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Update Azure OpenAI documentation to use v1 API

Browse source 
- 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- Removed outdated API version configuration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

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

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Daniel Chalef 2025-11-13 21:57:54 -08:00
parent 2ca14d61ec
commit 73d2a7c28f
8 changed files with 342 additions and 195 deletions
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79
README.md
View file

@ -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
endpoints for LLM and embedding services, and separate deployments for default and small models.
Graphiti supports Azure OpenAI for both LLM inference and embeddings using Azure's OpenAI v1 API compatibility layer.
> [!IMPORTANT]
> **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).
### Quick Start
```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.embedder.openai import OpenAIEmbedder, OpenAIEmbedderConfig
from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient
from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient
from graphiti_core.llm_client.config import LLMConfig
from graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient
# Azure OpenAI configuration - use separate endpoints for different services
api_key = "<your-api-key>"
api_version = "<your-api-version>"
llm_endpoint = "<your-llm-endpoint>" # e.g., "https://your-llm-resource.openai.azure.com/"
embedding_endpoint = "<your-embedding-endpoint>" # e.g., "https://your-embedding-resource.openai.azure.com/"
# Create separate Azure OpenAI clients for different services
llm_client_azure = AsyncAzureOpenAI(
api_key=api_key,
api_version=api_version,
azure_endpoint=llm_endpoint
# Initialize Azure OpenAI client using the standard OpenAI client
# with Azure's v1 API endpoint
azure_client = AsyncOpenAI(
base_url="https://your-resource-name.openai.azure.com/openai/v1/",
api_key="your-api-key",
)
embedding_client_azure = AsyncAzureOpenAI(
api_key=api_key,
api_version=api_version,
azure_endpoint=embedding_endpoint
# Create LLM and Embedder clients
llm_client = AzureOpenAILLMClient(
azure_client=azure_client,
config=LLMConfig(small_model="gpt-5-mini") # Your Azure deployment name
)
# Create LLM Config with your Azure deployment names
azure_llm_config = LLMConfig(
small_model="gpt-4.1-nano",
model="gpt-4.1-mini",
embedder_client = AzureOpenAIEmbedderClient(
azure_client=azure_client,
model="text-embedding-3-small" # Your Azure embedding deployment name
)
# Initialize Graphiti with Azure OpenAI clients
@ -438,29 +421,19 @@ graphiti = Graphiti(
"bolt://localhost:7687",
"neo4j",
"password",
llm_client=OpenAIClient(
config=azure_llm_config,
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
)
llm_client=llm_client,
embedder=embedder_client,
)
# 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
your Azure OpenAI service configuration.
**Key Points:**
- 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

3
examples/azure-openai/.env.example
View file

@ -6,6 +6,5 @@ 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-4.1
AZURE_OPENAI_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small

20
examples/azure-openai/README.md
View file

@ -36,8 +36,7 @@ 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-4.1
AZURE_OPENAI_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small
```
@ -80,18 +79,21 @@ uv run azure_openai_neo4j.py
### Azure OpenAI Integration
The example shows how to configure Graphiti to use Azure OpenAI:
The example shows how to configure Graphiti to use Azure OpenAI with the OpenAI v1 API:
```python
# Initialize Azure OpenAI client
azure_client = AsyncAzureOpenAI(
azure_endpoint=azure_endpoint,
# 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,
api_version=azure_api_version,
)
# Create LLM and Embedder clients
llm_client = AzureOpenAILLMClient(azure_client=azure_client)
llm_client = AzureOpenAILLMClient(
azure_client=azure_client,
config=LLMConfig(small_model=azure_deployment)
)
embedder_client = AzureOpenAIEmbedderClient(
azure_client=azure_client,
model=azure_embedding_deployment
@ -107,6 +109,8 @@ graphiti = Graphiti(
)
```
**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:

