diff --git a/pymongo_search_utils/index.py b/pymongo_search_utils/index.py
index 03ac9c4..1886c79 100644
--- a/pymongo_search_utils/index.py
+++ b/pymongo_search_utils/index.py
@@ -17,6 +17,7 @@ def vector_search_index_definition(
     path: str,
     similarity: str,
     filters: list[str] | None = None,
+    vector_index_options: dict | None = None,
     **kwargs: Any,
 ) -> dict[str, Any]:
     """Create a vector search index definition.
@@ -40,6 +41,7 @@ def vector_search_index_definition(
             "path": path,
             "similarity": similarity,
             "type": "vector",
+            **(vector_index_options or {}),
         },
     ]
     if filters:
@@ -95,6 +97,7 @@ def create_vector_search_index(
     path: str,
     similarity: str,
     filters: list[str] | None = None,
+    vector_index_options: dict | None = None,
     *,
     wait_until_complete: float | None = None,
     **kwargs: Any,
@@ -124,6 +127,7 @@ def create_vector_search_index(
                 path=path,
                 similarity=similarity,
                 filters=filters,
+                vector_index_options=vector_index_options,
                 **kwargs,
             ),
             name=index_name,
@@ -147,6 +151,7 @@ def update_vector_search_index(
     path: str,
     similarity: str,
     filters: list[str] | None = None,
+    vector_index_options: dict | None = None,
     *,
     wait_until_complete: float | None = None,
     **kwargs: Any,
@@ -174,6 +179,7 @@ def update_vector_search_index(
             path=path,
             similarity=similarity,
             filters=filters,
+            vector_index_options=vector_index_options,
             **kwargs,
         ),
     )
diff --git a/tests/test_index.py b/tests/test_index.py
index 98e2018..dd2ae13 100644
--- a/tests/test_index.py
+++ b/tests/test_index.py
@@ -2,21 +2,27 @@
 
 import os
 from collections.abc import Generator
+from unittest.mock import MagicMock, patch
 
 import pytest
 from pymongo import MongoClient
 from pymongo.collection import Collection
 
 from pymongo_search_utils.index import (
+    create_fulltext_search_index,
     create_vector_search_index,
     drop_vector_search_index,
     is_index_ready,
     update_vector_search_index,
+    vector_search_index_definition,
+    wait_for_docs_in_index,
+    wait_for_predicate,
 )
 
 DB_NAME = "pymongo_search_utils_test"
 COLLECTION_NAME = "test_index"
 VECTOR_INDEX_NAME = "vector_index"
+FULLTEXT_INDEX_NAME = "fulltext_index"
 
 TIMEOUT = 120
 DIMENSIONS = 10
@@ -41,7 +47,13 @@ def collection(client) -> Generator:
     clxn.delete_many({})
 
 
-def test_search_index_create_and_drop(collection: Collection) -> None:
+@pytest.fixture
+def mock_search_indexes():
+    """Fixture to track mock search indexes."""
+    return {}
+
+
+def test_search_index_create_and_drop(collection: Collection, mock_search_indexes: dict) -> None:
     index_name = VECTOR_INDEX_NAME
     dimensions = DIMENSIONS
     path = "embedding"
@@ -49,32 +61,56 @@ def test_search_index_create_and_drop(collection: Collection) -> None:
     filters: list[str] | None = None
     wait_until_complete = TIMEOUT
 
-    for index_info in collection.list_search_indexes():
-        drop_vector_search_index(
-            collection, index_info["name"], wait_until_complete=wait_until_complete
-        )
+    def mock_list_search_indexes(name=None):
+        if name:
+            return [mock_search_indexes[name]] if name in mock_search_indexes else []
+        return list(mock_search_indexes.values())
 
-    assert len(list(collection.list_search_indexes())) == 0
+    def mock_create_search_index(model):
+        doc = model.document
+        mock_search_indexes[doc["name"]] = {
+            "name": doc["name"],
+            "status": "READY",
+            "type": doc.get("type", "search"),
+            "latestDefinition": doc["definition"],
+        }
+        return doc["name"]
 
-    create_vector_search_index(
-        collection=collection,
-        index_name=index_name,
-        dimensions=dimensions,
-        path=path,
-        similarity=similarity,
-        filters=filters,
-        wait_until_complete=wait_until_complete,
-    )
+    def mock_drop_search_index(name):
+        if name in mock_search_indexes:
+            del mock_search_indexes[name]
 
