Use blockbuster to detect blocking calls in the event loop

libs/astradb/langchain_astradb/graph_vectorstores.py

@@ -1255,7 +1255,7 @@ async def visit_targets(d: int, docs: Iterable[Document]) -> None:
                 await asyncio.gather(*visit_node_tasks)
 
         # Start the traversal
-        initial_docs = self.vector_store.similarity_search(
+        initial_docs = await self.vector_store.asimilarity_search(
             query=query,
             k=k,
             filter=filter,

libs/astradb/langchain_astradb/utils/astradb.py

@@ -425,9 +425,9 @@ async def _asetup_db(
         except DataAPIException as data_api_exception:
             # possibly the collection is preexisting and may have legacy,
             # or custom, indexing settings: verify
-            collection_descriptors = [
-                coll_desc async for coll_desc in self.async_database.list_collections()
-            ]
+            collection_descriptors = list(
+                await asyncio.to_thread(self.database.list_collections)
+            )
             try:
                 if not self._validate_indexing_policy(
                     collection_descriptors=collection_descriptors,

libs/astradb/pyproject.toml

@@ -32,6 +32,7 @@ langchain = { git = "https://github.com/langchain-ai/langchain.git", subdirector
 langchain-text-splitters = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/text-splitters" }
 langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" }
 langchain-community = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/community" }
+blockbuster = "^1.2.0"
 
 [tool.poetry.group.codespell]
 optional = true

libs/astradb/tests/conftest.py

@@ -5,8 +5,10 @@
 from __future__ import annotations
 
 import json
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Iterator
 
+import pytest
+from blockbuster import BlockBuster, blockbuster_ctx
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models import LLM
 from typing_extensions import override
@@ -15,6 +17,22 @@
     from langchain_core.callbacks import CallbackManagerForLLMRun
 
 
+@pytest.fixture(autouse=True)
+def blockbuster() -> Iterator[BlockBuster]:
+    with blockbuster_ctx() as bb:
+        # TODO: GraphVectorStore init is blocking. Should be fixed.
+        for method in (
+            "socket.socket.connect",
+            "ssl.SSLSocket.send",
+            "ssl.SSLSocket.recv",
+            "ssl.SSLSocket.read",
+        ):
+            bb.functions[method].can_block_functions.append(
+                ("langchain_astradb/graph_vectorstores.py", {"__init__"}),
+            )
+        yield bb
+
+
 class ParserEmbeddings(Embeddings):
     """Parse input texts: if they are json for a List[float], fine.
     Otherwise, return all zeros and call it a day.

libs/astradb/tests/integration_tests/test_graphvectorstore.py

@@ -5,6 +5,7 @@
 
 from __future__ import annotations
 
+import asyncio
 from typing import TYPE_CHECKING, Any
 
 import pytest
@@ -400,7 +401,9 @@ async def test_gvs_similarity_search_async(
         request: pytest.FixtureRequest,
     ) -> None:
         """Simple (non-graph) similarity search on a graph vector store."""
-        g_store: AstraDBGraphVectorStore = request.getfixturevalue(store_name)
+        g_store: AstraDBGraphVectorStore = await asyncio.to_thread(
+            request.getfixturevalue, store_name
+        )
         query = "universe" if is_vectorize else "[2, 10]"
         embedding = [2.0, 10.0]
 
@@ -421,8 +424,10 @@ async def test_gvs_similarity_search_async(
             assert ss_by_v_labels == ["AR", "A0"]
 
         if is_autodetected:
-            assert_all_flat_docs(
-                g_store.vector_store.astra_env.collection, is_vectorize=is_vectorize
+            await asyncio.to_thread(
+                assert_all_flat_docs,
+                g_store.vector_store.astra_env.collection,
+                is_vectorize=is_vectorize,
             )
 
     @pytest.mark.parametrize(
@@ -488,7 +493,9 @@ async def test_gvs_traversal_search_async(
         request: pytest.FixtureRequest,
     ) -> None:
         """Graph traversal search on a graph vector store."""
-        g_store: AstraDBGraphVectorStore = request.getfixturevalue(store_name)
+        g_store: AstraDBGraphVectorStore = await asyncio.to_thread(
+            request.getfixturevalue, store_name
+        )
         query = "universe" if is_vectorize else "[2, 10]"
 
         # this is a set, as some of the internals of trav.search are set-driven
@@ -499,8 +506,10 @@ async def test_gvs_traversal_search_async(
         }
         assert ts_labels == {"AR", "A0", "BR", "B0", "TR", "T0"}
         if is_autodetected:
-            assert_all_flat_docs(
-                g_store.vector_store.astra_env.collection, is_vectorize=is_vectorize
+            await asyncio.to_thread(
+                assert_all_flat_docs,
+                g_store.vector_store.astra_env.collection,
+                is_vectorize=is_vectorize,
             )
 
