Ai
Leverages AI models and resource sets for tasks like semantic search, relationship extraction, and context-aware question answering.
About
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.
Key Features
- Embeddings generation using OpenAI and Ollama
- CLI for vector store management and semantic search
- Context-aware semantic search
- Vector store integration with hnswlib
- Chat completion capabilities
- 0 GitHub stars
Use Cases
- Context-aware question answering based on graph relationships
- Semantic search across interconnected models
- Generating embeddings for knowledge graph elements
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