Azure AI Search (formerly known as “Azure Cognitive Search”) provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.

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Usage

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"   #this key is used for embedding purpose

config = {
    "vector_store": {
        "provider": "azure_ai_search",
        "config": {
            "service_name": "ai-search-test",
            "api_key": "*****",
            "collection_name": "mem0", 
            "embedding_model_dims": 1536 ,
            "use_compression": False
        }
    }
}

m = Memory.from_config(config)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})

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Config

Let’s see the available parameters for the qdrant config: service_name (str): Azure Cognitive Search service name.

ParameterDescriptionDefault Value
service_nameAzure AI Search service nameNone
api_keyAPI key of the Azure AI Search serviceNone
collection_nameThe name of the collection/index to store the vectors, it will be created automatically if not existmem0
embedding_model_dimsDimensions of the embedding model1536
use_compressionUse scalar quantization vector compressionFalse
MilvusRedis