qdrant: new Qdrant implementation (#24164)

libs/partners/qdrant/langchain_qdrant/__init__.py

@@ -1,5 +1,13 @@
 from langchain_qdrant.fastembed_sparse import FastEmbedSparse
+from langchain_qdrant.qdrant import QdrantVectorStore, RetrievalMode
 from langchain_qdrant.sparse_embeddings import SparseEmbeddings, SparseVector
 from langchain_qdrant.vectorstores import Qdrant
 
-__all__ = ["Qdrant", "SparseEmbeddings", "SparseVector", "FastEmbedSparse"]
+__all__ = [
+    "Qdrant",
+    "QdrantVectorStore",
+    "SparseEmbeddings",
+    "SparseVector",
+    "FastEmbedSparse",
+    "RetrievalMode",
+]

libs/partners/qdrant/langchain_qdrant/vectorstores.py

@@ -23,6 +23,7 @@ from typing import (
 )
 
 import numpy as np
+from langchain_core._api.deprecation import deprecated
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.runnables.config import run_in_executor
@@ -65,6 +66,7 @@ def sync_call_fallback(method: Callable) -> Callable:
     return wrapper
 
 
+@deprecated(since="0.1.2", alternative="QdrantVectorStore", removal="0.5.0")
 class Qdrant(VectorStore):
     """`Qdrant` vector store.
 

libs/partners/qdrant/poetry.lock

@@ -207,13 +207,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastembed"
-version = "0.3.1"
+version = "0.3.3"
 description = "Fast, light, accurate library built for retrieval embedding generation"
 optional = true
 python-versions = "<3.13,>=3.8.0"
 files = [
-    {file = "fastembed-0.3.1-py3-none-any.whl", hash = "sha256:5e9fecf6031a010eff75c02986f556d0635102210c0b1765e784cfca07df8975"},
+    {file = "fastembed-0.3.3-py3-none-any.whl", hash = "sha256:1282a44dac187bbb0765b7e05a0612f1a7eb28c0c65ccb10b2b963467807fef6"},
-    {file = "fastembed-0.3.1.tar.gz", hash = "sha256:01a52c5c9cdfe2504dfa56b3c1b032d1ad88d621de144fc9a571371a79df6457"},
+    {file = "fastembed-0.3.3.tar.gz", hash = "sha256:2735777d8c4462a92ec289ad99534242deb2073cede7d4ad3b37d070994d767f"},
 ]
 
 [package.dependencies]
@@ -622,7 +622,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.2.11"
+version = "0.2.16"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -1530,13 +1530,13 @@ files = [
 
 [[package]]
 name = "qdrant-client"
-version = "1.10.0"
+version = "1.10.1"
 description = "Client library for the Qdrant vector search engine"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "qdrant_client-1.10.0-py3-none-any.whl", hash = "sha256:423c2586709ccf3db20850cd85c3d18954692a8faff98367dfa9dc82ab7f91d9"},
+    {file = "qdrant_client-1.10.1-py3-none-any.whl", hash = "sha256:b9fb8fe50dd168d92b2998be7c6135d5a229b3a3258ad158cc69c8adf9ff1810"},
-    {file = "qdrant_client-1.10.0.tar.gz", hash = "sha256:47c4f7abfab152fb7e5e4902ab0e2e9e33483c49ea5e80128ccd0295f342cf9b"},
+    {file = "qdrant_client-1.10.1.tar.gz", hash = "sha256:2284c8c5bb1defb0d9dbacb07d16f344972f395f4f2ed062318476a7951fd84c"},
 ]
 
 [package.dependencies]
@@ -1932,4 +1932,4 @@ fastembed = ["fastembed"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "192e50ec7768ff659cfe7d2bbc75497dbeba3ee9f1e245d12f2eb3cdf7fe07d9"
+content-hash = "a73d49a493892731f3fccc06217626d20b8114bc83ad107bc7336eabca479988"

libs/partners/qdrant/pyproject.toml

@@ -23,9 +23,9 @@ disallow_untyped_defs = true
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
 langchain-core = ">=0.1.52,<0.3"
-qdrant-client = "^1.9.0"
+qdrant-client = "^1.10.1"
 pydantic = "^2.7.4"
-fastembed = { version = "^0.3.1", python = ">=3.8.1,<3.13", optional = true}
+fastembed = { version = "^0.3.3", python = ">=3.8.1,<3.13", optional = true}
 
 [tool.poetry.extras]
 fastembed = [ "fastembed"]

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

@@ -31,29 +31,7 @@ def assert_documents_equals(actual: List[Document], expected: List[Document]):
         assert actual_doc.metadata == expected_doc.metadata
 
 
-class FakeEmbeddings(Embeddings):
+class ConsistentFakeEmbeddings(Embeddings):
-    """Fake embeddings functionality for testing."""
-
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Return simple embeddings.
-        Embeddings encode each text as its index."""
-        return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
-
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
-        return self.embed_documents(texts)
-
-    def embed_query(self, text: str) -> List[float]:
-        """Return constant query embeddings.
-        Embeddings are identical to embed_documents(texts)[0].
-        Distance to each text will be that text's index,
-        as it was passed to embed_documents."""
-        return [float(1.0)] * 9 + [float(0.0)]
-
-    async def aembed_query(self, text: str) -> List[float]:
-        return self.embed_query(text)
-
-
-class ConsistentFakeEmbeddings(FakeEmbeddings):
     """Fake embeddings which remember all the texts seen so far to return consistent
     vectors for the same texts."""
 

libs/partners/qdrant/tests/integration_tests/fastembed/test_fastembed_sparse.py

@@ -3,6 +3,8 @@ import pytest
 
 from langchain_qdrant import FastEmbedSparse
 
+pytest.importorskip("fastembed", reason="'fastembed' package is not installed")
+
 
 @pytest.mark.parametrize(
     "model_name", ["Qdrant/bm25", "Qdrant/bm42-all-minilm-l6-v2-attentions"]

libs/partners/qdrant/tests/unit_tests/test_imports.py

@@ -1,6 +1,13 @@
 from langchain_qdrant import __all__
 
-EXPECTED_ALL = ["Qdrant", "SparseEmbeddings", "SparseVector", "FastEmbedSparse"]
+EXPECTED_ALL = [
+    "Qdrant",
+    "QdrantVectorStore",
+    "SparseEmbeddings",
+    "SparseVector",
+    "FastEmbedSparse",
+    "RetrievalMode",
+]
 
 
 def test_all_imports() -> None: