Building with GraphRAG: N-Hop Expansion and Practical Integration

Introduction: Beyond Simple Chunks – The Power of GraphRAG

Welcome back, intrepid RAG explorers! In our previous chapters, we’ve journeyed through the foundations of RAG, tackled advanced embeddings, and even explored the nuances of hybrid search. We’ve seen how these techniques significantly improve context retrieval compared to basic chunking. However, even with powerful vector and keyword searches, standard RAG can still struggle with a particular class of questions: those requiring multi-hop reasoning or a deeper understanding of relationships between entities.

Imagine asking a system, “What are the key projects of the manager of Alice’s team lead?” A simple vector search might find documents about Alice, her team, or specific projects, but it struggles to connect these disparate pieces of information across multiple layers of relationships. This is where GraphRAG steps in, transforming your unstructured text into a structured knowledge graph that allows for sophisticated, relational queries.

In this chapter, we’re going to dive deep into GraphRAG. We’ll learn how to extract entities and their relationships from text, build a knowledge graph, and most importantly, perform N-hop expansion to retrieve context that spans multiple connections. By the end, you’ll understand how to integrate these powerful graph-based retrieval methods into your RAG 2.0 system, unlocking new levels of accuracy and contextual richness. Ready to connect the dots? Let’s go!

Core Concepts of GraphRAG

GraphRAG isn’t just a fancy buzzword; it’s a paradigm shift in how we think about context retrieval. Instead of treating documents as flat blocks of text or vectors, we transform them into a network of interconnected entities.

What is GraphRAG and Why Does It Matter?

At its heart, GraphRAG leverages knowledge graphs to enrich the retrieval process. A knowledge graph represents information as a network of nodes (entities like people, organizations, concepts) and edges (relationships between these entities, like “works for,” “is a part of,” “founded”).

Why is this a game-changer for RAG?

1. Multi-Hop Reasoning: Traditional RAG is often limited to “single-hop” retrieval – finding information directly related to the query. Graphs excel at “multi-hop” queries, allowing us to traverse relationships to find indirect connections. This is crucial for complex questions that require synthesizing information from various parts of your knowledge base.
2. Contextual Richness: By understanding the relationships, we can assemble a more coherent and relevant context for the LLM. Instead of just chunks of text, the LLM receives a structured snippet of a knowledge graph, making it easier to reason and generate accurate answers.
3. Disambiguation: Graphs can help disambiguate entities. If “Apple” appears, is it the company or the fruit? Relationships (e.g., “Apple produces iPhones” vs. “Apple is a type of fruit”) provide the necessary context.
4. Explainability: The path traversed in a graph to find an answer can often be presented to the user, improving the explainability of the RAG system’s output.

Thought Experiment: Imagine you’re building a RAG system for a vast corporate knowledge base. A user asks, “What is the budget for the project managed by the person who reports to Sarah, in the AI department?”

• Basic RAG: Might retrieve documents about Sarah, the AI department, or various projects. It would struggle to connect which specific project is managed by which specific person who reports to Sarah.
• GraphRAG: Would model “Sarah –[manages]–> Team X –[contains]–> Employee Y –[manages]–> Project Z –[has_budget]–> $$. It can precisely trace this path.

Entity and Relation Extraction: Building the Foundation

The first step in GraphRAG is transforming unstructured text into structured graph components. This process involves:

1. Named Entity Recognition (NER): Identifying and classifying named entities in text into predefined categories (e.g., person, organization, location, product, date). For example, in “Elon Musk founded SpaceX,” “Elon Musk” is a Person, “SpaceX” is an Organization.
2. Relation Extraction (RE): Identifying the semantic relationships between these entities. For example, in “Elon Musk founded SpaceX,” the relationship is “founded.”

Modern LLMs are incredibly adept at performing both NER and RE. We can prompt an LLM to read a piece of text and output structured triplets in the format (Subject, Predicate, Object).

Example Triplet:

• Text: “Dr. Alice Smith, a leading AI researcher, works at Google DeepMind.”
• Triplets:
  • (Dr. Alice Smith, is_a, AI Researcher)
  • (Dr. Alice Smith, works_at, Google DeepMind)
  • (Google DeepMind, is_a, Organization)

Knowledge Graph Construction: Connecting the Dots

Once we have our extracted entities and relations, we need a place to store them: a graph database. Graph databases, like Neo4j, are specifically designed to store and query highly connected data.

