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# Mistral AIParameter dan inferensi besar (24,07)
API penyelesaian Mistral AI obrolan memungkinkan Anda membuat aplikasi percakapan. Anda juga dapat menggunakan Amazon Bedrock Converse API dengan model ini. Anda dapat menggunakan alat untuk melakukan panggilan fungsi.
**Tip**
Anda dapat menggunakan API penyelesaian Mistral AI obrolan dengan operasi inferensi dasar ([InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html)atau [InvokeModelWithResponseStream](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html)). Namun, kami menyarankan Anda menggunakan Converse API untuk mengimplementasikan pesan dalam aplikasi Anda. ConverseAPI menyediakan serangkaian parameter terpadu yang bekerja di semua model yang mendukung pesan. Untuk informasi selengkapnya, lihat [Melakukan percakapan dengan operasi Converse API](conversation-inference.md).
Mistral AImodel tersedia di bawah [lisensi Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt). Untuk informasi selengkapnya tentang penggunaan Mistral AI model, lihat [Mistral AIdokumentasi](https://docs.mistral.ai/).
**Topics**
+ [Model yang didukung](#mistral-supported-models-chat-completion)
+ [Contoh Permintaan dan Respons](#model-parameters-mistral-large-2407-request-response)
## Model yang didukung
Anda dapat menggunakan Mistral AI model berikut dengan contoh kode di halaman ini..
+ Mistral Large 2 (24.07)
Anda memerlukan ID model untuk model yang ingin Anda gunakan. Untuk mendapatkan ID model, lihat[Model pondasi yang didukung di Amazon Bedrock](models-supported.md).
## Contoh Permintaan dan Respons
------
#### [ Request ]
Mistral AIContoh model panggilan besar (24,07).
```
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 AIContoh sebaliknya besar (24,07).
```
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\$1model\$1with\$1response\$1stream ]
Mistral AIContoh invoke\$1model\$1with\$1response\$1stream besar (24,07).
```
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\$1stream ]
Mistral AIContoh converse\$1stream besar (24,07).
```
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 AIContoh keluaran JSON besar (24,07).
```
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 AIContoh alat besar (24,07).
```
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()
```
------