core[minor]: Add async methods to MaxMarginalRelevanceExampleSelector (#19639)

libs/core/langchain_core/example_selectors/semantic_similarity.py

@@ -1,6 +1,7 @@
 """Example selector that selects examples based on SemanticSimilarity."""
 from __future__ import annotations
 
+from abc import ABC
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type
 
 from langchain_core.documents import Document
@@ -17,7 +18,7 @@ def sorted_values(values: Dict[str, str]) -> List[Any]:
     return [values[val] for val in sorted(values)]
 
 
-class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
+class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
     """Example selector that selects examples based on SemanticSimilarity."""
 
     vectorstore: VectorStore
@@ -70,6 +71,10 @@ class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
         )
         return ids[0]
 
+
+class SemanticSimilarityExampleSelector(_VectorStoreExampleSelector):
+    """Example selector that selects examples based on SemanticSimilarity."""
+
     def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
         """Select which examples to use based on semantic similarity."""
         # Get the docs with the highest similarity.
@@ -116,6 +121,9 @@ class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
             k: Number of examples to select
             input_keys: If provided, the search is based on the input variables
                 instead of all variables.
+            example_keys: If provided, keys to filter examples to.
+            vectorstore_kwargs: Extra arguments passed to similarity_search function
+                of the vectorstore.
             vectorstore_cls_kwargs: optional kwargs containing url for vector store
 
         Returns:
@@ -157,6 +165,9 @@ class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
             k: Number of examples to select
             input_keys: If provided, the search is based on the input variables
                 instead of all variables.
+            example_keys: If provided, keys to filter examples to.
+            vectorstore_kwargs: Extra arguments passed to similarity_search function
+                of the vectorstore.
             vectorstore_cls_kwargs: optional kwargs containing url for vector store
 
         Returns:
@@ -175,7 +186,7 @@ class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
         )
 
 
-class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
+class MaxMarginalRelevanceExampleSelector(_VectorStoreExampleSelector):
     """ExampleSelector that selects examples based on Max Marginal Relevance.
 
     This was shown to improve performance in this paper:
@@ -186,21 +197,20 @@ class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
     """Number of examples to fetch to rerank."""
 
     def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
-        """Select which examples to use based on semantic similarity."""
-        # Get the docs with the highest similarity.
-        if self.input_keys:
-            input_variables = {key: input_variables[key] for key in self.input_keys}
-        query = " ".join(sorted_values(input_variables))
         example_docs = self.vectorstore.max_marginal_relevance_search(
-            query, k=self.k, fetch_k=self.fetch_k
+            self._example_to_text(input_variables, self.input_keys),
+            k=self.k,
+            fetch_k=self.fetch_k,
         )
-        # Get the examples from the metadata.
-        # This assumes that examples are stored in metadata.
-        examples = [dict(e.metadata) for e in example_docs]
-        # If example keys are provided, filter examples to those keys.
-        if self.example_keys:
-            examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
-        return examples
+        return self._documents_to_examples(example_docs)
+
+    async def aselect_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+        example_docs = await self.vectorstore.amax_marginal_relevance_search(
+            self._example_to_text(input_variables, self.input_keys),
+            k=self.k,
+            fetch_k=self.fetch_k,
+        )
+        return self._documents_to_examples(example_docs)
 
     @classmethod
     def from_examples(
@@ -211,32 +221,86 @@ class MaxMarginalRelevanceExampleSelector(SemanticSimilarityExampleSelector):
         k: int = 4,
         input_keys: Optional[List[str]] = None,
         fetch_k: int = 20,
+        example_keys: Optional[List[str]] = None,
+        vectorstore_kwargs: Optional[dict] = None,
         **vectorstore_cls_kwargs: Any,
     ) -> MaxMarginalRelevanceExampleSelector:
         """Create k-shot example selector using example list and embeddings.
 
-        Reshuffles examples dynamically based on query similarity.
+        Reshuffles examples dynamically based on Max Marginal Relevance.
 
