diff --git a/mem0/memory/main.py b/mem0/memory/main.py
index 5b35a40f..9ff40f08 100644
--- a/mem0/memory/main.py
+++ b/mem0/memory/main.py
@@ -1,10 +1,10 @@
-import os
 import asyncio
 import concurrent
 import gc
 import hashlib
 import json
 import logging
+import os
 import uuid
 import warnings
 from datetime import datetime
@@ -20,7 +20,7 @@ from mem0.configs.prompts import (
     get_update_memory_messages,
 )
 from mem0.memory.base import MemoryBase
-from mem0.memory.setup import setup_config, mem0_dir
+from mem0.memory.setup import mem0_dir, setup_config
 from mem0.memory.storage import SQLiteManager
 from mem0.memory.telemetry import capture_event
 from mem0.memory.utils import (
@@ -67,7 +67,7 @@ class Memory(MemoryBase):
             self.graph = MemoryGraph(self.config)
             self.enable_graph = True
 
-        self.config.vector_store.config.collection_name = "mem0_migrations"
+        self.config.vector_store.config.collection_name = "mem0-migrations"
         if self.config.vector_store.provider in ["faiss", "qdrant"]:
             provider_path = f"migrations_{self.config.vector_store.provider}"
             self.config.vector_store.config.path = os.path.join(mem0_dir, provider_path)
@@ -765,13 +765,6 @@ class Memory(MemoryBase):
             Recreates the vector store with a new client
         """
         logger.warning("Resetting all memories")
-        self.vector_store.delete_col()
-
-        gc.collect()
-
-        # Close the client if it has a close method
-        if hasattr(self.vector_store, 'client') and hasattr(self.vector_store.client, 'close'):
-            self.vector_store.client.close()
 
         # Close the old connection if possible
         if hasattr(self.db, 'connection') and self.db.connection:
@@ -780,10 +773,14 @@ class Memory(MemoryBase):
 
         self.db = SQLiteManager(self.config.history_db_path)
 
-        # Create a new vector store with the same configuration
-        self.vector_store = VectorStoreFactory.create(
-            self.config.vector_store.provider, self.config.vector_store.config
-        )
+        if hasattr(self.vector_store, 'reset'):
+            self.vector_store = VectorStoreFactory.reset(self.vector_store)
+        else:
+            logger.warning("Vector store does not support reset. Skipping.")
+            self.vector_store.delete_col()
+            self.vector_store = VectorStoreFactory.create(
+                self.config.vector_store.provider, self.config.vector_store.config
+            )
         capture_event("mem0.reset", self, {"sync_type": "sync"})
 
