AI Search is Broken. Let’s Fix It.
AI-powered search is evolving beyond traditional methods. You’ve probably seen it firsthand. You ask an enterprise chatbot, “What’s our latest sustainability report?” and instead of a useful document, it pulls up a random marketing PDF from five years ago.
Or worse, an AI-powered fraud detection system flags a legitimate transaction because it “looks similar” to previous fraud cases without considering real-world connections that prove it’s actually safe.
Why does this happen? Because AI systems today rely on vector search alone, which finds things that look similar but doesn’t understand relationships.
This is where Hybrid Search comes in – a powerful approach that merges graph traversal with vector embedding search to deliver more accurate, context-aware results.
TigerGraph now supports native vector search, enabling seamless graph + vector hybrid queries in a single system. Even better, this capability is also available in TigerGraph DB Community Edition – free to use, even for production workloads.
This blog explores:
- What is Hybrid Search and why does it matter?
- How Graph + Vector Hybrid Search enhances AI-powered applications
- Key features of TigerGraph’s Hybrid Search
Let’s dive into why Hybrid Search is a game-changer and how TigerGraph enables it at scale.
What is Hybrid Search?
Hybrid Search is the integration of two complementary search techniques:
- Graph Search – Finds results based on relationships and structures. It is used to identify multi-hop connections, discover communities, and enhance contextual understanding.
- Vector Search – Finds similar entities based on numerical representations (embeddings). This is commonly used in LLMs, recommendation systems, and image/text similarity search.
By combining both approaches, Hybrid Search delivers results that are both semantically relevant and relationally meaningful. This is critical for applications that require explainability, deeper insights, and precise filtering.
Why Hybrid Search Matters
AI-powered retrieval is no longer just about fetching documents or matching keywords. Agentic AI systems are changing the way AI interacts with data.
Agentic AI represents a shift from static AI models to dynamic, decision-making AI agents – models that can reason, optimize workflows, and adapt autonomously.
But for Agentic AI to work, it needs more than just vector search or keyword retrieval. It needs a structured way to manage tasks, dependencies, and context-aware retrieval.
- Graph search enables structured knowledge retrieval – AI agents need a graph-based memory system to store their learnings, track interactions, and recall previous decisions.
- Vector search allows fast, semantic-based retrieval – AI agents require instant access to similar examples, past actions, and relevant external data sources.
- Graph + Vector Hybrid search makes AI reasoning possible – AI systems don’t just retrieve information; they understand relationships, infer context, and execute complex workflows dynamically.
This is why Hybrid Search is not just an optimization. It’s a necessity for building AI-powered applications that are intelligent, reliable, and contextually aware.
Let’s say you’re searching for research papers on renewable energy policies in Europe:
- A graph search will identify papers authored by the same researchers, cited within the same community, or linked to a specific policy group.
- A vector search will return papers with similar semantic meaning, based on textual embeddings.
- Graph + Vector Hybrid search retrieves semantically relevant documents while ensuring they are factually connected, providing more accurate and explainable AI retrieval.
How Graph+Vector Hybrid Search Enhances AI Applications
1. Improving LLM Retrieval with GraphRAG
Large Language Models (LLMs) rely on retrieval-augmented generation (RAG) to fetch relevant context before generating responses. However, pure vector search in RAG can return results that lack contextual accuracy. This leads to:
- Missing key relationships between retrieved documents.
- Inconsistencies in retrieved facts, causing AI-generated responses to be misleading.
- Lower retrieval precision, resulting in more API calls and increased computational costs.
GraphRAG (Graph + Retrieval-Augmented Generation) improves LLM workflows by:
- Using Graph Search to ensure those results are contextually connected and factually linked.
- Using Vector Search to retrieve semantically relevant documents.
For example, in an enterprise AI search system:
- Graph traversal ensures the retrieved documents belong to the same legal framework or precedent group.
- Vector search finds relevant documents for a legal case.
This ensures more relevant AI-generated responses, reducing hallucinations and false correlations.
