Refactor Qdrant integration (#8634)

This small PR introduces new parameters into Qdrant (`on_disk`), fixes
some tests and changes the error message to be more clear.

Tagging: @baskaryan, @rlancemartin, @eyurtsev

libs/langchain/langchain/vectorstores/mongodb_atlas.py

@@ -142,7 +142,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
             for t, m, embedding in zip(texts, metadatas, embeddings)
         ]
         # insert the documents in MongoDB Atlas
-        insert_result = self._collection.insert_many(to_insert)
+        insert_result = self._collection.insert_many(to_insert)  # type: ignore
         return insert_result.inserted_ids
 
     def _similarity_search_with_score(
@@ -170,7 +170,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
         ]
         if post_filter_pipeline is not None:
             pipeline.extend(post_filter_pipeline)
-        cursor = self._collection.aggregate(pipeline)
+        cursor = self._collection.aggregate(pipeline)  # type: ignore[arg-type]
         docs = []
         for res in cursor:
             text = res.pop(self._text_key)

libs/langchain/langchain/vectorstores/qdrant.py

@@ -981,6 +981,7 @@ class Qdrant(VectorStore):
         wal_config: Optional[common_types.WalConfigDiff] = None,
         quantization_config: Optional[common_types.QuantizationConfig] = None,
         init_from: Optional[common_types.InitFrom] = None,
+        on_disk: Optional[bool] = None,
         force_recreate: bool = False,
         **kwargs: Any,
     ) -> Qdrant:
@@ -1090,8 +1091,6 @@ class Qdrant(VectorStore):
         qdrant = cls._construct_instance(
             texts,
             embedding,
-            metadatas,
-            ids,
             location,
             url,
             port,
@@ -1117,6 +1116,7 @@ class Qdrant(VectorStore):
             wal_config,
             quantization_config,
             init_from,
+            on_disk,
             force_recreate,
             **kwargs,
         )
@@ -1157,6 +1157,7 @@ class Qdrant(VectorStore):
         wal_config: Optional[common_types.WalConfigDiff] = None,
         quantization_config: Optional[common_types.QuantizationConfig] = None,
         init_from: Optional[common_types.InitFrom] = None,
+        on_disk: Optional[bool] = None,
         force_recreate: bool = False,
         **kwargs: Any,
     ) -> Qdrant:
@@ -1266,8 +1267,6 @@ class Qdrant(VectorStore):
         qdrant = cls._construct_instance(
             texts,
             embedding,
-            metadatas,
-            ids,
             location,
             url,
             port,
@@ -1293,6 +1292,7 @@ class Qdrant(VectorStore):
             wal_config,
             quantization_config,
             init_from,
+            on_disk,
             force_recreate,
             **kwargs,
         )
@@ -1304,8 +1304,6 @@ class Qdrant(VectorStore):
         cls: Type[Qdrant],
         texts: List[str],
         embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        ids: Optional[Sequence[str]] = None,
         location: Optional[str] = None,
         url: Optional[str] = None,
         port: Optional[int] = 6333,
@@ -1331,6 +1329,7 @@ class Qdrant(VectorStore):
         wal_config: Optional[common_types.WalConfigDiff] = None,
         quantization_config: Optional[common_types.QuantizationConfig] = None,
         init_from: Optional[common_types.InitFrom] = None,
+        on_disk: Optional[bool] = None,
         force_recreate: bool = False,
         **kwargs: Any,
     ) -> Qdrant:
@@ -1421,16 +1420,17 @@ class Qdrant(VectorStore):
             if current_distance_func != distance_func:
                 raise QdrantException(
                     f"Existing Qdrant collection is configured for "
-                    f"{current_vector_config.distance} "  # type: ignore[union-attr]
-                    f"similarity. Please set `distance_func` parameter to "
-                    f"`{distance_func}` if you want to reuse it. If you want to "
-                    f"recreate the collection, set `force_recreate` parameter to "
-                    f"`True`."
+                    f"{current_distance_func} similarity, but requested "
+                    f"{distance_func}. Please set `distance_func` parameter to "
+                    f"`{current_distance_func}` if you want to reuse it. "
+                    f"If you want to recreate the collection, set `force_recreate` "
+                    f"parameter to `True`."
                 )
         except (UnexpectedResponse, RpcError, ValueError):
             vectors_config = rest.VectorParams(
                 size=vector_size,
                 distance=rest.Distance[distance_func],
+                on_disk=on_disk,
             )
 
