Simplify RAG App Development
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.
BENEFITS
Proven RAG Architecture
Iterate and Experiment Faster
Improve 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.
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.
Continuous Evolution, Seamless Updates
The architecture is continually updated to include the latest RAG techniques (such as GraphRAG), to improve GenAI relevancy. The RAG stack adds new open-source software to provide enterprise users a predictable upgrade path as new techniques emerge.
Cutting-Edge Advancements, Robust Search Capabilities
A stack that supports searching across all data types with faster, more accurate results. Improve search with Knowledge Graphs and implement hybrid searches with ColBERT.
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.
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 easily with the increase in data and user base, and lower the cost of LLMs by caching a large percentage of calls.
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.
Reduce Complexity with Langflow
Adding Langflow into your RAG stack enables any GenAI app builder to design RAG applications, switch between embedding modes, LLMs, retrievers, etc. easily and test with real data without the need to write code or learn the ins and outs of new frameworks.