Reuse the existing collection if configured properly in Qdrant.from_texts (#7530)

This PR changes the behavior of `Qdrant.from_texts` so the collection is
reused if not requested to recreate it. Previously, calling
`Qdrant.from_texts` or `Qdrant.from_documents` resulted in removing the
old data which was confusing for many.

tests/integration_tests/vectorstores/fake_embeddings.py

@@ -27,8 +27,9 @@ class ConsistentFakeEmbeddings(FakeEmbeddings):
     """Fake embeddings which remember all the texts seen so far to return consistent
     vectors for the same texts."""
 
-    def __init__(self) -> None:
+    def __init__(self, dimensionality: int = 10) -> None:
         self.known_texts: List[str] = []
+        self.dimensionality = dimensionality
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Return consistent embeddings for each text seen so far."""
@@ -36,7 +37,9 @@ class ConsistentFakeEmbeddings(FakeEmbeddings):
         for text in texts:
             if text not in self.known_texts:
                 self.known_texts.append(text)
-            vector = [float(1.0)] * 9 + [float(self.known_texts.index(text))]
+            vector = [float(1.0)] * (self.dimensionality - 1) + [
+                float(self.known_texts.index(text))
+            ]
             out_vectors.append(vector)
         return out_vectors
 
@@ -44,8 +47,10 @@ class ConsistentFakeEmbeddings(FakeEmbeddings):
         """Return consistent embeddings for the text, if seen before, or a constant
         one if the text is unknown."""
         if text not in self.known_texts:
-            return [float(1.0)] * 9 + [float(0.0)]
-        return [float(1.0)] * 9 + [float(self.known_texts.index(text))]
+            return [float(1.0)] * (self.dimensionality - 1) + [float(0.0)]
+        return [float(1.0)] * (self.dimensionality - 1) + [
+            float(self.known_texts.index(text))
+        ]
 
 
 class AngularTwoDimensionalEmbeddings(Embeddings):

tests/integration_tests/vectorstores/test_qdrant.py

@@ -9,6 +9,7 @@ from qdrant_client.http import models as rest
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
 from langchain.vectorstores import Qdrant
+from langchain.vectorstores.qdrant import QdrantException
 from tests.integration_tests.vectorstores.fake_embeddings import (
     ConsistentFakeEmbeddings,
 )
@@ -537,3 +538,148 @@ def test_qdrant_similarity_search_with_relevance_scores(
     assert all(
         (1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
     )
+
+
+@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
+def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) -> None:
+    """Test if Qdrant.from_texts reuses the same collection"""
+    from qdrant_client import QdrantClient
+
+    collection_name = "test"
+    embeddings = ConsistentFakeEmbeddings()
+    with tempfile.TemporaryDirectory() as tmpdir:
+        vec_store = Qdrant.from_texts(
+            ["lorem", "ipsum", "dolor", "sit", "amet"],
+            embeddings,
+            collection_name=collection_name,
+            path=str(tmpdir),
+            vector_name=vector_name,
+        )
+        del vec_store
+
+        vec_store = Qdrant.from_texts(
+            ["foo", "bar"],
+            embeddings,
+            collection_name=collection_name,
+            path=str(tmpdir),
+            vector_name=vector_name,
+        )
+        del vec_store
+
+        client = QdrantClient(path=str(tmpdir))
+        assert 7 == client.count(collection_name).count
+
+
+@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
+def test_qdrant_from_texts_raises_error_on_different_dimensionality(
+    vector_name: Optional[str],
+) -> None:
+    """Test if Qdrant.from_texts raises an exception if dimensionality does not match"""
+    collection_name = "test"
+    with tempfile.TemporaryDirectory() as tmpdir:
+        vec_store = Qdrant.from_texts(
+            ["lorem", "ipsum", "dolor", "sit", "amet"],
+            ConsistentFakeEmbeddings(dimensionality=10),
+            collection_name=collection_name,
+            path=str(tmpdir),
+            vector_name=vector_name,
+        )
+        del vec_store
+
+        with pytest.raises(QdrantException):
+            Qdrant.from_texts(
+                ["foo", "bar"],
+                ConsistentFakeEmbeddings(dimensionality=5),
+                collection_name=collection_name,
+                path=str(tmpdir),
+                vector_name=vector_name,
+            )
+
+
+@pytest.mark.parametrize(
+    ["first_vector_name", "second_vector_name"],
+    [
+        (None, "custom-vector"),
+        ("custom-vector", None),
+        ("my-first-vector", "my-second_vector"),
+    ],
+)
+def test_qdrant_from_texts_raises_error_on_different_vector_name(
+    first_vector_name: Optional[str],
+    second_vector_name: Optional[str],
+) -> None:
+    """Test if Qdrant.from_texts raises an exception if vector name does not match"""
+    collection_name = "test"
+    with tempfile.TemporaryDirectory() as tmpdir:
+        vec_store = Qdrant.from_texts(
+            ["lorem", "ipsum", "dolor", "sit", "amet"],
+            ConsistentFakeEmbeddings(dimensionality=10),
+            collection_name=collection_name,
+            path=str(tmpdir),
+            vector_name=first_vector_name,
+        )
+        del vec_store
+
+        with pytest.raises(QdrantException):
+            Qdrant.from_texts(
+                ["foo", "bar"],
+                ConsistentFakeEmbeddings(dimensionality=5),
+                collection_name=collection_name,
+                path=str(tmpdir),
+                vector_name=second_vector_name,
+            )
+
+
+def test_qdrant_from_texts_raises_error_on_different_distance() -> None:
+    """Test if Qdrant.from_texts raises an exception if distance does not match"""
+    collection_name = "test"
+    with tempfile.TemporaryDirectory() as tmpdir:
+        vec_store = Qdrant.from_texts(
+            ["lorem", "ipsum", "dolor", "sit", "amet"],
+            ConsistentFakeEmbeddings(dimensionality=10),
+            collection_name=collection_name,
+            path=str(tmpdir),
+            distance_func="Cosine",
+        )
+        del vec_store
+
+        with pytest.raises(QdrantException):
+            Qdrant.from_texts(
+                ["foo", "bar"],
+                ConsistentFakeEmbeddings(dimensionality=5),
+                collection_name=collection_name,
+                path=str(tmpdir),
+                distance_func="Euclid",
+            )
+
+
+@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
+def test_qdrant_from_texts_recreates_collection_on_force_recreate(
+    vector_name: Optional[str],
+) -> None:
+    """Test if Qdrant.from_texts recreates the collection even if config mismatches"""
+    from qdrant_client import QdrantClient
+
+    collection_name = "test"
+    with tempfile.TemporaryDirectory() as tmpdir:
+        vec_store = Qdrant.from_texts(
+            ["lorem", "ipsum", "dolor", "sit", "amet"],
+            ConsistentFakeEmbeddings(dimensionality=10),
+            collection_name=collection_name,
+            path=str(tmpdir),
+            vector_name=vector_name,
+        )
+        del vec_store
+
+        vec_store = Qdrant.from_texts(
+            ["foo", "bar"],
+            ConsistentFakeEmbeddings(dimensionality=5),
+            collection_name=collection_name,
+            path=str(tmpdir),
+            vector_name=vector_name,
+            force_recreate=True,
+        )
+        del vec_store
+
+        client = QdrantClient(path=str(tmpdir))
+        assert 2 == client.count(collection_name).count