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'Adaptive #RAG' is another type of agentic RAG that can accommodate its strategies to various user query intents.
Yes, It can accommodate its strategies to various user query intents:
⮕ Open-domain question-answering: Generates answers directly through LLM without relying on retrieval through RAG.
⮕ Multi-hop question-answering: Breaks multi-hop queries down to multiple single-hop queries, iteratively uses these more basic queries to access LLM and the RAG retriever, and combines the retrieved results to generate the final answer.
⮕ Adaptive retrieval: Applicable to complex queries requiring multi-step reasoning. Complex question-answering often involves synthesizing information from multiple data sources and performing multi-step reasoning. Adaptive retrieval iteratively accesses LLM and the RAG retriever to progressively build the information chain necessary for answering the complex questions.
As shown in the diagram below, Adaptive-RAG follows a similar workflow to Self-RAG. By implementing an extra query analysis at the beginning of its workflow, Adaptive-RAG offers a wider range of question-answering strategies.
Know more in the original article: medium.com/@infiniflowai/…
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Building a multi-document ReAct agent for financial analysis 💸
Check out @pavan_mantha1's article on using @llama_index and Qdrant to create a financial analysis system. Learn to set up the environment, manage documents, and build a ReAct Agent.
👉 blog.stackademic.com/building-a-mul…
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