Support named vectors in Qdrant (#6871)

# Description

This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.

## Example

```python
Qdrant.from_documents(
    docs,
    embeddings,
    location=":memory:",
    collection_name="my_documents",
    vector_name="custom_vector",
)
```

### Issue: #2594 

Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.

tests/integration_tests/vectorstores/test_qdrant.py

@@ -14,15 +14,8 @@ from tests.integration_tests.vectorstores.fake_embeddings import (
 
 
 @pytest.mark.parametrize("batch_size", [1, 64])
-@pytest.mark.parametrize(
-    ["content_payload_key", "metadata_payload_key"],
-    [
-        (Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
-        ("foo", "bar"),
-        (Qdrant.CONTENT_KEY, "bar"),
-        ("foo", Qdrant.METADATA_KEY),
-    ],
-)
+@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
 def test_qdrant_similarity_search(
     batch_size: int, content_payload_key: str, metadata_payload_key: str
 ) -> None:
@@ -41,15 +34,8 @@ def test_qdrant_similarity_search(
 
 
 @pytest.mark.parametrize("batch_size", [1, 64])
-@pytest.mark.parametrize(
-    ["content_payload_key", "metadata_payload_key"],
-    [
-        (Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
-        ("foo", "bar"),
-        (Qdrant.CONTENT_KEY, "bar"),
-        ("foo", Qdrant.METADATA_KEY),
-    ],
-)
+@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
 def test_qdrant_similarity_search_by_vector(
     batch_size: int, content_payload_key: str, metadata_payload_key: str
 ) -> None:
@@ -69,15 +55,8 @@ def test_qdrant_similarity_search_by_vector(
 
 
 @pytest.mark.parametrize("batch_size", [1, 64])
-@pytest.mark.parametrize(
-    ["content_payload_key", "metadata_payload_key"],
-    [
-        (Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
-        ("foo", "bar"),
-        (Qdrant.CONTENT_KEY, "bar"),
-        ("foo", Qdrant.METADATA_KEY),
-    ],
-)
+@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
 def test_qdrant_similarity_search_with_score_by_vector(
     batch_size: int, content_payload_key: str, metadata_payload_key: str
 ) -> None:
@@ -100,11 +79,16 @@ def test_qdrant_similarity_search_with_score_by_vector(
 
 
 @pytest.mark.parametrize("batch_size", [1, 64])
-def test_qdrant_add_documents(batch_size: int) -> None:
+@pytest.mark.parametrize("vector_name", [None, "my-vector"])
+def test_qdrant_add_documents(batch_size: int, vector_name: Optional[str]) -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
     docsearch: Qdrant = Qdrant.from_texts(
-        texts, ConsistentFakeEmbeddings(), location=":memory:", batch_size=batch_size
+        texts,
+        ConsistentFakeEmbeddings(),
+        location=":memory:",
+        batch_size=batch_size,
+        vector_name=vector_name,
     )
 
     new_texts = ["foobar", "foobaz"]
@@ -122,6 +106,7 @@ def test_qdrant_add_documents(batch_size: int) -> None:
 
 @pytest.mark.parametrize("batch_size", [1, 64])
 def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
+    """Test end to end Qdrant.add_texts returns unique ids."""
     docsearch: Qdrant = Qdrant.from_texts(
         ["foobar"],
         ConsistentFakeEmbeddings(),
@@ -135,15 +120,8 @@ def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
 
 
 @pytest.mark.parametrize("batch_size", [1, 64])
-@pytest.mark.parametrize(
-    ["content_payload_key", "metadata_payload_key"],
-    [
-        (Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
-        ("test_content", "test_payload"),
-        (Qdrant.CONTENT_KEY, "payload_test"),
-        ("content_test", Qdrant.METADATA_KEY),
-    ],
-)
+@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
 def test_qdrant_with_metadatas(
     batch_size: int, content_payload_key: str, metadata_payload_key: str
 ) -> None:
@@ -190,7 +168,10 @@ def test_qdrant_similarity_search_filters(batch_size: int) -> None:
     ]
 
 
-def test_qdrant_similarity_search_with_relevance_score_no_threshold() -> None:
+@pytest.mark.parametrize("vector_name", [None, "my-vector"])
+def test_qdrant_similarity_search_with_relevance_score_no_threshold(
+    vector_name: Optional[str],
+) -> None:
     """Test end to end construction and search."""
     texts = ["foo", "bar", "baz"]
     metadatas = [
@@ -202,6 +183,7 @@ def test_qdrant_similarity_search_with_relevance_score_no_threshold() -> None:
         ConsistentFakeEmbeddings(),
         metadatas=metadatas,
         location=":memory:",
+        vector_name=vector_name,
     )
     output = docsearch.similarity_search_with_relevance_scores(
         "foo", k=3, score_threshold=None
@@ -302,17 +284,14 @@ def test_qdrant_similarity_search_filters_with_qdrant_filters() -> None:
 
