我現(xiàn)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 創(chuàng)建一個學習伙伴。

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(chuàng)建一個生成式人工智能，可以回答用戶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(diào)模型、實施高級提示工程，并利用檢索增強生成 (RAG) 為學生提供準確的答案。

讓我們開始吧！

第 1 步：在 AWS 上設置您的環(huán)境

首先，請確保您的 AWS 賬戶已設置有訪問 Amazon Bedrock、S3 和 Lambda 所需的權(quán)限（在我發(fā)現(xiàn)必須存入借記卡后，我才了解到這一點:( ) .您將使用 Amazon S3、Lambda 和 Bedrock 等 AWS 服務。

• 創(chuàng)建一個S3 Bucket來存儲您的學習材料
• 這將允許模型訪問材料以進行微調(diào)和檢索。
• 轉(zhuǎn)到 Amazon S3 控制臺并創(chuàng)建一個新存儲桶，例如“study-materials”。

將教育內(nèi)容上傳到 S3。就我而言，我創(chuàng)建了合成數(shù)據(jù)來添加與我的碩士課程相關的數(shù)據(jù)。您可以根據(jù)需要創(chuàng)建自己的數(shù)據(jù)集或添加 Kaggle 中的其他數(shù)據(jù)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 構(gòu)建基礎模型

然后啟動 Amazon Bedrock：

• 前往 Amazon Bedrock 控制臺。
• 創(chuàng)建一個新項目并選擇您想要的基礎模型（例如 GPT-3、T5）。
• 選擇您的用例，在本例中為學習伙伴。
• 選擇微調(diào)選項（如果需要）并上傳數(shù)據(jù)集（來自 S3 的教育內(nèi)容）進行微調(diào)。
• 微調(diào)基礎模型：

Bedrock 將自動微調(diào)您數(shù)據(jù)集上的基礎模型。例如，如果您使用 GPT-3，Amazon Bedrock 將對其進行調(diào)整，以更好地理解教育內(nèi)容并為特定主題生成準確的答案。

這是一個使用 Amazon Bedrock SDK 來微調(diào)模型的快速 Python 代碼片段：

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(diào)后的模型：微調(diào)后，模型將被保存并準備部署。您可以在 Amazon S3 存儲桶中名為fine-tuned-model 的新文件夾下找到它。

第 3 步：實施檢索增強生成 (RAG)

1。設置 Amazon Lambda 函數(shù)：

• Lambda 將處理請求并與微調(diào)模型交互以生成響應。
• Lambda函數(shù)會根據(jù)用戶的查詢從S3中獲取相關學習資料，并使用RAG生成準確的答案。

用于生成答案的 Lambda 代碼： 以下示例說明了如何配置 Lambda 函數(shù)以使用微調(diào)模型來生成答案：

[
    {
        "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 函數(shù)： 在 AWS 上部署此 Lambda 函數(shù)。它將通過API網(wǎng)關調(diào)用來處理實時用戶查詢。

第 4 步：通過 API 網(wǎng)關公開模型

創(chuàng)建 API 網(wǎng)關：

轉(zhuǎn)到 API Gateway 控制臺并創(chuàng)建一個新的 REST API。
設置 POST 端點來調(diào)用處理答案生成的 Lambda 函數(shù)。

部署 API：

部署 API 并使用來自 AWS 的自定義域或默認 URL 使其可公開訪問。

第 5 步：構(gòu)建 Streamlit 界面

最后，構(gòu)建一個簡單的 Streamlit 應用程序，以允許用戶與您的學習伙伴互動。

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 上托管此 Streamlit 應用程序。

如果一切順利，恭喜你。你剛剛成為了你的學習伙伴。如果我必須評估這個項目，我可以為我的合成數(shù)據(jù)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(shù)據(jù)集。

感謝您的閱讀！讓我知道你的想法！

以上是使用 Amazon Bedrock 構(gòu)建個性化學習伴侶的詳細內(nèi)容。更多信息請關注PHP中文網(wǎng)其他相關文章！