Elevate Your AI Development with a RAG Stack

A RAG stack reference architecture that includes the best open-source tools for implementing RAG, giving developers a comprehensive GenAI stack leveraging Langflow, LangChain, LlamaIndex, and more.

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Benefits of a RAG Stack

Proven RAG Architecture

A RAG stack improves developer productivity and system performance with orchestration and prompt templates, unstructured data store abstraction, natural language to structured query abstraction, agent memory abstraction, and LLM caching abstraction. Our advanced RAG system optimizes the retrieval process, ensuring you get the most relevant context for your AI applications.

Improved GenAI Performance

A RAG stack designed to enhance GenAI app performance with tested techniques for prompt engineering, prompt retrieval and different data types—reducing hallucinations and improving contextual relevance. Our vector database contributes to this by enabling efficient semantic querying and semantic search capabilities. Unlock knowledge graphs to boost the RAG stack's performance. Knowledge graphs contribute to more accurate and contextually relevant results.

Continuous Evolution, Seamless Updates

The architecture is continually updated to include the latest RAG techniques (such as graph RAG), to improve GenAI relevancy. The RAG stack adds new open-source software to provide users a predictable upgrade path as new techniques emerge. This commitment to evolution ensures that your RAG systems stay at the cutting edge of AI technology.

Cutting-Edge Advancements, Robust Search Capabilities

A stack that supports searching across all data types with faster, more accurate results. Automate knowledge graph creation to improve search and implement hybrid searches with ColBERT for superior performance and relevance. Our advanced retrieval systems can handle complex queries, making it easier to extract valuable insights from your data store, including unstructured data.

Enterprise Governance and Compliance with Support

Develop with confidence! Our RAG stack architecture is backed with enterprise support when running with Astra DB, and it meets HIPAA, TRUSTe, SOC2 compliance requirements. Ensure your applications adhere to essential security and privacy regulations. The RAG stack architecture is also crucial for enabling enterprise adoption of generative AI technologies, facilitating the integration of large language models (LLMs) and addressing the financial implications of successful AI deployment.

Scalability and Cost-Effectiveness

Scale easily with the increase in data and usage when using Astra. This reference architecture is designed to improve response times, scale effortlessly with the increase in data and user base, and lower the cost of LLMs by caching a large percentage of calls. As enterprises transition from experimental innovation budgets to software and labor budgets, our RAG stack's cost-effectiveness becomes even more crucial for true enterprise adoption.

For Developers

Simplify and Accelerate RAG App Development

Langflow's visual IDE gives 'drag-and-drop' access to prebuilt RAG components and flows. Build, iterate, and deploy AI applications with ease, even with limited coding experience. This workflow automation platform streamlines the development process, allowing developers to focus on innovation rather than implementation details.

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Reduce Complexity with Langflow Workflow Automation Platform

Adding Langflow into your RAG stack enables any GenAI app builder to design RAG applications, easily switch between embedding modes, LLMs, retrievers, etc., and test with real data without the need to write code or learn the ins and outs of new frameworks. Langflow is a powerful workflow automation platform that simplifies the development process, making it easier to create sophisticated RAG systems.

RAG Stack Success Stories

Resources

VIDEO

Unleash Your App’s Full Potential with GenAI Powered by a RAG Stack with Langflow

Give your AI a turbo boost, making it more reliable and insightful without drowning in code and complex data setups.

Watch Now

BLOG POST

Finding the Right Movie with Semantic Search: How We Built Movies++

Learn how we used a combination of Astra DB and Langflow together with OpenAI and TMDB to build a GenAI app that revolutionizes the movie search experience.

Learn More

WHITEPAPER

Reference Architecture: Demystifying LLM-based Systems

Design patterns, architectural examples, and tools to help navigate the complex world of LLMs—and get generative AI apps to production.

Get the Whitepaper

Get Started with RAG Now

Accelerate your use of GenAI today with Langflow, the visual IDE that gives drag-and-drop access to prebuilt RAG components and flows. Build, iterate, and deploy AI applications with ease.

GET STARTED WITH LANGFLOW Read Langflow Docs

Frequently Asked Questions

What are the key components of a RAG stack, and how do they contribute to building effective generative AI applications?

A RAG stack includes tools like Langflow, LangChain, and LlamaIndex. Langflow offers a visual interface for building and managing RAG workflows, simplifying the development process. LangChain integrates with various retrieval data sources, while LlamaIndex enhances data retrieval and indexing. Together, these components streamline development, improve performance, and handle complex queries effectively. Additionally, the integration of advanced retrieval systems plays a crucial role in enhancing enterprise productivity by managing intricate data workflows more efficiently.

How does a RAG stack improve developer productivity and system performance with proprietary data?

A RAG stack boosts productivity with features like orchestration, prompt templates, and data storage abstraction. It simplifies the management of components and caching for LLMs, which speeds up response times and reduces costs. These features help developers iterate faster and create more efficient applications. As enterprises transition from experimental innovation budgets to software and labor budgets in the context of generative AI adoption, a RAG stack's capabilities become even more critical in meeting boardroom expectations and achieving substantial labor cost savings.

What are the benefits of using Langflow within a RAG stack?

Langflow provides a visual 'drag-and-drop' interface for RAG development, allowing easy construction and deployment of GenAI applications. It simplifies switching between different models and retrievers and speeds up testing with real data, making the development process more accessible and efficient. As a workflow automation platform, Langflow streamlines the entire RAG pipeline, from data extraction to model outputs.

How does a RAG stack ensure continuous evolution and relevance?

A RAG stack is designed for ongoing updates, incorporating new RAG techniques and open-source tools as they become available. This ensures that applications remain up-to-date with the latest advancements and continue to perform effectively. By staying at the forefront of emerging advanced RAG technologies, a RAG stack helps businesses maintain a competitive edge in the rapidly evolving AI landscape.

What are the enterprise governance and compliance features of a RAG stack?

A RAG stack supports enterprise governance with compliance to standards like HIPAA, TRUSTe, and SOC2 when used with Astra DB. It provides robust support and ensures that applications meet essential security and privacy regulations. This commitment to compliance is crucial for true enterprise adoption, particularly in industries dealing with sensitive data like healthcare or finance.