Refactor Python bindings to prioritize SQL/OpenCypher for database operations

- Updated documentation to reflect the removal of `BatchContext` and promote SQL/OpenCypher for transactions and data manipulation.
- Changed examples to use SQL commands for creating and managing vertices, edges, and transactions.
- Adjusted test coverage counts in documentation to reflect the current state (260 tests across 24 files).
- Removed legacy importer tests in favor of SQL `IMPORT DATABASE` usage, enhancing focus on SQL-first approaches.
- Improved performance tips and best practices for using NumPy and vector operations.
- Cleaned up references to batch context in various documentation files, emphasizing chunked transactions instead.

Author: tae898

bindings/python/README.md

@@ -2,7 +2,7 @@
 
 Native Python bindings for ArcadeDB - the multi-model database that supports Graph, Document, Key/Value, Search Engine, Time Series, and Vector models.
 
-**Status**: ✅ Production Ready | **Tests**: 271 Passing | **Platforms**: 4 Supported
+**Status**: ✅ Production Ready | **Tests**: 260 Passing Across 24 Test Files | **Platforms**: 4 Supported
 
 ---
 
@@ -89,7 +89,7 @@ Import: `import arcadedb_embedded as arcadedb`
 
 ## 🧪 Testing
 
-**Status**: 271 tests + example scripts passing on all 4 platforms
+**Status**: 260 tests passing across 24 test files
 
 ```bash
 # Run all tests
@@ -148,11 +148,10 @@ arcadedb_embedded/
 ├── transactions.py      # TransactionContext manager
 ├── schema.py            # Schema management API
 ├── vector.py            # Vector search and HNSW (JVector) indexing
-├── importer.py          # Data import (CSV, JSONL)
+├── graph.py             # Graph wrappers
+├── importer.py          # Data import helpers (CSV/XML) + SQL import guidance
 ├── exporter.py          # Data export (JSONL, GraphML, GraphSON, CSV)
-├── graph.py             # Graph API helpers
-├── batch.py             # Batch operations context
-├── async_executor.py    # Asynchronous query execution
+├── async_executor.py    # Asynchronous command/query + record execution
 ├── type_conversion.py   # Python-Java type conversion utilities
 ├── exceptions.py        # ArcadeDBError exception
 ├── jvm.py               # JVM lifecycle management

bindings/python/docs/api-access-methods.md

@@ -8,7 +8,7 @@ Direct JVM method calls via JPype for embedded/local runtime access.
 
 !!! note "DSL-first guidance"
     Prefer SQL/OpenCypher via `db.command(...)` and `db.query(...)` for schema, CRUD, and graph operations.
-    Wrapper APIs remain available, but examples and guides are standardized on DSL usage.
+    Wrapper APIs remain available as compatibility/reference features, but examples and guides are standardized on DSL usage.
 
 ### Characteristics
 

bindings/python/docs/api/async_executor.md

@@ -1,11 +1,12 @@
 # AsyncExecutor API
 
-!!! warning "Experimental"
-    `async_executor()` is experimental and not advised for production use yet. Prefer
-    standard transactions and synchronous workflows unless you are explicitly testing
-    or benchmarking.
+!!! note "Recommended usage"
+    For application code, prefer async SQL/OpenCypher via `async_exec.command(...)`
+    and `async_exec.query(...)`. Record-level helpers remain available for lower-level
+    workflows and tests.
 
-The AsyncExecutor provides low-level async operations for parallel processing, automatic batching, and optimized WAL operations.
+The AsyncExecutor provides low-level async operations for parallel processing,
+automatic batching, and optimized WAL operations.
 
 !!! tip "Using Context Managers"
     For automatic resource cleanup, prefer using context managers:
@@ -45,11 +46,15 @@ async_exec.set_parallel_level(8)       # 8 worker threads
 async_exec.set_commit_every(5000)      # Auto-commit every 5K ops
 async_exec.set_back_pressure(75)       # Queue back-pressure at 75%
 
-# Use for bulk operations
+# Use for bulk SQL operations
 for i in range(100000):
-    vertex = db.new_vertex("User")
-    vertex.set("userId", i)
-    async_exec.create_record(vertex)
+    async_exec.command(
+        "sql",
+        "INSERT INTO User SET userId = :id, name = :name",
+        callback=lambda rs: None,
+        id=i,
+        name=f"User {i}",
+    )
 
 # Wait for completion
 async_exec.wait_completion()
@@ -210,6 +215,10 @@ async_exec.create_record(
 
 Schedule asynchronous record creation.
 
+!!! note "Wrapper-specific API"
+    `AsyncExecutor.create_record()` and `update_record()` operate on record wrapper
+    objects. For non-async CRUD examples elsewhere in the docs, prefer SQL/OpenCypher.
+
 **Parameters:**
 
 - `record`: Document, Vertex, or Edge object to create
@@ -578,26 +587,6 @@ cpu_count = os.cpu_count() or 4
 async_exec.set_parallel_level(min(cpu_count, 16))
 ```
 
-## Comparison with BatchContext
-
-| Feature | AsyncExecutor | BatchContext |
-|---------|---------------|-------------|
-| **Control** | ✅ Full control | ⚠️ High-level API |
-| **Complexity** | ⚠️ More complex | ✅ Simple |
-| **Progress Bar** | ❌ Manual | ✅ Built-in |
-| **Error Handling** | ⚠️ Callbacks | ✅ Automatic collection |
-| **Use Case** | Advanced tuning | Most bulk ops |
-
-**When to use AsyncExecutor:**
-- Need fine-grained control
-- Custom callbacks required
-- Performance tuning critical
-
-**When to use BatchContext:**
-- Standard bulk operations
-- Want progress tracking
-- Prefer simple API
-
 ## Troubleshooting
 
 ### Out of Memory Errors
@@ -636,7 +625,6 @@ if async_exec.is_pending():
 
 ## See Also
 
-- **[BatchContext API](batch.md)** - High-level batch processing
 - **[Transactions API](transactions.md)** - Transaction management
 - **[Database API](database.md)** - Database operations
 - **[Example 05: CSV Import](../examples/05_csv_import_graph.md)** - Real-world usage