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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- Removed outdated API version configuration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 ## 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 ```python
from openai import AsyncAzureOpenAI from openai import AsyncOpenAI
from graphiti_core import Graphiti 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(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 # Initialize Graphiti with Azure OpenAI clients
@ -438,29 +421,19 @@ graphiti = Graphiti(
"bolt://localhost:7687", "bolt://localhost:7687",
"neo4j", "neo4j",
"password", "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 # 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 ## Using Graphiti with Google Gemini

3
examples/azure-openai/.env.example
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@ -6,6 +6,5 @@ NEO4J_PASSWORD=password
# Azure OpenAI settings # Azure OpenAI settings
AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com
AZURE_OPENAI_API_KEY=your-api-key-here AZURE_OPENAI_API_KEY=your-api-key-here
AZURE_OPENAI_API_VERSION=2024-10-21 AZURE_OPENAI_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_DEPLOYMENT=gpt-4.1
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small

20
examples/azure-openai/README.md
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@ -36,8 +36,7 @@ NEO4J_PASSWORD=your-password
# Azure OpenAI settings # Azure OpenAI settings
AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com
AZURE_OPENAI_API_KEY=your-api-key-here AZURE_OPENAI_API_KEY=your-api-key-here
AZURE_OPENAI_API_VERSION=2024-10-21 AZURE_OPENAI_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_DEPLOYMENT=gpt-4.1
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small
``` ```
@ -80,18 +79,21 @@ uv run azure_openai_neo4j.py
### Azure OpenAI Integration ### 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 ```python
# Initialize Azure OpenAI client # Initialize Azure OpenAI client using the standard OpenAI client
azure_client = AsyncAzureOpenAI( # with Azure's v1 API endpoint
azure_endpoint=azure_endpoint, azure_client = AsyncOpenAI(
base_url=f"{azure_endpoint}/openai/v1/",
api_key=azure_api_key, api_key=azure_api_key,
api_version=azure_api_version,
) )
# Create LLM and Embedder clients # 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( embedder_client = AzureOpenAIEmbedderClient(
azure_client=azure_client, azure_client=azure_client,
model=azure_embedding_deployment 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
Episodes are the primary units of information in Graphiti. They can be: Episodes are the primary units of information in Graphiti. They can be:

126
examples/azure-openai/azure_openai_neo4j.py
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@ -22,11 +22,12 @@ from datetime import datetime, timezone
from logging import INFO from logging import INFO
from dotenv import load_dotenv from dotenv import load_dotenv
from openai import AsyncAzureOpenAI from openai import AsyncOpenAI
from graphiti_core import Graphiti from graphiti_core import Graphiti
from graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient from graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient
from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient
from graphiti_core.llm_client.config import LLMConfig
from graphiti_core.nodes import EpisodeType from graphiti_core.nodes import EpisodeType
################################################# #################################################
@ -39,8 +40,8 @@ from graphiti_core.nodes import EpisodeType
# Configure logging # Configure logging
logging.basicConfig( logging.basicConfig(
level=INFO, level=INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
datefmt='%Y-%m-%d %H:%M:%S', datefmt="%Y-%m-%d %H:%M:%S",
) )
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -48,24 +49,23 @@ load_dotenv()
# Neo4j connection parameters # Neo4j connection parameters
# Make sure Neo4j Desktop is running with a local DBMS started # Make sure Neo4j Desktop is running with a local DBMS started
neo4j_uri = os.environ.get('NEO4J_URI', 'bolt://localhost:7687') neo4j_uri = os.environ.get("NEO4J_URI", "bolt://localhost:7687")
neo4j_user = os.environ.get('NEO4J_USER', 'neo4j') neo4j_user = os.environ.get("NEO4J_USER", "neo4j")
neo4j_password = os.environ.get('NEO4J_PASSWORD', 'password') neo4j_password = os.environ.get("NEO4J_PASSWORD", "password")
if not neo4j_uri or not neo4j_user or not neo4j_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 OpenAI connection parameters
azure_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT') azure_endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
azure_api_key = os.environ.get('AZURE_OPENAI_API_KEY') 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_deployment = os.environ.get('AZURE_OPENAI_DEPLOYMENT', 'gpt-4.1')
azure_embedding_deployment = os.environ.get( 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: 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(): async def main():
@ -78,14 +78,15 @@ async def main():
################################################# #################################################
# Initialize Azure OpenAI client # Initialize Azure OpenAI client
azure_client = AsyncAzureOpenAI( azure_client = AsyncOpenAI(
