I am trying to get the scores of the documents retrieved when using langchain retrievers.
Below is a snippet from my implementation of the retriever without scores currently.

vectorstore = FAISS.from_documents(docs, embeddings_model)    
semantic_retriever = vectorstore.as_retriever(k = 4)
result = semantic_retriever.invoke(query)

So I looked it up and langchain themselves have defined a way to do the same by writing a custom retriever wrapper and adding the similarity score to the metadata of the document. Here https://python.langchain.com/v0.2/docs/how_to/add_scores_retriever/

Honestly that approach may work but I am not sure if it makes sense. It doesn’t to me. Why should a score become a part of the permanent metadata of the document. Also what’s the difference between invoke and similarity_search_with_score?
This is langchain 0.2 by the way.