Agentic RAG with SingleStore

A significant advancement in this domain is the concept of agentic RAG, which introduces intelligent agents capable of dynamic decision-making and tool utilization to refine information retrieval and generation processes. When combined with SingleStore's capabilities, agentic RAG offers a powerful framework for developing sophisticated AI applications.

Understanding RAG

RAG is an innovative approach in natural language processing that combines the strengths of retrieval-based and generation-based models to enhance the quality of generated text. This hybrid model leverages vast amounts of information available in large-scale databases or knowledge bases, enabling the generation of more accurate and contextually relevant responses. If you would like to learn more about traditional RAG, this blog post by one of my colleagues goes into greater depth on RAG itself.

What is an AI agent?

An artificial intelligence (AI) agent is a software program capable of autonomously performing tasks on behalf of a user or another system by designing its workflow, breaking down large tasks into subtasks, and utilizing available tools. AI agents can encompass a wide range of functionalities beyond natural language processing, including decision-making, problem-solving, interacting with external environments and executing actions.

What is agentic RAG?

Agentic RAG extends traditional RAG by incorporating autonomous agents that can:

• Reason and plan. Decompose complex user queries into manageable sub-questions
• Utilize tools. Employ various tools, such as search engines or databases, to gather pertinent information
• Filter dataset. Run basic to advanced SQL queries to filter down dataset before running vector search, ensuring only relevant data is passed through to the LLM
• Reflect and adapt. Assess the relevance of retrieved data and adjust strategies accordingly

This approach enables LLMs to handle intricate tasks more effectively, providing nuanced and contextually accurate responses.

SingleStore's unified querying capabilities

SingleStore is a modern distributed SQL database that seamlessly integrates vector search functionalities. This integration allows developers to perform both traditional SQL operations and vector similarity searches within a single query, streamlining the development of AI-driven applications.

Advantages of combining SQL and vector search in a single query

• Simplified development workflow. By enabling combined SQL and vector search operations, SingleStore reduces the complexity associated with managing separate systems for relational and vector data. This unification simplifies the development process and minimizes potential integration issues.
• Enhanced performance. SingleStore's architecture is optimized for both Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP) workloads. The ability to perform vector searches directly within the database eliminates the need for data movement between systems, resulting in faster query execution and reduced latency.
• Comprehensive data analysis. Integrating vector search with SQL allows for complex queries that combine semantic similarity assessments with traditional relational data operations. This capability is particularly beneficial for applications requiring hybrid search functionalities, like combining full-text search with vector similarity and SQL to deliver more relevant results.

Implementing agentic RAG with SingleStore

To leverage SingleStore's unified querying capabilities in an agentic RAG framework, consider the following steps:

• Data preparation. Store your textual data and their corresponding vector embeddings in SingleStore's vector data type. This setup facilitates efficient vector similarity searches.
• Indexing. Utilize SingleStore's vector indexing features to create indexes on your vector columns. Indexed Approximate Nearest Neighbor (ANN) search can significantly enhance query performance, especially with large datasets.
• Query execution. Develop SQL queries that incorporate both traditional relational operations and vector similarity searches. For example, you can perform a vector range search to find records with embeddings similar to a query vector and apply additional SQL filters to refine the results.
• Agent integration. Implement intelligent agents that can dynamically construct and execute these unified queries based on the context of the user query. These agents can decompose complex questions, determine the appropriate retrieval strategies and utilize SingleStore's querying capabilities to fetch and process the necessary information.

Try agentic RAG with SingleStore

Integrating agentic RAG with SingleStore's unified querying capabilities offers a unique and powerful framework for developing modern AI applications. The ability to perform SQL and vector search operations in one place simplifies development workflows, enhances performance, and enables comprehensive data analysis. By leveraging these capabilities, developers can create intelligent systems capable of delivering accurate and contextually relevant information, thereby significantly improving user experience and satisfaction.

Ready to modernize your RAG applications? Start your free trial with SingleStore today and unlock the full potential of unified querying in your applications.