Add Azure OpenAI example with Neo4j (#1064)

* Add Azure OpenAI example with Neo4j

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Co-Authored-By: Claude <noreply@anthropic.com>

* Convert Azure OpenAI example to use uv

- Remove requirements.txt (uv uses pyproject.toml)
- Update README to use 'uv sync' and 'uv run'

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Co-Authored-By: Claude <noreply@anthropic.com>

* Update Azure OpenAI example to use gpt-4.1

- Change default deployment from gpt-4 to gpt-4.1
- Update README recommendations to prioritize gpt-4.1 models

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* Remove model recommendations from Azure OpenAI example

Model recommendations quickly become outdated.

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Co-Authored-By: Claude <noreply@anthropic.com>

* Add default Neo4j credentials to docker-compose

Set sensible defaults (neo4j/password) to prevent NEO4J_AUTH error
when .env file is not present.

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Co-Authored-By: Claude <noreply@anthropic.com>

* Update Azure OpenAI documentation to use v1 API

- Simplified Azure OpenAI setup using AsyncOpenAI with v1 endpoint
- Updated main README with clearer Quick Start example
- Removed outdated API version configuration
- Updated example deployment to gpt-5-mini
- Added note about v1 API endpoint format

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Co-Authored-By: Claude <noreply@anthropic.com>

* Update LLMConfig to include both model and small_model

Both parameters are needed for proper LLM configuration.

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Co-Authored-By: Claude <noreply@anthropic.com>

* Address PR review feedback

- Remove flawed validation check in azure_openai_neo4j.py
- Remove unused azure-identity dependency
- Update docstrings to reflect dual client support (AsyncAzureOpenAI and AsyncOpenAI)

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Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>

README.md

@@ -388,49 +388,32 @@ graphiti = Graphiti(graph_driver=driver)
 
 ## Using Graphiti with Azure OpenAI
 
-Graphiti supports Azure OpenAI for both LLM inference and embeddings. Azure deployments often require different
+Graphiti supports Azure OpenAI for both LLM inference and embeddings using Azure's OpenAI v1 API compatibility layer.
-endpoints for LLM and embedding services, and separate deployments for default and small models.
 
