Fix for SVM retriever discarding document metadata (#9141)

As stated in the title the SVM retriever discarded the metadata of
passed in docs. This code fixes that. I also added one unit test that
should test that.
---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/retrievers/svm.py

@@ -38,6 +38,8 @@ class SVMRetriever(BaseRetriever):
     """Index of embeddings."""
     texts: List[str]
     """List of texts to index."""
+    metadatas: Optional[List[dict]] = None
+    """List of metadatas corresponding with each text."""
     k: int = 4
     """Number of results to return."""
     relevancy_threshold: Optional[float] = None
@@ -51,10 +53,20 @@ class SVMRetriever(BaseRetriever):
 
     @classmethod
     def from_texts(
-        cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
+        cls,
+        texts: List[str],
+        embeddings: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
     ) -> SVMRetriever:
         index = create_index(texts, embeddings)
-        return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
+        return cls(
+            embeddings=embeddings,
+            index=index,
+            texts=texts,
+            metadatas=metadatas,
+            **kwargs,
+        )
 
     @classmethod
     def from_documents(
@@ -64,7 +76,9 @@ class SVMRetriever(BaseRetriever):
         **kwargs: Any,
     ) -> SVMRetriever:
         texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
-        return cls.from_texts(texts=texts, embeddings=embeddings, **kwargs)
+        return cls.from_texts(
+            texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs
+        )
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
@@ -108,5 +122,7 @@ class SVMRetriever(BaseRetriever):
                 self.relevancy_threshold is None
                 or normalized_similarities[row] >= self.relevancy_threshold
             ):
-                top_k_results.append(Document(page_content=self.texts[row - 1]))
+                metadata = self.metadatas[row - 1] if self.metadatas else {}
+                doc = Document(page_content=self.texts[row - 1], metadata=metadata)
+                top_k_results.append(doc)
         return top_k_results

libs/langchain/tests/unit_tests/retrievers/test_svm.py

@@ -25,3 +25,18 @@ class TestSVMRetriever:
             documents=input_docs, embeddings=FakeEmbeddings(size=100)
         )
         assert len(svm_retriever.texts) == 3
+
+    @pytest.mark.requires("sklearn")
+    def test_metadata_persists(self) -> None:
+        input_docs = [
+            Document(page_content="I have a pen.", metadata={"foo": "bar"}),
+            Document(page_content="How about you?", metadata={"foo": "baz"}),
+            Document(page_content="I have a bag.", metadata={"foo": "qux"}),
+        ]
+        svm_retriever = SVMRetriever.from_documents(
+            documents=input_docs, embeddings=FakeEmbeddings(size=100)
+        )
+        query = "Have anything?"
+        output_docs = svm_retriever.get_relevant_documents(query=query)
+        for doc in output_docs:
+            assert "foo" in doc.metadata