17 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 walkthrough of how BlueRobin transforms any uploaded document — PDF, image, or Office file — into a fully analysed, encrypted, and semantically indexed record.
How BlueRobin extracts named entities from every document, resolves them into a canonical taxonomy, and stores a property graph that connects people, organisations, dates, and events across your entire archive.
Revolutionizing cluster operations by replacing kubectl with natural language interaction using the Kubernetes MCP Server.
How we use Model Context Protocol to create a closed-loop design system where agents verify implementation against Figma specs automatically.
Bridging the gap between design and development by using the Model Context Protocol (MCP) to automate UI generation from Figma to Blazor.
A pragmatic analysis of the costs, performance, and complexity of running local LLMs versus cloud providers, and why we adopted a hybrid architecture.
How to extract entities (NER) from documents using LLMs and model them effectively in FalkorDB for knowledge graphs.
Extract named entities from documents using spaCy NLP service for building knowledge graphs and improving search relevance.
Techniques for rewriting user queries (HyDE, Expansion) and reranking results to boost retrieval accuracy in Retrieval-Augmented Generation.
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.
Implement effective text chunking strategies for RAG pipelines with semantic boundaries, overlap, and metadata preservation.
Learn how to integrate Docling, an AI-powered document understanding library, into your .NET application for high-quality OCR with layout preservation.
A practical cluster setup guide for running AI services alongside traditional backends with reliable data, messaging, and observability.