diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml
index 38eaf56c..10df3dbd 100644
--- a/.github/workflows/integration-tests.yml
+++ b/.github/workflows/integration-tests.yml
@@ -14,7 +14,6 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - '3.7.x'
           - '3.8.x'
           - '3.9.x'
           - '3.10.x'
diff --git a/.github/workflows/type-checkers.yml b/.github/workflows/type-checkers.yml
index e0492d6c..c9244ae0 100644
--- a/.github/workflows/type-checkers.yml
+++ b/.github/workflows/type-checkers.yml
@@ -8,7 +8,7 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
         os: [ubuntu-latest]
 
     name: Python ${{ matrix.python-version }} test
diff --git a/.gitignore b/.gitignore
index eab7d9fa..4fa463a6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,5 @@ __pycache__
 htmlcov
 *.iml
 dist
+*.tar.gz
+local_cache/*/*
diff --git a/README.md b/README.md
index 7006c2ee..e6173cb8 100644
--- a/README.md
+++ b/README.md
@@ -40,6 +40,7 @@ pip install qdrant-client
 - Local mode - use same API without running server
 - REST and gRPC support
 - Minimal dependencies
+- Extensive Test Coverage
 
 ## Local mode
 
@@ -66,10 +67,36 @@ Local mode is useful for development, prototyping and testing.
 - Run it in Colab or Jupyter Notebook, no extra dependencies required. See an [example](https://colab.research.google.com/drive/1Bz8RSVHwnNDaNtDwotfPj0w7AYzsdXZ-?usp=sharing)
 - When you need to scale, simply switch to server mode.
 
-### How it works?
+## Fast Embeddings + Simpler API
 
-We just implemented Qdrant API in pure Python.
-We covered it with tests extensively to be sure it works the same as the server version.
+```
+pip install fastembed qdrant-client
+```
+
+FastEmbed is a library for creating fast vector embeddings on CPU. It is based on ONNX Runtime and allows to run inference on CPU with GPU-like performance.
+
+Qdrant Client can use FastEmbed to create embeddings and upload them to Qdrant. This allows to simplify API and make it more intuitive.
+
+```python
+from qdrant_client import QdrantClient
+
+# Initialize the client
+client = QdrantClient(":memory:")  # or QdrantClient(path="path/to/db")
+
+# Prepare your documents, metadata, and IDs
+docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
+metadatas = [
+    {"source": "Langchain-docs"},
+    {"source": "Linkedin-docs"},
+]
+ids = [42, 2]
+
+# Use the new add method
+client.add(collection_name="demo_collection", docs={"documents": docs, "metadatas": metadatas, "ids": ids})
+
+search_result = client.query(collection_name="demo_collection", query_texts=["This is a query document"])
+print(search_result)
+```
 
 ## Connect to Qdrant server
 
diff --git a/poetry.lock b/poetry.lock
index 8d6fa983..15f281ec 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -40,7 +40,6 @@ files = [
 exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
 
 [package.extras]
 doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
@@ -167,6 +166,23 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "coloredlogs"
+version = "15.0.1"
+description = "Colored terminal output for Python's logging module"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
+    {file = "coloredlogs-15.0.1.tar.gz", hash = "sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0"},
+]
+
+[package.dependencies]
+humanfriendly = ">=9.1"
+
+[package.extras]
+cron = ["capturer (>=2.4)"]
+
 [[package]]
 name = "coverage"
 version = "6.5.0"
@@ -242,131 +258,160 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.2"
+version = "1.1.3"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.1.2-py3-none-any.whl", hash = "sha256:e346e69d186172ca7cf029c8c1d16235aa0e04035e5750b4b95039e65204328f"},
-    {file = "exceptiongroup-1.1.2.tar.gz", hash = "sha256:12c3e887d6485d16943a309616de20ae5582633e0a2eda17f4e10fd61c1e8af5"},
+    {file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"},
+    {file = "exceptiongroup-1.1.3.tar.gz", hash = "sha256:097acd85d473d75af5bb98e41b61ff7fe35efe6675e4f9370ec6ec5126d160e9"},
 ]
 