Key Components:

• Nodes: Represent entities (e.g., (Person: "Alice Smith"), (Organization: "Google DeepMind")).
• Relationships (Edges): Connect nodes and describe how they are related (e.g., (Alice Smith)-[:WORKS_AT]->(Google DeepMind)). Relationships can also have properties (e.g., [:WORKS_AT {start_date: "2020-01-15"}]).

Graph Schema Design: While graphs are flexible, having a basic schema helps. For example, you might decide on node labels like Person, Organization, Project, and relationship types like WORKS_FOR, MANAGES, PARTICIPATES_IN.

Figure 6.1: Simple Knowledge Graph Example

N-Hop Graph Expansion for Context Retrieval

This is where GraphRAG truly shines! N-hop expansion allows us to retrieve context by traversing a specified number of “hops” (relationships) away from an initial entity or set of entities.

How it works:

1. A user query is processed to identify key entities.
2. These entities become starting points in the knowledge graph.
3. A graph query (e.g., using Cypher for Neo4j) is executed to find all nodes and relationships within ‘N’ hops from the starting entities, optionally filtered by specific relationship types.
4. The retrieved graph subgraph is then serialized into a coherent text format to be passed as context to the LLM.

Let’s illustrate with an example:

Query: “What projects is Sarah involved in, and who manages those projects?”

1. Identify Entity: “Sarah” (Person).
2. Start Node: Find (Person: "Sarah") in the graph.
3. 1-Hop Expansion: Find (Sarah)-[:PARTICIPATES_IN]->(Project)
   • Result: Project A, Project B
4. 2-Hop Expansion (from projects): From Project A and Project B, find (Project)-[:MANAGES_BY]->(Person)
   • Result: Project A managed by John, Project B managed by Emily

The N-hop query allows us to build a comprehensive context snippet: “Sarah participates in Project A, which is managed by John. Sarah also participates in Project B, which is managed by Emily.” This is far more informative than just “Sarah works on projects.”

Figure 6.2: GraphRAG N-Hop Retrieval Process

Multi-Hop Retrieval and Agentic Integration

N-hop expansion is the backbone of multi-hop retrieval, directly addressing queries that require connecting distant concepts. Furthermore, in RAG 2.0, this can be integrated with agentic retrieval. An LLM agent can act as a “query planner,” deciding when to use vector search, when to use keyword search, and when to leverage the knowledge graph for multi-hop reasoning. The agent might even decide to perform multiple graph queries, synthesize the results, and then pass this rich context to the final generation LLM.

Step-by-Step Implementation: Building a Simple GraphRAG Pipeline

Let’s get our hands dirty! We’ll build a simplified GraphRAG pipeline in Python. This example will focus on:

1. Using an LLM to extract entities and relations.
2. Storing these in a Neo4j graph database.
3. Performing a basic N-hop style retrieval using Cypher.

Prerequisites:

• Python 3.9+ (as of 2026-03-20)
• Access to an LLM (we’ll use a local Ollama instance for simplicity, but you could substitute OpenAI, Azure OpenAI, etc.)
• A running Neo4j instance (Community Edition 5.x or newer is great for local development). You can run it via Docker: docker run --name neo4j-graphrag -p 7687:7687 -p 7474:7474 -e NEO4J_AUTH=neo4j/password -e NEO4J_PLUGINS='["apoc"]' neo4j:5.18.0 (adjust version as needed).
• Install Python libraries: pip install langchain==0.1.13 neo4j==5.19.0 ollama==0.1.7 (These versions are current as of 2026-03-20. Please verify for the latest stable releases).

Step 1: Data Preparation & LLM Setup

First, let’s set up our environment and define some sample text. We’ll use langchain for LLM orchestration and ollama for a local LLM.

Create a file named graphrag_pipeline.py.

# graphrag_pipeline.py

import os
from langchain_community.llms import Ollama
from neo4j import GraphDatabase, basic_auth
import json

# --- 1. LLM Setup (using Ollama for local LLM) ---
# Ensure you have Ollama running and a model pulled (e.g., 'llama2')
# Example: ollama run llama2
try:
    llm = Ollama(model="llama2", temperature=0.0) # Using a low temperature for consistent extraction
    print("Ollama LLM initialized successfully.")
except Exception as e:
    print(f"Error initializing Ollama LLM: {e}")
    print("Please ensure Ollama is running and 'llama2' model is pulled.")
    exit()

