Crossing Minds has developed embeddings specifically optimized for Retrieval-Augmented Generation (RAG) systems. These specialized vector representations are crucial for superior information retrieval and language model integration.
Our RAG-optimized embeddings ensure precise and relevant information retrieval, maintaining efficiency even with vast datasets.They capture nuanced meanings and intent, adapting through continuous learning from interactions.
These embeddings also enable cross-modal understanding, connecting information across text, images, and structured data. By focusing on these advanced embeddings, we provide a solid foundation for next-generation RAG systems, significantly enhancing AI's ability to process and utilize information effectively.
Our embedding models employ a flexible architecture that adapts to the unique characteristics of your data. Whether you're working with user behavior, product attributes, or multimedia content, our system optimizes the embedding process to capture the most relevant features and relationships.
This adaptive approach ensures that your embeddings reflect the subtle nuances and complex interactions within your data, providing a solid foundation for downstream tasks such as recommendation systems, search engines, and personalization algorithms.
Our adaptive learning architecture excels at capturing the complexities of user behavior and preferences. User embeddings encode long-term preferences, demographic information, and historical interactions, creating a comprehensive profile of each individual.
Session embeddings, on the other hand, capture real-time intent and context from current user activities, allowing for immediate responsiveness to user needs.
These embeddings evolve dynamically as user behavior changes, ensuring up-to-date representations that reflect the latest trends and individual preferences. This dynamic nature allows for personalized experiences that adapt in real-time, significantly enhancing user engagement and satisfaction.
Valuable information often spans multiple data types. Our Embedding Training system excels at integrating diverse data modalities:
By fusing these different modalities into unified embeddings, we enable your AI systems to make more informed decisions based on a holistic view of your data ecosystem.
Your business has unique needs. Our Embedding Training system offers extensive customization options, allowing you to fine-tune the embedding process to align with your specific objectives.
Whether you're optimizing for recommendation accuracy, search relevance, or user engagement, we provide the tools and expertise to tailor your embeddings for maximum impact.
Our embeddings are designed with interpretability in mind, allowing you to trace back from high-level model outputs to the underlying data features that influenced those decisions. This transparency builds trust and provides valuable insights into your data and AI processes.
Context: FinTech Data
Example
New York Home Hardware Distributors
NY HOME HARDWARE D
NEW YORK HHDW DISTR
Task: Entity Deduplication with LLM
Method
Accuracy
Total Time (min)
Static Few Shots
0.65
5
Fine Tuning
0.88
660
RAGSys
0.91
6
Context
Example
LLM Tags completion
Stone Nail File, Nail Art, Manicure
Amazing Stone Nail File
"best nail file i have ever used".
its never wears outs, and the tapered chiseled end is
wonderful
Measures 4" long x 1/4" wide
you get 1
Pink or Green
Hidden Tags:
stone nail file, nail art, nail polish, nail tools, manicure, manipedi, pedicure, gift, nail health, nail file
Generated Tags:
stone nail file, nail art, manicure, nail tools, nail care, nail file, pink nail file, green nail file, nail grooming, nail accessories, nail health, pedicure
Method
Precision
Recall
Zero Shot
0.1196
0.1326
Static Few Shots
0.1295
0.1415
RAGSys
0.2286
0.2559
LLM Tags Completion: Results. Average over 1k items in the test set. LLM: gpt-4o
Context: B2B Marketplace Catalog
Task: Entity Deduplication with LLM
Given two items titles, are they the same product?
Example 1
Product A:
Google Chromecast Ultra 4K Streaming Media Player
Product B:
Chromecast Ultra
Answer:
Yes
Example 2
Product A:
Dell Alienware M15
Product B:
Alienware M17
Answer:
No
