core[patch]: add standard tracing params for retrievers (#25240)

libs/community/tests/unit_tests/retrievers/test_base.py

@@ -74,6 +74,11 @@ async def test_fake_retriever_v1_upgrade_async(
     assert callbacks.retriever_errors == 0
 
 
+def test_fake_retriever_v1_standard_params(fake_retriever_v1: BaseRetriever) -> None:
+    ls_params = fake_retriever_v1._get_ls_params()
+    assert ls_params == {"ls_retriever_name": "fakeretrieverv1"}
+
+
 @pytest.fixture
 def fake_retriever_v1_with_kwargs() -> BaseRetriever:
     # Test for things like the Weaviate V1 Retriever.
@@ -213,3 +218,8 @@ async def test_fake_retriever_v2_async(
         await fake_erroring_retriever_v2.ainvoke(
             "Foo", config={"callbacks": [callbacks]}
         )
+
+
+def test_fake_retriever_v2_standard_params(fake_retriever_v2: BaseRetriever) -> None:
+    ls_params = fake_retriever_v2._get_ls_params()
+    assert ls_params == {"ls_retriever_name": "fakeretrieverv2"}

libs/community/tests/unit_tests/retrievers/test_bedrock.py

@@ -33,6 +33,11 @@ def test_create_client(amazon_retriever: AmazonKnowledgeBasesRetriever) -> None:
         amazon_retriever.create_client({})
 
 
+def test_standard_params(amazon_retriever: AmazonKnowledgeBasesRetriever) -> None:
+    ls_params = amazon_retriever._get_ls_params()
+    assert ls_params == {"ls_retriever_name": "amazonknowledgebases"}
+
+
 def test_get_relevant_documents(
     amazon_retriever: AmazonKnowledgeBasesRetriever, mock_client: MagicMock
 ) -> None:

libs/community/tests/unit_tests/vectorstores/test_databricks_vector_search.py

@@ -633,6 +633,28 @@ def test_similarity_score_threshold(index_details: dict, threshold: float) -> No
         assert len(search_result) == 0
 
 
+@pytest.mark.requires("databricks", "databricks.vector_search")
+def test_standard_params() -> None:
+    index = mock_index(DIRECT_ACCESS_INDEX)
+    vectorstore = default_databricks_vector_search(index)
+    retriever = vectorstore.as_retriever()
+    ls_params = retriever._get_ls_params()
+    assert ls_params == {
+        "ls_retriever_name": "vectorstore",
+        "ls_vector_store_provider": "DatabricksVectorSearch",
+        "ls_embedding_provider": "FakeEmbeddingsWithDimension",
+    }
+
+    index = mock_index(DELTA_SYNC_INDEX_MANAGED_EMBEDDINGS)
+    vectorstore = default_databricks_vector_search(index)
+    retriever = vectorstore.as_retriever()
+    ls_params = retriever._get_ls_params()
+    assert ls_params == {
+        "ls_retriever_name": "vectorstore",
+        "ls_vector_store_provider": "DatabricksVectorSearch",
+    }
+
+
 @pytest.mark.requires("databricks", "databricks.vector_search")
 @pytest.mark.parametrize(
     "index_details", [DELTA_SYNC_INDEX_SELF_MANAGED_EMBEDDINGS, DIRECT_ACCESS_INDEX]

libs/community/tests/unit_tests/vectorstores/test_faiss.py

@@ -49,6 +49,15 @@ def test_faiss() -> None:
     output = docsearch.similarity_search("foo", k=1)
     assert output == [Document(page_content="foo")]
 
+    # Retriever standard params
+    retriever = docsearch.as_retriever()
+    ls_params = retriever._get_ls_params()
+    assert ls_params == {
+        "ls_retriever_name": "vectorstore",
+        "ls_vector_store_provider": "FAISS",
+        "ls_embedding_provider": "FakeEmbeddings",
+    }
+
 
 @pytest.mark.requires("faiss")
 async def test_faiss_afrom_texts() -> None:

libs/core/langchain_core/retrievers.py

@@ -26,6 +26,8 @@
 from inspect import signature
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
+from typing_extensions import TypedDict
+
 from langchain_core._api import deprecated
 from langchain_core.documents import Document
 from langchain_core.load.dump import dumpd
@@ -50,6 +52,19 @@
 RetrieverOutputLike = Runnable[Any, RetrieverOutput]
 
