Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps

What if you could visually design and deploy a complete RAG pipeline in minutes, without writing complex code?

#1about 5 minutes

Addressing the core challenges of large language models

LLMs face issues with hallucinations, data security, and cost control when they lack relevant, private context.

#2about 2 minutes

Solving LLM limitations with RAG and vector databases

The Retrieval-Augmented Generation (RAG) pattern uses a vector database to perform semantic searches and inject relevant, real-time context into LLM prompts.

#3about 3 minutes

Comparing generic LLM responses with RAG-powered results

A demo of a bicycle recommendation service shows how RAG provides relevant, contextual product suggestions from a private catalog versus generic, unhelpful ones.

#4about 3 minutes

Leveraging Astra DB for high-relevance vector search

Astra DB, built on Apache Cassandra, provides a scalable, enterprise-ready vector database with the high-performance JVector search algorithm.

#5about 2 minutes

Introducing RAGStack as an opinionated development framework

RAGStack is a curated framework that simplifies GenAI development by integrating key tools like LangChain and LlamaIndex for use in enterprise settings.

#6about 3 minutes

How to easily vectorize data in the Astra DB UI

A demonstration shows how to upload a JSON dataset to an Astra DB collection and enable automatic vectorization for semantic search with just a few clicks.

#7about 4 minutes

Building enterprise-ready RAG applications with RAGStack

RAGStack ensures enterprise readiness by providing dependency-tested and vulnerability-scanned packages, demonstrated through a code example of a RAG application.

#8about 6 minutes

Building RAG pipelines visually with the Langflow platform

A demonstration of Langflow shows how to build, configure, and execute a complete RAG pipeline using a drag-and-drop interface without writing complex code.

#9about 1 minute