#embeddings (65)
- Designing Memory and State Management for Autonomous AI Agents
Learn to design and implement memory and state management systems that enable autonomous AI agents to achieve persistent understanding and robust, long-running workflows.
- Develop Long-Term Memory for AI Agents with Knowledge Retrieval
Learn to equip AI agents with long-term memory by leveraging external knowledge bases, vector databases, and Retrieval Augmented Generation for robust performance.
- Why Your LLM Doesn't Always Need a New Vector Database
Learn how many AI systems can effectively use existing traditional databases for better data integrity, cost-efficiency, and operational familiarity.
- Apple, Meta, OpenAI Multimodal Embedding Models Compared
Learn to objectively compare multimodal embedding models from Apple, Meta, and OpenAI to make an informed choice for your AI applications.
- Build Your First RAG System Using Embeddings and Vector Databases
Readers will build a RAG system, integrating document chunking, text embeddings, and vector databases to enhance LLM knowledge with external data.
- Introduction to Retrieval-Augmented Generation (RAG)
Grasp Retrieval-Augmented Generation fundamentals, see its value for production LLM applications, and build a functional RAG system using Python.
- Build AI Agents with Persistent Short-Term and Long-Term Memory
Learn to equip AI agents with persistent short-term context and long-term knowledge, making them more capable, consistent, and useful in applications.
- Understanding AI Agent Memory Systems: A Practical Guide
Explore the essential role of memory in AI agents, covering different memory types, storage, retrieval, and how agents use them to learn and maintain context.
- Stoolap Practical Field Guide
Embark on a comprehensive journey to master Stoolap, a modern embedded SQL database, from the ground up, with practical steps and engaging challenges for developers and data engineers.
- Multimodal AI: Integrate Diverse Data for Intelligent Apps
Integrate diverse data types like text, images, audio, and video to build sophisticated multimodal AI systems for intelligent real-world applications.
- Advanced RAG Techniques for Building Intelligent AI Systems
Learn to implement advanced Retrieval-Augmented Generation techniques like hybrid search and GraphRAG to build highly accurate and robust AI applications.
- Equip AI Agents with Long-Term Memory Using Agentic RAG
You will build a Retrieval-Augmented Generation system using vector databases, enabling AI agents to access external knowledge and reduce hallucinations.
- Advanced Concepts & Best Practices for Production-Ready Memory Systems
Explore advanced concepts and best practices for designing and implementing robust, scalable, and secure memory systems for AI agents in production environments.
- Vector Memory and Embeddings: The Power of Similarity
Explore vector memory and embeddings, understanding how AI agents leverage numerical representations for efficient similarity-based information retrieval and enhanced contextual understanding.
- Storing Agent Memories: From Files to Databases and Vector Stores
Explore how AI agents store their memories, from simple file systems to advanced vector databases, understanding the trade-offs and practical implementations.
- Retrieving AI Agent Memories for Contextual Awareness
Learn how AI agents retrieve information from memory using various strategies to achieve contextual awareness and overcome LLM limitations.
- Advanced Architectures for Multi-Agent AI Systems
Learn to design and build robust, scalable, and intelligent multi-agent AI systems using advanced architectures and orchestration patterns.
- AI-Native Databases: Storing and Querying for Intelligent Applications
Explore AI-Native Databases, understanding their unique features like vector search and knowledge graph integration for intelligent applications and agentic systems.
- Beyond the Prompt: Building Multi-Source Context Pipelines (RAG)
Explore Retrieval-Augmented Generation (RAG) to overcome LLM limitations, integrate external knowledge, and build dynamic, multi-source context pipelines for production AI systems.
- Breaking Down Information: Smart Chunking Strategies
Master smart chunking strategies to effectively break down large documents for LLMs, improving context management, relevance, and RAG system performance.
- Build an End-to-End Production RAG System with LLMOps
Build a robust, scalable, and cost-efficient Retrieval Augmented Generation system using LLMOps best practices for real-world production.
- Advanced Indexing Strategies for HTAP Workloads
Learn to apply advanced indexing techniques in Stoolap to optimize performance for transactional, analytical, and vector search workloads in HTAP applications.
