diff --git a/backend/open_webui/config.py b/backend/open_webui/config.py
index 58810c9e3e..93efa56fb9 100644
--- a/backend/open_webui/config.py
+++ b/backend/open_webui/config.py
@@ -2276,6 +2276,12 @@ QDRANT_TIMEOUT = int(os.environ.get('QDRANT_TIMEOUT', '5'))
 QDRANT_HNSW_M = int(os.environ.get('QDRANT_HNSW_M', '16'))
 ENABLE_QDRANT_MULTITENANCY_MODE = os.environ.get('ENABLE_QDRANT_MULTITENANCY_MODE', 'true').lower() == 'true'
 QDRANT_COLLECTION_PREFIX = os.environ.get('QDRANT_COLLECTION_PREFIX', 'open-webui')
+QDRANT_HYBRID_SEARCH_ENABLED = os.environ.get('QDRANT_HYBRID_SEARCH_ENABLED', 'true').lower() == 'true'
+QDRANT_SPARSE_EMBEDDING_MODEL = os.environ.get('QDRANT_SPARSE_EMBEDDING_MODEL', 'Qdrant/bm25')
+QDRANT_DENSE_VECTOR_NAME = os.environ.get('QDRANT_DENSE_VECTOR_NAME', 'dense')
+QDRANT_SPARSE_VECTOR_NAME = os.environ.get('QDRANT_SPARSE_VECTOR_NAME', 'sparse')
+QDRANT_HYBRID_SEARCH_RRF_K = int(os.environ.get('QDRANT_HYBRID_SEARCH_RRF_K', '60'))
+QDRANT_SPARSE_ON_DISK = os.environ.get('QDRANT_SPARSE_ON_DISK', 'false').lower() == 'true'
 
 WEAVIATE_HTTP_HOST = os.environ.get('WEAVIATE_HTTP_HOST', '')
 WEAVIATE_GRPC_HOST = os.environ.get('WEAVIATE_GRPC_HOST', '')
diff --git a/backend/open_webui/retrieval/utils.py b/backend/open_webui/retrieval/utils.py
index 4ab8bdf7c0..86568437ec 100644
--- a/backend/open_webui/retrieval/utils.py
+++ b/backend/open_webui/retrieval/utils.py
@@ -125,6 +125,7 @@ class VectorSearchRetriever(BaseRetriever):
             collection_name=self.collection_name,
             vectors=[embedding],
             limit=self.top_k,
+            query=query,
         )
 
         ids = result.ids[0]
@@ -144,13 +145,14 @@ class VectorSearchRetriever(BaseRetriever):
         return results
 
 
-def query_doc(collection_name: str, query_embedding: list[float], k: int, user: UserModel = None):
+def query_doc(collection_name: str, query_embedding: list[float], k: int, user: UserModel = None, query_text: Optional[str] = None):
     try:
         log.debug(f'query_doc:doc {collection_name}')
         result = VECTOR_DB_CLIENT.search(
             collection_name=collection_name,
             vectors=[query_embedding],
             limit=k,
+            query=query_text,
         )
 
         if result:
@@ -436,13 +438,14 @@ async def query_collection(
     results = []
     error = False
 
-    def process_query_collection(collection_name, query_embedding):
+    def process_query_collection(collection_name, query_embedding, query_text : Optional[str] = None):
         try:
             if collection_name:
                 result = query_doc(
                     collection_name=collection_name,
                     k=k,
                     query_embedding=query_embedding,
+                    query_text=query_text,
                 )
                 if result is not None:
                     return result.model_dump(), None
@@ -457,9 +460,9 @@ async def query_collection(
 
     with ThreadPoolExecutor() as executor:
         future_results = []
-        for query_embedding in query_embeddings:
+        for query_text, query_embedding in zip(queries, query_embeddings):
             for collection_name in collection_names:
-                result = executor.submit(process_query_collection, collection_name, query_embedding)
+                result = executor.submit(process_query_collection, collection_name, query_embedding, query_text)
                 future_results.append(result)
         task_results = [future.result() for future in future_results]
 
