Vector Databases Are the Base of RAG Retrieval
Tags: vectordatabase, rag, ai, machinelearning
Published: 2024-04-29 22:20:38


## Why Will RAG Stay Despite LLMs’ Advancements?

Implementing a chatbot powered by Retrieval Augmented Generation ([RAG](https://zilliz.com/learn/Retrieval-Augmented-Generation)) technology is a game-changer for businesses looking to enhance their customer support. This approach combines the conversational abilities of [large language models ](https://zilliz.com/glossary/large-language-models-\(llms\))with knowledge stored in vector databases from diverse fields, such as legal advising, educational assistance, healthcare, and more. 

A standard RAG framework consists of two core systems: the Retriever and the Generator. The Retriever segments data (like documents), encodes data into vector embeddings, creates indices (Chunks Vectors), and retrieves the semantically relevant results through vector embeddings. The Generator, on the other hand, uses the context gleaned from the retrieval process to prompt the LLM, which in turn generates precise responses.

![](https://assets.zilliz.com/A_generic_RAG_architecture_ce16755424.png)


_A generic RAG architecture. The user queries, spanning different modalities, serve as input to both the retriever and the generator. The retriever extracts relevant information from data sources. The generator interacts with the retrieval results and ultimately produces outcomes of various modalities. Source: [https://arxiv.org/pdf/2402.19473](https://arxiv.org/pdf/2402.19473)_

The effectiveness of RAG systems stems from its synergistic combination of retrieval systems and generative models. Retrieval systems deliver precise, relevant facts and data, while generative models craft flexible, contextually enriched responses. This dual approach allows RAG to handle complex inquiries and produce rich, informative answers effectively, proving invaluable in systems requiring nuanced natural language understanding and generation.

RAG technology is advantageous over traditional LLMs, including

- **Reduction of "Hallucination" Issues**: RAG leverages external information to aid LLMs in generating more accurate responses, enhancing the reliability and traceability of the output.

- **Enhanced Data Privacy and Security**: RAG can securely manage private data as an external knowledge base extension, preventing potential data breaches after model training.

- **Real-time Information Retrieval**: RAG facilitates the real-time acquisition of up-to-date, domain-specific knowledge, addressing the challenge of out-of-date information.

While ongoing advancements in LLMs also address these issues through strategies like fine-tuning on private datasets and providing longer-text windows, RAG remains a robust, reliable, and cost-effective solution in broader GenAI applications due to its:

- **Transparency and Operability**: Unlike the opaque processes of fine-tuning and long-text management, RAG offers clearer, more interconnected module relationships, improving tunability and interpretability.

- **Cost Efficiency and Quick Respons**e: RAG requires less training time and incurs lower costs than fine-tuned models. It also outpaces long-context processing LLMs in response speed and operational costs.

- **Private Data Management**: By separating the knowledge base from LLMs, RAG secures a practical implementation ground and effectively manages existing and newly acquired enterprise knowledge.

Even though many people predict RAG is on the verge of demise as LLMs continue to evolve and advance, I still believe that RAG technology will stay. RAG is inherently complementary to LLMs, which ensures its prolonged relevance and success across multiple applications. 


## Vector Databases Are the Base of RAG Retrieval

In real-world production applications, RAG retrieval is often tightly integrated with [vector databases](https://zilliz.com/learn/what-is-vector-database), leading to the development of a popular RAG solution known as the [CVP stack](https://zilliz.com/blog/ChatGPT-VectorDB-Prompt-as-code), comprising **C**hatGPT, **V**ector Database, and **P**rompt-as-code technologies. This innovative solution leverages vector databases' efficient similarity retrieval capabilities to enhance LLMs' performance. The RAG system can rapidly retrieve relevant knowledge entries within the vector database by transforming user queries into vector embeddings. This approach enables LLMs to access the most up-to-date information stored in the database when responding to user queries, effectively addressing issues such as delays in knowledge updates and the occasional inaccuracies in generated content, often called "hallucinations."

Many other retrieval technologies, including search engines, relational databases, and document databases, are available on the market in addition to vector databases. However, vector databases are the most favored option in RAG implementations due to their superior capabilities in efficiently storing and retrieving vast quantities of vector embeddings. Produced by machine learning models, these vectors represent a wide range of data types, including text, images, videos, and sounds, while capturing intricate semantic details. 

