agentic-rag-for-dummies

Build modular multi-agent RAG systems using LangGraph by decomposing complex queries into focused sub-queries and orchestrating parallel agents to accelerate cross-source reasoning and synthesis across documents.

Hierarchical indexing organizes documents into parent and child chunks to balance retrieval precision and scope. It is needed for complex information tasks requiring decomposition and cross-source synthesis.

Yes, agentic RAG orchestration uses a provider-agnostic design that works with local or cloud LLMs. It supports environments like Ollama and OpenAI for multi-agent retrieval and reasoning.

Maintain context in long-running retrieval-augmented generation workflows by using built-in conversation memory. This ensures coherent interactions and applies context compression to minimize redundant retrievals.

Ambiguous queries cause irrelevant retrievals because the system lacks query clarification. Applying query clarification and decomposition splits complex questions into focused sub-queries, enabling self-correction and re-querying.

Orchestrate parallel agents for multi-source document synthesis using configurable LangGraph controls. This multi-agent coordination decomposes tasks across agents, applying self-correction to accelerate reasoning.