diff --git a/docs/source/providers/vector_io/qdrant.md b/docs/source/providers/vector_io/qdrant.md
index a0de0be98..8b0cbeef8 100644
--- a/docs/source/providers/vector_io/qdrant.md
+++ b/docs/source/providers/vector_io/qdrant.md
@@ -3,21 +3,36 @@ orphan: true
 ---
 # Qdrant
 
-[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It
+[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
 That means you'll get fast and efficient vector retrieval.
 
+> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
+> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
+>
+> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
+
+
+
 ## Features
 
-- Easy to use
+- Lightweight and easy to use
 - Fully integrated with Llama Stack
+- Apache 2.0 license terms
+- Store embeddings and their metadata
+- Supports search by
+  [Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
+  and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
+- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
+- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
+- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
 
 ## Usage
 
 To use Qdrant in your Llama Stack project, follow these steps:
 
 1. Install the necessary dependencies.
-2. Configure your Llama Stack project to use Faiss.
+2. Configure your Llama Stack project to use Qdrant.
 3. Start storing and querying vectors.
 
 ## Installation
diff --git a/llama_stack/providers/inline/vector_io/qdrant/__init__.py b/llama_stack/providers/inline/vector_io/qdrant/__init__.py
new file mode 100644
index 000000000..8f0b91c61
--- /dev/null
+++ b/llama_stack/providers/inline/vector_io/qdrant/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
+
+from .config import QdrantVectorIOConfig
+
+
+async def get_adapter_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]):
+    from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
+
+    impl = QdrantVectorIOAdapter(config, deps[Api.inference])
+    await impl.initialize()
+    return impl
diff --git a/llama_stack/providers/inline/vector_io/qdrant/config.py b/llama_stack/providers/inline/vector_io/qdrant/config.py
new file mode 100644
index 000000000..282e951b0
--- /dev/null
+++ b/llama_stack/providers/inline/vector_io/qdrant/config.py
@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+from typing import Any, Dict
+
+from pydantic import BaseModel
+
+from llama_stack.schema_utils import json_schema_type
+
+
+@json_schema_type
+class QdrantVectorIOConfig(BaseModel):
+    path: str
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
+        return {
+            "path": "${env.QDRANT_PATH:~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
+        }
diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py
index fbc495d83..93031763d 100644
--- a/llama_stack/providers/registry/vector_io.py
+++ b/llama_stack/providers/registry/vector_io.py
@@ -92,6 +92,14 @@ def available_providers() -> List[ProviderSpec]:
             ),
             api_dependencies=[Api.inference],
         ),
+        InlineProviderSpec(
+            api=Api.vector_io,
+            provider_type="inline::qdrant",
+            pip_packages=["qdrant-client"],
+            module="llama_stack.providers.inline.vector_io.qdrant",
+            config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
+            api_dependencies=[Api.inference],
+        ),
         remote_provider_spec(
             Api.vector_io,
             AdapterSpec(
diff --git a/llama_stack/providers/remote/vector_io/qdrant/config.py b/llama_stack/providers/remote/vector_io/qdrant/config.py
index ce68aa492..6d7eebe23 100644
--- a/llama_stack/providers/remote/vector_io/qdrant/config.py
+++ b/llama_stack/providers/remote/vector_io/qdrant/config.py
@@ -23,7 +23,6 @@ class QdrantVectorIOConfig(BaseModel):
     prefix: Optional[str] = None
     timeout: Optional[int] = None
     host: Optional[str] = None
-    path: Optional[str] = None
 
     @classmethod
     def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]:
diff --git a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py
index 586b8ca95..9e7788dc0 100644
--- a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py
+++ b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py
@@ -6,7 +6,7 @@
 
 import logging
 import uuid
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 from numpy.typing import NDArray
 from qdrant_client import AsyncQdrantClient, models
@@ -16,12 +16,13 @@ from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
 )
 