azure_endpoint=azure_endpoint, base_url=f"{azure_endpoint}/openai/v1/",
api_key=azure_api_key, api_key=azure_api_key,
api_version=azure_api_version,
) )
# Create LLM and Embedder clients # 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( embedder_client = AzureOpenAIEmbedderClient(
azure_client=azure_client, model=azure_embedding_deployment azure_client=azure_client, model=azure_embedding_deployment
) )
@ -113,38 +114,40 @@ async def main():
# Episodes list containing both text and JSON episodes # Episodes list containing both text and JSON episodes
episodes = [ episodes = [
{ {
'content': 'Kamala Harris is the Attorney General of California. She was previously ' "content": "Kamala Harris is the Attorney General of California. She was previously "
'the district attorney for San Francisco.', "the district attorney for San Francisco.",
'type': EpisodeType.text, "type": EpisodeType.text,
'description': 'podcast transcript', "description": "podcast transcript",
}, },
{ {
'content': 'As AG, Harris was in office from January 3, 2011 – January 3, 2017', "content": "As AG, Harris was in office from January 3, 2011 – January 3, 2017",
'type': EpisodeType.text, "type": EpisodeType.text,
'description': 'podcast transcript', "description": "podcast transcript",
}, },
{ {
'content': { "content": {
'name': 'Gavin Newsom', "name": "Gavin Newsom",
'position': 'Governor', "position": "Governor",
'state': 'California', "state": "California",
'previous_role': 'Lieutenant Governor', "previous_role": "Lieutenant Governor",
'previous_location': 'San Francisco', "previous_location": "San Francisco",
}, },
'type': EpisodeType.json, "type": EpisodeType.json,
'description': 'podcast metadata', "description": "podcast metadata",
}, },
] ]
# Add episodes to the graph # Add episodes to the graph
for i, episode in enumerate(episodes): for i, episode in enumerate(episodes):
await graphiti.add_episode( await graphiti.add_episode(
name=f'California Politics {i}', name=f"California Politics {i}",
episode_body=episode['content'] episode_body=(
if isinstance(episode['content'], str) episode["content"]
else json.dumps(episode['content']), if isinstance(episode["content"], str)
source=episode['type'], else json.dumps(episode["content"])
source_description=episode['description'], ),
source=episode["type"],
source_description=episode["description"],
reference_time=datetime.now(timezone.utc), reference_time=datetime.now(timezone.utc),
) )
print(f'Added episode: California Politics {i} ({episode["type"].value})') 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 # Perform a hybrid search combining semantic similarity and BM25 retrieval
print("\nSearching for: 'Who was the California Attorney General?'") 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 search results
print('\nSearch Results:') print("\nSearch Results:")
for result in results: for result in results:
print(f'UUID: {result.uuid}') print(f"UUID: {result.uuid}")
print(f'Fact: {result.fact}') print(f"Fact: {result.fact}")
if hasattr(result, 'valid_at') and result.valid_at: if hasattr(result, "valid_at") and result.valid_at:
print(f'Valid from: {result.valid_at}') print(f"Valid from: {result.valid_at}")
if hasattr(result, 'invalid_at') and result.invalid_at: if hasattr(result, "invalid_at") and result.invalid_at:
print(f'Valid until: {result.invalid_at}') print(f"Valid until: {result.invalid_at}")
print('---') print("---")
################################################# #################################################
# CENTER NODE SEARCH # CENTER NODE SEARCH
@ -186,25 +189,26 @@ async def main():
# Get the source node UUID from the top result # Get the source node UUID from the top result
center_node_uuid = results[0].source_node_uuid center_node_uuid = results[0].source_node_uuid
print('\nReranking search results based on graph distance:') print("\nReranking search results based on graph distance:")
print(f'Using center node UUID: {center_node_uuid}') print(f"Using center node UUID: {center_node_uuid}")
reranked_results = await graphiti.search( 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 reranked search results
print('\nReranked Search Results:') print("\nReranked Search Results:")
for result in reranked_results: for result in reranked_results:
print(f'UUID: {result.uuid}') print(f"UUID: {result.uuid}")
print(f'Fact: {result.fact}') print(f"Fact: {result.fact}")
if hasattr(result, 'valid_at') and result.valid_at: if hasattr(result, "valid_at") and result.valid_at:
print(f'Valid from: {result.valid_at}') print(f"Valid from: {result.valid_at}")
if hasattr(result, 'invalid_at') and result.invalid_at: if hasattr(result, "invalid_at") and result.invalid_at:
print(f'Valid until: {result.invalid_at}') print(f"Valid until: {result.invalid_at}")
print('---') print("---")
else: 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: finally:
################################################# #################################################
@ -216,8 +220,8 @@ async def main():
# Close the connection # Close the connection
await graphiti.close() await graphiti.close()
print('\nConnection closed') print("\nConnection closed")
if __name__ == '__main__': if __name__ == "__main__":
asyncio.run(main()) asyncio.run(main())

20
graphiti_core/embedder/azure_openai.py
View file

@ -17,7 +17,7 @@ limitations under the License.
