We want to solve a problem that has plagued companies for years: How can employees quickly find the information they need? 

Retrieval-augmented generation (RAG) technology is expected to be the key to solving this problem, but how to choose the most appropriate data storage solution?

Vector database? Graph database? Knowledge graph? Let’s find out.

Vector databases: efficient but lacking context

The vector database divides the document into small chunks (about 100-200 characters) and converts them intoVector Storage.

When a user asks a question, the system converts the question into a vector and then usesKNN(K nearest neighbors) orANN(Approximate Nearest Neighbor) algorithm finds the most similar content.

Core advantages :

• Can store multiple types of data (text, images, etc.)
• Ability to handle unstructured data
• Supports semantic similarity search, not limited to keyword matching

Key questions :

Context Loss.

Let's take a simple example: a document about Apple contains "Apple was founded on April 1, 1976 by Steve Wozniak and Steve Jobs... Apple launched the Lisa in 1983 and the Macintosh in 1984..."

When a user asks "When did Apple launch the first Macintosh?", the vector database may mistakenly associate "1983" with "Macintosh" due to the blocking and similarity search mechanisms and give a wrong answer.

Graph databases: Relationship-first but inefficient

Graph databases throughNodes and edges organize data points into a network of relationships.

Each node represents an entity (such as a person, company, product), and the edges represent the relationships between entities (such as "created", "belongs to", "launched").

Core advantages :

• Directly store and represent relationships between entities
• Allows developers to assign weights and directionality to relationships
• Intuitive structure, easy to understand visually

The previous Apple case will be significantly improved in the graph database.

Through a clear relationship path (Apple - [Introduced] -> Macintosh - [Released in] -> 1984), the system can accurately answer the question "When did Apple introduce the Macintosh?"

Key questions :

Inefficiency in processing large-scale data, especially a mix of sparse and dense data in enterprise environments.

The extended query effect across databases is poor, and the larger the database size, the lower the query efficiency.

Knowledge graph: the best choice for integrating semantics and relationships

Knowledge graph is not just another database technology, but a data storage technology that simulates the way human thinking works .

It collects and connects concepts, entities, relations, and events through semantic descriptions to form aOverall network.

Core advantages :

• Preserve full semantic context and relationships
• Ability to encode structural relationships and hierarchies
• Supports data integration across multiple sources
• Higher query accuracy

Research shows that the accuracy can be improved from 16% based on GPT4 and SQL database to 54% when using the knowledge graph representation of the same SQL database. This gap is crucial to the reliability of the RAG system.

The knowledge graph further optimizes the Apple case, answering not only "When did Apple launch the Macintosh?" but also more complex questions such as "What innovative features did this computer have?" because it retains the relationship between the product and its features (such as the Macintosh was the first to use a graphical user interface and a mouse).

Key challenges : Knowledge graphs require a lot of computing power, some operations are expensive, and may be difficult to scale.

Best Practices for Enterprise-Level RAG: Hybrid Architecture

Faced with the complex needs of enterprise-level RAG, the best solution is often to combine the advantages of various technologies.Hybrid Architecture.

Core Strategies :

1. Hybrid Search : Vector Database ProcessingFuzzy semantic query, knowledge graph processingStructured relational query.

2. Saving Tokens :

• Graph clipping: only return entities and relationships that are directly relevant to the question
• Use the shortest path algorithm to reduce the number of returned nodes
• Summarize the results to generate refined knowledge representation

• Entity Disambiguation :

• Using contextual information to enhance the semantic representation of ambiguous words
• Set type and attribute constraints on entities
• Mutual verification of entity meanings through joint retrieval of vector database and knowledge graph

In Apple's example, a hybrid architecture can more fully answer user questions:

• "What company is Apple?" → Vector database provides overview information
• "When did Apple launch the Macintosh?" → Knowledge Graph provides an accurate timeline
• "What innovative features does the Macintosh have?" → The knowledge graph provides relational information, and the vector database provides detailed descriptions

An enterprise's choice of RAG data storage technology is not an either-or competition, but should be based on comprehensive considerations of specific needs and application scenarios.

For enterprise-level RAG systems,Knowledge graphs are known for their ability to preserve semantic relationships and encode structural information., often becomes the first choice; and combinedThe hybrid architecture of the vector database can provide the most complete and accurate solution.

Remember, users only need an answer to continue working. The ultimate goal of RAG technology is to enable enterprise employees to quickly obtain accurate information, no longer waste time waiting for answers, and no longer answer the same questions repeatedly . Choosing the right data storage technology is a key step for enterprises to achieve this goal.