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]

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

@@ -5,8 +5,8 @@ from qdrant_client import QdrantClient
 from tests.integration_tests.fixtures import qdrant_locations
 
 
-def pytest_sessionfinish() -> None:
-    """Clean up all collections after the test session."""
+def pytest_runtest_teardown() -> None:
+    """Clean up all collections after the each test."""
     for location in qdrant_locations():
         client = QdrantClient(location=location, api_key=os.getenv("QDRANT_API_KEY"))
         collections = client.get_collections().collections

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

@@ -2,6 +2,7 @@ import logging
 import os
 from typing import List
 
+from langchain_qdrant.qdrant import RetrievalMode
 from tests.integration_tests.common import qdrant_running_locally
 
 logger = logging.getLogger(__name__)
@@ -23,3 +24,20 @@ def qdrant_locations(use_in_memory: bool = True) -> List[str]:
         locations.append(qdrant_url)
 
     return locations
+
+
+def retrieval_modes(
+    *, dense: bool = True, sparse: bool = True, hybrid: bool = True
+) -> List[RetrievalMode]:
+    modes = []
+
+    if dense:
+        modes.append(RetrievalMode.DENSE)
+
+    if sparse:
+        modes.append(RetrievalMode.SPARSE)
+
+    if hybrid:
+        modes.append(RetrievalMode.HYBRID)
+
+    return modes

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_add_texts.py

@@ -0,0 +1,143 @@
+import uuid
+from typing import List, Union
+
+import pytest
+from langchain_core.documents import Document
+from qdrant_client import QdrantClient, models
+
+from langchain_qdrant import QdrantVectorStore, RetrievalMode
+from tests.integration_tests.common import (
+    ConsistentFakeEmbeddings,
+    ConsistentFakeSparseEmbeddings,
+    assert_documents_equals,
+)
+from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+def test_qdrant_add_documents_extends_existing_collection(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    sparse_vector_name: str,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    new_texts = ["foobar", "foobaz"]
+    docsearch.add_documents([Document(page_content=content) for content in new_texts])
+    output = docsearch.similarity_search("foobar", k=1)
+    assert_documents_equals(output, [Document(page_content="foobar")])
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+@pytest.mark.parametrize("batch_size", [1, 64])
+def test_qdrant_add_texts_returns_all_ids(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    sparse_vector_name: str,
+    batch_size: int,
+) -> None:
+    """Test end to end Qdrant.add_texts returns unique ids."""
+    docsearch = QdrantVectorStore.from_texts(
+        ["foobar"],
+        ConsistentFakeEmbeddings(),
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+        batch_size=batch_size,
+    )
+
+    ids = docsearch.add_texts(["foo", "bar", "baz"])
+    assert 3 == len(ids)
+    assert 3 == len(set(ids))
+    assert 3 == len(docsearch.get_by_ids(ids))
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+def test_qdrant_add_texts_stores_duplicated_texts(
+    location: str,
+    vector_name: str,
+) -> None:
+    """Test end to end Qdrant.add_texts stores duplicated texts separately."""
+
+    client = QdrantClient(location)
+    collection_name = uuid.uuid4().hex
+    vectors_config = {
+        vector_name: models.VectorParams(size=10, distance=models.Distance.COSINE)
+    }
+    client.recreate_collection(collection_name, vectors_config=vectors_config)
+
+    vec_store = QdrantVectorStore(
+        client,
+        collection_name,
+        embedding=ConsistentFakeEmbeddings(),
+        vector_name=vector_name,
+    )
+    ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
+
+    assert 2 == len(set(ids))
+    assert 2 == client.count(collection_name).count
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+@pytest.mark.parametrize("batch_size", [1, 64])
+def test_qdrant_add_texts_stores_ids(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    sparse_vector_name: str,
+    batch_size: int,
+) -> None:
+    """Test end to end Qdrant.add_texts stores provided ids."""
+    ids: List[Union[str, int]] = [
+        "fa38d572-4c31-4579-aedc-1960d79df6df",
+        432,
+        432145435,
+    ]
+    collection_name = uuid.uuid4().hex
+    vec_store = QdrantVectorStore.from_texts(
+        ["abc", "def", "ghi"],
+        ConsistentFakeEmbeddings(),
+        ids=ids,
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+        batch_size=batch_size,
+    )
+
+    assert 3 == vec_store.client.count(collection_name).count
+    stored_ids = [point.id for point in vec_store.client.scroll(collection_name)[0]]
+    assert set(ids) == set(stored_ids)
+    assert 3 == len(vec_store.get_by_ids(ids))

