HybridRAG and Why Combine Vector Embeddings with Knowledge Graphs for RAG?
If you’re exploring Retrieval-Augmented Generation (RAG) to make Large Language Models (LLMs) smarter, you’ve probably heard of HybridRAG. It’s the next evolution in RAG systems, and it combines the best of both worlds: vector databases and graph databases. But why do you need both? Why isn’t one enough? Let’s break it down in plain terms.
What Are Vector Embeddings?
Vector embeddings transform text, images, or other data into vectors. Vectors are points in a multi-dimensional space that capture semantic meaning. This allows you to compare information based on similarity of meaning, not just exact matches.
What is Vector Search?
Vector search captures meaning beyond exact terms. It finds relevant results even when queries include different phrasing, typos, slang, or vague language. That’s its biggest advantage: semantic matching.
You’d use vector search when:
- Queries are ambiguous or lack specificity.
- You need to handle synonyms or highly contextual language.
- You're retrieving context that doesn't exactly match the knowledge graph's structured nodes and relationships but shares conceptual similarity.
Read more: Simplify Data Retrieval with Memgraph’s Vector Search and Decoding Vector Search: The Secret Sauce Behind Smarter Data Retrieval.
How does Vector Search work?
- Generate embeddings for your data and your query.
- Compare them within the same vector space using similarity calculations.
- The most common comparison method is cosine similarity, which effectively captures semantic relationships, especially in text-based tasks.
How can you create and store embeddings? Vector databases like Pinecone or Weaviate. You can use Memgraph when combining vector search with graph-based reasoning.
What is Graph Data Model?
A graph data model organizes information as nodes (entities like customers, products, or patients) and edges (relationships between those entities). This model is designed to answer questions about how things are connected.
You’d use a graph model when:
- Relationships are core to your data.
- You need multi-hop reasoning (like tracing symptoms → patients → treatments).
- You're working with complex, highly connected data (such as healthcare records, fraud detection, or social networks).
Why Use a Hybrid Approach?
While both databases are powerful, they excel at different things. Combining them opens new possibilities and overcomes limitations of each. Here’s why:
1. You need both similarity and context
- What vector dbs do well: They’re great for finding items that are semantically similar. For instance, you can find documents that are “about the same topic” even if they don’t share the same exact keywords.
- What vector dbs struggle with: Context and relationships. A vector database might tell you that two documents are similar, but it can’t explain why or how they’re connected.
- What graph dbs do well: They thrive on context. Graphs let you explore relationships and perform reasoning across multiple entities.
- What graph dbs struggle with: Semantic similarity. Graphs are amazing for relationships, but they can’t tell you that a paragraph about “neural networks” is similar to one about “machine learning.”
Why Do HybridRAG?
By combining vector and graph databases, you can use a vector database to find relevant entities or documents based on semantic similarity. Then you can use a graph database to explore the relationships between those entities and extract meaningful context.
For example, “Which patients have similar symptoms to Mark, and what treatments worked for them?”
With HybridRAG, you would use a vector database to find patients with similar symptom descriptions (semantic similarity). Then use a graph database to trace the relationships between those patients, their treatments, and outcomes.
2. Efficient Data Retrieval at Scale
In real-world use cases, your data isn’t just big—it’s massive. HybridRAG makes retrieval efficient by splitting the workload. The vector database handles the unstructured or semi-structured data (e.g., embeddings of text or images).
On the other side, the graph database handles structured, relationship-heavy data.
Example use case:
Cedars-Sinai’s Alzheimer’s Disease Knowledge Base (AlzKB) uses a HybridRAG approach by combining Memgraph’s graph database (Memgraph) and a vector database to enhance query accuracy and machine learning outcomes.
The graph database stores biomedical entities (e.g., genes, drugs, diseases) and their relationships, enabling multi-hop reasoning and dynamic updates, while the vector database enables semantic similarity searches to match natural language queries with relevant graph data. This integration powers tools like KRAGEN (Knowledge Graph-Enhanced RAG) and ESCARGOT (Dynamic Graph of Thoughts) to address complex compound queries, guide automated machine learning pipelines, and discover novel drug and gene targets for Alzheimer’s.
Together, this system delivers high-performance, context-rich insights and has already identified two FDA-approved drugs (Temazepam and Ibuprofen) as potential candidates for Alzheimer’s treatment.
Read more: Using Memgraph for Knowledge-Driven AutoML in Alzheimer’s Research at Cedars-Sinai
3. Dynamic and Adaptive Queries
Sometimes, you don’t know exactly what you’re looking for. A hybrid system adapts dynamically:
- Start broad with a vector search to find semantically relevant data.
- Then narrow down with a graph search to analyze relationships or clusters of related items.
Example use case:
A financial application identifies similar companies using a vector database (e.g., based on earnings reports) and then uses a graph database to uncover relationships like partnerships or shared investors.
4. Enhanced Relevance with Multi-Hop Reasoning
Vector databases are good at finding what is similar, but they don’t understand connections between entities. HybridRAG brings graph-powered multi-hop reasoning into the mix:
After finding similar items in the vector database, use the graph database to explore multi-step relationships and generate deeper insights.
5. Real-Time Updates + Historical Context
Graph dbs dynamically update in real time, reflecting the latest connections or events. Vector dbs provide historical semantic understanding by indexing embeddings.
This combination ensures your system is both fresh (real-time) and insightful (context-rich).
Example use case:
Precina Health uses both a graph database (Memgraph) and a vector database (Qdrant) in their system to manage and optimize Type 2 diabetes care.
Memgraph is the backbone of their knowledge graph, storing and managing relationships between patient data, behavioral insights, and medical information. It enables multi-hop reasoning, connecting complex relationships like how social and behavioral factors impact medical outcomes.
On the other side, they use a vector database, Qdrant. It supports fast semantic searches, retrieving relevant nodes and documents based on contextual similarity to a query. It helps identify initial relevant data points before relational reasoning occurs in the graph database.
TL;DR
By combining vector and graph databases, you get the best of both worlds. Vectors handle semantic similarity, graphs tackle relationships.
Need to dive deep?
Vectors start broad; graphs drill down.
The result?
Faster, smarter data retrieval that uncovers patterns through multi-hop reasoning while keeping real-time updates and historical context in check.
This setup is flexible enough for industries ranging from healthcare to e-commerce. If scalable, context-rich RAG systems are your goal, HybridRAG might be your ideal option.
