Update Async explainer after review

Async explainer.md

@@ -1,26 +1,26 @@
-# Design Rationale: Mixed Interface PGVectorStore
+# Design Overview for `PGVectorStore`
 
-This document outlines the design choices behind the PGVectorStore integration for LangChain, focusing on its dual interface that supports both synchronous and asynchronous usage patterns.
+This document outlines the design choices behind the PGVectorStore integration for LangChain, focusing on how an async PostgreSQL driver can supports both synchronous and asynchronous usage.
 
 ## Motivation: Performance through Asynchronicity
 
 Database interactions are often I/O-bound, making asynchronous programming crucial for performance.
 
--   **Non-Blocking Operations:** Asynchronous code prevents the application from stalling while waiting for database responses, improving throughput and responsiveness.
--  **Asynchronous Foundation (`asyncio` and Drivers):** Built upon Python's `asyncio`, the integration is designed to work with asynchronous PostgreSQL drivers to handle database operations efficiently. While compatible drivers are supported, the `asyncpg` driver is specifically recommended due to its high performance in concurrent scenarios. You can explore its benefits ([link](https://magic.io/blog/asyncpg-1m-rows-from-postgres-to-python/)) and performance benchmarks ([link](https://fernandoarteaga.dev/blog/psycopg-vs-asyncpg/)) for more details.
+-  **Non-Blocking Operations:** Asynchronous code prevents the application from stalling while waiting for database responses, improving throughput and responsiveness.
+-  **Asynchronous Foundation (`asyncio` and Drivers):** Built upon Python's `asyncio` library, the integration is designed to work with asynchronous PostgreSQL drivers to handle database operations efficiently. While compatible drivers are supported, the `asyncpg` driver is specifically recommended due to its high performance in concurrent scenarios. You can explore its benefits ([link](https://magic.io/blog/asyncpg-1m-rows-from-postgres-to-python/)) and performance benchmarks ([link](https://fernandoarteaga.dev/blog/psycopg-vs-asyncpg/)) for more details.
 
-This foundation ensures the core database interactions are fast and scalable.
+This native async foundation ensures the core database interactions are fast and scalable.
 
 ## The Two-Class Approach: Enabling a Mixed Interface
 
 To cater to different application architectures while maintaining performance, we provide two classes:
 
 1.  **`AsyncPGVectorStore` (Core Asynchronous Implementation):**
-    * This class contains the pure `async/await` logic for all database operations using `asyncpg`.
+    * This class contains the pure `async/await` logic for all database operations.
     * It's designed for **direct use within asynchronous applications**. Users working in an `asyncio` environment can `await` its methods for maximum efficiency and direct control within the event loop.
     * It represents the fundamental, non-blocking way of interacting with the database.
 
-2.  **`PGVectorStore` (Synchronous Interface / Asynchronous Internals):**
+2.   **`PGVectorStore` (Mixed Sync/Async API ):**
     * This class provides both asynchronous & synchronous APIs.
     * When one of its methods is called, it internally invokes the corresponding `async` method from `AsyncPGVectorStore`.
     * It **manages the execution of this underlying asynchronous logic**, handling the necessary `asyncio` event loop interactions (e.g., starting/running the coroutine) behind the scenes.