87 lines
3.2 KiB
Plaintext
87 lines
3.2 KiB
Plaintext
# Azure AI Search
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[Azure AI Search](https://learn.microsoft.com/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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"compression_type": "none"
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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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## Advanced Usage
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```python
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# Search with specific filter mode
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result = m.search(
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"sci-fi movies",
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filters={"user_id": "alice"},
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limit=5,
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vector_filter_mode="preFilter" # Apply filters before vector search
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)
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# Using binary compression for large vector collections
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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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"compression_type": "binary",
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"use_float16": True # Use half precision for storage efficiency
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}
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}
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}
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```
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## Configuration Parameters
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| Parameter | Description | Default Value | Options |
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| --- | --- | --- | --- |
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| `service_name` | Azure AI Search service name | Required | - |
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| `api_key` | API key of the Azure AI Search service | Required | - |
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| `collection_name` | The name of the collection/index to store vectors | `mem0` | Any valid index name |
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| `embedding_model_dims` | Dimensions of the embedding model | `1536` | Any integer value |
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| `compression_type` | Type of vector compression to use | `none` | `none`, `scalar`, `binary` |
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| `use_float16` | Store vectors in half precision (Edm.Half) | `False` | `True`, `False` |
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## Notes on Configuration Options
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- **compression_type**:
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- `none`: No compression, uses full vector precision
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- `scalar`: Scalar quantization with reasonable balance of speed and accuracy
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- `binary`: Binary quantization for maximum compression with some accuracy trade-off
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- **vector_filter_mode**:
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- `preFilter`: Applies filters before vector search (faster)
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- `postFilter`: Applies filters after vector search (may provide better relevance)
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- **use_float16**: Using half precision (float16) reduces storage requirements but may slightly impact accuracy. Useful for very large vector collections.
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- **Filterable Fields**: The implementation automatically extracts `user_id`, `run_id`, and `agent_id` fields from payloads for filtering. |