names_to_functions[function_name](**function_params)

    message.append({"role": "tool", "content": function_result, "tool_call_id":tool_call.get("id")})
   
    new_mistral_params = {
        "body": json.dumps({
                "messages": message,
                "tools": tools           
        }),
        "modelId":"mistral.mistral-large-2407-v1:0",
    }
    response = bedrock.invoke_model(**new_mistral_params)
    body = response.get('body').read().decode('utf-8')
    body = json.loads(body)
    print(body)
invoke_bedrock_mistral_tool()