+Jimmeny Marvelton
+
+
+ moderation_callback is different from LangChain Chain Callbacks. You can still use LangChain Chain callbacks with AmazonComprehendModerationChain via the callbacks parameter. Example: \n", + "from langchain.callbacks.stdout import StdOutCallbackHandler\n", + "comp_moderation_with_config = AmazonComprehendModerationChain(verbose=True, callbacks=[StdOutCallbackHandler()])\n", + "\n", + "
{source}
+
| Team | "Payroll (millions)" | "Wins" |
|---|---|---|
| str | f64 | i64 |
| "Nationals" | 81.34 | 98 |
| "Reds" | 82.2 | 97 |
| "Yankees" | 197.96 | 95 |
| "Giants" | 117.62 | 94 |
| "Braves" | 83.31 | 94 |
Content {page_id}
"}}, + "body": { + f"{content_format.name.lower()}": {"value": f"Content {page_id}
"} + }, "status": "current", "type": "page", "_links": { diff --git a/libs/langchain/tests/unit_tests/document_loaders/test_git.py b/libs/langchain/tests/unit_tests/document_loaders/test_git.py index c667cbdc8..269f5e2bb 100644 --- a/libs/langchain/tests/unit_tests/document_loaders/test_git.py +++ b/libs/langchain/tests/unit_tests/document_loaders/test_git.py @@ -23,7 +23,7 @@ def init_repo(tmpdir: py.path.local, dir_name: str) -> str: git.add([sample_file]) git.commit(m="Initial commit") - return repo_dir + return str(repo_dir) @pytest.mark.requires("git") diff --git a/libs/langchain/tests/unit_tests/indexes/test_api.py b/libs/langchain/tests/unit_tests/indexes/test_api.py new file mode 100644 index 000000000..3104084d0 --- /dev/null +++ b/libs/langchain/tests/unit_tests/indexes/test_api.py @@ -0,0 +1,13 @@ +from langchain.indexes import __all__ + + +def test_all() -> None: + """Use to catch obvious breaking changes.""" + assert __all__ == sorted(__all__, key=str.lower) + assert __all__ == [ + "GraphIndexCreator", + "index", + "IndexingResult", + "SQLRecordManager", + "VectorstoreIndexCreator", + ] diff --git a/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py b/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py new file mode 100644 index 000000000..24bbd1152 --- /dev/null +++ b/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py @@ -0,0 +1,50 @@ +import pytest + +from langchain.indexes._api import _HashedDocument +from langchain.schema import Document + + +def test_hashed_document_hashing() -> None: + hashed_document = _HashedDocument( + uid="123", page_content="Lorem ipsum dolor sit amet", metadata={"key": "value"} + ) + assert isinstance(hashed_document.hash_, str) + + +def test_hashing_with_missing_content() -> None: + """Check that ValueError is raised if page_content is missing.""" + with pytest.raises(ValueError): + _HashedDocument( + metadata={"key": "value"}, + ) + + +def test_uid_auto_assigned_to_hash() -> None: + """Test uid is auto-assigned to the hashed_document hash.""" + hashed_document = _HashedDocument( + page_content="Lorem ipsum dolor sit amet", metadata={"key": "value"} + ) + assert hashed_document.uid == hashed_document.hash_ + + +def test_to_document() -> None: + """Test to_document method.""" + hashed_document = _HashedDocument( + page_content="Lorem ipsum dolor sit amet", metadata={"key": "value"} + ) + doc = hashed_document.to_document() + assert isinstance(doc, Document) + assert doc.page_content == "Lorem ipsum dolor sit amet" + assert doc.metadata == {"key": "value"} + + +def test_from_document() -> None: + """Test from document class method.""" + document = Document( + page_content="Lorem ipsum dolor sit amet", metadata={"key": "value"} + ) + + hashed_document = _HashedDocument.from_document(document) + # hash should be deterministic + assert hashed_document.hash_ == "fd1dc827-051b-537d-a1fe-1fa043e8b276" + assert hashed_document.uid == hashed_document.hash_ diff --git a/libs/langchain/tests/unit_tests/indexes/test_indexing.py b/libs/langchain/tests/unit_tests/indexes/test_indexing.py new file mode 100644 index 000000000..70ebf4fcb --- /dev/null +++ b/libs/langchain/tests/unit_tests/indexes/test_indexing.py @@ -0,0 +1,474 @@ +from datetime import datetime +from typing import Any, Dict, Iterable, Iterator, List, Optional, Sequence, Type +from unittest.mock import patch + +import pytest + +from langchain.document_loaders.base import BaseLoader +from langchain.embeddings.base import Embeddings +from langchain.indexes import index +from langchain.indexes._sql_record_manager import SQLRecordManager +from langchain.schema import Document +from langchain.vectorstores.base import VST, VectorStore + + +class ToyLoader(BaseLoader): + """Toy loader that always returns the same documents.""" + + def __init__(self, documents: Sequence[Document]) -> None: + """Initialize with the documents to return.""" + self.documents = documents + + def lazy_load( + self, + ) -> Iterator[Document]: + yield from self.documents + + def load(self) -> List[Document]: + """Load the documents from the source.""" + return list(self.lazy_load()) + + +class InMemoryVectorStore(VectorStore): + """In-memory implementation of VectorStore using a dictionary.""" + + def __init__(self) -> None: + """Vector store interface for testing things in memory.""" + self.store: Dict[str, Document] = {} + + def delete(self, ids: Optional[Sequence[str]] = None, **kwargs: Any) -> None: + """Delete the given documents from the store using their IDs.""" + if ids: + for _id in ids: + self.store.pop(_id, None) + + def add_documents( # type: ignore + self, + documents: Sequence[Document], + *, + ids: Optional[Sequence[str]] = None, + **kwargs: Any, + ) -> None: + """Add the given documents to the store (insert behavior).""" + if ids and len(ids) != len(documents): + raise ValueError( + f"Expected {len(ids)} ids, got {len(documents)} documents." + ) + + if not ids: + raise