Mixedbread Changelog

Mixedbread MCP

Introducing the Mixedbread Model Context Protocol (MCP) server, a TypeScript-based integration that exposes powerful vector 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
• Vector Store Operations: Direct interaction with vector 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 vector 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

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