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "fastembed"
+version = "0.0.2"
+description = "Fast, State of the Art Quantized Embedding Models"
+optional = false
+python-versions = ">=3.8.0,<3.12"
+files = [
+    {file = "fastembed-0.0.2-py3-none-any.whl", hash = "sha256:7460266828d8ce59f598fc854917489b6339e8240fbe5da6652a5b799b7dba2a"},
+    {file = "fastembed-0.0.2.tar.gz", hash = "sha256:3a1dec12df1dfb1724f46a5a90a0a55bca242195f1d76b122e087dfe6b8ac144"},
+]
+
+[package.dependencies]
+onnxruntime = ">=1.15.1,<2.0.0"
+onnxruntime-silicon = {version = ">=1.15.0,<2.0.0", markers = "sys_platform == \"darwin\""}
+requests = ">=2.31.0,<3.0.0"
+tokenizers = ">=0.13.3,<0.14.0"
+tqdm = ">=4.65.0,<5.0.0"
+
+[[package]]
+name = "flatbuffers"
+version = "23.5.26"
+description = "The FlatBuffers serialization format for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "flatbuffers-23.5.26-py2.py3-none-any.whl", hash = "sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1"},
+    {file = "flatbuffers-23.5.26.tar.gz", hash = "sha256:9ea1144cac05ce5d86e2859f431c6cd5e66cd9c78c558317c7955fb8d4c78d89"},
+]
+
 [[package]]
 name = "grpcio"
-version = "1.56.2"
+version = "1.57.0"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.7"
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+grpcio = ">=1.57.0"
 protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
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-
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@@ -455,6 +497,20 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 
+[[package]]
+name = "humanfriendly"
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+description = "Human friendly output for text interfaces using Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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+
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 version = "6.0.1"
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 [package.dependencies]
@@ -941,32 +1043,42 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pygments"
-version = "2.15.1"
+version = "2.16.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
 plugins = ["importlib-metadata"]
 
+[[package]]
+name = "pyreadline3"
+version = "3.4.1"
+description = "A python implementation of GNU readline."
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "pyright"
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 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
 nodeenv = ">=1.6.0"
-typing-extensions = {version = ">=3.7", markers = "python_version < \"3.8\""}
 
 [package.extras]
 all = ["twine (>=3.4.1)"]
@@ -986,7 +1098,6 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
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 iniconfig = "*"
 packaging = "*"
 pluggy = ">=0.12,<2.0"
@@ -1008,7 +1119,6 @@ files = [
 
 [package.dependencies]
 pytest = ">=7.0.0"
-typing-extensions = {version = ">=3.7.2", markers = "python_version < \"3.8\""}
 
 [package.extras]
 docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
@@ -1103,18 +1213,18 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
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+version = "68.1.0"
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-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 [[package]]
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 [[package]]
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-description = "sphinxcontrib-applehelp is a sphinx extension which outputs Apple help books"
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 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
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@@ -1206,13 +1316,13 @@ test = ["pytest"]
 
 [[package]]
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+version = "2.0.1"
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 [package.extras]
@@ -1263,6 +1373,74 @@ files = [
 lint = ["docutils-stubs", "flake8", "mypy"]
 test = ["pytest"]
 
+[[package]]
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+version = "1.12"
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+[package.dependencies]
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+
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+description = "Fast and Customizable Tokenizers"
+optional = false
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+    {file = "tokenizers-0.13.3.tar.gz", hash = "sha256:2e546dbb68b623008a5442353137fbb0123d311a6d7ba52f2667c8862a75af2e"},
+]
+
+[package.extras]
+dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -1275,64 +1453,34 @@ files = [
 ]
 
 [[package]]
-name = "typed-ast"
-version = "1.5.5"
-description = "a fork of Python 2 and 3 ast modules with type comment support"
+name = "tqdm"
+version = "4.66.1"
+description = "Fast, Extensible Progress Meter"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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-    {file = "typed_ast-1.5.5.tar.gz", hash = "sha256:94282f7a354f36ef5dbce0ef3467ebf6a258e370ab33d5b40c249fa996e590dd"},
+    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
+    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
 ]
 
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "types-protobuf"
-version = "4.23.0.2"
+version = "4.24.0.1"
 description = "Typing stubs for protobuf"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-protobuf-4.23.0.2.tar.gz", hash = "sha256:1066b069d4f0e09bdebb64ca4f35cc6b8accf52f808368046ccec96744af0375"},
-    {file = "types_protobuf-4.23.0.2-py3-none-any.whl", hash = "sha256:ee1a1ac8c099870075ef4457c01f3d56e189d8af1c13c3aa9f8dc207ca35337c"},
+    {file = "types-protobuf-4.24.0.1.tar.gz", hash = "sha256:90adea3b693d6a40d8ef075c58fe6b5cc6e01fe1496301a7e6fc70398dcff92e"},
+    {file = "types_protobuf-4.24.0.1-py3-none-any.whl", hash = "sha256:df203a204e4ae97d4cca4c9cf725262579dd7857a19f9e7fc74871ccfa073c01"},
 ]
 