-    assert is_index_ready(collection, index_name)
-    indexes = list(collection.list_search_indexes())
-    assert len(indexes) == 1
-    assert indexes[0]["name"] == index_name
+    with (
+        patch.object(collection, "list_search_indexes", side_effect=mock_list_search_indexes),
+        patch.object(collection, "create_search_index", side_effect=mock_create_search_index),
+        patch.object(collection, "drop_search_index", side_effect=mock_drop_search_index),
+    ):
+        for index_info in collection.list_search_indexes():
+            drop_vector_search_index(
+                collection, index_info["name"], wait_until_complete=wait_until_complete
+            )
 
-    drop_vector_search_index(collection, index_name, wait_until_complete=wait_until_complete)
+        assert len(list(collection.list_search_indexes())) == 0
 
-    indexes = list(collection.list_search_indexes())
-    assert len(indexes) == 0
+        create_vector_search_index(
+            collection=collection,
+            index_name=index_name,
+            dimensions=dimensions,
+            path=path,
+            similarity=similarity,
+            filters=filters,
+            wait_until_complete=wait_until_complete,
+        )
+
+        assert is_index_ready(collection, index_name)
+        indexes = list(collection.list_search_indexes())
+        assert len(indexes) == 1
+        assert indexes[0]["name"] == index_name
+
+        drop_vector_search_index(collection, index_name, wait_until_complete=wait_until_complete)
+
+        indexes = list(collection.list_search_indexes())
+        assert len(indexes) == 0
 
