Create an abstract MemoryProviderSingleton class. Pass config instead of instantiating a new one where used.

scripts/commands.py

@@ -1,6 +1,6 @@
 import browse
 import json
-from memory import PineconeMemory
+from memory.pinecone import PineconeMemory
 import datetime
 import agent_manager as agents
 import speak
@@ -52,7 +52,7 @@ def get_command(response):
 
 
 def execute_command(command_name, arguments):
-    memory = PineconeMemory()
+    memory = PineconeMemory(cfg=cfg)
     try:
         if command_name == "google":
             

scripts/config.py

@@ -1,3 +1,4 @@
+import abc
 import os
 import openai
 from dotenv import load_dotenv
@@ -5,7 +6,7 @@ from dotenv import load_dotenv
 load_dotenv()
 
 
-class Singleton(type):
+class Singleton(abc.ABCMeta, type):
     """
     Singleton metaclass for ensuring only one instance of a class.
     """
@@ -20,6 +21,10 @@ class Singleton(type):
         return cls._instances[cls]
 
 
+class AbstractSingleton(abc.ABC, metaclass=Singleton):
+    pass
+
+
 class Config(metaclass=Singleton):
     """
     Configuration class to store the state of bools for different scripts access.

scripts/main.py

@@ -1,7 +1,7 @@
 import json
 import random
 import commands as cmd
-from memory import PineconeMemory
+from memory.pinecone import PineconeMemory
 import data
 import chat
 from colorama import Fore, Style
@@ -283,7 +283,7 @@ user_input = "Determine which next command to use, and respond using the format
 
 # Initialize memory and make sure it is empty.
 # this is particularly important for indexing and referencing pinecone memory
-memory = PineconeMemory()
+memory = PineconeMemory(cfg)
 memory.clear()
 
 print('Using memory of type: ' + memory.__class__.__name__)

scripts/memory/base.py

@@ -0,0 +1,34 @@
+import abc
+from config import AbstractSingleton
+import openai
+
+
+def get_ada_embedding(text):
+    text = text.replace("\n", " ")
+    return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
+
+
+def get_text_from_embedding(embedding):
+    return openai.Embedding.retrieve(embedding, model="text-embedding-ada-002")["data"][0]["text"]
+
+
+class MemoryProviderSingleton(AbstractSingleton):
+    @abc.abstractmethod
+    def add(self, data):
+        pass
+
+    @abc.abstractmethod
+    def get(self, data):
+        pass
+
+    @abc.abstractmethod
+    def clear(self):
+        pass
+
+    @abc.abstractmethod
+    def get_relevant(self, data, num_relevant=5):
+        pass
+
+    @abc.abstractmethod
+    def get_stats(self):
+        pass

scripts/memory/pinecone.py

@@ -1,21 +1,11 @@
-from config import Config, Singleton
+
 import pinecone
-import openai
 
-cfg = Config()
+from memory.base import MemoryProviderSingleton, get_ada_embedding
 
 
-def get_ada_embedding(text):
-    text = text.replace("\n", " ")
-    return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
-
-
-def get_text_from_embedding(embedding):
-    return openai.Embedding.retrieve(embedding, model="text-embedding-ada-002")["data"][0]["text"]
-
-
-class PineconeMemory(metaclass=Singleton):
-    def __init__(self):
+class PineconeMemory(MemoryProviderSingleton):
+    def __init__(self, cfg):
         pinecone_api_key = cfg.pinecone_api_key
         pinecone_region = cfg.pinecone_region
         pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)