mirror of
https://github.com/getzep/graphiti.git
synced 2024-09-08 19:13:11 +03:00
895afc7be1
* chore: Add romeo runner * fix: Linter * wip * wip dump * chore: Update romeo parser * chore: Anthropic model fix * wip * allbirds * allbirds runner * format * wip * wip * mypy updates * update * remove r * update tests * format * wip * chore: Strategically update the message * rebase and fix import issues * Update package imports for graphiti_core in examples and utils * nits * chore: Update OpenAI GPT-4o model to gpt-4o-2024-08-06 * implement groq * improvments & linting * cleanup and nits * Refactor package imports for graphiti_core in examples and utils * Refactor package imports for graphiti_core in examples and utils * implement diskcache * remove debug stuff * log cache hit when debugging only * Improve LLM config. Fix bugs (#41) Refactor LLMConfig class to allow None values for model and base_url * chore: Resolve mc --------- Co-authored-by: paulpaliychuk <pavlo.paliychuk.ca@gmail.com> Co-authored-by: prestonrasmussen <prasmuss15@gmail.com>
75 lines
2.3 KiB
Python
75 lines
2.3 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 hashlib
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import json
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import logging
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import typing
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from abc import ABC, abstractmethod
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from diskcache import Cache
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from ..prompts.models import Message
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from .config import LLMConfig
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DEFAULT_TEMPERATURE = 0
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DEFAULT_CACHE_DIR = './llm_cache'
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logger = logging.getLogger(__name__)
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class LLMClient(ABC):
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def __init__(self, config: LLMConfig | None, cache: bool = False):
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if config is None:
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config = LLMConfig()
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self.config = config
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self.model = config.model
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self.temperature = config.temperature
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self.max_tokens = config.max_tokens
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self.cache_enabled = cache
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self.cache_dir = Cache(DEFAULT_CACHE_DIR) # Create a cache directory
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@abstractmethod
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def get_embedder(self) -> typing.Any:
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pass
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@abstractmethod
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async def _generate_response(self, messages: list[Message]) -> dict[str, typing.Any]:
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pass
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def _get_cache_key(self, messages: list[Message]) -> str:
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# Create a unique cache key based on the messages and model
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message_str = json.dumps([m.model_dump() for m in messages], sort_keys=True)
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key_str = f'{self.model}:{message_str}'
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return hashlib.md5(key_str.encode()).hexdigest()
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async def generate_response(self, messages: list[Message]) -> dict[str, typing.Any]:
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if self.cache_enabled:
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cache_key = self._get_cache_key(messages)
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cached_response = self.cache_dir.get(cache_key)
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if cached_response is not None:
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logger.debug(f'Cache hit for {cache_key}')
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return cached_response
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response = await self._generate_response(messages)
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if self.cache_enabled:
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self.cache_dir.set(cache_key, response)
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return response
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