23 articles
Complete 2026 comparison of top vector databases: Pinecone, Weaviate, ChromaDB, Qdrant, and Milvus. Performance benchmarks, pricing, self-hosted vs managed, and which to choose for RAG and semantic search.
Build a production semantic search engine using OpenAI embeddings and cosine similarity in Python. Covers hybrid search, caching, FastAPI deployment, and cost optimization for high-volume use cases.
Comprehensive comparison of the top vector databases for LLM applications in 2025 — Pinecone, Chroma, Weaviate, and Qdrant. Covers performance, cost, features, and when to use each for RAG and semantic search.
Master Pinecone from account setup to production RAG — serverless indexes, metadata filtering, hybrid search, and performance tuning. The only Pinecone tutorial you need for building LLM-powered search.
Complete guide to Chroma DB for local vector embeddings and semantic search — installation, collections, filtering, and LangChain integration. The fastest way to add vector search to your LLM project without an API key.
Understand how text embeddings enable semantic search — from word vectors to transformer-based embeddings, cosine similarity, and practical implementation for RAG systems. The foundation of every LLM retrieval application.
Master the OpenAI Embeddings API — text-embedding-3-small vs large, batch embedding, dimensionality reduction, cost optimization, and full RAG integration. Everything you need to build production semantic search.
Generate high-quality text embeddings locally using Sentence Transformers — no API keys, no cost per call. Covers model selection, batch encoding, fine-tuning, and integration with vector databases for production RAG.
Master Together AI — the cloud platform for running 200+ open-source LLMs at production scale. This guide covers setup, chat completions, streaming, embeddings, fine-tuning, custom model deployment, and cost optimization for developers and ML engineers.
Build scalable AI personalization systems using user profiles, embedding-based preference learning, and privacy-preserving context injection. Covers cold start, preference drift, and A/B testing.
Build production-grade semantic search using embeddings, vector databases, and hybrid retrieval. Covers indexing pipelines, query rewriting, re-ranking, and handling edge cases at scale.
Build recommendation systems using embeddings, two-tower models, and solve cold start with hybrid approaches.
Build production DPR systems: train dual encoders, fine-tune on domain data, scale with FAISS, and outperform BM25 on specialized domains.
Fine-tune embeddings for specialized domains. Generate training pairs with LLMs, train with sentence-transformers, and deploy custom embedding models in production.
Compare text-embedding-3-small vs 3-large, Cohere embed v3, sentence-transformers, multilingual models, and how to choose embeddings for your stack.
Master multimodal embeddings: CLIP for text-image, ImageBind for audio/3D, cross-modal search, and production storage strategies.
Master pgvector setup, index tuning, hybrid search patterns, and embedding pipelines for production semantic search at scale.
pgai extends PostgreSQL with AI capabilities: auto-embedding, semantic search, and LLM function calls—all in SQL. No external vector database required.
Explore chunking strategies from fixed-size to semantic splitting, including sentence-window retrieval and late chunking techniques that dramatically improve retrieval quality.
Master semantic chunking, recursive splitting, parent-child strategies, and late chunking to maximize RAG retrieval quality and cut retrieval latency.
Explore why dense embeddings alone fail, and how hybrid search combining vector similarity with BM25 sparse retrieval dramatically improves RAG quality.
Understand why vector similarity ranks poorly, how cross-encoder rerankers fix it, and implement production-grade reranking with latency optimization.
Compare the top vector databases in 2026: Pinecone serverless, Weaviate multi-tenancy, Qdrant quantization, pgvector for Postgres, and when to use each.