community[patch]: Added missing from_documents method to KNNRetriever. (#18411)

- Description: Added missing `from_documents` method to `KNNRetriever`,
providing the ability to supply metadata to LangChain `Document`s, and
to give it parity to the other retrievers, which do have
`from_documents`.
- Issue: None
- Dependencies: None
- Twitter handle: None

Co-authored-by: Victor Adan <vadan@netroadshow.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

libs/community/langchain_community/retrievers/knn.py

@@ -5,7 +5,7 @@ https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
 from __future__ import annotations
 
 import concurrent.futures
-from typing import Any, List, Optional
+from typing import Any, Iterable, List, Optional
 
 import numpy as np
 from langchain_core.callbacks import CallbackManagerForRetrieverRun
@@ -38,6 +38,8 @@ class KNNRetriever(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 +53,32 @@ class KNNRetriever(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,
     ) -> KNNRetriever:
         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(
+        cls,
+        documents: Iterable[Document],
+        embeddings: Embeddings,
+        **kwargs: Any,
+    ) -> KNNRetriever:
+        texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
+        return cls.from_texts(
+            texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs
+        )
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
@@ -71,7 +95,10 @@ class KNNRetriever(BaseRetriever):
         normalized_similarities = (similarities - np.min(similarities)) / denominator
 
         top_k_results = [
-            Document(page_content=self.texts[row])
+            Document(
+                page_content=self.texts[row],
+                metadata=self.metadatas[row] if self.metadatas else {},
+            )
             for row in sorted_ix[0 : self.k]
             if (
                 self.relevancy_threshold is None

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

@@ -1,3 +1,5 @@
+from langchain_core.documents import Document
+
 from langchain_community.embeddings import FakeEmbeddings
 from langchain_community.retrievers.knn import KNNRetriever
 
@@ -9,3 +11,19 @@ class TestKNNRetriever:
             texts=input_texts, embeddings=FakeEmbeddings(size=100)
         )
         assert len(knn_retriever.texts) == 3
+
+    def test_from_documents(self) -> None:
+        input_docs = [
+            Document(page_content="I have a pen.", metadata={"page": 1}),
+            Document(page_content="Do you have a pen?", metadata={"page": 2}),
+            Document(page_content="I have a bag.", metadata={"page": 3}),
+        ]
+        knn_retriever = KNNRetriever.from_documents(
+            documents=input_docs, embeddings=FakeEmbeddings(size=100)
+        )
+        assert knn_retriever.texts == [
+            "I have a pen.",
+            "Do you have a pen?",
+            "I have a bag.",
+        ]
+        assert knn_retriever.metadatas == [{"page": 1}, {"page": 2}, {"page": 3}]