Below is a comparative analysis of vector databases against other technological options in information retrieval, highlighting why vector databases have become the preferred choice in building RAG applications. 

|                                                                   |                                                                                           |                                                                                                                        |                                                                                                                               |                                                                                                                                                   |
| ----------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| Category                                                          | Search Engines                                                                            | Relational Databases                                                                                                   | Document Databases                                                                                                            | Vector Databases                                                                                                                                  |
| Primary Products                                                  | Elasticsearch                                                                             | MySQL                                                                                                                  | MongoDB                                                                                                                       | Milvus                                                                                                                                            |
| Implementation Principle                                          | Utilizes inverted indexing for fast text searches with limited vector search capabilities | Employs standardized data models and SQL for optimal transaction processing, struggles with handling unstructured data | Stores data in JSON format, offering flexible data models and basic full-text search but limited semantic search capabilities | Designed specifically for high-dimensional vectors, uses Approximate Nearest Neighbor (ANN) algorithms for effective semantic similarity searches |
| Use Cases                                                         | Ideal for full-text searches and straightforward data analysis                            | Best for applications demanding high consistency and intricate transaction management                                  | Well-suited for rapid development and environments with frequent data model changes                                           | Optimal for unstructured data-based retrieval like image and semantic text searches                                                               |
| Large Vector Dataset Retrieval Efficiency (The higher the better) | Medium                                                                                    | Low                                                                                                                    | Low                                                                                                                           | High                                                                                                                                              |
| Multimodal Generalization Capability (The higher the better)      | Medium                                                                                    | Low                                                                                                                    | Low                                                                                                                           | High                                                                                                                                              |
| Suitability for RAG Retrieval(The higher the better)              | Medium                                                                                    | Low                                                                                                                    | Low                                                                                                                           | High                                                                                                                                              |
| Total Costs(The lower the better)                                 | High                                                                                      | High                                                                                                                   | Medium                                                                                                                        | Low                                                                                                                                               |

_Compare vector databases with other information retrieval technologies_ 

As shown in the table above, vector databases are advantageous in the following fields: 

**Implementation Principle:** Vectors encode semantic meanings, and vector databases decode query semantics using deep learning models, moving beyond simple keyword searches. The precision of semantic understanding has improved with AI advancements, making vector distance a standard measure of semantic similarity in NLP and positioning embeddings as the preferred format for managing diverse data types.

**Retrieval Efficiency:** High-dimensional vectors allow for advanced indexing and quantization techniques that significantly boost retrieval speed and reduce storage demands. Vector databases can scale horizontally to manage growing data volumes while maintaining quick response times, essential for RAG systems dealing with extensive datasets.

**Generalization Capability:** Unlike traditional databases that mostly handle text, vector databases store and process various types unstructured data, including images, videos, and audio. This versatility enhances the flexibility and functionality of RAG systems.

**Total Ownership Cost:** Vector databases are easier to deploy and integrate with existing machine learning frameworks due to their straightforward setup and comprehensive APIs. This accessibility, coupled with lower overall costs, makes them a favorite among developers of RAG applications.


## Enhancing Vector Databases for More Performant RAG Applications

Vector databases have been a fundamental retrieval technology in RAG systems. However, as developers build increasingly complicated RAG applications and use them in production environments, their demand for higher-quality and more precise answers grows, posing challenges to vector databases. 


![RAG use case.png](https://assets.zilliz.com/RAG_use_case_7bcb047dd1.png)


A standard RAG construction process usually involves several steps: preprocessing data through segmentation, data cleaning, and embedding; building and managing indexes; and using vector search to locate similar segments to improve prompt generation. Most vector databases handle index building and management and vector data retrieval, with just a few, like [Milvus](https://zilliz.com/what-is-milvus), providing built-in embedding functions. Therefore, the quality of vector data retrieval directly impacts the relevance and effectiveness of the LLM-generated content. 

Numerous engineering optimizations emerge to enhance the retrieval quality of vector databases, including selecting appropriate chunk sizes, deciding on the necessity of overlapping segments, choosing appropriate embedding models, adding content tags, integrating lexicon-based retrieval for a hybrid search approach, and selecting rerankers. We could integrate many of these tasks within vector databases. 

Specifically, vector databases should improve the following areas: 

- **High Precision in retrieval**: Vector databases must accurately recall the most relevant documents or data snippets corresponding to user queries. This requires vector databases to process and interpret complex semantic relationships within high-dimensional vector spaces, ensuring the retrieved content is highly relevant to the query. 

- **Rapid Response**: For optimal user experience, we must execute retrievals within milliseconds. This requires vector databases to quickly access and retrieve information from expansive datasets. As data volumes expand and query requirements evolve, these databases must also scale flexibly to accommodate larger data sets and more complex queries while maintaining stable and reliable recall performance.

- **Multimodal Data Handling**: With the diversification of use cases, vector databases need to manage not just textual data but also images, videos, and other forms of multimodal data. This requires the databases to support embeddings of various data types and to efficiently retrieve information based on diverse modal queries.

- **Interpretability and Debuggability**: It is also crucial for vector databases to provide sufficient diagnostic and optimization tools when results are not retrieved well. 


## Wrapping Up

As the demand for RAG applications increases among developers for diverse purposes, RAG technology will continue to expand and ultimately revolutionize a broad spectrum of industries by significantly enhancing the efficiency of information retrieval and knowledge acquisition.

Vector databases represent a largely untapped resource with vast potential for application as the core infrastructure for RAG systems. Milvus vector database is proactively refining and addressing the challenges of developing and enhancing RAG applications, informed by insights from GenAI developers. In my following post, I will introduce the open-source [Milvus](https://zilliz.com/what-is-milvus) vector database and its new features, exploring why Milvus is a perfect vector database for enterprise-ready RAG applications. Stay tuned.