44 lines
1.8 KiB
Plaintext
44 lines
1.8 KiB
Plaintext
[Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-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
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```python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "sk-xx" #this key is used for embedding purpose
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config = {
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"vector_store": {
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"provider": "azure_ai_search",
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"config": {
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"service_name": "ai-search-test",
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"api_key": "*****",
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"collection_name": "mem0",
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"embedding_model_dims": 1536 ,
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"use_compression": False
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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### Config
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Let's see the available parameters for the `qdrant` config:
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service_name (str): Azure Cognitive Search service name.
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `service_name` | Azure AI Search service name | `None` |
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| `api_key` | API key of the Azure AI Search service | `None` |
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| `collection_name` | The name of the collection/index to store the vectors, it will be created automatically if not exist | `mem0` |
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| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
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| `use_compression` | Use scalar quantization vector compression | False | |