6 articles found
How BlueRobin combines multi-model vector search, Reciprocal Rank Fusion, knowledge graph traversal, and full-document context assembly to answer complex questions across your entire archive.
A production blueprint for combining graph traversal and vector search to improve recall and precision in document retrieval.
Implement a complete Retrieval-Augmented Generation pipeline that combines semantic search with local LLM inference for intelligent document Q&A.
Build a powerful hybrid search system that combines vector embeddings with traditional keyword search for comprehensive document retrieval.
Deploy and configure Qdrant for production semantic search with collection management, filtering, and high availability on Kubernetes.
Create a production-ready embedding pipeline in .NET that generates vector embeddings with Ollama and stores them in Qdrant for semantic search.