Update README.md

Author: murrayhu-mdb

README.md

@@ -7,7 +7,7 @@ and [`pydantic-ai-harness`](https://github.com/pydantic/pydantic-ai-harness).
 
 Drop `MongoDBMemoryCapability` into an agent's `capabilities=[...]` array and it gains
 durable, cross-session, semantically-searchable memory — backed by MongoDB documents +
-Atlas Vector Search. Anchored to [pydantic-ai-harness#255](https://github.com/pydantic/pydantic-ai-harness/issues/255).
+MongoDB Vector Search. Anchored to [pydantic-ai-harness#255](https://github.com/pydantic/pydantic-ai-harness/issues/255).
 
 ## Why
 
@@ -16,7 +16,7 @@ inflates tokens and hits context limits. This capability gives the agent a tiere
 
 - **Short-term / episodic** — recent turns, semantically searchable via `$vectorSearch`.
 - **Long-term structured profile** — durable user preferences (always injected).
-- **Semantic recall** — Voyage 3.5 embeddings + Atlas Vector Search, with **prefiltering**.
+- **Semantic recall** — Voyage 3.5 embeddings + MongoDB Vector Search, with **prefiltering**.
 
 ## Install
 
@@ -33,7 +33,7 @@ from pydantic_ai_mongodb_memory import MongoDBMemoryCapability
 memory = MongoDBMemoryCapability(
     connection_string="mongodb+srv://...",
     database_name="agent_memory",
-    semantic_recall=True,            # uses Atlas Vector Search + voyage-3.5
+    semantic_recall=True,            # uses MongoDB Vector Search + voyage-3.5
 )
 
 agent = Agent("google:gemini-2.0-flash", capabilities=[memory])
@@ -70,7 +70,7 @@ Both auto-load `demo/.env` (`ATLAS_URI`, `VOYAGE_API_KEY`, `GEMINI_API_KEY`).
 
 ```bash
 python demo/main.py        # data-level: seed team dataset, multi-turn, scope isolation, $vectorSearch
-python demo/agent_demo.py  # a Gemini agent: cross-session memory + Atlas Vector Search w/ department prefilter
+python demo/agent_demo.py  # a Gemini agent: cross-session memory + MongoDB Vector Search w/ department prefilter
 ```
 
 `agent_demo.py` proves the headline value: an agent told "only staff Engineering" in