Leverages AI models and resource sets for tasks like semantic search, relationship extraction, and context-aware question answering.
This tool utilizes AI models and interconnected resource sets to abstract AI components from low-level implementation details. It employs "Narrator" processors to describe model elements and their relationships, enabling the generation of embeddings and vector stores for semantic search and RAG (Retrieval-Augmented Generation). This approach considers both semantic and graph distance, allowing for context-aware question answering and information retrieval within complex data structures.
主な機能
01Embeddings generation using OpenAI and Ollama
02CLI for vector store management and semantic search
03Context-aware semantic search
04Vector store integration with hnswlib
05Chat completion capabilities
060 GitHub stars
ユースケース
01Context-aware question answering based on graph relationships
02Semantic search across interconnected models
03Generating embeddings for knowledge graph elements
