neo4j-graph-rag

You can search across Mattermost threads and HubSpot emails by using a Neo4j knowledge graph that applies semantic retrieval and cross-source aggregation. This surfaces relevant conversations and documents from multiple unified sources.

Yes, you can combine vector similarity search with Cypher filtering for RAG to achieve precise semantic retrieval. This approach filters the knowledge graph using structured queries alongside vector embeddings stored on the corresponding nodes.

Yes, you need a stable Neo4j instance and an embedding model to query transcripts and issues. The knowledge graph requires these components to generate vector embeddings and perform semantic retrieval operations.

The best way to retrieve business insights from Fireflies transcripts and GitHub issues is using a graph-based RAG approach. It enables cross-source stitching to build coherent summaries from multiple data sources like product discussions and support history.

Graph RAG handles cross-source aggregation for customer support history by applying semantic retrieval across a multi-source knowledge graph. It surfaces relevant threads and emails, using embeddings stored on nodes to connect related data points.