-> [!IMPORTANT]
+### Quick Start
-> **Azure OpenAI v1 API Opt-in Required for Structured Outputs**
->
-> Graphiti uses structured outputs via the `client.beta.chat.completions.parse()` method, which requires Azure OpenAI
-> deployments to opt into the v1 API. Without this opt-in, you'll encounter 404 Resource not found errors during episode
-> ingestion.
->
-> To enable v1 API support in your Azure OpenAI deployment, follow Microsoft's
-> guide: [Azure OpenAI API version lifecycle](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/api-version-lifecycle?tabs=key#api-evolution).
 
 ```python
-from openai import AsyncAzureOpenAI
+from openai import AsyncOpenAI
 from graphiti_core import Graphiti
-from graphiti_core.llm_client import LLMConfig, OpenAIClient
+from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient
-from graphiti_core.embedder.openai import OpenAIEmbedder, OpenAIEmbedderConfig
+from graphiti_core.llm_client.config import LLMConfig
-from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient
+from graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient
 
-# Azure OpenAI configuration - use separate endpoints for different services
+# Initialize Azure OpenAI client using the standard OpenAI client
-api_key = "<your-api-key>"
+# with Azure's v1 API endpoint
-api_version = "<your-api-version>"
+azure_client = AsyncOpenAI(
-llm_endpoint = "<your-llm-endpoint>"  # e.g., "https://your-llm-resource.openai.azure.com/"
+    base_url="https://your-resource-name.openai.azure.com/openai/v1/",
-embedding_endpoint = "<your-embedding-endpoint>"  # e.g., "https://your-embedding-resource.openai.azure.com/"
+    api_key="your-api-key",
-
-# Create separate Azure OpenAI clients for different services
-llm_client_azure = AsyncAzureOpenAI(
-    api_key=api_key,
-    api_version=api_version,
-    azure_endpoint=llm_endpoint
 )
 
-embedding_client_azure = AsyncAzureOpenAI(
+# Create LLM and Embedder clients
-    api_key=api_key,
+llm_client = AzureOpenAILLMClient(
-    api_version=api_version,
+    azure_client=azure_client,
-    azure_endpoint=embedding_endpoint
+    config=LLMConfig(model="gpt-5-mini", small_model="gpt-5-mini")  # Your Azure deployment name
 )
-
+embedder_client = AzureOpenAIEmbedderClient(
-# Create LLM Config with your Azure deployment names
+    azure_client=azure_client,
-azure_llm_config = LLMConfig(
+    model="text-embedding-3-small"  # Your Azure embedding deployment name
-    small_model="gpt-4.1-nano",
-    model="gpt-4.1-mini",
 )
 
 # Initialize Graphiti with Azure OpenAI clients
@@ -438,29 +421,19 @@ graphiti = Graphiti(
     "bolt://localhost:7687",
     "neo4j",
     "password",
-    llm_client=OpenAIClient(
+    llm_client=llm_client,
-        config=azure_llm_config,
+    embedder=embedder_client,
-        client=llm_client_azure
-    ),
-    embedder=OpenAIEmbedder(
-        config=OpenAIEmbedderConfig(
-            embedding_model="text-embedding-3-small-deployment"  # Your Azure embedding deployment name
-        ),
-        client=embedding_client_azure
-    ),
-    cross_encoder=OpenAIRerankerClient(
-        config=LLMConfig(
-            model=azure_llm_config.small_model  # Use small model for reranking
-        ),
-        client=llm_client_azure
-    )
 )
 
 # Now you can use Graphiti with Azure OpenAI
 ```
 
-Make sure to replace the placeholder values with your actual Azure OpenAI credentials and deployment names that match
+**Key Points:**
-your Azure OpenAI service configuration.
+- Use the standard `AsyncOpenAI` client with Azure's v1 API endpoint format: `https://your-resource-name.openai.azure.com/openai/v1/`
+- The deployment names (e.g., `gpt-5-mini`, `text-embedding-3-small`) should match your Azure OpenAI deployment names
+- See `examples/azure-openai/` for a complete working example
+
+Make sure to replace the placeholder values with your actual Azure OpenAI credentials and deployment names.
 
 ## Using Graphiti with Google Gemini
 

docker-compose.yml

@@ -22,8 +22,8 @@ services:
     environment:
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - NEO4J_URI=bolt://neo4j:${NEO4J_PORT:-7687}
-      - NEO4J_USER=${NEO4J_USER}
+      - NEO4J_USER=${NEO4J_USER:-neo4j}
-      - NEO4J_PASSWORD=${NEO4J_PASSWORD}
+      - NEO4J_PASSWORD=${NEO4J_PASSWORD:-password}
       - PORT=8000
       - db_backend=neo4j
   neo4j:
@@ -45,7 +45,7 @@ services:
     volumes:
       - neo4j_data:/data
     environment:
-      - NEO4J_AUTH=${NEO4J_USER}/${NEO4J_PASSWORD}
+      - NEO4J_AUTH=${NEO4J_USER:-neo4j}/${NEO4J_PASSWORD:-password}
 
   falkordb:
     image: falkordb/falkordb:latest

examples/azure-openai/.env.example

@@ -0,0 +1,10 @@
+# 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_DEPLOYMENT=gpt-5-mini
+AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small

examples/azure-openai/README.md

@@ -0,0 +1,154 @@
+# 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
+
+```bash
+uv sync
+```
+
+### 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_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](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
+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:
+
+```python
+# 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

examples/azure-openai/azure_openai_neo4j.py

@@ -0,0 +1,225 @@
+"""
+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 AsyncOpenAI
+
+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.llm_client.config import LLMConfig
+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")
+
+# Azure OpenAI connection parameters
+azure_endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
+azure_api_key = os.environ.get("AZURE_OPENAI_API_KEY")
+azure_deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt-4.1")
+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 = 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 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())

graphiti_core/embedder/azure_openai.py

@@ -17,7 +17,7 @@ limitations under the License.
 import logging
 from typing import Any
 
