Overview
Mixedbread Vector Stores transform any data into an AI-native search engine that understands meaning, not just keywords. Whether it's PDFs, images, code repositories, or product catalogs, our platform makes previously unsearchable content instantly discoverable through natural language queries and AI agents. Powered by Mixedbread's multimodal embedding models, we automatically handle the complex process of making your data AI-ready and searchable.
Making Data Searchable: Simple Workflow
Create a named vector store for your specific data domain (e.g., "Company Knowledge", "Product Catalog", "Support Docs").
Upload documents, images, code files, or structured data. We automatically process everything into searchable format.
Use conversational queries or integrate with AI agents to find exactly what you need across all your data, regardless of format.
Monitor performance and expand your searchable knowledge base as your data grows.
Key Features
Mixedbread Vector Stores provide everything you need to transform static data into intelligent, searchable experiences:
| Capability | What It Means | Benefits |
|---|---|---|
| Universal Data Support | Text, images, code, PDFs, product catalogs—any content type | No data left behind; comprehensive search across all your information |
| Multimodal AI Understanding | Single platform that processes both text and visual content | Bridge the gap between different data formats seamlessly |
| Zero Setup Required | Upload and search immediately | Get results in minutes, not weeks of infrastructure setup |
| Natural Language + AI Agents | Conversational search and autonomous AI querying | Users find answers naturally; agents can explore data independently |
| Enterprise-Ready Scale | Millions of documents, thousands of queries per second | Grows with your business without performance degradation |
| Developer-First Integration | Simple APIs, comprehensive SDKs | Works with any tech stack; minimal integration complexity |
Getting the Most from Your Search Engine
Transform your approach to data organization and search optimization with these proven strategies:
Smart Organization: Create purpose-built search engines for different domains—customer support, product catalogs, internal knowledge. This improves both performance and user experience by keeping related content together.
Rich Metadata Strategy: Add contextual tags and attributes during upload. This combination of semantic understanding plus structured filtering delivers precision that neither approach achieves alone.
Iterative Growth: Start with your most critical data sources and expand systematically. Monitor search quality metrics and user feedback to guide optimization—what gets measured gets improved.
Automated Freshness: Set up workflows to sync updated content automatically. Stale data leads to poor user experiences and reduced trust in your search system.
Quality-First Approach: Clean, well-structured source data produces dramatically better search results. Invest time upfront in removing duplicates and standardizing formats—your users will notice the difference.
Check out the Vector Store API for detailed endpoints and code examples.
Last updated: June 17, 2025