Embedding Studio
An open-source framework that transforming embedding models and vector databases into self-improving search engines that enhance both the recall of similarity search and precision of exact matching.
Key Features
Full-Cycle Search Engine
Transform your vector database into a complete search solution with built-in clickstream collection and continuous model refinement.
Intelligent Query Processing
Combine structured and unstructured search with our LLM for natural query search parsing.
User Feedback Collection
Automatically gather clickstream and session data for resource-saving and accuracy-effective fine-tuning.
Python Ecosystem
Use Embedding Studio together with your preferred Python libraries.
Plugin-Based System
Connect your own Vector DB, Data Source and Embedding model by implementing a Python plugin.
Continuous Improvement
Enhance search quality on-the-fly without long waiting periods through iterative fine-tuning.
Open Source
All our code is open sourced under the Apache 2.0 license.
Easy Deployment
Simple deployment with Docker Compose and blue-green deployment for zero-downtime updates.
Performance Monitoring
Track search quality metrics through comprehensive dashboards using MLflow.
When to Use Embedding Studio
Rich Content Collections
Businesses with extensive catalogs and unstructured data
Customer-Centric Platforms
Applications prioritizing personalized user experiences
Dynamic Content
Platforms with evolving content and changing user preferences
Complex Queries
Systems handling nuanced and multifaceted search needs
Mixed Data Types
Applications integrating different data formats in search
Cost-Conscious
Teams looking for powerful yet affordable solutions
Core Capabilities
Embedding Studio is a framework which transforms your Vector Database into a feature-rich, self-improving Search Engine.
Semantic & Similarity Search
Build powerful search systems for images inspired by True Detective or Breaking Bad, or find blogposts related to vector search topics.
Using multimodal embeddings like CLIP or BLIP, a vector database like Pgvector, QDrant or Milvus, Embedding Studio enables powerful search without hashtags, classifiers, or classic text indexes.
Iterative Fine-Tuning
Embedding Studio implements a full feedback loop that enhances search quality over time based on real user interactions. This creates systems that improve automatically with minimal additional input.
With features like vector optimization and blue-green deployment, you can apply incremental improvements and deploy enhanced embedding models with zero downtime.
Structured & Unstructured Search
Businesses rarely use unstructured search alone. When users search for brick red houses san francisco area for april, they want houses in San Francisco for April rental, with brick-red color as a preference.
Embedding Studio provides LLM instruct fine-tuning for Zero-Shot query parsing to bridge the gap before a company collects sufficient data, potentially eliminating complex rule implementation entirely.
Multi-Modal Support
Work with text, images, and structured data in one unified framework. Embedding Studio supports:
Zero-Shot queries parser which needs only a filters schema.
Vector database capabilities to implement query-to-filters mapping that improves session by session.
Personalization support for user-specific vector adjustments based on individual behavior.
Contact Information
EulerSearch Inc.
3416, 1007 N Orange St. 4th Floor,
Wilmington, DE, New Castle, US, 19801
Email: aleksandr.iudaev@eulersearch.com
Phone: +34 (691) 454 148
LinkedIn: alexanderyudaev