126
examples/azure-openai/azure_openai_neo4j.py
View file

@ -22,11 +22,12 @@ from datetime import datetime, timezone
from logging import INFO
from dotenv import load_dotenv
from openai import AsyncAzureOpenAI
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
#################################################
@ -39,8 +40,8 @@ from graphiti_core.nodes import EpisodeType
# Configure logging
logging.basicConfig(
level=INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logger = logging.getLogger(__name__)
@ -48,24 +49,23 @@ 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')
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')
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-4.1')
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'
"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')
raise ValueError("AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY must be set")
async def main():
@ -78,14 +78,15 @@ async def main():
#################################################
# Initialize Azure OpenAI client
azure_client = AsyncAzureOpenAI(
azure_endpoint=azure_endpoint,
azure_client = AsyncOpenAI(
base_url=f"{azure_endpoint}/openai/v1/",
api_key=azure_api_key,
api_version=azure_api_version,
)
# Create LLM and Embedder clients
llm_client = AzureOpenAILLMClient(azure_client=azure_client)
llm_client = AzureOpenAILLMClient(
azure_client=azure_client, config=LLMConfig(small_model=azure_deployment)
)
embedder_client = AzureOpenAIEmbedderClient(
azure_client=azure_client, model=azure_embedding_deployment
)
@ -113,38 +114,40 @@ async def main():
# 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": "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": "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',
"content": {
"name": "Gavin Newsom",
"position": "Governor",
"state": "California",
"previous_role": "Lieutenant Governor",
"previous_location": "San Francisco",
},
'type': EpisodeType.json,
'description': 'podcast metadata',
"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'],
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})')
@ -160,18 +163,18 @@ async def main():
# 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?')
results = await graphiti.search("Who was the California Attorney General?")
# Print search results
print('\nSearch 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('---')
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
@ -186,25 +189,26 @@ async def main():
# 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}')
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
"Who was the California Attorney General?",
center_node_uuid=center_node_uuid,
)
# Print reranked search results
print('\nReranked 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('---')
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.')
print("No results found in the initial search to use as center node.")
finally:
#################################################
@ -216,8 +220,8 @@ async def main():
# Close the connection
await graphiti.close()
print('\nConnection closed')
print("\nConnection closed")
if __name__ == '__main__':
if __name__ == "__main__":
asyncio.run(main())

20
graphiti_core/embedder/azure_openai.py
View file

@ -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
@ -27,7 +27,11 @@ logger = logging.getLogger(__name__)
class AzureOpenAIEmbedderClient(EmbedderClient):
"""Wrapper class for AsyncAzureOpenAI that implements the EmbedderClient interface."""
def __init__(self, azure_client: AsyncAzureOpenAI, model: str = 'text-embedding-3-small'):
def __init__(
self,
azure_client: AsyncAzureOpenAI | AsyncOpenAI,
model: str = "text-embedding-3-small",
):
self.azure_client = azure_client
self.model = model
@ -37,18 +41,22 @@ 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 +68,5 @@ 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

30
graphiti_core/llm_client/azure_openai_client.py
View file

@ -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
@ -35,7 +35,7 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
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 +63,21 @@ 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,
'input': messages,
'max_output_tokens': max_tokens,
'text_format': response_model, # type: ignore
"model": model,
"input": messages,
"max_output_tokens": max_tokens,
"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 +93,20 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
supports_reasoning = self._supports_reasoning_features(model)
request_kwargs = {
'model': model,
'messages': messages,
'max_tokens': max_tokens,
'response_format': {'type': 'json_object'},
"model": model,
"messages": messages,
"max_tokens": max_tokens,
"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)

3
pyproject.toml
View file

@ -36,7 +36,9 @@ neo4j-opensearch = ["boto3>=1.39.16", "opensearch-py>=3.0.0"]
sentence-transformers = ["sentence-transformers>=3.2.1"]
neptune = ["langchain-aws>=0.2.29", "opensearch-py>=3.0.0", "boto3>=1.39.16"]
tracing = ["opentelemetry-api>=1.20.0", "opentelemetry-sdk>=1.20.0"]
azure = ["azure-identity>=1.25.1"]
dev = [
"azure-identity>=1.25.1",
"pyright>=1.1.404",
"groq>=0.2.0",
"anthropic>=0.49.0",
@ -103,3 +105,4 @@ docstring-code-format = true
include = ["graphiti_core"]
pythonVersion = "3.10"
typeCheckingMode = "basic"

256
uv.lock generated
View file

@ -262,6 +262,35 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815, upload-time = "2025-03-13T11:10:21.14Z" },
]
[[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 = [
{ url = "https://files.pythonhosted.org/packages/b1/3c/b90d5afc2e47c4a45f4bba00f9c3193b0417fad5ad3bb07869f9d12832aa/azure_core-1.36.0-py3-none-any.whl", hash = "sha256:fee9923a3a753e94a259563429f3644aaf05c486d45b1215d098115102d91d3b", size = 213302, upload-time = "2025-10-15T00:33:51.058Z" },
]
[[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 = [
{ url = "https://files.pythonhosted.org/packages/83/7b/5652771e24fff12da9dde4c20ecf4682e606b104f26419d139758cc935a6/azure_identity-1.25.1-py3-none-any.whl", hash = "sha256:e9edd720af03dff020223cd269fa3a61e8f345ea75443858273bcb44844ab651", size = 191317, upload-time = "2025-10-06T20:30:04.251Z" },
]
[[package]]
name = "babel"
version = "2.17.0"
@ -358,59 +387,84 @@ wheels = [
[[package]]
name = "cffi"
version = "1.17.1"
version = "2.0.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "pycparser" },
{ name = "pycparser", marker = "implementation_name != 'PyPy'" },
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