//- 💫 DOCS > USAGE > SPACY 101 > VOCAB & STRINGSTORE

p
    |  Whenever possible, spaCy tries to store data in a vocabulary, the
    |  #[+api("vocab") #[code Vocab]], that will be
    |  #[strong shared by multiple documents]. To save memory, spaCy also
    |  encodes all strings to #[strong hash values] – in this case for example,
    |  "coffee" has the hash #[code 3197928453018144401]. Entity labels like
    |  "ORG" and part-of-speech tags like "VERB" are also encoded. Internally,
    |  spaCy only "speaks" in hash values.

+aside
    |  #[strong Token]: A word, punctuation mark etc. #[em in context], including
    |  its attributes, tags and dependencies.#[br]
    |  #[strong Lexeme]: A "word type" with no context. Includes the word shape
    |  and flags, e.g. if it's lowercase, a digit or punctuation.#[br]
    |  #[strong Doc]: A processed container of tokens in context.#[br]
    |  #[strong Vocab]: The collection of lexemes.#[br]
    |  #[strong StringStore]: The dictionary mapping hash values to strings, for
    |  example #[code 3197928453018144401] &rarr; "coffee".

+image
    include ../../../assets/img/docs/vocab_stringstore.svg
    .u-text-right
        +button("/assets/img/docs/vocab_stringstore.svg", false, "secondary").u-text-tag View large graphic

p
    |  If you process lots of documents containing the word "coffee" in all
    |  kinds of different contexts, storing the exact string "coffee" every time
    |  would take up way too much space. So instead, spaCy hashes the string
    |  and stores it in the #[+api("stringstore") #[code StringStore]]. You can
    |  think of the #[code StringStore] as a
    |  #[strong lookup table that works in both directions] – you can look up a
    |  string to get its hash, or a hash to get its string:

+code.
    doc = nlp(u'I like coffee')
    assert doc.vocab.strings[u'coffee'] == 3197928453018144401
    assert doc.vocab.strings[3197928453018144401] == u'coffee'

+aside("What does 'L' at the end of a hash mean?")
    |  If you return a hash value in the #[strong Python 2 interpreter], it'll
    |  show up as #[code 3197928453018144401L]. The #[code L] just means "long
    |  integer" – it's #[strong not] actually a part of the hash value.

p
    |  Now that all strings are encoded, the entries in the vocabulary
    |  #[strong don&apos;t need to include the word text] themselves. Instead,
    |  they can look it up in the #[code StringStore] via its hash value. Each
    |  entry in the vocabulary, also called #[+api("lexeme") #[code Lexeme]],
    |  contains the #[strong context-independent] information about a word.
    |  For example, no matter if "love" is used as a verb or a noun in some
    |  context, its spelling and whether it consists of alphabetic characters
    |  won't ever change. Its hash value will also always be the same.

+code.
    for word in doc:
        lexeme = doc.vocab[word.text]
        print(lexeme.text, lexeme.orth, lexeme.shape_, lexeme.prefix_, lexeme.suffix_,
              lexeme.is_alpha, lexeme.is_digit, lexeme.is_title, lexeme.lang_)

+aside
    |  #[strong Text]: The original text of the lexeme.#[br]
    |  #[strong Orth]: The hash value of the lexeme.#[br]
    |  #[strong Shape]: The abstract word shape of the lexeme.#[br]
    |  #[strong Prefix]: By default, the first letter of the word string.#[br]
    |  #[strong Suffix]: By default, the last three letters of the word string.#[br]
    |  #[strong is alpha]: Does the lexeme consist of alphabetic characters?#[br]
    |  #[strong is digit]: Does the lexeme consist of digits?#[br]

+table(["text", "orth", "shape", "prefix", "suffix", "is_alpha", "is_digit"])
    - var style = [0, 1, 1, 0, 0, 1, 1]
    +annotation-row(["I", "4690420944186131903", "X", "I", "I", true, false], style)
    +annotation-row(["love", "3702023516439754181", "xxxx", "l", "ove", true, false], style)
    +annotation-row(["coffee", "3197928453018144401", "xxxx", "c", "ffe", true, false], style)

p
    |  The mapping of words to hashes doesn't depend on any state. To make sure
    |  each value is unique, spaCy uses a
    |  #[+a("https://en.wikipedia.org/wiki/Hash_function") hash function] to
    |  calculate the hash #[strong based on the word string]. This also means
    |  that the hash for "coffee" will always be the same, no matter which model
    |  you're using or how you've configured spaCy.

p
    |  However, hashes #[strong cannot be reversed] and there's no way to
    |  resolve #[code 3197928453018144401] back to "coffee". All spaCy can do
    |  is look it up in the vocabulary. That's why you always need to make
    |  sure all objects you create have access to the same vocabulary. If they
    |  don't, spaCy might not be able to find the strings it needs.

+code.
    from spacy.tokens import Doc
    from spacy.vocab import Vocab

    doc = nlp(u'I like coffee') # original Doc
    assert doc.vocab.strings[u'coffee'] == 3197928453018144401 # get hash
    assert doc.vocab.strings[3197928453018144401] == u'coffee' # 👍

    empty_doc = Doc(Vocab()) # new Doc with empty Vocab
    # doc.vocab.strings[3197928453018144401] will raise an error :(

    empty_doc.vocab.strings.add(u'coffee') # add "coffee" and generate hash
    assert doc.vocab.strings[3197928453018144401] == u'coffee' # 👍

    new_doc = Doc(doc.vocab) # create new doc with first doc's vocab
    assert doc.vocab.strings[3197928453018144401] == u'coffee' # 👍

p
    |  If the vocabulary doesn't contain a string for #[code 3197928453018144401],
    |  spaCy will raise an error. You can re-add "coffee" manually, but this
    |  only works if you actually #[em know] that the document contains that
    |  word. To prevent this problem, spaCy will also export the #[code Vocab]
    |  when you save a #[code Doc] or #[code nlp] object. This will give you
    |  the object and its encoded annotations, plus they "key" to decode it.
