Feature - Support Azure AI Search as a Vector DB (#1967)

Co-authored-by: Sidney Phoon <sidneyphoon17@gmail.com>

docs/components/vectordbs/config.mdx

@@ -6,7 +6,7 @@ Config in mem0 is a dictionary that specifies the settings for your vector datab
 
 The config is defined as a Python dictionary with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus","azure_ai_search")
   - `config`: A nested dictionary containing provider-specific settings
 
 ## How to Use Config

docs/components/vectordbs/dbs/azure_ai_search.mdx

@@ -0,0 +1,38 @@
+[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.
+
+### Usage
+
+```python
+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"})
+```
+
+### Config
+
+Let's see the available parameters for the `qdrant` config:
+service_name (str): Azure Cognitive Search service name.  
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `service_name` | Azure AI Search service name | `None` |
+| `api_key` | API key of the Azure AI Search service | `None` |
+| `collection_name` | The name of the collection/index to store the vectors, it will be created automatically if not exist | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `use_compression` | Use scalar quantization vector compression | False |

docs/components/vectordbs/overview.mdx

@@ -12,6 +12,7 @@ See the list of supported vector databases below.
   <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
   <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
   <Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
+  <Card title="Azure AI Search" href="/components/vectordbs/dbs/azure_ai_search"></Card>
 </CardGroup>
 
 ## Usage

docs/mint.json

@@ -110,7 +110,8 @@
                 "components/vectordbs/dbs/chroma",
                 "components/vectordbs/dbs/pgvector",
                 "components/vectordbs/dbs/qdrant",
-                "components/vectordbs/dbs/milvus"
+                "components/vectordbs/dbs/milvus",
+                "components/vectordbs/dbs/azure_ai_search"
               ]
             }
           ]