使用 Amazon Bedrock 构建个性化学习伴侣

我现在正在攻读硕士学位，我一直想找到方法来减少每天的学习时间。瞧！这是我的解决方案：使用 Amazon Bedrock 创建一个学习伙伴。

我们将利用 Amazon Bedrock 来利用 GPT-4 或 T5 等基础模型 (FM) 的力量。

这些模型将帮助我们创建一个生成式人工智能，可以回答用户对我的硕士课程中各种主题的查询，例如量子物理、机器学习等。我们将探索如何微调模型、实施高级提示工程，并利用检索增强生成 (RAG) 为学生提供准确的答案。

让我们开始吧！

第 1 步：在 AWS 上设置您的环境

首先，请确保您的 AWS 账户已设置有访问 Amazon Bedrock、S3 和 Lambda 所需的权限（在我发现必须存入借记卡后，我才了解到这一点:( ) .您将使用 Amazon S3、Lambda 和 Bedrock 等 AWS 服务。

• 创建一个S3 Bucket来存储您的学习材料
• 这将允许模型访问材料以进行微调和检索。
• 转到 Amazon S3 控制台并创建一个新存储桶，例如“study-materials”。

将教育内容上传到 S3。就我而言，我创建了合成数据来添加与我的硕士课程相关的数据。您可以根据需要创建自己的数据集或添加 Kaggle 中的其他数据集。

[
    {
        "topic": "Advanced Economics",
        "question": "How does the Lucas Critique challenge traditional macroeconomic policy analysis?",
        "answer": "The Lucas Critique argues that traditional macroeconomic models' parameters are not policy-invariant because economic agents adjust their behavior based on expected policy changes, making historical relationships unreliable for policy evaluation."
    },
    {
        "topic": "Quantum Physics",
        "question": "Explain quantum entanglement and its implications for quantum computing.",
        "answer": "Quantum entanglement is a physical phenomenon where pairs of particles remain fundamentally connected regardless of distance. This property enables quantum computers to perform certain calculations exponentially faster than classical computers through quantum parallelism and superdense coding."
    },
    {
        "topic": "Advanced Statistics",
        "question": "What is the difference between frequentist and Bayesian approaches to statistical inference?",
        "answer": "Frequentist inference treats parameters as fixed and data as random, using probability to describe long-run frequency of events. Bayesian inference treats parameters as random variables with prior distributions, updated through data to form posterior distributions, allowing direct probability statements about parameters."
    },
    {
        "topic": "Machine Learning",
        "question": "How do transformers solve the long-range dependency problem in sequence modeling?",
        "answer": "Transformers use self-attention mechanisms to directly model relationships between all positions in a sequence, eliminating the need for recurrent connections. This allows parallel processing and better capture of long-range dependencies through multi-head attention and positional encodings."
    },
    {
        "topic": "Molecular Biology",
        "question": "What are the implications of epigenetic inheritance for evolutionary theory?",
        "answer": "Epigenetic inheritance challenges the traditional neo-Darwinian model by demonstrating that heritable changes in gene expression can occur without DNA sequence alterations, suggesting a Lamarckian component to evolution through environmentally-induced modifications."
    },
    {
        "topic": "Advanced Computer Architecture",
        "question": "How do non-volatile memory architectures impact traditional memory hierarchy design?",
        "answer": "Non-volatile memory architectures blur the traditional distinction between storage and memory, enabling persistent memory systems that combine storage durability with memory-like performance, requiring fundamental redesign of memory hierarchies and system software."
    }
]

第 2 步：利用 Amazon Bedrock 构建基础模型

然后启动 Amazon Bedrock：

• 前往 Amazon Bedrock 控制台。
• 创建一个新项目并选择您想要的基础模型（例如 GPT-3、T5）。
• 选择您的用例，在本例中为学习伙伴。
• 选择微调选项（如果需要）并上传数据集（来自 S3 的教育内容）进行微调。
• 微调基础模型：

Bedrock 将自动微调您数据集上的基础模型。例如，如果您使用 GPT-3，Amazon Bedrock 将对其进行调整，以更好地理解教育内容并为特定主题生成准确的答案。

