AI/ML
Vector Database
What a vector database is and why it matters
Definition
A vector database is a purpose-built database that stores data as high-dimensional numerical vectors (embeddings) and supports fast similarity search across millions or billions of records. Vector databases like Pinecone, Weaviate, and Qdrant are essential infrastructure for RAG pipelines, semantic search, recommendation engines, and any AI application that needs to find similar items by meaning rather than exact keyword match.
How it works
Traditional databases search by exact match or pattern. You query for records where the title contains "machine learning" and get back only records with those exact words. Vector databases search by meaning. You query with a concept, and the database returns records that are semantically similar - even if they use completely different words.
This works because embeddings capture semantic meaning as numbers. The sentence "How do I cancel my subscription?" and "I want to stop my monthly plan" would have very similar vector representations, even though they share almost no words. The vector database finds this similarity using algorithms like HNSW (Hierarchical Navigable Small World) that make billion-scale searches fast.
Choosing a vector database depends on your scale and requirements. Pinecone offers a managed service with minimal operational overhead. Weaviate and Qdrant can be self-hosted for more control. For smaller datasets, PostgreSQL with the pgvector extension can be sufficient without adding new infrastructure.
How 1Raft uses Vector Database
We use vector databases in every RAG pipeline and semantic search feature we build. In a martech project, we indexed 200,000+ articles in Pinecone to power content recommendations based on topic similarity. We evaluate the right vector database for each project based on dataset size, query latency requirements, filtering needs, and whether the client prefers managed or self-hosted infrastructure.
Related terms
AI/ML
Embeddings
Embeddings are numerical representations of data (text, images, audio) in a high-dimensional space where similar items are located near each other. They allow AI systems to measure similarity, search by meaning, and cluster related content.
AI/ML
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that combines a language model with a searchable knowledge base. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents first, then generates answers grounded in that specific data.
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Natural Language Processing (NLP)
Natural language processing is the branch of AI focused on enabling machines to understand, interpret, and generate human language. It covers everything from sentiment analysis and text classification to machine translation and conversational AI.
Related services
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