Mixedbread Changelog

Mixedbread MCP

Introducing the Mixedbread Model Context Protocol (MCP) server, a TypeScript-based integration that exposes powerful store capabilities to AI assistants like Claude Desktop. Built as an open standard, it enables secure and controlled access to external data sources.

Key Features

• Claude Desktop Integration: Seamless integration with Claude and other MCP-compatible AI assistants
• Store Operations: Direct interaction with stores through standardized MCP tools
• Semantic Search: Enable AI assistants to search and retrieve information using natural language
• File Management: Upload, manage, and search through documents in stores
• Secure Data Access: Controlled interface for AI systems to access external data sources

Installation

npm install -g @mixedbread/mcp

mxbai CLI

Introducing the Mixedbread CLI, a command-line interface for managing Mixedbread's services directly from your terminal. Built on top of the Mixedbread SDK, it provides efficient command-line access to all core platform features.

Key Features

• Store Management: Create, list, update, and manage stores with comprehensive control
• Store File Upload & Processing: Upload files with intelligent processing strategies, metadata, and batch operations
• Semantic Search: Search through stores using natural language queries with advanced filtering
• Question Answering: Get AI-powered answers based on your store content
• Intelligent Sync: Sync files with change detection and smart processing strategies to stores

Installation

npm install -g @mixedbread/cli

Platform Alpha

Today we're excited to announce the alpha of the Mixedbread platform, bringing together all our capabilities in a unified API. Experience state-of-the-art embeddings, reranking, document parsing, and stores in one integrated solution.

Now Available in Alpha

• Embeddings API: Transform text into semantic vectors with our award-winning models
• Reranking API: Boost search relevance with advanced cross-encoder models
• Document Parsing API: Extract LLM-ready content from PDFs, DOCX, PPTX, and more
• Stores API: Fully-managed multi-modal search with automatic ingestion pipelines

mxbai-rerank-v2

The second generation of our reranking models features reinforcement learning (GRPO), extended context handling, and support for 100+ languages. These models are 8x faster than comparable alternatives while achieving higher accuracy across all benchmarks.

Models

• mxbai-rerank-base-v2 (0.5B): Balanced performance for production use
• mxbai-rerank-large-v2 (1.5B): Maximum accuracy for critical applications

mxbai-embed-xsmall-v1

Our smallest and most efficient English embedding model. Perfect for edge deployments and resource-constrained environments, it delivers competitive performance in an extra small footprint.

Batched - Dynamic Batching Library

We've open-sourced Batched, our dynamic batching library that powers Mixedbread's inference infrastructure. Achieve up to 10x throughput improvements with minimal latency impact.

Baguetter - Retrieval Testing Framework

Introducing Baguetter, our open-source framework for testing and evaluating retrieval systems. Make your search better with comprehensive testing tools designed for real-world scenarios.

BMX Algorithm

We've developed BMX, a modern take on the classic BM25 algorithm. Combining lexical and semantic signals, BMX delivers superior hybrid search performance.

deepset-mxbai-embed-de-large-v1

In collaboration with deepset, we've released a German/English embedding model that sets new performance standards among open source alternatives.

mxbai-colbert-large-v1

Our ColBERT model brings late interaction capabilities. Built on our mxbai-embed-large-v1 architecture, it achieves state-of-the-art performance on 13 BEIR benchmarks.

mxbai-embed-large-v1

Our flagship English embedding model delivers state-of-the-art performance, outperforming closed source alternatives like OpenAI's text-embedding-v3.

mxbai-embed-2d-large-v1

The world's first 2D-Matryoshka embedding model. This innovative approach allows you to reduce both the number of layers and dimensions while maintaining competitive performance.

mxbai-rerank-v1

We're launching our first generation of reranking models, available in three sizes. These models add a powerful semantic layer to existing search systems, dramatically improving result relevance.

Models

• mxbai-rerank-xsmall-v1: Ultra-efficient for high-volume applications
• mxbai-rerank-base-v1: Balanced performance and speed
• mxbai-rerank-large-v1: Maximum accuracy for critical applications