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Building Advanced RAG Applications Using FalkorDB, LangChain, Diffbot API, and OpenAI
This article explores the benefits of using a knowledge graph database, FalkorDB, for building Retrieval-Augmented Generation (RAG) applications.
RAG applications aim to enhance the capabilities of large
language models (LLMs) by combining them with knowledge graphs. The article discusses how FalkorDB offers a powerful alternative to vector databases for storing and querying data, providing LLMs with more
comprehensive context and reducing the risk of hallucinations.
The author presents a step-by-step guide to implementing RAG using FalkorDB, LangChain, Diffbot API, and OpenAI, showcasing how to construct a knowledge graph from documents and utilize it for question-answering.
The key takeaway is that integrating knowledge graphs with LLMs through FalkorDB significantly improves the accuracy and relevance of responses generated by LLMs.
To read more: https://www.falkordb.com/blog/rag-applications-using-falkordb-langchain-diffbot-api-and-openai/
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By FalkorDBThis article explores the benefits of using a knowledge graph database, FalkorDB, for building Retrieval-Augmented Generation (RAG) applications.
RAG applications aim to enhance the capabilities of large
language models (LLMs) by combining them with knowledge graphs. The article discusses how FalkorDB offers a powerful alternative to vector databases for storing and querying data, providing LLMs with more
comprehensive context and reducing the risk of hallucinations.
The author presents a step-by-step guide to implementing RAG using FalkorDB, LangChain, Diffbot API, and OpenAI, showcasing how to construct a knowledge graph from documents and utilize it for question-answering.
The key takeaway is that integrating knowledge graphs with LLMs through FalkorDB significantly improves the accuracy and relevance of responses generated by LLMs.
To read more: https://www.falkordb.com/blog/rag-applications-using-falkordb-langchain-diffbot-api-and-openai/