diff --git a/backend/open_webui/retrieval/vector/dbs/chroma.py b/backend/open_webui/retrieval/vector/dbs/chroma.py
index 4ace732b2d..8ec0a85efd 100755
--- a/backend/open_webui/retrieval/vector/dbs/chroma.py
+++ b/backend/open_webui/retrieval/vector/dbs/chroma.py
@@ -72,6 +72,7 @@ class ChromaClient(VectorDBBase):
         vectors: list[list[float | int]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
         try:
diff --git a/backend/open_webui/retrieval/vector/dbs/elasticsearch.py b/backend/open_webui/retrieval/vector/dbs/elasticsearch.py
index 201a5e1706..9316bbc69b 100644
--- a/backend/open_webui/retrieval/vector/dbs/elasticsearch.py
+++ b/backend/open_webui/retrieval/vector/dbs/elasticsearch.py
@@ -160,6 +160,7 @@ class ElasticsearchClient(VectorDBBase):
         vectors: list[list[float]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         query = {
             'size': limit,
diff --git a/backend/open_webui/retrieval/vector/dbs/mariadb_vector.py b/backend/open_webui/retrieval/vector/dbs/mariadb_vector.py
index 1cb3563382..c7ed64da0c 100644
--- a/backend/open_webui/retrieval/vector/dbs/mariadb_vector.py
+++ b/backend/open_webui/retrieval/vector/dbs/mariadb_vector.py
@@ -384,6 +384,7 @@ class MariaDBVectorClient(VectorDBBase):
         vectors: List[List[float]],
         filter: Optional[Dict[str, Any]] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         """
         Perform a vector similarity search.
diff --git a/backend/open_webui/retrieval/vector/dbs/milvus.py b/backend/open_webui/retrieval/vector/dbs/milvus.py
index 2f3d8f3890..99a445bd4e 100644
--- a/backend/open_webui/retrieval/vector/dbs/milvus.py
+++ b/backend/open_webui/retrieval/vector/dbs/milvus.py
@@ -182,6 +182,7 @@ class MilvusClient(VectorDBBase):
         vectors: list[list[float | int]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
         collection_name = collection_name.replace('-', '_')
diff --git a/backend/open_webui/retrieval/vector/dbs/milvus_multitenancy.py b/backend/open_webui/retrieval/vector/dbs/milvus_multitenancy.py
index 93b4a8cbc4..fc6e5ad79d 100644
--- a/backend/open_webui/retrieval/vector/dbs/milvus_multitenancy.py
+++ b/backend/open_webui/retrieval/vector/dbs/milvus_multitenancy.py
@@ -160,6 +160,7 @@ class MilvusClient(VectorDBBase):
         vectors: List[List[float]],
         filter: Optional[Dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         if not vectors:
             return None
diff --git a/backend/open_webui/retrieval/vector/dbs/opengauss.py b/backend/open_webui/retrieval/vector/dbs/opengauss.py
index ac97cf01fa..64d8861290 100644
--- a/backend/open_webui/retrieval/vector/dbs/opengauss.py
+++ b/backend/open_webui/retrieval/vector/dbs/opengauss.py
@@ -222,6 +222,7 @@ class OpenGaussClient(VectorDBBase):
         vectors: List[List[float]],
         filter: Optional[Dict[str, Any]] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         try:
             if not vectors:
diff --git a/backend/open_webui/retrieval/vector/dbs/opensearch.py b/backend/open_webui/retrieval/vector/dbs/opensearch.py
index a08dca7865..af00e242ab 100644
--- a/backend/open_webui/retrieval/vector/dbs/opensearch.py
+++ b/backend/open_webui/retrieval/vector/dbs/opensearch.py
@@ -120,6 +120,7 @@ class OpenSearchClient(VectorDBBase):
         vectors: list[list[float | int]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         try:
             if not self.has_collection(collection_name):
diff --git a/backend/open_webui/retrieval/vector/dbs/oracle23ai.py b/backend/open_webui/retrieval/vector/dbs/oracle23ai.py
index 9a5bd638d9..2b7a0e3f76 100644
--- a/backend/open_webui/retrieval/vector/dbs/oracle23ai.py
+++ b/backend/open_webui/retrieval/vector/dbs/oracle23ai.py
@@ -518,6 +518,7 @@ class Oracle23aiClient(VectorDBBase):
         vectors: List[List[Union[float, int]]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         """
         Search for similar vectors in the database.
diff --git a/backend/open_webui/retrieval/vector/dbs/pgvector.py b/backend/open_webui/retrieval/vector/dbs/pgvector.py
index 4775ff21f4..a37f5612de 100644
--- a/backend/open_webui/retrieval/vector/dbs/pgvector.py
+++ b/backend/open_webui/retrieval/vector/dbs/pgvector.py
@@ -396,6 +396,7 @@ class PgvectorClient(VectorDBBase):
         vectors: List[List[float]],
         filter: Optional[Dict[str, Any]] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         try:
             if not vectors:
diff --git a/backend/open_webui/retrieval/vector/dbs/pinecone.py b/backend/open_webui/retrieval/vector/dbs/pinecone.py
index 6469ac9172..be998073a4 100644
--- a/backend/open_webui/retrieval/vector/dbs/pinecone.py
+++ b/backend/open_webui/retrieval/vector/dbs/pinecone.py
@@ -354,6 +354,7 @@ class PineconeClient(VectorDBBase):
         vectors: List[List[Union[float, int]]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         """Search for similar vectors in a collection."""
         if not vectors or not vectors[0]:
diff --git a/backend/open_webui/retrieval/vector/dbs/qdrant.py b/backend/open_webui/retrieval/vector/dbs/qdrant.py
index f050bebeb5..42c11268cd 100644
--- a/backend/open_webui/retrieval/vector/dbs/qdrant.py
+++ b/backend/open_webui/retrieval/vector/dbs/qdrant.py
@@ -148,6 +148,7 @@ class QdrantClient(VectorDBBase):
         vectors: list[list[float | int]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
         if limit is None:
diff --git a/backend/open_webui/retrieval/vector/dbs/qdrant_multitenancy.py b/backend/open_webui/retrieval/vector/dbs/qdrant_multitenancy.py
index c3c2ba41d0..c048094379 100644
--- a/backend/open_webui/retrieval/vector/dbs/qdrant_multitenancy.py
+++ b/backend/open_webui/retrieval/vector/dbs/qdrant_multitenancy.py
@@ -16,7 +16,14 @@ from open_webui.config import (
     QDRANT_COLLECTION_PREFIX,
     QDRANT_TIMEOUT,
     QDRANT_HNSW_M,
+    QDRANT_HYBRID_SEARCH_ENABLED,
+    QDRANT_SPARSE_EMBEDDING_MODEL,
+    QDRANT_DENSE_VECTOR_NAME,
+    QDRANT_SPARSE_VECTOR_NAME,
+    QDRANT_HYBRID_SEARCH_RRF_K,
+    QDRANT_SPARSE_ON_DISK,
 )
+
 from open_webui.retrieval.vector.main import (
     GetResult,
     SearchResult,
@@ -25,9 +32,16 @@ from open_webui.retrieval.vector.main import (
 )
 from qdrant_client import QdrantClient as Qclient
 from qdrant_client.http.exceptions import UnexpectedResponse
-from qdrant_client.http.models import PointStruct
+from qdrant_client.http.models import PointStruct, SparseVector
 from qdrant_client.models import models
 