2. Fraud Detection with Hybrid Search
Fraud detection in banking and e-commerce involves analyzing transaction patterns and behavioral similarities. Traditional fraud detection relies on rule-based graph analytics, but this alone misses cases where fraudsters adapt their strategies.
Hybrid Search enables multi-layered fraud detection:
- Graph Search identifies suspicious transaction networks (e.g., accounts linked to known fraud rings).
- Vector Search finds accounts with similar transaction behaviors, even if they have no direct connection to known fraudsters.
This approach detects previously unseen fraud patterns by comparing behavioral embeddings within detected fraud rings.
3. Personalized Recommendations with Graph + Vector Hybrid Search
Recommendation engines power streaming services, e-commerce platforms, and social media applications. However, pure vector-based recommendations lack personalization:
- Graph search alone finds user-item relationships but may not generalize well to unseen items.
- Vector search alone can suggest similar items but doesn’t capture user context (e.g., a customer’s past purchases or social network influence).
Hybrid Search enhances recommendations by:
- Using graph-based filters to tailor results based on user preferences, purchase history, or social network proximity.
- Using vector embeddings to rank items by similarity.
For example, in an e-commerce platform:
- Graph Search ensures results match the user’s brand preferences or price range.
- Vector Search finds products similar to past purchases.
4. Enterprise AI Search with Knowledge Graphs
Enterprises use AI-powered search to connect employees with relevant internal knowledge. However, traditional search struggles with:
- Understanding relationships between internal documents, reports, and expert contacts.
- Ensuring the credibility of retrieved information in large organizations.
Hybrid Search solves this by:
- Using Graph Search to ensure results come from verified sources, trusted expert authors, or approved corporate documents.
- Using Vector Search to retrieve similar knowledge base articles.
This enables more reliable and explainable enterprise search applications.
5. Supply Chain Optimization with Graph + Vector Hybrid Search
Supply chains are complex networks with suppliers, manufacturers, logistics hubs, and distribution points. Traditional supply chain analytics rely on graph modeling for network optimization, but vector search enhances predictive insights:
Supplier Risk Analysis:
- Graph Search identifies supplier dependencies and potential bottlenecks.
- Vector Search finds suppliers with similar risk profiles (e.g., based on historical performance, geopolitical risks, or financial health).
Logistics Route Optimization:
- Graph Search models transportation networks, warehouse locations, and shipment paths.
- Vector Search retrieves historically similar shipping delays and disruptions, helping optimize routes dynamically.
Demand Forecasting and Inventory Management:
- Graph Search identifies relationships between products, suppliers, and seasonal trends.
- Vector Search matches historical demand patterns to current market conditions for better inventory planning.
By combining graph relationships with vector-based similarity analysis, Hybrid Search allows supply chain leaders to make smarter, data-driven decisions while adapting to real-time disruptions.
Key Features of TigerGraph’s Hybrid Search
1. Multi-Modal Graph + Vector Analytics
- Store vector embeddings alongside graph vertices.
- Build GraphRAG, fraud detection, and supply chain optimization applications with a single query engine.
- Retrieve semantically relevant results while filtering by graph relationships.
2. High-Performance Vector Search
- 5.2X faster vector searches with 23% higher recall than competitors to rapidly uncover the most similar items while using 22.4X fewer resources and reducing operational costs.
3. Native Hybrid Querying
- Filter vector search results using graph-based constraints.
- Combine graph algorithms with vector similarity ranking.
- Run multi-hop traversals with vector search for knowledge graphs and fraud detection.
4. Real-Time Vector Indexing
- 6X Faster Indexing – Vectors are indexed on ingestion, eliminating manual reindexing.
- Incremental Index Updates – Supports real-time updates to keep retrieval accurate.
5. Free, Production-Ready in Community Edition
- 200GB of graph data and 100GB of vector data at zero cost.
- Full hybrid search capabilities for real-world AI workloads.
TigerGraph’s Hybrid Search enables faster, more accurate, and explainable AI applications, making it a must-have for LLMs, fraud detection, supply chain optimization, and personalized recommendations.
Download TigerGraph DB Community Edition
Try it today and unlock a new level of AI-powered retrieval.
