agentic-rag

To build autonomous RAG agents, you need a framework enabling query decomposition, adaptive retrieval, and multi-step planning. This approach breaks down complex questions into manageable sub-queries and synthesizes information from diverse sources.

Query decomposition in multi-step retrieval breaks complex user questions into smaller, manageable sub-questions. This allows autonomous RAG agents to execute multiple retrieval steps and combine information from various sources for accurate synthesis.

Adaptive retrieval intelligently decides when to retrieve and expands search queries if initial results are poor. It also reranks results for quality, ensuring autonomous RAG agents synthesize accurate information from diverse documentation sources.

Yes, you can use LangGraph to compare pricing across platforms by developing autonomous RAG agents. These agents decompose queries, retrieve relevant details from different documentation sources, and synthesize a comprehensive answer.

Agentic RAG supports tool integration by leveraging various tools like search and calculators. This enhances retrieval and reasoning capabilities, allowing the autonomous agent to handle intricate user queries beyond simple retrieve-and-generate limitations.