Change format into a table of features

README.md

@@ -92,16 +92,13 @@ See example for the [PGVector vectorstore here](https://github.com/langchain-ai/
 
 * [`langchain-google-cloud-sql-pg`](https://github.com/googleapis/langchain-google-cloud-sql-pg-python)
 
-Using the Google Cloud provided integrations provides the following benefits:
+Using the Google Cloud integrations provides the following benefits:
 
-- **Enhanced security**: Easily and securely connect to Google Cloud databases utilizing IAM for authorization and database authentication without needing to manage SSL certificates, configure firewall rules, or enable authorized networks.
+- **Enhanced Security**: Securely connect to Google Cloud databases utilizing IAM for authorization and database authentication without needing to manage SSL certificates, configure firewall rules, or enable authorized networks.
 - **Simplified and Secure Connections:** Connect to Google Cloud databases effortlessly using the instance name instead of complex connection strings. The integrations creates a secure connection pool that can be easily shared across your application using the `engine` object.
 
-Learn how to [migrate a `PGVector` vector store to `AlloyDBVectorStore`](https://github.com/googleapis/langchain-google-alloydb-pg-python/blob/main/samples/migrations/migrate_pgvector_to_alloydb.md) to gain the following benefits:
+| Vector Store             | Metadata filtering | Async support  | Schema Flexibility | Improved metadata handling | Hybrid Search         |
+|--------------------------|--------------------|----------------|--------------------|----------------------------|---------------|
+| Google AlloyDB           | ✓                  | ✓              | ✓                  | ✓                          | ✓             |
+| Google Cloud SQL Postgres| ✓                  | ✓              | ✓                  | ✓                          | ✓             |
 
-- **Simplified management**: a single table contains data corresponding to a single collection, making it easier to query, update, and maintain.
-- **Schema flexibility**: allow users to add tables into any database schema.
-- **Improved performance**: using a single-table schema can lead to faster query execution, especially for large collections.
-- **Improved metadata handling**: store metadata in columns instead of JSON, resulting in significant performance improvements.
-- **Clear separation**: clearly separate table and extension creation, allowing for distinct permissions and streamlined workflows.
-- **Better integration with AlloyDB**: take advantage of AlloyDB's advanced indexing and scalability capabilities.