Implementing GraphReader with Neo4j and LangGraph

This blog post discusses the implementation of the GraphReader agent, which is designed to retrieve information from a structured knowledge graph. The GraphReader agent is part of the RAG applications, which are becoming a compelling approach to answering complex questions. The agent uses a large language model (LLM) to identify atomic facts and key elements from a given text, and then stores this information in a graph database. The text is first split into chunks, and then the LLM extracts atomic facts and key elements from each chunk. The extracted information is then imported into a graph database, with relationships established between consecutive chunks. The blog post provides a step-by-step guide on how to implement the GraphReader agent using Neo4j as the storage layer and LangChain in combination with LangGraph to define the agent and its flow.