fix(qdrant): Allow async-only initialization with hybrid search

llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/llama_index/vector_stores/qdrant/base.py

@@ -219,6 +219,10 @@ def __init__(
             dense_vector_name=dense_vector_name,
             sparse_vector_name=sparse_vector_name,
         )
+        # Track if the user provided their own sparse functions. This is to prevent
+        # them from being overwritten by the lazy-init correction for async clients.
+        self._user_provided_sparse_doc_fn = sparse_doc_fn is not None
+        self._user_provided_sparse_query_fn = sparse_query_fn is not None
 
         if (
             client is None
@@ -1545,12 +1549,13 @@ def get_default_sparse_doc_encoder(
     ) -> SparseEncoderCallable:
         """
         Get the default sparse document encoder.
-        Use old format for backward compatibility if detected.
+        For async-only clients, assumes new format initially.
+        Will be auto-corrected on first async operation if collection uses old format.
         """
-        if self.use_old_sparse_encoder(collection_name):
-            # Update the sparse vector name to use the old format
-            self.sparse_vector_name = DEFAULT_SPARSE_VECTOR_NAME_OLD
-            return default_sparse_encoder("naver/efficient-splade-VI-BT-large-doc")
+        if self._client is not None:
+            if self.use_old_sparse_encoder(collection_name):
+                self.sparse_vector_name = DEFAULT_SPARSE_VECTOR_NAME_OLD
+                return default_sparse_encoder("naver/efficient-splade-VI-BT-large-doc")
 
         if fastembed_sparse_model is not None:
             return fastembed_sparse_encoder(model_name=fastembed_sparse_model)
@@ -1564,12 +1569,16 @@ def get_default_sparse_query_encoder(
     ) -> SparseEncoderCallable:
         """
         Get the default sparse query encoder.
-        Use old format for backward compatibility if detected.
+        For async-only clients, assumes new format initially.
+        Will be auto-corrected on first async operation if collection uses old format.
         """
-        if self.use_old_sparse_encoder(collection_name):
-            # Update the sparse vector name to use the old format
-            self.sparse_vector_name = DEFAULT_SPARSE_VECTOR_NAME_OLD
-            return default_sparse_encoder("naver/efficient-splade-VI-BT-large-query")
+        if self._client is not None:
+            if self.use_old_sparse_encoder(collection_name):
+                # Update the sparse vector name to use the old format
+                self.sparse_vector_name = DEFAULT_SPARSE_VECTOR_NAME_OLD
+                return default_sparse_encoder(
+                    "naver/efficient-splade-VI-BT-large-query"
+                )
 
         if fastembed_sparse_model is not None:
             return fastembed_sparse_encoder(model_name=fastembed_sparse_model)
@@ -1613,10 +1622,10 @@ def _detect_vector_format(self, collection_name: str) -> None:
     async def _adetect_vector_format(self, collection_name: str) -> None:
         """
         Asynchronous method to detect and handle old vector formats from existing collections.
-        - named vs non-named vectors
-        - new sparse vector field name vs old sparse vector field name
         """
         try:
+            old_sparse_name = self.sparse_vector_name  # Store state before detection
+
             collection_info = await self._aclient.get_collection(collection_name)
             vectors_config = collection_info.config.params.vectors
             sparse_vectors = collection_info.config.params.sparse_vectors or {}
@@ -1632,18 +1641,49 @@ async def _adetect_vector_format(self, collection_name: str) -> None:
                 self._legacy_vector_format = True
                 self.dense_vector_name = LEGACY_UNNAMED_VECTOR
 
-            # Detect sparse vector name if any sparse vectors configured
+            # Detect sparse vector name and correct if necessary
             if isinstance(sparse_vectors, dict) and len(sparse_vectors) > 0:
                 if self.sparse_vector_name in sparse_vectors:
                     pass
                 elif DEFAULT_SPARSE_VECTOR_NAME_OLD in sparse_vectors:
                     self.sparse_vector_name = DEFAULT_SPARSE_VECTOR_NAME_OLD
 
+            # If the name changed, our initial assumption was wrong. Correct it.
+            if self.enable_hybrid and old_sparse_name != self.sparse_vector_name:
+                self._reinitialize_sparse_encoders()
+
         except Exception as e:
             logger.warning(
                 f"Could not detect vector format for collection {collection_name}: {e}"
             )
 
+    def _reinitialize_sparse_encoders(self) -> None:
+        """Recreate default sparse encoders after vector format detection, respecting user-provided functions."""
+        if not self.enable_hybrid:
+            return
+
+        # Only override the doc function if the user did NOT provide one
+        if not self._user_provided_sparse_doc_fn:
+            if self.sparse_vector_name == DEFAULT_SPARSE_VECTOR_NAME_OLD:
+                self._sparse_doc_fn = default_sparse_encoder(
+                    "naver/efficient-splade-VI-BT-large-doc"
+                )
+            else:
+                self._sparse_doc_fn = fastembed_sparse_encoder(
+                    model_name=self.fastembed_sparse_model
+                )
+
+        # Only override the query function if the user did NOT provide one
+        if not self._user_provided_sparse_query_fn:
+            if self.sparse_vector_name == DEFAULT_SPARSE_VECTOR_NAME_OLD:
+                self._sparse_query_fn = default_sparse_encoder(
+                    "naver/efficient-splade-VI-BT-large-query"
+                )
+            else:
+                self._sparse_query_fn = fastembed_sparse_encoder(
+                    model_name=self.fastembed_sparse_model
+                )
+
     def _validate_custom_sharding(
         self,
     ):

llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/pyproject.toml

@@ -28,7 +28,7 @@ dev = [
 
 [project]
 name = "llama-index-vector-stores-qdrant"
-version = "0.8.5"
+version = "0.8.6"
 description = "llama-index vector_stores qdrant integration"
 authors = [{name = "Your Name", email = "[email protected]"}]
 requires-python = ">=3.9,<3.14"

llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/tests/conftest.py

@@ -326,3 +326,21 @@ async def collection_initialized_payload_indexed_vector_store() -> AsyncGenerato
             client.close()
         except Exception:
             pass
+
+
+@pytest_asyncio.fixture
+async def async_only_hybrid_vector_store() -> QdrantVectorStore:
+    """
+    Fixture to test initialization with only an async client and hybrid search enabled.
+    This specifically reproduces the bug conditions.
+    """
+    # Note: Each in-memory client has its own isolated storage.
+    aclient = qdrant_client.AsyncQdrantClient(":memory:")
+
+    # The key part: initialize with *only* the aclient.
+    return QdrantVectorStore(
+        "test_async_only",
+        aclient=aclient,
+        enable_hybrid=True,
+        fastembed_sparse_model="Qdrant/bm25",
+    )

llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant/tests/test_vector_stores_qdrant.py

@@ -13,6 +13,7 @@
 
 from llama_index.core.vector_stores.types import BasePydanticVectorStore
 from llama_index.vector_stores.qdrant import QdrantVectorStore
+from llama_index.core.schema import TextNode
 from llama_index.core.vector_stores.types import (
     VectorStoreQuery,
     VectorStoreQueryMode,
@@ -818,3 +819,53 @@ async def test_async_query_initializes_with_async_client_only() -> None:
     assert result is not None
     assert len(result.nodes) == 1
     assert getattr(result.nodes[0], "text", None) == "hello"
+
+
+# --- Test async-only initialization with hybrid search enabled ---
+@pytest.mark.asyncio
+async def test_init_with_async_client_only_and_hybrid_succeeds(
+    async_only_hybrid_vector_store: QdrantVectorStore,
+) -> None:
+    """
+    Tests that QdrantVectorStore initializes without errors when only
+    an async client is provided and hybrid search is enabled.
+    """
+    # The test passes if the fixture is created successfully without raising an exception.
+    # We add a simple assertion to confirm the object is of the correct type.
+    assert isinstance(async_only_hybrid_vector_store, QdrantVectorStore)
+    assert async_only_hybrid_vector_store.enable_hybrid is True
+
+
+# --- Test for async-only legacy collection correction ---
+@pytest.mark.asyncio
+async def test_async_only_hybrid_legacy_collection() -> None:
+    """Test that async-only mode correctly handles legacy sparse vector format."""
+    collection_name = "test_legacy_async"
+    aclient = AsyncQdrantClient(":memory:")
+
+    # Create collection with OLD sparse vector name
+    await aclient.create_collection(
+        collection_name=collection_name,
+        vectors_config={
+            "text-dense": qmodels.VectorParams(size=2, distance=qmodels.Distance.COSINE)
+        },
+        sparse_vectors_config={"text-sparse": qmodels.SparseVectorParams()},  # OLD name
+    )
+
+    # Initialize with async client only
+    store = QdrantVectorStore(
+        collection_name=collection_name,
+        aclient=aclient,
+        enable_hybrid=True,
+        fastembed_sparse_model="Qdrant/bm25",
+    )
+
+    # Initially assumes new format
+    assert store.sparse_vector_name == "text-sparse-new"
+
+    # After first async operation, should detect and correct to old format
+    node = TextNode(text="test", embedding=[1.0, 0.0])
+    await store.async_add([node])
+
+    # Should now be corrected
+    assert store.sparse_vector_name == "text-sparse"