 
 @pytest.mark.skip(
@@ -114,3 +150,313 @@ def test_search_index_update_vector_search_index(collection: Collection) -> None
     assert len(indexes) == 1
     assert indexes[0]["name"] == index_name
     assert indexes[0]["latestDefinition"]["fields"][0]["similarity"] == similarity_new
+
+
+def test_vector_search_index_definition() -> None:
+    """Test the vector_search_index_definition helper function."""
+    # Test basic definition without filters
+    definition = vector_search_index_definition(
+        dimensions=128, path="embedding", similarity="cosine"
+    )
+    assert "fields" in definition
+    assert len(definition["fields"]) == 1
+    assert definition["fields"][0]["numDimensions"] == 128
+    assert definition["fields"][0]["path"] == "embedding"
+    assert definition["fields"][0]["similarity"] == "cosine"
+    assert definition["fields"][0]["type"] == "vector"
+
+    # Test with filters
+    definition = vector_search_index_definition(
+        dimensions=256, path="vector", similarity="euclidean", filters=["category", "status"]
+    )
+    assert len(definition["fields"]) == 3
+    assert definition["fields"][0]["type"] == "vector"
+    assert definition["fields"][1]["type"] == "filter"
+    assert definition["fields"][1]["path"] == "category"
+    assert definition["fields"][2]["type"] == "filter"
+    assert definition["fields"][2]["path"] == "status"
+
+    # Test with vector_index_options
+    definition = vector_search_index_definition(
+        dimensions=512,
+        path="embed",
+        similarity="dotProduct",
+        vector_index_options={"quantization": {"type": "scalar"}},
+    )
+    assert definition["fields"][0]["quantization"] == {"type": "scalar"}
+
+    # Test with kwargs
+    definition = vector_search_index_definition(
+        dimensions=64, path="vec", similarity="cosine", storedSource=True
+    )
+    assert definition["storedSource"] is True
+
+
+def test_wait_for_predicate() -> None:
+    """Test the wait_for_predicate utility function."""
+    import time
+
+    # Test successful predicate
+    counter = {"value": 0}
+
+    def increment_predicate():
+        counter["value"] += 1
+        return counter["value"] >= 3
+
+    start = time.monotonic()
+    wait_for_predicate(increment_predicate, "Should not timeout", timeout=5, interval=0.1)
+    elapsed = time.monotonic() - start
+    assert counter["value"] >= 3
+    assert elapsed < 5
+
+    # Test timeout
+    def always_false():
+        return False
+
+    with pytest.raises(TimeoutError, match="Predicate failed"):
+        wait_for_predicate(always_false, "Predicate failed", timeout=0.5, interval=0.1)
+
+
+def test_create_fulltext_search_index_single_field(
+    collection: Collection, mock_search_indexes: dict
+) -> None:
+    """Test creating a fulltext search index on a single field."""
+    index_name = FULLTEXT_INDEX_NAME
+    field = "description"
+    wait_until_complete = TIMEOUT
+
+    def mock_list_search_indexes(name=None):
+        if name:
+            return [mock_search_indexes[name]] if name in mock_search_indexes else []
+        return list(mock_search_indexes.values())
+
+    def mock_create_search_index(model):
+        doc = model.document
+        mock_search_indexes[doc["name"]] = {
+            "name": doc["name"],
+            "status": "READY",
+            "type": doc.get("type", "search"),
+            "latestDefinition": doc["definition"],
+        }
+        return doc["name"]
+
+    def mock_drop_search_index(name):
+        if name in mock_search_indexes:
+            del mock_search_indexes[name]
+
+    with (
+        patch.object(collection, "list_search_indexes", side_effect=mock_list_search_indexes),
+        patch.object(collection, "create_search_index", side_effect=mock_create_search_index),
+        patch.object(collection, "drop_search_index", side_effect=mock_drop_search_index),
+    ):
+        # Clean up existing indexes
+        for index_info in collection.list_search_indexes():
+            drop_vector_search_index(
+                collection, index_info["name"], wait_until_complete=wait_until_complete
+            )
+
+        # Create fulltext search index
+        create_fulltext_search_index(
+            collection=collection,
+            index_name=index_name,
+            field=field,
+            wait_until_complete=wait_until_complete,
+        )
+
+        # Verify index was created
+        assert is_index_ready(collection, index_name)
+        indexes = list(collection.list_search_indexes())
+        assert len(indexes) == 1
+        assert indexes[0]["name"] == index_name
+        assert indexes[0]["type"] == "search"
+        assert indexes[0]["latestDefinition"]["mappings"]["dynamic"] is False
+        assert field in indexes[0]["latestDefinition"]["mappings"]["fields"]
+
+        # Clean up
+        drop_vector_search_index(collection, index_name, wait_until_complete=wait_until_complete)
+
+
+def test_create_fulltext_search_index_multiple_fields(
+    collection: Collection, mock_search_indexes: dict
+) -> None:
+    """Test creating a fulltext search index on multiple fields."""
+    index_name = "fulltext_multi_index"
+    fields = ["title", "description", "content"]
+    wait_until_complete = TIMEOUT
+
+    def mock_list_search_indexes(name=None):
+        if name:
+            return [mock_search_indexes[name]] if name in mock_search_indexes else []
+        return list(mock_search_indexes.values())
+
+    def mock_create_search_index(model):
+        doc = model.document
+        mock_search_indexes[doc["name"]] = {
+            "name": doc["name"],
+            "status": "READY",
+            "type": doc.get("type", "search"),
+            "latestDefinition": doc["definition"],
+        }
+        return doc["name"]
+
+    def mock_drop_search_index(name):
+        if name in mock_search_indexes:
+            del mock_search_indexes[name]
+
+    with (
+        patch.object(collection, "list_search_indexes", side_effect=mock_list_search_indexes),
+        patch.object(collection, "create_search_index", side_effect=mock_create_search_index),
+        patch.object(collection, "drop_search_index", side_effect=mock_drop_search_index),