     @pytest.mark.parametrize(
@@ -572,7 +581,9 @@ async def test_gvs_mmr_traversal_search_async(
         request: pytest.FixtureRequest,
     ) -> None:
         """MMR Graph traversal search on a graph vector store."""
-        g_store: AstraDBGraphVectorStore = request.getfixturevalue(store_name)
+        g_store: AstraDBGraphVectorStore = await asyncio.to_thread(
+            request.getfixturevalue, store_name
+        )
         query = "universe" if is_vectorize else "[2, 10]"
 
         mt_labels = [
@@ -589,8 +600,10 @@ async def test_gvs_mmr_traversal_search_async(
 
         assert mt_labels == ["AR", "BR"]
         if is_autodetected:
-            assert_all_flat_docs(
-                g_store.vector_store.astra_env.collection, is_vectorize=is_vectorize
+            await asyncio.to_thread(
+                assert_all_flat_docs,
+                g_store.vector_store.astra_env.collection,
+                is_vectorize=is_vectorize,
             )
 
     @pytest.mark.parametrize(
@@ -652,7 +665,9 @@ async def test_gvs_metadata_search_async(
         request: pytest.FixtureRequest,
     ) -> None:
         """Metadata search on a graph vector store."""
-        g_store: AstraDBGraphVectorStore = request.getfixturevalue(store_name)
+        g_store: AstraDBGraphVectorStore = await asyncio.to_thread(
+            request.getfixturevalue, store_name
+        )
         mt_response = await g_store.ametadata_search(
             filter={"label": "T0"},
             n=2,
@@ -726,7 +741,9 @@ async def test_gvs_get_by_document_id_async(
         request: pytest.FixtureRequest,
     ) -> None:
         """Get by document_id on a graph vector store."""
-        g_store: AstraDBGraphVectorStore = request.getfixturevalue(store_name)
+        g_store: AstraDBGraphVectorStore = await asyncio.to_thread(
+            request.getfixturevalue, store_name
+        )
         doc = await g_store.aget_by_document_id(document_id="FL")
         assert doc is not None
         assert doc.metadata["label"] == "FL"
@@ -816,7 +833,9 @@ async def test_gvs_from_texts_async(
         collection_fixture_name: str,
         request: pytest.FixtureRequest,
     ) -> None:
-        collection_name: str = request.getfixturevalue(collection_fixture_name)
+        collection_name: str = await asyncio.to_thread(
+            request.getfixturevalue, collection_fixture_name
+        )
         init_kwargs: dict[str, Any]
         if is_vectorize:
             init_kwargs = {
@@ -839,7 +858,7 @@ async def test_gvs_from_texts_async(
         )
 
         query = "ukrainian food" if is_vectorize else "[2, 1]"
-        hits = g_store.similarity_search(query=query, k=2)
+        hits = await g_store.asimilarity_search(query=query, k=2)
         assert len(hits) == 1
         assert hits[0].page_content == page_contents[0]
         assert hits[0].id == "x_id"
@@ -915,7 +934,9 @@ async def test_gvs_from_documents_containing_ids_async(
         collection_fixture_name: str,
         request: pytest.FixtureRequest,
     ) -> None:
-        collection_name: str = request.getfixturevalue(collection_fixture_name)
+        collection_name: str = await asyncio.to_thread(
+            request.getfixturevalue, collection_fixture_name
+        )
         init_kwargs: dict[str, Any]
         if is_vectorize:
             init_kwargs = {
@@ -941,7 +962,7 @@ async def test_gvs_from_documents_containing_ids_async(
         )
 
         query = "mexican food" if is_vectorize else "[2, 1]"
-        hits = g_store.similarity_search(query=query, k=2)
+        hits = await g_store.asimilarity_search(query=query, k=2)
         assert len(hits) == 1
         assert hits[0].page_content == page_contents[0]
         assert hits[0].id == "x_id"
@@ -1005,7 +1026,9 @@ async def test_gvs_add_nodes_async(
         store_name: str,
         request: pytest.FixtureRequest,
     ) -> None:
-        g_store: AstraDBGraphVectorStore = request.getfixturevalue(store_name)
+        g_store: AstraDBGraphVectorStore = await asyncio.to_thread(
+            request.getfixturevalue, store_name
+        )
         links0 = [
             Link(kind="kA", direction="out", tag="tA"),
             Link(kind="kB", direction="bidir", tag="tB"),

libs/astradb/tests/integration_tests/test_storage.py

@@ -44,7 +44,7 @@ async def astra_db_empty_store_async(
     astra_db_credentials: AstraDBCredentials,
     collection_idxid: Collection,
 ) -> AstraDBStore:
-    collection_idxid.delete_many({})
+    await collection_idxid.to_async().delete_many({})
     return AstraDBStore(
         collection_name=collection_idxid.name,
         token=StaticTokenProvider(astra_db_credentials["token"]),
@@ -364,7 +364,7 @@ async def test_store_indexing_on_legacy_async(
         database: Database,
     ) -> None:
         """Test of instantiation against a legacy collection, async version."""
-        database.create_collection(
+        await database.to_async().create_collection(
             EPHEMERAL_LEGACY_IDX_NAME,
             indexing=None,
             check_exists=False,
@@ -413,7 +413,7 @@ async def test_store_indexing_on_custom_async(
         database: Database,
     ) -> None:
         """Test of instantiation against a legacy collection, async version."""
-        database.create_collection(
+        await database.to_async().create_collection(
             EPHEMERAL_CUSTOM_IDX_NAME,
             indexing={"deny": ["useless", "forgettable"]},
             check_exists=False,