Fix azure ai vector store

docs/components/vectordbs/dbs/azure_ai_search.mdx

@@ -33,17 +33,6 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-## Advanced Usage
-
-```python
-# Search with specific filter mode
-result = m.search(
-    "sci-fi movies", 
-    filters={"user_id": "alice"}, 
-    limit=5,
-    vector_filter_mode="preFilter"  # Apply filters before vector search
-)
-
 # Using binary compression for large vector collections
 config = {
     "vector_store": {
@@ -78,10 +67,6 @@ config = {
   - `scalar`: Scalar quantization with reasonable balance of speed and accuracy
   - `binary`: Binary quantization for maximum compression with some accuracy trade-off
 
-- **vector_filter_mode**:
-  - `preFilter`: Applies filters before vector search (faster)
-  - `postFilter`: Applies filters after vector search (may provide better relevance)
-
 - **use_float16**: Using half precision (float16) reduces storage requirements but may slightly impact accuracy. Useful for very large vector collections.
 
 - **Filterable Fields**: The implementation automatically extracts `user_id`, `run_id`, and `agent_id` fields from payloads for filtering.

mem0/vector_stores/azure_ai_search.py

@@ -169,7 +169,7 @@ def insert(self, vectors, payloads=None, ids=None):
         ]
         response = self.search_client.upload_documents(documents)
         for doc in response:
-            if not doc.get("status", False):
+            if not hasattr(doc, "status_code") and doc.get("status_code") != 201:
                 raise Exception(f"Insert failed for document {doc.get('id')}: {doc}")
         return response
 
@@ -189,16 +189,14 @@ def _build_filter_expression(self, filters):
         filter_expression = " and ".join(filter_conditions)
         return filter_expression
 
-    def search(self, query, limit=5, filters=None, vector_filter_mode="preFilter"):
+    def search(self, query, limit=5, filters=None):
         """
         Search for similar vectors.
 
         Args:
             query (List[float]): Query vector.
             limit (int, optional): Number of results to return. Defaults to 5.
             filters (Dict, optional): Filters to apply to the search. Defaults to None.
-            vector_filter_mode (str): Determines whether filters are applied before or after the vector search.
-                Known values: "preFilter" (default) and "postFilter".
 
         Returns:
             List[OutputData]: Search results.
@@ -213,8 +211,7 @@ def search(self, query, limit=5, filters=None, vector_filter_mode="preFilter"):
         search_results = self.search_client.search(
             vector_queries=[vector_query],
             filter=filter_expression,
-            top=limit,
-            vector_filter_mode=vector_filter_mode,  
+            top=limit
         )
 
         results = []
@@ -236,7 +233,7 @@ def delete(self, vector_id):
         """
         response = self.search_client.delete_documents(documents=[{"id": vector_id}])
         for doc in response:
-            if not doc.get("status", False):
+            if not hasattr(doc, "status_code") and doc.get("status_code") != 200:
                 raise Exception(f"Delete failed for document {vector_id}: {doc}")
         logger.info(f"Deleted document with ID '{vector_id}' from index '{self.index_name}'.")
         return response
@@ -260,7 +257,7 @@ def update(self, vector_id, vector=None, payload=None):
                 document[field] = payload.get(field)
         response = self.search_client.merge_or_upload_documents(documents=[document])
         for doc in response:
-            if not doc.get("status", False):
+            if not hasattr(doc, "status_code") and doc.get("status_code") != 200:
                 raise Exception(f"Update failed for document {vector_id}: {doc}")
         return response
 
@@ -335,7 +332,7 @@ def list(self, filters=None, limit=100):
                     id=result["id"], score=result["@search.score"], payload=payload
                 )
             )
-        return results
+        return [results]
 
     def __del__(self):
         """Close the search client when the object is deleted."""

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mem0ai"
-version = "0.1.69"
+version = "0.1.70"
 description = "Long-term memory for AI Agents"
 authors = ["Mem0 <[email protected]>"]
 exclude = [