--- title: Best Embeddings posts of 2025 | daily.dev description: The most upvoted Embeddings posts of 2025, curated by the daily.dev community. canonical: https://daily.dev/tags/embeddings/best-of/2025 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/embeddings/best-of/2025 og:type: website og:site_name: daily.dev og:title: Best Embeddings posts of 2025 | daily.dev og:description: The most upvoted Embeddings posts of 2025, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Embeddings — 2025 1. 1 [](/posts/implementing-vector-search-from-scratch-a-step-by-step-tutorial-c4rk9mfup "Implementing Vector Search from Scratch: A Step-by-Step Tutorial") Article Machine Learning Mastery · 1y Implementing Vector Search from Scratch: A Step-by-Step Tutorial A comprehensive tutorial demonstrating how to build a vector search engine from scratch using Python. Covers the three core steps of vector search: converting text to numerical vectors, calculating similarity using cosine similarity, and retrieving the most relevant results. Includes practical code examples with NumPy and Matplotlib, visualization of vector spaces, and explains the connection to RAG systems. The implementation uses simplified word embeddings and averaging techniques to make concepts accessible while maintaining the fundamental principles of semantic search. 118 2. 2 [](/posts/how-ai-agents-remember-things-the-role-of-vector-stores-in-llm-memory-tqesfcprx "How AI Agents Remember Things: The Role of Vector Stores in LLM Memory") Article freeCodeCamp · 1y How AI Agents Remember Things: The Role of Vector Stores in LLM Memory Large language models don't have inherent memory, but vector stores enable AI agents to simulate memory by converting text into numerical embeddings and storing them in specialized databases. When users interact with AI, the system searches for semantically similar stored vectors to retrieve relevant past information. Popular vector databases include FAISS for local deployments and Pinecone for cloud-based solutions. This approach, called retrieval-augmented generation (RAG), allows AI to appear contextually aware despite technical limitations around similarity-based matching and static embeddings. 98 3. 3 [](/posts/top-5-vector-databases-to-learn-in-2025-with-courses-and-books-to-master-them--l6xzilsmr "Top 5 Vector Databases to Learn in 2025 (with Courses and Books to Master Them)") Article Javarevisited · 1y Top 5 Vector Databases to Learn in 2025 (with Courses and Books to Master Them) Vector databases have become essential infrastructure for AI applications in 2025, powering semantic search, RAG systems, and recommendation engines. The top 5 vector databases to learn are Pinecone (production-ready managed service), Weaviate (open-source with hybrid search), ChromaDB (lightweight local option), FAISS (industry-standard similarity search library), and Qdrant (high-performance Rust-based solution). Each database has specific strengths and learning resources including Udemy courses, Coursera programs, and technical books to help developers master these technologies for building modern GenAI applications. 60 4. 4 [](/posts/make-rag-systems-32x-memory-efficient--qxzqnyhia "Make RAG systems 32x Memory Efficient!") Article Daily Dose of Data Science | Avi Chawla | Substack · 1y Make RAG systems 32x Memory Efficient! Binary quantization can make RAG systems 32x more memory efficient by converting float32 embeddings to binary vectors. The technique involves ingesting documents, generating binary embeddings, storing them in a vector database like Milvus, and using Hamming distance for retrieval. A complete implementation demonstrates querying 36M+ vectors in under 30ms using LlamaIndex, Milvus, and Groq for inference, with deployment via Beam Cloud. 54 1 5. 5 [](/posts/teilomillet-raggo-a-lightweight-production-ready-rag-retrieval-augmented-generation-library-in-g-aopova1gb "teilomillet/raggo: A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go.") Article Community Picks · 1y teilomillet/raggo: A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go. Raggo is a lightweight Go library for building Retrieval Augmented Generation (RAG) systems. It provides document processing, text chunking, embeddings generation, and vector storage capabilities. The library supports multiple RAG implementations including SimpleRAG for basic Q&A, ContextualRAG for advanced document understanding, and MemoryContext for chat applications. Features include flexible configuration, concurrent processing, rate limiting, and integration with various vector databases and embedding providers. 22 6. 6 [](/posts/a-guide-to-llm-embeddings-6oxc5tkzw "A Guide to LLM Embeddings") Article Couchbase · 2y A Guide to LLM Embeddings LLM embeddings are numerical representations generated by large language models (LLMs) like GPT and BERT, capturing semantic meaning to enable efficient text processing, similarity search, and retrieval. They are used in various applications such as search engines, recommendation systems, and AI agents. Embeddings can be fine-tuned for specific domains and integrated into solutions using tools like Couchbase Capella. The key components of LLMs include tokenization, embedding layers, attention mechanisms, and feedforward layers. Different types of embeddings serve various tasks and can be tailored for optimal performance based on the use case. 22 7. 7 [](/posts/beaconbay-ck-semantic-grep-tool-for-use-by-ai-and-humans--2p5oa89dv "BeaconBay/ck: Semantic grep tool for use by AI and humans!") Article Hacker News · 1y BeaconBay/ck: Semantic grep tool for use by AI and humans! ck is a semantic grep tool that finds code by meaning rather than keywords. It combines traditional grep functionality with AI-powered semantic search using embeddings, allowing developers to search for concepts like "error handling" and find relevant code even without exact keyword matches. The tool offers three search modes (regex, semantic, hybrid), maintains full grep compatibility, provides JSON output for automation, and works completely offline with local embedding models. 19 8. 8 [](/posts/3-insane-algorithms-netflix-uses-to-scan-billions-of-frames-m5ojporwj "3 Insane Algorithms Netflix Uses to Scan BILLIONS of Frames") Video Coding with Lewis · 1y 3 Insane Algorithms Netflix Uses to Scan BILLIONS of Frames Netflix uses three sophisticated computer vision algorithms to analyze billions of video frames: match cut transitions that automatically find visually similar shots for seamless editing, video search capabilities that convert text queries into mathematical embeddings to find specific scenes, and scene detection that combines screenplay alignment with multimodal analysis of video and audio tracks. These systems leverage instance segmentation, optical flow, and bidirectional neural networks to automate video editing tasks that would otherwise require thousands of manual hours. 14 9. 9 [](/posts/chat-with-your-documents-tool-rag-vector-dbs-cosine-sim-claude-api-implementation-mkz42fsyc "Chat with your documents tool — RAG (vector DBs + cosine sim.) & Claude API implementation") Article Medium · 1y Chat with your documents tool — RAG (vector DBs + cosine sim.) & Claude API implementation A detailed implementation of a RAG system for a law firm that processes 1TB of legal documents using vector embeddings, FAISS indexing, and Claude API. The system chunks documents, creates embeddings with a trilingual MiniLM model, performs cosine similarity search, and includes citation verification to prevent hallucinations. Key features include OCR processing, privacy-focused local deployment, sub-20ms query response times, and costs around $0.02 per query. 10 10. 10 [](/posts/a-guide-to-embeddings-model-api-in-spring-ai-bt7d3rsgt "A Guide to Embeddings Model API in Spring AI") Article Baeldung · 1y A Guide to Embeddings Model API in Spring AI Spring AI's embeddings model API provides an abstraction layer for integrating text embedding capabilities into Java applications. The tutorial covers the core API components including EmbeddingModel, EmbeddingRequest, EmbeddingResponse, and Embedding classes. It demonstrates both auto-configuration and manual configuration approaches for integrating OpenAI's embedding models, showing how to convert text into high-dimensional vector representations that AI models can process for semantic understanding. 10 11. 11 [](/posts/5-chunking-strategies-for-rag-v4er48yne "5 Chunking Strategies For RAG") Article Daily Dose of Data Science | Avi Chawla | Substack · 1y 5 Chunking Strategies For RAG Five chunking strategies for Retrieval Augmented Generation (RAG) are explained: fixed-size chunking splits text into uniform segments with overlap, semantic chunking groups segments based on embedding similarity, recursive chunking uses natural separators then splits oversized chunks, document structure-based chunking follows inherent document organization, and LLM-based chunking uses language models for semantic accuracy. Each approach has trade-offs between simplicity, semantic preservation, and computational cost, with semantic chunking often performing well in practice. 10 [See all Embeddings archives](/tags/embeddings/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/tags/embeddings/best-of/2025#page","url":"https://daily.dev/tags/embeddings/best-of/2025","name":"Best Embeddings Posts of 2025","description":"The most upvoted Embeddings posts of 2025, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/tags/embeddings/best-of/2025#items","numberOfItems":11,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/implementing-vector-search-from-scratch-a-step-by-step-tutorial-c4rk9mfup","name":"Implementing Vector Search from Scratch: A Step-by-Step Tutorial"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/how-ai-agents-remember-things-the-role-of-vector-stores-in-llm-memory-tqesfcprx","name":"How AI Agents Remember Things: The Role of Vector Stores in LLM Memory"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/top-5-vector-databases-to-learn-in-2025-with-courses-and-books-to-master-them--l6xzilsmr","name":"Top 5 Vector Databases to Learn in 2025 (with Courses and Books to Master Them)"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/make-rag-systems-32x-memory-efficient--qxzqnyhia","name":"Make RAG systems 32x Memory Efficient!"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/teilomillet-raggo-a-lightweight-production-ready-rag-retrieval-augmented-generation-library-in-g-aopova1gb","name":"teilomillet/raggo: A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go."},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/a-guide-to-llm-embeddings-6oxc5tkzw","name":"A Guide to LLM Embeddings"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/beaconbay-ck-semantic-grep-tool-for-use-by-ai-and-humans--2p5oa89dv","name":"BeaconBay/ck: Semantic grep tool for use by AI and humans!"},{"@type":"ListItem","position":8,"url":"https://daily.dev/posts/3-insane-algorithms-netflix-uses-to-scan-billions-of-frames-m5ojporwj","name":"3 Insane Algorithms Netflix Uses to Scan BILLIONS of Frames"},{"@type":"ListItem","position":9,"url":"https://daily.dev/posts/chat-with-your-documents-tool-rag-vector-dbs-cosine-sim-claude-api-implementation-mkz42fsyc","name":"Chat with your documents tool — RAG (vector DBs + cosine sim.) & Claude API implementation"},{"@type":"ListItem","position":10,"url":"https://daily.dev/posts/a-guide-to-embeddings-model-api-in-spring-ai-bt7d3rsgt","name":"A Guide to Embeddings Model API in Spring AI"},{"@type":"ListItem","position":11,"url":"https://daily.dev/posts/5-chunking-strategies-for-rag-v4er48yne","name":"5 Chunking Strategies For RAG"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Tags","item":"https://daily.dev/tags"},{"@type":"ListItem","position":3,"name":"Embeddings","item":"https://daily.dev/tags/embeddings"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/tags/embeddings/best-of"},{"@type":"ListItem","position":5,"name":"2025"}]}]} ```