Leveraging Vector Datastores for Generative AI Applications on AWS

Generative AI is transforming industries with its ability to answer questions, write stories, create art, and generate code. Many AWS customers are exploring how to leverage generative AI within their organizations. A key component of this strategy involves utilizing domain-specific data, which provides unique insights into their businesses and industries. Vector datastores are becoming increasingly important in generative AI applications, and this article explores their role and how AWS solutions can help you harness the power of generative AI.

At the heart of generative AI applications are Large Language Models (LLMs). These machine learning models are trained on vast amounts of content, such as data available on the internet. After training on publicly accessible data, LLMs are considered foundation models (FMs). These models can be adapted and optimized for various use cases. Amazon SageMaker JumpStart offers pre-trained proprietary and open-source foundation models, including Stability AI's Text2Image and Hugging Face's Text2Text Flan T-5. Amazon Bedrock simplifies building and scaling generative AI applications by providing API access to models from AI21 Labs, Anthropic, Stability AI, and Amazon Titan.

While generative AI applications can leverage FMs for broad knowledge, customizing them is crucial for accurate results in specific or specialized domains. Prompt engineering, or in-context learning, is a simple method to ground generative AI applications in a specific domain and improve accuracy. While it doesn't eliminate hallucinations entirely, it narrows the semantic meaning to your domain. FMs infer the next token based on a set of input tokens. The more context you provide, the more likely the inferred token will be relevant. The prompt used to query the FM should contain input tokens and as much contextually relevant data as possible. Vector datastores help design prompts with semantically relevant inputs, a method known as Retrieval Augmented Generation (RAG). In practice, you might design a prompt using contextually relevant personalized data and data with similar semantics.

RAG uses embeddings (vectors) to enhance the accuracy of generative AI applications. Domain-specific data is split into semantic elements, and FMs calculate vectors for these elements. These vectors are stored in a vector datastore, enabling similarity searches. In a generative AI application, a user's query is split into semantic elements, and the vector datastore is queried to find the nearest neighbors in the vector space. This provides contextually similar semantic elements, which are added to the prompt. This process helps the LLM build on your domain-specific context, increasing the likelihood of accurate and contextually relevant output.

Several factors are crucial when considering vector datastores. The amount of domain-specific data and the process of splitting it into semantic elements determine the number of embeddings the vector datastore needs to support. This can impact indexing efficiency and performance at scale. The dimensionality of embedding vectors also matters. Different FMs generate vectors with varying dimensions. Higher dimensions can represent richer context, but there are diminishing returns and increased query latency. Data governance is another key consideration, as domain-specific datasets may contain sensitive data. It's important to control the data flow through systems that create, store, and query embeddings.

AWS offers several options for vector datastores. Aurora PostgreSQL-compatible relational database with the pgvector extension provides vector data types and similarity search operators. Amazon OpenSearch Service with the k-NN plugin and Amazon OpenSearch Serverless's vector engine also offer vector capabilities. The choice depends on factors like where you currently store data, familiarity with these technologies, vector dimension scaling, the number of embeddings, and performance needs.

The best AWS vector datastore depends on your specific use case and priorities. If you're heavily invested in relational databases, especially PostgreSQL, Aurora PostgreSQL with the pgvector extension is a good option. For large-scale vector data repositories, OpenSearch Service is a strong choice due to its distributed nature. OpenSearch Serverless's vector engine provides an easy way to get started with vector similarity search. Consider Amazon Kendra for a fully managed semantic search experience. LangChain supports Aurora PostgreSQL with pgvector, OpenSearch Serverless's vector engine, and OpenSearch Service with k-NN.

Embeddings should be stored and managed close to your domain-specific datasets. This allows you to combine embedding data with other metadata without external data sources. Storing embeddings close to the source data simplifies data pipelines and keeps embeddings up to date. Aurora PostgreSQL with pgvector, OpenSearch Serverless's vector engine, and OpenSearch Service with the k-NN plugin are all viable options. You can combine prompt engineering with foundation models from SageMaker JumpStart and Amazon Bedrock to build innovative generative AI solutions. This is a rapidly evolving field, so stay updated with the latest developments. Start building generative AI applications on AWS today!