import logging import logging
from typing import Any from typing import Any
from openai import AsyncAzureOpenAI from openai import AsyncAzureOpenAI, AsyncOpenAI
from .client import EmbedderClient from .client import EmbedderClient
@ -27,7 +27,11 @@ logger = logging.getLogger(__name__)
class AzureOpenAIEmbedderClient(EmbedderClient): class AzureOpenAIEmbedderClient(EmbedderClient):
"""Wrapper class for AsyncAzureOpenAI that implements the EmbedderClient interface.""" """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.azure_client = azure_client
self.model = model self.model = model
@ -37,18 +41,22 @@ class AzureOpenAIEmbedderClient(EmbedderClient):
# Handle different input types # Handle different input types
if isinstance(input_data, str): if isinstance(input_data, str):
text_input = [input_data] 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 text_input = input_data
else: else:
# Convert to string list for other types # Convert to string list for other types
text_input = [str(input_data)] 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 the first embedding as a list of floats
return response.data[0].embedding return response.data[0].embedding
except Exception as e: except Exception as e:
logger.error(f'Error in Azure OpenAI embedding: {e}') logger.error(f"Error in Azure OpenAI embedding: {e}")
raise raise
async def create_batch(self, input_data_list: list[str]) -> list[list[float]]: 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] return [embedding.embedding for embedding in response.data]
except Exception as e: 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 raise

30
graphiti_core/llm_client/azure_openai_client.py
View file

@ -17,7 +17,7 @@ limitations under the License.
import logging import logging
from typing import ClassVar from typing import ClassVar
from openai import AsyncAzureOpenAI from openai import AsyncAzureOpenAI, AsyncOpenAI
from openai.types.chat import ChatCompletionMessageParam from openai.types.chat import ChatCompletionMessageParam
from pydantic import BaseModel from pydantic import BaseModel
@ -35,7 +35,7 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
def __init__( def __init__(
self, self,
azure_client: AsyncAzureOpenAI, azure_client: AsyncAzureOpenAI | AsyncOpenAI,
config: LLMConfig | None = None, config: LLMConfig | None = None,
max_tokens: int = DEFAULT_MAX_TOKENS, max_tokens: int = DEFAULT_MAX_TOKENS,
reasoning: str | None = None, reasoning: str | None = None,
@ -63,21 +63,21 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
"""Create a structured completion using Azure OpenAI's responses.parse API.""" """Create a structured completion using Azure OpenAI's responses.parse API."""
supports_reasoning = self._supports_reasoning_features(model) supports_reasoning = self._supports_reasoning_features(model)
request_kwargs = { 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 temperature_value = temperature if not supports_reasoning else None
if temperature_value is not None: if temperature_value is not None:
request_kwargs['temperature'] = temperature_value request_kwargs["temperature"] = temperature_value
if supports_reasoning and reasoning: if supports_reasoning and reasoning:
request_kwargs['reasoning'] = {'effort': reasoning} # type: ignore request_kwargs["reasoning"] = {"effort": reasoning} # type: ignore
if supports_reasoning and verbosity: 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) return await self.client.responses.parse(**request_kwargs)
@ -93,20 +93,20 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
supports_reasoning = self._supports_reasoning_features(model) supports_reasoning = self._supports_reasoning_features(model)
request_kwargs = { 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 temperature_value = temperature if not supports_reasoning else None
if temperature_value is not 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) return await self.client.chat.completions.create(**request_kwargs)
@staticmethod @staticmethod
def _supports_reasoning_features(model: str) -> bool: def _supports_reasoning_features(model: str) -> bool:
"""Return True when the Azure model supports reasoning/verbosity options.""" """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) 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"] sentence-transformers = ["sentence-transformers>=3.2.1"]
neptune = ["langchain-aws>=0.2.29", "opensearch-py>=3.0.0", "boto3>=1.39.16"] 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"] tracing = ["opentelemetry-api>=1.20.0", "opentelemetry-sdk>=1.20.0"]
azure = ["azure-identity>=1.25.1"]
dev = [ dev = [
"azure-identity>=1.25.1",
"pyright>=1.1.404", "pyright>=1.1.404",
"groq>=0.2.0", "groq>=0.2.0",
"anthropic>=0.49.0", "anthropic>=0.49.0",
@ -103,3 +105,4 @@ docstring-code-format = true
include = ["graphiti_core"] include = ["graphiti_core"]
pythonVersion = "3.10" pythonVersion = "3.10"
typeCheckingMode = "basic" typeCheckingMode = "basic"

256
uv.lock generated
View file

@ -262,6 +262,35 @@ wheels = [
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@ -801,8 +920,12 @@ dependencies = [
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{ name = "anthropic" }, { name = "anthropic" },
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azure = [
{ name = "azure-identity" },
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@ -863,6 +986,8 @@ voyageai = [
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