NotImplementedError("This is not implemented yet.") + + for _id, document in zip(ids, documents): + if _id in self.store: + raise ValueError( + f"Document with uid {_id} already exists in the store." + ) + self.store[_id] = document + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> List[str]: + """Add the given texts to the store (insert behavior).""" + raise NotImplementedError() + + @classmethod + def from_texts( + cls: Type[VST], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> VST: + """Create a vector store from a list of texts.""" + raise NotImplementedError() + + def similarity_search( + self, query: str, k: int = 4, **kwargs: Any + ) -> List[Document]: + """Find the most similar documents to the given query.""" + raise NotImplementedError() + + +@pytest.fixture +def record_manager() -> SQLRecordManager: + """Timestamped set fixture.""" + record_manager = SQLRecordManager("kittens", db_url="sqlite:///:memory:") + record_manager.create_schema() + return record_manager + + +@pytest.fixture +def vector_store() -> InMemoryVectorStore: + """Vector store fixture.""" + return InMemoryVectorStore() + + +def test_indexing_same_content( + record_manager: SQLRecordManager, vector_store: InMemoryVectorStore +) -> None: + """Indexing some content to confirm it gets added only once.""" + loader = ToyLoader( + documents=[ + Document( + page_content="This is a test document.", + ), + Document( + page_content="This is another document.", + ), + ] + ) + + assert index(loader, record_manager, vector_store) == { + "num_added": 2, + "num_deleted": 0, + "num_skipped": 0, + "num_updated": 0, + } + + assert len(list(vector_store.store)) == 2 + + for _ in range(2): + # Run the indexing again + assert index(loader, record_manager, vector_store) == { + "num_added": 0, + "num_deleted": 0, + "num_skipped": 2, + "num_updated": 0, + } + + +def test_index_simple_delete_full( + record_manager: SQLRecordManager, vector_store: InMemoryVectorStore +) -> None: + """Indexing some content to confirm it gets added only once.""" + loader = ToyLoader( + documents=[ + Document( + page_content="This is a test document.", + ), + Document( + page_content="This is another document.", + ), + ] + ) + + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 1).timestamp() + ): + assert index(loader, record_manager, vector_store, delete_mode="full") == { + "num_added": 2, + "num_deleted": 0, + "num_skipped": 0, + "num_updated": 0, + } + + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 1).timestamp() + ): + assert index(loader, record_manager, vector_store, delete_mode="full") == { + "num_added": 0, + "num_deleted": 0, + "num_skipped": 2, + "num_updated": 0, + } + + loader = ToyLoader( + documents=[ + Document( + page_content="mutated document 1", + ), + Document( + page_content="This is another document.", # <-- Same as original + ), + ] + ) + + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + assert index(loader, record_manager, vector_store, delete_mode="full") == { + "num_added": 1, + "num_deleted": 1, + "num_skipped": 1, + "num_updated": 0, + } + + doc_texts = set( + # Ignoring type since doc should be in the store and not a None + vector_store.store.get(uid).page_content # type: ignore + for uid in vector_store.store + ) + assert doc_texts == {"mutated document 1", "This is another document."} + + # Attempt to index again verify that nothing changes + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + assert index(loader, record_manager, vector_store, delete_mode="full") == { + "num_added": 0, + "num_deleted": 0, + "num_skipped": 2, + "num_updated": 0, + } + + +def test_incremental_fails_with_bad_source_ids( + record_manager: SQLRecordManager, vector_store: InMemoryVectorStore +) -> None: + """Test indexing with incremental deletion strategy.""" + loader = ToyLoader( + documents=[ + Document( + page_content="This is a test document.", + metadata={"source": "1"}, + ), + Document( + page_content="This is another document.", + metadata={"source": "2"}, + ), + Document( + page_content="This is yet another document.", + metadata={"source": None}, + ), + ] + ) + + with pytest.raises(ValueError): + # Should raise an error because no source id function was specified + index(loader, record_manager, vector_store, delete_mode="incremental") + + with pytest.raises(ValueError): + # Should raise an error because no source id function was specified + index( + loader, + record_manager, + vector_store, + delete_mode="incremental", + source_id_key="source", + ) + + +def test_no_delete( + record_manager: SQLRecordManager, vector_store: InMemoryVectorStore +) -> None: + """Test indexing without a deletion strategy.""" + loader = ToyLoader( + documents=[ + Document( + page_content="This is a test document.", + metadata={"source": "1"}, + ), + Document( + page_content="This is another document.", + metadata={"source": "2"}, + ), + ] + ) + + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + assert index( + loader, + record_manager, + vector_store, + delete_mode=None, + source_id_key="source", + ) == { + "num_added": 2, + "num_deleted": 0, + "num_skipped": 0, + "num_updated": 0, + } + + # If we add the same content twice it should be skipped + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + assert index( + loader, + record_manager, + vector_store, + delete_mode=None, + source_id_key="source", + ) == { + "num_added": 0, + "num_deleted": 0, + "num_skipped": 2, + "num_updated": 0, + } + + loader = ToyLoader( + documents=[ + Document( + page_content="mutated content", + metadata={"source": "1"}, + ), + Document( + page_content="This is another document.", + metadata={"source": "2"}, + ), + ] + ) + + # Should result in no updates