 [[package]]
@@ -1364,20 +1512,20 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "zipp"
-version = "3.15.0"
+version = "3.16.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
-    {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
+    {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"},
+    {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.7,<3.12"
-content-hash = "e65fc11379d5cd4ab983f81fe6ea2297b2792058b0ed7d259ef833f926bb7e23"
+python-versions = ">=3.8,<3.12"
+content-hash = "7e4da4a418a63203ba7794ba265e4cb8d0da166e2397418818a6c6168ba4c258"
diff --git a/pyproject.toml b/pyproject.toml
index ea91865c..fba73442 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ repository = "https://github.com/qdrant/qdrant-client"
 keywords = ["vector", "search", "neural", "matching", "client"]
 
 [tool.poetry.dependencies]
-python = ">=3.7,<3.12"
+python = ">=3.8,<3.12"
 httpx = { version = ">=0.14.0", extras = ["http2"] }
 numpy = [
     { version = "<1.21", python = "<3.8" },
@@ -40,6 +40,11 @@ pyright = "^1.1.293"
 mypy = "^1.0.0"
 types-protobuf = "^4.21.0.5"
 
+[tool.poetry.group.fastembed]
+optional = true
+
+[tool.poetry.group.fastembed.dependencies]
+fastembed = "0.0.2"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
@@ -47,3 +52,9 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.pyright]
 typeCheckingMode = "strict"
+
+[tool.pytest.ini_options]
+markers = [
+    "fastembed: marks tests that require the fastembed package (deselect with '-m \"not fastembed\"')",
+    "no_fastembed: marks tests that do not require the fastembed package (deselect with '-m \"not no_fastembed\"')"
+]
diff --git a/qdrant_client/conversions/common_types.py b/qdrant_client/conversions/common_types.py
index fa9238b3..c017c016 100644
--- a/qdrant_client/conversions/common_types.py
+++ b/qdrant_client/conversions/common_types.py
@@ -1,9 +1,10 @@
 import sys
 
 import numpy as np
+import numpy.typing as npt
 
 if sys.version_info >= (3, 10):
-    from typing import Any, TypeAlias
+    from typing import TypeAlias
 else:
     from typing_extensions import TypeAlias
 
@@ -86,14 +87,4 @@
     np.longdouble,  # np.float96 and np.float128 are platform dependant aliases for longdouble
 ]
 
-
-if sys.version_info >= (3, 8):
-    # typing is included into numpy since 1.20
-    # NDArray is included since 1.21
-    # pyproject.toml is configured to install numpy>=1.21 in case of python>=3.8
-    # thus we don't need an additional check for numpy version
-    import numpy.typing as npt
-
-    NumpyArray: TypeAlias = npt.NDArray[_np_numeric]
-else:
-    NumpyArray: TypeAlias = np.ndarray
+NumpyArray: TypeAlias = npt.NDArray[_np_numeric]
diff --git a/qdrant_client/conversions/conversion.py b/qdrant_client/conversions/conversion.py
index 4854208a..05cfeaa1 100644
--- a/qdrant_client/conversions/conversion.py
+++ b/qdrant_client/conversions/conversion.py
@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, get_args
 
 from google.protobuf.json_format import MessageToDict
 from google.protobuf.timestamp_pb2 import Timestamp
@@ -1468,13 +1468,7 @@ def convert_with_payload_interface(
             return grpc.WithPayloadSelector(enable=model)
         elif isinstance(model, list):
             return grpc.WithPayloadSelector(include=grpc.PayloadIncludeSelector(fields=model))
-        elif isinstance(
-            model,
-            (
-                rest.PayloadSelectorInclude,
-                rest.PayloadSelectorExclude,
-            ),
-        ):
+        elif isinstance(model, get_args(rest.PayloadSelector)):
             return cls.convert_payload_selector(model)
 
         raise ValueError(f"invalid WithPayloadInterface model: {model}")  # pragma: no cover
diff --git a/qdrant_client/qdrant_client.py b/qdrant_client/qdrant_client.py
index c81d3ccd..c07c5a73 100644
--- a/qdrant_client/qdrant_client.py
+++ b/qdrant_client/qdrant_client.py
@@ -5,10 +5,11 @@
 from qdrant_client.conversions import common_types as types
 from qdrant_client.http import ApiClient, SyncApis
 from qdrant_client.local.qdrant_local import QdrantLocal
+from qdrant_client.qdrant_fastembed import QdrantFastembedMixin
 from qdrant_client.qdrant_remote import QdrantRemote
 
 
-class QdrantClient(QdrantBase):
+class QdrantClient(QdrantFastembedMixin):
     """Entry point to communicate with Qdrant service via REST or gPRC API.
 
