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feat: eliminate ghost variables with configurable provider defaults system

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Replace hardcoded DEFAULT_MODEL and DEFAULT_SMALL_MODEL constants across all LLM clients with a centralized, configurable provider defaults system.

Key changes:
- Created provider_defaults.py with centralized configuration for all providers
- Added environment variable support for easy customization (e.g., GEMINI_DEFAULT_MODEL)
- Updated all LLM clients to use configurable defaults instead of hardcoded constants
- Made edge operations max_tokens configurable via EXTRACT_EDGES_MAX_TOKENS
- Updated cross-encoder reranker clients to use provider defaults
- Maintained full backward compatibility with existing configurations

This resolves the issue where Gemini's flash-lite model has location constraints in Vertex AI that differ from the regular flash model, and users couldn't easily override these without editing source code.

Environment variables now supported:
- {PROVIDER}_DEFAULT_MODEL
- {PROVIDER}_DEFAULT_SMALL_MODEL  
- {PROVIDER}_DEFAULT_MAX_TOKENS
- {PROVIDER}_DEFAULT_TEMPERATURE
- {PROVIDER}_EXTRACT_EDGES_MAX_TOKENS
- EXTRACT_EDGES_MAX_TOKENS (global fallback)

Fixes #681

Co-authored-by: Daniel Chalef <danielchalef@users.noreply.github.com>
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167
PROVIDER_CONFIGURATION.md Normal file
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@ -0,0 +1,167 @@
# Provider Configuration System
This document describes the new provider configuration system that replaces hardcoded "ghost variables" with configurable defaults.
## Overview
Previously, each LLM provider client had hardcoded model names and configuration values that could not be easily customized without modifying the source code. This created several issues:
1. **Maintenance burden**: Updating to newer models required code changes
2. **Limited flexibility**: Users couldn't easily switch to different models
3. **Provider constraints**: Some models (like Gemini's flash-lite) have specific location constraints that differed from defaults
4. **Hidden configurations**: Token limits and other operational parameters were buried in the code
## New Configuration System
The new system introduces a centralized `provider_defaults.py` module that:
1. **Centralizes all provider defaults** in a single location
2. **Supports environment variable overrides** for easy customization
3. **Maintains backward compatibility** with existing configurations
4. **Provides provider-specific configurations** for different LLM providers
## Environment Variables
You can now override any provider default using environment variables with the following pattern:
```bash
# For OpenAI
export OPENAI_DEFAULT_MODEL="gpt-4"
export OPENAI_DEFAULT_SMALL_MODEL="gpt-4-mini"
export OPENAI_DEFAULT_MAX_TOKENS="8192"
export OPENAI_DEFAULT_TEMPERATURE="0.0"
export OPENAI_EXTRACT_EDGES_MAX_TOKENS="16384"
# For Gemini
export GEMINI_DEFAULT_MODEL="gemini-2.5-flash"
export GEMINI_DEFAULT_SMALL_MODEL="gemini-2.5-flash-lite"
export GEMINI_DEFAULT_MAX_TOKENS="8192"
export GEMINI_DEFAULT_TEMPERATURE="0.0"
export GEMINI_EXTRACT_EDGES_MAX_TOKENS="16384"
# For Anthropic
export ANTHROPIC_DEFAULT_MODEL="claude-3-5-sonnet-latest"
export ANTHROPIC_DEFAULT_SMALL_MODEL="claude-3-5-haiku-latest"
export ANTHROPIC_DEFAULT_MAX_TOKENS="8192"
export ANTHROPIC_DEFAULT_TEMPERATURE="0.0"
export ANTHROPIC_EXTRACT_EDGES_MAX_TOKENS="16384"
# For Groq
export GROQ_DEFAULT_MODEL="llama-3.1-70b-versatile"
export GROQ_DEFAULT_SMALL_MODEL="llama-3.1-8b-instant"
export GROQ_DEFAULT_MAX_TOKENS="8192"
export GROQ_DEFAULT_TEMPERATURE="0.0"
export GROQ_EXTRACT_EDGES_MAX_TOKENS="16384"
# General configuration (for edge operations)
export EXTRACT_EDGES_MAX_TOKENS="16384"
```
## Supported Providers
The system currently supports the following providers:
- **openai**: OpenAI GPT models
- **gemini**: Google Gemini models
- **anthropic**: Anthropic Claude models
- **groq**: Groq models