or deletions! + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + assert index( + loader, + record_manager, + vector_store, + delete_mode=None, + source_id_key="source", + ) == { + "num_added": 1, + "num_deleted": 0, + "num_skipped": 1, + "num_updated": 0, + } + + +def test_incremental_delete( + record_manager: SQLRecordManager, vector_store: InMemoryVectorStore +) -> None: + """Test indexing with incremental deletion strategy.""" + loader = ToyLoader( + documents=[ + Document( + page_content="This is a test document.", + metadata={"source": "1"}, + ), + Document( + page_content="This is another document.", + metadata={"source": "2"}, + ), + ] + ) + + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + assert index( + loader, + record_manager, + vector_store, + delete_mode="incremental", + source_id_key="source", + ) == { + "num_added": 2, + "num_deleted": 0, + "num_skipped": 0, + "num_updated": 0, + } + + doc_texts = set( + # Ignoring type since doc should be in the store and not a None + vector_store.store.get(uid).page_content # type: ignore + for uid in vector_store.store + ) + assert doc_texts == {"This is another document.", "This is a test document."} + + # Attempt to index again verify that nothing changes + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + assert index( + loader, + record_manager, + vector_store, + delete_mode="incremental", + source_id_key="source", + ) == { + "num_added": 0, + "num_deleted": 0, + "num_skipped": 2, + "num_updated": 0, + } + + # Create 2 documents from the same source all with mutated content + loader = ToyLoader( + documents=[ + Document( + page_content="mutated document 1", + metadata={"source": "1"}, + ), + Document( + page_content="mutated document 2", + metadata={"source": "1"}, + ), + Document( + page_content="This is another document.", # <-- Same as original + metadata={"source": "2"}, + ), + ] + ) + + # Attempt to index again verify that nothing changes + with patch.object( + record_manager, "get_time", return_value=datetime(2021, 1, 3).timestamp() + ): + assert index( + loader, + record_manager, + vector_store, + delete_mode="incremental", + source_id_key="source", + ) == { + "num_added": 2, + "num_deleted": 1, + "num_skipped": 1, + "num_updated": 0, + } + + doc_texts = set( + # Ignoring type since doc should be in the store and not a None + vector_store.store.get(uid).page_content # type: ignore + for uid in vector_store.store + ) + assert doc_texts == { + "mutated document 1", + "mutated document 2", + "This is another document.", + } + + +def test_indexing_with_no_docs( + record_manager: SQLRecordManager, vector_store: VectorStore +) -> None: + """Check edge case when loader returns no new docs.""" + loader = ToyLoader(documents=[]) + + assert index(loader, record_manager, vector_store, delete_mode="full") == { + "num_added": 0, + "num_deleted": 0, + "num_skipped": 0, + "num_updated": 0, + } + + +def test_deduplication( + record_manager: SQLRecordManager, vector_store: VectorStore +) -> None: + """Check edge case when loader returns no new docs.""" + docs = [ + Document( + page_content="This is a test document.", + metadata={"source": "1"}, + ), + Document( + page_content="This is a test document.", + metadata={"source": "1"}, + ), + ] + + # Should result in only a single document being added + assert index(docs, record_manager, vector_store, delete_mode="full") == { + "num_added": 1, + "num_deleted": 0, + "num_skipped": 0, + "num_updated": 0, + } diff --git a/libs/langchain/tests/unit_tests/indexes/test_sql_record_manager.py b/libs/langchain/tests/unit_tests/indexes/test_sql_record_manager.py new file mode 100644 index 000000000..d7d95212a --- /dev/null +++ b/libs/langchain/tests/unit_tests/indexes/test_sql_record_manager.py @@ -0,0 +1,276 @@ +from datetime import datetime +from unittest.mock import patch + +import pytest + +from langchain.indexes._sql_record_manager import SQLRecordManager, UpsertionRecord + + +@pytest.fixture() +def manager() -> SQLRecordManager: + """Initialize the test database and yield the TimestampedSet instance.""" + # Initialize and yield the TimestampedSet instance + record_manager = SQLRecordManager("kittens", db_url="sqlite:///:memory:") + record_manager.create_schema() + return record_manager + + +def test_update(manager: SQLRecordManager) -> None: + """Test updating records in the database.""" + # no keys should be present in the set + read_keys = manager.list_keys() + assert read_keys == [] + # Insert records + keys = ["key1", "key2", "key3"] + manager.update(keys) + # Retrieve the records + read_keys = manager.list_keys() + assert read_keys == ["key1", "key2", "key3"] + + +def test_update_timestamp(manager: SQLRecordManager) -> None: + """Test updating records in the database.""" + # no keys should be present in the set + with patch.object( + manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() + ): + manager.update(["key1"]) + + with manager._make_session() as session: + records = ( + session.query(UpsertionRecord) + .filter(UpsertionRecord.namespace == manager.namespace) + .all() # type: ignore[attr-defined] + ) + + assert [ + { + "key": record.key, + "namespace": record.namespace, + "updated_at": record.updated_at, + "group_id": record.group_id, + } + for record in records + ] == [ + { + "group_id": None, + "key": "key1", + "namespace": "kittens", + "updated_at": datetime(2021, 1, 2, 0, 0).timestamp(), + } + ] + + with patch.object( + manager, "get_time", return_value=datetime(2023, 1, 2).timestamp() + ): + manager.update(["key1"]) + + with manager._make_session() as session: + records = ( + session.query(UpsertionRecord) + .filter(UpsertionRecord.namespace == manager.namespace) + .all() # type: ignore[attr-defined] + ) + + assert [ + { + "key": record.key, + "namespace": record.namespace, + "updated_at": record.updated_at, + "group_id": record.group_id, + } + for record in records + ] == [ + { + "group_id": None, + "key": "key1", + "namespace": "kittens", + "updated_at": datetime(2023, 1, 2, 0, 0).timestamp(), + } + ] + + with patch.object( + manager, "get_time", return_value=datetime(2023, 2, 2).timestamp() + ): + manager.update(["key1"], group_ids=["group1"]) + + with manager._make_session() as session: + records = ( + session.query(UpsertionRecord) + .filter(UpsertionRecord.namespace == manager.namespace) + .all() # type: ignore[attr-defined] + ) + + assert [ + { + "key": record.key, + "namespace": record.namespace, + "updated_at": record.updated_at, + "group_id": record.group_id, + } + for record in records + ] == [ + { + "group_id": "group1", + "key": "key1", + "namespace": "kittens", + "updated_at": datetime(2023, 2, 2, 0, 0).timestamp(), + } + ] + + +def test_update_with_group_ids(manager: SQLRecordManager) -> None: + """Test updating records in the database.""" + # no keys should be present in the set + read_keys = manager.list_keys() + assert read_keys == [] + # Insert records + keys = ["key1", "key2", "key3"] + manager.update(keys) + # Retrieve the records + read_keys = manager.list_keys() + assert read_keys == ["key1", "key2", "key3"] + + +def test_exists(manager: SQLRecordManager) -> None: + """Test checking if keys exist in the database.""" + # Insert records + keys = ["key1", "key2", "key3"] + manager.update(keys) + # Check if the keys exist in the database + exists = manager.exists(keys) + assert len(exists) == len(keys) + assert exists == [True, True, True] + + exists = manager.exists(["key1", "key4"]) + assert len(exists) == 2 + assert exists == [True, False] + + +def test_list_keys(manager: SQLRecordManager) -> None: + """Test listing keys based on the provided date range.""" + # Insert records + assert manager.list_keys() == [] + with manager._make_session() as session: + # Add some keys with explicit updated_ats + session.add( + UpsertionRecord( + key="key1", + updated_at=datetime(2021, 1, 1).timestamp(), + namespace="kittens", + ) + ) + session.add( + UpsertionRecord( + key="key2", + updated_at=datetime(2022, 1, 1).timestamp(), + namespace="kittens", + ) + ) + session.add( + UpsertionRecord( + key="key3", + updated_at=datetime(2023, 1, 1).timestamp(), + namespace="kittens", + ) + ) + session.add( + UpsertionRecord( + key="key4", + group_id="group1", + updated_at=datetime(2024, 1, 1).timestamp(), + namespace="kittens", + ) + ) + # Insert keys from a different namespace, these should not be visible! + session.add( + UpsertionRecord( + key="key1", + updated_at=datetime(2021, 1, 1).timestamp(), + namespace="puppies", + ) + ) + session.add( + UpsertionRecord( + key="key5", + updated_at=datetime(2021, 1, 1).timestamp(), + namespace="puppies", + ) + ) + session.commit() + + # Retrieve all keys + assert manager.list_keys() == ["key1", "key2", "key3", "key4"] + + # Retrieve keys updated after a certain date + assert manager.list_keys(after=datetime(2022, 2, 1).timestamp()) == ["key3", "key4"] + + # Retrieve keys updated after a certain date + assert manager.list_keys(before=datetime(2022, 2, 1).timestamp()) == [ + "key1", + "key2", + ] + + # Retrieve keys updated after a certain date + assert manager.list_keys(before=datetime(2019, 2, 1).timestamp()) == [] + + # Retrieve keys in a time range + assert manager.list_keys( + before=datetime(2022, 2, 1).timestamp(), + after=datetime(2021, 11, 1).timestamp(), + ) == ["key2"] + + assert manager.list_keys(group_ids=["group1", "group2"]) == ["key4"] + + # Test multiple filters + assert ( + manager.list_keys( + group_ids=["group1", "group2"], before=datetime(2019, 1, 1).timestamp() + ) + == [] + ) + assert manager.list_keys( + group_ids=["group1", "group2"], after=datetime(2019, 1, 1).timestamp() + ) == ["key4"] + + +def test_namespace_is_used(manager: SQLRecordManager) -> None: + """Verify that namespace is taken into account for all operations.""" + assert manager.namespace == "kittens" + with manager._make_session() as session: + # Add some keys with explicit updated_ats + session.add(UpsertionRecord(key="key1", namespace="kittens")) + session.add(UpsertionRecord(key="key2", namespace="kittens")) + session.add(UpsertionRecord(key="key1", namespace="puppies")) + session.add(UpsertionRecord(key="key3", namespace="puppies")) + session.commit() + + assert manager.list_keys() == ["key1", "key2"] + manager.delete_keys(["key1"]) + assert manager.list_keys() == ["key2"] + manager.update(["key3"], group_ids=["group3"]) + + with manager._make_session() as session: + results = session.query(UpsertionRecord).all() + + assert sorted([(r.namespace, r.key, r.group_id) for r in results]) == [ + ("kittens", "key2", None), + ("kittens", "key3", "group3"), + ("puppies", "key1", None), + ("puppies", "key3", None), + ] + + +def test_delete_keys(manager: SQLRecordManager) -> None: + """Test deleting keys from the database.""" + # Insert records + keys = ["key1", "key2", "key3"] + manager.update(keys) + + # Delete some keys + keys_to_delete = ["key1", "key2"] + manager.delete_keys(keys_to_delete) + + # Check if the deleted keys are no longer in the database + remaining_keys = manager.list_keys() + assert remaining_keys == ["key3"] diff --git a/libs/langchain/tests/unit_tests/retrievers/test_web_research.py b/libs/langchain/tests/unit_tests/retrievers/test_web_research.py new file mode 100644 index 000000000..a052e59b7 --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/test_web_research.py @@ -0,0 +1,36 @@ +from typing import List + +import pytest + +from langchain.retrievers.web_research import QuestionListOutputParser + + +@pytest.mark.parametrize( + "text,expected", + ( + ( + "1. Line one.