-from .config import QdrantVectorIOConfig
+from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
 
 log = logging.getLogger(__name__)
 CHUNK_ID_KEY = "_chunk_id"
@@ -99,17 +100,19 @@ class QdrantIndex(EmbeddingIndex):
 
 
 class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
-    def __init__(self, config: QdrantVectorIOConfig, inference_api: Api.inference) -> None:
+    def __init__(
+        self, config: Union[RemoteQdrantVectorIOConfig, InlineQdrantVectorIOConfig], inference_api: Api.inference
+    ) -> None:
         self.config = config
-        self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
+        self.client: AsyncQdrantClient = None
         self.cache = {}
         self.inference_api = inference_api
 
     async def initialize(self) -> None:
-        pass
+        self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
 
     async def shutdown(self) -> None:
-        self.client.close()
+        await self.client.close()
 
     async def register_vector_db(
         self,
@@ -123,6 +126,11 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
 
         self.cache[vector_db.identifier] = index
 
+    async def unregister_vector_db(self, vector_db_id: str) -> None:
+        if vector_db_id in self.cache:
+            await self.cache[vector_db_id].index.delete()
+            del self.cache[vector_db_id]
+
     async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
         if vector_db_id in self.cache:
             return self.cache[vector_db_id]
diff --git a/pyproject.toml b/pyproject.toml
index 7972bf211..f57b91462 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,7 +56,7 @@ dev = [
     "ruamel.yaml",      # needed for openapi generator
 ]
 # These are the dependencies required for running unit tests.
-unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet"]
+unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet", "qdrant-client"]
 # These are the core dependencies required for running integration tests. They are shared across all
 # providers. If a provider requires additional dependencies, please add them to your environment
 # separately. If you are using "uv" to execute your tests, you can use the "--with" flag to specify extra
@@ -247,6 +247,7 @@ exclude = [
     "^llama_stack/providers/inline/vector_io/chroma/",
     "^llama_stack/providers/inline/vector_io/faiss/",
     "^llama_stack/providers/inline/vector_io/milvus/",
+    "^llama_stack/providers/inline/vector_io/qdrant/",
     "^llama_stack/providers/inline/vector_io/sqlite_vec/",
     "^llama_stack/providers/remote/agents/sample/",
     "^llama_stack/providers/remote/datasetio/huggingface/",
diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py
new file mode 100644
index 000000000..3bcd0613f
--- /dev/null
+++ b/tests/unit/providers/vector_io/conftest.py
@@ -0,0 +1,42 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import random
+
+import numpy as np
+import pytest
+
+from llama_stack.apis.vector_io import Chunk
+
+EMBEDDING_DIMENSION = 384
+
+
+@pytest.fixture
+def vector_db_id() -> str:
+    return f"test-vector-db-{random.randint(1, 100)}"
+
+
+@pytest.fixture(scope="session")
+def embedding_dimension() -> int:
+    return EMBEDDING_DIMENSION
+
+
+@pytest.fixture(scope="session")
+def sample_chunks():
+    """Generates chunks that force multiple batches for a single document to expose ID conflicts."""
+    n, k = 10, 3
+    sample = [
+        Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
+        for j in range(k)
+        for i in range(n)
+    ]
+    return sample
+
+
+@pytest.fixture(scope="session")
+def sample_embeddings(sample_chunks):
+    np.random.seed(42)
+    return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
diff --git a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py
new file mode 100644
index 000000000..bc97719c0
--- /dev/null
+++ b/tests/unit/providers/vector_io/test_qdrant.py
@@ -0,0 +1,135 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import asyncio
+import os
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+import pytest_asyncio
+
+from llama_stack.apis.inference import EmbeddingsResponse, Inference
+from llama_stack.apis.vector_io import (
+    QueryChunksResponse,
+    VectorDB,