- **azure_openai**: Azure OpenAI models
## Usage Examples
### Basic Usage
The configuration system works transparently with existing code:
```python
from graphiti_core.llm_client import OpenAIClient
from graphiti_core.llm_client.config import LLMConfig
# Uses default models (configurable via environment variables)
client = OpenAIClient()
# Or with explicit configuration (still uses provider defaults as fallback)
config = LLMConfig(model="gpt-4", small_model="gpt-4-mini")
client = OpenAIClient(config)
```
### Customizing Model Defaults
Instead of hardcoding model names in your application, you can now use environment variables:
```bash
# Set up your preferred models
export OPENAI_DEFAULT_MODEL="gpt-4"
export OPENAI_DEFAULT_SMALL_MODEL="gpt-4-mini"
# Your application will automatically use these defaults
python your_app.py
```
### Provider-Specific Configuration
Each provider can have different default models and configurations:
```python
from graphiti_core.llm_client.provider_defaults import get_provider_defaults
# Get defaults for a specific provider
openai_defaults = get_provider_defaults('openai')
print(f"OpenAI default model: {openai_defaults.model}")
print(f"OpenAI small model: {openai_defaults.small_model}")
gemini_defaults = get_provider_defaults('gemini')
print(f"Gemini default model: {gemini_defaults.model}")
print(f"Gemini small model: {gemini_defaults.small_model}")
```
## Migration Guide
### Before (with ghost variables)
```python
# In gemini_client.py
DEFAULT_MODEL = 'gemini-2.5-flash'
DEFAULT_SMALL_MODEL = 'models/gemini-2.5-flash-lite-preview-06-17'
def _get_model_for_size(self, model_size: ModelSize) -> str:
if model_size == ModelSize.small:
return self.small_model or DEFAULT_SMALL_MODEL
else:
return self.model or DEFAULT_MODEL
```
### After (with configurable defaults)
```python
# Configuration is now externalized
from .provider_defaults import get_model_for_size
def _get_model_for_size(self, model_size: ModelSize) -> str:
return get_model_for_size(
provider='gemini',
model_size=model_size.value,
user_model=self.model,
user_small_model=self.small_model
)
```
## Benefits
1. **No More Ghost Variables**: All defaults are now configurable
2. **Easy Model Updates**: Update models via environment variables
3. **Provider Flexibility**: Each provider can have optimized defaults
4. **Backward Compatibility**: Existing code continues to work unchanged
5. **Environment-Specific Configuration**: Different environments can use different models
6. **Reduced Maintenance**: No need to modify source code for model updates
## Implementation Details
The new system is implemented in `graphiti_core/llm_client/provider_defaults.py` and includes:
- `ProviderDefaults` dataclass for configuration structure
- `get_provider_defaults()` function with environment variable support
- `get_model_for_size()` centralized model selection logic
- `get_extract_edges_max_tokens_default()` for operational parameters
All existing LLM client implementations have been updated to use this new system while maintaining full backward compatibility.

5
graphiti_core/cross_encoder/gemini_reranker_client.py
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@ -20,6 +20,7 @@ from typing import TYPE_CHECKING
from ..helpers import semaphore_gather
from ..llm_client import LLMConfig, RateLimitError
from ..llm_client.provider_defaults import get_provider_defaults
from .client import CrossEncoderClient
if TYPE_CHECKING:
@ -37,8 +38,6 @@ else:
logger = logging.getLogger(__name__)
DEFAULT_MODEL = 'gemini-2.5-flash-lite-preview-06-17'
class GeminiRerankerClient(CrossEncoderClient):
"""
@ -103,7 +102,7 @@ Provide only a number between 0 and 100 (no explanation, just the number):"""
responses = await semaphore_gather(
*[
self.client.aio.models.generate_content(
model=self.config.model or DEFAULT_MODEL,
model=self.config.model or get_provider_defaults('gemini').model,
contents=prompt_messages, # type: ignore
config=types.GenerateContentConfig(
system_instruction='You are an expert at rating passage relevance. Respond with only a number from 0-100.',

