docs: Update README for v0.3.5 - Vector Convenience Methods

Documentation Updates
=====================
- Add 'What's New in 0.3.5' section
- Document new vector operations on Database class
- Show simplified API compared to separate VectorIndex class
- Add code examples for create_index(), insert_vectors(), search()
- Document tokio-optional compatibility

API Examples
============
Before (0.3.4):
    from toondb.vector import VectorIndex
    index = VectorIndex(dimension=384)

After (0.3.5):
    db = Database.open('./mydb')
    db.create_index('embeddings', dimension=384)

Benefits
========
- Simpler API - all operations on Database class
- No need to manage separate VectorIndex instances
- Index metadata stored in database
- Backwards compatible with VectorIndex class

Author: sushanthpy

README.md

@@ -1,8 +1,39 @@
-# ToonDB Python SDK v0.3.4
+# ToonDB Python SDK v0.3.5
 
 **Dual-mode architecture: Embedded (FFI) + Server (gRPC/IPC)**  
 Choose the deployment mode that fits your needs.
 
+## What's New in 0.3.5
+
+### 🔢 Vector Operations in Database Class
+No need for separate `VectorIndex` class anymore - vector operations are now directly available on the `Database` class:
+
+```python
+from toondb import Database
+
+db = Database.open('./mydb')
+
+# Create vector index
+db.create_index('embeddings', dimension=384)
+
+# Insert vectors
+db.insert_vectors('embeddings', [1, 2, 3], [
+    [0.1, 0.2, ...],  # vector 1
+    [0.3, 0.4, ...],  # vector 2
+    [0.5, 0.6, ...],  # vector 3
+])
+
+# Search
+results = db.search('embeddings', [0.1, 0.2, ...], k=5)
+for id, distance in results:
+    print(f'ID: {id}, Distance: {distance:.4f}')
+```
+
+### 🏗️ Works with Tokio-Optional Architecture
+- Supports both sync and async Rust backend (v0.3.5)
+- No breaking changes to existing code
+- Smaller binaries (~500KB reduction)
+
 ## Architecture: Flexible Deployment
 
 ```
@@ -72,6 +103,19 @@ with Database.open("./mydb") as db:
     db.put(b"key", b"value")
     value = db.get(b"key")
 
+    # Vector operations (NEW in 0.3.5)
+    db.create_index('embeddings', dimension=384)
+    db.insert_vectors('embeddings', [1, 2, 3], [
+        [0.1, 0.2, ...],  # vector 1
+        [0.3, 0.4, ...],  # vector 2
+        [0.5, 0.6, ...],  # vector 3
+    ])
+    
+    # Search for similar vectors
+    results = db.search('embeddings', [0.1, 0.2, ...], k=5)
+    for id, distance in results:
+        print(f'ID: {id}, Distance: {distance}')
+    
     # Namespaces
     ns = db.namespace("tenant_123")
     collection = ns.collection("documents", dimension=384)