# --- 2. Neo4j Database Setup ---
NEO4J_URI = os.getenv("NEO4J_URI", "bolt://localhost:7687")
NEO4J_USERNAME = os.getenv("NEO4J_USERNAME", "neo4j")
NEO4J_PASSWORD = os.getenv("NEO4J_PASSWORD", "password")

try:
    driver = GraphDatabase.driver(NEO4J_URI, auth=basic_auth(NEO4J_USERNAME, NEO4J_PASSWORD))
    driver.verify_connectivity()
    print("Neo4j database connection established.")
except Exception as e:
    print(f"Error connecting to Neo4j: {e}")
    print("Please ensure your Neo4j instance is running and accessible.")
    exit()

# --- 3. Sample Data ---
sample_texts = [
    "Dr. Alice Smith is a senior AI researcher at Google DeepMind. She leads Project Aurora.",
    "Project Aurora focuses on advanced neural networks. Bob Johnson is a key contributor.",
    "Bob Johnson previously worked at Microsoft Research on the Project Athena team.",
    "Sarah Connor, the head of engineering at Google DeepMind, oversees Project Aurora."
]

print("\nSetup complete. Ready for entity extraction and graph building!")

Explanation:

• We import necessary libraries. langchain_community.llms.Ollama allows us to interact with a local Ollama instance. neo4j is the official Neo4j Python driver.
• The Ollama LLM is initialized with llama2 (you might need to ollama pull llama2 first). We set temperature=0.0 to encourage deterministic and consistent output for extraction.
• Neo4j connection details are set. It’s good practice to use environment variables, but for this example, we have fallbacks. driver.verify_connectivity() checks if the connection is successful.
• sample_texts provides our raw data for graph construction.

Step 2: Entity & Relation Extraction (LLM-powered)

Now, let’s create a function that uses our LLM to extract (subject, predicate, object) triplets from text. We’ll define a clear prompt for the LLM.

Add the following function to graphrag_pipeline.py:

# ... (previous code) ...

def extract_triplets_with_llm(text: str, llm: Ollama) -> list:
    """
    Uses an LLM to extract (subject, predicate, object) triplets from text.
    """
    prompt = f"""
    You are an expert at extracting structured information from text.
    Your task is to identify entities and the relationships between them,
    and express them as (subject, predicate, object) triplets.

    Follow these rules:
    - Extract as many relevant triplets as possible.
    - Subjects and objects should be specific named entities or concepts.
    - Predicates should be concise and descriptive verbs or phrases.
    - Ensure predicates clearly define the relationship.
    - Output should be a JSON array of objects, where each object has 'subject', 'predicate', and 'object' keys.
    - Example: {{"subject": "Elon Musk", "predicate": "founded", "object": "SpaceX"}}

    Text: "{text}"

    JSON Triplet Output:
    """
    
    try:
        # LangChain's invoke method sends the prompt to the LLM and gets the response
        llm_response = llm.invoke(prompt)
        # Attempt to parse the LLM's JSON output
        triplets = json.loads(llm_response)
        if not isinstance(triplets, list):
            print(f"Warning: LLM output for text '{text}' was not a list. Attempting to wrap it.")
            triplets = [triplets] if isinstance(triplets, dict) else []
        
        # Basic validation for triplet structure
        valid_triplets = []
        for t in triplets:
            if all(key in t for key in ['subject', 'predicate', 'object']):
                valid_triplets.append(t)
            else:
                print(f"Skipping malformed triplet: {t}")
        return valid_triplets
    except json.JSONDecodeError as e:
        print(f"Error parsing JSON from LLM: {e}")
        print(f"LLM Response was: {llm_response}")
        return []
    except Exception as e:
        print(f"An unexpected error occurred during triplet extraction: {e}")
        return []

# Test extraction with one sample text
print("\n--- Testing Triplet Extraction ---")
first_text = sample_texts[0]
extracted_triplets = extract_triplets_with_llm(first_text, llm)
print(f"Original Text: {first_text}")
print(f"Extracted Triplets: {json.dumps(extracted_triplets, indent=2)}")

Explanation:

• The extract_triplets_with_llm function takes text and the LLM instance.
• A detailed prompt guides the LLM to output JSON triplets. This is crucial for consistent structured output.
• llm.invoke(prompt) sends the request.
• json.loads() parses the LLM’s response. We include error handling for malformed JSON and basic validation to ensure the output structure.

Step 3: Graph Database Integration (Neo4j)

Now, let’s write functions to add these extracted triplets to our Neo4j database. We’ll create nodes for subjects and objects, and relationships for predicates.

