Enables document ingestion, semantic search, and retrieval-augmented generation via MCP tools and REST API, using vector embeddings and intelligent chunking.
Enables LLMs to manage VAST Data storage infrastructure, including views, quotas, snapshots, and network configurations, through natural language commands.
Enables intelligent ingestion and querying of PDF, Markdown, and text files using hybrid search that combines keyword matching and semantic embeddings with citations.
Enables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.
Enables large language models to interact with Milvus vector databases through natural language, supporting semantic search with built-in OpenAI-compatible embedding services and comprehensive collection management.