这是一个使用 Amazon Bedrock SDK 来微调模型的快速 Python 代码片段：

import boto3

# Initialize Bedrock client
client = boto3.client("bedrock-runtime")

# Define S3 path for your dataset
dataset_path = 's3://study-materials/my-educational-dataset.json'

# Fine-tune the model
response = client.start_training(
    modelName="GPT-3",
    datasetLocation=dataset_path,
    trainingParameters={"batch_size": 16, "epochs": 5}
)
print(response)

保存微调后的模型：微调后，模型将被保存并准备部署。您可以在 Amazon S3 存储桶中名为fine-tuned-model 的新文件夹下找到它。

第 3 步：实施检索增强生成 (RAG)

1。设置 Amazon Lambda 函数：

• Lambda 将处理请求并与微调模型交互以生成响应。
• Lambda函数会根据用户的查询从S3中获取相关学习资料，并使用RAG生成准确的答案。

用于生成答案的 Lambda 代码： 以下示例说明了如何配置 Lambda 函数以使用微调模型来生成答案：

[
    {
        "topic": "Advanced Economics",
        "question": "How does the Lucas Critique challenge traditional macroeconomic policy analysis?",
        "answer": "The Lucas Critique argues that traditional macroeconomic models' parameters are not policy-invariant because economic agents adjust their behavior based on expected policy changes, making historical relationships unreliable for policy evaluation."
    },
    {
        "topic": "Quantum Physics",
        "question": "Explain quantum entanglement and its implications for quantum computing.",
        "answer": "Quantum entanglement is a physical phenomenon where pairs of particles remain fundamentally connected regardless of distance. This property enables quantum computers to perform certain calculations exponentially faster than classical computers through quantum parallelism and superdense coding."
    },
    {
        "topic": "Advanced Statistics",
        "question": "What is the difference between frequentist and Bayesian approaches to statistical inference?",
        "answer": "Frequentist inference treats parameters as fixed and data as random, using probability to describe long-run frequency of events. Bayesian inference treats parameters as random variables with prior distributions, updated through data to form posterior distributions, allowing direct probability statements about parameters."
    },
    {
        "topic": "Machine Learning",
        "question": "How do transformers solve the long-range dependency problem in sequence modeling?",
        "answer": "Transformers use self-attention mechanisms to directly model relationships between all positions in a sequence, eliminating the need for recurrent connections. This allows parallel processing and better capture of long-range dependencies through multi-head attention and positional encodings."
    },
    {
        "topic": "Molecular Biology",
        "question": "What are the implications of epigenetic inheritance for evolutionary theory?",
        "answer": "Epigenetic inheritance challenges the traditional neo-Darwinian model by demonstrating that heritable changes in gene expression can occur without DNA sequence alterations, suggesting a Lamarckian component to evolution through environmentally-induced modifications."
    },
    {
        "topic": "Advanced Computer Architecture",
        "question": "How do non-volatile memory architectures impact traditional memory hierarchy design?",
        "answer": "Non-volatile memory architectures blur the traditional distinction between storage and memory, enabling persistent memory systems that combine storage durability with memory-like performance, requiring fundamental redesign of memory hierarchies and system software."
    }
]

3。部署 Lambda 函数： 在 AWS 上部署此 Lambda 函数。它将通过API网关调用来处理实时用户查询。

第 4 步：通过 API 网关公开模型

创建 API 网关：

转到 API Gateway 控制台并创建一个新的 REST API。
设置 POST 端点来调用处理答案生成的 Lambda 函数。

部署 API：

部署 API 并使用来自 AWS 的自定义域或默认 URL 使其可公开访问。

第 5 步：构建 Streamlit 界面

最后，构建一个简单的 Streamlit 应用程序，以允许用户与您的学习伙伴互动。

import boto3

# Initialize Bedrock client
client = boto3.client("bedrock-runtime")

# Define S3 path for your dataset
dataset_path = 's3://study-materials/my-educational-dataset.json'

# Fine-tune the model
response = client.start_training(
    modelName="GPT-3",
    datasetLocation=dataset_path,
    trainingParameters={"batch_size": 16, "epochs": 5}
)
print(response)

您可以在 AWS EC2 或 Elastic Beanstalk 上托管此 Streamlit 应用程序。

如果一切顺利，恭喜你。你刚刚成为了你的学习伙伴。如果我必须评估这个项目，我可以为我的合成数据添加更多示例（废话？），或者获取另一个与我的目标完美契合的教育数据集。

感谢您的阅读！让我知道你的想法！

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