\n", + ["1. Line one.\n"], + ), + ( + "1. Line one.", + ["1. Line one."], + ), + ( + "1. Line one.\n2. Line two.\n", + ["1. Line one.\n", "2. Line two.\n"], + ), + ( + "1. Line one.\n2. Line two.", + ["1. Line one.\n", "2. Line two."], + ), + ( + "1. Line one.\n2. Line two.\n3. Line three.", + ["1. Line one.\n", "2. Line two.\n", "3. Line three."], + ), + ), +) +def test_list_output_parser(text: str, expected: List[str]) -> None: + parser = QuestionListOutputParser() + result = parser.parse(text) + assert result.lines == expected diff --git a/libs/langchain/tests/unit_tests/schema/runnable/__init__.py b/libs/langchain/tests/unit_tests/schema/runnable/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr similarity index 85% rename from libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr rename to libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr index 321edb7a2..c48d4edbd 100644 --- a/libs/langchain/tests/unit_tests/schema/__snapshots__/test_runnable.ambr +++ b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr @@ -464,6 +464,218 @@ Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, example=False)}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- +# name: test_each + ''' + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "schema", + "runnable", + "RunnableSequence" + ], + "kwargs": { + "first": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "ChatPromptTemplate" + ], + "kwargs": { + "messages": [ + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "SystemMessagePromptTemplate" + ], + "kwargs": { + "prompt": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "prompt", + "PromptTemplate" + ], + "kwargs": { + "input_variables": [], + "template": "You are a nice assistant.", + "template_format": "f-string", + "partial_variables": {} + } + } + } + }, + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "HumanMessagePromptTemplate" + ], + "kwargs": { + "prompt": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "prompt", + "PromptTemplate" + ], + "kwargs": { + "input_variables": [ + "question" + ], + "template": "{question}", + "template_format": "f-string", + "partial_variables": {} + } + } + } + } + ], + "input_variables": [ + "question" + ] + } + }, + "middle": [ + { + "lc": 1, + "type": "not_implemented", + "id": [ + "langchain", + "llms", + "fake", + "FakeStreamingListLLM" + ] + }, + { + "lc": 1, + "type": "constructor", + "id": [ + "runnable", + "test_runnable", + "FakeSplitIntoListParser" + ], + "kwargs": {} + } + ], + "last": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "schema", + "runnable", + "RunnableEach" + ], + "kwargs": { + "bound": { + "lc": 1, + "type": "not_implemented", + "id": [ + "langchain", + "llms", + "fake", + "FakeStreamingListLLM" + ] + } + } + } + } + } + ''' +# --- +# name: test_higher_order_lambda_runnable + ''' + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "schema", + "runnable", + "RunnableSequence" + ], + "kwargs": { + "first": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "schema", + "runnable", + "RunnableMap" + ], + "kwargs": { + "steps": { + "key": { + "lc": 1, + "type": "not_implemented", + "id": [ + "langchain", + "schema", + "runnable", + "base", + "RunnableLambda" + ] + }, + "input": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "schema", + "runnable", + "RunnableMap" + ], + "kwargs": { + "steps": { + "question": { + "lc": 1, + "type": "not_implemented", + "id": [ + "langchain", + "schema", + "runnable", + "base", + "RunnableLambda" + ] + } + } + } + } + } + } + }, + "last": { + "lc": 1, + "type": "not_implemented", + "id": [ + "langchain", + "schema", + "runnable", + "base", + "RunnableLambda" + ] + } + } + } + ''' +# --- # name: test_llm_with_fallbacks[llm_chain_with_fallbacks] ''' { @@ -1052,6 +1264,125 @@ Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your favorite color?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- +# name: test_prompt_with_llm_and_async_lambda + ''' + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "schema", + "runnable", + "RunnableSequence" + ], + "kwargs": { + "first": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "ChatPromptTemplate" + ], + "kwargs": { + "messages": [ + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "SystemMessagePromptTemplate" + ], + "kwargs": { + "prompt": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "prompt", + "PromptTemplate" + ], + "kwargs": { + "input_variables": [], + "template": "You are a nice assistant.", + "template_format": "f-string", + "partial_variables": {} + } + } + } + }, + { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "chat", + "HumanMessagePromptTemplate" + ], + "kwargs": { + "prompt": { + "lc": 1, + "type": "constructor", + "id": [ + "langchain", + "prompts", + "prompt", + "PromptTemplate" + ], + "kwargs": { + "input_variables": [ + "question" + ], + "template": "{question}", + "template_format": "f-string", + "partial_variables": {} + } + } + } + } + ], + "input_variables": [ + "question" + ] + } + }, + "middle": [ + { + "lc": 1, + "type": "not_implemented", + "id": [ + "langchain", + "llms", + "fake", + "FakeListLLM" + ] + } + ], + "last": { + "lc": 1, + "type": "not_implemented", + "id": [ + "langchain", + "schema", + "runnable", + "base", + "RunnableLambda" + ] + } + } + } + ''' +# --- +# name: test_prompt_with_llm_and_async_lambda.1 + list([ + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}], 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + ]) +# --- # name: test_router_runnable ''' { @@ -1352,6 +1683,7 @@ "lc": 1, "type": "not_implemented", "id": [ + "runnable", "test_runnable", "FakeRetriever" ] diff --git a/libs/langchain/tests/unit_tests/schema/runnable/test_locals.py b/libs/langchain/tests/unit_tests/schema/runnable/test_locals.py new file mode 100644 index 000000000..ee07c0cfc --- /dev/null +++ b/libs/langchain/tests/unit_tests/schema/runnable/test_locals.py @@ -0,0 +1,93 @@ +from typing import Any, Callable, Type + +import pytest + +from langchain import PromptTemplate +from langchain.llms import FakeListLLM +from langchain.schema.runnable import ( + GetLocalVar, + PutLocalVar, + RunnablePassthrough, + RunnableSequence, +) + + +@pytest.mark.parametrize( + ("method", "input", "output"), + [ + (lambda r, x: r.invoke(x), "foo", "foo"), + (lambda r, x: r.batch(x), ["foo", "bar"], ["foo", "bar"]), + (lambda r, x: list(r.stream(x))[0], "foo", "foo"), + ], +) +def test_put_get(method: Callable, input: Any, output: Any) -> None: + runnable = PutLocalVar("input") | GetLocalVar("input") + assert method(runnable, input) == output + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("method", "input", "output"), + [ + (lambda r, x: r.ainvoke(x), "foo", "foo"), + (lambda r, x: r.abatch(x), ["foo", "bar"], ["foo", "bar"]), + ], +) +async def test_put_get_async(method: Callable, input: Any, output: Any) -> None: + runnable = PutLocalVar("input") | GetLocalVar("input") + assert await method(runnable, input) == output + + +@pytest.mark.parametrize( + ("runnable", "error"), + [ + (PutLocalVar("input"), ValueError), + (GetLocalVar("input"), ValueError), + (PutLocalVar("input") | GetLocalVar("missing"), KeyError), + ], +) +def test_incorrect_usage(runnable: RunnableSequence, error: Type[Exception]) -> None: + with pytest.raises(error): + runnable.invoke("foo") + + +def test_get_in_map() -> None: + runnable: RunnableSequence = PutLocalVar("input") | {"bar": GetLocalVar("input")} + assert runnable.invoke("foo") == {"bar": "foo"} + + +def test_put_in_map() -> None: + runnable: RunnableSequence = {"bar": PutLocalVar("input")} | GetLocalVar("input") + with pytest.raises(KeyError): + runnable.invoke("foo") + + +@pytest.mark.parametrize( + "runnable", + [ + PutLocalVar("input") | GetLocalVar("input", passthrough_key="output"), + ( + PutLocalVar("input") + | {"input": RunnablePassthrough()} + | PromptTemplate.from_template("say {input}") + | FakeListLLM(responses=["hello"]) + | GetLocalVar("input", passthrough_key="output") + ), + ], +) +@pytest.mark.parametrize( + ("method", "input", "output"), + [ + (lambda r, x: r.invoke(x), "hello", {"input": "hello", "output": "hello"}), + (lambda r, x: r.batch(x), ["hello"], [{"input": "hello", "output": "hello"}]), + ( + lambda r, x: list(r.stream(x))[0], + "hello", + {"input": "hello", "output": "hello"}, + ), + ], +) +def test_put_get_sequence( + runnable: RunnableSequence, method: Callable, input: Any, output: Any +) -> None: + assert method(runnable, input) == output diff --git a/libs/langchain/tests/unit_tests/schema/test_runnable.py b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py similarity index 79% rename from libs/langchain/tests/unit_tests/schema/test_runnable.py rename to libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py index 352ddea40..f24475331 100644 --- a/libs/langchain/tests/unit_tests/schema/test_runnable.py +++ b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py @@ -1,4 +1,5 @@ -from typing import Any, Dict, List, Optional +from operator import itemgetter +from typing import Any, Dict, List, Optional, Union from uuid import UUID import pytest @@ -27,7 +28,7 @@ from langchain.schema.messages import ( HumanMessage, SystemMessage, ) -from langchain.schema.output_parser import StrOutputParser +from langchain.schema.output_parser import BaseOutputParser, StrOutputParser from langchain.schema.retriever import BaseRetriever from langchain.schema.runnable import ( RouterRunnable, @@ -131,17 +132,25 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None: assert fake.batch( ["hello", "wooorld"], [dict(tags=["a-tag"]), dict(metadata={"key": "value"})] ) == [5, 7] - assert spy.call_args_list == [ - mocker.call("hello", dict(tags=["a-tag"])), - mocker.call("wooorld", dict(metadata={"key": "value"})), - ] + + assert len(spy.call_args_list) == 2 + for i, call in enumerate(spy.call_args_list): + assert call.args[0] == ("hello" if i == 0 else "wooorld") + if i == 0: + assert call.args[1].get("tags") == ["a-tag"] + assert call.args[1].get("metadata") == {} + else: + assert call.args[1].get("tags") == [] + assert call.args[1].get("metadata") == {"key": "value"} + spy.reset_mock() assert fake.batch(["hello", "wooorld"], dict(tags=["a-tag"])) == [5, 7] - assert spy.call_args_list == [ - mocker.call("hello", dict(tags=["a-tag"])), - mocker.call("wooorld", dict(tags=["a-tag"])), - ] + assert len(spy.call_args_list) == 2 + for i, call in enumerate(spy.call_args_list): + assert call.args[0] == ("hello" if i == 0 else "wooorld") + assert call.args[1].get("tags") == ["a-tag"] + assert call.args[1].get("metadata") == {} spy.reset_mock() assert await fake.ainvoke("hello", config={"callbacks": []}) == 5 @@ -161,8 +170,26 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None: 7, ] assert spy.call_args_list == [ - mocker.call("hello", dict(metadata={"key": "value"})), - mocker.call("wooorld", dict(metadata={"key": "value"})), + mocker.call( + "hello", + dict( + metadata={"key": "value"}, + tags=[], + callbacks=None, + _locals={}, + recursion_limit=10, + ), + ), + mocker.call( + "wooorld", + dict( + metadata={"key": "value"}, + tags=[], + callbacks=None, + _locals={}, + recursion_limit=10, + ), + ), ] @@ -414,6 +441,50 @@ async def test_prompt_with_llm( ) +@pytest.mark.asyncio +@freeze_time("2023-01-01") +async def test_prompt_with_llm_and_async_lambda( + mocker: MockerFixture, snapshot: SnapshotAssertion +) -> None: + prompt = ( + SystemMessagePromptTemplate.from_template("You are a nice assistant.") + + "{question}" + ) + llm = FakeListLLM(responses=["foo", "bar"]) + + async def passthrough(input: Any) -> Any: + return input + + chain = prompt | llm | passthrough + + assert isinstance(chain, RunnableSequence) + assert chain.first == prompt + assert chain.middle == [llm] + assert chain.last == RunnableLambda(func=passthrough) + assert dumps(chain, pretty=True) == snapshot + + # Test invoke + prompt_spy = mocker.spy(prompt.