Propagate "filter" arg in Chroma similarity_search (#1869)

Technically a duplicate fix to #1619 but with unit tests and a small
documentation update
- Propagate `filter` arg in Chroma `similarity_search` to delegated call
to `similarity_search_with_score`
- Add `filter` arg to `similarity_search_by_vector`
- Clarify doc strings on FakeEmbeddings

langchain/vectorstores/chroma.py

@@ -130,13 +130,14 @@ class Chroma(VectorStore):
         Returns:
             List[Document]: List of documents most simmilar to the query text.
         """
-        docs_and_scores = self.similarity_search_with_score(query, k)
+        docs_and_scores = self.similarity_search_with_score(query, k, where=filter)
         return [doc for doc, _ in docs_and_scores]
 
     def similarity_search_by_vector(
         self,
         embedding: List[float],
         k: int = 4,
+        filter: Optional[Dict[str, str]] = None,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs most similar to embedding vector.
@@ -146,7 +147,9 @@ class Chroma(VectorStore):
         Returns:
             List of Documents most similar to the query vector.
         """
-        results = self._collection.query(query_embeddings=embedding, n_results=k)
+        results = self._collection.query(
+            query_embeddings=embedding, n_results=k, where=filter
+        )
         return _results_to_docs(results)
 
     def similarity_search_with_score(

tests/integration_tests/vectorstores/fake_embeddings.py

@@ -10,9 +10,13 @@ class FakeEmbeddings(Embeddings):
     """Fake embeddings functionality for testing."""
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Return simple embeddings."""
+        """Return simple embeddings.
+        Embeddings encode each text as its index."""
         return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
 
     def embed_query(self, text: str) -> List[float]:
-        """Return simple embeddings."""
+        """Return constant query embeddings.
+        Embeddings are identical to embed_documents(texts)[0].
+        Distance to each text will be that text's index,
+        as it was passed to embed_documents."""
         return [float(1.0)] * 9 + [float(0.0)]

tests/integration_tests/vectorstores/test_chroma.py

@@ -29,7 +29,7 @@ def test_chroma_with_metadatas() -> None:
 
 
 def test_chroma_with_metadatas_with_scores() -> None:
-    """Test end to end construction and search."""
+    """Test end to end construction and scored search."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": str(i)} for i in range(len(texts))]
     docsearch = Chroma.from_texts(
@@ -39,7 +39,47 @@ def test_chroma_with_metadatas_with_scores() -> None:
         metadatas=metadatas,
     )
     output = docsearch.similarity_search_with_score("foo", k=1)
-    assert output == [(Document(page_content="foo", metadata={"page": "0"}), 1.0)]
+    assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
+
+
+def test_chroma_search_filter() -> None:
+    """Test end to end construction and search with metadata filtering."""
+    texts = ["far", "bar", "baz"]
+    metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
+    docsearch = Chroma.from_texts(
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metadatas=metadatas,
+    )
+    output = docsearch.similarity_search("far", k=1, filter={"first_letter": "f"})
+    assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
+    output = docsearch.similarity_search("far", k=1, filter={"first_letter": "b"})
+    assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
+
+
+def test_chroma_search_filter_with_scores() -> None:
+    """Test end to end construction and scored search with metadata filtering."""
+    texts = ["far", "bar", "baz"]
+    metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
+    docsearch = Chroma.from_texts(
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metadatas=metadatas,
+    )
+    output = docsearch.similarity_search_with_score(
+        "far", k=1, filter={"first_letter": "f"}
+    )
+    assert output == [
+        (Document(page_content="far", metadata={"first_letter": "f"}), 0.0)
+    ]
+    output = docsearch.similarity_search_with_score(
+        "far", k=1, filter={"first_letter": "b"}
+    )
+    assert output == [
+        (Document(page_content="bar", metadata={"first_letter": "b"}), 1.0)
+    ]
 
 
 def test_chroma_with_persistence() -> None: