diff --git a/examples/azure-openai/.env.example b/examples/azure-openai/.env.example
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+# Neo4j connection settings
+NEO4J_URI=bolt://localhost:7687
+NEO4J_USER=neo4j
+NEO4J_PASSWORD=password
+
+# Azure OpenAI settings
+AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com
+AZURE_OPENAI_API_KEY=your-api-key-here
+AZURE_OPENAI_API_VERSION=2024-10-21
+AZURE_OPENAI_DEPLOYMENT=gpt-4
+AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small
diff --git a/examples/azure-openai/README.md b/examples/azure-openai/README.md
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+# 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
+
+From the project root:
+
+```bash
+uv sync --extra dev
+```
+
+### 2. Configure Environment Variables
+
+Copy the `.env.example` file to `.env` and fill in your credentials:
+
+```bash
+cd examples/azure-openai
+cp .env.example .env
+```
+
+Edit `.env` with your actual values:
+
+```env
+# 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_API_VERSION=2024-10-21
+AZURE_OPENAI_DEPLOYMENT=gpt-4
+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
+   - Recommended: GPT-4, GPT-4 Turbo, or GPT-4.1
+   - Set the deployment name in `AZURE_OPENAI_DEPLOYMENT`
+
+2. **Embedding Model**: Used for semantic search
+   - Recommended: text-embedding-3-small or text-embedding-3-large
+   - 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](https://neo4j.com/download/)
+- Create a new database
+- Start the database
+- Use the credentials in your `.env` file
+
+## Running the Example
+
+```bash
+cd examples/azure-openai
+python 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:
+
+```python
+# Initialize Azure OpenAI client
+azure_client = AsyncAzureOpenAI(
+    azure_endpoint=azure_endpoint,
+    api_key=azure_api_key,
+    api_version=azure_api_version,
+)
+
+# Create LLM and Embedder clients
+llm_client = AzureOpenAILLMClient(azure_client=azure_client)
+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,
+)
+```
+
+### 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
diff --git a/examples/azure-openai/azure_openai_neo4j.py b/examples/azure-openai/azure_openai_neo4j.py
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+++ b/examples/azure-openai/azure_openai_neo4j.py
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+"""
+Copyright 2025, Zep Software, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import asyncio
+import json
+import logging
+import os
+from datetime import datetime, timezone
+from logging import INFO
+
+from dotenv import load_dotenv
+from openai import AsyncAzureOpenAI
+
+from graphiti_core import Graphiti
+from graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient
+from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient
+from graphiti_core.nodes import EpisodeType
+
+#################################################
+# CONFIGURATION
+#################################################
+# Set up logging and environment variables for
+# connecting to Neo4j database and Azure OpenAI
+#################################################
+
+# Configure logging
+logging.basicConfig(
+    level=INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S',
+)
+logger = logging.getLogger(__name__)
+
+load_dotenv()
+
+# Neo4j connection parameters
+# Make sure Neo4j Desktop is running with a local DBMS started
+neo4j_uri = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
+neo4j_user = os.environ.get('NEO4J_USER', 'neo4j')
+neo4j_password = os.environ.get('NEO4J_PASSWORD', 'password')
+
+if not neo4j_uri or not neo4j_user or not neo4j_password:
+    raise ValueError('NEO4J_URI, NEO4J_USER, and NEO4J_PASSWORD must be set')
+
+# Azure OpenAI connection parameters
+azure_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT')
+azure_api_key = os.environ.get('AZURE_OPENAI_API_KEY')
+azure_api_version = os.environ.get('AZURE_OPENAI_API_VERSION', '2024-10-21')
+azure_deployment = os.environ.get('AZURE_OPENAI_DEPLOYMENT', 'gpt-4')
+azure_embedding_deployment = os.environ.get(
+    'AZURE_OPENAI_EMBEDDING_DEPLOYMENT', 'text-embedding-3-small'
+)
+
+if not azure_endpoint or not azure_api_key:
+    raise ValueError('AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY must be set')
+
+
+async def main():
+    #################################################
+    # INITIALIZATION
+    #################################################
+    # Connect to Neo4j and Azure OpenAI, then set up
+    # Graphiti indices. This is required before using
+    # other Graphiti functionality
+    #################################################
+
+    # Initialize Azure OpenAI client
+    azure_client = AsyncAzureOpenAI(
+        azure_endpoint=azure_endpoint,
+        api_key=azure_api_key,
+        api_version=azure_api_version,
+    )
+
+    # Create LLM and Embedder clients
+    llm_client = AzureOpenAILLMClient(azure_client=azure_client)
+    embedder_client = AzureOpenAIEmbedderClient(
+        azure_client=azure_client, model=azure_embedding_deployment
+    )
+
+    # Initialize Graphiti with Neo4j connection and Azure OpenAI clients
+    graphiti = Graphiti(
+        neo4j_uri,
+        neo4j_user,
+        neo4j_password,
+        llm_client=llm_client,
+        embedder=embedder_client,
+    )
+
+    try:
+        #################################################
+        # ADDING EPISODES
+        #################################################
+        # Episodes are the primary units of information
+        # in Graphiti. They can be text or structured JSON
+        # and are automatically processed to extract entities
+        # and relationships.
+        #################################################
+
+        # Example: Add Episodes
+        # Episodes list containing both text and JSON episodes
+        episodes = [
+            {
+                'content': 'Kamala Harris is the Attorney General of California. She was previously '
+                'the district attorney for San Francisco.',
+                'type': EpisodeType.text,
+                'description': 'podcast transcript',
+            },
+            {
+                'content': 'As AG, Harris was in office from January 3, 2011 – January 3, 2017',
+                'type': EpisodeType.text,
+                'description': 'podcast transcript',
+            },
+            {
+                'content': {
+                    'name': 'Gavin Newsom',
+                    'position': 'Governor',
+                    'state': 'California',
+                    'previous_role': 'Lieutenant Governor',
+                    'previous_location': 'San Francisco',
+                },
+                'type': EpisodeType.json,
+                'description': 'podcast metadata',
+            },
+        ]
+
+        # Add episodes to the graph
+        for i, episode in enumerate(episodes):
+            await graphiti.add_episode(
+                name=f'California Politics {i}',
+                episode_body=episode['content']
+                if isinstance(episode['content'], str)
+                else json.dumps(episode['content']),
+                source=episode['type'],
+                source_description=episode['description'],
+                reference_time=datetime.now(timezone.utc),
+            )
+            print(f'Added episode: California Politics {i} ({episode["type"].value})')
+
+        #################################################
+        # BASIC SEARCH
+        #################################################
+        # The simplest way to retrieve relationships (edges)
+        # from Graphiti is using the search method, which
+        # performs a hybrid search combining semantic
+        # similarity and BM25 text retrieval.
+        #################################################
+
+        # Perform a hybrid search combining semantic similarity and BM25 retrieval
+        print("\nSearching for: 'Who was the California Attorney General?'")
+        results = await graphiti.search('Who was the California Attorney General?')
+
+        # Print search results
+        print('\nSearch Results:')
+        for result in results:
+            print(f'UUID: {result.uuid}')
+            print(f'Fact: {result.fact}')
+            if hasattr(result, 'valid_at') and result.valid_at:
+                print(f'Valid from: {result.valid_at}')
+            if hasattr(result, 'invalid_at') and result.invalid_at:
+                print(f'Valid until: {result.invalid_at}')
+            print('---')
+
+        #################################################
+        # CENTER NODE SEARCH
+        #################################################
+        # For more contextually relevant results, you can
+        # use a center node to rerank search results based
+        # on their graph distance to a specific node
+        #################################################
+
+        # Use the top search result's UUID as the center node for reranking
+        if results and len(results) > 0:
+            # Get the source node UUID from the top result
+            center_node_uuid = results[0].source_node_uuid
+
+            print('\nReranking search results based on graph distance:')
+            print(f'Using center node UUID: {center_node_uuid}')
+
+            reranked_results = await graphiti.search(
+                'Who was the California Attorney General?', center_node_uuid=center_node_uuid
+            )
+
+            # Print reranked search results
+            print('\nReranked Search Results:')
+            for result in reranked_results:
+                print(f'UUID: {result.uuid}')
+                print(f'Fact: {result.fact}')
+                if hasattr(result, 'valid_at') and result.valid_at:
+                    print(f'Valid from: {result.valid_at}')
+                if hasattr(result, 'invalid_at') and result.invalid_at:
+                    print(f'Valid until: {result.invalid_at}')
+                print('---')
+        else:
+            print('No results found in the initial search to use as center node.')
+
+    finally:
+        #################################################
+        # CLEANUP
+        #################################################
+        # Always close the connection to Neo4j when
+        # finished to properly release resources
+        #################################################
+
+        # Close the connection
+        await graphiti.close()
+        print('\nConnection closed')
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
diff --git a/examples/azure-openai/requirements.txt b/examples/azure-openai/requirements.txt
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+graphiti-core
+python-dotenv
+openai