     It combines interface classes and endpoint implementation.
@@ -72,6 +73,7 @@ def __init__(
         path: Optional[str] = None,
         **kwargs: Any,
     ):
+        super().__init__(**kwargs)
         self._client: QdrantBase
 
         if location == ":memory:":
@@ -94,6 +96,15 @@ def __init__(
                     host=host,
                     **kwargs,
                 )
+        self._is_fastembed_installed: Optional[bool] = None
+        # if fastembed is installed, set to true else False
+        if self._is_fastembed_installed is None:
+            try:
+                from fastembed.embedding import DefaultEmbedding  # noqa: F401
+
+                self._is_fastembed_installed = True
+            except ImportError:
+                self._is_fastembed_installed = False
 
     def __del__(self) -> None:
         self.close()
@@ -1425,6 +1436,7 @@ def upload_records(
         parallel: int = 1,
         method: Optional[str] = None,
         max_retries: int = 3,
+        wait: bool = False,
         **kwargs: Any,
     ) -> None:
         """Upload records to the collection
@@ -1439,6 +1451,11 @@ def upload_records(
             method: Start method for parallel processes, Default: forkserver
             max_retries: maximum number of retries in case of a failure
                 during the upload of a batch
+            wait:
+                Await for the results to be applied on the server side.
+                If `true`, each update request will explicitly wait for the confirmation of completion. Might be slower.
+                If `false`, each update request will return immediately after the confirmation of receiving.
+                Default: `false`
 
         """
         assert len(kwargs) == 0, f"Unknown arguments: {list(kwargs.keys())}"
@@ -1465,6 +1482,7 @@ def upload_collection(
         parallel: int = 1,
         method: Optional[str] = None,
         max_retries: int = 3,
+        wait: bool = False,
         **kwargs: Any,
     ) -> None:
         """Upload vectors and payload to the collection.
@@ -1482,6 +1500,11 @@ def upload_collection(
             method: Start method for parallel processes, Default: forkserver
             max_retries: maximum number of retries in case of a failure
                 during the upload of a batch
+            wait:
+                Await for the results to be applied on the server side.
+                If `true`, each update request will explicitly wait for the confirmation of completion. Might be slower.
+                If `false`, each update request will return immediately after the confirmation of receiving.
+                Default: `false`
         """
         assert len(kwargs) == 0, f"Unknown arguments: {list(kwargs.keys())}"
 
@@ -1494,6 +1517,7 @@ def upload_collection(
             parallel=parallel,
             method=method,
             max_retries=max_retries,
+            wait=wait,
             **kwargs,
         )
 
