Hash function for text documents that maps similar documents to the same value I have a web site that process text documents (typically 10-100 pages) submitted by users.  Each time a user submits a document, I'd like to store a hash of the document, but I'd like similar documents to map to the same hash value.  I essentially want to know whether a user is resubmitting a slightly changed document or a new document.  

I don't store the documents, so I can only compare hash values and I can't compare the documents to each other.

I've done a lot of reading about MinHash and LSH, but these all seem to be based on having a corpus of a large number of documents and then finding similar documents within the corpus.  I think these don't work for me because I need to compute my hash vector on a single document at a time without knowing anything about other documents.

In some ways I feel like this should be an easy problem.  Something like computing a hash of a bag-of-words vector, but I'm struggling to figure out a good way to do this.

My comparison is based on text and not meaning so I don't need anything like word embeddings.
Hashing the unique copies of anything, including documents, is most commonly called fingerprinting. 

Picking the fingerprinting hash function depends on your use case. For your use case, pick a rolling, non-cryptographic hash function. One of the simplest examples is Rabin–Karp algorithm. Once applied, similar documents will have similar hash values. 

Another issue is comparing hash values to identify near-duplicates. Exact nearest-neighbors algorithms work best but are not scalable. Approximate nearest-neighbors algorithms are scalable but can have errors. Locality-sensitive hashing (LSH) is an example of an approximate nearest-neighbors search algorithm. You'll have to decide the trade-off between scale and potential errors.