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