))
Building Knowledge Graphs for RAG: Exploring GraphRAG with Neo4j and LangChain
Manage episode 446468982 series 3570694
Konten disediakan oleh HackerNoon. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh HackerNoon atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.
This story was originally published on HackerNoon at: https://hackernoon.com/building-knowledge-graphs-for-rag-exploring-graphrag-with-neo4j-and-langchain.
Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #graphrag, #retrieval-augmented-generation, #knowledge-graph, #neo4j, #langchain, #llms, #llmgraphtransformer, #good-company, and more.
This story was written by: @neo4j. Learn more about this writer by checking @neo4j's about page, and for more stories, please visit hackernoon.com.
This article explores the implementation of a "From Local to Global" GraphRAG pipeline using Neo4j and LangChain. It covers the process of constructing knowledge graphs from text, summarizing communities of entities using Large Language Models (LLMs), and enhancing Retrieval-Augmented Generation (RAG) accuracy by combining graph algorithms with LLM-based summarization. The approach condenses information from multiple sources into structured graphs and generates natural language summaries, offering an efficient method for complex information retrieval.
986 episode
Bagikan
