PlanRAG技术：它通过两个主要的推理类型来回答问题：首先是制定计划，其次是基于检索结果的推理。PlanRAG技术的核心在于，它使用单个语言模型来执行这两种类型的推理，以减少使用不同语言模型可能带来的副作用。
PlanRAG的推理过程：PlanRAG的推理过程包括三个主要步骤：
规划（Planning）：语言模型接收决策问题、数据库架构和业务规则作为输入，生成一个初始的数据分析计划。
检索与回答（Retrieving & Answering）：与之前的RAG技术不同，PlanRAG在这一步骤中不仅考虑问题和规则，还包括初始计划，以更有效地生成数据分析查询。
重新规划（Re-planning）：如果初始计划不足以解决问题，PlanRAG会根据每次检索的结果评估当前计划，并生成新的计划或纠正先前分析的方向。
在定位场景中，先前的迭代RAG与PlanRAG 推理过程的示例
IterRAG-LM和PlanRAGLM 在简单回答（SR）和多步回答（MR）问题上的准确率（%）
# PrefixYou are a decision-making agent answering a given question.You should collect the data to answer the question:# Tool descriptionsGraph DB: Useful for when you need to collect the data thatfollows the following schema (You MUST generate a Cypher querystatement to interact with this tool):(n:Trade_node {{name, local_value, is_inland, total_power,outgoing, ingoing}});(m:Country {{name, home_node, development}});(Trade_node)-[r:source {{flow}}]->[Trade_node](Country)-[NodeCountry{{is_home,has_merchant,base_trading_power,calculated_trading_power}}]->(Trade_node), args: {{{{'tool_input': {{{{'type': 'string'}}}}}}}}Self thinking: Useful for when there is no available tool., args:{{{{'tool_input': {{{{'type': 'string'}}}}}}}}# Format instructionsUse the following Strict format:Question: the input question you must answer.Thought: you should always think about what to do.Action: a suitable database name, MUST be one of [‘Graph DB’,‘Self-thinking’].Action input: a syntactically correct query statement only, MUSTbe written by Cypher query language.Observation: the result of the action.Thought: I now know the answer.Final answer: the final answer to the question based on theobserved data.# SuffixBegin! Keep in mind that Your response MUST follow the validformat above.# PrefixYou are a decision-making agent answering a given question.You have already collected the data to answer the question.Indeed, you should make your Final answer immediately.:# Tool descriptionsGraph DB: Useful for when you need to collect the data thatfollows the following schema (You MUST generate a Cypher querystatement to interact with this tool):(n:Trade_node {{name, local_value, is_inland, total_power,outgoing, ingoing}});(m:Country {{name, home_node, development}});(Trade_node)-[r:source {{flow}}]->[Trade_node](Country)-[NodeCountry{{is_home,has_merchant,base_trading_power,calculated_trading_power}}]->(Trade_node), args: {{{{'tool_input': {{{{'type': 'string'}}}}}}}}Self thinking: Useful for when there is no available tool., args:{{{{'tool_input': {{{{'type': 'string'}}}}}}}}# Format instructionsUse the following Strict format:Final answer: the final answer to the question based on theobserved data.# SuffixBegin!https://arxiv.org/pdf/2406.12430PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makershttps://github.com/myeon9h/PlanRAG.
