Dieter Flick & Michel de Ru

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?

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

Final takeaways and how to get started

The key to successful GenAI is leveraging your own data, and you can get started by trying Astra DB for free.

Related jobs
Jobs that call for the skills explored in this talk.

Featured Partners

Related Videos

See all videos
Building Real-Time AI/ML Agents with Distributed Data using Apache Cassandra and Astra DB28:20

Building Real-Time AI/ML Agents with Distributed Data using Apache Cassandra and Astra DB

Dieter Flick

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation21:17

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Carl Lapierre

Large Language Models ❤️ Knowledge Graphs29:11

Large Language Models ❤️ Knowledge Graphs

Michael Hunger

Building AI Applications with LangChain and Node.js26:42

Building AI Applications with LangChain and Node.js

Julián Duque

Langchain4J - An Introduction for Impatient Developers31:59

Langchain4J - An Introduction for Impatient Developers

Juarez Junior

WeAreDevelopers LIVE - Vector Similarity Search Patterns for Efficiency and more1:05:44

WeAreDevelopers LIVE - Vector Similarity Search Patterns for Efficiency and more

Chris Heilmann, Daniel Cranney, Raphael De Lio & Developer Advocate at Redis

Enter the Brave New World of GenAI with Vector Search1:00:56

Enter the Brave New World of GenAI with Vector Search

Mary Grygleski

RAG like a hero with Docling28:17

RAG like a hero with Docling

Alex Soto & Markus Eisele

From learning to earning

Jobs that call for the skills explored in this talk.