add with score option for max marginal relevance (#6867)

### Adding the functionality to return the scores with retrieved
documents when using the max marginal relevance
- Description: Add the method
`max_marginal_relevance_search_with_score_by_vector` to the FAISS
wrapper. Functionality operates the same as
`similarity_search_with_score_by_vector` except for using the max
marginal relevance retrieval framework like is used in the
`max_marginal_relevance_search_by_vector` method.
  - Dependencies: None
  - Tag maintainer: @rlancemartin @eyurtsev 
  - Twitter handle: @RianDolphin

---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>

tests/integration_tests/vectorstores/test_faiss.py

@@ -46,9 +46,19 @@ def test_faiss_vector_sim() -> None:
     output = docsearch.similarity_search_by_vector(query_vec, k=1)
     assert output == [Document(page_content="foo")]
 
+
+def test_faiss_mmr() -> None:
+    texts = ["foo", "foo", "fou", "foy"]
+    docsearch = FAISS.from_texts(texts, FakeEmbeddings())
+    query_vec = FakeEmbeddings().embed_query(text="foo")
     # make sure we can have k > docstore size
-    output = docsearch.max_marginal_relevance_search_by_vector(query_vec, k=10)
+    output = docsearch.max_marginal_relevance_search_with_score_by_vector(
+        query_vec, k=10, lambda_mult=0.1
+    )
     assert len(output) == len(texts)
+    assert output[0][0] == Document(page_content="foo")
+    assert output[0][1] == 0.0
+    assert output[1][0] != Document(page_content="foo")
 
 
 def test_faiss_with_metadatas() -> None: