Improved Knowledge Graph Creation with LangChain and LlamaIndex
We’re excited to share the latest updates to Memgraph’s integrations with LangChain and LlamaIndex, two frameworks that make it easier than ever to create, manage, and query knowledge graphs (KGs) from unstructured data.
These integrations are vital for building advanced Graph Retrieval-Augmented Generation (GraphRAG) solutions, where knowledge graphs provide the structure and context to make large language models (LLMs) smarter and more effective.
LangChain and LlamaIndex are frameworks designed to improve large language models (LLMs) by integrating external knowledge, enabling them to process and reason over structured data, like that in a graph database such as Memgraph. Here's a simplified breakdown of their roles and how they intersect with graph databases for knowledge creation:
LangChain is primarily focused on building applications that use LLMs in a modular and composable way. It enables LLMs to interface with external tools, APIs, and databases. It allows you to create pipelines where structured knowledge (like that stored in a graph database) is used alongside LLM capabilities.
