Add bedrock (#2119)
Co-authored-by: takatost <takatost@users.noreply.github.com> Co-authored-by: Garfield Dai <dai.hai@foxmail.com> Co-authored-by: Joel <iamjoel007@gmail.com> Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com> Co-authored-by: Charlie.Wei <luowei@cvte.com> Co-authored-by: crazywoola <427733928@qq.com> Co-authored-by: Benjamin <benjaminx@gmail.com>
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117
api/tests/integration_tests/model_runtime/bedrock/test_llm.py
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117
api/tests/integration_tests/model_runtime/bedrock/test_llm.py
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import os
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from typing import Generator
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import pytest
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from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
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from core.model_runtime.entities.message_entities import AssistantPromptMessage, SystemPromptMessage, UserPromptMessage
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from core.model_runtime.errors.validate import CredentialsValidateFailedError
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from core.model_runtime.model_providers.bedrock.llm.llm import BedrockLargeLanguageModel
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def test_validate_credentials():
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model = BedrockLargeLanguageModel()
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with pytest.raises(CredentialsValidateFailedError):
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model.validate_credentials(
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model='meta.llama2-13b-chat-v1',
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credentials={
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'anthropic_api_key': 'invalid_key'
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}
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)
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model.validate_credentials(
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model='meta.llama2-13b-chat-v1',
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credentials={
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"aws_region": os.getenv("AWS_REGION"),
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"aws_access_key": os.getenv("AWS_ACCESS_KEY"),
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"aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
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}
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)
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def test_invoke_model():
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model = BedrockLargeLanguageModel()
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response = model.invoke(
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model='meta.llama2-13b-chat-v1',
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credentials={
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"aws_region": os.getenv("AWS_REGION"),
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"aws_access_key": os.getenv("AWS_ACCESS_KEY"),
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"aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
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},
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prompt_messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Hello World!'
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)
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],
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model_parameters={
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'temperature': 0.0,
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'top_p': 1.0,
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'max_tokens_to_sample': 10
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},
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stop=['How'],
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stream=False,
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user="abc-123"
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)
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assert isinstance(response, LLMResult)
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assert len(response.message.content) > 0
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def test_invoke_stream_model():
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model = BedrockLargeLanguageModel()
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response = model.invoke(
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model='meta.llama2-13b-chat-v1',
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credentials={
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"aws_region": os.getenv("AWS_REGION"),
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"aws_access_key": os.getenv("AWS_ACCESS_KEY"),
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"aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
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},
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prompt_messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Hello World!'
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)
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],
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model_parameters={
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'temperature': 0.0,
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'max_tokens_to_sample': 100
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},
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stream=True,
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user="abc-123"
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)
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assert isinstance(response, Generator)
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for chunk in response:
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print(chunk)
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assert isinstance(chunk, LLMResultChunk)
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assert isinstance(chunk.delta, LLMResultChunkDelta)
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assert isinstance(chunk.delta.message, AssistantPromptMessage)
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assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
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def test_get_num_tokens():
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model = BedrockLargeLanguageModel()
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num_tokens = model.get_num_tokens(
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model='meta.llama2-13b-chat-v1',
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credentials = {
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"aws_region": os.getenv("AWS_REGION"),
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"aws_access_key": os.getenv("AWS_ACCESS_KEY"),
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"aws_secret_access_key": os.getenv("AWS_SECRET_ACCESS_KEY")
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},
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messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Hello World!'
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)
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]
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)
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assert num_tokens == 18
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