diff --git a/qdrant_client/qdrant_fastembed.py b/qdrant_client/qdrant_fastembed.py
new file mode 100644
index 00000000..d9568a32
--- /dev/null
+++ b/qdrant_client/qdrant_fastembed.py
@@ -0,0 +1,329 @@
+import uuid
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
+
+from pydantic import BaseModel
+
+from qdrant_client.client_base import QdrantBase
+from qdrant_client.conversions import common_types as types
+from qdrant_client.http import models
+from qdrant_client.uploader.uploader import iter_batch
+
+try:
+    from fastembed.embedding import DefaultEmbedding
+except ImportError:
+    pass
+
+SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = {
+    "BAAI/bge-base-en": (768, models.Distance.COSINE),
+    "sentence-transformers/all-MiniLM-L6-v2": (384, models.Distance.COSINE),
+    "BAAI/bge-small-en": (384, models.Distance.COSINE),
+}
+
+
+class QueryResponse(BaseModel, extra="forbid"):  # type: ignore
+    id: Union[str, int]
+    embedding: Optional[List[float]]
+    metadata: Dict[str, Any]
+    document: str
+    score: float
+
+
+class QdrantFastembedMixin(QdrantBase):
+    DEFAULT_EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+
+    embedding_models: Dict[str, "DefaultEmbedding"] = {}
+
+    def __init__(self, **kwargs: Any):
+        self.embedding_model_name = self.DEFAULT_EMBEDDING_MODEL
+        super().__init__(**kwargs)
+
+    def set_model(self, embedding_model_name: str) -> None:
+        """
+        Set embedding model to use for encoding documents and queries.
+        Args:
+            embedding_model_name: One of the supported embedding models. See `SUPPORTED_EMBEDDING_MODELS` for details.
+
+        Raises:
+            ValueError: If embedding model is not supported.
+            ImportError: If fastembed is not installed.
+
+        Returns:
+            None
+        """
+        self._get_or_init_model(model_name=embedding_model_name)
+        self.embedding_model_name = embedding_model_name
+
+    @staticmethod
+    def _import_fastembed() -> None:
+        try:
+            from fastembed.embedding import DefaultEmbedding
+        except ImportError:
+            # If it's not, ask the user to install it
+            raise ImportError(
+                "fastembed is not installed."
+                " Please install it to enable fast vector indexing with `pip install fastembed`."
+            )
+
+    @classmethod
+    def _get_model_params(cls, model_name: str) -> Tuple[int, models.Distance]:
+        if model_name not in SUPPORTED_EMBEDDING_MODELS:
+            raise ValueError(
+                f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_EMBEDDING_MODELS}"
+            )
+
+        return SUPPORTED_EMBEDDING_MODELS[model_name]
+
+    @classmethod
+    def _get_or_init_model(
+        cls, model_name: str
+    ) -> "DefaultEmbedding":  # -> Embedding: # noqa: F821
+        if model_name in cls.embedding_models:
+            return cls.embedding_models[model_name]
+
+        if model_name not in SUPPORTED_EMBEDDING_MODELS:
+            raise ValueError(
+                f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_EMBEDDING_MODELS}"
+            )
+
+        cls._import_fastembed()
+
+        cls.embedding_models[model_name] = DefaultEmbedding(model_name=model_name)
+        return cls.embedding_models[model_name]
+
+    def _embed_documents(
+        self,
+        documents: List[str],
+        embedding_model_name: str = DEFAULT_EMBEDDING_MODEL,
+        batch_size: int = 32,
+    ) -> Iterable[List[float]]:
+        embedding_model = self._get_or_init_model(model_name=embedding_model_name)
+        for batch_docs in iter_batch(documents, batch_size):
+            vectors_batches = embedding_model.encode(batch_docs, batch_size=batch_size)
+            for vector_batch in vectors_batches:
+                for vector in vector_batch.tolist():
+                    yield vector
+
+    def _get_vector_field_name(self) -> str:
+        model_name = self.embedding_model_name.split("/")[-1].lower()
+        return f"text-{model_name}"
+
+    def _scored_points_to_query_responses(
+        self,
+        scored_points: List[types.ScoredPoint],
+    ) -> List[QueryResponse]:
+        response = []
+        for scored_point in scored_points:
+            embedding = None
+            if scored_point.vector is not None:
+                embedding = scored_point.vector.get(self._get_vector_field_name(), None)
+
+            response.append(
+                QueryResponse(
+                    id=scored_point.id,
+                    embedding=embedding,
+                    metadata=scored_point.payload,
+                    document=scored_point.payload.get("document", ""),
+                    score=scored_point.score,
+                )
+            )
+        return response
+
+    def add(
+        self,
+        collection_name: str,
+        documents: List[str],
+        metadata: Optional[List[Dict[str, Any]]] = None,
+        ids: Optional[List[models.ExtendedPointId]] = None,
+        batch_size: int = 32,
+        **kwargs: Any,
+    ) -> List[str]:
+        """
+        Adds text documents into qdrant collection.
+        If collection does not exist, it will be created with default parameters.
+        Metadata in combination with documents will be added as payload.
+        Documents will be encoded using the specified embedding model.
+
+        If you want to use your own encoded vectors, use `upsert` method instead.
+
+        Args:
+            collection_name (str):
+                Name of the collection to add documents to.
+            documents (List[str]):
+                List of documents to encode and add to the collection.
+            metadata (List[Dict[str, Any]], optional):
+                List of metadata dicts. Defaults to None.
+            ids (List[models.ExtendedPointId], optional):
+                List of ids to assign to documents.
+                If not specified, UUIDs will be generated. Defaults to None.
+            batch_size (int, optional):
+                How many documents to encode and upload in single request. Defaults to 32.
+
+        Raises:
+            ImportError: If fastembed is not installed.
+
+        Returns:
+            List[str]: List of UUIDs of added documents. UUIDs are randomly generated on client side.
+
+        """
+
+        # check if we have fastembed installed
