Use Aurora as a Knowledge Base for Amazon Bedrock for RAG

Generative AI has revolutionized the way we interact with technology, enabling applications to produce human-like text, images, and even code. However, leveraging generative AI for practical, real-world applications often requires integrating proprietary data and ensuring efficient data retrieval. In this blog post, we’ll explore how Amazon Bedrock and PostgreSQL with the PGVector extension can be used to build advanced generative AI applications that are both personalized and efficient.

Understanding the Challenge

While large language models (LLMs) like GPT-3 and others are trained on vast amounts of public data, they lack access to private or proprietary information. This limitation becomes evident when building applications that require personalized responses or company-specific data.

For example, a travel booking company might want to provide personalized hotel recommendations based on a user’s past bookings, loyalty status, or special discounts. An LLM trained solely on public data cannot access this proprietary information, leading to generic or even incorrect responses—a phenomenon known as “hallucination” in AI.

The Role of Databases in Generative AI

To overcome these limitations, integrating a knowledge base that contains proprietary data is essential. Databases like PostgreSQL can store structured and unstructured data, such as customer profiles, transaction histories, and company documents. By connecting an LLM to this database, applications can generate responses that are both accurate and personalized.

Introducing Vectors and Embeddings

To effectively integrate database information with LLMs, we use vector embeddings. An embedding is a numerical representation of data—be it text, images, or other forms—that captures its semantic meaning. By converting data into vectors, we can perform similarity searches to find relevant information based on user queries.

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation combines the strengths of information retrieval and generative AI. The RAG pipeline involves:

1. Document Ingestion: Uploading documents to a storage system.
2. Chunking: Breaking down documents into manageable pieces.
3. Vectorization: Converting chunks into embeddings using an embedding model.
4. Storage: Storing embeddings in a vector database.
5. Similarity Matching: Comparing query embeddings with stored embeddings to find relevant data.
6. Response Generation: Using the retrieved data to generate accurate and context-rich responses via the LLM.

Building with Amazon Bedrock and PostgreSQL

Amazon Bedrock

Amazon Bedrock is a fully managed service that provides access to foundation models from leading AI companies. It simplifies the deployment of generative AI applications by handling the underlying infrastructure and offering a choice of models suitable for various tasks.

Key Features:

• Foundation Models as a Service: Access to pre-trained models without the need for extensive setup.
• Knowledge Bases: A feature that allows you to integrate proprietary data with foundation models seamlessly.
• Serverless Architecture: Pay-as-you-go model ensures cost efficiency.

PostgreSQL with PGVector

PostgreSQL, enhanced with the PGVector extension, becomes a powerful vector database capable of handling high-dimensional embeddings.

Advantages:

• Extensibility: PostgreSQL’s architecture allows for extensions like PGVector to add new functionalities.
• Scalability: Features like Amazon Aurora’s Serverless V2 and Optimized Reads help scale workloads efficiently.
• Integrated Data Storage: Store both traditional relational data and vector embeddings in one place.

Examples of use cases

Text-Based Knowledge Integration

Scenario: Enhancing an LLM’s knowledge with the latest PostgreSQL 16 release notes, which are not part of the model’s training data.

Steps:

1. Upload Document: Save the PostgreSQL 16 release notes as a PDF and upload it to an Amazon S3 bucket.
2. Create Knowledge Base: Use Amazon Bedrock to create a knowledge base that points to the S3 bucket.
3. Vectorize and Store: Bedrock automatically vectorizes the document and stores the embeddings in PostgreSQL with PGVector.
4. Query the Model: Ask the LLM about the new features in PostgreSQL 16.
5. Retrieve Enhanced Response: The model provides an accurate answer augmented by the data from the knowledge base.

Image-Based Semantic Search

Scenario: An animal shelter wants to improve its pet adoption website by enabling users to search for pets using images or text descriptions.

Steps:

1. Prepare Data: Collect images and descriptions of available pets.
2. Vectorize Data: Use an embedding model to convert both images and text into embeddings.
3. Store in PostgreSQL: Save the embeddings using PGVector.
4. Implement Search Interface: Allow users to upload an image or enter a description of the pet they’re interested in.
5. Retrieve Results: Perform a similarity search to find matching pets and display them to the user.

Tools Used:

• LangChain: An open-source framework that simplifies working with embeddings and vector databases.
• Jupyter Notebooks: For prototyping and running the application code.

Processing Multimodal Financial Documents

Scenario: A financial institution needs to process loan applications that include text, tables, and images.

Challenges:

• Traditional text embeddings may not capture information from tables and images effectively.
• Financial documents require precise data extraction for compliance and decision-making.

Solution:

1. Segment Documents: Separate the document into text, tables, and images.
2. Summarize Content: Use specialized models to summarize each segment accurately.
3. Vectorize Summaries: Convert the summaries into embeddings.
4. Store and Retrieve: Save embeddings in PostgreSQL and perform similarity searches when querying.
5. Generate Responses: Use the LLM to provide detailed and accurate information based on the retrieved data.

Performance Optimization with Amazon Aurora

For applications dealing with large volumes of vector data, performance is crucial. Amazon Aurora with PostgreSQL offers features like:

• Optimized Reads: Improves query throughput by leveraging local SSD storage, resulting in up to 9x performance gains for workloads exceeding memory.
• Serverless Scaling: Automatically adjusts capacity based on workload demands.
• Enhanced Indexing: Improvements in PGVector have led to significant reductions in index build times and query latency.

Benchmark Highlights:

• Index Build Time: Over 150x improvement, enabling faster data ingestion.
• Query Latency: Up to 30x reduction in P99 latency for high-recall queries.

Integrating generative AI with proprietary data unlocks new possibilities for creating personalized and context-aware applications. By leveraging Amazon Bedrock and PostgreSQL with PGVector, developers can build scalable, efficient, and intelligent systems that meet the demands of real-world use cases.

Key Takeaways:

• Seamless Integration: Amazon Bedrock’s knowledge bases simplify connecting proprietary data with LLMs.
• Versatility: PostgreSQL with PGVector handles various data types, including text and images.
• Performance: Amazon Aurora’s features ensure applications remain responsive, even with large datasets.

Whether you’re enhancing customer experiences, automating document processing, or building advanced search functionalities, the combination of Amazon Bedrock and PostgreSQL provides a robust foundation for your generative AI applications.

Get Started Today

As a Select AWS Partner with a validated solution and an Advanced AWS Lambda Delivery practice, Zircon is ready to help you explore these technologies to transform your applications with generative AI. With accessible tools and managed services, integrating advanced AI capabilities has never been easier.