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_existing.py

@@ -0,0 +1,51 @@
+import uuid
+
+import pytest
+
+from langchain_qdrant.qdrant import QdrantVectorStore, RetrievalMode
+from tests.integration_tests.common import (
+    ConsistentFakeEmbeddings,
+    ConsistentFakeSparseEmbeddings,
+)
+from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
+
+
+@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+def test_qdrant_from_existing_collection_uses_same_collection(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    sparse_vector_name: str,
+) -> None:
+    """Test if the QdrantVectorStore.from_existing_collection reuses the collection."""
+
+    collection_name = uuid.uuid4().hex
+    docs = ["foo"]
+    QdrantVectorStore.from_texts(
+        docs,
+        embedding=ConsistentFakeEmbeddings(),
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    qdrant = QdrantVectorStore.from_existing_collection(
+        collection_name,
+        embedding=ConsistentFakeEmbeddings(),
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+    qdrant.add_texts(["baz", "bar"])
+
+    assert 3 == qdrant.client.count(collection_name).count

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py

@@ -0,0 +1,385 @@
+import uuid
+from typing import List, Union
+
+import pytest
+from langchain_core.documents import Document
+from qdrant_client import models
+
+from langchain_qdrant import QdrantVectorStore, RetrievalMode
+from langchain_qdrant.qdrant import QdrantVectorStoreError
+from tests.integration_tests.common import (
+    ConsistentFakeEmbeddings,
+    ConsistentFakeSparseEmbeddings,
+    assert_documents_equals,
+)
+from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+def test_vectorstore_from_texts(location: str, retrieval_mode: RetrievalMode) -> None:
+    """Test end to end Qdrant.from_texts stores texts."""
+    collection_name = uuid.uuid4().hex
+
+    vec_store = QdrantVectorStore.from_texts(
+        ["Lorem ipsum dolor sit amet", "Ipsum dolor sit amet"],
+        ConsistentFakeEmbeddings(),
+        collection_name=collection_name,
+        location=location,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    assert 2 == vec_store.client.count(collection_name).count
+
+
+@pytest.mark.parametrize("batch_size", [1, 64])
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+def test_qdrant_from_texts_stores_ids(
+    batch_size: int,
+    vector_name: str,
+    sparse_vector_name: str,
+    location: str,
+    retrieval_mode: RetrievalMode,
+) -> None:
+    """Test end to end Qdrant.from_texts stores provided ids."""
+    collection_name = uuid.uuid4().hex
+    ids: List[Union[str, int]] = [
+        "fa38d572-4c31-4579-aedc-1960d79df6df",
+        786,
+    ]
+    vec_store = QdrantVectorStore.from_texts(
+        ["abc", "def"],
+        ConsistentFakeEmbeddings(),
+        ids=ids,
+        collection_name=collection_name,
+        location=location,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+        batch_size=batch_size,
+        vector_name=vector_name,
+        sparse_vector_name=sparse_vector_name,
+    )
+
+    assert 2 == vec_store.client.count(collection_name).count
+    stored_ids = [point.id for point in vec_store.client.retrieve(collection_name, ids)]
+    assert set(ids) == set(stored_ids)
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+def test_qdrant_from_texts_stores_embeddings_as_named_vectors(
+    location: str,
+    retrieval_mode: RetrievalMode,
+    vector_name: str,
+    sparse_vector_name: str,
+) -> None:
+    """Test end to end Qdrant.from_texts stores named vectors if name is provided."""
+
+    collection_name = uuid.uuid4().hex
+    vec_store = QdrantVectorStore.from_texts(
+        ["lorem", "ipsum", "dolor", "sit", "amet"],
+        ConsistentFakeEmbeddings(),
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    assert 5 == vec_store.client.count(collection_name).count
+    if retrieval_mode in retrieval_modes(sparse=False):
