Retrieval-Augmented Generation
A technique that enhances LLM responses by retrieving relevant information from external knowledge bases. Combines the power of search with generative AI for grounded, factual outputs.
Who it's for
Teams building AI that answers from their own documents instead of guessing.
Keep exploring
Concepts in the glossary
14 in catalog
RAG is featured by
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LlamaIndex
The Data Framework for LLM Applications
Framework -
Cohere
The enterprise RAG platform
AI as a Service -
Pinecone
The vector database for AI
Vector Database -
Qdrant
The vector search engine
Vector Database -
Weaviate
The vector database with hybrid search
Vector Database -
Chroma
The AI-native embedding database
Vector Database -
Milvus
The vector DB built for scale
Vector Database -
pgvector
Vector search inside Postgres
Vector Database -
Turbopuffer
Serverless vector search on object storage
Vector Database -
LanceDB
The embedded vector database
Vector Database -
Firecrawl
Turn websites into LLM-ready data
Service -
MongoDB
The document database
Hosting Service -
Neo4j
The graph database
Hosting Service -
RAGFlow
Build a superior context layer for AI agents
Framework
1 in catalog