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

import os
from collections.abc 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