Add the following functions to graphrag_pipeline.py:

# ... (previous code) ...

def add_triplet_to_graph(tx, subject: str, predicate: str, object: str):
    """
    Adds a single (subject, predicate, object) triplet to the Neo4j graph.
    Creates nodes if they don't exist and the relationship.
    """
    # Cypher query to create or merge nodes and relationships
    # MERGE ensures uniqueness: if a node/relationship exists, it's used; otherwise, it's created.
    cypher_query = """
    MERGE (s:Entity {name: $subject})
    MERGE (o:Entity {name: $object})
    MERGE (s)-[r:RELATION {type: $predicate}]->(o)
    RETURN s, r, o
    """
    tx.run(cypher_query, subject=subject, predicate=predicate, object=object)

def populate_graph_from_texts(driver, texts: list, llm: Ollama):
    """
    Processes a list of texts, extracts triplets, and populates the Neo4j graph.
    """
    print("\n--- Populating Graph Database ---")
    with driver.session() as session:
        # Clear existing data for a clean run (optional, for development)
        session.run("MATCH (n) DETACH DELETE n")
        print("Graph cleared for new data.")

        for i, text in enumerate(texts):
            print(f"Processing text {i+1}/{len(texts)}: '{text}'")
            triplets = extract_triplets_with_llm(text, llm)
            for triplet in triplets:
                subject = triplet['subject']
                predicate = triplet['predicate'].replace(" ", "_").upper() # Standardize predicate
                object = triplet['object']
                try:
                    session.write_transaction(add_triplet_to_graph, subject, predicate, object)
                    # print(f"  Added: ({subject})-[:{predicate}]->({object})")
                except Exception as e:
                    print(f"  Error adding triplet ({subject}, {predicate}, {object}): {e}")
    print("Graph population complete.")

# Populate the graph
populate_graph_from_texts(driver, sample_texts, llm)

Explanation:

• add_triplet_to_graph: This function takes a Neo4j transaction (tx) and the triplet components.
  • MERGE (s:Entity {name: $subject}): This Cypher command ensures that a node with the label Entity and a name property equal to $subject exists. If it doesn’t, it creates it.
  • MERGE (o:Entity {name: $object}): Does the same for the object.
  • MERGE (s)-[r:RELATION {type: $predicate}]->(o): Creates a directed relationship of type RELATION (we use a generic type here, but in a real system you’d use specific types like WORKS_AT, LEADS) between s and o, with a type property storing the actual predicate. We also standardize the predicate by replacing spaces with underscores and making it uppercase.
• populate_graph_from_texts: This orchestrates the process.
  • It first clears the graph (MATCH (n) DETACH DELETE n) for a fresh start – be cautious with this in production!
  • It iterates through each sample_text, extracts triplets, and then uses session.write_transaction to add each triplet to the graph. Transactions ensure atomicity.

Step 4: N-Hop Retrieval

Now that our graph is populated, let’s query it using Cypher to perform N-hop retrieval. We’ll define a function that takes a starting entity and the number of hops, then returns relevant context.

Add the following function to graphrag_pipeline.py:

# ... (previous code) ...

def retrieve_context_from_graph(driver, starting_entity: str, max_hops: int = 2) -> str:
    """
    Retrieves context from the graph by performing an N-hop expansion
    around a starting entity and converts it into a readable text format.
    """
    print(f"\n--- Retrieving Context for '{starting_entity}' (max {max_hops} hops) ---")
    context_parts = []
    
    with driver.session() as session:
        # Cypher query for N-hop expansion
        # It finds the starting entity, then paths up to `max_hops` away.
        # It collects all nodes and relationships along these paths.
        cypher_query = f"""
        MATCH (start_node:Entity {{name: $entity_name}})
        MATCH path = (start_node)-[*1..{max_hops}]-(related_node:Entity)
        RETURN nodes(path) AS nodes, relationships(path) AS rels
        """
        
        result = session.run(cypher_query, entity_name=starting_entity)
        
        seen_statements = set()
        for record in result:
            nodes = record["nodes"]
            rels = record["rels"]

            for rel in rels:
                start_node = next((n for n in nodes if n.id == rel.start_node.id), None)
                end_node = next((n for n in nodes if n.id == rel.end_node.id), None)
                
                if start_node and end_node:
                    statement = f"{start_node['name']} {rel['type'].replace('_', ' ').lower()} {end_node['name']}."
                    if statement not in seen_statements:
                        context_parts.append(statement)
                        seen_statements.add(statement)

    if not context_parts:
        return f"No related information found for '{starting_entity}' within {max_hops} hops."
    
