使用 Amazon Bedrock 建立個人化學習伴侶

我現在正在攻讀碩士學位，我一直想找到方法來減少每天的學習時間。瞧！這是我的解決方案：使用 Amazon Bedrock 建立一個學習夥伴。

我們將利用 Amazon Bedrock 來利用 GPT-4 或 T5 等基礎模型 (FM) 的力量。

這些模型將幫助我們創建一個生成式人工智慧，可以回答使用者對我的碩士課程中各種主題的查詢，例如量子物理、機器學習等。我們將探索如何微調模型、實施高階提示工程，並利用檢索增強生成 (RAG) 為學生提供準確的答案。

讓我們開始吧！

第 1 步：在 AWS 上設定您的環境

首先，請確保您的AWS 帳戶已設定有訪問Amazon Bedrock、S3 和Lambda 所需的權限（在我發現必須存入借記卡後，我才了解到這一點:( ) .您將使用Amazon S3、Lambda 和Bedrock 等AWS 服務。

建立一個S3 Bucket來儲存您的學習材料
這將允許模型存取材料以進行微調和檢索。
前往 Amazon S3 控制台並建立一個新儲存桶，例如「study-materials」。

將教育內容上傳到 S3。就我而言，我創建了合成數據來添加與我的碩士課程相關的數據。您可以根據需要建立自己的資料集或新增 Kaggle 中的其他資料集。

[
    {
        "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 建立基礎模型

然後啟動 Amazon Bedrock：

前往 Amazon Bedrock 主機。
建立一個新專案並選擇您想要的基礎模型（例如 GPT-3、T5）。
選擇您的用例，在本例中為學習夥伴。
選擇微調選項（如果需要）並上傳資料集（來自 S3 的教育內容）進行微調。
微調基礎模型：

Bedrock 將自動微調您資料集上的基礎模型。例如，如果您使用 GPT-3，Amazon Bedrock 將對其進行調整，以更好地理解教育內容並為特定主題產生準確的答案。

這是一個使用 Amazon Bedrock SDK 來微調模型的快速 Python 程式碼片段：

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)

保存微調後的模型：微調後，模型將被保存並準備部署。您可以在 Amazon S3 儲存桶中名為fine-tuned-model 的新資料夾下找到它。

第 3 步：實作檢索增強產生 (RAG)

1。設定 Amazon Lambda 函數：

• Lambda 將處理請求並與微調模型互動以產生回應。
• Lambda函數會根據使用者的查詢從S3取得相關學習資料，並使用RAG產生精確的答案。

用於產生答案的 Lambda 程式碼： 以下範例說明如何設定 Lambda 函數以使用微調模型來產生答案：

[
    {
        "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 函數： 在 AWS 上部署此 Lambda 函數。它將透過API網關呼叫來處理即時用戶查詢。

第 4 步：透過 API 閘道公開模型

建立 API 閘道：

前往 API Gateway 控制台並建立新的 REST API。
設定 POST 端點來呼叫處理答案所產生的 La​​mbda 函數。

部署 API：

部署 API 並使用來自 AWS 的自訂網域或預設 URL 使其可公開存取。

第 5 步：建立 Streamlit 介面

最後，建立一個簡單的 Streamlit 應用程序，以允許用戶與您的學習夥伴互動。

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 上託管此 Streamlit 應用程式。

如果一切順利，恭喜你。你剛剛成為了你的學習夥伴。如果我必須評估這個項目，我可以為我的合成數據添加更多示例（廢話？），或者獲取另一個與我的目標完美契合的教育數據集。

感謝您的閱讀！讓我知道你的想法！

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