 
 @pytest.mark.parametrize("batch_size", [1, 64])
-@pytest.mark.parametrize(
-    ["content_payload_key", "metadata_payload_key"],
-    [
-        (Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
-        ("test_content", "test_payload"),
-        (Qdrant.CONTENT_KEY, "payload_test"),
-        ("content_test", Qdrant.METADATA_KEY),
-    ],
-)
+@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "test_content"])
+@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "test_metadata"])
+@pytest.mark.parametrize("vector_name", [None, "my-vector"])
 def test_qdrant_max_marginal_relevance_search(
-    batch_size: int, content_payload_key: str, metadata_payload_key: str
+    batch_size: int,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    vector_name: Optional[str],
 ) -> None:
     """Test end to end construction and MRR search."""
     texts = ["foo", "bar", "baz"]
@@ -325,6 +304,7 @@ def test_qdrant_max_marginal_relevance_search(
         content_payload_key=content_payload_key,
         metadata_payload_key=metadata_payload_key,
         batch_size=batch_size,
+        vector_name=vector_name,
     )
     output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
     assert output == [
@@ -344,6 +324,7 @@ def test_qdrant_max_marginal_relevance_search(
 def test_qdrant_embedding_interface(
     embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
 ) -> None:
+    """Test Qdrant may accept different types for embeddings."""
     from qdrant_client import QdrantClient
 
     client = QdrantClient(":memory:")
@@ -364,9 +345,10 @@ def test_qdrant_embedding_interface(
         (None, None),
     ],
 )
-def test_qdrant_embedding_interface_raises(
+def test_qdrant_embedding_interface_raises_value_error(
     embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
 ) -> None:
+    """Test Qdrant requires only one method for embeddings."""
     from qdrant_client import QdrantClient
 
     client = QdrantClient(":memory:")
@@ -381,21 +363,24 @@ def test_qdrant_embedding_interface_raises(
         )
 
 
-def test_qdrant_stores_duplicated_texts() -> None:
+@pytest.mark.parametrize("vector_name", [None, "my-vector"])
+def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
+    """Test end to end Qdrant.add_texts stores duplicated texts separately."""
     from qdrant_client import QdrantClient
     from qdrant_client.http import models as rest
 
     client = QdrantClient(":memory:")
     collection_name = "test"
-    client.recreate_collection(
-        collection_name,
-        vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
-    )
+    vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
+    if vector_name is not None:
+        vectors_config = {vector_name: vectors_config}  # type: ignore[assignment]
+    client.recreate_collection(collection_name, vectors_config=vectors_config)
 
     vec_store = Qdrant(
         client,
         collection_name,
         embeddings=ConsistentFakeEmbeddings(),
+        vector_name=vector_name,
     )
     ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
 
@@ -404,6 +389,7 @@ def test_qdrant_stores_duplicated_texts() -> None:
 
 
 def test_qdrant_from_texts_stores_duplicated_texts() -> None:
+    """Test end to end Qdrant.from_texts stores duplicated texts separately."""
     from qdrant_client import QdrantClient
 
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -420,7 +406,11 @@ def test_qdrant_from_texts_stores_duplicated_texts() -> None:
 
 
 @pytest.mark.parametrize("batch_size", [1, 64])
-def test_qdrant_from_texts_stores_ids(batch_size: int) -> None:
+@pytest.mark.parametrize("vector_name", [None, "my-vector"])
+def test_qdrant_from_texts_stores_ids(
+    batch_size: int, vector_name: Optional[str]
+) -> None:
+    """Test end to end Qdrant.from_texts stores provided ids."""
     from qdrant_client import QdrantClient
 
     with tempfile.TemporaryDirectory() as tmpdir:
@@ -435,6 +425,7 @@ def test_qdrant_from_texts_stores_ids(batch_size: int) -> None:
             collection_name="test",
             path=str(tmpdir),
             batch_size=batch_size,
+            vector_name=vector_name,
         )
         del vec_store
 
@@ -446,6 +437,7 @@ def test_qdrant_from_texts_stores_ids(batch_size: int) -> None:
 
 @pytest.mark.parametrize("batch_size", [1, 64])
 def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
+    """Test end to end Qdrant.add_texts stores provided ids."""
     from qdrant_client import QdrantClient
 
     ids = [
@@ -467,3 +459,57 @@ def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
     assert 2 == client.count("test").count
     stored_ids = [point.id for point in client.scroll("test")[0]]
     assert set(ids) == set(stored_ids)
+
+
+@pytest.mark.parametrize("vector_name", ["custom-vector"])
+def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
+    """Test end to end Qdrant.from_texts stores named vectors if name is provided."""
+    from qdrant_client import QdrantClient
+
+    collection_name = "test"
+    with tempfile.TemporaryDirectory() as tmpdir:
+        vec_store = Qdrant.from_texts(
+            ["lorem", "ipsum", "dolor", "sit", "amet"],
+            ConsistentFakeEmbeddings(),
+            collection_name=collection_name,
+            path=str(tmpdir),
+            vector_name=vector_name,
+        )
+        del vec_store
+
+        client = QdrantClient(path=str(tmpdir))
+        assert 5 == client.count("test").count
+        assert all(
+            vector_name in point.vector  # type: ignore[operator]
+            for point in client.scroll(collection_name, with_vectors=True)[0]
+        )
+
+
+@pytest.mark.parametrize("vector_name", ["custom-vector"])
+def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
+    """Test end to end Qdrant.add_texts stores named vectors if name is provided."""
+    from qdrant_client import QdrantClient
+
+    collection_name = "test"
+
+    client = QdrantClient(":memory:")
+    client.recreate_collection(
+        collection_name,
+        vectors_config={
+            vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
+        },
+    )
+
+    vec_store = Qdrant(
+        client,
+        collection_name,
+        ConsistentFakeEmbeddings(),
+        vector_name=vector_name,
+    )
+    vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"])
+
+    assert 5 == client.count("test").count
+    assert all(
+        vector_name in point.vector  # type: ignore[operator]
+        for point in client.scroll(collection_name, with_vectors=True)[0]
+    )