    # Assemble the context into a coherent string
    full_context = f"Information related to {starting_entity}:\n" + "\n".join(context_parts)
    return full_context

# Test retrieval
query_entity = "Dr. Alice Smith"
retrieved_context = retrieve_context_from_graph(driver, query_entity, max_hops=2)
print(retrieved_context)

query_entity_2 = "Project Aurora"
retrieved_context_2 = retrieve_context_from_graph(driver, query_entity_2, max_hops=2)
print(retrieved_context_2)

# Close the Neo4j driver
driver.close()
print("\nNeo4j driver closed.")

Explanation:

• retrieve_context_from_graph: This function takes the Neo4j driver, a starting_entity, and max_hops.
• Cypher Query: MATCH (start_node:Entity {name: $entity_name}) MATCH path = (start_node)-[*1..{max_hops}]-(related_node:Entity) RETURN nodes(path) AS nodes, relationships(path) AS rels
  • MATCH (start_node:Entity {name: $entity_name}): Finds the node corresponding to our starting_entity.
  • MATCH path = (start_node)-[*1..{max_hops}]-(related_node:Entity): This is the N-hop magic!
    • -[*1..{max_hops}]- means find any path of length between 1 and max_hops (inclusive).
    • The - without an arrow means relationships can be traversed in either direction. For directed relationships, you’d use -> or <-.
  • RETURN nodes(path) AS nodes, relationships(path) AS rels: Returns all nodes and relationships found along these paths.
• Context Assembly: We iterate through the returned records, reconstruct the relationships as simple English sentences (e.g., “Alice Smith works at Google DeepMind.”), and collect them into context_parts. A seen_statements set prevents duplicate sentences.
• Finally, these sentences are joined to form the full_context string, which would then be passed to the LLM for generation.

Running the Pipeline

To run this entire pipeline:

1. Ensure Ollama is running (ollama serve) and you have llama2 pulled (ollama pull llama2).
2. Ensure your Neo4j Docker container is running as per the docker run command provided earlier.
3. Execute the Python script: python graphrag_pipeline.py

You should see output similar to:

• LLM initialization success.
• Neo4j connection success.
• Extracted triplets from the first text.
• Graph population messages.
• Retrieved context for “Dr. Alice Smith” and “Project Aurora” demonstrating the N-hop connections.

This simple example illustrates the core mechanics. In a production system, you’d have more sophisticated entity/relation types, more robust error handling, and potentially integrate with a RAG orchestration framework like LlamaIndex or LangChain’s graph-specific modules.

Mini-Challenge: Expanding Your Graph Insights

You’ve built a basic GraphRAG pipeline! Now, let’s try to enhance its retrieval capabilities.

Challenge: Modify the retrieve_context_from_graph function to specifically find:

1. All entities that work at “Google DeepMind” (a 1-hop query, but with a specific relationship type and direction).
2. The projects managed by people who work at “Google DeepMind” (a multi-hop query focusing on specific relationship types).

Hint:

• For the first part, you’ll need to adjust the Cypher query to specify the relationship type [:WORKS_AT] and its direction <-[:WORKS_AT]-.
• For the second part, you’ll need to chain multiple relationships in your Cypher pattern, for example, (org)-[:HAS_EMPLOYEE]->(person)-[:MANAGES]->(project).

What to Observe/Learn:

• How precise Cypher queries allow you to navigate the graph with fine-grained control.
• The difference in retrieved context when specifying relationship types and directions.
• The power of multi-hop queries to answer complex questions that span different types of connections.

# --- Mini-Challenge: Example of how you might start the Cypher for part 1 ---
# Note: This is a hint, not the full solution.
# def retrieve_specific_context_from_graph(driver, target_entity: str, relationship_type: str, direction: str = "inbound") -> str:
#     with driver.session() as session:
#         cypher_query = f"""
#         MATCH (target:Entity {{name: $entity_name}})
#         MATCH (related_entity:Entity){'<-' if direction == 'inbound' else '-'}[r:{relationship_type}]{'->' if direction == 'outbound' else '-'}(target)
#         RETURN related_entity.name AS related, type(r) AS rel_type, target.name AS target_name
#         """
#         # ... complete the function

Common Pitfalls & Troubleshooting in GraphRAG

GraphRAG is powerful, but it’s not without its challenges. Being aware of these can save you a lot of debugging time.

