Update Python client to support batch insert and search

client/python/USAGE.md

@@ -39,6 +39,12 @@ The client supports usage as a context manager, which automatically closes the u
 Example available in:  
 ```examples/context_manager_usage.py```
 
+### Batch Insertion and Search Support  
+
+The client now supports batch insertion and batch search queries.  
+Methods of usage and examples available in:  
+```examples/batch_insert_usage.py``` & ```examples/search_query_usage.py```  
+
 ---
 
 ## Client API
@@ -78,6 +84,22 @@ Raises
 
 ---
 
+#### **Batch Insert**
+
+Insert multiple vectors with payloads in a single request  
+```
+batch_insert(*, items: list[tuple[DenseVector, Payload]]) -> list[str]
+```
+
+Returns
+- List of `point_id` (UUID string)
+
+Raises
+- `TypeError` if input structure is invalid
+- gRPC-mapped errors (see Error Handling)
+
+---
+
 #### **Get**
 
 Fetch a point by its ID
@@ -112,6 +134,32 @@ Raises
 
 ---
 
+#### **Batch Search**
+
+Search for nearest neighbours for multiple queries in a single request
+```
+batch_search(
+    *,
+    queries,
+    similarity: Similarity | None = None,
+    limit: int | None = None,
+) -> list[list[str]]
+```
+
+Returns 
+- `TypeError` for invalid query formats
+- `ValueError` if required parameters are missing
+
+Supported Input Formats:  
+The `queries` parameter is flexible and supports multiple formats:
+- List of `SearchQuery` objects
+- List of `(DenseVector, Similarity, Limit)` tuples
+- List of `(DenseVector, Similarity)` tuples with a global `Limit`
+- List of `(DenseVector, Limit)` tuples with a global `Similarity`
+- List of `DenseVector` with global `Similarity` and `Limit`
+
+---
+
 #### **Delete**
 
 Delete a point by its ID
@@ -177,6 +225,19 @@ All fields are directly accessible:
 
 ---
 
+### `SearchQuery`
+
+```
+SearchQuery(
+    vector: DenseVector,
+    similarity: Similarity,
+    limit: int,
+)
+```
+Structured representation of a search request  
+
+---
+
 ### `Similarity`
 
 Enum representing distance functions: 

client/python/examples/batch_insert_usage.py

@@ -0,0 +1,39 @@
+from vortexdb import VortexDB
+from vortexdb import DenseVector, Payload, to_dense_vectors
+
+
+def main():
+    db = VortexDB(
+        grpc_url="localhost:50051",
+        api_key="my-secret-password",
+    )
+
+    raw_vectors = [
+        [0.1, 0.2, 0.3],
+        [0.4, 0.5, 0.6],
+        [0.7, 0.8, 0.9],
+    ]
+    vectors = to_dense_vectors(raw_vectors)
+
+    p1 = Payload.text("hello world")
+    p2 = Payload.image("/img/a.png")
+    p3 = Payload.text("foo bar")
+
+    items = [
+        (vectors[0], p1),
+        (vectors[1], p2),
+        (vectors[2], p3),
+    ]
+
+    # Batch Insert
+    point_ids = db.batch_insert(items=items)
+    print("Inserted ids:\n", point_ids)
+
+    for pid in point_ids:
+        db.delete(point_id=pid)
+
+    db.close()
+
+
+if __name__ == "__main__":
+    main()

client/python/examples/search_query_usage.py

@@ -0,0 +1,69 @@
+from vortexdb import VortexDB
+from vortexdb import DenseVector, Similarity, SearchQuery, to_dense_vectors
+
+
+def main():
+    db = VortexDB(
+        grpc_url="localhost:50051",
+        api_key="my-secret-password",
+    )
+
+    raw_vectors = [
+        [0.1, 0.2, 0.3],
+        [0.4, 0.5, 0.6],
+        [0.7, 0.8, 0.9],
+    ]
+    vectors = to_dense_vectors(raw_vectors)
+
+    q = SearchQuery(
+        vector=vectors[0],
+        similarity=Similarity.COSINE,
+        limit=3,
+    )
+    res = db.search(query=q)
+    print("Single SearchQuery:\n", res)
+
+    # List of SearchQuery
+    queries = [
+        SearchQuery(vectors[0], Similarity.HAMMING, 3),
+        SearchQuery(vectors[1], Similarity.EUCLIDEAN, 2),
+        q,
+    ]
+    res = db.batch_search(queries=queries)
+    print("\nBatch SearchQuery:\n", res)
+
+    # List of vectors with global Similarity and Limit
+    res = db.batch_search(
+        queries=vectors,
+        similarity=Similarity.COSINE,
+        limit=3,
+    )
+    print("\nList of DenseVectors:\n", res)
+
+    # List of tuple (DenseVector, Similarity) with global Limit
+    queries = [
+        (vectors[0], Similarity.COSINE),
+        (vectors[1], Similarity.MANHATTAN),
+    ]
+    res = db.batch_search(
+        queries=queries,
+        limit=3,
+    )
+    print("\nList of (DenseVector, Similarity):\n", res)
+
+    # List of tuple (DenseVector, Limit) with global Similarity
+    queries = [
+        (vectors[0], 2),
+        (vectors[1], 4),
+    ]
+    res = db.batch_search(
+        queries=queries,
+        similarity=Similarity.COSINE,
+    )
+    print("\nList of (DenseVector, Limit):\n", res)
+
+    db.close()
+
+
+if __name__ == "__main__":
+    main()

client/python/tests/test_client.py

@@ -5,6 +5,7 @@
 from vortexdb.connection import GRPCConnection
 from vortexdb.models import DenseVector, Payload, Similarity, ContentType, Point
 from vortexdb.exceptions import InvalidArgumentError
+from vortexdb.models import SearchQuery
 
 
 
@@ -45,7 +46,6 @@ def test_insert_success(client, mock_connection):
     assert point_id == "point-123"
 
 
-
 def test_insert_rejects_invalid_vector(client):
     with pytest.raises(TypeError):
         client.insert(
@@ -54,6 +54,41 @@ def test_insert_rejects_invalid_vector(client):
         )
 
 
+# Batch Insert
+
+def test_batch_insert_success(client, mock_connection):
+    response = Mock()
+    response.ids = [
+        Mock(id=Mock(value="p1")),
+        Mock(id=Mock(value="p2")),
+    ]
+    mock_connection.call.return_value = response
+    items = [
+        (DenseVector([1, 2, 3]), Payload.text("a")),
+        (DenseVector([4, 5, 6]), Payload.text("b")),
+    ]
+    result = client.batch_insert(items=items)
+    assert result == ["p1", "p2"]
+
+def test_batch_insert_invalid_items_type(client):
+    with pytest.raises(TypeError):
+        client.batch_insert(items="not-a-list")
+
+def test_batch_insert_invalid_tuple_structure(client):
+    items = [
+        (DenseVector([1, 2, 3]),),  # only one element
+    ]
+    with pytest.raises(TypeError):
+        client.batch_insert(items=items)
+
+def test_batch_insert_invalid_vector(client):
+    items = [
+        ([1, 2, 3], Payload.text("a")),  # not DenseVector
+    ]
+    with pytest.raises(TypeError):
+        client.batch_insert(items=items)
+
+
 # Get
 
 def test_get_point_success(client, mock_connection):
@@ -118,6 +153,80 @@ def test_search_invalid_vector(client):
         )
 
 
+# Batch Search
+
+def test_batch_search_full_tuple(client, mock_connection):
+    mock_connection.call.return_value = Mock(
+        results=[
+            Mock(result_point_ids=[Mock(id=Mock(value="p1"))]),
+            Mock(result_point_ids=[Mock(id=Mock(value="p2"))]),
+        ]
+    )
+    queries = [
+        (DenseVector([1, 2, 3]), Similarity.COSINE, 2),
+        (DenseVector([4, 5, 6]), Similarity.EUCLIDEAN, 1),
+    ]
+    result = client.batch_search(queries=queries)
+    assert result == [["p1"], ["p2"]]
+
+def test_batch_search_vectors_with_global_params(client, mock_connection):
+    mock_connection.call.return_value = Mock(
+        results=[
+            Mock(result_point_ids=[Mock(id=Mock(value="p1"))]),
+        ]
+    )
+    queries = [DenseVector([1, 2, 3])]
+    result = client.batch_search(
+        queries=queries,
+        similarity=Similarity.MANHATTAN,
+        limit=2,
+    )
+    assert result == [["p1"]]
+
+def test_batch_search_vector_similarity_with_global_limit(client, mock_connection):
+    mock_connection.call.return_value = Mock(
+        results=[
+            Mock(result_point_ids=[Mock(id=Mock(value="p1"))]),
+        ]
+    )
+    queries = [
+        (DenseVector([1, 2, 3]), Similarity.COSINE),
+    ]
+    result = client.batch_search(
+        queries=queries,
+        limit=2,
+    )
+    assert result == [["p1"]]
+
+def test_batch_search_searchquery_objects(client, mock_connection):
+    mock_connection.call.return_value = Mock(
+        results=[
+            Mock(result_point_ids=[Mock(id=Mock(value="p1"))]),
+        ]
+    )
+    queries = [
+        SearchQuery(DenseVector([1, 2, 3]), Similarity.COSINE, 2),
+    ]
+    result = client.batch_search(queries=queries)
+    assert result == [["p1"]]
+
+def test_batch_search_missing_globals_for_vector(client):
+    queries = [DenseVector([1, 2, 3])]
+    with pytest.raises(ValueError):
+        client.batch_search(queries=queries)
+
+def test_batch_search_missing_limit(client):
+    queries = [
+        (DenseVector([1, 2, 3]), Similarity.COSINE),
+    ]
+    with pytest.raises(ValueError):
+        client.batch_search(queries=queries)
+
+def test_batch_search_invalid_format(client):
+    queries = ["invalid"]
+    with pytest.raises(TypeError):
+        client.batch_search(queries=queries)
+
 # Close
 
 def test_close_closes_connection(client, mock_connection):

client/python/vortexdb/__init__.py

@@ -6,6 +6,8 @@
     Payload,
     Point,
     Similarity,
+    SearchQuery,
+    to_dense_vectors,
 )
 from vortexdb.exceptions import (
     VortexDBError,
@@ -23,6 +25,7 @@
     "Payload",
     "Point",
     "Similarity",
+    "SearchQuery",
     "VortexDBError",
     "AuthenticationError",
     "NotFoundError",