93ab7375cd
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>
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import json
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import logging
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import typing
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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import groq
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from groq import AsyncGroq
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from groq.types.chat import ChatCompletionMessageParam
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else:
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try:
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import groq
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from groq import AsyncGroq
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from groq.types.chat import ChatCompletionMessageParam
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except ImportError:
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raise ImportError(
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'groq is required for GroqClient. Install it with: pip install graphiti-core[groq]'
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) from None
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from pydantic import BaseModel
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from ..prompts.models import Message
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from .client import LLMClient
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from .config import LLMConfig, ModelSize
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from .errors import RateLimitError
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from .provider_defaults import get_provider_defaults
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logger = logging.getLogger(__name__)
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DEFAULT_MAX_TOKENS = 2048
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class GroqClient(LLMClient):
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def __init__(self, config: LLMConfig | None = None, cache: bool = False):
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if config is None:
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config = LLMConfig(max_tokens=DEFAULT_MAX_TOKENS)
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elif config.max_tokens is None:
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config.max_tokens = DEFAULT_MAX_TOKENS
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super().__init__(config, cache)
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self.client = AsyncGroq(api_key=config.api_key)
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async def _generate_response(
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self,
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messages: list[Message],
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response_model: type[BaseModel] | None = None,
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max_tokens: int = DEFAULT_MAX_TOKENS,
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model_size: ModelSize = ModelSize.medium,
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) -> dict[str, typing.Any]:
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msgs: list[ChatCompletionMessageParam] = []
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for m in messages:
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if m.role == 'user':
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msgs.append({'role': 'user', 'content': m.content})
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elif m.role == 'system':
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msgs.append({'role': 'system', 'content': m.content})
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try:
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model = self.model or get_provider_defaults('groq').model
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response = await self.client.chat.completions.create(
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model=model,
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messages=msgs,
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temperature=self.temperature,
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max_tokens=max_tokens or self.max_tokens,
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response_format={'type': 'json_object'},
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)
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result = response.choices[0].message.content or ''
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return json.loads(result)
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except groq.RateLimitError as e:
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raise RateLimitError from e
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except Exception as e:
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logger.error(f'Error in generating LLM response: {e}')
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raise
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