docs: Rename Atlas Vector Search to MongoDB Vector Search and fix code examples

docs/reference/data-sources/mongodb.md

@@ -28,9 +28,7 @@ The full set of configuration options is available [here](https://rtd.feast.dev/
 
 ## Vector Search
 
-The MongoDB online store supports [Atlas Vector Search](https://www.mongodb.com/docs/atlas/atlas-vector-search/), enabling similarity search over feature embeddings stored in MongoDB Atlas. This is powered by the `$vectorSearch` aggregation stage and requires MongoDB Atlas (or the `mongodb/mongodb-atlas-local` Docker image for local development).
-
-See [PR #6344](https://github.com/feast-dev/feast/pull/6344) for full implementation details.
+The MongoDB online store supports [MongoDB Vector Search](https://www.mongodb.com/docs/atlas/atlas-vector-search/), enabling similarity search over feature embeddings stored in MongoDB. This is powered by the `$vectorSearch` aggregation stage and supports MongoDB Atlas, self-hosted MongoDB with Atlas Search indexes, and the `mongodb/mongodb-atlas-local` Docker image for local development.
 
 ### Configuration
 
@@ -41,7 +39,7 @@ project: my_project
 provider: local
 online_store:
   type: mongodb
-  connection_string: mongodb+srv://<user>:<pass>@cluster.mongodb.net
+  connection_string: mongodb+srv://<user>:<pass>@cluster.mongodb.net  # pragma: allowlist secret
   vector_enabled: true
   similarity: cosine  # cosine | euclidean | dotProduct
   vector_index_wait_timeout: 60  # seconds to wait for index to become queryable
@@ -76,32 +74,24 @@ item_embeddings = FeatureView(
 )
 ```
 
-When `feast apply` (or `store.update()`) runs with `vector_enabled=True`, Atlas vector search indexes are automatically created for any field with `vector_index=True`. Indexes are also automatically dropped when feature views are removed.
+When `feast apply` (or `store.update()`) runs with `vector_enabled=True`, MongoDB vector search indexes are automatically created for any field with `vector_index=True`. Indexes are also automatically dropped when feature views are removed.
 
 ### Retrieving Documents via Vector Search
 
 Use `retrieve_online_documents_v2()` to perform similarity search:
 
 ```python
-source = FeatureStore(repo_path=".")
+store = FeatureStore(repo_path=".")
 results = store.retrieve_online_documents_v2(
-    config=repo_config,
-    table=item_embeddings,
-    requested_features=["embedding", "title"],
-    embedding=[0.1, 0.2, ...],  # query vector
+    features=["item_embeddings:embedding", "item_embeddings:title"],
+    query=[0.1, 0.2, ...],  # query vector
     top_k=5,
 )
-
-# Each result is a (event_timestamp, entity_key_proto, feature_dict) tuple.
-# feature_dict includes a synthetic "distance" key with the vector search score.
-for ts, entity_key, features in results:
-    print(features["title"].string_val, features["distance"].float_val)
-```
 ```
 
 ### How It Works
 
-- **Index creation**: `update()` creates an Atlas vector search index named `<feature_view>__<field>__vs_index` for each vector-indexed field. It waits for the index to reach `READY` status before proceeding.
+- **Index creation**: `update()` creates a MongoDB vector search index named `<feature_view>__<field>__vs_index` for each vector-indexed field. It waits for the index to reach `READY` status before proceeding.
 - **Query execution**: `retrieve_online_documents_v2()` builds a `$vectorSearch` aggregation pipeline with `numCandidates = max(top_k * 10, 100)` and the specified `limit`.
 - **Score**: Results include a `distance` field populated from `$meta: "vectorSearchScore"`.
 - **BSON compatibility**: Query vectors are coerced to native Python floats to avoid numpy serialization issues.

docs/reference/offline-stores/mongodb.md

@@ -3,8 +3,6 @@
 ## Description
 
 The MongoDB offline store provides support for reading [MongoDBSource](../data-sources/mongodb.md).
-* Uses a single shared collection with a compound index for all FeatureViews, distinguished by a `feature_view` discriminator field.
-* Entity dataframes can be provided as a Pandas dataframe. The offline store converts entity identifiers into serialized entity keys for efficient lookup against the collection.
 
 ## Getting started