__class__, "ainvoke") + llm_spy = mocker.spy(llm.__class__, "ainvoke") + tracer = FakeTracer() + assert ( + await chain.ainvoke( + {"question": "What is your name?"}, dict(callbacks=[tracer]) + ) + == "foo" + ) + assert prompt_spy.call_args.args[1] == {"question": "What is your name?"} + assert llm_spy.call_args.args[1] == ChatPromptValue( + messages=[ + SystemMessage(content="You are a nice assistant."), + HumanMessage(content="What is your name?"), + ] + ) + assert tracer.runs == snapshot + mocker.stop(prompt_spy) + mocker.stop(llm_spy) + + @freeze_time("2023-01-01") def test_prompt_with_chat_model_and_parser( mocker: MockerFixture, snapshot: SnapshotAssertion @@ -698,6 +769,105 @@ async def test_router_runnable( assert len(router_run.child_runs) == 2 +@pytest.mark.asyncio +@freeze_time("2023-01-01") +async def test_higher_order_lambda_runnable( + mocker: MockerFixture, snapshot: SnapshotAssertion +) -> None: + math_chain = ChatPromptTemplate.from_template( + "You are a math genius. Answer the question: {question}" + ) | FakeListLLM(responses=["4"]) + english_chain = ChatPromptTemplate.from_template( + "You are an english major. Answer the question: {question}" + ) | FakeListLLM(responses=["2"]) + input_map: Runnable = RunnableMap( + { # type: ignore[arg-type] + "key": lambda x: x["key"], + "input": {"question": lambda x: x["question"]}, + } + ) + + def router(input: Dict[str, Any]) -> Runnable: + if input["key"] == "math": + return itemgetter("input") | math_chain + elif input["key"] == "english": + return itemgetter("input") | english_chain + else: + raise ValueError(f"Unknown key: {input['key']}") + + chain: Runnable = input_map | router + assert dumps(chain, pretty=True) == snapshot + + result = chain.invoke({"key": "math", "question": "2 + 2"}) + assert result == "4" + + result2 = chain.batch( + [{"key": "math", "question": "2 + 2"}, {"key": "english", "question": "2 + 2"}] + ) + assert result2 == ["4", "2"] + + result = await chain.ainvoke({"key": "math", "question": "2 + 2"}) + assert result == "4" + + result2 = await chain.abatch( + [{"key": "math", "question": "2 + 2"}, {"key": "english", "question": "2 + 2"}] + ) + assert result2 == ["4", "2"] + + # Test invoke + math_spy = mocker.spy(math_chain.__class__, "invoke") + tracer = FakeTracer() + assert ( + chain.invoke({"key": "math", "question": "2 + 2"}, dict(callbacks=[tracer])) + == "4" + ) + assert math_spy.call_args.args[1] == { + "key": "math", + "input": {"question": "2 + 2"}, + } + assert len([r for r in tracer.runs if r.parent_run_id is None]) == 1 + parent_run = next(r for r in tracer.runs if r.parent_run_id is None) + assert len(parent_run.child_runs) == 2 + router_run = parent_run.child_runs[1] + assert router_run.name == "RunnableLambda" + assert len(router_run.child_runs) == 1 + math_run = router_run.child_runs[0] + assert math_run.name == "RunnableSequence" + assert len(math_run.child_runs) == 3 + + # Test ainvoke + async def arouter(input: Dict[str, Any]) -> Runnable: + if input["key"] == "math": + return itemgetter("input") | math_chain + elif input["key"] == "english": + return itemgetter("input") | english_chain + else: + raise ValueError(f"Unknown key: {input['key']}") + + achain: Runnable = input_map | arouter + math_spy = mocker.spy(math_chain.__class__, "ainvoke") + tracer = FakeTracer() + assert ( + await achain.ainvoke( + {"key": "math", "question": "2 + 2"}, dict(callbacks=[tracer]) + ) + == "4" + ) + assert math_spy.call_args.args[1] == { + "key": "math", + "input": {"question": "2 + 2"}, + } + assert len([r for r in tracer.runs if r.parent_run_id is None]) == 1 + parent_run = next(r for r in tracer.runs if r.parent_run_id is None) + assert len(parent_run.child_runs) == 2 + router_run = parent_run.child_runs[1] + assert router_run.name == "RunnableLambda" + assert len(router_run.child_runs) == 1 + math_run = router_run.child_runs[0] + assert math_run.name == "RunnableSequence" + assert len(math_run.child_runs) == 3 + + @freeze_time("2023-01-01") def test_seq_prompt_map(mocker: MockerFixture, snapshot: SnapshotAssertion) -> None: passthrough = mocker.Mock(side_effect=lambda x: x) @@ -1062,3 +1232,67 @@ async def test_llm_with_fallbacks( assert await runnable.abatch(["hi", "hey", "bye"]) == ["bar"] * 3 assert list(await runnable.ainvoke("hello")) == list("bar") assert dumps(runnable, pretty=True) == snapshot + + +class FakeSplitIntoListParser(BaseOutputParser[List[str]]): + """Parse the output of an LLM call to a comma-separated list.""" + + @property + def lc_serializable(self) -> bool: + return True + + def get_format_instructions(self) -> str: + return ( + "Your response should be a list of comma separated values, " + "eg: `foo, bar, baz`" + ) + + def parse(self, text: str) -> List[str]: + """Parse the output of an LLM call.""" + return text.strip().split(", ") + + +def test_each_simple() -> None: + """Test that each() works with a simple runnable.""" + parser = FakeSplitIntoListParser() + assert parser.invoke("first