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Enhancing RAG with Knowledge Graphs: Integrating Llama 3.1, NVIDIA NIM, and LangChain for Dynamic AI
Manage episode 446468980 series 3570694
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This story was originally published on HackerNoon at: https://hackernoon.com/enhancing-rag-with-knowledge-graphs-integrating-llama-31-nvidia-nim-and-langchain-for-dynamic-ai.
Use Llama 3.1 native function-calling capabilities to retrieve structured data from a knowledge graph to power your RAG applications.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #knowledge-graph, #retrieval-augmented-generation, #llama-3.1, #nvidia-nim, #neo4j, #langchain, #graph-based-llm-agent, #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 demonstrates the use of Llama 3.1, NVIDIA NIM, and LangChain to create a knowledge graph-based agent for retrieval-augmented generation (RAG), leveraging structured data and dynamic query generation to improve information retrieval and response accuracy.
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