Enhancing RAG with Knowledge Graphs: Integrating Llama 3.1, NVIDIA NIM, and LangChain for Dynamic AI | HackerNoon
Briefly

from Hackernoon3 months ago
The best current solution for querying knowledge graphs is dynamic query generation, which uses a logic layer for deterministic database queries rather than relying solely on LLMs.
Hackernoonhttps://hackernoon.com/enhancing-rag-with-knowledge-graphs-integrating-llama-31-nvidia-nim-and-langchain-for-dynamic-ai
Dynamic query generation allows for controlled and consistent query processes while providing user input flexibility, enhancing the retrieval of structured information from knowledge graphs.
Hackernoonhttps://hackernoon.com/enhancing-rag-with-knowledge-graphs-integrating-llama-31-nvidia-nim-and-langchain-for-dynamic-ai
Implementing a retriever over a knowledge graph containing structured information, like FDA FAERS data, offers new opportunities for efficiently extracting relevant information.
Hackernoonhttps://hackernoon.com/enhancing-rag-with-knowledge-graphs-integrating-llama-31-nvidia-nim-and-langchain-for-dynamic-ai
GraphRAG, particularly Microsoft’s implementation, highlights the growing importance of merging unstructured text processing with structured data retrieval, shaping future data interaction.
Hackernoonhttps://hackernoon.com/enhancing-rag-with-knowledge-graphs-integrating-llama-31-nvidia-nim-and-langchain-for-dynamic-ai
Read at Hackernoon
#knowledge-graphs #dynamic-query-generation #fda-faers #retrieval-augmented-generation #structured-information
[
|
]