ChatGPT Memory

Allows to scale the ChatGPT API to multiple simultaneous sessions with infinite contextual and adaptive memory powered by GPT and Redis datastore.

Getting Started

1. Create your free Redis datastore here.
2. Get your OpenAI API key here.

## set the following ENVIRONMENT Variables before running this script
from chatgpt_memory.environment import OPENAI_API_KEY, REDIS_HOST, REDIS_PASSWORD, REDIS_PORT
from chatgpt_memory.datastore.config import RedisDataStoreConfig
from chatgpt_memory.datastore.redis import RedisDataStore
from chatgpt_memory.llm_client.openai.conversation.chatgpt_client import ChatGPTClient
from chatgpt_memory.llm_client.openai.conversation.config import ChatGPTConfig
from chatgpt_memory.llm_client.openai.embedding.config import EmbeddingConfig
from chatgpt_memory.llm_client.openai.embedding.embedding_client import EmbeddingClient
from chatgpt_memory.memory.manager import MemoryManager


embedding_config = EmbeddingConfig(api_key=OPENAI_API_KEY)
embed_client = EmbeddingClient(config=embedding_config)
redis_datastore_config = RedisDataStoreConfig(
    host=REDIS_HOST,
    port=REDIS_PORT,
    password=REDIS_PASSWORD,
)
redis_datastore = RedisDataStore(config=redis_datastore_config)
redis_datastore.connect()
redis_datastore.create_index()
memory_manager = MemoryManager(datastore=redis_datastore, embed_client=embed_client, topk=1)

chat_gpt_client = ChatGPTClient(
    config=ChatGPTConfig(api_key=OPENAI_API_KEY, verbose=True), memory_manager=memory_manager
)

conversation_id = None
while True:
    user_message = input("\n Please enter your message: ")
    response = chat_gpt_client.converse(message=user_message, conversation_id=conversation_id)
    conversation_id = response.conversation_id
    print(response.chat_gpt_answer)