Implement RAG systems, vector databases, and semantic search that give your applications perfect recall.
Google’s EmbeddingGemma packs 308M parameters into a powerful on-device embedding model, supporting 100+ languages for offline AI, RAG, and semantic search.
Build a multilingual chatbot that pulls answers from PDFs or threads using Retrieval-Augmented Generation (RAG) and the SUTRA model by TWO Platforms. This tutorial walks you through creating your own AI assistant in Hindi, Bengali, Spanish, and more.
Building a general-purpose LLM agent involves selecting the right model, managing memory, structuring control logic, and integrating tools. Frameworks like LangChain and ReAct enhance flexibility. Fine-tuning prompts and optimizing execution improve performance across tasks.
Semantic search redefines how we find information by focusing on meaning, not keywords. txtai, an open-source platform, combines embeddings with LLMs to power smart search and workflows. Learn how to build intuitive systems for FAQs, document search, and more in this comprehensive guide!
Unlock the power of Qdrant, a cutting-edge vector database for AI applications. Learn to store, search, and manage high-dimensional data for tasks like semantic search, recommendations, and more. This guide dives deep into setup, querying, visualization, and real-world use cases! 🚀
Pinecone is a scalable vector database optimized for high-dimensional data, enabling efficient similarity searches for AI applications like recommendation systems and semantic search. Its real-time capabilities and advanced features make it a powerful tool for managing unstructured data at scale.
Milvus, an open-source vector database, powers AI applications like recommendation systems and similarity search. This guide covers setting up Milvus, creating collections, inserting data, and performing searches with detailed steps.
ChromaDB is a powerful vector database designed to handle embedding-based data storage and retrieval. It enables efficient similarity search and content-based querying for multimodal data, including text, images, and more.
Create systems where multiple agents work together, building, orchestrating, and managing intelligent agents.
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