     def chat(self, query):
diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py
index 8d48c3ae..03fc0d2c 100644
--- a/mem0/utils/factory.py
+++ b/mem0/utils/factory.py
@@ -97,3 +97,9 @@ class VectorStoreFactory:
             return vector_store_instance(**config)
         else:
             raise ValueError(f"Unsupported VectorStore provider: {provider_name}")
+        
+    @classmethod
+    def reset(cls, instance):
+        instance.reset()
+        return instance
+        
diff --git a/mem0/vector_stores/azure_ai_search.py b/mem0/vector_stores/azure_ai_search.py
index 99c53454..efcd528a 100644
--- a/mem0/vector_stores/azure_ai_search.py
+++ b/mem0/vector_stores/azure_ai_search.py
@@ -65,6 +65,8 @@ class AzureAISearch(VectorStoreBase):
             hybrid_search (bool): Whether to use hybrid search. Default is False.
             vector_filter_mode (Optional[str]): Mode for vector filtering. Default is "preFilter".
         """
+        self.service_name = service_name
+        self.api_key = api_key
         self.index_name = collection_name
         self.collection_name = collection_name
         self.embedding_model_dims = embedding_model_dims
@@ -341,3 +343,38 @@ class AzureAISearch(VectorStoreBase):
         """Close the search client when the object is deleted."""
         self.search_client.close()
         self.index_client.close()
+
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.index_name}...")
+
+        try:
+            # Close the existing clients
+            self.search_client.close()
+            self.index_client.close()
+
+            # Delete the collection
+            self.delete_col()
+
+            # Reinitialize the clients
+            service_endpoint = f"https://{self.service_name}.search.windows.net"
+            self.search_client = SearchClient(
+                endpoint=service_endpoint,
+                index_name=self.index_name,
+                credential=AzureKeyCredential(self.api_key),
+            )
+            self.index_client = SearchIndexClient(
+                endpoint=service_endpoint,
+                credential=AzureKeyCredential(self.api_key),
+            )
+
+            # Add user agent
+            self.search_client._client._config.user_agent_policy.add_user_agent("mem0")
+            self.index_client._client._config.user_agent_policy.add_user_agent("mem0")
+
+            # Create the collection
+            self.create_col()
+        except Exception as e:
+            logger.error(f"Error resetting index {self.index_name}: {e}")
+            raise
+
diff --git a/mem0/vector_stores/base.py b/mem0/vector_stores/base.py
index 4f55d109..98f3503b 100644
--- a/mem0/vector_stores/base.py
+++ b/mem0/vector_stores/base.py
@@ -51,3 +51,8 @@ class VectorStoreBase(ABC):
     def list(self, filters=None, limit=None):
         """List all memories."""
         pass
+    
+    @abstractmethod
+    def reset(self):
+        """Reset by delete the collection and recreate it."""
+        pass
diff --git a/mem0/vector_stores/chroma.py b/mem0/vector_stores/chroma.py
index 696a3047..ae4b03f7 100644
--- a/mem0/vector_stores/chroma.py
+++ b/mem0/vector_stores/chroma.py
@@ -221,3 +221,9 @@ class ChromaDB(VectorStoreBase):
         """
         results = self.collection.get(where=filters, limit=limit)
         return [self._parse_output(results)]
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.collection = self.create_col(self.collection_name)
diff --git a/mem0/vector_stores/elasticsearch.py b/mem0/vector_stores/elasticsearch.py
index 7b7cc2b4..4d733a45 100644
--- a/mem0/vector_stores/elasticsearch.py
+++ b/mem0/vector_stores/elasticsearch.py
@@ -222,3 +222,9 @@ class ElasticsearchDB(VectorStoreBase):
             )
 
         return [results]
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_index()
diff --git a/mem0/vector_stores/faiss.py b/mem0/vector_stores/faiss.py
index 623a3bd7..c042738c 100644
--- a/mem0/vector_stores/faiss.py
+++ b/mem0/vector_stores/faiss.py
@@ -465,3 +465,9 @@ class FAISS(VectorStoreBase):
                 break
 
         return [results]
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_col(self.collection_name)
diff --git a/mem0/vector_stores/langchain.py b/mem0/vector_stores/langchain.py
index ad9a991d..aac04f06 100644
--- a/mem0/vector_stores/langchain.py
+++ b/mem0/vector_stores/langchain.py
@@ -1,3 +1,4 @@
+import logging
 from typing import Dict, List, Optional
 
 from pydantic import BaseModel
@@ -11,6 +12,7 @@ except ImportError:
 
 from mem0.vector_stores.base import VectorStoreBase
 
+logger = logging.getLogger(__name__)
 
 class OutputData(BaseModel):
     id: Optional[str]  # memory id
@@ -135,7 +137,14 @@ class Langchain(VectorStoreBase):
         """
         Delete a collection.
         """
-        self.client.delete(ids=None)
+        logger.warning("Deleting collection")
+        if hasattr(self.client, "delete_collection"):
+            self.client.delete_collection()
+        elif hasattr(self.client, "reset_collection"):
+            self.client.reset_collection()
+        else:
+            # Fallback to the generic delete method
+            self.client.delete(ids=None)
 
     def col_info(self):
         """
@@ -147,5 +156,27 @@ class Langchain(VectorStoreBase):
         """
         List all vectors in a collection.
         """
-        # This would require implementation-specific access to the underlying store
-        raise NotImplementedError("Listing all vectors not directly supported by LangChain vectorstores")
+        try:
+            if hasattr(self.client, "_collection") and hasattr(self.client._collection, "get"):
+                # Convert mem0 filters to Chroma where clause if needed
+                where_clause = None
+                if filters and "user_id" in filters:
+                    where_clause = {"user_id": filters["user_id"]}
+
+                result = self.client._collection.get(
+                    where=where_clause,
+                    limit=limit
+                )
+
+                # Convert the result to the expected format
+                if result and isinstance(result, dict):
+                    return [self._parse_output(result)]
+                return []
+        except Exception as e:
+            logger.error(f"Error listing vectors from Chroma: {e}")
+            return []
+
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting collection: {self.collection_name}")
+        self.delete_col()
diff --git a/mem0/vector_stores/milvus.py b/mem0/vector_stores/milvus.py
index ff48e306..775006ff 100644
--- a/mem0/vector_stores/milvus.py
+++ b/mem0/vector_stores/milvus.py
@@ -237,3 +237,9 @@ class MilvusDB(VectorStoreBase):
             obj = OutputData(id=data.get("id"), score=None, payload=data.get("metadata"))
             memories.append(obj)
         return [memories]
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_col(self.collection_name, self.embedding_model_dims, self.metric_type)
diff --git a/mem0/vector_stores/opensearch.py b/mem0/vector_stores/opensearch.py
index 18b0063c..72d39976 100644
--- a/mem0/vector_stores/opensearch.py
+++ b/mem0/vector_stores/opensearch.py
@@ -199,3 +199,9 @@ class OpenSearchDB(VectorStoreBase):
                 for hit in response["hits"]["hits"]
             ]
         ]
+        
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_index()
diff --git a/mem0/vector_stores/pgvector.py b/mem0/vector_stores/pgvector.py
index 85ff3e14..cc33077f 100644
--- a/mem0/vector_stores/pgvector.py
+++ b/mem0/vector_stores/pgvector.py
@@ -286,3 +286,9 @@ class PGVector(VectorStoreBase):
             self.cur.close()
         if hasattr(self, "conn"):
             self.conn.close()
+            
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_col(self.embedding_model_dims)
diff --git a/mem0/vector_stores/pinecone.py b/mem0/vector_stores/pinecone.py
index 1cb0be5a..ff350bb4 100644
--- a/mem0/vector_stores/pinecone.py
+++ b/mem0/vector_stores/pinecone.py
@@ -369,5 +369,6 @@ class PineconeDB(VectorStoreBase):
         """
         Reset the index by deleting and recreating it.
         """
+        logger.warning(f"Resetting index {self.collection_name}...")
         self.delete_col()
         self.create_col(self.embedding_model_dims, self.metric)
diff --git a/mem0/vector_stores/qdrant.py b/mem0/vector_stores/qdrant.py
index d50054ca..3703878c 100644
--- a/mem0/vector_stores/qdrant.py
+++ b/mem0/vector_stores/qdrant.py
@@ -67,6 +67,7 @@ class Qdrant(VectorStoreBase):
 
         self.collection_name = collection_name
         self.embedding_model_dims = embedding_model_dims
+        self.on_disk = on_disk
         self.create_col(embedding_model_dims, on_disk)
 
     def create_col(self, vector_size: int, on_disk: bool, distance: Distance = Distance.COSINE):
@@ -231,3 +232,9 @@ class Qdrant(VectorStoreBase):
             with_vectors=False,
         )
         return result
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_col(self.embedding_model_dims, self.on_disk)
diff --git a/mem0/vector_stores/redis.py b/mem0/vector_stores/redis.py
index 25cdcbb5..293d69ec 100644
--- a/mem0/vector_stores/redis.py
+++ b/mem0/vector_stores/redis.py
@@ -75,9 +75,48 @@ class RedisDB(VectorStoreBase):
         self.index.set_client(self.client)
         self.index.create(overwrite=True)
 
-    # TODO: Implement multiindex support.
-    def create_col(self, name, vector_size, distance):
-        raise NotImplementedError("Collection/Index creation not supported yet.")
+    def create_col(self, name=None, vector_size=None, distance=None):
+        """
+        Create a new collection (index) in Redis.
+
+        Args:
+            name (str, optional): Name for the collection. Defaults to None, which uses the current collection_name.
+            vector_size (int, optional): Size of the vector embeddings. Defaults to None, which uses the current embedding_model_dims.
+            distance (str, optional): Distance metric to use. Defaults to None, which uses 'cosine'.
+
+        Returns:
+            The created index object.
+        """
+        # Use provided parameters or fall back to instance attributes
+        collection_name = name or self.schema['index']['name']
+        embedding_dims = vector_size or self.embedding_model_dims
+        distance_metric = distance or "cosine"
+
+        # Create a new schema with the specified parameters
+        index_schema = {
+            "name": collection_name,
+            "prefix": f"mem0:{collection_name}",
+        }
+
+        # Copy the default fields and update the vector field with the specified dimensions
+        fields = DEFAULT_FIELDS.copy()
+        fields[-1]["attrs"]["dims"] = embedding_dims
+        fields[-1]["attrs"]["distance_metric"] = distance_metric
+
+        # Create the schema
+        schema = {"index": index_schema, "fields": fields}
+
+        # Create the index
+        index = SearchIndex.from_dict(schema)
+        index.set_client(self.client)
+        index.create(overwrite=True)
+
+        # Update instance attributes if creating a new collection
+        if name:
+            self.schema = schema
+            self.index = index
+
+        return index
 
