feat(qdrant): implement hybrid and keyword search support

- Implement hybrid search using Qdrant's native query filtering
- Add keyword search support
- Update test suites to include qdrant for keyword and hybrid modes

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>

src/llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -129,7 +129,63 @@ class QdrantIndex(EmbeddingIndex):
         return QueryChunksResponse(chunks=chunks, scores=scores)
 
     async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
-        raise NotImplementedError("Keyword search is not supported in Qdrant")
+        """
+        Performs keyword-based search using Qdrant's MatchText filter.
+
+        Uses Qdrant's query_filter with MatchText to search for chunks containing
+        the specified text query string in the chunk content.
+
+        Args:
+            query_string: The text query for keyword search
+            k: Number of results to return
+            score_threshold: Minimum similarity score threshold
+
+        Returns:
+            QueryChunksResponse with chunks and scores matching the keyword query
+        """
+        try:
+            results = (
+                await self.client.query_points(
+                    collection_name=self.collection_name,
+                    query_filter=models.Filter(
+                        must=[
+                            models.FieldCondition(
+                                key="chunk_content.content", match=models.MatchText(text=query_string)
+                            )
+                        ]
+                    ),
+                    limit=k,
+                    with_payload=True,
+                    with_vectors=False,
+                    score_threshold=score_threshold,
+                )
+            ).points
+        except Exception as e:
+            log.error(f"Error querying keyword search in Qdrant collection {self.collection_name}: {e}")
+            raise
+
+        chunks, scores = [], []
+        for point in results:
+            if not isinstance(point, models.ScoredPoint):
+                raise RuntimeError(f"Expected ScoredPoint from Qdrant query, got {type(point).__name__}")
+            if point.payload is None:
+                raise RuntimeError("Qdrant query returned point with no payload")
+
+            try:
+                chunk = Chunk(**point.payload["chunk_content"])
+            except Exception:
+                chunk_id = point.payload.get(CHUNK_ID_KEY, "unknown") if point.payload else "unknown"
+                point_id = getattr(point, "id", "unknown")
+                log.exception(
+                    f"Failed to parse chunk in collection {self.collection_name}: "
+                    f"chunk_id={chunk_id}, point_id={point_id}"
+                )
+                continue
+
+            chunks.append(chunk)
+            scores.append(point.score)
+
+        return QueryChunksResponse(chunks=chunks, scores=scores)
 
     async def query_hybrid(
         self,
@@ -140,7 +196,66 @@ class QdrantIndex(EmbeddingIndex):
         reranker_type: str,
         reranker_params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse:
-        raise NotImplementedError("Hybrid search is not supported in Qdrant")
+        """
+        Hybrid search combining vector similarity and keyword filtering in a single query.
+
+        Uses Qdrant's native capability to combine a vector query with a query_filter,
+        allowing vector similarity search to be filtered by keyword matches in one call.
+
+        Args:
+            embedding: The query embedding vector
+            query_string: The text query for keyword filtering
+            k: Number of results to return
+            score_threshold: Minimum similarity score threshold
+            reranker_type: Not used with this approach, but kept for API compatibility
+            reranker_params: Not used with this approach, but kept for API compatibility
+
+        Returns:
+            QueryChunksResponse with filtered vector search results
+        """
+        try:
+            results = (
+                await self.client.query_points(
+                    collection_name=self.collection_name,
+                    query=embedding.tolist(),
+                    query_filter=models.Filter(
+                        must=[
+                            models.FieldCondition(
+                                key="chunk_content.content", match=models.MatchText(text=query_string)
+                            )
+                        ]
+                    ),
+                    limit=k,
+                    with_payload=True,
+                    score_threshold=score_threshold,
+                )
+            ).points
+        except Exception as e:
+            log.error(f"Error querying hybrid search in Qdrant collection {self.collection_name}: {e}")
+            raise
+
+        chunks, scores = [], []
+        for point in results:
+            if not isinstance(point, models.ScoredPoint):
+                raise RuntimeError(f"Expected ScoredPoint from Qdrant query, got {type(point).__name__}")
+            if point.payload is None:
+                raise RuntimeError("Qdrant query returned point with no payload")
+
+            try:
+                chunk = Chunk(**point.payload["chunk_content"])
+            except Exception:
+                chunk_id = point.payload.get(CHUNK_ID_KEY, "unknown") if point.payload else "unknown"
+                point_id = getattr(point, "id", "unknown")
+                log.exception(
+                    f"Failed to parse chunk in collection {self.collection_name}: "
+                    f"chunk_id={chunk_id}, point_id={point_id}"
+                )
+                continue
+
+            chunks.append(chunk)
+            scores.append(point.score)
+
+        return QueryChunksResponse(chunks=chunks, scores=scores)
 
     async def delete(self):
         await self.client.delete_collection(collection_name=self.collection_name)

tests/integration/vector_io/test_openai_vector_stores.py

@@ -57,16 +57,20 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
         ],
         "keyword": [
             "inline::milvus",
+            "inline::qdrant",
             "inline::sqlite-vec",
             "remote::milvus",
             "remote::pgvector",
+            "remote::qdrant",
             "remote::weaviate",
         ],
         "hybrid": [
             "inline::milvus",
+            "inline::qdrant",
             "inline::sqlite-vec",
             "remote::milvus",
             "remote::pgvector",
+            "remote::qdrant",
             "remote::weaviate",
         ],
     }

tests/unit/providers/vector_io/conftest.py

@@ -14,17 +14,19 @@ from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreCo
 from llama_stack.core.storage.kvstore import register_kvstore_backends
 from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
 from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
+from llama_stack.providers.inline.vector_io.qdrant.config import QdrantVectorIOConfig
 from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig
 from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter
 from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
 from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter
+from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantIndex, QdrantVectorIOAdapter
 from llama_stack_api import Chunk, ChunkMetadata, QueryChunksResponse, VectorStore
 