 
+class LangSmithRetrieverParams(TypedDict, total=False):
+    """LangSmith parameters for tracing."""
+
+    ls_retriever_name: str
+    """Retriever name."""
+    ls_vector_store_provider: Optional[str]
+    """Vector store provider."""
+    ls_embedding_provider: Optional[str]
+    """Embedding provider."""
+    ls_embedding_model: Optional[str]
+    """Embedding model."""
+
+
 class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
     """Abstract base class for a Document retrieval system.
 
@@ -167,6 +182,19 @@ def __init_subclass__(cls, **kwargs: Any) -> None:
             len(set(parameters.keys()) - {"self", "query", "run_manager"}) > 0
         )
 
+    def _get_ls_params(self, **kwargs: Any) -> LangSmithRetrieverParams:
+        """Get standard params for tracing."""
+
+        default_retriever_name = self.get_name()
+        if default_retriever_name.startswith("Retriever"):
+            default_retriever_name = default_retriever_name[9:]
+        elif default_retriever_name.endswith("Retriever"):
+            default_retriever_name = default_retriever_name[:-9]
+        default_retriever_name = default_retriever_name.lower()
+
+        ls_params = LangSmithRetrieverParams(ls_retriever_name=default_retriever_name)
+        return ls_params
+
     def invoke(
         self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
     ) -> List[Document]:
@@ -191,13 +219,17 @@ def invoke(
         from langchain_core.callbacks.manager import CallbackManager
 
         config = ensure_config(config)
+        inheritable_metadata = {
+            **(config.get("metadata") or {}),
+            **self._get_ls_params(**kwargs),
+        }
         callback_manager = CallbackManager.configure(
             config.get("callbacks"),
             None,
             verbose=kwargs.get("verbose", False),
             inheritable_tags=config.get("tags"),
             local_tags=self.tags,
-            inheritable_metadata=config.get("metadata"),
+            inheritable_metadata=inheritable_metadata,
             local_metadata=self.metadata,
         )
         run_manager = callback_manager.on_retriever_start(
@@ -250,13 +282,17 @@ async def ainvoke(
         from langchain_core.callbacks.manager import AsyncCallbackManager
 
         config = ensure_config(config)
+        inheritable_metadata = {
+            **(config.get("metadata") or {}),
+            **self._get_ls_params(**kwargs),
+        }
         callback_manager = AsyncCallbackManager.configure(
             config.get("callbacks"),
             None,
             verbose=kwargs.get("verbose", False),
             inheritable_tags=config.get("tags"),
             local_tags=self.tags,
-            inheritable_metadata=config.get("metadata"),
+            inheritable_metadata=inheritable_metadata,
             local_metadata=self.metadata,
         )
         run_manager = await callback_manager.on_retriever_start(

libs/core/langchain_core/vectorstores/base.py

@@ -44,7 +44,7 @@
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import Field, root_validator
-from langchain_core.retrievers import BaseRetriever
+from langchain_core.retrievers import BaseRetriever, LangSmithRetrieverParams
 from langchain_core.runnables.config import run_in_executor
 
 if TYPE_CHECKING:
@@ -1014,6 +1014,25 @@ def validate_search_type(cls, values: Dict) -> Dict:
                 )
         return values
 
+    def _get_ls_params(self, **kwargs: Any) -> LangSmithRetrieverParams:
+        """Get standard params for tracing."""
+
+        ls_params = super()._get_ls_params(**kwargs)
+        ls_params["ls_vector_store_provider"] = self.vectorstore.__class__.__name__
+
+        if self.vectorstore.embeddings:
+            ls_params["ls_embedding_provider"] = (
+                self.vectorstore.embeddings.__class__.__name__
+            )
+        elif hasattr(self.vectorstore, "embedding") and isinstance(
+            self.vectorstore.embedding, Embeddings
+        ):
+            ls_params["ls_embedding_provider"] = (
+                self.vectorstore.embedding.__class__.__name__
+            )
+
+        return ls_params
+
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
     ) -> List[Document]: