qdrant: test new QdrantVectorStore (#24165)

## Description

This PR adds integration tests to follow up on #24164.

By default, the tests use an in-memory instance.

To run the full suite of tests, with both in-memory and Qdrant server:

```
$ docker run -p 6333:6333 qdrant/qdrant

$ make test

$ make integration_test
```

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/qdrant/tests/integration_tests/common.py

@@ -4,6 +4,8 @@ import requests  # type: ignore
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 
+from langchain_qdrant import SparseEmbeddings, SparseVector
+
 
 def qdrant_running_locally() -> bool:
     """Check if Qdrant is running at http://localhost:6333."""
@@ -55,3 +57,29 @@ class ConsistentFakeEmbeddings(Embeddings):
         """Return consistent embeddings for the text, if seen before, or a constant
         one if the text is unknown."""
         return self.embed_documents([text])[0]
+
+
+class ConsistentFakeSparseEmbeddings(SparseEmbeddings):
+    """Fake sparse embeddings which remembers all the texts seen so far "
+    "to return consistent vectors for the same texts."""
+
+    def __init__(self, dimensionality: int = 25) -> None:
+        self.known_texts: List[str] = []
+        self.dimensionality = 25
+
+    def embed_documents(self, texts: List[str]) -> List[SparseVector]:
+        """Return consistent embeddings for each text seen so far."""
+        out_vectors = []
+        for text in texts:
+            if text not in self.known_texts:
+                self.known_texts.append(text)
+            index = self.known_texts.index(text)
+            indices = [i + index for i in range(self.dimensionality)]
+            values = [1.0] * (self.dimensionality - 1) + [float(index)]
+            out_vectors.append(SparseVector(indices=indices, values=values))
+        return out_vectors
+
+    def embed_query(self, text: str) -> SparseVector:
+        """Return consistent embeddings for the text, "
+        "if seen before, or a constant one if the text is unknown."""
+        return self.embed_documents([text])[0]