item, second item") == ["first item", "second item"] + assert parser.map().invoke(["a, b", "c"]) == [["a", "b"], ["c"]] + assert parser.map().map().invoke([["a, b", "c"], ["c, e"]]) == [ + [["a", "b"], ["c"]], + [["c", "e"]], + ] + + +def test_each(snapshot: SnapshotAssertion) -> None: + prompt = ( + SystemMessagePromptTemplate.from_template("You are a nice assistant.") + + "{question}" + ) + first_llm = FakeStreamingListLLM(responses=["first item, second item, third item"]) + parser = FakeSplitIntoListParser() + second_llm = FakeStreamingListLLM(responses=["this", "is", "a", "test"]) + + chain = prompt | first_llm | parser | second_llm.map() + + assert dumps(chain, pretty=True) == snapshot + output = chain.invoke({"question": "What up"}) + assert output == ["this", "is", "a"] + + assert (parser | second_llm.map()).invoke("first item, second item") == [ + "test", + "this", + ] + + +def test_recursive_lambda() -> None: + def _simple_recursion(x: int) -> Union[int, Runnable]: + if x < 10: + return RunnableLambda(lambda *args: _simple_recursion(x + 1)) + else: + return x + + runnable = RunnableLambda(_simple_recursion) + assert runnable.invoke(5) == 10 + + with pytest.raises(RecursionError): + runnable.invoke(0, {"recursion_limit": 9}) diff --git a/libs/langchain/tests/unit_tests/test_dependencies.py b/libs/langchain/tests/unit_tests/test_dependencies.py index e446485ca..2e40a7ab4 100644 --- a/libs/langchain/tests/unit_tests/test_dependencies.py +++ b/libs/langchain/tests/unit_tests/test_dependencies.py @@ -26,10 +26,13 @@ def test_required_dependencies(poetry_conf: Mapping[str, Any]) -> None: # Get the dependencies from the [tool.poetry.dependencies] section dependencies = poetry_conf["dependencies"] - required_dependencies = [ - package_name + is_required = { + package_name: isinstance(requirements, str) + or not requirements.get("optional", False) for package_name, requirements in dependencies.items() - if isinstance(requirements, str) or not requirements.get("optional", False) + } + required_dependencies = [ + package_name for package_name, required in is_required.items() if required ] assert sorted(required_dependencies) == [ @@ -47,6 +50,12 @@ def test_required_dependencies(poetry_conf: Mapping[str, Any]) -> None: "tenacity", ] + unrequired_dependencies = [ + package_name for package_name, required in is_required.items() if not required + ] + in_extras = [dep for group in poetry_conf["extras"].values() for dep in group] + assert set(unrequired_dependencies) == set(in_extras) + def test_test_group_dependencies(poetry_conf: Mapping[str, Any]) -> None: """Check if someone is attempting to add additional test dependencies. diff --git a/libs/langchain/tests/unit_tests/tools/test_public_api.py b/libs/langchain/tests/unit_tests/tools/test_public_api.py index 43c2fb905..6d58dd92e 100644 --- a/libs/langchain/tests/unit_tests/tools/test_public_api.py +++ b/libs/langchain/tests/unit_tests/tools/test_public_api.py @@ -2,6 +2,11 @@ from langchain.tools import __all__ as public_api _EXPECTED = [ + "AINAppOps", + "AINOwnerOps", + "AINRuleOps", + "AINTransfer", + "AINValueOps", "AIPluginTool", "APIOperation", "ArxivQueryRun", diff --git a/libs/langchain/tests/unit_tests/utils/__init__.py b/libs/langchain/tests/unit_tests/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/libs/langchain/tests/unit_tests/utils/test_iter.py b/libs/langchain/tests/unit_tests/utils/test_iter.py new file mode 100644 index 000000000..f0fd8bf4c --- /dev/null +++ b/libs/langchain/tests/unit_tests/utils/test_iter.py @@ -0,0 +1,21 @@ +from typing import List + +import pytest + +from langchain.utils.iter import batch_iterate + + +@pytest.mark.parametrize( + "input_size, input_iterable, expected_output", + [ + (2, [1, 2, 3, 4, 5], [[1, 2], [3, 4], [5]]), + (3, [10, 20, 30, 40, 50], [[10, 20, 30], [40, 50]]), + (1, [100, 200, 300], [[100], [200], [300]]), + (4, [], []), + ], +) +def test_batch_iterate( + input_size: int, input_iterable: List[str], expected_output: List[str] +) -> None: + """Test batching function.""" + assert list(batch_iterate(input_size, input_iterable)) == expected_output diff --git a/poetry.lock b/poetry.lock index 7599ad353..8210605be 100644 --- a/poetry.lock +++ b/poetry.lock @@ -167,22 +167,23 @@ files = [ [[package]] name = "argon2-cffi" -version = "21.3.0" -description = "The secure Argon2 password hashing algorithm." +version = "23.1.0" +description = "Argon2 for Python" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" files = [ - {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"}, - {file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"}, + {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"}, + {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"}, ] [package.dependencies] argon2-cffi-bindings = "*" [package.extras] -dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"] -docs = ["furo", "sphinx", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"] +dev = ["argon2-cffi[tests,typing]", "tox (>4)"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"] +tests = ["hypothesis", "pytest"] +typing = ["mypy"] [[package]] name = "argon2-cffi-bindings" @@ -254,13 +255,13 @@ test = ["astroid", "pytest"] [[package]] name = "async-lru" -version = "2.0.3" +version = "2.0.4" description = "Simple LRU cache for asyncio" optional = false python-versions = ">=3.8" files = [ - {file = "async-lru-2.0.3.tar.gz", hash = "sha256:b714c9d1415fca4e264da72a9e2abc66880ce7430e03a973341f88ea4c0d4869"}, - {file = "async_lru-2.0.3-py3-none-any.whl", hash = "sha256:00c0a8899c20b9c88663a47732689ff98189c9fa08ad9f734d7722f934d250b1"}, + {file = 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file