5
graphiti_core/cross_encoder/openai_reranker_client.py
View file

@ -23,13 +23,12 @@ from openai import AsyncAzureOpenAI, AsyncOpenAI
from ..helpers import semaphore_gather
from ..llm_client import LLMConfig, OpenAIClient, RateLimitError
from ..llm_client.provider_defaults import get_provider_defaults
from ..prompts import Message
from .client import CrossEncoderClient
logger = logging.getLogger(__name__)
DEFAULT_MODEL = 'gpt-4.1-nano'
class OpenAIRerankerClient(CrossEncoderClient):
def __init__(
@ -84,7 +83,7 @@ class OpenAIRerankerClient(CrossEncoderClient):
responses = await semaphore_gather(
*[
self.client.chat.completions.create(
model=DEFAULT_MODEL,
model=self.config.model or get_provider_defaults('openai').model,
messages=openai_messages,
temperature=0,
max_tokens=1,

5
graphiti_core/llm_client/anthropic_client.py
View file

@ -27,6 +27,7 @@ from ..prompts.models import Message
from .client import LLMClient
from .config import DEFAULT_MAX_TOKENS, LLMConfig, ModelSize
from .errors import RateLimitError, RefusalError
from .provider_defaults import get_provider_defaults
if TYPE_CHECKING:
import anthropic
@ -62,8 +63,6 @@ AnthropicModel = Literal[
'claude-2.0',
]
DEFAULT_MODEL: AnthropicModel = 'claude-3-7-sonnet-latest'
class AnthropicClient(LLMClient):
"""
@ -99,7 +98,7 @@ class AnthropicClient(LLMClient):
config.max_tokens = max_tokens
if config.model is None:
config.model = DEFAULT_MODEL
config.model = get_provider_defaults('anthropic').model
super().__init__(config, cache)
# Explicitly set the instance model to the config model to prevent type checking errors

14
graphiti_core/llm_client/gemini_client.py
View file

@ -25,6 +25,7 @@ from ..prompts.models import Message
from .client import MULTILINGUAL_EXTRACTION_RESPONSES, LLMClient
from .config import DEFAULT_MAX_TOKENS, LLMConfig, ModelSize
from .errors import RateLimitError
from .provider_defaults import get_model_for_size
if TYPE_CHECKING:
from google import genai
@ -43,9 +44,6 @@ else:
logger = logging.getLogger(__name__)
DEFAULT_MODEL = 'gemini-2.5-flash'
DEFAULT_SMALL_MODEL = 'models/gemini-2.5-flash-lite-preview-06-17'
class GeminiClient(LLMClient):
"""
@ -141,10 +139,12 @@ class GeminiClient(LLMClient):
def _get_model_for_size(self, model_size: ModelSize) -> str:
"""Get the appropriate model name based on the requested size."""
if model_size == ModelSize.small:
return self.small_model or DEFAULT_SMALL_MODEL
else:
return self.model or DEFAULT_MODEL
return get_model_for_size(
provider='gemini',
model_size=model_size.value,
user_model=self.model,
user_small_model=self.small_model
)
async def _generate_response(
self,

5
graphiti_core/llm_client/groq_client.py
View file

@ -38,10 +38,10 @@ from ..prompts.models import Message
from .client import LLMClient
from .config import LLMConfig, ModelSize
from .errors import RateLimitError
from .provider_defaults import get_provider_defaults
logger = logging.getLogger(__name__)
DEFAULT_MODEL = 'llama-3.1-70b-versatile'
DEFAULT_MAX_TOKENS = 2048
@ -69,8 +69,9 @@ class GroqClient(LLMClient):
elif m.role == 'system':
msgs.append({'role': 'system', 'content': m.content})
try:
model = self.model or get_provider_defaults('groq').model
response = await self.client.chat.completions.create(
model=self.model or DEFAULT_MODEL,
model=model,
messages=msgs,
temperature=self.temperature,
max_tokens=max_tokens or self.max_tokens,

