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update summary character limit (#1073)

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* update

* update tests
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Preston Rasmussen 2025-11-18 17:16:02 -05:00 • committed by GitHub
parent 8b7ad6f84c
commit d2654003ff
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10 changed files with 91 additions and 87 deletions
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110
examples/azure-openai/azure_openai_neo4j.py
View file

@ -40,8 +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__)
@ -49,20 +49,20 @@ 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')
# 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_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():
@ -76,7 +76,7 @@ async def main():
# Initialize Azure OpenAI client
azure_client = AsyncOpenAI(
base_url=f"{azure_endpoint}/openai/v1/",
base_url=f'{azure_endpoint}/openai/v1/',
api_key=azure_api_key,
)
@ -112,40 +112,40 @@ 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}",
name=f'California Politics {i}',
episode_body=(
episode["content"]
if isinstance(episode["content"], str)
else json.dumps(episode["content"])
episode['content']
if isinstance(episode['content'], str)
else json.dumps(episode['content'])
),
source=episode["type"],
source_description=episode["description"],
source=episode['type'],
source_description=episode['description'],
reference_time=datetime.now(timezone.utc),
)
print(f'Added episode: California Politics {i} ({episode["type"].value})')
@ -161,18 +161,18 @@ 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
@ -187,26 +187,26 @@ 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?",
'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:
#################################################
@ -218,8 +218,8 @@ async def main():
# Close the connection
await graphiti.close()
print("\nConnection closed")
print('\nConnection closed')
if __name__ == "__main__":
if __name__ == '__main__':
asyncio.run(main())

2
graphiti_core/driver/neo4j_driver.py
View file

@ -106,7 +106,7 @@ class Neo4jDriver(GraphDriver):
for query in index_queries
]
)
async def health_check(self) -> None:
"""Check Neo4j connectivity by running the driver's verify_connectivity method."""
try:

14
graphiti_core/embedder/azure_openai.py
View file

@ -33,7 +33,7 @@ class AzureOpenAIEmbedderClient(EmbedderClient):
def __init__(
self,
azure_client: AsyncAzureOpenAI | AsyncOpenAI,
model: str = "text-embedding-3-small",
model: str = 'text-embedding-3-small',
):
self.azure_client = azure_client
self.model = model
@ -44,22 +44,18 @@ 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]]:
@ -71,5 +67,5 @@ 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

26
graphiti_core/llm_client/azure_openai_client.py
View file

@ -66,21 +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,
"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)
@ -96,20 +96,20 @@ 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)

8
graphiti_core/llm_client/openai_base_client.py
View file

@ -166,13 +166,17 @@ class BaseOpenAIClient(LLMClient):
except openai.RateLimitError as e:
raise RateLimitError from e
except openai.AuthenticationError as e:
logger.error(f'OpenAI Authentication Error: {e}. Please verify your API key is correct.')
logger.error(
f'OpenAI Authentication Error: {e}. Please verify your API key is correct.'
)
raise
except Exception as e:
# Provide more context for connection errors
error_msg = str(e)
if 'Connection error' in error_msg or 'connection' in error_msg.lower():
logger.error(f'Connection error communicating with OpenAI API. Please check your network connection and API key. Error: {e}')
logger.error(
f'Connection error communicating with OpenAI API. Please check your network connection and API key. Error: {e}'
)
else:
logger.error(f'Error in generating LLM response: {e}')
raise

8
graphiti_core/llm_client/openai_client.py
View file

@ -74,7 +74,9 @@ class OpenAIClient(BaseOpenAIClient):
):
"""Create a structured completion using OpenAI's beta parse API."""
# Reasoning models (gpt-5 family) don't support temperature
is_reasoning_model = model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
is_reasoning_model = (
model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
)
response = await self.client.responses.parse(
model=model,
@ -100,7 +102,9 @@ class OpenAIClient(BaseOpenAIClient):
):
"""Create a regular completion with JSON format."""
# Reasoning models (gpt-5 family) don't support temperature
is_reasoning_model = model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
is_reasoning_model = (
model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
)
return await self.client.chat.completions.create(
model=model,

2
graphiti_core/utils/text_utils.py
View file

@ -17,7 +17,7 @@ limitations under the License.
import re
# Maximum length for entity/node summaries
MAX_SUMMARY_CHARS = 250
MAX_SUMMARY_CHARS = 500
def truncate_at_sentence(text: str, max_chars: int) -> str:

2
pyproject.toml
View file

@ -1,7 +1,7 @@
[project]
name = "graphiti-core"
description = "A temporal graph building library"
version = "0.24.0"
version = "0.24.1"
authors = [
{ name = "Paul Paliychuk", email = "paul@getzep.com" },
{ name = "Preston Rasmussen", email = "preston@getzep.com" },

2
tests/test_text_utils.py
View file

@ -87,7 +87,7 @@ def test_truncate_at_sentence_strips_trailing_whitespace():
def test_max_summary_chars_constant():
"""Test that MAX_SUMMARY_CHARS is set to expected value."""
assert MAX_SUMMARY_CHARS == 250
assert MAX_SUMMARY_CHARS == 500
def test_truncate_at_sentence_realistic_summary():

4
uv.lock generated
View file

@ -1,5 +1,5 @@
version = 1
revision = 3
revision = 2
requires-python = ">=3.10, <4"
resolution-markers = [
"python_full_version >= '3.14'",
@ -808,7 +808,7 @@ wheels = [
[[package]]
name = "graphiti-core"
version = "0.24.0"
version = "0.24.1"
source = { editable = "." }
dependencies = [
{ name = "diskcache" },