Build Multimodal AI RAG/Search in Python using Weviate and ImageBind

HowCanAIHelp · Intermediate ·🔍 RAG & Vector Search ·1y ago

About this lesson

Build Multimodal AI RAG/Search in Python using Weviate and ImageBind Traditional RAG systems rely heavily on text. But the real world is multimodal. In this tutorial, we go beyond text-based retrieval to build a Multimodal Search Engine that can semantically understand and search video content using text, audio prompts, and image inputs. We will leverage Meta's open-source ImageBind to create joint embeddings across different modalities and store them in Weaviate, a production-ready vector database. 💻 GET THE SOURCE CODE: https://github.com/AminaKeldibek/VideoCompanion/tree/multimodal_search What You Will Build: A prototype that allows users to search a video archive using any sense. Example 1: Input an image of a lion 🖼️ The system returns video segments containing lions. Example 2: Input an audio file of a drumbeat 🎵 The system finds timestamps in videos where drums are playing. 🧠 Who Is This For? AI Engineers exploring multimodal embeddings. Founders building AI-native search products. ML teams working on video understanding and semantic search. New to RAG? Start with my previous text-only RAG tutorial: https://www.youtube.com/watch?v=GrmS-_vt96o&t=10s ⏱️ Timecodes 0:00 - Intro: The Multimodal Goal 00:58 - Demo: Searching Video with Audio & Images 02:39 - Architecture: Steps to build multimodal RAG 03:24 - Deep Dive: Meta’s ImageBind Model 05:31 - Deep Dive: How Weaviate Handles Multimodal Data 06:28 - Setup: ImageBind + Weaviate via Docker 08:49 - Vector Indexing Parameters 12:04 - Data Pipeline: Extracting Text, Audio, and Frames from Video 13:48 - Preprocessing Multimodal Inputs 14:27 - Generating & Storing Video Embeddings 14:45 - Running Multimodal Queries 📌 Tech Stack We use an entirely open-source stack for this project: Meta ImageBind, Weaviate Vector DB, Python, Docker, Gradio, OpenAI Whisper, and OpenCV. 📎 References ImageBind Paper/Code: https://github.com/facebookresearch/ImageBind Weaviate Documentation: https://weaviate.i

Original Description

Build Multimodal AI RAG/Search in Python using Weviate and ImageBind Traditional RAG systems rely heavily on text. But the real world is multimodal. In this tutorial, we go beyond text-based retrieval to build a Multimodal Search Engine that can semantically understand and search video content using text, audio prompts, and image inputs. We will leverage Meta's open-source ImageBind to create joint embeddings across different modalities and store them in Weaviate, a production-ready vector database. 💻 GET THE SOURCE CODE: https://github.com/AminaKeldibek/VideoCompanion/tree/multimodal_search What You Will Build: A prototype that allows users to search a video archive using any sense. Example 1: Input an image of a lion 🖼️ The system returns video segments containing lions. Example 2: Input an audio file of a drumbeat 🎵 The system finds timestamps in videos where drums are playing. 🧠 Who Is This For? AI Engineers exploring multimodal embeddings. Founders building AI-native search products. ML teams working on video understanding and semantic search. New to RAG? Start with my previous text-only RAG tutorial: https://www.youtube.com/watch?v=GrmS-_vt96o&t=10s ⏱️ Timecodes 0:00 - Intro: The Multimodal Goal 00:58 - Demo: Searching Video with Audio & Images 02:39 - Architecture: Steps to build multimodal RAG 03:24 - Deep Dive: Meta’s ImageBind Model 05:31 - Deep Dive: How Weaviate Handles Multimodal Data 06:28 - Setup: ImageBind + Weaviate via Docker 08:49 - Vector Indexing Parameters 12:04 - Data Pipeline: Extracting Text, Audio, and Frames from Video 13:48 - Preprocessing Multimodal Inputs 14:27 - Generating & Storing Video Embeddings 14:45 - Running Multimodal Queries 📌 Tech Stack We use an entirely open-source stack for this project: Meta ImageBind, Weaviate Vector DB, Python, Docker, Gradio, OpenAI Whisper, and OpenCV. 📎 References ImageBind Paper/Code: https://github.com/facebookresearch/ImageBind Weaviate Documentation: https://weaviate.i
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Chapters (11)

Intro: The Multimodal Goal
0:58 Demo: Searching Video with Audio & Images
2:39 Architecture: Steps to build multimodal RAG
3:24 Deep Dive: Meta’s ImageBind Model
5:31 Deep Dive: How Weaviate Handles Multimodal Data
6:28 Setup: ImageBind + Weaviate via Docker
8:49 Vector Indexing Parameters
12:04 Data Pipeline: Extracting Text, Audio, and Frames from Video
13:48 Preprocessing Multimodal Inputs
14:27 Generating & Storing Video Embeddings
14:45 Running Multimodal Queries
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