14
graphiti_core/llm_client/openai_base_client.py
View file

@ -28,12 +28,10 @@ from ..prompts.models import Message
from .client import MULTILINGUAL_EXTRACTION_RESPONSES, LLMClient
from .config import DEFAULT_MAX_TOKENS, LLMConfig, ModelSize
from .errors import RateLimitError, RefusalError
from .provider_defaults import get_model_for_size
logger = logging.getLogger(__name__)
DEFAULT_MODEL = 'gpt-4.1-mini'
DEFAULT_SMALL_MODEL = 'gpt-4.1-nano'
class BaseOpenAIClient(LLMClient):
"""
@ -100,10 +98,12 @@ class BaseOpenAIClient(LLMClient):
def _get_model_for_size(self, model_size: ModelSize) -> str:
"""Get the appropriate model name based on the requested size."""
if model_size == ModelSize.small:
return self.small_model or DEFAULT_SMALL_MODEL
else:
return self.model or DEFAULT_MODEL
return get_model_for_size(
provider='openai',
model_size=model_size.value,
user_model=self.model,
user_small_model=self.small_model
)
def _handle_structured_response(self, response: Any) -> dict[str, Any]:
"""Handle structured response parsing and validation."""

6
graphiti_core/llm_client/openai_generic_client.py
View file

@ -28,11 +28,10 @@ from ..prompts.models import Message
from .client import MULTILINGUAL_EXTRACTION_RESPONSES, LLMClient
from .config import DEFAULT_MAX_TOKENS, LLMConfig, ModelSize
from .errors import RateLimitError, RefusalError
from .provider_defaults import get_provider_defaults
logger = logging.getLogger(__name__)
DEFAULT_MODEL = 'gpt-4.1-mini'
class OpenAIGenericClient(LLMClient):
"""
@ -99,8 +98,9 @@ class OpenAIGenericClient(LLMClient):
elif m.role == 'system':
openai_messages.append({'role': 'system', 'content': m.content})
try:
model = self.model or get_provider_defaults('openai').model
response = await self.client.chat.completions.create(
model=self.model or DEFAULT_MODEL,
model=model,
messages=openai_messages,
temperature=self.temperature,
max_tokens=self.max_tokens,