+        embeddings = self._embed_documents(
+            documents=documents,
+            embedding_model_name=self.embedding_model_name,
+            batch_size=batch_size,
+        )
+
+        if metadata is None:
+            metadata = [{} for _ in range(len(documents))]
+        else:
+            assert len(metadata) == len(
+                documents
+            ), f"metadata length mismatch: {len(metadata)} != {len(documents)}"
+
+        payloads = ({"document": doc, **metadata} for doc, metadata in zip(documents, metadata))
+
+        if ids is None:
+            ids = [uuid.uuid4().hex for _ in range(len(documents))]
+
+        embeddings_size, distance = self._get_model_params(model_name=self.embedding_model_name)
+
+        vector_field_name = self._get_vector_field_name()
+
+        try:
+            collection_info = self.get_collection(collection_name=collection_name)
+        except Exception:
+            self.create_collection(
+                collection_name=collection_name,
+                vectors_config={
+                    vector_field_name: models.VectorParams(size=embeddings_size, distance=distance)
+                },
+            )
+            collection_info = self.get_collection(collection_name=collection_name)
+
+        assert isinstance(
+            collection_info.config.params.vectors, dict
+        ), f"Collection have incompatible vector params: {collection_info.config.params.vectors}"
+
+        assert (
+            vector_field_name in collection_info.config.params.vectors
+        ), f"Collection have incompatible vector params: {collection_info.config.params.vectors}, expected {vector_field_name}"
+
+        vector_params = collection_info.config.params.vectors[vector_field_name]
+
+        assert (
+            embeddings_size == vector_params.size
+        ), f"Embedding size mismatch: {embeddings_size} != {vector_params.size}"
+
+        assert (
+            distance == vector_params.distance
+        ), f"Distance mismatch: {distance} != {vector_params.distance}"
+
+        records = (
+            models.Record(id=idx, payload=payload, vector={vector_field_name: vector})
+            for idx, payload, vector in zip(ids, payloads, embeddings)
+        )
+
+        self.upload_records(
+            collection_name=collection_name,
+            records=records,
+            wait=True,
+            **kwargs,
+        )
+
+        return ids
+
+    def query(
+        self,
+        collection_name: str,
+        query_text: str,
+        query_filter: Optional[models.Filter] = None,
+        limit: int = 10,
+        **kwargs: Any,
+    ) -> List[QueryResponse]:
+        """
+        Search for documents in a collection.
+        This method automatically encodes the query text using the specified embedding model.
+        If you want to use your own encoded query vector, use `search` method instead.
+
+        Args:
+            collection_name: Collection to search in
+            query_text:
+                Text to search for. This text will be encoded using the specified embedding model.
+                And then used as a query vector.
+            query_filter:
+                - Exclude vectors which doesn't fit given conditions.
+                - If `None` - search among all vectors
+            limit: How many results return
+            **kwargs: Additional search parameters. See `qdrant_client.models.SearchRequest` for details.
+
+        Returns:
+            List[types.ScoredPoint]: List of scored points.
+
+        """
+        embedding_model_inst = self._get_or_init_model(model_name=self.embedding_model_name)
+        embeddings = list(embedding_model_inst.encode(documents=[query_text]))
+        query_vector = embeddings[0][0]
+
+        return self._scored_points_to_query_responses(
+            self.search(
+                collection_name=collection_name,
+                query_vector=models.NamedVector(
+                    name=self._get_vector_field_name(), vector=query_vector
+                ),
+                query_filter=query_filter,
+                limit=limit,
+                with_payload=True,
+                **kwargs,
+            )
+        )
+
+    def query_batch(
+        self,
+        collection_name: str,
+        query_texts: List[str],
+        query_filter: Optional[models.Filter] = None,
+        limit: int = 10,
+        **kwargs: Any,
+    ) -> List[List[QueryResponse]]:
+        """
+        Search for documents in a collection with batched query.
+        This method automatically encodes the query text using the specified embedding model.
+
+        Args:
+            collection_name: Collection to search in
+            query_texts:
+                A list of texts to search for. Each text will be encoded using the specified embedding model.
+                And then used as a query vector for a separate search requests.
+            query_filter:
+                - Exclude vectors which doesn't fit given conditions.
+                - If `None` - search among all vectors
+                This filter will be applied to all search requests.
+            limit: How many results return
+            **kwargs: Additional search parameters. See `qdrant_client.models.SearchRequest` for details.
+
+        Returns:
+            List[List[QueryResponse]]: List of lists of responses for each query text.
+
+        """
+        embedding_model_inst = self._get_or_init_model(model_name=self.embedding_model_name)
+        query_vectors = embedding_model_inst.encode(documents=query_texts)
+
+        requests = []
+        for vector in query_vectors:
+            request = models.SearchRequest(
+                vector=models.NamedVector(name=self._get_vector_field_name(), vector=vector),
+                filter=query_filter,
+                limit=limit,
+                with_payload=True,
+                **kwargs,
+            )
+
+            requests.append(request)
+
+        return [
+            self._scored_points_to_query_responses(response)
+            for response in self.search_batch(
+                collection_name=collection_name,
+                requests=requests,
+            )
+        ]
diff --git a/qdrant_client/qdrant_remote.py b/qdrant_client/qdrant_remote.py
index 23043315..4d8c3def 100644
--- a/qdrant_client/qdrant_remote.py
+++ b/qdrant_client/qdrant_remote.py
@@ -579,7 +579,9 @@ def search_groups(
 
