41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
# This example shows how to use vector config to use QDRANT CLOUD
|
|
import os
|
|
from dotenv import load_dotenv
|
|
from mem0 import Memory
|
|
|
|
# Loading OpenAI API Key
|
|
load_dotenv()
|
|
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
|
|
USER_ID = "test"
|
|
quadrant_host="xx.gcp.cloud.qdrant.io"
|
|
|
|
# creating the config attributes
|
|
collection_name="memory" # this is the collection I created in QDRANT cloud
|
|
api_key=os.environ.get("QDRANT_API_KEY") # Getting the QDRANT api KEY
|
|
host=quadrant_host
|
|
port=6333 #Default port for QDRANT cloud
|
|
|
|
# Creating the config dict
|
|
config = {
|
|
"vector_store": {
|
|
"provider": "qdrant",
|
|
"config": {
|
|
"collection_name": collection_name,
|
|
"host": host,
|
|
"port": port,
|
|
"path": None,
|
|
"api_key":api_key
|
|
}
|
|
}
|
|
}
|
|
|
|
# this is the change, create the memory class using from config
|
|
memory = Memory().from_config(config)
|
|
|
|
USER_DATA = """
|
|
I am a strong believer in memory architecture.
|
|
"""
|
|
|
|
response = memory.add(USER_DATA, user_id=USER_ID)
|
|
print(response)
|