     def insert(self, vectors: list, payloads: list = None, ids: list = None):
         data = []
@@ -194,6 +233,25 @@ class RedisDB(VectorStoreBase):
     def col_info(self, name):
         return self.index.info()
 
+    def reset(self):
+        """
+        Reset the index by deleting and recreating it.
+        """
+        collection_name = self.schema['index']['name']
+        logger.warning(f"Resetting index {collection_name}...")
+        self.delete_col()
+        
+        self.index = SearchIndex.from_dict(self.schema)
+        self.index.set_client(self.client)
+        self.index.create(overwrite=True)
+        
+        #or use 
+        #self.create_col(collection_name, self.embedding_model_dims)
+
+
+        # Recreate the index with the same parameters
+        self.create_col(collection_name, self.embedding_model_dims)
+
     def list(self, filters: dict = None, limit: int = None) -> list:
         """
         List all recent created memories from the vector store.
diff --git a/mem0/vector_stores/supabase.py b/mem0/vector_stores/supabase.py
index 65ea3684..9d0053d1 100644
--- a/mem0/vector_stores/supabase.py
+++ b/mem0/vector_stores/supabase.py
@@ -229,3 +229,9 @@ class Supabase(VectorStoreBase):
         records = self.collection.fetch(ids=ids)
 
         return [[OutputData(id=str(record[0]), score=None, payload=record[2]) for record in records]]
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_col(self.embedding_model_dims)
diff --git a/mem0/vector_stores/upstash_vector.py b/mem0/vector_stores/upstash_vector.py
index 66a010db..6d9b6a06 100644
--- a/mem0/vector_stores/upstash_vector.py
+++ b/mem0/vector_stores/upstash_vector.py
@@ -285,3 +285,10 @@ class UpstashVector(VectorStoreBase):
         - Per-namespace vector and pending vector counts
         """
         return self.client.info()
+    
+    def reset(self):
+        """
+        Reset the Upstash Vector index.
+        """
+        self.delete_col()
+
diff --git a/mem0/vector_stores/vertex_ai_vector_search.py b/mem0/vector_stores/vertex_ai_vector_search.py
index 6f526584..39aa9923 100644
--- a/mem0/vector_stores/vertex_ai_vector_search.py
+++ b/mem0/vector_stores/vertex_ai_vector_search.py
@@ -620,3 +620,10 @@ class GoogleMatchingEngine(VectorStoreBase):
         logger.debug("Starting similarity search")
         docs_and_scores = self.similarity_search_with_score(query, k, filter)
         return [doc for doc, _ in docs_and_scores]
+
+    def reset(self):
+        """
+        Reset the Google Matching Engine index.
+        """
+        logger.warning("Reset operation is not supported for Google Matching Engine")
+        pass
diff --git a/mem0/vector_stores/weaviate.py b/mem0/vector_stores/weaviate.py
index cdf20dee..b759780b 100644
--- a/mem0/vector_stores/weaviate.py
+++ b/mem0/vector_stores/weaviate.py
@@ -308,3 +308,9 @@ class Weaviate(VectorStoreBase):
             payload["id"] = str(obj.uuid).split("'")[0]
             results.append(OutputData(id=str(obj.uuid).split("'")[0], score=1.0, payload=payload))
         return [results]
+    
+    def reset(self):
+        """Reset the index by deleting and recreating it."""
+        logger.warning(f"Resetting index {self.collection_name}...")
+        self.delete_col()
+        self.create_col()