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World Congress 2024 • Aug 20, 2024 • Session details

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

David Leconte , Michel de Ru

Tired of LLM hallucinations and data security risks? Learn to rapidly build secure, low-code RAG pipelines using Astra DB and Langflow.

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#1 about 5 min

Challenges of applying large language models to enterprise data

Why public models struggle to provide relevance and present security risks when paired with proprietary information.

#2 about 3 min

Enabling contextual responses with retrieval-augmented generation and vector databases

How semantic search capabilities enable models to ingest and reference proprietary business data in real time.

#3 about 3 min

Demonstrating bicycle recommendations with and without retrieval-augmented generation

A comparison of large language model responses using generic public data versus a specific enterprise product catalog.

#4 about 4 min

Scaling semantic search with Astra DB and Apache Cassandra

How deploying enterprise-ready databases equipped with vector search algorithms improves generative query relevancy.

#5 about 2 min

Simplifying generative AI deployments using the RagStack opinionated framework

An introduction to an opinionated framework that streamlines enterprise AI integrations via embedding models and document parsers.

#6 about 4 min

Vectorizing enterprise catalog data within the Astra DB interface

A walkthrough of configuring a vector-enabled database collection to orchestrate semantic searches across JSON files.

#7 about 4 min

Managing complex model dependencies using the RagStack AI framework

How deploying a curated framework mitigates open-source vulnerabilities and stabilizes production pipelines.

#8 about 6 min

Building low-code AI application pipelines visually using Langflow interfaces

Leveraging a visual approach to integrate embedding providers, vector stores, and conversational interfaces without manual coding.

#9 about 1 min

Prioritizing enterprise data in generative AI tooling and development

Final takeaways validating that maintaining proprietary information yields a stronger market edge than generic models.

Matching moments

1:24 min

Expanding AI capabilities using retrieval-augmented generation

Cedric Clyburn Cedric Clyburn · WWC 2024

3:36 min

Overview of generative AI and the presentation agenda

juarezjunior juarezjunior · WWC 2024

2:08 min

Navigating the components of the modern generative AI stack

Julián Duque Julián Duque · WWC 2025

3:16 min

Integrating vector stores and RAG capabilities in Spring AI

Sandra Ahlgrimm Sandra Ahlgrimm +1 · WWC 2024

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 · WWC 2024

7:28 min

Accelerating product features using generative large language models

David Singleton David Singleton +1 · Coffee With Developers

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