1. Over-extraction or Under-extraction by LLMs:

   • Pitfall: Your LLM might extract too many trivial triplets or miss crucial ones, leading to a “noisy” or incomplete graph. The quality of graph construction directly impacts retrieval.
   • Troubleshooting:
     • Refine LLM Prompts: Experiment with prompt engineering. Be very explicit about what entities and relations to extract, and provide good few-shot examples.
     • Post-processing: Implement rules or a second LLM pass to filter or merge similar entities/relations.
     • Model Choice: Some LLMs are better at structured extraction than others. Fine-tuning a smaller LLM for your specific extraction task can also yield great results.

2. Suboptimal Graph Schema Design:

   • Pitfall: If your nodes and relationship types are too generic (e.g., all relationships are just RELATION), or too specific (hundreds of relationship types), querying becomes difficult or inefficient.
   • Troubleshooting:
     • Iterative Design: Start simple with Entity nodes and generic RELATION types. As you identify common patterns, introduce more specific node labels (e.g., Person, Project, Organization) and relationship types (e.g., WORKS_FOR, MANAGES).
     • Domain Expertise: Leverage domain experts to define key entities and relationships.
     • Review and Refine: Regularly review your graph’s structure and how well it answers your target queries.

3. Performance of N-Hop Queries on Large Graphs:

   • Pitfall: As your knowledge graph grows, deep N-hop queries (e.g., 5+ hops) can become computationally expensive, especially if not properly indexed.
   • Troubleshooting:
     • Index Nodes: Ensure your graph database has indexes on frequently queried node properties (e.g., CREATE INDEX FOR (n:Entity) ON (n.name) in Cypher).
     • Limit Hops: For most RAG use cases, 2-3 hops are often sufficient. Consider if deeper hops are truly necessary for the user’s query.
     • Optimize Cypher: Learn advanced Cypher query optimization techniques.
     • Graph Algorithms: For very complex scenarios, consider pre-computing relationships or using graph algorithms (e.g., shortest path) to optimize retrieval.

4. GraphRAG Degrading Retrieval Quality (compared to Vector Search):

   • Pitfall: Sometimes, a poorly constructed or queried knowledge graph can actually provide less relevant context than a well-tuned vector search. This often happens if the extracted relationships are sparse or inaccurate.
   • Troubleshooting:
     • Hybrid Approach: Remember GraphRAG is one tool. Often, the best RAG 2.0 systems use a hybrid retrieval approach, combining vector, keyword, and graph-based retrieval, often fused with techniques like Reciprocal Rank Fusion (RRF).
     • Benchmarking: Rigorously benchmark your GraphRAG component against other retrieval methods for different query types. Identify where it excels and where it falls short.
     • Evaluate Extraction Quality: Continuously evaluate the quality of your LLM’s entity and relation extraction. Garbage in, garbage out!

Summary: Connecting the Dots for Smarter RAG

Phew! You’ve just taken a significant leap forward in building truly intelligent RAG systems. In this chapter, we unpacked the power of GraphRAG and its crucial role in RAG 2.0:

• We understood that basic RAG struggles with multi-hop reasoning and complex relational queries.
• We learned how GraphRAG addresses these limitations by transforming unstructured text into a knowledge graph of nodes (entities) and edges (relationships).
• We explored the process of entity and relation extraction using powerful LLMs, turning raw text into structured triplets.
• We saw how to construct a knowledge graph in a graph database like Neo4j, using these triplets.
• Crucially, we delved into N-hop graph expansion, a technique that allows us to traverse multiple relationships to retrieve a rich, interconnected context for the LLM, enabling sophisticated multi-hop reasoning.
• Finally, we walked through a step-by-step practical implementation using Python, ollama, and Neo4j, illustrating how to build and query a simple knowledge graph for RAG.

GraphRAG is a powerful arrow in your RAG 2.0 quiver, enabling your systems to understand and reason over complex, interconnected information. While it introduces new complexities, the benefits in terms of accuracy and the ability to answer nuanced questions are immense.

What’s next? In our final chapters, we’ll explore Agentic Retrieval – how LLMs can act as intelligent orchestrators, dynamically planning retrieval strategies across multiple sources, including the graph you’ve just built! Get ready for the ultimate RAG 2.0 grand finale.

References

• RAG and Generative AI - Azure AI Search - Microsoft Learn
• Neo4j Developer Documentation
• LangChain Documentation - Ollama
• LangChain Documentation - Neo4j

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.