From 95f7021beccd519f62d71d870a5798453229a8c3 Mon Sep 17 00:00:00 2001
From: Anush <anushshetty90@gmail.com>
Date: Sat, 16 Nov 2024 02:52:23 +0530
Subject: [PATCH] feat: Qdrant vectorstore (#163)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Anush008 <anushshetty90@gmail.com>
Co-authored-by: Michał Pstrąg <michal.pstrag@icloud.com>
---
 .gitignore                                    |   1 +
 docs/api_reference/core/vector-stores.md      |   2 +
 examples/apps/documents_chat.py               |   2 +-
 examples/document-search/chroma.py            |  10 +-
 examples/document-search/chroma_otel.py       |   2 +-
 examples/document-search/multimodal.py        |   2 +-
 examples/document-search/qdrant.py            | 107 ++++++++++
 packages/ragbits-core/pyproject.toml          |   3 +
 .../ragbits/core/metadata_stores/in_memory.py |   2 +-
 .../ragbits/core/vector_stores/__init__.py    |  33 ++-
 .../src/ragbits/core/vector_stores/base.py    |  18 +-
 .../src/ragbits/core/vector_stores/chroma.py  |  55 ++---
 .../ragbits/core/vector_stores/in_memory.py   |  17 ++
 .../src/ragbits/core/vector_stores/qdrant.py  | 197 ++++++++++++++++++
 .../tests/unit/vector_stores/test_chroma.py   |  21 +-
 .../unit/vector_stores/test_in_memory.py      |   9 +-
 .../tests/unit/vector_stores/test_qdrant.py   | 140 +++++++++++++
 .../src/ragbits/document_search/_main.py      |   2 +-
 .../document_search/documents/element.py      |  41 ++--
 .../retrieval/rerankers/litellm.py            |   2 +-
 .../tests/unit/test_elements.py               |  14 +-
 pyproject.toml                                |   2 +-
 uv.lock                                       | 134 +++++++++++-
 23 files changed, 706 insertions(+), 110 deletions(-)
 create mode 100644 examples/document-search/qdrant.py
 create mode 100644 packages/ragbits-core/src/ragbits/core/vector_stores/qdrant.py
 create mode 100644 packages/ragbits-core/tests/unit/vector_stores/test_qdrant.py

diff --git a/.gitignore b/.gitignore
index b35330b9..088ec593 100644
--- a/.gitignore
+++ b/.gitignore
@@ -95,5 +95,6 @@ dist/
 
 # examples
 chroma/
+qdrant/
 
 .aider*
diff --git a/docs/api_reference/core/vector-stores.md b/docs/api_reference/core/vector-stores.md
index 34254658..0503cd97 100644
--- a/docs/api_reference/core/vector-stores.md
+++ b/docs/api_reference/core/vector-stores.md
@@ -9,3 +9,5 @@
 ::: ragbits.core.vector_stores.in_memory.InMemoryVectorStore
 
 ::: ragbits.core.vector_stores.chroma.ChromaVectorStore
+
+::: ragbits.core.vector_stores.qdrant.QdrantVectorStore
diff --git a/examples/apps/documents_chat.py b/examples/apps/documents_chat.py
index 3b1b2a43..8f7f24a4 100644
--- a/examples/apps/documents_chat.py
+++ b/examples/apps/documents_chat.py
@@ -125,7 +125,7 @@ async def _handle_message(
         if not self._documents_ingested:
             yield self.NO_DOCUMENTS_INGESTED_MESSAGE
         results = await self.document_search.search(message[-1])
-        prompt = RAGPrompt(QueryWithContext(query=message, context=[i.get_text_representation() for i in results]))
+        prompt = RAGPrompt(QueryWithContext(query=message, context=[i.text_representation for i in results]))
         response = await self._llm.generate(prompt)
         yield response.answer
 
diff --git a/examples/document-search/chroma.py b/examples/document-search/chroma.py
index b641c360..bdf27dc5 100644
--- a/examples/document-search/chroma.py
+++ b/examples/document-search/chroma.py
@@ -9,7 +9,7 @@
 
     1. Create a list of documents.
     2. Initialize the `LiteLLMEmbeddings` class with the OpenAI `text-embedding-3-small` embedding model.
-    3. Initialize the `ChromaVectorStore` class with a `PersistentClient` instance and an index name.
+    3. Initialize the `ChromaVectorStore` class with a `EphemeralClient` instance and an index name.
     4. Initialize the `DocumentSearch` class with the embedder and the vector store.
     5. Ingest the documents into the `DocumentSearch` instance.
     6. List all documents in the vector store.
@@ -33,7 +33,7 @@
 
 import asyncio
 
-from chromadb import PersistentClient
+from chromadb import EphemeralClient
 
 from ragbits.core.embeddings.litellm import LiteLLMEmbeddings
 from ragbits.core.vector_stores.chroma import ChromaVectorStore
@@ -72,7 +72,7 @@ async def main() -> None:
         model="text-embedding-3-small",
     )
     vector_store = ChromaVectorStore(
-        client=PersistentClient("./chroma"),
+        client=EphemeralClient(),
         index_name="jokes",
     )
     document_search = DocumentSearch(
@@ -91,7 +91,7 @@ async def main() -> None:
     query = "I'm boiling my water and I need a joke"
     vector_store_kwargs = {
         "k": 2,
-        "max_distance": None,
+        "max_distance": 0.6,
     }
     results = await document_search.search(
         query,
@@ -100,7 +100,7 @@ async def main() -> None:
 
     print()
     print(f"Documents similar to: {query}")
-    print([element.get_text_representation() for element in results])
+    print([element.text_representation for element in results])
 
 
 if __name__ == "__main__":
diff --git a/examples/document-search/chroma_otel.py b/examples/document-search/chroma_otel.py
index 64b5e198..7efd5195 100644
--- a/examples/document-search/chroma_otel.py
+++ b/examples/document-search/chroma_otel.py
@@ -130,7 +130,7 @@ async def main() -> None:
 
     print()
     print(f"Documents similar to: {query}")
-    print([element.get_text_representation() for element in results])
+    print([element.text_representation for element in results])
 
 
 if __name__ == "__main__":
diff --git a/examples/document-search/multimodal.py b/examples/document-search/multimodal.py
index 88dfb571..0e932bd5 100644
--- a/examples/document-search/multimodal.py
+++ b/examples/document-search/multimodal.py
@@ -91,7 +91,7 @@ async def main() -> None:
     print("Results for 'Fluffy teady bear toy':")
     for result in results:
         document = await result.document_meta.fetch()
-        print(f"Type: {result.element_type}, Location: {document.local_path}, Text: {result.get_text_representation()}")
+        print(f"Type: {result.element_type}, Location: {document.local_path}, Text: {result.text_representation}")
 
 
 if __name__ == "__main__":
diff --git a/examples/document-search/qdrant.py b/examples/document-search/qdrant.py
new file mode 100644
index 00000000..b1ea998e
--- /dev/null
+++ b/examples/document-search/qdrant.py
@@ -0,0 +1,107 @@
+"""
+Ragbits Document Search Example: Qdrant
+
+This example demonstrates how to use the `DocumentSearch` class to search for documents with a more advanced setup.
+We will use the `LiteLLMEmbeddings` class to embed the documents and the query, the `QdrantVectorStore` class to store
+the embeddings.
+
+The script performs the following steps:
+
+    1. Create a list of documents.
+    2. Initialize the `LiteLLMEmbeddings` class with the OpenAI `text-embedding-3-small` embedding model.
+    3. Initialize the `QdrantVectorStore` class with a `AsyncQdrantClient` in-memory instance and an index name.
+    4. Initialize the `DocumentSearch` class with the embedder and the vector store.
+    5. Ingest the documents into the `DocumentSearch` instance.
+    6. List all documents in the vector store.
+    7. Search for documents using a query.
+    8. Print the list of all documents and the search results.
+
+To run the script, execute the following command:
+
+    ```bash
+    uv run examples/document-search/qdrant.py
+    ```
+"""
+
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "ragbits-document-search",
+#     "ragbits-core[litellm,qdrant]",
+# ]
+# ///
+
+import asyncio
+
+from qdrant_client import AsyncQdrantClient
+
+from ragbits.core.embeddings.litellm import LiteLLMEmbeddings
+from ragbits.core.vector_stores.qdrant import QdrantVectorStore
+from ragbits.document_search import DocumentSearch, SearchConfig
+from ragbits.document_search.documents.document import DocumentMeta
+
+documents = [
+    DocumentMeta.create_text_document_from_literal(
+        """
+        RIP boiled water. You will be mist.
+        """
+    ),
+    DocumentMeta.create_text_document_from_literal(
+        """
+        Why doesn't James Bond fart in bed? Because it would blow his cover.
+        """
+    ),
+    DocumentMeta.create_text_document_from_literal(
+        """
+        Why programmers don't like to swim? Because they're scared of the floating points.
+        """
+    ),
+    DocumentMeta.create_text_document_from_literal(
+        """
+        This one is completely unrelated.
+        """
+    ),
+]
+
+
+async def main() -> None:
+    """
+    Run the example.
+    """
+    embedder = LiteLLMEmbeddings(
+        model="text-embedding-3-small",
+    )
+    vector_store = QdrantVectorStore(
+        client=AsyncQdrantClient(":memory:"),
+        index_name="jokes",
+    )
+    document_search = DocumentSearch(
+        embedder=embedder,
+        vector_store=vector_store,
+    )
+
+    await document_search.ingest(documents)
+
+    all_documents = await vector_store.list()
+
+    print()
+    print("All documents:")
+    print([doc.metadata["content"] for doc in all_documents])
+
+    query = "I'm boiling my water and I need a joke"
+    vector_store_kwargs = {
+        "k": 2,
+        "max_distance": 0.6,
+    }
+    results = await document_search.search(
+        query,
+        config=SearchConfig(vector_store_kwargs=vector_store_kwargs),
+    )
+
+    print()
+    print(f"Documents similar to: {query}")
+    print([element.text_representation for element in results])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/packages/ragbits-core/pyproject.toml b/packages/ragbits-core/pyproject.toml
index 5ed2672f..ecfe10f1 100644
--- a/packages/ragbits-core/pyproject.toml
+++ b/packages/ragbits-core/pyproject.toml
@@ -59,6 +59,9 @@ promptfoo = [
 otel = [
     "opentelemetry-api~=1.27.0",
 ]
+qdrant = [
+    "qdrant-client~=1.12.1",
+]
 
 [tool.uv]
 dev-dependencies = [
diff --git a/packages/ragbits-core/src/ragbits/core/metadata_stores/in_memory.py b/packages/ragbits-core/src/ragbits/core/metadata_stores/in_memory.py
index 0cd1f89f..6f8a73db 100644
--- a/packages/ragbits-core/src/ragbits/core/metadata_stores/in_memory.py
+++ b/packages/ragbits-core/src/ragbits/core/metadata_stores/in_memory.py
@@ -23,7 +23,7 @@ async def store(self, ids: list[str], metadatas: list[dict]) -> None:
             ids: list of unique ids of the entries
             metadatas: list of dicts with metadata.
         """
-        for _id, metadata in zip(ids, metadatas, strict=False):
+        for _id, metadata in zip(ids, metadatas, strict=True):
             self._storage[_id] = metadata
 
     @traceable
diff --git a/packages/ragbits-core/src/ragbits/core/vector_stores/__init__.py b/packages/ragbits-core/src/ragbits/core/vector_stores/__init__.py
index b9c16c34..7fb02751 100644
--- a/packages/ragbits-core/src/ragbits/core/vector_stores/__init__.py
+++ b/packages/ragbits-core/src/ragbits/core/vector_stores/__init__.py
@@ -1,34 +1,27 @@
 import sys
 
-from ..metadata_stores import get_metadata_store
-from ..utils.config_handling import get_cls_from_config
-from .base import VectorStore, VectorStoreEntry, VectorStoreOptions, WhereQuery
-from .in_memory import InMemoryVectorStore
+from ragbits.core.utils.config_handling import get_cls_from_config
+from ragbits.core.vector_stores.base import VectorStore, VectorStoreEntry, VectorStoreOptions, WhereQuery
+from ragbits.core.vector_stores.in_memory import InMemoryVectorStore
 
-__all__ = ["InMemoryVectorStore", "VectorStore", "VectorStoreEntry", "WhereQuery"]
+__all__ = ["InMemoryVectorStore", "VectorStore", "VectorStoreEntry", "VectorStoreOptions", "WhereQuery"]
 
-module = sys.modules[__name__]
 
-
-def get_vector_store(vector_store_config: dict) -> VectorStore:
+def get_vector_store(config: dict) -> VectorStore:
     """
     Initializes and returns a VectorStore object based on the provided configuration.
 
     Args:
-        vector_store_config: A dictionary containing configuration details for the VectorStore.
+        config: A dictionary containing configuration details for the VectorStore.
 
     Returns:
         An instance of the specified VectorStore class, initialized with the provided config
         (if any) or default arguments.
-    """
-    vector_store_cls = get_cls_from_config(vector_store_config["type"], module)
-    config = vector_store_config.get("config", {})
 
-    if vector_store_config["type"].endswith("ChromaVectorStore"):
-        return vector_store_cls.from_config(config)
-
-    metadata_store_config = vector_store_config.get("metadata_store_config")
-    return vector_store_cls(
-        default_options=VectorStoreOptions(**config.get("default_options", {})),
-        metadata_store=get_metadata_store(metadata_store_config),
-    )
+    Raises:
+        KeyError: If the provided configuration does not contain a valid "type" key.
+        InvalidConfigurationError: If the provided configuration is invalid.
+        NotImplementedError: If the specified VectorStore class cannot be created from the provided configuration.
+    """
+    vector_store_cls = get_cls_from_config(config["type"], sys.modules[__name__])
+    return vector_store_cls.from_config(config.get("config", {}))
diff --git a/packages/ragbits-core/src/ragbits/core/vector_stores/base.py b/packages/ragbits-core/src/ragbits/core/vector_stores/base.py
index 234ca441..9ba342e4 100644
--- a/packages/ragbits-core/src/ragbits/core/vector_stores/base.py
+++ b/packages/ragbits-core/src/ragbits/core/vector_stores/base.py
@@ -13,8 +13,8 @@ class VectorStoreEntry(BaseModel):
     """
 
     id: str
+    key: str
     vector: list[float]
-    content: str
     metadata: dict
 
 
@@ -48,6 +48,22 @@ def __init__(
         self._default_options = default_options or VectorStoreOptions()
         self._metadata_store = metadata_store
 
+    @classmethod
+    def from_config(cls, config: dict) -> "VectorStore":
+        """
+        Creates and returns an instance of the Reranker class from the given configuration.
+
+        Args:
+            config: A dictionary containing the configuration for initializing the Reranker instance.
+
+        Returns:
+            An initialized instance of the Reranker class.
+
+        Raises:
+            NotImplementedError: If the class cannot be created from the provided configuration.
+        """
+        raise NotImplementedError(f"Cannot create class {cls.__name__} from config.")
+
     @abstractmethod
     async def store(self, entries: list[VectorStoreEntry]) -> None:
         """
diff --git a/packages/ragbits-core/src/ragbits/core/vector_stores/chroma.py b/packages/ragbits-core/src/ragbits/core/vector_stores/chroma.py
index 00a147d7..e9f55c95 100644
--- a/packages/ragbits-core/src/ragbits/core/vector_stores/chroma.py
+++ b/packages/ragbits-core/src/ragbits/core/vector_stores/chroma.py
@@ -1,10 +1,7 @@
-from __future__ import annotations
-
 import json
 from typing import Literal
 
 import chromadb
-from chromadb import Collection
 from chromadb.api import ClientAPI
 
 from ragbits.core.audit import traceable
@@ -16,14 +13,14 @@
 
 class ChromaVectorStore(VectorStore):
     """
-    Class that stores text embeddings using [Chroma](https://docs.trychroma.com/).
+    Vector store implementation using [Chroma](https://docs.trychroma.com).
     """
 
     def __init__(
         self,
         client: ClientAPI,
         index_name: str,
-        distance_method: Literal["l2", "ip", "cosine"] = "l2",
+        distance_method: Literal["l2", "ip", "cosine"] = "cosine",
         default_options: VectorStoreOptions | None = None,
         metadata_store: MetadataStore | None = None,
     ) -> None:
@@ -41,22 +38,13 @@ def __init__(
         self._client = client
         self._index_name = index_name
         self._distance_method = distance_method
-        self._collection = self._get_chroma_collection()
-
-    def _get_chroma_collection(self) -> Collection:
-        """
-        Gets or creates a collection with the given name and metadata.
-
-        Returns:
-            The collection.
-        """
-        return self._client.get_or_create_collection(
+        self._collection = self._client.get_or_create_collection(
             name=self._index_name,
             metadata={"hnsw:space": self._distance_method},
         )
 
     @classmethod
-    def from_config(cls, config: dict) -> ChromaVectorStore:
+    def from_config(cls, config: dict) -> "ChromaVectorStore":
         """
         Creates and returns an instance of the ChromaVectorStore class from the given configuration.
 
@@ -70,7 +58,7 @@ def from_config(cls, config: dict) -> ChromaVectorStore:
         return cls(
             client=client_cls(**config["client"].get("config", {})),
             index_name=config["index_name"],
-            distance_method=config.get("distance_method", "l2"),
+            distance_method=config.get("distance_method", "cosine"),
             default_options=VectorStoreOptions(**config.get("default_options", {})),
             metadata_store=get_metadata_store(config.get("metadata_store")),
         )
@@ -83,15 +71,20 @@ async def store(self, entries: list[VectorStoreEntry]) -> None:
         Args:
             entries: The entries to store.
         """
+        if not entries:
+            return
+
         ids = [entry.id for entry in entries]
-        documents = [entry.content for entry in entries]
+        documents = [entry.key for entry in entries]
         embeddings = [entry.vector for entry in entries]
         metadatas = [entry.metadata for entry in entries]
+
         metadatas = (
             [{"__metadata": json.dumps(metadata, default=str)} for metadata in metadatas]
             if self._metadata_store is None
             else await self._metadata_store.store(ids, metadatas)  # type: ignore
         )
+
         self._collection.add(ids=ids, embeddings=embeddings, metadatas=metadatas, documents=documents)  # type: ignore
 
     @traceable
@@ -116,14 +109,13 @@ async def retrieve(self, vector: list[float], options: VectorStoreOptions | None
             n_results=options.k,
             include=["metadatas", "embeddings", "distances", "documents"],
         )
+
         ids = results.get("ids") or []
-        metadatas = results.get("metadatas") or []
         embeddings = results.get("embeddings") or []
         distances = results.get("distances") or []
         documents = results.get("documents") or []
-
         metadatas = [
-            [json.loads(metadata["__metadata"]) for batch in metadatas for metadata in batch]  # type: ignore
+            [json.loads(metadata["__metadata"]) for batch in results.get("metadatas", []) for metadata in batch]  # type: ignore
             if self._metadata_store is None
             else await self._metadata_store.get(*ids)
         ]
@@ -131,12 +123,12 @@ async def retrieve(self, vector: list[float], options: VectorStoreOptions | None
         return [
             VectorStoreEntry(
                 id=id,
-                content=document,
+                key=document,
                 vector=list(embeddings),
                 metadata=metadata,  # type: ignore
             )
-            for batch in zip(ids, metadatas, embeddings, distances, documents, strict=False)
-            for id, metadata, embeddings, distance, document in zip(*batch, strict=False)
+            for batch in zip(ids, metadatas, embeddings, distances, documents, strict=True)
+            for id, metadata, embeddings, distance, document in zip(*batch, strict=True)
             if options.max_distance is None or distance <= options.max_distance
         ]
 
@@ -162,19 +154,18 @@ async def list(
         # Cast `where` to chromadb's Where type
         where_chroma: chromadb.Where | None = dict(where) if where else None
 
-        get_results = self._collection.get(
+        results = self._collection.get(
             where=where_chroma,
             limit=limit,
             offset=offset,
             include=["metadatas", "embeddings", "documents"],
         )
-        ids = get_results.get("ids") or []
-        metadatas = get_results.get("metadatas") or []
-        embeddings = get_results.get("embeddings") or []
-        documents = get_results.get("documents") or []
 
+        ids = results.get("ids") or []
+        embeddings = results.get("embeddings") or []
+        documents = results.get("documents") or []
         metadatas = (
-            [json.loads(metadata["__metadata"]) for metadata in metadatas]  # type: ignore
+            [json.loads(metadata["__metadata"]) for metadata in results.get("metadatas", [])]  # type: ignore
             if self._metadata_store is None
             else await self._metadata_store.get(ids)
         )
@@ -182,9 +173,9 @@ async def list(
         return [
             VectorStoreEntry(
                 id=id,
-                content=document,
+                key=document,
                 vector=list(embedding),
                 metadata=metadata,  # type: ignore
             )
-            for id, metadata, embedding, document in zip(ids, metadatas, embeddings, documents, strict=False)
+            for id, metadata, embedding, document in zip(ids, metadatas, embeddings, documents, strict=True)
         ]
diff --git a/packages/ragbits-core/src/ragbits/core/vector_stores/in_memory.py b/packages/ragbits-core/src/ragbits/core/vector_stores/in_memory.py
index d1a1c27e..28f69608 100644
--- a/packages/ragbits-core/src/ragbits/core/vector_stores/in_memory.py
+++ b/packages/ragbits-core/src/ragbits/core/vector_stores/in_memory.py
@@ -3,6 +3,7 @@
 import numpy as np
 
 from ragbits.core.audit import traceable
+from ragbits.core.metadata_stores import get_metadata_store
 from ragbits.core.metadata_stores.base import MetadataStore
 from ragbits.core.vector_stores.base import VectorStore, VectorStoreEntry, VectorStoreOptions, WhereQuery
 
@@ -27,6 +28,22 @@ def __init__(
         super().__init__(default_options=default_options, metadata_store=metadata_store)
         self._storage: dict[str, VectorStoreEntry] = {}
 
+    @classmethod
+    def from_config(cls, config: dict) -> "InMemoryVectorStore":
+        """
+        Creates and returns an instance of the InMemoryVectorStore class from the given configuration.
+
+        Args:
+            config: A dictionary containing the configuration for initializing the InMemoryVectorStore instance.
+
+        Returns:
+            An initialized instance of the InMemoryVectorStore class.
+        """
+        return cls(
+            default_options=VectorStoreOptions(**config.get("default_options", {})),
+            metadata_store=get_metadata_store(config.get("metadata_store")),
+        )
+
     @traceable
     async def store(self, entries: list[VectorStoreEntry]) -> None:
         """
diff --git a/packages/ragbits-core/src/ragbits/core/vector_stores/qdrant.py b/packages/ragbits-core/src/ragbits/core/vector_stores/qdrant.py
new file mode 100644
index 00000000..d5bfeb4f
--- /dev/null
+++ b/packages/ragbits-core/src/ragbits/core/vector_stores/qdrant.py
@@ -0,0 +1,197 @@
+import json
+
+import qdrant_client
+from qdrant_client import AsyncQdrantClient
+from qdrant_client.models import Distance, Filter, VectorParams
+
+from ragbits.core.audit import traceable
+from ragbits.core.metadata_stores import get_metadata_store
+from ragbits.core.metadata_stores.base import MetadataStore
+from ragbits.core.utils.config_handling import get_cls_from_config
+from ragbits.core.vector_stores.base import VectorStore, VectorStoreEntry, VectorStoreOptions
+
+
+class QdrantVectorStore(VectorStore):
+    """
+    Vector store implementation using [Qdrant](https://qdrant.tech).
+    """
+
+    def __init__(
+        self,
+        client: AsyncQdrantClient,
+        index_name: str,
+        distance_method: Distance = Distance.COSINE,
+        default_options: VectorStoreOptions | None = None,
+        metadata_store: MetadataStore | None = None,
+    ) -> None:
+        """
+        Constructs a new QdrantVectorStore instance.
+
+        Args:
+            client: An instance of the Qdrant client.
+            index_name: The name of the index.
+            distance_method: The distance metric to use when creating the collection.
+            default_options: The default options for querying the vector store.
+            metadata_store: The metadata store to use. If None, the metadata will be stored in Qdrant.
+        """
+        super().__init__(default_options=default_options, metadata_store=metadata_store)
+        self._client = client
+        self._index_name = index_name
+        self._distance_method = distance_method
+
+    @classmethod
+    def from_config(cls, config: dict) -> "QdrantVectorStore":
+        """
+        Creates and returns an instance of the QdrantVectorStore class from the given configuration.
+
+        Args:
+            config: A dictionary containing the configuration for initializing the QdrantVectorStore instance.
+
+        Returns:
+            An initialized instance of the QdrantVectorStore class.
+        """
+        client_cls = get_cls_from_config(config["client"]["type"], qdrant_client)
+        return cls(
+            client=client_cls(**config["client"].get("config", {})),
+            index_name=config["index_name"],
+            distance_method=config.get("distance_method", Distance.COSINE),
+            default_options=VectorStoreOptions(**config.get("default_options", {})),
+            metadata_store=get_metadata_store(config.get("metadata_store")),
+        )
+
+    @traceable
+    async def store(self, entries: list[VectorStoreEntry]) -> None:
+        """
+        Stores vector entries in the Qdrant collection.
+
+        Args:
+            entries: List of VectorStoreEntry objects to store
+
+        Raises:
+            QdrantException: If upload to collection fails.
+        """
+        if not entries:
+            return
+
+        if not await self._client.collection_exists(self._index_name):
+            await self._client.create_collection(
+                collection_name=self._index_name,
+                vectors_config=VectorParams(size=len(entries[0].vector), distance=self._distance_method),
+            )
+
+        ids = [entry.id for entry in entries]
+        embeddings = [entry.vector for entry in entries]
+        payloads = [{"document": entry.key} for entry in entries]
+        metadatas = [entry.metadata for entry in entries]
+
+        metadatas = (
+            [{"metadata": json.dumps(metadata, default=str)} for metadata in metadatas]
+            if self._metadata_store is None
+            else await self._metadata_store.store(ids, metadatas)  # type: ignore
+        )
+        if metadatas is not None:
+            payloads = [{**payload, **metadata} for (payload, metadata) in zip(payloads, metadatas, strict=True)]
+
+        self._client.upload_collection(
+            collection_name=self._index_name,
+            vectors=embeddings,
+            payload=payloads,
+            ids=ids,
+            wait=True,
+        )
+
+    @traceable
+    async def retrieve(self, vector: list[float], options: VectorStoreOptions | None = None) -> list[VectorStoreEntry]:
+        """
+        Retrieves entries from the Qdrant collection based on vector similarity.
+
+        Args:
+            vector: The vector to query.
+            options: The options for querying the vector store.
+
+        Returns:
+            The retrieved entries.
+
+        Raises:
+            MetadataNotFoundError: If metadata cannot be retrieved
+        """
+        options = options or self._default_options
+        score_threshold = 1 - options.max_distance if options.max_distance else None
+
+        results = await self._client.query_points(
+            collection_name=self._index_name,
+            query=vector,
+            limit=options.k,
+            score_threshold=score_threshold,
+            with_payload=True,
+            with_vectors=True,
+        )
+
+        ids = [point.id for point in results.points]
+        vectors = [point.vector for point in results.points]
+        documents = [point.payload["document"] for point in results.points]  # type: ignore
+        metadatas = (
+            [json.loads(point.payload["metadata"]) for point in results.points]  # type: ignore
+            if self._metadata_store is None
+            else await self._metadata_store.get(ids)  # type: ignore
+        )
+
+        return [
+            VectorStoreEntry(
+                id=str(id),
+                key=document,
+                vector=vector,  # type: ignore
+                metadata=metadata,
+            )
+            for id, document, vector, metadata in zip(ids, documents, vectors, metadatas, strict=True)
+        ]
+
+    @traceable
+    async def list(  # type: ignore
+        self,
+        where: Filter | None = None,  # type: ignore
+        limit: int | None = None,
+        offset: int = 0,
+    ) -> list[VectorStoreEntry]:
+        """
+        List entries from the vector store. The entries can be filtered, limited and offset.
+
+        Args:
+            where: Conditions for filtering results.
+            Reference: https://qdrant.tech/documentation/concepts/filtering
+            limit: The maximum number of entries to return.
+            offset: The number of entries to skip.
+
+        Returns:
+            The entries.
+
+        Raises:
+            MetadataNotFoundError: If the metadata is not found.
+        """
+        results = await self._client.query_points(
+            collection_name=self._index_name,
+            query_filter=where,
+            limit=limit or 10,
+            offset=offset,
+            with_payload=True,
+            with_vectors=True,
+        )
+
+        ids = [point.id for point in results.points]
+        vectors = [point.vector for point in results.points]
+        documents = [point.payload["document"] for point in results.points]  # type: ignore
+        metadatas = (
+            [json.loads(point.payload["metadata"]) for point in results.points]  # type: ignore
+            if self._metadata_store is None
+            else await self._metadata_store.get(ids)  # type: ignore
+        )
+
+        return [
+            VectorStoreEntry(
+                id=str(id),
+                key=document,
+                vector=vector,  # type: ignore
+                metadata=metadata,
+            )
+            for id, document, vector, metadata in zip(ids, documents, vectors, metadatas, strict=True)
+        ]
diff --git a/packages/ragbits-core/tests/unit/vector_stores/test_chroma.py b/packages/ragbits-core/tests/unit/vector_stores/test_chroma.py
index b3d038ec..f79a5126 100644
--- a/packages/ragbits-core/tests/unit/vector_stores/test_chroma.py
+++ b/packages/ragbits-core/tests/unit/vector_stores/test_chroma.py
@@ -14,16 +14,11 @@ def mock_chromadb_store() -> ChromaVectorStore:
     )
 
 
-async def test_get_chroma_collection(mock_chromadb_store: ChromaVectorStore) -> None:
-    _ = mock_chromadb_store._get_chroma_collection()
-    assert mock_chromadb_store._client.get_or_create_collection.call_count == 2  # type: ignore
-
-
 async def test_store(mock_chromadb_store: ChromaVectorStore) -> None:
     data = [
         VectorStoreEntry(
             id="test_key",
-            content="test content",
+            key="test content",
             vector=[0.1, 0.2, 0.3],
             metadata={
                 "content": "test content",
@@ -70,8 +65,7 @@ async def test_retrieve(
     mock_chromadb_store: ChromaVectorStore, max_distance: float | None, results: list[dict]
 ) -> None:
     vector = [0.1, 0.2, 0.3]
-    mock_collection = mock_chromadb_store._get_chroma_collection()
-    mock_collection.query.return_value = {  # type: ignore
+    mock_chromadb_store._collection.query.return_value = {  # type: ignore
         "metadatas": [
             [
                 {
@@ -93,17 +87,16 @@ async def test_retrieve(
     entries = await mock_chromadb_store.retrieve(vector, options=VectorStoreOptions(max_distance=max_distance))
 
     assert len(entries) == len(results)
-    for entry, result in zip(entries, results, strict=False):
+    for entry, result in zip(entries, results, strict=True):
         assert entry.metadata["content"] == result["content"]
         assert entry.metadata["document"]["title"] == result["title"]
         assert entry.vector == result["vector"]
         assert entry.id == f"test_id_{results.index(result) + 1}"
-        assert entry.content == result["content"]
+        assert entry.key == result["content"]
 
 
 async def test_list(mock_chromadb_store: ChromaVectorStore) -> None:
-    mock_collection = mock_chromadb_store._get_chroma_collection()
-    mock_collection.get.return_value = {  # type: ignore
+    mock_chromadb_store._collection.get.return_value = {  # type: ignore
         "metadatas": [
             {
                 "__metadata": '{"content": "test content", "document": {"title": "test title", "source":'
@@ -125,10 +118,10 @@ async def test_list(mock_chromadb_store: ChromaVectorStore) -> None:
     assert entries[0].metadata["content"] == "test content"
     assert entries[0].metadata["document"]["title"] == "test title"
     assert entries[0].vector == [0.12, 0.25, 0.29]
-    assert entries[0].content == "test content 1"
+    assert entries[0].key == "test content 1"
     assert entries[0].id == "test_id_1"
     assert entries[1].metadata["content"] == "test content 2"
     assert entries[1].metadata["document"]["title"] == "test title 2"
     assert entries[1].vector == [0.13, 0.26, 0.30]
-    assert entries[1].content == "test content2"
+    assert entries[1].key == "test content2"
     assert entries[1].id == "test_id_2"
diff --git a/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py b/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py
index d47e19a2..2c167330 100644
--- a/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py
+++ b/packages/ragbits-core/tests/unit/vector_stores/test_in_memory.py
@@ -1,6 +1,7 @@
 from pathlib import Path
 
 import pytest
+from pydantic import computed_field
 
 from ragbits.core.vector_stores.base import VectorStoreOptions
 from ragbits.core.vector_stores.in_memory import InMemoryVectorStore
@@ -20,12 +21,14 @@ class AnimalElement(Element):
     type: str
     age: int
 
-    def get_text_representation(self) -> str:
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def text_representation(self) -> str:
         """
         Get the text representation of the element.
 
         Returns:
-            The key.
+            The text representation.
         """
         return self.name
 
@@ -66,7 +69,7 @@ async def test_retrieve(store: InMemoryVectorStore, k: int, max_distance: float
     entries = await store.retrieve(search_vector, options=VectorStoreOptions(k=k, max_distance=max_distance))
 
     assert len(entries) == len(results)
-    for entry, result in zip(entries, results, strict=False):
+    for entry, result in zip(entries, results, strict=True):
         assert entry.metadata["name"] == result
 
 
diff --git a/packages/ragbits-core/tests/unit/vector_stores/test_qdrant.py b/packages/ragbits-core/tests/unit/vector_stores/test_qdrant.py
new file mode 100644
index 00000000..22cb9391
--- /dev/null
+++ b/packages/ragbits-core/tests/unit/vector_stores/test_qdrant.py
@@ -0,0 +1,140 @@
+from unittest.mock import AsyncMock
+
+import pytest
+from qdrant_client.http import models
+
+from ragbits.core.vector_stores.base import VectorStoreEntry
+from ragbits.core.vector_stores.qdrant import QdrantVectorStore
+
+
+@pytest.fixture
+def mock_qdrant_store() -> QdrantVectorStore:
+    return QdrantVectorStore(
+        client=AsyncMock(),
+        index_name="test_collection",
+    )
+
+
+async def test_store(mock_qdrant_store: QdrantVectorStore) -> None:
+    data = [
+        VectorStoreEntry(
+            id="1c7d6b27-4ef1-537c-ad7c-676edb8bc8a8",
+            key="test_key",
+            vector=[0.1, 0.2, 0.3],
+            metadata={
+                "content": "test content",
+                "document": {
+                    "title": "test title",
+                    "source": {"path": "/test/path"},
+                    "document_type": "test_type",
+                },
+            },
+        )
+    ]
+
+    mock_qdrant_store._client.collection_exists.return_value = False  # type: ignore
+    await mock_qdrant_store.store(data)
+
+    mock_qdrant_store._client.collection_exists.assert_called_once()  # type: ignore
+    mock_qdrant_store._client.create_collection.assert_called_once()  # type: ignore
+    mock_qdrant_store._client.upload_collection.assert_called_with(  # type: ignore
+        collection_name="test_collection",
+        vectors=[[0.1, 0.2, 0.3]],
+        payload=[
+            {
+                "document": "test_key",
+                "metadata": '{"content": "test content", '
+                '"document": {"title": "test title", "source": {"path": "/test/path"}, "document_type": "test_type"}}',
+            }
+        ],
+        ids=["1c7d6b27-4ef1-537c-ad7c-676edb8bc8a8"],
+        wait=True,
+    )
+
+
+async def test_retrieve(mock_qdrant_store: QdrantVectorStore) -> None:
+    mock_qdrant_store._client.query_points.return_value = models.QueryResponse(  # type: ignore
+        points=[
+            models.ScoredPoint(
+                version=1,
+                id="1f908deb-bc9f-4b5a-8b73-2e72d8b44dc5",
+                vector=[0.12, 0.25, 0.29],
+                score=0.9,
+                payload={
+                    "document": "test_key 1",
+                    "metadata": '{"content": "test content 1",'
+                    '"document": {"title": "test title 1", '
+                    '"source": {"path": "/test/path-1"}, "document_type": "txt"}}',
+                },
+            ),
+            models.ScoredPoint(
+                version=1,
+                id="827cad0b-058f-4b85-b8ed-ac741948d502",
+                vector=[0.13, 0.26, 0.30],
+                score=0.9,
+                payload={
+                    "document": "test_key 2",
+                    "metadata": '{"content": "test content 2", '
+                    '"document": {"title": "test title 2", '
+                    '"source": {"path": "/test/path-2"}, "document_type": "txt"}}',
+                },
+            ),
+        ]
+    )
+
+    results = [
+        {"content": "test content 1", "title": "test title 1", "vector": [0.12, 0.25, 0.29]},
+        {"content": "test content 2", "title": "test title 2", "vector": [0.13, 0.26, 0.30]},
+    ]
+
+    entries = await mock_qdrant_store.retrieve([0.12, 0.25, 0.29])
+
+    assert len(entries) == len(results)
+    for entry, result in zip(entries, results, strict=True):
+        assert entry.metadata["content"] == result["content"]
+        assert entry.metadata["document"]["title"] == result["title"]
+        assert entry.vector == result["vector"]
+
+
+async def test_list(mock_qdrant_store: QdrantVectorStore) -> None:
+    mock_qdrant_store._client.query_points.return_value = models.QueryResponse(  # type: ignore
+        points=[
+            models.ScoredPoint(
+                version=1,
+                id="1f908deb-bc9f-4b5a-8b73-2e72d8b44dc5",
+                vector=[0.12, 0.25, 0.29],
+                score=0.9,
+                payload={
+                    "document": "test_key 1",
+                    "metadata": '{"content": "test content 1",'
+                    '"document": {"title": "test title 1", '
+                    '"source": {"path": "/test/path-1"}, "document_type": "txt"}}',
+                },
+            ),
+            models.ScoredPoint(
+                version=1,
+                id="827cad0b-058f-4b85-b8ed-ac741948d502",
+                vector=[0.13, 0.26, 0.30],
+                score=0.9,
+                payload={
+                    "document": "test_key 2",
+                    "metadata": '{"content": "test content 2", '
+                    '"document": {"title": "test title 2", '
+                    '"source": {"path": "/test/path-2"}, "document_type": "txt"}}',
+                },
+            ),
+        ]
+    )
+
+    results = [
+        {"content": "test content 1", "title": "test title 1", "vector": [0.12, 0.25, 0.29]},
+        {"content": "test content 2", "title": "test title 2", "vector": [0.13, 0.26, 0.30]},
+    ]
+
+    entries = await mock_qdrant_store.list()
+
+    assert len(entries) == len(results)
+    for entry, result in zip(entries, results, strict=True):
+        assert entry.metadata["content"] == result["content"]
+        assert entry.metadata["document"]["title"] == result["title"]
+        assert entry.vector == result["vector"]
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/_main.py b/packages/ragbits-document-search/src/ragbits/document_search/_main.py
index 521ea0ff..daa56dc0 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/_main.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/_main.py
@@ -170,7 +170,7 @@ async def insert_elements(self, elements: list[Element]) -> None:
         Args:
             elements: The list of Elements to insert.
         """
-        vectors = await self.embedder.embed_text([element.get_text_for_embedding() for element in elements])
+        vectors = await self.embedder.embed_text([element.key for element in elements])
 
         image_elements = [element for element in elements if isinstance(element, ImageElement)]
         entries = [element.to_vector_db_entry(vector) for element, vector in zip(elements, vectors, strict=False)]
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py b/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py
index 893d7d3c..63bf82bd 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/documents/element.py
@@ -42,24 +42,27 @@ def id(self) -> str:
         id_components = [
             self.document_meta.id,
             self.element_type,
-            self.get_text_for_embedding(),
-            self.get_text_representation(),
+            self.key,
+            self.text_representation,
             str(self.location),
         ]
-
         return str(uuid.uuid5(uuid.NAMESPACE_OID, ";".join(id_components)))
 
-    def get_text_for_embedding(self) -> str:
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def key(self) -> str:
         """
-        Get the text representation of the element for embedding.
+        Get the representation of the element for embedding.
 
         Returns:
-            The text representation for embedding.
+            The representation for embedding.
         """
-        return self.get_text_representation()
+        return self.text_representation
 
+    @computed_field  # type: ignore[prop-decorator]
+    @property
     @abstractmethod
-    def get_text_representation(self) -> str:
+    def text_representation(self) -> str:
         """
         Get the text representation of the element.
 
@@ -68,12 +71,10 @@ def get_text_representation(self) -> str:
         """
 
     @classmethod
-    def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:  # pylint: disable=unused-argument #noqa: ANN401
+    def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:  # noqa: ANN401
         element_type_default = cls.model_fields["element_type"].default
-
         if element_type_default is None:
             raise ValueError("Element type must be defined")
-
         Element._elements_registry[element_type_default] = cls
 
     @classmethod
@@ -87,11 +88,9 @@ def from_vector_db_entry(cls, db_entry: VectorStoreEntry) -> "Element":
         Returns:
             The element.
         """
-        meta = db_entry.metadata
-        element_type = meta["element_type"]
+        element_type = db_entry.metadata["element_type"]
         element_cls = Element._elements_registry[element_type]
-
-        return element_cls(**meta)
+        return element_cls(**db_entry.metadata)
 
     def to_vector_db_entry(self, vector: list[float]) -> VectorStoreEntry:
         """
@@ -105,9 +104,9 @@ def to_vector_db_entry(self, vector: list[float]) -> VectorStoreEntry:
         """
         return VectorStoreEntry(
             id=self.id,
+            key=self.key,
             vector=vector,
-            content=self.get_text_for_embedding(),
-            metadata=self.model_dump(),
+            metadata=self.model_dump(exclude={"id", "key"}),
         )
 
 
@@ -119,7 +118,9 @@ class TextElement(Element):
     element_type: str = "text"
     content: str
 
-    def get_text_representation(self) -> str:
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def text_representation(self) -> str:
         """
         Get the text representation of the element.
 
@@ -139,7 +140,9 @@ class ImageElement(Element):
     ocr_extracted_text: str
     image_bytes: bytes
 
-    def get_text_representation(self) -> str:
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def text_representation(self) -> str:
         """
         Get the text representation of the element.
 
diff --git a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py
index c83906ce..a27e982f 100644
--- a/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py
+++ b/packages/ragbits-document-search/src/ragbits/document_search/retrieval/rerankers/litellm.py
@@ -58,7 +58,7 @@ async def rerank(
             The reranked elements.
         """
         options = self._default_options if options is None else options
-        documents = [element.get_text_representation() for element in elements]
+        documents = [element.text_representation for element in elements]
 
         response = await litellm.arerank(
             model=self.model,
diff --git a/packages/ragbits-document-search/tests/unit/test_elements.py b/packages/ragbits-document-search/tests/unit/test_elements.py
index 7f98307c..f4b1fa78 100644
--- a/packages/ragbits-document-search/tests/unit/test_elements.py
+++ b/packages/ragbits-document-search/tests/unit/test_elements.py
@@ -1,20 +1,24 @@
+from pydantic import computed_field
+
 from ragbits.core.vector_stores.base import VectorStoreEntry
 from ragbits.document_search.documents.document import DocumentType
 from ragbits.document_search.documents.element import Element
 
 
-def test_resolving_element_type():
+def test_resolving_element_type() -> None:
     class MyElement(Element):
         element_type: str = "custom_element"
         foo: str
 
-        def get_text_representation(self) -> str:
+        @computed_field  # type: ignore[prop-decorator]
+        @property
+        def text_representation(self) -> str:
             return self.foo + self.foo
 
     element = Element.from_vector_db_entry(
         db_entry=VectorStoreEntry(
             id="test id",
-            content="test content",
+            key="test content",
             vector=[0.1, 0.2],
             metadata={
                 "element_type": "custom_element",
@@ -29,7 +33,7 @@ def get_text_representation(self) -> str:
 
     assert isinstance(element, MyElement)
     assert element.foo == "bar"
-    assert element.get_text_for_embedding() == "barbar"
-    assert element.get_text_representation() == "barbar"
+    assert element.key == "barbar"
+    assert element.text_representation == "barbar"
     assert element.document_meta.document_type == DocumentType.TXT
     assert element.document_meta.source.source_type == "local_file_source"
diff --git a/pyproject.toml b/pyproject.toml
index b56cc198..0ee989d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
     "ragbits-cli",
-    "ragbits-core[chroma,lab,litellm,local,otel]",
+    "ragbits-core[chroma,lab,litellm,local,otel,qdrant]",
     "ragbits-document-search[gcs,huggingface]",
     "ragbits-evaluate[relari]",
     "ragbits-guardrails[openai]",
diff --git a/uv.lock b/uv.lock
index a3b1b1b8..c89e5221 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1368,6 +1368,43 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/df/133216989fe7e17caeafd7ff5b17cc82c4e722025d0b8d5d2290c11fe2e6/grpcio-1.66.2-cp313-cp313-win_amd64.whl", hash = "sha256:fb70487c95786e345af5e854ffec8cb8cc781bcc5df7930c4fbb7feaa72e1cdf", size = 4278018 },
 ]
 
+[[package]]
+name = "grpcio-tools"
+version = "1.62.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "grpcio" },
+    { name = "protobuf" },
+    { name = "setuptools" },
+]
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 name = "pyyaml"
 version = "6.0.2"
@@ -3647,6 +3751,24 @@ wheels = [
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 ]
 
+[[package]]
+name = "qdrant-client"
+version = "1.12.1"
+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/15/5e/ec560881e086f893947c8798949c72de5cfae9453fd05c2250f8dfeaa571/qdrant_client-1.12.1.tar.gz", hash = "sha256:35e8e646f75b7b883b3d2d0ee4c69c5301000bba41c82aa546e985db0f1aeb72", size = 237441 }
+wheels = [
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+]
+
 [[package]]
 name = "ragbits"
 version = "0.0.1"
@@ -3703,6 +3825,9 @@ otel = [
 promptfoo = [
     { name = "pyyaml" },
 ]
+qdrant = [
+    { name = "qdrant-client" },
+]
 
 [package.dev-dependencies]
 dev = [
@@ -3723,6 +3848,7 @@ requires-dist = [
     { name = "opentelemetry-api", marker = "extra == 'otel'", specifier = "~=1.27.0" },
     { name = "pydantic", specifier = ">=2.9.1" },
     { name = "pyyaml", marker = "extra == 'promptfoo'", specifier = "~=6.0.2" },
+    { name = "qdrant-client", marker = "extra == 'qdrant'", specifier = "~=1.12.1" },
     { name = "tomli", specifier = "~=2.0.2" },
     { name = "torch", marker = "extra == 'local'", specifier = "~=2.2.1" },
     { name = "transformers", marker = "extra == 'local'", specifier = "~=4.44.2" },
@@ -3873,7 +3999,7 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "ragbits-cli" },
-    { name = "ragbits-core", extra = ["chroma", "lab", "litellm", "local", "otel"] },
+    { name = "ragbits-core", extra = ["chroma", "lab", "litellm", "local", "otel", "qdrant"] },
     { name = "ragbits-document-search", extra = ["gcs", "huggingface"] },
     { name = "ragbits-evaluate", extra = ["relari"] },
     { name = "ragbits-guardrails", extra = ["openai"] },
@@ -3901,7 +4027,7 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "ragbits-cli", editable = "packages/ragbits-cli" },
-    { name = "ragbits-core", extras = ["chroma", "lab", "litellm", "local", "otel"], editable = "packages/ragbits-core" },
+    { name = "ragbits-core", extras = ["chroma", "lab", "litellm", "local", "otel", "qdrant"], editable = "packages/ragbits-core" },
     { name = "ragbits-document-search", extras = ["gcs", "huggingface"], editable = "packages/ragbits-document-search" },
     { name = "ragbits-evaluate", extras = ["relari"], editable = "packages/ragbits-evaluate" },
     { name = "ragbits-guardrails", extras = ["openai"], editable = "packages/ragbits-guardrails" },