 EMBEDDING_DIMENSION = 768
 COLLECTION_PREFIX = "test_collection"
 
 
-@pytest.fixture(params=["sqlite_vec", "faiss", "pgvector"])
+@pytest.fixture(params=["sqlite_vec", "faiss", "pgvector", "qdrant"])
 def vector_provider(request):
     return request.param
 
@@ -317,12 +319,116 @@ async def pgvector_vec_adapter(unique_kvstore_config, mock_inference_api, embedd
                         await adapter.shutdown()
 
 
+@pytest.fixture
+async def qdrant_vec_index(embedding_dimension):
+    from qdrant_client import models
+
+    mock_client = AsyncMock()
+    mock_client.collection_exists.return_value = False
+    mock_client.create_collection = AsyncMock()
+    mock_client.query_points = AsyncMock(return_value=AsyncMock(points=[]))
+    mock_client.delete_collection = AsyncMock()
+
+    collection_name = f"test-qdrant-collection-{random.randint(1, 1000000)}"
+    index = QdrantIndex(mock_client, collection_name)
+    index._test_chunks = []
+
+    async def mock_add_chunks(chunks, embeddings):
+        index._test_chunks = list(chunks)
+        # Create mock query response with test chunks
+        mock_points = []
+        for chunk in chunks:
+            mock_point = MagicMock(spec=models.ScoredPoint)
+            mock_point.score = 1.0
+            mock_point.payload = {"chunk_content": chunk.model_dump(), "_chunk_id": chunk.chunk_id}
+            mock_points.append(mock_point)
+
+        async def query_points_mock(**kwargs):
+            # Return chunks in order when queried
+            query_k = kwargs.get("limit", len(index._test_chunks))
+            return AsyncMock(points=mock_points[:query_k])
+
+        mock_client.query_points = query_points_mock
+
+    index.add_chunks = mock_add_chunks
+
+    async def mock_query_vector(embedding, k, score_threshold):
+        chunks = index._test_chunks[:k] if hasattr(index, "_test_chunks") else []
+        scores = [1.0] * len(chunks)
+        return QueryChunksResponse(chunks=chunks, scores=scores)
+
+    index.query_vector = mock_query_vector
+
+    yield index
+
+
+@pytest.fixture
+async def qdrant_vec_adapter(unique_kvstore_config, mock_inference_api, embedding_dimension):
+    config = QdrantVectorIOConfig(
+        path=":memory:",
+        persistence=unique_kvstore_config,
+    )
+
+    adapter = QdrantVectorIOAdapter(config, mock_inference_api, None)
+
+    from unittest.mock import patch
+
+    mock_client = AsyncMock()
+    mock_client.collection_exists.return_value = False
+    mock_client.create_collection = AsyncMock()
+    mock_client.query_points = AsyncMock(return_value=AsyncMock(points=[]))
+    mock_client.delete_collection = AsyncMock()
+    mock_client.close = AsyncMock()
+    mock_client.upsert = AsyncMock()
+
+    with patch("llama_stack.providers.remote.vector_io.qdrant.qdrant.AsyncQdrantClient") as mock_client_class:
+        mock_client_class.return_value = mock_client
+
+        with patch("llama_stack.core.storage.kvstore.kvstore_impl") as mock_kvstore_impl:
+            mock_kvstore = AsyncMock()
+            mock_kvstore.values_in_range.return_value = []
+            mock_kvstore_impl.return_value = mock_kvstore
+
+            with patch.object(adapter, "initialize_openai_vector_stores", new_callable=AsyncMock):
+                await adapter.initialize()
+                adapter.client = mock_client
+
+                async def mock_insert_chunks(vector_store_id, chunks, ttl_seconds=None):
+                    index = await adapter._get_and_cache_vector_store_index(vector_store_id)
+                    if not index:
+                        raise ValueError(f"Vector DB {vector_store_id} not found")
+                    await index.insert_chunks(chunks)
+
+                adapter.insert_chunks = mock_insert_chunks
+
+                async def mock_query_chunks(vector_store_id, query, params=None):
+                    index = await adapter._get_and_cache_vector_store_index(vector_store_id)
+                    if not index:
+                        raise ValueError(f"Vector DB {vector_store_id} not found")
+                    return await index.query_chunks(query, params)
+
+                adapter.query_chunks = mock_query_chunks
+
+                test_vector_store = VectorStore(
+                    identifier=f"qdrant_test_collection_{random.randint(1, 1_000_000)}",
+                    provider_id="test_provider",
+                    embedding_model="test_model",
+                    embedding_dimension=embedding_dimension,
+                )
+                await adapter.register_vector_store(test_vector_store)
+                adapter.test_collection_id = test_vector_store.identifier
+
+                yield adapter
+                await adapter.shutdown()
+
+
 @pytest.fixture
 def vector_io_adapter(vector_provider, request):
     vector_provider_dict = {
         "faiss": "faiss_vec_adapter",
         "sqlite_vec": "sqlite_vec_adapter",
         "pgvector": "pgvector_vec_adapter",
+        "qdrant": "qdrant_vec_adapter",
     }
     return request.getfixturevalue(vector_provider_dict[vector_provider])