Embedding model

What is Embedding?

In the RAG (Retrieval-Augmented Generation) processing workflow,Embedding the core task is to convert large amounts of text data processed by the Parser, such as documents and knowledge content, into numerical forms that computers can understand and compare — namely vectors.

This conversion process enables RAG systems to:

1. Understand semantics

Embedding models capture the deep semantic meaning of text, not just surface vocabulary. This means that even if the wording in the query and documents differs, the system can identify their relevance as long as their meanings are similar.

2. Efficient retrieval

After converting text into vectors, the system can use efficient vector similarity search algorithms to quickly find the text fragments most relevant to the user's query from a massive database.

3. Improve generation quality

The retrieved relevant text fragments are provided to large language models (LLMs) as contextual references, helping the LLM generate more accurate answers.

Embedding plays two key roles in MaiAgent's RAG technology

1. Converting knowledge base content into vectors , and storing them in a vector database

2. Vectorizing the user's question, comparing it with vectors in the database to find the most relevant content

RAG process

In short, Embedding is the cornerstone of RAG systems; it transforms unstructured text data into computable, comparable vectors and is the critical preprocessing step for achieving precise information retrieval and high-quality content generation.

Impact of Embedding on RAG systems

1. Retrieval quality

• Depth of semantic understanding: High-quality Embedding models can more accurately capture the semantic content of text, improving retrieval relevance

• Context awareness: Excellent Embedding can understand contextual relationships in text, ensuring coherence of retrieval results

• Multilingual Support: Powerful multilingual Embedding models can handle cross-language knowledge retrieval needs

2. System performance

• Retrieval speed: The vector dimensionality and computational efficiency of Embedding models directly affect retrieval response time

• Resource consumption: Different Embedding models have different computational resource requirements, affecting system scalability

• Parallel processing: Efficient Embedding models can support large-scale parallel retrieval

Embedding models supported by MaiAgent

	Cohere Embed Multilingual v3.0 (Bedrock)	MaiAgent Embedding (open source)	OpenAI text-embedding-3-Large
Characteristics	Highest performance, cloud deployment	Open source, lightweight, highly customizable	Strong semantic understanding
Supported languages	Multilingual (including English, French, German, Spanish, Chinese, etc.)	Primarily targeted at English, but also supports some other languages	Multilingual, especially strong in English contexts
Deployment options	Requires AWS infrastructure	Can be deployed cloud-based or on-premises according to needs	Only supports cloud; data needs to be sent to OpenAI
Applicable scenarios	Information retrieval, recommendation systems, text classification, translation	Suitable for small tasks and basic applications	Information retrieval, recommendation systems

MaiAgent's Embedding technical advantages

1. Flexible model selection:

Offers multiple Embedding model choices to meet different needs

2. Diverse deployment options:

Supports cloud and on-premises deployment to ensure data security

3. Performance optimization:

Specifically optimized for RAG scenarios to provide the best retrieval results

4. Cost-effectiveness:

Choose the appropriate model based on actual needs to balance performance and cost