-from openai import AsyncAzureOpenAI
+from openai import AsyncAzureOpenAI, AsyncOpenAI
 
 from .client import EmbedderClient
 
@@ -25,9 +25,16 @@ logger = logging.getLogger(__name__)
 
 
 class AzureOpenAIEmbedderClient(EmbedderClient):
-    """Wrapper class for AsyncAzureOpenAI that implements the EmbedderClient interface."""
+    """Wrapper class for Azure OpenAI that implements the EmbedderClient interface.
 
-    def __init__(self, azure_client: AsyncAzureOpenAI, model: str = 'text-embedding-3-small'):
+    Supports both AsyncAzureOpenAI and AsyncOpenAI (with Azure v1 API endpoint).
+    """
+
+    def __init__(
+        self,
+        azure_client: AsyncAzureOpenAI | AsyncOpenAI,
+        model: str = "text-embedding-3-small",
+    ):
         self.azure_client = azure_client
         self.model = model
 
@@ -37,18 +44,22 @@ class AzureOpenAIEmbedderClient(EmbedderClient):
             # Handle different input types
             if isinstance(input_data, str):
                 text_input = [input_data]
-            elif isinstance(input_data, list) and all(isinstance(item, str) for item in input_data):
+            elif isinstance(input_data, list) and all(
+                isinstance(item, str) for item in input_data
+            ):
                 text_input = input_data
             else:
                 # Convert to string list for other types
                 text_input = [str(input_data)]
 
-            response = await self.azure_client.embeddings.create(model=self.model, input=text_input)
+            response = await self.azure_client.embeddings.create(
+                model=self.model, input=text_input
+            )
 
             # Return the first embedding as a list of floats
             return response.data[0].embedding
         except Exception as e:
-            logger.error(f'Error in Azure OpenAI embedding: {e}')
+            logger.error(f"Error in Azure OpenAI embedding: {e}")
             raise
 
     async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
@@ -60,5 +71,5 @@ class AzureOpenAIEmbedderClient(EmbedderClient):
 
             return [embedding.embedding for embedding in response.data]
         except Exception as e:
-            logger.error(f'Error in Azure OpenAI batch embedding: {e}')
+            logger.error(f"Error in Azure OpenAI batch embedding: {e}")
             raise

graphiti_core/llm_client/azure_openai_client.py

@@ -17,7 +17,7 @@ limitations under the License.
 import logging
 from typing import ClassVar
 
-from openai import AsyncAzureOpenAI
+from openai import AsyncAzureOpenAI, AsyncOpenAI
 from openai.types.chat import ChatCompletionMessageParam
 from pydantic import BaseModel
 
@@ -28,14 +28,17 @@ logger = logging.getLogger(__name__)
 
 
 class AzureOpenAILLMClient(BaseOpenAIClient):
-    """Wrapper class for AsyncAzureOpenAI that implements the LLMClient interface."""
+    """Wrapper class for Azure OpenAI that implements the LLMClient interface.
+
+    Supports both AsyncAzureOpenAI and AsyncOpenAI (with Azure v1 API endpoint).
+    """
 
