About Vecstor

Vecstor is an advanced infrastructure service designed to provide robust context storage and preprocessing capabilities, specifically optimized for Multi-Context Prompting (MCP) applications and AI assistants. Our goal is to offer a unified, efficient API for handling semantic context, performing vector operations, and integrating seamlessly with modern AI workflows.

Core Mission

We aim to simplify the complexities of semantic data management for developers building next-generation AI systems. Vecstor handles the heavy lifting of embedding generation, vector storage, semantic search, and data preparation, allowing you to focus on creating intelligent applications.

Key Features

• Persistent Semantic Memory: Efficiently store and retrieve long-term context using state-of-the-art embedding models and vector databases (like SQLite, ChromaDB, Qdrant, Pinecone).
• Vector Database Wrapper: A simplified, MCP-compatible interface abstracting interactions with various popular vector databases.
• Context Pre-processing Pipeline: An API-driven pipeline for cleaning, chunking, summarizing, and generating optimized embeddings from raw data sources (text, documents, etc.).
• Flexible Deployment: Available as a cloud-hosted service or a self-hosted solution (via Docker) for maximum control.
• Developer-Friendly API: Secure and well-documented RESTful API for easy integration.

Whether you're building sophisticated AI agents, enhancing chatbot capabilities, or developing advanced RAG (Retrieval-Augmented Generation) systems, Vecstor provides the foundational infrastructure for managing semantic context effectively.