----Take you to understand the 10 common vector databases in RAG applications and their differences

introduction

Large language models (LLMs) have shown great capabilities in building intelligent applications that can understand and generate natural language. However, these models usually rely on the knowledge they have during training, and may not be able to grasp specific domains or the latest information. To make up for this shortcoming, retrieval-augmented generation (RAG) technology has emerged.

RAG significantly improves the performance and knowledge coverage of LLM on specific tasks by retrieving relevant information from external knowledge bases before LLM generates answers. In RAG applications, vector databases play a crucial role in storing and efficiently retrieving this external knowledge, allowing LLM to generate more accurate and contextual answers based on the latest, domain-specific information.

This article will explore in depth the 10 common vector databases used in RAG applications and introduce their characteristics and differences in detail to help readers better understand and choose a database that suits their needs.

Detailed explanation of the characteristics and differences of common vector databases

1. Weaviate

Core Features :

• Open source, AI-native vector database
• Stores both the object and its corresponding vector representation
• Multiple deployment options: Weaviate Cloud, Docker, Kubernetes, and embedded

Unique advantages :

• Pre-integrated vectorization and RAG modules
• Support for multimodal data (text, audio, video)
• Provides vector, keyword and hybrid search

2. Qdrant

Core Features :

• High-performance, large-scale vector similarity search engine
• Written entirely in Rust
• Supports arbitrary JSON format metadata attached to vectors

Unique advantages :

• Supports dense and sparse vectors
• Provide RESTful API and gRPC API
• Deep integration with LangChain, LlamaIndex and other frameworks

3. Milvus

Core Features :

• An open source database focused on vector similarity search
• Supports multiple index types and search algorithms
• Provide distributed architecture

Unique advantages :

• Optimized for large-scale vector searches
• Support multiple similarity measurement methods
• Active open source community

4. MyScale

Core Features :

• Fully SQL-compliant vector database
• Support SQL-vector join query
• Provides two deployment modes: cloud service and self-hosting

Unique advantages :

• Friendly to developers familiar with SQL
• Combining vector search and relational query
• Claims to be three times faster and cheaper than other solutions

5. pgvector

Core Features :

• Open Source Extensions for PostgreSQL
• Add vector similarity search function to PostgreSQL
• Supports multiple vector data types and distance metrics

Unique advantages :

• Directly integrated into PostgreSQL
• Supports exact search and approximate search
• Inheriting PostgreSQL's ACID characteristics

6. pgvecto-rs

Core Features :

• PostgreSQL vector search extension developed with Rust
• Supports vectors up to 65535 dimensions
• Introducing the VBASE method for filtering

Unique advantages :

• Optimizing performance using SIMD instructions
• Independent management of index storage and memory
• Support for binary and sparse vectors

7. Chroma

Core Features :

• Lightweight, easy-to-use vector database
• Focus on embedded storage and retrieval
• Simple API design

Unique advantages :

• Quick start and prototyping
• Deep integration with the Python ecosystem
• Suitable for small to medium applications

8. OpenSearch

Core Features :

• Open source search and analytics suite
• Built-in vector search function
• Supports approximate k-NN and exact k-NN search

Unique advantages :

• Unified platform to handle search and analysis needs
• Automatically generate vector embeddings from text
• Support multi-modal search

9. TiDB Vector Search

Core Features :

• Vector Search Extension for Distributed SQL Database
• Unified processing of vector search, knowledge graph and operation data
• Providing Serverless Options

Unique advantages :

• Simplify data management for AI applications
• Support GraphRAG technology
• Integration with tools such as OpenAI Embedding

10. Elasticsearch

Core Features :

• Popular search and analytics engines
• Supports vector fields and similarity search
• Rich query DSL

Unique advantages :

• Mature ecosystem
• Powerful combination of full-text search and vector search
• Enterprise-grade features and support

Vector database comparison table

in conclusion

This article provides a detailed summary and comparison of 10 common vector databases used in RAG applications. Each database has its own unique characteristics and advantages, and is suitable for different application scenarios and requirements. When choosing a suitable vector database, developers need to comprehensively consider deployment methods, scalability, performance, ease of use, integration with other technologies, support for hybrid search, and cost.

For example, for developers familiar with the PostgreSQL ecosystem, pgvector and pgvecto-rs may be more natural choices; for cloud-native applications that require high flexibility and powerful functions, Weaviate, Qdrant, and Milvus provide rich functions and good scalability; MyScale has attracted a large number of users with its SQL compatibility and high performance; OpenSearch and Elasticsearch, as mature search engines, also provide users with a unified solution with their integrated vector search functions; TiDB Vector Search brings convenience to AI application development with its unified data management and Serverless features.

As RAG applications become increasingly popular, choosing a suitable vector database will be a key step in building efficient and intelligent applications.