         Args:
             examples: List of examples to use in the prompt.
-            embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
+            embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
             vectorstore_cls: A vector store DB interface class, e.g. FAISS.
             k: Number of examples to select
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
             input_keys: If provided, the search is based on the input variables
                 instead of all variables.
+            example_keys: If provided, keys to filter examples to.
+            vectorstore_kwargs: Extra arguments passed to similarity_search function
+                of the vectorstore.
             vectorstore_cls_kwargs: optional kwargs containing url for vector store
 
         Returns:
             The ExampleSelector instantiated, backed by a vector store.
         """
-        if input_keys:
-            string_examples = [
-                " ".join(sorted_values({k: eg[k] for k in input_keys}))
-                for eg in examples
-            ]
-        else:
-            string_examples = [" ".join(sorted_values(eg)) for eg in examples]
+        string_examples = [cls._example_to_text(eg, input_keys) for eg in examples]
         vectorstore = vectorstore_cls.from_texts(
             string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
         )
-        return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
+        return cls(
+            vectorstore=vectorstore,
+            k=k,
+            fetch_k=fetch_k,
+            input_keys=input_keys,
+            example_keys=example_keys,
+            vectorstore_kwargs=vectorstore_kwargs,
+        )
+
+    @classmethod
+    async def afrom_examples(
+        cls,
+        examples: List[dict],
+        embeddings: Embeddings,
+        vectorstore_cls: Type[VectorStore],
+        *,
+        k: int = 4,
+        input_keys: Optional[List[str]] = None,
+        fetch_k: int = 20,
+        example_keys: Optional[List[str]] = None,
+        vectorstore_kwargs: Optional[dict] = None,
+        **vectorstore_cls_kwargs: Any,
+    ) -> MaxMarginalRelevanceExampleSelector:
+        """Create k-shot example selector using example list and embeddings.
+
+        Reshuffles examples dynamically based on Max Marginal Relevance.
+
+        Args:
+            examples: List of examples to use in the prompt.
+            embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings().
+            vectorstore_cls: A vector store DB interface class, e.g. FAISS.
+            k: Number of examples to select
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            input_keys: If provided, the search is based on the input variables
+                instead of all variables.
+            example_keys: If provided, keys to filter examples to.
+            vectorstore_kwargs: Extra arguments passed to similarity_search function
+                of the vectorstore.
+            vectorstore_cls_kwargs: optional kwargs containing url for vector store
+
+        Returns:
+            The ExampleSelector instantiated, backed by a vector store.
+        """
+        string_examples = [cls._example_to_text(eg, input_keys) for eg in examples]
+        vectorstore = await vectorstore_cls.afrom_texts(
+            string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
+        )
+        return cls(
+            vectorstore=vectorstore,
+            k=k,
+            fetch_k=fetch_k,
+            input_keys=input_keys,
+            example_keys=example_keys,
+            vectorstore_kwargs=vectorstore_kwargs,
+        )

libs/core/langchain_core/vectorstores.py

@@ -462,7 +462,22 @@ class VectorStore(ABC):
         lambda_mult: float = 0.5,
         **kwargs: Any,
     ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
 
         # This is a temporary workaround to make the similarity search
         # asynchronous. The proper solution is to make the similarity search

libs/core/tests/unit_tests/example_selectors/test_similarity.py

@@ -2,7 +2,10 @@ from typing import Any, Iterable, List, Optional, cast
 
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings, FakeEmbeddings
-from langchain_core.example_selectors import SemanticSimilarityExampleSelector
+from langchain_core.example_selectors import (
+    MaxMarginalRelevanceExampleSelector,
+    SemanticSimilarityExampleSelector,
+)
 from langchain_core.vectorstores import VectorStore
 
 
@@ -32,7 +35,24 @@ class DummyVectorStore(VectorStore):
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]:
         return [
-            Document(page_content=query, metadata={"metadata": query, "other": "other"})
+            Document(
+                page_content=query, metadata={"query": query, "k": k, "other": "other"}
+            )
+        ] * k
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        return [
+            Document(
+                page_content=query,
+                metadata={"query": query, "k": k, "fetch_k": fetch_k, "other": "other"},
+            )
         ] * k
 
