In today’s business landscape, corporate data exhibits high diversity and complexity. Emails, invoices, resumes, support tickets, log messages, and tabular data all contain intricate conceptual relationships and contextual information. However, traditional single-vector embedding models struggle to capture and understand this complex multidimensional data structure, posing significant challenges for data retrieval and mining.
The Current State and Dilemma of Vectorization
High Complexity and Diversity of Corporate Data
Corporate data often contains multiple concepts and relationships; for example, an email may include information from different levels such as sender, subject, and attachment content. This intricate multidimensional feature presents significant difficulties for data processing.
Defects of Traditional Single-Vector Embedding Models
Limited Understanding of Classification Layers
Developers often need to create classification layers to identify and match queries with different aspects of document metadata. However, this approach is limited by the understanding scope of the classifiers and incurs high deployment costs.
Loss of Multifaceted Information
Existing embedding models (such as Cohere Embed v3) map documents into a single semantic vector space. When data contains multiple concepts, it inevitably leads to the loss of multifaceted semantic information from the documents.
Innovative Solutions from Cohere Compass
Multidimensional Representation Format
Cohere Compass employs a novel embedding format that effectively captures and stores multiple concepts and their relationships within the data. It merges originally independent vectors into the same space, forming a rich semantic network.
End-to-End One-Stop Toolchain
Compass provides end-to-end tool support. Users only need to integrate raw data into a standard JSON input using the SDK, which is then processed by the embedding model to generate multidimensional representations, finally stored in any vector database.
Addressing the Challenges of Multidimensional Data
With its innovative multidimensional representation and comprehensive toolchain, Compass can efficiently tackle the retrieval challenges posed by traditional models regarding multidimensional data. Both text data and structured data can achieve high-quality vector representations.
How Compass Works
Compass SDK Parses Multidimensional Data into JSON
In traditional retrieval processes, emails and PDF attachments are processed separately. The Compass SDK can parse them along with metadata such as sender and time into a single JSON file.
Compass Model Generates Multidimensional Vector Representations
The JSON file is fed into the Compass embedding model, outputting a multidimensional vector representation that captures various aspects and interrelations within the data.
Stored in Any Vector Database
The embedding output can be directly stored in any supported vector database, preparing for subsequent semantic retrieval.
Advantages and Applications of Compass
Example Comparison: Superior to Traditional Models
For example, when asked, “What was the first PR (Pull Request) I received about the Cohere embedding model?” Compass can accurately distinguish and satisfy this query, which involves multiple aspects including time, subject, and type, whereas traditional models fail.
Fully Unleashing the Value of Corporate Data
Through efficient retrieval of multidimensional data, Compass is expected to release the full potential value of corporate data. It can be applied not only in traditional scenarios such as emails and invoices but also has significant implications for software development, customer support, and more.
Looking Ahead
Although Compass is still in the private testing phase, its innovative concepts and initial performance have been refreshing. As a new paradigm for multidimensional retrieval, Cohere Compass contributes new solutions to the challenges posed by the high complexity and diversity of corporate data through its innovative representation and toolchain support, opening up new possibilities for the efficient utilization of corporate data. We look forward to Cohere’s further performance in the RAG field!