+        assert all(
+            (vector_name in point.vector or isinstance(point.vector, list))  # type: ignore
+            for point in vec_store.client.scroll(collection_name, with_vectors=True)[0]
+        )
+    if retrieval_mode in retrieval_modes(dense=False):
+        assert all(
+            sparse_vector_name in point.vector  # type: ignore
+            for point in vec_store.client.scroll(collection_name, with_vectors=True)[0]
+        )
+
+
+@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+def test_qdrant_from_texts_reuses_same_collection(
+    location: str,
+    retrieval_mode: RetrievalMode,
+    vector_name: str,
+    sparse_vector_name: str,
+) -> None:
+    """Test if Qdrant.from_texts reuses the same collection"""
+    collection_name = uuid.uuid4().hex
+    embeddings = ConsistentFakeEmbeddings()
+    sparse_embeddings = ConsistentFakeSparseEmbeddings()
+    vec_store = QdrantVectorStore.from_texts(
+        ["lorem", "ipsum", "dolor", "sit", "amet"],
+        embeddings,
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=sparse_embeddings,
+    )
+    del vec_store
+
+    vec_store = QdrantVectorStore.from_texts(
+        ["foo", "bar"],
+        embeddings,
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=sparse_embeddings,
+    )
+
+    assert 7 == vec_store.client.count(collection_name).count
+
+
+@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
+def test_qdrant_from_texts_raises_error_on_different_dimensionality(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+) -> None:
+    """Test if Qdrant.from_texts raises an exception if dimensionality does not match"""
+    collection_name = uuid.uuid4().hex
+    QdrantVectorStore.from_texts(
+        ["lorem", "ipsum", "dolor", "sit", "amet"],
+        ConsistentFakeEmbeddings(dimensionality=10),
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    with pytest.raises(QdrantVectorStoreError) as excinfo:
+        QdrantVectorStore.from_texts(
+            ["foo", "bar"],
+            ConsistentFakeEmbeddings(dimensionality=5),
+            collection_name=collection_name,
+            location=location,
+            vector_name=vector_name,
+            retrieval_mode=retrieval_mode,
+            sparse_embedding=ConsistentFakeSparseEmbeddings(),
+        )
+
+        expected_message = "collection is configured for dense vectors "
+        "with 10 dimensions. Selected embeddings are 5-dimensional"
+        assert expected_message in str(excinfo.value)
+
+
+@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
+@pytest.mark.parametrize(
+    ["first_vector_name", "second_vector_name"],
+    [
+        ("", "custom-vector"),
+        ("custom-vector", ""),
+        ("my-first-vector", "my-second_vector"),
+    ],
+)
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
+def test_qdrant_from_texts_raises_error_on_different_vector_name(
+    location: str,
+    first_vector_name: str,
+    second_vector_name: str,
+    retrieval_mode: RetrievalMode,
+) -> None:
+    """Test if Qdrant.from_texts raises an exception if vector name does not match"""
+    collection_name = uuid.uuid4().hex
+    QdrantVectorStore.from_texts(
+        ["lorem", "ipsum", "dolor", "sit", "amet"],
+        ConsistentFakeEmbeddings(dimensionality=10),
+        collection_name=collection_name,
+        location=location,
+        vector_name=first_vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    with pytest.raises(QdrantVectorStoreError) as excinfo:
+        QdrantVectorStore.from_texts(
+            ["foo", "bar"],
+            ConsistentFakeEmbeddings(dimensionality=10),
+            collection_name=collection_name,
+            location=location,
+            vector_name=second_vector_name,
+            retrieval_mode=retrieval_mode,
+            sparse_embedding=ConsistentFakeSparseEmbeddings(),
+        )
+
+        expected_message = "does not contain dense vector named"
+        assert expected_message in str(excinfo.value)
+
+
+@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
+def test_qdrant_from_texts_raises_error_on_different_distance(
+    location: str, vector_name: str, retrieval_mode: RetrievalMode
+) -> None:
+    """Test if Qdrant.from_texts raises an exception if distance does not match"""
+    collection_name = uuid.uuid4().hex
+    QdrantVectorStore.from_texts(
+        ["lorem", "ipsum", "dolor", "sit", "amet"],
+        ConsistentFakeEmbeddings(),
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        distance=models.Distance.COSINE,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    with pytest.raises(QdrantVectorStoreError) as excinfo:
+        QdrantVectorStore.from_texts(
+            ["foo", "bar"],
+            ConsistentFakeEmbeddings(),
+            collection_name=collection_name,
+            location=location,
+            vector_name=vector_name,
+            distance=models.Distance.EUCLID,
+            retrieval_mode=retrieval_mode,
+            sparse_embedding=ConsistentFakeSparseEmbeddings(),
+        )
+
+        expected_message = "configured for COSINE similarity, but requested EUCLID"
+        assert expected_message in str(excinfo.value)
+
+
+@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+def test_qdrant_from_texts_recreates_collection_on_force_recreate(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    sparse_vector_name: str,
+) -> None:
+    collection_name = uuid.uuid4().hex
+    vec_store = QdrantVectorStore.from_texts(
+        ["lorem", "ipsum", "dolor", "sit", "amet"],
+        ConsistentFakeEmbeddings(dimensionality=10),
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    vec_store = QdrantVectorStore.from_texts(
+        ["foo", "bar"],
+        ConsistentFakeEmbeddings(dimensionality=5),
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+        force_recreate=True,
+    )
+
+    assert 2 == vec_store.client.count(collection_name).count
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
+)
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+def test_qdrant_from_texts_stores_metadatas(
+    location: str,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    sparse_vector_name: str,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["fabrin", "barizda"]
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        location=location,
+        content_payload_key=content_payload_key,
+        metadata_payload_key=metadata_payload_key,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+    output = docsearch.similarity_search("fabrin", k=1)
+    assert_documents_equals(
+        output, [Document(page_content="fabrin", metadata={"page": 0})]
+    )
+
+
+@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
+@pytest.mark.parametrize(
+    "sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
+)
+def test_from_texts_passed_optimizers_config_and_on_disk_payload(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    sparse_vector_name: str,
+) -> None:
+    collection_name = uuid.uuid4().hex
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": i} for i in range(len(texts))]
+    optimizers_config = models.OptimizersConfigDiff(memmap_threshold=1000)
+    vec_store = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        collection_create_options={
+            "on_disk_payload": True,
+            "optimizers_config": optimizers_config,
+        },
+        vector_params={
+            "on_disk": True,
+        },
+        collection_name=collection_name,
+        location=location,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_vector_name=sparse_vector_name,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    collection_info = vec_store.client.get_collection(collection_name)
+    assert collection_info.config.params.vectors[vector_name].on_disk is True  # type: ignore
+    assert collection_info.config.optimizer_config.memmap_threshold == 1000
+    assert collection_info.config.params.on_disk_payload is True

