agentic-rag-for-dummies and context-aware-rag
These are complements: the educational framework for building agentic RAG systems (A) could leverage the knowledge graph ingestion and retrieval capabilities (B) as a concrete implementation pattern for the retrieval component.
About agentic-rag-for-dummies
GiovanniPasq/agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
Built on LangGraph's agentic framework, this system implements hierarchical parent-child chunk indexing for precision search paired with context-rich retrieval, conversation memory across turns, and human-in-the-loop query clarification. Multi-agent map-reduce parallelizes sub-query resolution with self-correction and context compression, while supporting pluggable LLM providers (Ollama, OpenAI, Anthropic, Google) and Qdrant vector storage—all orchestrated through observable graph execution with Langfuse integration.
About context-aware-rag
NVIDIA/context-aware-rag
Context-Aware RAG library for Knowledge Graph ingestion and retrieval functions.
Supports multiple data sources and storage backends (Neo4j, Milvus, ArangoDB, MinIO) with pluggable ingestion and retrieval strategies, including GraphRAG for automatic knowledge graph extraction. Built as microservices with separate ingestion and retrieval APIs, integrated OpenTelemetry observability via Phoenix and Prometheus, and experimental Model Context Protocol (MCP) support for agentic AI workflows. Uses component-based architecture enabling custom function composition while maintaining compatibility with existing data pipelines.
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