Vector Search
Searching documents by semantic similarity using high-dimensional embedding vectors.
Vector search retrieves documents based on semantic similarity rather than keyword matching. Documents are encoded as high-dimensional embedding vectors (typically 384-3072 dimensions) and queries are encoded into the same vector space. Approximate nearest neighbor (ANN) algorithms like HNSW, IVF, and ScaNN find the closest matches efficiently. Vector search powers semantic search, recommendation systems, and retrieval-augmented generation (RAG). Major engines: Pinecone, Weaviate, Qdrant, Milvus, pgvector. Empire325 designs vector search architectures with deliberate chunking strategy, embedding model selection, and reranking pipelines for production-grade retrieval quality.
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Large Language Model (LLM)
A neural network trained on massive text corpora to understand and generate human language.
Retrieval-Augmented Generation (RAG)
An AI architecture combining LLM generation with real-time retrieval from external knowledge sources.
AI Agent
An autonomous LLM-based system that plans, takes actions via tools, and accomplishes multi-step goals.
Fine-Tuning
Adapting a pretrained foundation model to specific tasks or domains via additional training.