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added dotenv in .toml, added an example to use qdrant, fixed the code in main.py #1653

Merged
merged 9 commits into from
Aug 14, 2024
40 changes: 40 additions & 0 deletions cookbooks/add_memory_using_qdrant_cloud.py
Original file line number Diff line number Diff line change
@@ -0,0 1,40 @@
# 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)
2 changes: 1 addition & 1 deletion mem0/memory/main.py
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 33,7 @@ def __init__(self, config: MemoryConfig = MemoryConfig()):
self.vector_store = VectorStoreFactory.create(self.config.vector_store.provider, self.config.vector_store.config)
self.llm = LlmFactory.create(self.config.llm.provider, self.config.llm.config)
self.db = SQLiteManager(self.config.history_db_path)
self.collection_name = self.config.vector_store.config.collection_name if "collection_name" in self.config.vector_store.config else "mem0"
self.collection_name = self.config.vector_store.config.collection_name

capture_event("mem0.init", self)

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