+try:
+    from fastembed.sparse import SparseTextEmbedding
+
+    FASTEMBED_AVAILABLE = True
+except ImportError:
+    FASTEMBED_AVAILABLE = False
+
 NO_LIMIT = 999999999
 TENANT_ID_FIELD = 'tenant_id'
 DEFAULT_DIMENSION = 384
@@ -53,6 +67,12 @@ class QdrantClient(VectorDBBase):
         self.GRPC_PORT = QDRANT_GRPC_PORT
         self.QDRANT_TIMEOUT = QDRANT_TIMEOUT
         self.QDRANT_HNSW_M = QDRANT_HNSW_M
+        self.QDRANT_HYBRID_SEARCH_ENABLED = QDRANT_HYBRID_SEARCH_ENABLED
+        self.QDRANT_SPARSE_EMBEDDING_MODEL = QDRANT_SPARSE_EMBEDDING_MODEL
+        self.QDRANT_DENSE_VECTOR_NAME = QDRANT_DENSE_VECTOR_NAME
+        self.QDRANT_SPARSE_VECTOR_NAME = QDRANT_SPARSE_VECTOR_NAME
+        self.QDRANT_HYBRID_SEARCH_RRF_K = QDRANT_HYBRID_SEARCH_RRF_K
+        self.QDRANT_SPARSE_ON_DISK = QDRANT_SPARSE_ON_DISK
 
         if not self.QDRANT_URI:
             raise ValueError('QDRANT_URI is not set. Please configure it in the environment variables.')
@@ -86,6 +106,42 @@ class QdrantClient(VectorDBBase):
         self.WEB_SEARCH_COLLECTION = f'{self.collection_prefix}_web-search'
         self.HASH_BASED_COLLECTION = f'{self.collection_prefix}_hash-based'
 
+        # Initialize sparse encoder if hybrid search is enabled
+        self.sparse_encoder = None
+        if self.QDRANT_HYBRID_SEARCH_ENABLED:
+            if FASTEMBED_AVAILABLE:
+                try:
+                    self.sparse_encoder = SparseTextEmbedding(model_name=self.QDRANT_SPARSE_EMBEDDING_MODEL)
+                    log.info(f'Hybrid search enabled with sparse model: {self.QDRANT_SPARSE_EMBEDDING_MODEL}')
+                except Exception as e:
+                    log.warning(f'Failed to load sparse encoder, hybrid search disabled: {e}')
+            else:
+                log.warning(
+                    "fastembed not installed, hybrid search disabled. Install with: pip install 'fastembed>=0.6.1'"
+                )
+    @property
+    def _hybrid_enabled(self) -> bool:
+        return self.QDRANT_HYBRID_SEARCH_ENABLED and self.sparse_encoder is not None
+
+    def _is_hybrid_collection(self, mt_collection_name: str) -> bool:
+        """Check if an existing collection has sparse vectors (i.e. was created as hybrid)."""
+        try:
+            info = self.client.get_collection(mt_collection_name)
+            return bool(info.config.params.sparse_vectors)
+        except Exception:
+            return False
+
+    def _encode_sparse(self, texts: list[str]) -> list[SparseVector]:
+        """Encode texts into sparse vectors using fastembed."""
+        embeddings = list(self.sparse_encoder.embed(texts))
+        return [SparseVector(indices=e.indices.tolist(), values=e.values.tolist()) for e in embeddings]
+
+    def _encode_sparse_query(self, text: str) -> SparseVector:
+        """Encode a query text into a sparse vector."""
+        embeddings = list(self.sparse_encoder.query_embed(text))
+        e = embeddings[0]
+        return SparseVector(indices=e.indices.tolist(), values=e.values.tolist())
+
     def _result_to_get_result(self, points) -> GetResult:
         ids, documents, metadatas = [], [], []
         for point in points:
@@ -134,21 +190,46 @@ class QdrantClient(VectorDBBase):
         """
         Creates a collection with multi-tenancy configuration and payload indexes for tenant_id and metadata fields.
         """
-        self.client.create_collection(
-            collection_name=mt_collection_name,
-            vectors_config=models.VectorParams(
-                size=dimension,
-                distance=models.Distance.COSINE,
-                on_disk=self.QDRANT_ON_DISK,
-            ),
-            # Disable global index building due to multitenancy
-            # For more details https://qdrant.tech/documentation/guides/multiple-partitions/#calibrate-performance
-            hnsw_config=models.HnswConfigDiff(
-                payload_m=self.QDRANT_HNSW_M,
-                m=0,
-            ),
-        )
-        log.info(f'Multi-tenant collection {mt_collection_name} created with dimension {dimension}!')
+        if self._hybrid_enabled:
+            self.client.create_collection(
+                collection_name=mt_collection_name,
+                vectors_config={
+                    self.QDRANT_DENSE_VECTOR_NAME: models.VectorParams(
+                        size=dimension,
+                        distance=models.Distance.COSINE,
+                        on_disk=self.QDRANT_ON_DISK,
+                    )
+                },
+                sparse_vectors_config={
+                    self.QDRANT_SPARSE_VECTOR_NAME: models.SparseVectorParams(
+                        index=models.SparseIndexParams(on_disk=self.QDRANT_SPARSE_ON_DISK)
+                    )
+                },
+                hnsw_config=models.HnswConfigDiff(
+                    payload_m=self.QDRANT_HNSW_M,
+                    m=0,
+                ),
+            )
+            log.info(
+                f'Multi-tenant hybrid collection {mt_collection_name} created with '
+                f'dense ({dimension} dims) + sparse vectors'
+            )
+        else:
+            self.client.create_collection(
+                collection_name=mt_collection_name,
+                vectors_config=models.VectorParams(
+                    size=dimension,
+                    distance=models.Distance.COSINE,
+                    on_disk=self.QDRANT_ON_DISK,
+                ),
+                # Disable global index building due to multitenancy
+                # For more details https://qdrant.tech/documentation/guides/multiple-partitions/#calibrate-performance
+                hnsw_config=models.HnswConfigDiff(
+                    payload_m=self.QDRANT_HNSW_M,
+                    m=0,
+                ),
+            )
+            log.info(f'Multi-tenant collection {mt_collection_name} created with dimension {dimension}')
 
         self.client.create_payload_index(
             collection_name=mt_collection_name,
@@ -170,22 +251,60 @@ class QdrantClient(VectorDBBase):
                 ),
             )
 
-    def _create_points(self, items: List[VectorItem], tenant_id: str) -> List[PointStruct]:
+    def _create_points(
+        self, items: list[VectorItem], tenant_id: str, collection_is_hybrid: bool = False
+    ) -> list[PointStruct]:
         """
         Create point structs from vector items with tenant ID.
+        Uses named vectors when collection_is_hybrid is True.
+        If hybrid is also enabled in config, adds sparse vectors; otherwise dense-only named vector.
         """
-        return [
-            PointStruct(
-                id=item['id'],
-                vector=item['vector'],
-                payload={
-                    'text': item['text'],
-                    'metadata': item['metadata'],
-                    TENANT_ID_FIELD: tenant_id,
-                },
-            )
-            for item in items
-        ]
+        if collection_is_hybrid:
+            if self._hybrid_enabled:
+                texts = [item['text'] for item in items]
+                sparse_vectors = self._encode_sparse(texts)
+                return [
+                    PointStruct(
+                        id=item['id'],
+                        vector={
+                            self.QDRANT_DENSE_VECTOR_NAME: item['vector'],
+                            self.QDRANT_SPARSE_VECTOR_NAME: sparse_vec,
+                        },
+                        payload={
+                            'text': item['text'],
+                            'metadata': item['metadata'],
+                            TENANT_ID_FIELD: tenant_id,
+                        },
+                    )
+                    for item, sparse_vec in zip(items, sparse_vectors)
+                ]
+            else:
+                # Collection is hybrid but encoder unavailable — store only dense named vector
+                return [
+                    PointStruct(
+                        id=item['id'],
+                        vector={self.QDRANT_DENSE_VECTOR_NAME: item['vector']},
+                        payload={
+                            'text': item['text'],
+                            'metadata': item['metadata'],
+                            TENANT_ID_FIELD: tenant_id,
+                        },
+                    )
+                    for item in items
+                ]
+        else:
+            return [
+                PointStruct(
+                    id=item['id'],
+                    vector=item['vector'],
+                    payload={
+                        'text': item['text'],
+                        'metadata': item['metadata'],
+                        TENANT_ID_FIELD: tenant_id,
+                    },
+                )
+                for item in items
+            ]
 
     def _ensure_collection(self, mt_collection_name: str, dimension: int = DEFAULT_DIMENSION):
         """
@@ -245,9 +364,11 @@ class QdrantClient(VectorDBBase):
         vectors: List[List[float | int]],
         filter: Optional[Dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         """
         Search for the nearest neighbor items based on the vectors with tenant isolation.
+        Uses hybrid search (dense + sparse RRF) when enabled and query is provided.
         """
         if not self.client or not vectors:
             return None
@@ -257,12 +378,42 @@ class QdrantClient(VectorDBBase):
             return None
 
         tenant_filter = _tenant_filter(tenant_id)
-        query_response = self.client.query_points(
-            collection_name=mt_collection,
-            query=vectors[0],
-            limit=limit,
-            query_filter=models.Filter(must=[tenant_filter]),
-        )
+        combined_filter = models.Filter(must=[tenant_filter])
+
+        collection_is_hybrid = self._is_hybrid_collection(mt_collection)
+
+        # Hybrid search with RRF fusion
+        if collection_is_hybrid and self._hybrid_enabled and query:
+            sparse_query = self._encode_sparse_query(query)
+            query_response = self.client.query_points(
+                collection_name=mt_collection,
+                query=models.FusionQuery(fusion=models.Fusion.RRF),
+                prefetch=[
+                    models.Prefetch(
+                        query=vectors[0],
+                        using=self.QDRANT_DENSE_VECTOR_NAME,
+                        filter=combined_filter,
+                        limit=limit * 2,
+                    ),
+                    models.Prefetch(
+                        query=sparse_query,
+                        using=self.QDRANT_SPARSE_VECTOR_NAME,
+                        filter=combined_filter,
+                        limit=limit * 2,
+                    ),
+                ],
+                limit=limit,
+            )
+        else:
+            # Dense-only search — use named vector if collection schema requires it
+            query_vector = (self.QDRANT_DENSE_VECTOR_NAME, vectors[0]) if collection_is_hybrid else vectors[0]
+            query_response = self.client.query_points(
+                collection_name=mt_collection,
+                query=query_vector,
+                limit=limit,
+                query_filter=combined_filter,
+            )
+
         get_result = self._result_to_get_result(query_response.points)
         return SearchResult(
             ids=get_result.ids,
@@ -320,7 +471,8 @@ class QdrantClient(VectorDBBase):
         mt_collection, tenant_id = self._get_collection_and_tenant_id(collection_name)
         dimension = len(items[0]['vector'])
         self._ensure_collection(mt_collection, dimension)
-        points = self._create_points(items, tenant_id)
+        collection_is_hybrid = self._is_hybrid_collection(mt_collection)
+        points = self._create_points(items, tenant_id, collection_is_hybrid)
         self.client.upload_points(mt_collection, points)
         return None
 
diff --git a/backend/open_webui/retrieval/vector/dbs/s3vector.py b/backend/open_webui/retrieval/vector/dbs/s3vector.py
index 8877d206e6..1c548aaa03 100644
--- a/backend/open_webui/retrieval/vector/dbs/s3vector.py
+++ b/backend/open_webui/retrieval/vector/dbs/s3vector.py
@@ -285,6 +285,7 @@ class S3VectorClient(VectorDBBase):
         vectors: List[List[Union[float, int]]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         """
         Search for similar vectors in a collection using multiple query vectors.
diff --git a/backend/open_webui/retrieval/vector/dbs/weaviate.py b/backend/open_webui/retrieval/vector/dbs/weaviate.py
index 2cf4c135c5..d7284b75d7 100644
--- a/backend/open_webui/retrieval/vector/dbs/weaviate.py
+++ b/backend/open_webui/retrieval/vector/dbs/weaviate.py
@@ -162,6 +162,7 @@ class WeaviateClient(VectorDBBase):
         vectors: List[List[Union[float, int]]],
         filter: Optional[dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         sane_collection_name = self._sanitize_collection_name(collection_name)
         if not self.client.collections.exists(sane_collection_name):
diff --git a/backend/open_webui/retrieval/vector/main.py b/backend/open_webui/retrieval/vector/main.py
index f7904baa20..25cb454f95 100644
--- a/backend/open_webui/retrieval/vector/main.py
+++ b/backend/open_webui/retrieval/vector/main.py
@@ -58,6 +58,7 @@ class VectorDBBase(ABC):
         vectors: List[List[Union[float, int]]],
         filter: Optional[Dict] = None,
         limit: int = 10,
+        query: Optional[str] = None,
     ) -> Optional[SearchResult]:
         """Search for similar vectors in a collection."""
         pass
diff --git a/backend/open_webui/routers/memories.py b/backend/open_webui/routers/memories.py
index 4557f0c44d..dbd4287efa 100644
--- a/backend/open_webui/routers/memories.py
+++ b/backend/open_webui/routers/memories.py
@@ -142,6 +142,7 @@ async def query_memory(
         collection_name=f'user-memory-{user.id}',
         vectors=[vector],
         limit=form_data.k,
+        query=form_data.content,
     )
 
     return results
diff --git a/backend/open_webui/routers/retrieval.py b/backend/open_webui/routers/retrieval.py
index 6c9e988dd6..bfb2bac4f2 100644
--- a/backend/open_webui/routers/retrieval.py
+++ b/backend/open_webui/routers/retrieval.py
@@ -2409,6 +2409,7 @@ async def query_doc_handler(
                 query_embedding=query_embedding,
                 k=form_data.k if form_data.k else request.app.state.config.TOP_K,
                 user=user,
+                query_text=form_data.query,
             )
     except Exception as e:
         log.exception(e)
diff --git a/backend/open_webui/tools/builtin.py b/backend/open_webui/tools/builtin.py
index f02a082c42..3ca311d4dc 100644
--- a/backend/open_webui/tools/builtin.py
+++ b/backend/open_webui/tools/builtin.py
@@ -2221,6 +2221,7 @@ async def query_knowledge_bases(
                 vectors=[query_embedding],
                 filter={'knowledge_base_id': {'$in': accessible_ids}},
                 limit=count,
+                query=query,
             )
 
             if search_results and search_results.ids and search_results.ids[0]:
diff --git a/backend/requirements.txt b/backend/requirements.txt
index a9275beaf3..9b8f85a930 100644
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -124,6 +124,7 @@ boto3==1.42.62
 
 pymilvus==2.6.9
 qdrant-client==1.17.0
+fastembed==0.8.0 # fastembed for qdrant SparseTextEmbedding
 playwright==1.58.0 # Caution: version must match docker-compose.playwright.yaml - Update the docker-compose.yaml if necessary
 elasticsearch==9.3.0
 pinecone==6.0.2