The process for building a vector retriever model involves sending chunked documents to an embedding model (such as nomic-embed-text) to generate numerical representations (embeddings) that capture semantic meaning, and then storing these embeddings in a vector database like Neo4j for efficient similarity searches.

Authors

• Person: Not available Organization: Procogia
  RAG Using Knowledge Graph: Mastering Advanced Techniques

Sources

• RAG Using Knowledge Graph: Mastering Advanced Techniques procogia.com Procogia via serper

Referenced by nodes (2)

• vector database concept
• Neo4j concept