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Build GraphRAG applications using Spanner Graph and LangChain
- Spanner Graph integrates graph, relational, search, and AI capabilities for scalable data management, with GraphRAG leading in question-answering systems extraction.
- LangChain and Spanner Graph are demonstrated to build robust GraphRAG applications for extracting insights in interconnected data.
- RAG systems improve performance by querying external data during inference and integrating it for contextually relevant responses.
- GraphRAG enhances context retrieval by creating knowledge graphs from varied data sources for detailed responses in gen AI applications.
- LangChain simplifies building RAG apps by integrating data sources and models, while Spanner Graph provides scalability and reliability.
- Building a retail application using GraphRAG enables a contextualized understanding of data relationships like product specifications and customer preferences.
- Steps involve transforming data into a knowledge graph, generating vector embeddings for semantic search, storing the graph in Spanner Graph, and inspecting the graph.
- Retrieval of context in GraphRAG applications is demonstrated using SpannerGraphVectorContextRetriever for enhanced answers.
- GraphRAG stands out by providing richer, more informative answers compared to conventional RAG, as showcased with a beginner drone recommendation scenario.
- Combining Spanner Graph and LangChain accelerates GraphRAG development for intelligent applications with reliable data insights.
- To get started, the GitHub repository, reference notebook tutorial, and setup guide for Spanner Graph capabilities are recommended resources.
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