RAG Agent Builder

Build powerful Retrieval-Augmented Generation (RAG) applications that enhance LLM capabilities with external knowledge sources, enabling accurate, contextualized AI responses.

Quick Start

Get started with RAG implementations in the examples and utilities:

• Examples: See examples/ directory for complete implementations:

  • basic_rag.py - Simple chunk-embed-retrieve-generate pipeline
  • retrieval_strategies.py - Hybrid search, reranking, and filtering
  • agentic_rag.py - Agent-controlled retrieval with iterative refinement

• Utilities: See scripts/ directory for helper modules:

  • embedding_management.py - Embedding generation, normalization, and caching
  • vector_db_manager.py - Vector database abstraction and factory
  • rag_evaluation.py - Retrieval and answer quality metrics

Overview