             # If vector name was provided, we're going to use the named vectors feature

libs/langchain/tests/integration_tests/vectorstores/qdrant/async_api/test_add_texts.py

@@ -1,3 +1,4 @@
+import uuid
 from typing import Optional
 
 import pytest
@@ -42,7 +43,7 @@ async def test_qdrant_aadd_texts_stores_duplicated_texts(
     from qdrant_client.http import models as rest
 
     client = QdrantClient(location=qdrant_location)
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     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]
@@ -75,7 +76,7 @@ async def test_qdrant_aadd_texts_stores_ids(
     ]
 
     client = QdrantClient(location=qdrant_location)
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     client.recreate_collection(
         collection_name,
         vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
@@ -101,7 +102,7 @@ async def test_qdrant_aadd_texts_stores_embeddings_as_named_vectors(
     """Test end to end Qdrant.aadd_texts stores named vectors if name is provided."""
     from qdrant_client import QdrantClient
 
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
 
     client = QdrantClient(location=qdrant_location)
     client.recreate_collection(

libs/langchain/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py

@@ -224,6 +224,10 @@ async def test_qdrant_from_texts_recreates_collection_on_force_recreate(
 
     client = QdrantClient()
     assert 2 == client.count(collection_name).count
+    vector_params = client.get_collection(collection_name).config.params.vectors
+    if vector_name is not None:
+        vector_params = vector_params[vector_name]  # type: ignore[index]
+    assert 5 == vector_params.size  # type: ignore[union-attr]
 
 
 @pytest.mark.asyncio

libs/langchain/tests/integration_tests/vectorstores/qdrant/test_add_texts.py

@@ -1,3 +1,4 @@
+import uuid
 from typing import Optional
 
 import pytest
@@ -58,7 +59,7 @@ def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) ->
     from qdrant_client.http import models as rest
 
     client = QdrantClient(":memory:")
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     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]
@@ -87,7 +88,7 @@ def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
     ]
 
     client = QdrantClient(":memory:")
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     client.recreate_collection(
         collection_name,
         vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
@@ -107,7 +108,7 @@ def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -
     """Test end to end Qdrant.add_texts stores named vectors if name is provided."""
     from qdrant_client import QdrantClient
 
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
 
     client = QdrantClient(":memory:")
     client.recreate_collection(

libs/langchain/tests/integration_tests/vectorstores/qdrant/test_embedding_interface.py

@@ -1,3 +1,4 @@
+import uuid
 from typing import Callable, Optional
 
 import pytest
@@ -24,7 +25,7 @@ def test_qdrant_embedding_interface(
     from qdrant_client import QdrantClient
 
     client = QdrantClient(":memory:")
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
 
     Qdrant(
         client,
@@ -48,7 +49,7 @@ def test_qdrant_embedding_interface_raises_value_error(
     from qdrant_client import QdrantClient
 
     client = QdrantClient(":memory:")
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
 
     with pytest.raises(ValueError):
         Qdrant(

libs/langchain/tests/integration_tests/vectorstores/qdrant/test_from_texts.py

@@ -1,4 +1,5 @@
 import tempfile
+import uuid
 from typing import Optional
 
 import pytest
@@ -9,13 +10,14 @@ from langchain.vectorstores.qdrant import QdrantException
 from tests.integration_tests.vectorstores.fake_embeddings import (
     ConsistentFakeEmbeddings,
 )
+from tests.integration_tests.vectorstores.qdrant.common import qdrant_is_not_running
 
 
 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
 
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
 
     with tempfile.TemporaryDirectory() as tmpdir:
         vec_store = Qdrant.from_texts(
@@ -38,7 +40,7 @@ def test_qdrant_from_texts_stores_ids(
     """Test end to end Qdrant.from_texts stores provided ids."""
     from qdrant_client import QdrantClient
 