162
graphiti_core/llm_client/provider_defaults.py Normal file
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@ -0,0 +1,162 @@
"""
Copyright 2024, 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 os
from dataclasses import dataclass
from typing import Dict, Optional
@dataclass
class ProviderDefaults:
"""
Configuration for provider-specific model defaults.
This class replaces hardcoded DEFAULT_MODEL constants with configurable
provider-specific defaults that can be overridden via environment variables.
"""
model: str
small_model: str
max_tokens: int = 8192
temperature: float = 0.0
extract_edges_max_tokens: int = 16384
# Provider-specific default configurations
# These can be overridden via environment variables (see get_provider_defaults)
PROVIDER_DEFAULTS: Dict[str, ProviderDefaults] = {
'openai': ProviderDefaults(
model='gpt-4.1-mini',
small_model='gpt-4.1-nano',
extract_edges_max_tokens=16384,
),
'gemini': ProviderDefaults(
model='gemini-2.5-flash',
small_model='models/gemini-2.5-flash-lite-preview-06-17',
extract_edges_max_tokens=16384,
),
'anthropic': ProviderDefaults(
model='claude-3-7-sonnet-latest',
small_model='claude-3-7-haiku-latest',
extract_edges_max_tokens=16384,
),
'groq': ProviderDefaults(
model='llama-3.1-70b-versatile',
small_model='llama-3.1-8b-instant',
extract_edges_max_tokens=16384,
),
'azure_openai': ProviderDefaults(
model='gpt-4.1-mini',
small_model='gpt-4.1-nano',
extract_edges_max_tokens=16384,
),
}
def get_provider_defaults(provider: str) -> ProviderDefaults:
"""
Get provider-specific defaults with optional environment variable overrides.
Environment variables can override defaults using the pattern:
- {PROVIDER}_DEFAULT_MODEL
- {PROVIDER}_DEFAULT_SMALL_MODEL
- {PROVIDER}_DEFAULT_MAX_TOKENS
- {PROVIDER}_DEFAULT_TEMPERATURE
- {PROVIDER}_EXTRACT_EDGES_MAX_TOKENS
Args:
provider: The provider name (e.g., 'openai', 'gemini', 'anthropic', etc.)
Returns:
ProviderDefaults object with defaults for the specified provider
Raises:
ValueError: If the provider is not supported
"""
if provider not in PROVIDER_DEFAULTS:
raise ValueError(f"Unsupported provider: {provider}. Supported providers: {list(PROVIDER_DEFAULTS.keys())}")
defaults = PROVIDER_DEFAULTS[provider]
# Check for environment variable overrides
env_prefix = provider.upper()
model = os.getenv(f'{env_prefix}_DEFAULT_MODEL', defaults.model)
small_model = os.getenv(f'{env_prefix}_DEFAULT_SMALL_MODEL', defaults.small_model)
max_tokens = int(os.getenv(f'{env_prefix}_DEFAULT_MAX_TOKENS', str(defaults.max_tokens)))
temperature = float(os.getenv(f'{env_prefix}_DEFAULT_TEMPERATURE', str(defaults.temperature)))
extract_edges_max_tokens = int(os.getenv(f'{env_prefix}_EXTRACT_EDGES_MAX_TOKENS', str(defaults.extract_edges_max_tokens)))
return ProviderDefaults(
model=model,
small_model=small_model,
max_tokens=max_tokens,
temperature=temperature,
extract_edges_max_tokens=extract_edges_max_tokens
)
def get_model_for_size(provider: str, model_size: str, user_model: Optional[str] = None, user_small_model: Optional[str] = None) -> str:
"""
Get the appropriate model name based on the requested size and provider.
This function replaces the _get_model_for_size methods in individual clients
with a centralized implementation that uses configurable provider defaults.
Args:
provider: The provider name (e.g., 'openai', 'gemini', 'anthropic', etc.)
model_size: The size of the model requested ('small' or 'medium')
user_model: User-configured model override
user_small_model: User-configured small model override
Returns:
The appropriate model name for the requested size
"""
defaults = get_provider_defaults(provider)
if model_size == 'small':
return user_small_model or defaults.small_model
else:
return user_model or defaults.model
def get_extract_edges_max_tokens(provider: str) -> int:
"""
Get the maximum tokens for edge extraction operations.
This function replaces hardcoded extract_edges_max_tokens constants
with configurable provider-specific defaults.
Args:
provider: The provider name (e.g., 'openai', 'gemini', 'anthropic', etc.)
Returns:
The maximum tokens for edge extraction operations
"""
defaults = get_provider_defaults(provider)
return defaults.extract_edges_max_tokens
def get_extract_edges_max_tokens_default() -> int:
"""
Get the default maximum tokens for edge extraction operations.
This function provides a configurable default that can be overridden
via the EXTRACT_EDGES_MAX_TOKENS environment variable.
Returns:
The maximum tokens for edge extraction operations
"""
return int(os.getenv('EXTRACT_EDGES_MAX_TOKENS', '16384'))

3
graphiti_core/utils/maintenance/edge_operations.py
View file

@ -32,6 +32,7 @@ from graphiti_core.graphiti_types import GraphitiClients
from graphiti_core.helpers import DEFAULT_DATABASE, MAX_REFLEXION_ITERATIONS, semaphore_gather
from graphiti_core.llm_client import LLMClient
from graphiti_core.llm_client.config import ModelSize
from graphiti_core.llm_client.provider_defaults import get_extract_edges_max_tokens_default
from graphiti_core.nodes import CommunityNode, EntityNode, EpisodicNode
from graphiti_core.prompts import prompt_library
from graphiti_core.prompts.dedupe_edges import EdgeDuplicate, UniqueFacts
@ -114,7 +115,7 @@ async def extract_edges(
) -> list[EntityEdge]:
start = time()
extract_edges_max_tokens = 16384
extract_edges_max_tokens = get_extract_edges_max_tokens_default()
llm_client = clients.llm_client
edge_type_signature_map: dict[str, tuple[str, str]] = {