111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
import logging
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from typing import Any
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logging.basicConfig(level=logging.INFO)
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from dotenv import load_dotenv
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load_dotenv()
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from langchain import OpenAI
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from langchain.chat_models import ChatOpenAI
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from typing import Optional, Dict, List, Union
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import tracemalloc
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tracemalloc.start()
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import os
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import uuid
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class EpisodicBuffer(DynamicBaseMemory):
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def __init__(
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self,
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user_id: str,
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memory_id: Optional[str],
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index_name: Optional[str],
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db_type: str = "weaviate",
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):
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super().__init__('EpisodicBuffer',
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user_id, memory_id, index_name, db_type, namespace="BUFFERMEMORY"
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)
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self.st_memory_id = str( uuid.uuid4())
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self.llm = ChatOpenAI(
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temperature=0.0,
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max_tokens=1200,
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openai_api_key=os.environ.get("OPENAI_API_KEY"),
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model_name="gpt-4-0613",
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# callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],
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)
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self.llm_base = OpenAI(
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temperature=0.0,
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max_tokens=1200,
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openai_api_key=os.environ.get("OPENAI_API_KEY"),
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model_name="gpt-4-0613",
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)
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async def handle_modulator(
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self,
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modulator_name: str,
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attention_modulators: Dict[str, float],
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observation: str,
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namespace: Optional[str] = None,
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memory: Optional[Dict[str, Any]] = None,
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) -> Optional[List[Union[str, float]]]:
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"""
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Handle the given modulator based on the observation and namespace.
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Parameters:
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- modulator_name: Name of the modulator to handle.
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- attention_modulators: Dictionary of modulator values.
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- observation: The current observation.
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- namespace: An optional namespace.
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Returns:
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- Result of the modulator if criteria met, else None.
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"""
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modulator_value = attention_modulators.get(modulator_name, 0.0)
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modulator_functions = {
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"freshness": lambda obs, ns, mem: self.freshness(observation=obs, namespace=ns, memory=mem),
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"frequency": lambda obs, ns, mem: self.frequency(observation=obs, namespace=ns, memory=mem),
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"relevance": lambda obs, ns, mem: self.relevance(observation=obs, namespace=ns, memory=mem),
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"saliency": lambda obs, ns, mem: self.saliency(observation=obs, namespace=ns, memory=mem),
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}
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result_func = modulator_functions.get(modulator_name)
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if not result_func:
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return None
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result = await result_func(observation, namespace, memory)
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if not result:
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return None
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try:
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logging.info("Modulator %s", modulator_name)
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logging.info("Modulator value %s", modulator_value)
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logging.info("Result %s", result[0])
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if float(result[0]) >= float(modulator_value):
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return result
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except ValueError:
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pass
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return None
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async def available_operations(self) -> list[str]:
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"""Determines what operations are available for the user to process PDFs"""
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return [
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"retrieve over time",
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"save to personal notes",
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"translate to german"
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# "load to semantic memory",
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# "load to episodic memory",
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# "load to buffer",
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]
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