How to Build a Better Movie Search with AI Using LLMs and Vector Search
This video offers a practical guide for developers to easily build GenAI applications for an advanced movie seach app using DataStax's GenAI products. We cover using OpenAI's text embedding model for vector similarity searches on IMDb data, loading and vectorizing movie synopses, and designing a RAG (Retrieval Augmented Generation) pipeline in Langflow for enhanced search results. Langflow’s drag-and-drop IDE simplifies RAG-based app creation with powerful open-source tools and integrations to accelerate GenAI development. Additionally, we show how to build an interactive UI using the Vercel AI SDK.
⭐️Check out Langflow on GitHub: https://dtsx.io/3JN5OJH
ABOUT LANGFLOW:
➡️DataStax+Langflow makes it 100x easier for all developers; So you can focus on what you actually want to focus on: Building your apps!
➡️It's an open source, visual framework for building GenAI and RAG apps with LangChain and prebuilt components for any kind of app, API, or data source.
➡️You can easily drag and drop components, make connections, see how data will flow, and swap pieces in and out to iterate incredibly fast with fine-grained control over AI applications within seconds (instead of weeks and months).
➡️Langflow then creates the workflow code and LangChain objects you need to get into production quickly.
Resources:
DataStax Developer Hub: https://dtsx.io/devhub
DataStax Blog: https://dtsx.io/howto
Try Langflow: https://dtsx.io/trylangflow
Try Astra DB: https://dtsx.io/40kQpI6
Stay in touch:
Join our Discord Community: / discord
Follow us on X: https://x.com/DataStaxDevs
Chapters:
[00:00:00] Introduction to GenAI
[00:00:18] Keyword vs Vector Search
[00:01:41] Astra DB Overview
[00:02:28] Creating a Vector Collection
[00:03:38] Embeddings Explained
[00:04:04] Loading Data for Search
[00:05:00] Movie Search Demo
[00:05:57] Langflow and RAG Pipelines
[00:07:00] Building a RAG Pipeline
[00:08:22] API Integration
[00:09:03] Movies++ Demo
[00:10:22] Generating UI and Trailers
[00:11:01] Conclusion