community[patch]: Added a function from_existing_collection in Qdrant vector database. (#20779)

Issue: #20514 
The current implementation of `construct_instance` expects a `texts:
List[str]` that will call the embedding function. This might not be
needed when we already have a client with collection and `path, you
don't want to add any text.

This PR adds a class method that returns a qdrant instance with an
existing client.

Here everytime
cb6e5e56c2/libs/community/langchain_community/vectorstores/qdrant.py (L1592)
`construct_instance` is called, this line sends some text for embedding
generation.

---------

Co-authored-by: Anush <anushshetty90@gmail.com>

libs/community/langchain_community/vectorstores/qdrant.py

@@ -1367,6 +1367,51 @@ class Qdrant(VectorStore):
         qdrant.add_texts(texts, metadatas, ids, batch_size)
         return qdrant
 
+    @classmethod
+    def from_existing_collection(
+        cls: Type[Qdrant],
+        embedding: Embeddings,
+        path: str,
+        collection_name: str,
+        location: Optional[str] = None,
+        url: Optional[str] = None,
+        port: Optional[int] = 6333,
+        grpc_port: int = 6334,
+        prefer_grpc: bool = False,
+        https: Optional[bool] = None,
+        api_key: Optional[str] = None,
+        prefix: Optional[str] = None,
+        timeout: Optional[float] = None,
+        host: Optional[str] = None,
+        **kwargs: Any,
+    ) -> Qdrant:
+        """
+        Get instance of an existing Qdrant collection.
+        This method will return the instance of the store without inserting any new
+        embeddings
+        """
+        client, async_client = cls._generate_clients(
+            location=location,
+            url=url,
+            port=port,
+            grpc_port=grpc_port,
+            prefer_grpc=prefer_grpc,
+            https=https,
+            api_key=api_key,
+            prefix=prefix,
+            timeout=timeout,
+            host=host,
+            path=path,
+            **kwargs,
+        )
+        return cls(
+            client=client,
+            async_client=async_client,
+            collection_name=collection_name,
+            embeddings=embedding,
+            **kwargs,
+        )
+
     @classmethod
     @sync_call_fallback
     async def afrom_texts(

libs/community/tests/integration_tests/vectorstores/qdrant/test_from_existing_collection.py

@@ -0,0 +1,39 @@
+import tempfile
+import uuid
+
+import pytest
+
+from langchain_community.vectorstores import Qdrant
+from tests.integration_tests.vectorstores.fake_embeddings import (
+    ConsistentFakeEmbeddings,
+)
+
+
+@pytest.mark.parametrize("vector_name", ["custom-vector"])
+def test_qdrant_from_existing_collection_uses_same_collection(vector_name: str) -> None:
+    """Test if the Qdrant.from_existing_collection reuses the same collection."""
+    from qdrant_client import QdrantClient
+
+    collection_name = uuid.uuid4().hex
+    with tempfile.TemporaryDirectory() as tmpdir:
+        docs = ["foo"]
+        qdrant = Qdrant.from_texts(
+            docs,
+            embedding=ConsistentFakeEmbeddings(),
+            path=str(tmpdir),
+            collection_name=collection_name,
+            vector_name=vector_name,
+        )
+        del qdrant
+
+        qdrant = Qdrant.from_existing_collection(
+            embedding=ConsistentFakeEmbeddings(),
+            path=str(tmpdir),
+            collection_name=collection_name,
+            vector_name=vector_name,
+        )
+        qdrant.add_texts(["baz", "bar"])
+        del qdrant
+
+        client = QdrantClient(path=str(tmpdir))
+        assert 3 == client.count(collection_name).count