Working with Retrievers

Introduction

In this lesson, you will work hands-on with all three retriever types to see how they work in practice.

There are three Jupyter notebooks to explore:

01_01_vector_retriever.ipynb - Vector Retriever
01_02_vector_cypher_retriever.ipynb - Vector + Cypher Retriever
01_03_text2cypher_retriever.ipynb - Text2Cypher Retriever

Hands-On: Retriever Notebooks

Open the first notebook: 01_01_vector_retriever.ipynb

Each notebook demonstrates:

Setting up the retriever with the knowledge graph we built
Customizing the retriever for your requirements
Using the retriever as part of a GraphRAG pipeline
Comparing results from different retrieval methods

What You’ll Build:

As you work through the notebooks, take time to review the code snippets and understand how each retriever is initialized and used.

Note how each retriever has its place in a complete GraphRAG system!

Initialize Models:

All retrievers will use the same LLM and embedder for consistency:

python

from neo4j_graphrag.retrievers import VectorRetriever, VectorCypherRetriever, Text2CypherRetriever

llm = OpenAILLM(model_name="gpt-4o", api_key=OPENAI_API_KEY)
embedder = OpenAIEmbeddings(api_key=OPENAI_API_KEY)

Vector Retriever:

Returns semantically similar text chunks
Good for exploratory questions
May miss entity-specific context

python

vector_retriever = VectorRetriever(
    driver=driver,
    index_name="chunkEmbeddings",
    embedder=embedder
)

Vector + Cypher Retriever:

Provides both content and relationships
Richer context with entity information
Better for comprehensive answers

python

vector_cypher_retriever = VectorCypherRetriever(
    driver=driver,
    index_name="chunkEmbeddings",
    retrieval_query=cypher_query,
    embedder=embedder
)

Text2Cypher Retriever:

Direct, precise answers from graph structure
Perfect for factual queries
Handles aggregations and counts

python

text2cypher_retriever = Text2CypherRetriever(
    driver=driver,
    llm=llm,
    neo4j_schema=neo4j_schema
)

Testing Different Retrievers

Try these questions with each retriever:

Broad Semantic Questions:

"What are the main cybersecurity threats in financial services?"
"Tell me about risk factors mentioned in the documents"

Entity-Specific Questions:

"What products does Apple mention in their filings?"
"Which companies face regulatory challenges?"

Precise Data Questions:

"How many companies mention cloud computing?"
"Count the risk factors for Microsoft"

Compare the results - you’ll see how each retriever approaches the same question differently!

Summary

In this hands-on lesson, you worked with all three retriever types in practice:

What You Built:

Vector Retriever for semantic search
Vector + Cypher Retriever for contextual search
Text2Cypher Retriever for precise queries

Key Insights:

Different retrievers excel at different question types
Combining approaches gives comprehensive coverage
Understanding retriever strengths guides selection

Preparation:

You now understand how each retriever works in practice
You’ve seen their different strengths and limitations
You’re ready to wrap these retrievers as conversational agent tools

In the next module, you will learn how to combine these retrievers into intelligent agents that can choose the right retrieval method automatically.

Neo4j & Generative AI Hands-On Workshop

Generative AI

Retrievers

Agents

Working with Retrievers

Introduction

Hands-On: Retriever Notebooks

Testing Different Retrievers

Summary

Chatbot