     @classmethod
@@ -72,19 +92,19 @@ async def test_aadd_example() -> None:
 def test_select_examples() -> None:
     vector_store = DummyVectorStore()
     selector = SemanticSimilarityExampleSelector(
-        vectorstore=vector_store, input_keys=["foo2"], example_keys=["metadata"], k=2
+        vectorstore=vector_store, input_keys=["foo2"], example_keys=["query", "k"], k=2
     )
     examples = selector.select_examples({"foo": "bar", "foo2": "bar2"})
-    assert examples == [{"metadata": "bar2"}] * 2
+    assert examples == [{"query": "bar2", "k": 2}] * 2
 
 
 async def test_aselect_examples() -> None:
     vector_store = DummyVectorStore()
     selector = SemanticSimilarityExampleSelector(
-        vectorstore=vector_store, input_keys=["foo2"], example_keys=["metadata"], k=2
+        vectorstore=vector_store, input_keys=["foo2"], example_keys=["query", "k"], k=2
     )
     examples = await selector.aselect_examples({"foo": "bar", "foo2": "bar2"})
-    assert examples == [{"metadata": "bar2"}] * 2
+    assert examples == [{"query": "bar2", "k": 2}] * 2
 
 
 def test_from_examples() -> None:
@@ -137,3 +157,85 @@ async def test_afrom_examples() -> None:
     assert vector_store.init_arg == "some_init_arg"
     assert vector_store.texts == ["bar"]
     assert vector_store.metadatas == [{"foo": "bar"}]
+
+
+def test_mmr_select_examples() -> None:
+    vector_store = DummyVectorStore()
+    selector = MaxMarginalRelevanceExampleSelector(
+        vectorstore=vector_store,
+        input_keys=["foo2"],
+        example_keys=["query", "k", "fetch_k"],
+        k=2,
+        fetch_k=5,
+    )
+    examples = selector.select_examples({"foo": "bar", "foo2": "bar2"})
+    assert examples == [{"query": "bar2", "k": 2, "fetch_k": 5}] * 2
+
+
+async def test_mmr_aselect_examples() -> None:
+    vector_store = DummyVectorStore()
+    selector = MaxMarginalRelevanceExampleSelector(
+        vectorstore=vector_store,
+        input_keys=["foo2"],
+        example_keys=["query", "k", "fetch_k"],
+        k=2,
+        fetch_k=5,
+    )
+    examples = await selector.aselect_examples({"foo": "bar", "foo2": "bar2"})
+    assert examples == [{"query": "bar2", "k": 2, "fetch_k": 5}] * 2
+
+
+def test_mmr_from_examples() -> None:
+    examples = [{"foo": "bar"}]
+    embeddings = FakeEmbeddings(size=1)
+    selector = MaxMarginalRelevanceExampleSelector.from_examples(
+        examples=examples,
+        embeddings=embeddings,
+        vectorstore_cls=DummyVectorStore,
+        k=2,
+        fetch_k=5,
+        input_keys=["foo"],
+        example_keys=["some_example_key"],
+        vectorstore_kwargs={"vs_foo": "vs_bar"},
+        init_arg="some_init_arg",
+    )
+    assert selector.input_keys == ["foo"]
+    assert selector.example_keys == ["some_example_key"]
+    assert selector.k == 2
+    assert selector.fetch_k == 5
+    assert selector.vectorstore_kwargs == {"vs_foo": "vs_bar"}
+
+    assert isinstance(selector.vectorstore, DummyVectorStore)
+    vector_store = cast(DummyVectorStore, selector.vectorstore)
+    assert vector_store.embeddings is embeddings
+    assert vector_store.init_arg == "some_init_arg"
+    assert vector_store.texts == ["bar"]
+    assert vector_store.metadatas == [{"foo": "bar"}]
+
+
+async def test_mmr_afrom_examples() -> None:
+    examples = [{"foo": "bar"}]
+    embeddings = FakeEmbeddings(size=1)
+    selector = await MaxMarginalRelevanceExampleSelector.afrom_examples(
+        examples=examples,
+        embeddings=embeddings,
+        vectorstore_cls=DummyVectorStore,
+        k=2,
+        fetch_k=5,
+        input_keys=["foo"],
+        example_keys=["some_example_key"],
+        vectorstore_kwargs={"vs_foo": "vs_bar"},
+        init_arg="some_init_arg",
+    )
+    assert selector.input_keys == ["foo"]
+    assert selector.example_keys == ["some_example_key"]
+    assert selector.k == 2
+    assert selector.fetch_k == 5
+    assert selector.vectorstore_kwargs == {"vs_foo": "vs_bar"}
+
+    assert isinstance(selector.vectorstore, DummyVectorStore)
+    vector_store = cast(DummyVectorStore, selector.vectorstore)
+    assert vector_store.embeddings is embeddings
+    assert vector_store.init_arg == "some_init_arg"
+    assert vector_store.texts == ["bar"]
+    assert vector_store.metadatas == [{"foo": "bar"}]