+    VectorDBStore,
+)
+from llama_stack.providers.inline.vector_io.qdrant.config import (
+    QdrantVectorIOConfig as InlineQdrantVectorIOConfig,
+)
+from llama_stack.providers.remote.vector_io.qdrant.qdrant import (
+    QdrantVectorIOAdapter,
+)
+
+# This test is a unit test for the QdrantVectorIOAdapter class. This should only contain
+# tests which are specific to this class. More general (API-level) tests should be placed in
+# tests/integration/vector_io/
+#
+# How to run this test:
+#
+# pytest tests/unit/providers/vector_io/test_qdrant.py \
+# -v -s --tb=short --disable-warnings --asyncio-mode=auto
+
+
+@pytest.fixture
+def qdrant_config(tmp_path) -> InlineQdrantVectorIOConfig:
+    return InlineQdrantVectorIOConfig(path=os.path.join(tmp_path, "qdrant.db"))
+
+
+@pytest.fixture(scope="session")
+def loop():
+    return asyncio.new_event_loop()
+
+
+@pytest.fixture
+def mock_vector_db(vector_db_id) -> MagicMock:
+    mock_vector_db = MagicMock(spec=VectorDB)
+    mock_vector_db.embedding_model = "embedding_model"
+    mock_vector_db.identifier = vector_db_id
+    return mock_vector_db
+
+
+@pytest.fixture
+def mock_vector_db_store(mock_vector_db) -> MagicMock:
+    mock_store = MagicMock(spec=VectorDBStore)
+    mock_store.get_vector_db = AsyncMock(return_value=mock_vector_db)
+    return mock_store
+
+
+@pytest.fixture
+def mock_api_service(sample_embeddings):
+    mock_api_service = MagicMock(spec=Inference)
+    mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings))
+    return mock_api_service
+
+
+@pytest_asyncio.fixture
+async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter:
+    adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service)
+    adapter.vector_db_store = mock_vector_db_store
+    await adapter.initialize()
+    yield adapter
+    await adapter.shutdown()
+
+
+__QUERY = "Sample query"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 30)])
+async def test_qdrant_adapter_returns_expected_chunks(
+    qdrant_adapter: QdrantVectorIOAdapter,
+    vector_db_id,
+    sample_chunks,
+    sample_embeddings,
+    max_query_chunks,
+    expected_chunks,
+) -> None:
+    assert qdrant_adapter is not None
+    await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
+
+    index = await qdrant_adapter._get_and_cache_vector_db_index(vector_db_id=vector_db_id)
+    assert index is not None
+
+    response = await qdrant_adapter.query_chunks(
+        query=__QUERY,
+        vector_db_id=vector_db_id,
+        params={"max_chunks": max_query_chunks},
+    )
+    assert isinstance(response, QueryChunksResponse)
+    assert len(response.chunks) == expected_chunks
+
+
+# To by-pass attempt to convert a Mock to JSON
+def _prepare_for_json(value: Any) -> str:
+    return str(value)
+
+
+@patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json)
+@pytest.mark.asyncio
+async def test_qdrant_register_and_unregister_vector_db(
+    qdrant_adapter: QdrantVectorIOAdapter,
+    mock_vector_db,
+    sample_chunks,
+) -> None:
+    # Initially, no collections
+    vector_db_id = mock_vector_db.identifier
+    assert len((await qdrant_adapter.client.get_collections()).collections) == 0
+
+    # Register does not create a collection
+    assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
+    await qdrant_adapter.register_vector_db(mock_vector_db)
+    assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
+
+    # First insert creates the collection
+    await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
+    assert await qdrant_adapter.client.collection_exists(vector_db_id)
+
+    # Unregister deletes the collection
+    await qdrant_adapter.unregister_vector_db(vector_db_id)
+    assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
+    assert len((await qdrant_adapter.client.get_collections()).collections) == 0
diff --git a/tests/unit/providers/vector_io/test_sqlite_vec.py b/tests/unit/providers/vector_io/test_sqlite_vec.py
index eb5660a85..cff988c53 100644
--- a/tests/unit/providers/vector_io/test_sqlite_vec.py
+++ b/tests/unit/providers/vector_io/test_sqlite_vec.py
@@ -29,8 +29,6 @@ from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
 # -v -s --tb=short --disable-warnings --asyncio-mode=auto
 
