Learn multimodal AI through practical tutorials that demonstrate text, image, and video processing with Amazon Bedrock and AWS services.
| 📓 Notebook | 🎯 Focus & Key Learning | ⏱️ Time | 📊 Level | 🖼️ Diagram |
|---|---|---|---|---|
| 01 - Semantic Search with LangChain, Amazon Titan Embeddings, and FAISS | Text embeddings and PDF processing - Document chunking, embeddings generation, FAISS vector store operations | 30 min |  |
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| 02 - Building a Multimodal Image Search App with Titan Embeddings | Visual search capabilities - Image embeddings, multimodal search, natural language image queries | 45 min |  |
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| 03 - Supercharging Vector Similarity Search with Amazon Aurora and pgvector | Production database setup - PostgreSQL vector operations, pgvector extension, scalable similarity search | 60 min | |
|
| 04 - Video Understanding | Video content analysis - Nova models for video processing, content extraction, video understanding workflows | 45 min |  |
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| 05 - Video and Audio Content Analysis with Amazon Bedrock | Audio processing workflows - Transcription, audio embeddings, multimedia content analysis | 40 min | |
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| 06 - Building Agentic Video RAG with Strands Agents - Local | AI agents for video analysis - Local agent implementation, memory-enhanced agents, persistent context storage | 90 min |  |
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| 07 - Building Agentic Video RAG with Strands Agents - Cloud | Production agent deployment - Cloud-based agent architecture, ECS deployment, scalable agent workflows | 120 min | |
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| 🔧 Service | 🎯 Purpose | ⚡ Key Capabilities |
|---|---|---|
| Amazon Bedrock | AI model access | Titan Embeddings, Nova models for multimodal processing |
| Amazon Aurora PostgreSQL | Vector database | pgvector extension for similarity search operations |
| Amazon S3 | Object storage | Document, image, and video content storage |
| Amazon Transcribe | Speech-to-text | Audio content extraction from video files |
📋 Before You Begin:
- ✅ AWS Account with Amazon Bedrock access enabled
- ✅ Python 3.8+ installed locally
- ✅ AWS CLI configured with appropriate permissions
- ✅ Jupyter Notebook or JupyterLab installed
📦 Required Python Packages:
# All requirements are in requirements.txt
# Install after creating virtual environment (see Quick Start Guide)🔑 AWS Credentials Setup: Follow the AWS credentials configuration guide to configure your environment.
git clone https://github.com/build-on-aws/langchain-embeddings.git
cd langchain-embeddings/notebooks
# Create virtual environment
python -m venv venv
# Activate environment (macOS/Linux)
source venv/bin/activate
# Or on Windows
# venv\Scripts\activate
# Install requirements
pip install -r requirements.txtjupyter notebook 01_build_pdf_vector_db.ipynbComplete notebooks 01-07 in sequence for the best learning experience.
| 💰 Notebook Range | 🔧 AWS Services Used |
|---|---|
| 01-02 | Bedrock, S3 |
| 03 | Aurora PostgreSQL |
| 04-05 | Bedrock, Transcribe, S3 |
| 06-07 | Full stack |
💡 Pro Tip: Use AWS Free Tier when possible and monitor costs through AWS Cost Explorer.
| 🎯 Tip | 📝 Description |
|---|---|
| 📚 Start Sequential | Follow the numbered order for best learning experience |
| 🔬 Experiment | Modify code examples to understand concepts better |
| 💰 Monitor Costs | Check AWS usage, especially for Bedrock API calls |
| 💾 Save Work | Export important results before closing notebooks |
- 📖 Amazon Bedrock Documentation
- 🦜 LangChain Python Documentation
- 🔍 FAISS Documentation
- 🗄️ pgvector Documentation
Completed the notebooks? Take your learning to production!
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This library is licensed under the MIT-0 License. See the LICENSE file for details.