Elevate Your LLMs with RAG at Scale Using AWS Glue for Apache Spark | Amazon Onboarding with Learning Manager Chanci Turner

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Large language models (LLMs) represent a significant advancement in deep learning, having been trained on extensive datasets. These models exhibit remarkable versatility, capable of executing various tasks such as answering inquiries, summarizing content, translating languages, and completing sentences. The transformative potential of LLMs in content creation and the utilization of search engines and virtual assistants is immense. Retrieval Augmented Generation (RAG) enhances the output of an LLM by referencing a reliable knowledge base outside its training data prior to generating a response. While LLMs are built on a vast amount of information and utilize billions of parameters to produce unique outputs, RAG further amplifies their capabilities by integrating specific domains or an organization’s internal knowledge base—eliminating the need for retraining the LLMs. RAG is an efficient and economical method to enhance LLM performance, ensuring that results are pertinent, precise, and contextually relevant.

RAG incorporates an information retrieval component that utilizes user input to extract information from external sources, commonly referred to as external data. This data may come in diverse formats, including files, database entries, or lengthy texts. To facilitate comprehension by generative AI models, embedding language models are employed to convert this external data into numerical representations, which are then stored in a vector database, creating a knowledge library.

Implementing RAG

Implementing RAG necessitates additional data engineering processes:

• Scalable retrieval indexes must accommodate large text corpora that cover essential knowledge domains.
• Data preprocessing is crucial to enable semantic search during inference, encompassing normalization, vectorization, and index optimization.
• The indexes must continually expand with incoming documents, and data pipelines should seamlessly integrate new data at scale.
• Varied data sources amplify the requirement for customizable cleaning and transformation logic to address the unique characteristics of different data types.

In this article, we will delve into constructing a reusable RAG data pipeline utilizing LangChain—an open-source framework for LLM-based applications—and its integration with AWS Glue and Amazon OpenSearch Serverless. The resulting solution serves as a reference architecture for scalable RAG indexing and deployment. We provide sample notebooks that address ingestion, transformation, vectorization, and index management, empowering teams to convert diverse data into high-performing RAG applications.

Data Preprocessing for RAG

Effective data preprocessing is vital for responsible retrieval from external data using RAG. High-quality, clean data leads to enhanced accuracy in RAG outputs, while privacy and ethical considerations necessitate thorough data filtering, thus laying the groundwork for LLMs with RAG to maximize their potential in downstream applications. To ensure effective retrieval from external data, a standard practice is to first sanitize and clean the documents. Tools like Amazon Comprehend or AWS Glue‘s sensitive data detection capabilities can identify sensitive information, which can then be processed with Spark. Subsequently, the documents should be divided into manageable segments, converted to embeddings, and stored in a vector index while maintaining a reference to the original document. This method enables the determination of semantic similarity between queries and the text from the data sources.

Solution Overview

This solution leverages LangChain in conjunction with AWS Glue for Apache Spark and Amazon OpenSearch Serverless. By utilizing Apache Spark’s distributed capabilities and the flexibility of PySpark scripting, we ensure scalability and customization. OpenSearch Serverless serves as a sample vector store, and we will employ the Llama 3.1 model.

Key advantages of this solution include:

• The ability to efficiently manage data cleaning, sanitizing, and quality control alongside chunking and embedding.
• The capacity to build and oversee an incremental data pipeline that updates embeddings in the Vectorstore at scale.
• The option to select from a wide array of embedding models.
• The flexibility to incorporate various data sources, including databases, data warehouses, and SaaS applications supported in AWS Glue.

This solution encompasses:

• Processing unstructured data such as HTML, Markdown, and text files through Apache Spark, which includes distributed data cleaning, sanitizing, chunking, and vector embedding for downstream use.
• Integrating everything into a Spark pipeline that incrementally processes sources and publishes vectors to an OpenSearch Serverless.
• Querying the indexed content using your preferred LLM model to provide natural language responses.

Prerequisites

Before proceeding with this tutorial, you must set up the following AWS resources:

• An Amazon Simple Storage Service (Amazon S3) bucket for data storage.
• An AWS Identity and Access Management (IAM) role for your AWS Glue notebook, as detailed in the guide on setting up IAM permissions for AWS Glue Studio. This role requires permissions for OpenSearch Service Serverless. Here’s an example policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "OpenSearchServerless",
            "Effect": "Allow",
            "Action": [
                "aoss:APIAccessAll",
                "aoss:CreateAccessPolicy",
                "aoss:CreateCollection",
                "aoss:CreateSecurityPolicy",
                "aoss:DeleteAccessPolicy",
                "aoss:DeleteCollection",
                "aoss:DeleteSecurityPolicy",
                "aoss:ListCollections"
            ],
            "Resource": "*"
        }
    ]
}

To launch an AWS Glue Studio notebook, follow these steps:

1. Download the Jupyter Notebook file.
2. In the AWS Glue console, select Notebooks from the navigation pane.
3. Under Create job, opt for Notebook.
4. For Options, choose Upload Notebook.
5. Select Create notebook, which will start the notebook in a minute.

Run the first two cells to configure an AWS Glue interactive session, thereby setting up the necessary parameters for your AWS Glue notebook.

Vector Store Setup

The initial step is to create a vector store which enables efficient vector similarity searches through specialized indexes. RAG enhances LLMs by utilizing an external knowledge base, which is typically constructed using a vector database populated with vector-encoded knowledge articles. In this example, Amazon OpenSearch Serverless will be used due to its simplicity and scalability, supporting vector searches with low latency and the capability to handle billions of vectors. For more information, refer to Amazon OpenSearch Service’s vector database capabilities.

To set up OpenSearch Serverless, complete the following:

1. In the vector store setup cell, replace <your-iam-role-arn> with your IAM role’s Amazon Resource Name (ARN) and <region> with your AWS Region, then execute the cell.
2. Run the next cell to establish the OpenSearch Serverless collection, along with the necessary security and access policies.

You have successfully provisioned OpenSearch Serverless, and you are now prepared to inject documents.

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