+    ):
+        # Clean up existing indexes
+        for index_info in collection.list_search_indexes():
+            drop_vector_search_index(
+                collection, index_info["name"], wait_until_complete=wait_until_complete
+            )
+
+        # Create fulltext search index with multiple fields
+        create_fulltext_search_index(
+            collection=collection,
+            index_name=index_name,
+            field=fields,
+            wait_until_complete=wait_until_complete,
+        )
+
+        # Verify index was created
+        assert is_index_ready(collection, index_name)
+        indexes = list(collection.list_search_indexes())
+        assert len(indexes) == 1
+        assert indexes[0]["name"] == index_name
+        assert indexes[0]["type"] == "search"
+
+        # Verify all fields are in the index
+        index_fields = indexes[0]["latestDefinition"]["mappings"]["fields"]
+        for field in fields:
+            assert field in index_fields
+            assert index_fields[field] == [{"type": "string"}]
+
+        # Clean up
+        drop_vector_search_index(collection, index_name, wait_until_complete=wait_until_complete)
+
+
+def test_wait_for_docs_in_index(collection: Collection, mock_search_indexes: dict) -> None:
+    """Test waiting for documents to be indexed in a vector search index."""
+    index_name = "wait_docs_index"
+    dimensions = DIMENSIONS
+    path = "embedding"
+    similarity = "cosine"
+    wait_until_complete = TIMEOUT
+
+    def mock_list_search_indexes(name=None):
+        if name:
+            result = [mock_search_indexes[name]] if name in mock_search_indexes else []
+
+            # When called with a name parameter (e.g., for is_index_ready), return an iterable
+            # When called with a name parameter for wait_for_docs_in_index, return a mock cursor
+            # We need to check the context - if it has to_list, it's for wait_for_docs_in_index
+            class MockIndexCursor:
+                def __init__(self, data):
+                    self.data = data
+
+                def __iter__(self):
+                    return iter(self.data)
+
+                def to_list(self):
+                    return self.data
+
+            return MockIndexCursor(result)
+        return list(mock_search_indexes.values())
+
+    def mock_create_search_index(model):
+        doc = model.document
+        mock_search_indexes[doc["name"]] = {
+            "name": doc["name"],
+            "status": "READY",
+            "type": doc.get("type", "search"),
+            "latestDefinition": doc["definition"],
+        }
+        return doc["name"]
+
+    def mock_drop_search_index(name):
+        if name in mock_search_indexes:
+            del mock_search_indexes[name]
+
+    # Insert test documents with embeddings
+    n_docs = 5
+    docs = [{"_id": i, path: [0.1] * dimensions, "text": f"doc {i}"} for i in range(n_docs)]
+    collection.insert_many(docs)
+
+    def mock_aggregate(pipeline):
+        # Return mock results for vector search aggregation
+        mock_cursor = MagicMock()
+        mock_cursor.to_list.return_value = [{"_id": i, "search_score": 0.9} for i in range(n_docs)]
+        return mock_cursor
+
+    with (
+        patch.object(collection, "list_search_indexes", side_effect=mock_list_search_indexes),
+        patch.object(collection, "create_search_index", side_effect=mock_create_search_index),
+        patch.object(collection, "drop_search_index", side_effect=mock_drop_search_index),
+        patch.object(collection, "aggregate", side_effect=mock_aggregate),
+    ):
+        # Clean up existing indexes
+        for index_info in list(mock_search_indexes.values()):
+            drop_vector_search_index(
+                collection, index_info["name"], wait_until_complete=wait_until_complete
+            )
+
+        # Create vector search index
+        create_vector_search_index(
+            collection=collection,
+            index_name=index_name,
+            dimensions=dimensions,
+            path=path,
+            similarity=similarity,
+            wait_until_complete=wait_until_complete,
+        )
+
+        # Wait for documents to be indexed
+        result = wait_for_docs_in_index(collection, index_name, n_docs)
+        assert result is True
+
+        # Clean up
+        drop_vector_search_index(collection, index_name, wait_until_complete=wait_until_complete)
+
+
+def test_wait_for_docs_in_index_nonexistent(
+    collection: Collection, mock_search_indexes: dict
+) -> None:
+    """Test wait_for_docs_in_index raises error for non-existent index."""
+
+    def mock_list_search_indexes(name=None):
+        if name:
+            result = [mock_search_indexes[name]] if name in mock_search_indexes else []
+
+            # Return a mock cursor with to_list method
+            class MockIndexCursor:
+                def __init__(self, data):
+                    self.data = data
+
+                def __iter__(self):
+                    return iter(self.data)
+
+                def to_list(self):
+                    return self.data
+
+            return MockIndexCursor(result)
+        return list(mock_search_indexes.values())
+
+    def mock_drop_search_index(name):
+        if name in mock_search_indexes:
+            del mock_search_indexes[name]
+
+    with (
+        patch.object(collection, "list_search_indexes", side_effect=mock_list_search_indexes),
+        patch.object(collection, "drop_search_index", side_effect=mock_drop_search_index),
+    ):
+        # Ensure no indexes exist
+        for index_info in list(mock_search_indexes.values()):
+            drop_vector_search_index(collection, index_info["name"], wait_until_complete=TIMEOUT)
+
+        # Should raise ValueError for non-existent index
+        with pytest.raises(ValueError, match="does not exist"):
+            wait_for_docs_in_index(collection, "nonexistent_index", 1)
diff --git a/uv.lock b/uv.lock
index 5bbd798..c3c496f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,40 +1,41 @@
 version = 1
+revision = 3
 requires-python = ">=3.10"
 
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