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Tagged: Vector Databases
One Retriever, Three Backends: Porting NumPy Search to Chroma, FAISS, Pinecone
Port a hand-rolled NumPy cosine retriever to Chroma, FAISS and Pinecone, and learn what indexes, metadata filters and persistence add, plus the mistakes that break relevance.
11 min readHow a Vector Database Works: Embeddings, Vector Search and Human Memory
A memory-based mental model for vector search: how embeddings, similarity, nearest neighbor indexes and metadata filters work inside a vector database for RAG.
9 min readTuning HNSW Indexes and Scaling Vector Search for Production RAG
Learn how HNSW's M and ef parameters trade recall, latency and memory, how to set them in Chroma, and when to scale a vector database vertically or by sharding.
4 min readHow Vector Databases Really Work: From Embeddings to Hybrid Search
Explains how embeddings encode meaning, how similarity search and indexing scale, and when hybrid search and vector databases actually fit enterprise AI systems.
8 min readBenchmarking Vector Databases for High-Throughput Semantic Search
Learn a practical Node.js and Python methodology for benchmarking vector databases under realistic load to guide sound architectural and scaling decisions.
14 min readWhy Simpler Beats Elaborate in Today's AI Agent Architectures
This piece examines three 2024 arguments against vector databases, hypergraph memory, and orchestration complexity, showing simpler systems often outperform elaborate agent stacks.
15 min readVector Databases Explained: The Engine Behind RAG and AI Search
Learn how vector databases turn text into embeddings, power semantic search and RAG pipelines, and drive real-world AI applications like recommendations.
7 min readUnderstanding AI Memory: Context, Embeddings, RAG, and Model Weights Explained
This article breaks down how AI systems actually remember information, covering context windows, embeddings, vector databases, RAG, and model parameters.
14 min read
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