我現(xiàn)在正在攻讀碩士學(xué)位，我一直想找到方法來(lái)減少每天的學(xué)習(xí)時(shí)間。瞧！這是我的解決方案：使用 Amazon Bedrock 建立一個(gè)學(xué)習(xí)夥伴。

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

這些模型將幫助我們創(chuàng)建一個(gè)生成式人工智慧，可以回答使用者對(duì)我的碩士課程中各種主題的查詢(xún)，例如量子物理、機(jī)器學(xué)習(xí)等。我們將探索如何微調(diào)模型、實(shí)施高階提示工程，並利用檢索增強(qiáng)生成 (RAG) 為學(xué)生提供準(zhǔn)確的答案。

讓我們開(kāi)始吧！

第 1 步：在 AWS 上設(shè)定您的環(huán)境

首先，請(qǐng)確保您的AWS 帳戶(hù)已設(shè)定有訪問(wèn)Amazon Bedrock、S3 和Lambda 所需的權(quán)限（在我發(fā)現(xiàn)必須存入借記卡後，我才了解到這一點(diǎn):( ) .您將使用Amazon S3、Lambda 和Bedrock 等AWS 服務(wù)。

建立一個(gè)S3 Bucket來(lái)儲(chǔ)存您的學(xué)習(xí)材料
這將允許模型存取材料以進(jìn)行微調(diào)和檢索。
前往 Amazon S3 控制臺(tái)並建立一個(gè)新儲(chǔ)存桶，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(lái)添加與我的碩士課程相關(guān)的數(shù)據(jù)。您可以根據(jù)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(chǔ)模型

然後啟動(dòng) Amazon Bedrock：

前往 Amazon Bedrock 主機(jī)。
建立一個(gè)新專(zhuān)案並選擇您想要的基礎(chǔ)模型（例如 GPT-3、T5）。
選擇您的用例，在本例中為學(xué)習(xí)夥伴。
選擇微調(diào)選項(xiàng)（如果需要）並上傳資料集（來(lái)自 S3 的教育內(nèi)容）進(jìn)行微調(diào)。
微調(diào)基礎(chǔ)模型：

Bedrock 將自動(dòng)微調(diào)您資料集上的基礎(chǔ)模型。例如，如果您使用 GPT-3，Amazon Bedrock 將對(duì)其進(jìn)行調(diào)整，以更好地理解教育內(nèi)容並為特定主題產(chǎn)生準(zhǔn)確的答案。

這是一個(gè)使用 Amazon Bedrock SDK 來(lái)微調(diào)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(diào)後的模型：微調(diào)後，模型將被保存並準(zhǔn)備部署。您可以在 Amazon S3 儲(chǔ)存桶中名為fine-tuned-model 的新資料夾下找到它。

第 3 步：實(shí)作檢索增強(qiáng)產(chǎn)生 (RAG)

1。設(shè)定 Amazon Lambda 函數(shù)：

• Lambda 將處理請(qǐng)求並與微調(diào)模型互動(dòng)以產(chǎn)生回應(yīng)。
• Lambda函數(shù)會(huì)根據(jù)使用者的查詢(xún)從S3取得相關(guān)學(xué)習(xí)資料，並使用RAG產(chǎn)生精確的答案。

用於產(chǎn)生答案的 Lambda 程式碼： 以下範(fàn)例說(shuō)明如何設(shè)定 Lambda 函數(shù)以使用微調(diào)模型來(lái)產(chǎn)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(shù)： 在 AWS 上部署此 Lambda 函數(shù)。它將透過(guò)API網(wǎng)關(guān)呼叫來(lái)處理即時(shí)用戶(hù)查詢(xún)。

第 4 步：透過(guò) API 閘道公開(kāi)模型

建立 API 閘道：

前往 API Gateway 控制臺(tái)並建立新的 REST API。
設(shè)定 POST 端點(diǎn)來(lái)呼叫處理答案所產(chǎn)生的 La??mbda 函數(shù)。

部署 API：

部署 API 並使用來(lái)自 AWS 的自訂網(wǎng)域或預(yù)設(shè) URL 使其可公開(kāi)存取。

第 5 步：建立 Streamlit 介面

最後，建立一個(gè)簡(jiǎn)單的 Streamlit 應(yīng)用程序，以允許用戶(hù)與您的學(xué)習(xí)夥伴互動(dòng)。

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(yīng)用程式。

如果一切順利，恭喜你。你剛剛成為了你的學(xué)習(xí)夥伴。如果我必須評(píng)估這個(gè)項(xiàng)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(gè)與我的目標(biāo)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 建立個(gè)人化學(xué)習(xí)伴侶的詳細(xì)內(nèi)容。更多資訊請(qǐng)關(guān)注PHP中文網(wǎng)其他相關(guān)文章！