- Build a Hybrid OLTP/OLAP Analytics Dashboard with Stoolap
Readers will learn to build an analytics dashboard leveraging Stoolap's HTAP features for real-time data ingestion and complex analytical queries.
- Welcome to Stoolap: A New Generation Embedded Database
Discover Stoolap, a modern embedded SQL database built with Rust, and learn how its unique features like MVCC, parallel execution, and vector search redefine embedded data management.
- Beyond Relational: Vector Search and Semantic Queries
Explore Stoolap's advanced vector search capabilities. Learn how to store, index, and query vector embeddings for semantic similarity, enhancing your applications with AI-driven search.
- Mastering Stoolap Database: A Complete Guide
Unlock Stoolap database mastery from basics to advanced features. Explore its unique architecture, MVCC, parallel execution, vector search, and unified OLTP/OLAP capabilities.
- Architecting Multimodal Encoders for AI Perception
Understand how to design and implement multimodal encoders, enabling AI systems to process and unify diverse data types such as text, images, and audio.
- Building Robust Pipelines: From Ingestion to Vectorization
Explore the critical steps of data ingestion, preprocessing, and vectorization for multimodal AI systems, focusing on robust and high-performance pipeline design.
- Hands-On Project: Building a Multimodal Search Assistant
Build a practical multimodal search assistant from scratch using Python, CLIP, and FAISS. Learn to index and query text and images in a shared embedding space.
- Build Multimodal RAG Systems with Diverse Data Sources
Learn to implement Multimodal RAG, integrating diverse data types to enhance AI knowledge bases and overcome Large Language Model limitations.
- Representing Reality: From Raw Data to Embeddings
Unlock the secret behind multimodal AI: learn how raw text, image, audio, and video data are transformed into powerful numerical embeddings for AI understanding.
- Weaving Information: Data Fusion Strategies
Explore the critical data fusion strategies—early, late, and hybrid—that enable multimodal AI systems to combine text, image, audio, and video inputs for comprehensive understanding.
- Move Beyond Simple RAG Chunking with Advanced Context Assembly
Learn to implement advanced context assembly techniques to overcome simple chunking limitations and build highly coherent contexts for RAG systems.
- Implementing Advanced Embeddings and Hybrid Search for RAG 2.0
Apply advanced embedding models and hybrid search strategies to improve retrieval accuracy and relevance for LLMs, enhancing RAG 2.0 systems.
- Retrieval-Augmented Generation: Basics, Architecture, Limitations
Learn the core architecture and functionality of basic Retrieval-Augmented Generation and identify its key limitations for AI systems.
- GraphRAG: Model Relationships for Better LLM Context
Discover how GraphRAG leverages knowledge graphs to model complex relationships, enhancing retrieval for multi-hop reasoning and richer LLM context.
- Modern RAG 2.0: Advanced Retrieval Guide
Dive deep into modern RAG 2.0, exploring advanced techniques like hybrid search, GraphRAG, and multi-hop retrieval. Learn to overcome basic RAG limitations for superior accuracy and relevance.
- Deploying RAG 2.0: Best Practices, Evaluation, and Real-World Projects
Explore best practices for deploying RAG 2.0 systems, learn crucial evaluation methodologies, and discover real-world applications to build robust and accurate Generative AI solutions.
- Generate Face Embeddings for Accurate Biometric Recognition
Learn to generate and compare face embeddings using deep learning to transform faces into unique numerical representations for accurate recognition.
- USearch & ScyllaDB Vector Search Practical Field Guide
Embark on a comprehensive journey to master the USearch library for high-performance vector search, particularly with ScyllaDB, from fundamentals to advanced deployment.
- What are Vector Embeddings? The Language of AI
Unlock the mystery of vector embeddings! Learn what they are, why they're vital for modern AI, and how they transform data into a language machines understand for tasks like similarity search.
- USearch: Core Concepts, Installation, and Vector Search
Learn USearch core concepts, install the library, and perform your first vector search using practical Python examples for a strong foundation.