     # Class-level constants
     MAX_RETRIES: ClassVar[int] = 2
 
     def __init__(
         self,
-        azure_client: AsyncAzureOpenAI,
+        azure_client: AsyncAzureOpenAI | AsyncOpenAI,
         config: LLMConfig | None = None,
         max_tokens: int = DEFAULT_MAX_TOKENS,
         reasoning: str | None = None,
@@ -63,21 +66,21 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
         """Create a structured completion using Azure OpenAI's responses.parse API."""
         supports_reasoning = self._supports_reasoning_features(model)
         request_kwargs = {
-            'model': model,
+            "model": model,
-            'input': messages,
+            "input": messages,
-            'max_output_tokens': max_tokens,
+            "max_output_tokens": max_tokens,
-            'text_format': response_model,  # type: ignore
+            "text_format": response_model,  # type: ignore
         }
 
         temperature_value = temperature if not supports_reasoning else None
         if temperature_value is not None:
-            request_kwargs['temperature'] = temperature_value
+            request_kwargs["temperature"] = temperature_value
 
         if supports_reasoning and reasoning:
-            request_kwargs['reasoning'] = {'effort': reasoning}  # type: ignore
+            request_kwargs["reasoning"] = {"effort": reasoning}  # type: ignore
 
         if supports_reasoning and verbosity:
-            request_kwargs['text'] = {'verbosity': verbosity}  # type: ignore
+            request_kwargs["text"] = {"verbosity": verbosity}  # type: ignore
 
         return await self.client.responses.parse(**request_kwargs)
 
@@ -93,20 +96,20 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
         supports_reasoning = self._supports_reasoning_features(model)
 
         request_kwargs = {
-            'model': model,
+            "model": model,
-            'messages': messages,
+            "messages": messages,
-            'max_tokens': max_tokens,
+            "max_tokens": max_tokens,
-            'response_format': {'type': 'json_object'},
+            "response_format": {"type": "json_object"},
         }
 
         temperature_value = temperature if not supports_reasoning else None
         if temperature_value is not None:
-            request_kwargs['temperature'] = temperature_value
+            request_kwargs["temperature"] = temperature_value
 
         return await self.client.chat.completions.create(**request_kwargs)
 
     @staticmethod
     def _supports_reasoning_features(model: str) -> bool:
         """Return True when the Azure model supports reasoning/verbosity options."""
-        reasoning_prefixes = ('o1', 'o3', 'gpt-5')
+        reasoning_prefixes = ("o1", "o3", "gpt-5")
         return model.startswith(reasoning_prefixes)

pyproject.toml

@@ -103,3 +103,4 @@ docstring-code-format = true
 include = ["graphiti_core"]
 pythonVersion = "3.10"
 typeCheckingMode = "basic"
+

uv.lock

@@ -262,6 +262,35 @@ wheels = [
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 ]
 
+[[package]]
+name = "azure-core"
+version = "1.36.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "requests" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0a/c4/d4ff3bc3ddf155156460bff340bbe9533f99fac54ddea165f35a8619f162/azure_core-1.36.0.tar.gz", hash = "sha256:22e5605e6d0bf1d229726af56d9e92bc37b6e726b141a18be0b4d424131741b7", size = 351139, upload-time = "2025-10-15T00:33:49.083Z" }
+wheels = [
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+]
+
+[[package]]
+name = "azure-identity"
+version = "1.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "azure-core" },
+    { name = "cryptography" },
+    { name = "msal" },
+    { name = "msal-extensions" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/8d/1a6c41c28a37eab26dc85ab6c86992c700cd3f4a597d9ed174b0e9c69489/azure_identity-1.25.1.tar.gz", hash = "sha256:87ca8328883de6036443e1c37b40e8dc8fb74898240f61071e09d2e369361456", size = 279826, upload-time = "2025-10-06T20:30:02.194Z" }
+wheels = [
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+]
+
 [[package]]
 name = "babel"
 version = "2.17.0"
@@ -358,59 +387,84 @@ wheels = [
 
 [[package]]
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+version = "2.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pycparser" },
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
 ]
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 [[package]]
 name = "debugpy"
 version = "1.8.14"
@@ -801,8 +920,12 @@ dependencies = [
 anthropic = [
     { name = "anthropic" },
 ]
+azure = [
+    { name = "azure-identity" },
+]
 dev = [
     { name = "anthropic" },
+    { name = "azure-identity" },
     { name = "boto3" },
     { name = "diskcache-stubs" },
     { name = "falkordb" },
@@ -863,6 +986,8 @@ voyageai = [
 requires-dist = [
     { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.49.0" },
     { name = "anthropic", marker = "extra == 'dev'", specifier = ">=0.49.0" },
+    { name = "azure-identity", marker = "extra == 'azure'", specifier = ">=1.25.1" },
+    { name = "azure-identity", marker = "extra == 'dev'", specifier = ">=1.25.1" },
     { name = "boto3", marker = "extra == 'dev'", specifier = ">=1.39.16" },
     { name = "boto3", marker = "extra == 'neo4j-opensearch'", specifier = ">=1.39.16" },
     { name = "boto3", marker = "extra == 'neptune'", specifier = ">=1.39.16" },
@@ -908,7 +1033,7 @@ requires-dist = [
     { name = "voyageai", marker = "extra == 'dev'", specifier = ">=0.2.3" },
     { name = "voyageai", marker = "extra == 'voyageai'", specifier = ">=0.2.3" },
 ]
-provides-extras = ["anthropic", "groq", "google-genai", "kuzu", "falkordb", "voyageai", "neo4j-opensearch", "sentence-transformers", "neptune", "tracing", "dev"]
+provides-extras = ["anthropic", "groq", "google-genai", "kuzu", "falkordb", "voyageai", "neo4j-opensearch", "sentence-transformers", "neptune", "tracing", "azure", "dev"]
 
 [[package]]
 name = "groq"
@@ -1686,6 +1811,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" },
 ]
 
+[[package]]
+name = "msal"
+version = "1.34.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cryptography" },
+    { name = "pyjwt", extra = ["crypto"] },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cf/0e/c857c46d653e104019a84f22d4494f2119b4fe9f896c92b4b864b3b045cc/msal-1.34.0.tar.gz", hash = "sha256:76ba83b716ea5a6d75b0279c0ac353a0e05b820ca1f6682c0eb7f45190c43c2f", size = 153961, upload-time = "2025-09-22T23:05:48.989Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/dc/18d48843499e278538890dc709e9ee3dea8375f8be8e82682851df1b48b5/msal-1.34.0-py3-none-any.whl", hash = "sha256:f669b1644e4950115da7a176441b0e13ec2975c29528d8b9e81316023676d6e1", size = 116987, upload-time = "2025-09-22T23:05:47.294Z" },
+]
+
+[[package]]
+name = "msal-extensions"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "msal" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/01/99/5d239b6156eddf761a636bded1118414d161bd6b7b37a9335549ed159396/msal_extensions-1.3.1.tar.gz", hash = "sha256:c5b0fd10f65ef62b5f1d62f4251d51cbcaf003fcedae8c91b040a488614be1a4", size = 23315, upload-time = "2025-03-14T23:51:03.902Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5e/75/bd9b7bb966668920f06b200e84454c8f3566b102183bc55c5473d96cb2b9/msal_extensions-1.3.1-py3-none-any.whl", hash = "sha256:96d3de4d034504e969ac5e85bae8106c8373b5c6568e4c8fa7af2eca9dbe6bca", size = 20583, upload-time = "2025-03-14T23:51:03.016Z" },
+]
+
 [[package]]
 name = "multidict"
 version = "6.6.3"
@@ -2789,6 +2940,11 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/79/84/0fdf9b18ba31d69877bd39c9cd6052b47f3761e9910c15de788e519f079f/PyJWT-2.9.0-py3-none-any.whl", hash = "sha256:3b02fb0f44517787776cf48f2ae25d8e14f300e6d7545a4315cee571a415e850", size = 22344, upload-time = "2024-08-01T15:01:06.481Z" },
 ]
 
+[package.optional-dependencies]
+crypto = [
+    { name = "cryptography" },
+]
+
 [[package]]
 name = "pyright"
 version = "1.1.404"