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_mmr.py

@@ -0,0 +1,116 @@
+import pytest  # type: ignore[import-not-found]
+from langchain_core.documents import Document
+from qdrant_client import models
+
+from langchain_qdrant import QdrantVectorStore, RetrievalMode
+from langchain_qdrant.qdrant import QdrantVectorStoreError
+from tests.integration_tests.common import (
+    ConsistentFakeEmbeddings,
+    ConsistentFakeSparseEmbeddings,
+    assert_documents_equals,
+)
+from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
+
+
+# MMR is supported when dense embeddings are available
+# i.e. In Dense and Hybrid retrieval modes
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize(
+    "content_payload_key", [QdrantVectorStore.CONTENT_KEY, "test_content"]
+)
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "test_metadata"]
+)
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+def test_qdrant_mmr_search(
+    location: str,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    retrieval_mode: RetrievalMode,
+    vector_name: str,
+) -> None:
+    """Test end to end construction and MRR search."""
+    filter = models.Filter(
+        must=[
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.page",
+                match=models.MatchValue(
+                    value=2,
+                ),
+            ),
+        ],
+    )
+
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        content_payload_key=content_payload_key,
+        metadata_payload_key=metadata_payload_key,
+        location=location,
+        retrieval_mode=retrieval_mode,
+        vector_name=vector_name,
+        distance=models.Distance.EUCLID,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+    output = docsearch.max_marginal_relevance_search(
+        "foo", k=2, fetch_k=3, lambda_mult=0.0
+    )
+    assert_documents_equals(
+        output,
+        [
+            Document(page_content="foo", metadata={"page": 0}),
+            Document(page_content="baz", metadata={"page": 2}),
+        ],
+    )
+
+    output = docsearch.max_marginal_relevance_search(
+        "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter
+    )
+    assert_documents_equals(
+        output,
+        [Document(page_content="baz", metadata={"page": 2})],
+    )
+
+
+# MMR shouldn't work with only sparse retrieval mode
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize(
+    "content_payload_key", [QdrantVectorStore.CONTENT_KEY, "test_content"]
+)
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "test_metadata"]
+)
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes(dense=False, hybrid=False))
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+def test_invalid_qdrant_mmr_with_sparse(
+    location: str,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    retrieval_mode: RetrievalMode,
+    vector_name: str,
+) -> None:
+    """Test end to end construction and MRR search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": i} for i in range(len(texts))]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        content_payload_key=content_payload_key,
+        metadata_payload_key=metadata_payload_key,
+        location=location,
+        retrieval_mode=retrieval_mode,
+        vector_name=vector_name,
+        distance=models.Distance.EUCLID,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    with pytest.raises(QdrantVectorStoreError) as excinfo:
+        docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3, lambda_mult=0.0)
+
+        expected_message = "does not contain dense vector named"
+        assert expected_message in str(excinfo.value)

libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py

@@ -0,0 +1,278 @@
+import pytest
+from langchain_core.documents import Document
+from qdrant_client import models
+
+from langchain_qdrant import QdrantVectorStore, RetrievalMode
+from tests.integration_tests.common import (
+    ConsistentFakeEmbeddings,
+    ConsistentFakeSparseEmbeddings,
+    assert_documents_equals,
+)
+from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+@pytest.mark.parametrize("batch_size", [1, 64])
+def test_similarity_search(
+    location: str,
+    vector_name: str,
+    retrieval_mode: RetrievalMode,
+    batch_size: int,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        location=location,
+        batch_size=batch_size,
+        vector_name=vector_name,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert_documents_equals(actual=output, expected=[Document(page_content="foo")])
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
+)
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+@pytest.mark.parametrize("batch_size", [1, 64])
+def test_similarity_search_by_vector(
+    location: str,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    vector_name: str,
+    batch_size: int,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        location=location,
+        content_payload_key=content_payload_key,
+        metadata_payload_key=metadata_payload_key,
+        batch_size=batch_size,
+        vector_name=vector_name,
+    )
+    embeddings = ConsistentFakeEmbeddings().embed_query("foo")
+    output = docsearch.similarity_search_by_vector(embeddings, k=1)
+    assert_documents_equals(output, [Document(page_content="foo")])
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
+)
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+def test_similarity_search_filters(
+    location: str,
+    metadata_payload_key: str,
+    retrieval_mode: RetrievalMode,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [
+        {"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
+        for i in range(len(texts))
+    ]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        location=location,
+        metadata_payload_key=metadata_payload_key,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    qdrant_filter = models.Filter(
+        must=[
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.page", match=models.MatchValue(value=1)
+            )
+        ]
+    )
+    output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
+
+    assert_documents_equals(
+        actual=output,
+        expected=[
+            Document(
+                page_content="bar",
+                metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
+            )
+        ],
+    )
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+def test_similarity_relevance_search_no_threshold(
+    location: str,
+    vector_name: str,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [
+        {"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
+        for i in range(len(texts))
+    ]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        location=location,
+        vector_name=vector_name,
+    )
+    output = docsearch.similarity_search_with_relevance_scores(
+        "foo", k=3, score_threshold=None
+    )
+    assert len(output) == 3
+    for i in range(len(output)):
+        assert round(output[i][1], 2) >= 0
+        assert round(output[i][1], 2) <= 1
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+def test_relevance_search_with_threshold(
+    location: str,
+    vector_name: str,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [
+        {"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
+        for i in range(len(texts))
+    ]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        location=location,
+        vector_name=vector_name,
+    )
+
+    score_threshold = 0.99
+    kwargs = {"score_threshold": score_threshold}
+    output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
+    assert len(output) == 1
+    assert all([score >= score_threshold for _, score in output])
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
+)
+@pytest.mark.parametrize("vector_name", ["", "my-vector"])
+def test_relevance_search_with_threshold_and_filter(
+    location: str,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    vector_name: str,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [
+        {"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
+        for i in range(len(texts))
+    ]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        location=location,
+        content_payload_key=content_payload_key,
+        metadata_payload_key=metadata_payload_key,
+        vector_name=vector_name,
+    )
+    score_threshold = 0.99  # for almost exact match
+    negative_filter = models.Filter(
+        must=[
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.page", match=models.MatchValue(value=1)
+            )
+        ]
+    )
+    kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
+    output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
+    assert len(output) == 0
+    positive_filter = models.Filter(
+        must=[
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.page", match=models.MatchValue(value=0)
+            )
+        ]
+    )
+    kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
+    output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
+    assert len(output) == 1
+    assert all([score >= score_threshold for _, score in output])
+
+
+@pytest.mark.parametrize("location", qdrant_locations())
+@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
+@pytest.mark.parametrize(
+    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
+)
+@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
+def test_similarity_search_filters_with_qdrant_filters(
+    location: str,
+    content_payload_key: str,
+    metadata_payload_key: str,
+    retrieval_mode: RetrievalMode,
+) -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [
+        {"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
+        for i in range(len(texts))
+    ]
+    docsearch = QdrantVectorStore.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        location=location,
+        metadatas=metadatas,
+        content_payload_key=content_payload_key,
+        metadata_payload_key=metadata_payload_key,
+        retrieval_mode=retrieval_mode,
+        sparse_embedding=ConsistentFakeSparseEmbeddings(),
+    )
+
+    qdrant_filter = models.Filter(
+        must=[
+            models.FieldCondition(
+                key=content_payload_key, match=models.MatchValue(value="bar")
+            ),
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.page",
+                match=models.MatchValue(value=1),
+            ),
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.details.page",
+                match=models.MatchValue(value=2),
+            ),
+            models.FieldCondition(
+                key=f"{metadata_payload_key}.details.pages",
+                match=models.MatchAny(any=[3]),
+            ),
+        ]
+    )
+    output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
+    assert_documents_equals(
+        actual=output,
+        expected=[
+            Document(
+                page_content="bar",
+                metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
+            )
+        ],
+    )