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Overview

Embedding Studio

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version Python 3.10 CUDA 11.7.1 Docker Compose Version

Website • Documentation • Challenges & Solutions • Use Cases

Embedding Studio is an innovative open-source framework designed to transform embedding models and vector databases into comprehensive, self-improving search engines. With built-in clickstream collection, continuous model refinement, and intelligent vector optimization, it creates a feedback loop that enhances search quality over time based on real user interactions.

Search Quality Evolution

RED: On the graph, typical search solutions without enhancements, such as Full Text Searching (FTS), Nearest Neighbor Search (NNS), and others, are marked in red. Without the use of additional tools, the search quality remains unchanged over time.

ORANGE: Solutions are depicted that accumulate some feedback (clicks, reviews, votes, discussions, etc.) and then initiate a full model retraining. The primary issue with these solutions is that full model retraining is a time-consuming and expensive procedure, thus lacking reactive adjustments (for example, when a product suddenly experiences increased demand, and the search system has not yet adapted to it).

INDIGO: We propose a solution that allows collecting user feedback and rapidly retraining the model on the difference between the old and new versions. This enables a smoother and more relevant search quality curve for your system.

Features

Core Capabilities

  • 🔄 Full-Cycle Search Engine - Transform your vector database into a complete search solution
  • 🖱️ User Feedback Collection - Automatically gather clickstream and session data
  • 🚀 Continuous Improvement - Enhance search quality on-the-fly without long waiting periods
  • 📊 Performance Monitoring - Track search quality metrics through comprehensive dashboards
  • 🎯 Iterative Fine-Tuning - Improve your embedding model through user interaction data
  • 🔍 Blue-Green Deployment - Zero-downtime deployment of improved embedding models
  • 💾 Multi-Source Integration - Connect to various data sources (S3, GCP, PostgreSQL, etc.)
  • 🧠 Vector Optimization - Apply post-training adjustments for incremental improvements

Specialized Features

  • 📈 Personalization Support - User-specific vector adjustments based on behavior
  • 💬 Suggestion System - Intelligent query autocompletions from usage patterns
  • 🔎 Category Prediction - Identify relevant categories from user queries
  • 🔤 Multi-Modal Support - Work with text, images, and structured data
  • 🧩 Plugin Architecture - Easily extend functionality

In Development

  • 📑 Zero-Shot Query Parser
  • 📚 Catalog Pre-Training
  • 📊 Advanced Analytics

When is Embedding Studio the Best Fit?

  • 📚💼 Rich Content Collections – Extensive catalogs and unstructured data
  • 🛍️🤝 Customer-Centric Platforms – Personalized UX
  • 🔄📊 Dynamic Content – Evolving information
  • 🔍🧠 Complex Queries – Multifaceted search
  • 🔄📊 Mixed Data Types – Structured + unstructured
  • 💵💡 Cost-Conscious Organizations – Optimize without high cost

More at: docs/when-to-use-the-embeddingstudio.md

How it works

Follow our tutorial to get acquainted with the main functions.

Challenges Solved

Embedding Studio is not a vector DB. It transforms your vector DB into a full search engine.

  • ✅ Cold Start Problems
  • ✅ Static Search Quality
  • ✅ Long Improvement Cycles
  • ✅ Expensive Reindexing
  • ✅ Hybrid Search Complexity
  • ✅ Query Understanding
  • ✅ New Content Discovery

System Architecture

Core Components

  • API Service - Main orchestrator
  • Vector DB - PostgreSQL with pgvector
  • Clickstream System - Track user interactions
  • Workers:
  • Fine-Tuning Worker
  • Inference Worker (Triton)
  • Improvement Worker
  • Upsertion Worker

Data Flow

  1. Ingest content
  2. Track user behavior
  3. Fine-tune on feedback
  4. Redeploy improved models
  5. Serve better search results

📬 Contact Us

EulerSearch Inc.
3416, 1007 N Orange St. 4th Floor,
Wilmington, DE, New Castle, US, 19801
📧 aleksandr.iudaev@eulersearch.com
📞 +34 (691) 454 148
🔗 LinkedIn