RAG & Vector Databases

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

Lither works with every major vector database provider, so you keep full control over where your embeddings live. Start on self-hosted pgvector with zero extra infrastructure, or connect a managed service, then switch adapters later without touching application code.

  • pgvector: self-hosted PostgreSQL extension with 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

Intelligent Chunking & Embeddings

Ingestion is tuned for the content you actually have. Choose from five chunking strategies and your preferred embedding model, set workspace-level defaults, and keep retrieval consistent across every agent and pipeline.

  • Fixed-size, recursive, semantic, sentence-level, and document-aware chunking
  • OpenAI, Cohere, BGE, and E5 embedding model support
  • Workspace-level defaults for consistent retrieval
  • Optimized for different content types out of the box

Knowledge Graphs & AI Memory

Go beyond flat vector search with entity-relation knowledge storage and persistent agent memory. Build interconnected knowledge bases for deep reasoning and give your agents context that carries across conversations.

  • Entity-relation knowledge storage with graph queries
  • Persistent agent memory with time decay
  • Episodic, semantic, procedural, and working memory types
  • Unified datasources dashboard for DBs, files, and API connectors

RAG at Enterprise Scale

0+
Vector DB Providers
0
Chunking Strategies
0
Embedding Models
0
Memory Types

Frequently Asked Questions

Which vector databases does Lither support?

Lither supports self-hosted pgvector plus managed providers including Pinecone, Qdrant, Weaviate, and Milvus. You can switch adapters without changing application code, so you keep full control over where your embeddings live.

Can I use my own embedding models?

Yes. Lither supports OpenAI, Cohere, BGE, and E5 embedding models, and you can set workspace-level defaults so retrieval stays consistent across every agent and pipeline.

What chunking strategies are available?

Five strategies ship out of the box: fixed-size, recursive, semantic, sentence-level, and document-aware chunking. Each is optimized for different content types so ingestion matches the documents you actually have.

How does AI memory work?

Agents get persistent memory with time decay across four types: episodic, semantic, procedural, and working memory. Combined with entity-relation knowledge graphs, this gives agents context-rich reasoning that carries across conversations.

Build smarter AI with RAG

Give your AI agents access to your knowledge base with production-ready RAG pipelines.

Get Started Free