dify/api/tests/integration_tests/model_runtime/bedrock/test_llm.py

import os
from typing import Generator

import pytest
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import AssistantPromptMessage, SystemPromptMessage, UserPromptMessage
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.bedrock.llm.llm import BedrockLargeLanguageModel

def test_validate_credentials():
    model = BedrockLargeLanguageModel()

    with pytest.raises(CredentialsValidateFailedError):
        model.validate_credentials(
            model='meta.llama2-13b-chat-v1',
            credentials={
                'anthropic_api_key': 'invalid_key'
            }
        )

    model.validate_credentials(
        model='meta.llama2-13b-chat-v1',
        credentials={
            "aws_region": os.getenv("AWS_REGION"),
            "aws_access_key": os.getenv("AWS_ACCESS_KEY"),
            "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
        }
    )

def test_invoke_model():
    model = BedrockLargeLanguageModel()

    response = model.invoke(
        model='meta.llama2-13b-chat-v1',
        credentials={
            "aws_region": os.getenv("AWS_REGION"),
            "aws_access_key": os.getenv("AWS_ACCESS_KEY"),
            "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'top_p': 1.0,
            'max_tokens_to_sample': 10
        },
        stop=['How'],
        stream=False,
        user="abc-123"
    )

    assert isinstance(response, LLMResult)
    assert len(response.message.content) > 0

def test_invoke_stream_model():
    model = BedrockLargeLanguageModel()

    response = model.invoke(
        model='meta.llama2-13b-chat-v1',
        credentials={
            "aws_region": os.getenv("AWS_REGION"),
            "aws_access_key": os.getenv("AWS_ACCESS_KEY"),
            "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'max_tokens_to_sample': 100
        },
        stream=True,
        user="abc-123"
    )

    assert isinstance(response, Generator)

    for chunk in response:
        print(chunk)
        assert isinstance(chunk, LLMResultChunk)
        assert isinstance(chunk.delta, LLMResultChunkDelta)
        assert isinstance(chunk.delta.message, AssistantPromptMessage)
        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True


def test_get_num_tokens():
    model = BedrockLargeLanguageModel()

    num_tokens = model.get_num_tokens(
        model='meta.llama2-13b-chat-v1',
        credentials = {
            "aws_region": os.getenv("AWS_REGION"),
            "aws_access_key": os.getenv("AWS_ACCESS_KEY"),
            "aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
        },
        messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content='Hello World!'
            )
        ]
    )

    assert num_tokens == 18