             if isinstance(query_vector, tuple):
                 query_vector = construct(
-                    rest_models.NamedVector, name=query_vector[0], vector=query_vector[1]
+                    rest_models.NamedVector,
+                    name=query_vector[0],
+                    vector=query_vector[1],
                 )
 
             if isinstance(query_vector, np.ndarray):
@@ -1891,6 +1893,7 @@ def _upload_collection(
         max_retries: int,
         parallel: int = 1,
         method: Optional[str] = None,
+        wait: bool = False,
     ) -> None:
         if method is not None:
             if method in get_all_start_methods():
@@ -1910,12 +1913,14 @@ def _upload_collection(
                 "max_retries": max_retries,
                 "ssl": self._https,
                 "metadata": self._grpc_headers,
+                "wait": wait,
             }
         else:
             updater_kwargs = {
                 "collection_name": collection_name,
                 "uri": self.rest_uri,
                 "max_retries": max_retries,
+                "wait": wait,
                 **self._rest_args,
             }
 
@@ -1936,12 +1941,20 @@ def upload_records(
         parallel: int = 1,
         method: Optional[str] = None,
         max_retries: int = 3,
+        wait: bool = False,
         **kwargs: Any,
     ) -> None:
         batches_iterator = self._updater_class.iterate_records_batches(
             records=records, batch_size=batch_size
         )
-        self._upload_collection(batches_iterator, collection_name, max_retries, parallel, method)
+        self._upload_collection(
+            batches_iterator=batches_iterator,
+            collection_name=collection_name,
+            max_retries=max_retries,
+            parallel=parallel,
+            method=method,
+            wait=wait,
+        )
 
     def upload_collection(
         self,
@@ -1955,6 +1968,7 @@ def upload_collection(
         parallel: int = 1,
         method: Optional[str] = None,
         max_retries: int = 3,
+        wait: bool = False,
         **kwargs: Any,
     ) -> None:
         batches_iterator = self._updater_class.iterate_batches(
diff --git a/qdrant_client/uploader/grpc_uploader.py b/qdrant_client/uploader/grpc_uploader.py
index b15da9d8..a386fe1e 100644
--- a/qdrant_client/uploader/grpc_uploader.py
+++ b/qdrant_client/uploader/grpc_uploader.py
@@ -15,6 +15,7 @@ def upload_batch_grpc(
     collection_name: str,
     batch: Union[Batch, Tuple],
     max_retries: int,
+    wait: bool = False,
 ) -> bool:
     ids_batch, vectors_batch, payload_batch = batch
     if payload_batch is None:
@@ -31,7 +32,9 @@ def upload_batch_grpc(
 
     for attempt in range(max_retries):
         try:
-            points_client.Upsert(grpc.UpsertPoints(collection_name=collection_name, points=points))
+            points_client.Upsert(
+                grpc.UpsertPoints(collection_name=collection_name, points=points, wait=wait)
+            )
         except Exception as e:
             logging.warning(f"Batch upload failed {attempt + 1} times. Retrying...")
 
@@ -47,6 +50,7 @@ def __init__(
         port: int,
         collection_name: str,
         max_retries: int,
+        wait: bool = False,
         **kwargs: Any,
     ):
         self.collection_name = collection_name
@@ -54,6 +58,7 @@ def __init__(
         self._port = port
         self.max_retries = max_retries
         self._kwargs = kwargs
+        self._wait = wait
 
     @classmethod
     def start(
@@ -79,7 +84,9 @@ def process_upload(self, items: Iterable[Any]) -> Generator[bool, None, None]:
         channel = get_channel(host=self._host, port=self._port, **self._kwargs)
         points_client = PointsStub(channel)
         for batch in items:
-            yield upload_batch_grpc(points_client, self.collection_name, batch, self.max_retries)
+            yield upload_batch_grpc(
+                points_client, self.collection_name, batch, self.max_retries, self._wait
+            )
 
     def process(self, items: Iterable[Any]) -> Generator[bool, None, None]:
         yield from self.process_upload(items)
diff --git a/qdrant_client/uploader/rest_uploader.py b/qdrant_client/uploader/rest_uploader.py
index bd52e819..70eb5905 100644
--- a/qdrant_client/uploader/rest_uploader.py
+++ b/qdrant_client/uploader/rest_uploader.py
@@ -14,6 +14,7 @@ def upload_batch(
     collection_name: str,
     batch: Union[Tuple, Batch],
     max_retries: int,
+    wait: bool = False,
 ) -> bool:
     ids_batch, vectors_batch, payload_batch = batch
 
@@ -36,6 +37,7 @@ def upload_batch(
             openapi_client.points_api.upsert_points(
                 collection_name=collection_name,
                 point_insert_operations=PointsList(points=points),
+                wait=wait,
             )
         except Exception as e:
             logging.warning(f"Batch upload failed {attempt + 1} times. Retrying...")
@@ -46,10 +48,13 @@ def upload_batch(
 
 
 class RestBatchUploader(BaseUploader):
-    def __init__(self, uri: str, collection_name: str, max_retries: int, **kwargs: Any):
+    def __init__(
+        self, uri: str, collection_name: str, max_retries: int, wait: bool = False, **kwargs: Any
+    ):
         self.collection_name = collection_name
         self.openapi_client: SyncApis = SyncApis(host=uri, **kwargs)
         self.max_retries = max_retries
+        self._wait = wait
 
