Vector vs Graph vs Hybrid Search

How your data is stored determines how intelligently your agent can find it. For most simple RAG projects, we start with Vector Search. But as agents become more sophisticated, they need more than just "Similar concepts." They need "Relationships" and "Exact matches."

In this lesson, we will compare the three pillars of modern retrieval and learn which one to choose for your agentic architecture.

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1. Vector Search (Semantic)

Vector search converts text into numbers (Embeddings) and finds "Nearby" concepts in a mathematical space.

Best For:

• Conceptual Queries: "How do I feel better?" (Finds articles on health, sleep, and diet).
• Unstructured Data: PDFs, Emails, Slack messages.

The Downside:

• Exactness: If you search for "Order ID 9876", a vector search might return "Order ID 9875" because the numbers are mathematically similar, even though the meaning is 100% different.

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2. Graph Search (Relationships)

Knowledge Graphs store data as Entities (Nodes) and Relationships (Edges).

• Entity A: "John Smith"
• Relationship: "IS_EMPLOYED_BY"
• Entity B: "Google"

Best For:

• Multi-Hop Reasoning: "Who is the manager of the person who wrote the Q3 report?"
• Complex Hierarchies: Legal documents, corporate structures, and scientific research.

The Downside:

• Complexity: Building and maintaining a graph is hard. You have to explicitly define every relationship.

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3. Hybrid Search (The Winner)

Hybrid Search combines Keyword Search (BM25) and Vector Search.

How it Works:

1. Search the Exact Keywords (to find IDs, names, and specific codes).
2. Search the Semantic Concepts (to find meaning).
3. Merge the results using RRF (Reciprocal Rank Fusion).

Production Reality: 90% of professional RAG systems use Hybrid search because it is the only way to avoid the "Order ID" problem mentioned above.

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4. GraphRAG: The New Frontier

GraphRAG is a technique where an agent uses an LLM to build a knowledge graph from your text files automatically.

1. The agent reads 1,000 PDFs.
2. It extracts entities and links.
3. When you ask a question, the agent navigates the Graph and the Vector DB simultaneously.

Result: Deep, "Contextual" intelligence that "Simple RAG" can't touch.

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5. Choosing for Your Agent

Goal	Search Type	Recommended Choice
Chatting with a PDF	Simple Vector	Chroma / Pinecone
Analyzing Customer Journeys	Graph	Neo4j / FalkorDB
Technical Support Bot	Hybrid	Elasticsearch / Weaviate
Complex Logic / ERP	GraphRAG	Microsoft GraphRAG / LlamaIndex

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6. Real-World Tip: Metadata Filtering

Regardless of the search type, Metadata is your best friend. An agent shouldn't just "Search." It should Filter.

• "Find all emails from 'Sudeep' (Metadata) about 'The Course' (Vector)." This is 10x more accurate than just searching for "Sudeep's emails about the course."

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Summary and Mental Model

Think of Vector Search like a Feeling. ("I'm looking for a vibe like X"). Think of Graph Search like a Family Tree. ("I'm looking for X's sister's husband"). Think of Hybrid Search like a Google Search. ("I'm looking for 'Specific Keyword' and things related to it").

An agent with all three is an agent that never forgets.

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Exercise: Retrieval Strategy

1. Selection: You are building an agent for a Medical Clinic.
   • The user asks: "In which year did Patient 123 have their appendectomy?"
   • Should you use Vector or Keyword/Graph search? Why?
2. Design: Draft a "Search Configuration" for a hybrid system.
   • Which should have a higher "Weight" (Importance): the Exact Match of a patient name or the Concept of a symptom?
3. Logic: Why is it harder to implement a Graph search for a 1-million-page library than a Vector search?
   • (Hint: Think about "Schema Complexity"). Ready to avoid the bottleneck? Next lesson: Avoiding the Retrieval Bottleneck.