Mixedbread

Overview

Mixedbread Vector Stores provide a fully-managed, scalable semantic search platform. Simply upload your data, and let us handle parsing, chunking, embedding, and indexing for seamless, intuitive retrieval across diverse data types and languages.

Introduction

A vector store is a specialized database optimized for efficiently storing, indexing, and searching high-dimensional vectors known as embeddings. Unlike traditional search systems, vector stores perform semantic search — retrieving documents based on conceptual similarity to your query. Mixedbread automates the journey from raw documents to a fully searchable knowledge base, making advanced retrieval capabilities accessible to everyone.

Typical Workflow: Vector Store Lifecycle

Create a named vector store to organize and manage your documents.

Upload your documents to the vector store.

Use natural language to search through one or more vectors stores and retrieve relevant documents.

Keep track of your vector stores, documents, and usage metrics through the dashboard or API.

Key Features

  • Fully Managed: No need for manual setup or ongoing infrastructure maintenance.
  • Automated End-to-End Pipeline: Seamless handling of parsing, chunking, embedding, and indexing.
  • Advanced Semantic Search: Understands user intent and context beyond simple keywords.
  • Powerful Metadata Filtering: Refine searches based on detailed document attributes.
  • High Scalability: Built to effortlessly scale with large datasets and high query volumes.
  • Developer-Friendly API: Easy-to-use APIs and SDKs for rapid integration.
  • Multimodal & Multilingual: Robust support for diverse data types and languages, enhancing global usability.

Best Practices for Vector Stores

  1. Use Multiple Stores: Create distinct stores for logically separate datasets, environments or specific topics to simplify management.

  2. Leverage Metadata for Filtering: Apply consistent metadata during document ingestion and utilize filters in your search queries for targeted results.

  3. Tune Retrieval Quantity: Experiment with the number of chunks retrieved to find the right balance between precision and recall for your application.

  4. Keep Content Current: Implement workflows to regularly update or delete documents within your vector store as the source information changes.

  5. Prepare Source Documents: Clean and preprocess your source documents before uploading to enhance the quality of chunking and embeddings.

Last updated: May 2, 2025