- Your First Vector Search with USearch
Take your first steps with USearch! Learn to initialize a vector index, add data, and perform similarity searches, understanding the core concepts of high-dimensional data retrieval.
- ScyllaDB: A Real-time Database for AI (Overview)
Explore ScyllaDB's architecture, its role in real-time AI applications, and how it provides massive-scale vector search capabilities, powered by USearch.
- Storing Vectors in ScyllaDB: The Vector Data Type
Dive into ScyllaDB's native vector data type, learn how to define vector columns, understand distance metrics, and store vector embeddings for efficient similarity search.
- Perform Vector Similarity Search Directly in ScyllaDB
Readers will learn to integrate vector search into ScyllaDB, create vector columns, build indexes, and perform similarity searches using CQL.
- Understanding USearch Indexing Strategies
Dive deep into USearch indexing strategies, focusing on HNSW, understanding their impact on performance and recall, and applying them for efficient vector search with ScyllaDB.
- Vector Distance Metrics and Their Impact
Explore vector distance metrics like Euclidean, Cosine, and Dot Product, understanding their role in USearch and ScyllaDB for accurate similarity search.
- Optimizing USearch Performance: Memory & Latency
Dive deep into optimizing USearch performance within ScyllaDB, focusing on memory management, latency reduction, and fine-tuning vector index parameters for real-time AI applications.
- Scaling ScyllaDB Vector Search for Billions of Vectors
Unlock the power of ScyllaDB and USearch to build highly scalable vector search solutions capable of handling billions of vectors with low latency and high throughput.
- Advanced USearch Features: Quantization & Compression
Dive into advanced USearch features: quantization and compression. Optimize vector search for memory, speed, and scale, balancing accuracy with performance in your AI applications.
- Designing Real-world Vector Search Systems with ScyllaDB and USearch
Learn to design and understand production-ready architectures combining USearch and ScyllaDB for scalable, high-performance vector search applications.
- Building a Movie Recommendation System
Learn to build a real-time movie recommendation system using USearch for efficient vector similarity search and ScyllaDB for scalable, low-latency vector storage.
- Implementing Semantic Search for Documents
Dive into practical semantic search by building a document search engine. Learn to generate embeddings, store them in ScyllaDB, and query with USearch for intelligent results.
- Fraud Detection with Vector Similarity
Learn to apply USearch and ScyllaDB for real-time fraud detection using vector similarity, building on prior knowledge of embeddings and scalable databases.
- Monitoring and Debugging Vector Search Systems
Master monitoring and debugging USearch-powered vector search with ScyllaDB. Learn to identify performance bottlenecks, troubleshoot issues, and ensure system reliability using Prometheus and Grafana.
- Deployment Strategies for High-Availability
Explore robust deployment strategies for USearch-powered vector search with ScyllaDB, focusing on achieving high-availability, fault tolerance, and scalability for critical AI applications.
- Data Lifecycle Management for Embeddings
Master the critical aspects of managing the full lifecycle of vector embeddings, from creation to updates and deletion, using USearch and ScyllaDB for optimal performance and data freshness.
- The Future of Vector Search with USearch and ScyllaDB
Learn about emerging vector database trends like hybrid search and multimodal AI, and how USearch and ScyllaDB will shape real-time AI applications.
- USearch and ScyllaDB for Vector Search Guide
Learn to implement efficient vector search applications using the USearch library and its integration with ScyllaDB, covering fundamentals and advanced techniques.
- Embeddings, Vector Databases & Semantic Search
Learn about embeddings, vector databases, and semantic search to build advanced AI applications.
- Building a Semantic Search Engine Using Embeddings
Readers will learn to build a semantic search engine from scratch, preparing data, generating embeddings, and performing intelligent searches for relevant results.
- Enhance LLMs with Custom Data using Retrieval-Augmented Generation
After this chapter, you will implement a RAG system to enhance large language models with external, up-to-date, and domain-specific knowledge.
- Deep Dive into Embeddings
Learn about embeddings, their importance in AI and NLP applications, and how to use them with any-llm.
- Advanced Topics: Redis Modules and Beyond
Explore advanced Redis modules for efficient data processing, including JSON management, time-series analysis, and vector search.