Doc2Vec How to find most similar document - gensim

Question

I am using Gensim's Doc2Vec, and was wondering if there is a way to get the most similar document to another document that is outside the list of TaggedDocuments used to train the Doc2Vec model.
Right now I can infer a vector from a document not in the training set:
# 'model' here is a instance of Doc2Vec class that has been trained
# Inferring a vector
doc_not_in_training_set = "Foo Foo Foo Foo Foo Foo Fie"
v1 = model.infer_vector(word_tokenize(doc_not_in_training_set.lower()))
print("V1_infer", v1)
This prints out a vector representation of the 'doc_not_in_training_set' string. However, is there a way to use this vector to find the n most similar documents to the 'doc_not_in_training_set' string (in the TaggedDocuments training set for this word2vec model)?
Looking under the documentation, the closest I could find was the model.docvec.most_similar() method:
# Finding most similar to first
similar_doc = model.docvecs.most_similar('0')
This returns the document in the training set most similar to the document in the training set with tag '0'.
In the documentation of this method, it looks like there is not yet the functionality I am looking for:
TODO: Accept vectors of out-of-training-set docs, as if from inference.
Is there another method I can use to find documents similar to a document not in the training set?

Answer 1

The .most_similar() method will also take a raw vectors as the target position.
It helps to explicitly name the positive parameter, to prevent other logic of that method, which tries to intuit what other strings/etc supplied as arguments might mean, from misinterpreting a single raw vector.
So try:
similar_docs = model.docvecs.most_similar(positive=[v1])
You should get back a list of nearest-neighbors to the v1 vector that you'd previously inferred.