feat: Qdrant vector store support

docs/core-components/vector-store.md

@@ -80,14 +80,14 @@ vectordb_new_pod = PineconeVectorDb(
 
 BeyondLLM currently integrates with Weaviate, a versatile and scalable vector database designed for high-performance similarity search and efficient management of vector embeddings.
 
-#### Parameters for WeaviateVectorDb:
+**Parameters:**
 
 * **url** : Specifies the URL of your Weaviate cluster. This is essential for connecting to the Weaviate instance where your embeddings will be stored.
 * **index\_name** : The name of the index within Weaviate where your embeddings will be organized and managed.
 * **api\_key** : The API key for authenticated access to your Weaviate instance. If not provided, the connection will be unauthenticated.
 * **additional\_headers** : Additional headers for the Weaviate request in JSON format. This is useful for custom configurations or additional authentication methods.
 
-#### Code Example:
+**Code Example:**
 
 ```python
 from beyondllm.vectordb import WeaviateVectorDb
@@ -101,6 +101,34 @@ vectordb = WeaviateVectorDb(
 )
 ```
 
+### 4. Qdrant
+
+[Qdrant](http://qdrant.tech) is an open-source, high-performance, vector search engine. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support.
+
+**Parameters:**
+
+* **collection** : Name of the collection to be used. If the collection doesn't exist, it is created automatically.
+* **client** : An instance of [`qdrant_client.QdrantClient`](https://python-client.qdrant.tech/qdrant_client.qdrant_client) for interfacing with the Qdrant server.
+* **llamaindex_kwargs** : Additional options to pass when instantiating [`llama_index.vector_stores.qdrant.QdrantVectorStore`](https://docs.llamaindex.ai/en/stable/api_reference/storage/vector_store/qdrant/#llama_index.vector_stores.qdrant.QdrantVectorStore).
+
+**Code Example:**
+
+```python
+# Install dependencies with:
+# $ pip install llama-index-vector-stores-qdrant
+
+from beyondllm.vectordb import QdrantVectorDb
+from qdrant_client import QdrantClient
+
+vdb = QdrantVectorDb(
+    collection_name="my-collection-name",
+    client=QdrantClient(url="http://localhost:6333"),
+    llamaindex_kwargs={
+        "batch_size": 64
+    }
+)
+```
+
 ### **Integrating with BeyondLLM Retrievers:**
 
 VectorDB instances can be used with the auto\_retriever functionality provided by BeyondLLM, by simply passing instance within the auto\_retriever function to enable efficient retrieval from your Vector Store index:

src/beyondllm/vectordb/__init__.py

@@ -1,4 +1,5 @@
 from .chroma import ChromaVectorDb
 from .pinecone import PineconeVectorDb
 from .weaviate import WeaviateVectorDb
-from .redis import RedisVectorDb
+from .redis import RedisVectorDb
+from .qdrant import QdrantVectorDb

src/beyondllm/vectordb/qdrant.py

@@ -0,0 +1,86 @@
+from typing import Any
+from beyondllm.vectordb.base import VectorDbConfig
+from dataclasses import dataclass, field
+import warnings
+
+warnings.filterwarnings("ignore")
+import subprocess, sys
+
+
+@dataclass
+class QdrantVectorDb:
+    """
+    Vector store implementation using Qdrant - https://qdrant.tech/.
+    REQUIRES the `"llama-index-vector-stores-qdrant"` package.
+
+    >>> from beyondllm.vectordb import QdrantVectorDb
+    >>> from qdrant_client import QdrantClient
+
+    >>> vdb = QdrantVectorDb(
+    ...     collection_name="my-collection-name",
+    ...     client=QdrantClient(url="http://localhost:6333"),
+    ...     llamaindex_kwargs={
+    ...         "batch_size": 64
+    ...     }
+    ... )
+    """
+
+    client: Any
+    collection_name: str
+    llamaindex_kwargs: dict = field(default_factory=dict)
+
+    def __post_init__(self):
+        try:
+            from llama_index.vector_stores.qdrant import QdrantVectorStore
+
+            self.client: QdrantVectorStore
+
+        except ImportError:
+            user_agree = input(
+                "The feature requires an additional package: llama-index-vector-stores-qdrant. Would you like to install it now? [y/n]: "
+            )
+            if user_agree.lower() == "y":
+                subprocess.check_call(
+                    [
+                        sys.executable,
+                        "-m",
+                        "pip",
+                        "install",
+                        "pip install llama-index-vector-stores-qdrant",
+                    ]
+                )
+                from llama_index.vector_stores.qdrant import QdrantVectorStore
+            else:
+                raise ImportError(
+                    "The 'llama-index-vector-stores-qdrant' package couldn't be installed."
+                )
+
+        self.load()
+
+    def load(self):
+        from llama_index.vector_stores.qdrant import QdrantVectorStore
+
+        self.client = QdrantVectorStore(
+            collection_name=self.collection_name,
+            client=self.client,
+            **self.llamaindex_kwargs,
+        )
+        return self.client
+
+    def add(self, *args, **kwargs):
+        return self.client.add(*args, **kwargs)
+
+    def stores_text(self, *args, **kwargs):
+        return self.client.stores_text(*args, **kwargs)
+
+    def is_embedding_query(self, *args, **kwargs):
+        return self.client.is_embedding_query(*args, **kwargs)
+
+    def query(self, *args, **kwargs):
+        return self.client.query(*args, **kwargs)
+
+    @staticmethod
+    def load_from_kwargs(self, kwargs):
+        embed_config = VectorDbConfig(**kwargs)
+        self.config = embed_config
+        self.load()