Azure OpenAI with Neo4j Example

This example demonstrates how to use Graphiti with Azure OpenAI and Neo4j to build a knowledge graph.

Prerequisites

• Python 3.10+
• Neo4j database (running locally or remotely)
• Azure OpenAI subscription with deployed models

Setup

1. Install Dependencies

uv sync

2. Configure Environment Variables

Copy the .env.example file to .env and fill in your credentials:

cd examples/azure-openai
cp .env.example .env

Edit .env with your actual values:

# Neo4j connection settings
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your-password

# Azure OpenAI settings
AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com
AZURE_OPENAI_API_KEY=your-api-key-here
AZURE_OPENAI_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small

3. Azure OpenAI Model Deployments

This example requires two Azure OpenAI model deployments:

1. Chat Completion Model: Used for entity extraction and relationship analysis

   • Set the deployment name in AZURE_OPENAI_DEPLOYMENT

2. Embedding Model: Used for semantic search

   • Set the deployment name in AZURE_OPENAI_EMBEDDING_DEPLOYMENT

4. Neo4j Setup

Make sure Neo4j is running and accessible at the URI specified in your .env file.

For local development:

• Download and install Neo4j Desktop
• Create a new database
• Start the database
• Use the credentials in your .env file

Running the Example

cd examples/azure-openai
uv run azure_openai_neo4j.py

What This Example Does

1. Initialization: Sets up connections to Neo4j and Azure OpenAI
2. Adding Episodes: Ingests text and JSON data about California politics
3. Basic Search: Performs hybrid search combining semantic similarity and BM25 retrieval
4. Center Node Search: Reranks results based on graph distance to a specific node
5. Cleanup: Properly closes database connections

Key Concepts

Azure OpenAI Integration

The example shows how to configure Graphiti to use Azure OpenAI with the OpenAI v1 API:

# Initialize Azure OpenAI client using the standard OpenAI client
# with Azure's v1 API endpoint
azure_client = AsyncOpenAI(
    base_url=f"{azure_endpoint}/openai/v1/",
    api_key=azure_api_key,
)

# Create LLM and Embedder clients
llm_client = AzureOpenAILLMClient(
    azure_client=azure_client,
    config=LLMConfig(model=azure_deployment, small_model=azure_deployment)
)
embedder_client = AzureOpenAIEmbedderClient(
    azure_client=azure_client,
    model=azure_embedding_deployment
)

# Initialize Graphiti with custom clients
graphiti = Graphiti(
    neo4j_uri,
    neo4j_user,
    neo4j_password,
    llm_client=llm_client,
    embedder=embedder_client,
)

Note: This example uses Azure OpenAI's v1 API compatibility layer, which allows using the standard AsyncOpenAI client. The endpoint format is https://your-resource-name.openai.azure.com/openai/v1/.

Episodes

Episodes are the primary units of information in Graphiti. They can be:

• Text: Raw text content (e.g., transcripts, documents)
• JSON: Structured data with key-value pairs

Hybrid Search

Graphiti combines multiple search strategies:

• Semantic Search: Uses embeddings to find semantically similar content
• BM25: Keyword-based text retrieval
• Graph Traversal: Leverages relationships between entities

Troubleshooting

Azure OpenAI API Errors

• Verify your endpoint URL is correct (should end in .openai.azure.com)
• Check that your API key is valid
• Ensure your deployment names match actual deployments in Azure
• Verify API version is supported by your deployment

Neo4j Connection Issues

• Ensure Neo4j is running
• Check firewall settings
• Verify credentials are correct
• Check URI format (should be bolt:// or neo4j://)

Next Steps

• Explore other search recipes in graphiti_core/search/search_config_recipes.py
• Try different episode types and content
• Experiment with custom entity definitions
• Add more episodes to build a larger knowledge graph

Related Examples

• examples/quickstart/ - Basic Graphiti usage with OpenAI
• examples/podcast/ - Processing longer content
• examples/ecommerce/ - Domain-specific knowledge graphs