Production-Ready RAG Pipelines
Build retrieval-augmented generation systems with your choice of vector database. Knowledge graphs, AI memory, and intelligent chunking for enterprise AI applications.
RAG Capabilities
Enterprise-grade retrieval-augmented generation infrastructure
Multi-Vector DB Support
Self-hosted pgvector or connect Pinecone, Qdrant, Weaviate, and Milvus. Switch adapters without code changes.
5 Chunking Strategies
Fixed-size, recursive, semantic, sentence-level, and document-aware chunking. Optimized for different content types.
Knowledge Graphs
Entity-relation knowledge storage with graph queries. Build interconnected knowledge bases for deep reasoning.
Episodic AI Memory
Persistent agent memory with time decay. Episodic, semantic, procedural, and working memory types for context-rich agents.
Embedding Models
OpenAI, Cohere, BGE, and E5 embedding model support. Configure workspace-level defaults for consistency.
Datasource Management
Manage vector DBs, relational databases, file storage, and API connectors from a unified datasources dashboard.
Bring Your Own Vector DB
Works with all major vector database providers
pgvector
Self-hosted PostgreSQL extension. Zero extra infrastructure.
Pinecone
Fully managed vector database with serverless scaling.
Qdrant
High-performance vector similarity search engine.
Weaviate
Open-source vector database with hybrid search.
Build smarter AI with RAG
Give your AI agents access to your knowledge base with production-ready RAG pipelines.