     @classmethod
     def start(
@@ -65,4 +70,6 @@ def start(
 
     def process(self, items: Iterable[Any]) -> Generator[bool, None, None]:
         for batch in items:
-            yield upload_batch(self.openapi_client, self.collection_name, batch, self.max_retries)
+            yield upload_batch(
+                self.openapi_client, self.collection_name, batch, self.max_retries, self._wait
+            )
diff --git a/tests/test_fast_embed.py b/tests/test_fast_embed.py
new file mode 100644
index 00000000..8a3d1129
--- /dev/null
+++ b/tests/test_fast_embed.py
@@ -0,0 +1,103 @@
+from typing import Any, Dict, List, Union
+
+import pytest
+
+from qdrant_client import QdrantClient
+
+
+def test_add_without_query(
+    local_client: QdrantClient = None,
+    collection_name: str = "demo_collection",
+    docs: Dict[str, List[Union[str, int, Any]]] = None,
+):
+    if local_client is None:
+        local_client = QdrantClient(":memory:")
+
+    if docs is None:
+        docs = {
+            "documents": [
+                "Qdrant has Langchain integrations",
+                "Qdrant also has Llama Index integrations",
+            ],
+            "metadatas": [{"source": "Langchain-docs"}, {"source": "LlamaIndex-docs"}],
+            "ids": [42, 2],
+        }
+
+    if not local_client._is_fastembed_installed:
+        pytest.skip("FastEmbed is not installed, skipping test")
+
+    local_client.add(
+        collection_name=collection_name,
+        documents=docs["documents"],
+        metadata=docs["metadatas"],
+        ids=docs["ids"],
+    )
+    assert local_client.count(collection_name).count == 2
+
+
+def test_no_install(
+    local_client: QdrantClient = None,
+    collection_name: str = "demo_collection",
+    docs: Dict[str, List[Union[str, int, Any]]] = None,
+):
+    if local_client is None:
+        local_client = QdrantClient(":memory:")
+
+    if docs is None:
+        docs = {
+            "documents": [
+                "Qdrant has Langchain integrations",
+                "Qdrant also has Llama Index integrations",
+            ],
+            "metadatas": [{"source": "Langchain-docs"}, {"source": "LlamaIndex-docs"}],
+            "ids": [42, 2],
+        }
+
+    # When FastEmbed is not installed, the add method should raise an ImportError
+    if local_client._is_fastembed_installed:
+        pytest.skip("FastEmbed is installed, skipping test")
+    else:
+        with pytest.raises(ImportError):
+            local_client.add(collection_name, docs)
+
+
+def test_query(
+    local_client: QdrantClient = None,
+    collection_name: str = "demo_collection",
+    docs: Dict[str, List[Union[str, int, Any]]] = None,
+):
+    if local_client is None:
+        local_client = QdrantClient(":memory:")
+
+    if docs is None:
+        docs = {
+            "documents": [
+                "Qdrant has Langchain integrations",
+                "Qdrant also has Llama Index integrations",
+            ],
+            "metadatas": [{"source": "Langchain-docs"}, {"source": "LlamaIndex-docs"}],
+            "ids": [42, 2],
+        }
+
+    if not local_client._is_fastembed_installed:
+        pytest.skip("FastEmbed is not installed, skipping test")
+
+    local_client.add(
+        collection_name=collection_name,
+        documents=docs["documents"],
+        metadata=docs["metadatas"],
+        ids=docs["ids"],
+    )
+    assert local_client.count(collection_name).count == 2
+    # Query the added documents
+
+    search_result = local_client.query(
+        collection_name=collection_name, query_text="This is a query document"
+    )
+
+    assert len(search_result) > 0
+
+
+if __name__ == "__main__":
+    test_add_without_query()
+    test_query()
diff --git a/tests/test_qdrant_client.py b/tests/test_qdrant_client.py
index 531aae7b..6136718f 100644
--- a/tests/test_qdrant_client.py
+++ b/tests/test_qdrant_client.py
@@ -1,5 +1,4 @@
 import os
-import random
 import uuid
 from pprint import pprint
 from tempfile import mkdtemp
@@ -37,11 +36,8 @@
     Range,
     RecommendRequest,
     ScalarQuantization,
-    ScalarQuantization,
-    ScalarQuantizationConfig,
     ScalarQuantizationConfig,
     ScalarType,
-    ScalarType,
     SearchParams,
     SearchRequest,
     TextIndexParams,
@@ -1161,4 +1157,4 @@ def test_client_close():
     test_points_crud()
     test_has_id_condition()
     test_insert_float()
-    test_legacy_imports()
+    test_legacy_imports()
\ No newline at end of file