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     with tempfile.TemporaryDirectory() as tmpdir:
         ids = [
             "fa38d572-4c31-4579-aedc-1960d79df6df",
@@ -66,7 +68,7 @@ def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str)
     """Test end to end Qdrant.from_texts stores named vectors if name is provided."""
     from qdrant_client import QdrantClient
 
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     with tempfile.TemporaryDirectory() as tmpdir:
         vec_store = Qdrant.from_texts(
             ["lorem", "ipsum", "dolor", "sit", "amet"],
@@ -90,7 +92,7 @@ def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) ->
     """Test if Qdrant.from_texts reuses the same collection"""
     from qdrant_client import QdrantClient
 
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     embeddings = ConsistentFakeEmbeddings()
     with tempfile.TemporaryDirectory() as tmpdir:
         vec_store = Qdrant.from_texts(
@@ -120,7 +122,7 @@ 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"
+    collection_name = uuid.uuid4().hex
     with tempfile.TemporaryDirectory() as tmpdir:
         vec_store = Qdrant.from_texts(
             ["lorem", "ipsum", "dolor", "sit", "amet"],
@@ -154,7 +156,7 @@ def test_qdrant_from_texts_raises_error_on_different_vector_name(
     second_vector_name: Optional[str],
 ) -> None:
     """Test if Qdrant.from_texts raises an exception if vector name does not match"""
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     with tempfile.TemporaryDirectory() as tmpdir:
         vec_store = Qdrant.from_texts(
             ["lorem", "ipsum", "dolor", "sit", "amet"],
@@ -177,26 +179,32 @@ def test_qdrant_from_texts_raises_error_on_different_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"
+    collection_name = uuid.uuid4().hex
     with tempfile.TemporaryDirectory() as tmpdir:
         vec_store = Qdrant.from_texts(
             ["lorem", "ipsum", "dolor", "sit", "amet"],
-            ConsistentFakeEmbeddings(dimensionality=10),
+            ConsistentFakeEmbeddings(),
             collection_name=collection_name,
             path=str(tmpdir),
             distance_func="Cosine",
         )
         del vec_store
 
-        with pytest.raises(QdrantException):
+        with pytest.raises(QdrantException) as excinfo:
             Qdrant.from_texts(
                 ["foo", "bar"],
-                ConsistentFakeEmbeddings(dimensionality=5),
+                ConsistentFakeEmbeddings(),
                 collection_name=collection_name,
                 path=str(tmpdir),
                 distance_func="Euclid",
             )
 
+        expected_message = (
+            "configured for COSINE similarity, but requested EUCLID. Please set "
+            "`distance_func` parameter to `COSINE`"
+        )
+        assert expected_message in str(excinfo.value)
+
 
 @pytest.mark.parametrize("vector_name", [None, "custom-vector"])
 def test_qdrant_from_texts_recreates_collection_on_force_recreate(
@@ -205,7 +213,7 @@ def test_qdrant_from_texts_recreates_collection_on_force_recreate(
     """Test if Qdrant.from_texts recreates the collection even if config mismatches"""
     from qdrant_client import QdrantClient
 
-    collection_name = "test"
+    collection_name = uuid.uuid4().hex
     with tempfile.TemporaryDirectory() as tmpdir:
         vec_store = Qdrant.from_texts(
             ["lorem", "ipsum", "dolor", "sit", "amet"],
@@ -250,3 +258,27 @@ def test_qdrant_from_texts_stores_metadatas(
     )
     output = docsearch.similarity_search("foo", k=1)
     assert output == [Document(page_content="foo", metadata={"page": 0})]
+
+
+@pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running")
+def test_from_texts_passed_optimizers_config_and_on_disk_payload() -> None:
+    from qdrant_client import models
+
+    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 = Qdrant.from_texts(
+        texts,
+        ConsistentFakeEmbeddings(),
+        metadatas=metadatas,
+        optimizers_config=optimizers_config,
+        on_disk_payload=True,
+        on_disk=True,
+        collection_name=collection_name,
+    )
+
+    collection_info = vec_store.client.get_collection(collection_name)
+    assert collection_info.config.params.vectors.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