 SQLITE_VEC_PROVIDER = "sqlite_vec"
-EMBEDDING_DIMENSION = 384
-EMBEDDING_MODEL = "all-MiniLM-L6-v2"
 
 
 @pytest.fixture(scope="session")
@@ -50,26 +48,8 @@ def sqlite_connection(loop):
 
 
 @pytest_asyncio.fixture(scope="session", autouse=True)
-async def sqlite_vec_index(sqlite_connection):
-    return await SQLiteVecIndex.create(dimension=EMBEDDING_DIMENSION, connection=sqlite_connection, bank_id="test_bank")
-
-
-@pytest.fixture(scope="session")
-def sample_chunks():
-    """Generates chunks that force multiple batches for a single document to expose ID conflicts."""
-    n, k = 10, 3
-    sample = [
-        Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
-        for j in range(k)
-        for i in range(n)
-    ]
-    return sample
-
-
-@pytest.fixture(scope="session")
-def sample_embeddings(sample_chunks):
-    np.random.seed(42)
-    return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
+async def sqlite_vec_index(sqlite_connection, embedding_dimension):
+    return await SQLiteVecIndex.create(dimension=embedding_dimension, connection=sqlite_connection, bank_id="test_bank")
 
 
 @pytest.mark.asyncio
@@ -82,21 +62,21 @@ async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
 
 
 @pytest.mark.asyncio
-async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
+async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings, embedding_dimension):
     await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings)
-    query_embedding = np.random.rand(EMBEDDING_DIMENSION).astype(np.float32)
+    query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
     response = await sqlite_vec_index.query(query_embedding, k=2, score_threshold=0.0)
     assert isinstance(response, QueryChunksResponse)
     assert len(response.chunks) == 2
 
 
 @pytest.mark.asyncio
-async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks):
+async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dimension):
     """Test that chunk IDs do not conflict across batches when inserting chunks."""
     # Reduce batch size to force multiple batches for same document
     # since there are 10 chunks per document and batch size is 2
     batch_size = 2
-    sample_embeddings = np.random.rand(len(sample_chunks), EMBEDDING_DIMENSION).astype(np.float32)
+    sample_embeddings = np.random.rand(len(sample_chunks), embedding_dimension).astype(np.float32)
 
     await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=batch_size)
 
diff --git a/uv.lock b/uv.lock
index 860b29241..b63d23b14 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.10"
 resolution-markers = [
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
@@ -8,9 +7,12 @@ resolution-markers = [
     "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
 ]
 
 [[package]]
@@ -793,6 +795,107 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/89/30/2bd0eb03a7dee7727cd2ec643d1e992979e62d5e7443507381cce0455132/googleapis_common_protos-1.67.0-py2.py3-none-any.whl", hash = "sha256:579de760800d13616f51cf8be00c876f00a9f146d3e6510e19d1f4111758b741", size = 164985 },
 ]
 
+[[package]]
+name = "grpcio"
+version = "1.71.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/95/aa11fc09a85d91fbc7dd405dcb2a1e0256989d67bf89fa65ae24b3ba105a/grpcio-1.71.0.tar.gz", hash = "sha256:2b85f7820475ad3edec209d3d89a7909ada16caab05d3f2e08a7e8ae3200a55c", size = 12549828 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7c/c5/ef610b3f988cc0cc67b765f72b8e2db06a1db14e65acb5ae7810a6b7042e/grpcio-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:c200cb6f2393468142eb50ab19613229dcc7829b5ccee8b658a36005f6669fdd", size = 5210643 },
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 version = "1.0.7"
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+[package.optional-dependencies]
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+]
+
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 version = "0.4.0"
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+[[package]]
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+version = "6.1.0"
+source = { registry = "https://pypi.org/simple" }
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 version = "2.6.7"
@@ -1250,6 +1389,7 @@ unit = [
     { name = "chardet" },
     { name = "openai" },
     { name = "pypdf" },
+    { name = "qdrant-client" },
     { name = "sqlite-vec" },
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     { name = "pytest-html", marker = "extra == 'dev'" },
     { name = "python-dotenv" },
+    { name = "qdrant-client", marker = "extra == 'unit'" },
     { name = "requests" },
     { name = "rich" },
     { name = "rich", marker = "extra == 'codegen'" },
@@ -1314,7 +1455,6 @@ requires-dist = [
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+[[package]]
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+version = "2.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+]
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+wheels = [
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 version = "4.1.0"
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+[[package]]
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+version = "1.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "grpcio" },
+    { name = "grpcio-tools" },
+    { name = "httpx", extra = ["http2"] },
+    { name = "numpy" },
+    { name = "portalocker" },
+    { name = "pydantic" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/eb/58/1e4acd7ff7637ed56a66e5044699e7af6067232703d0b34f05068fc6234b/qdrant_client-1.13.3.tar.gz", hash = "sha256:61ca09e07c6d7ac0dfbdeb13dca4fe5f3e08fa430cb0d74d66ef5d023a70adfc", size = 266278 }
+wheels = [
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+
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 version = "3.12.2"
@@ -3417,7 +3587,8 @@ source = { registry = "https://download.pytorch.org/whl/cpu" }
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     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
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-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
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+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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 resolution-markers = [
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 dependencies = [
     { name = "numpy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },