AI Vector Databases & RAG Infrastructure
Power semantic search and retrieval-augmented generation (RAG) apps with a database built for AI embeddings. Store and query millions of vectors fast — the infrastructure layer behind AI applications that need to search documents, memories, or knowledge bases by meaning, not just keywords.
Amazon Bedrock
The fully managed AWS platform for building generative AI applications and agents at production scale
Chroma
The open-source search infrastructure for AI — fast, serverless, and scalable
MongoDB Atlas Vector Search
Build intelligent applications with vector search, hybrid search, and generative AI on your live data
LangChain
The open-source framework and platform for building reliable AI agents
Linkly AI
Local AI knowledge engine — index all your files, search from any AI agent, files never leave your computer
AI Vector Databases & RAG Infrastructure
When you want an AI to answer questions based on your own documents — your product documentation, internal knowledge base, past support tickets — the standard approach is called retrieval-augmented generation (RAG). The documents get converted into numerical representations called embeddings and stored in a vector database, which can then find the most relevant chunks when a user asks a question.
What vector databases do that regular databases can't
Traditional databases search by exact match — a document either contains the word "refund" or it doesn't. Vector databases search by meaning — they can find documents about returns, cancellations, and money-back guarantees when someone asks about "getting my money back," even if the exact phrase never appears.
Choosing the right one for your project
- Pinecone — fully managed, easiest to get started with.
- Weaviate and Qdrant — self-hostable for teams that want data control.
- Chroma — lightweight, popular for local development and prototyping.
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