Metadata-Version: 2.1
Name: lmdb-embeddings
Version: 0.2.1
Summary: Fast querying of word embeddings using the LMDB "Lightning" Database.
Home-page: https://www.thoughtriver.com
Author: Dom Hudson
Author-email: dom.hudson@thoughtriver.com
License: GNU General Public License v3.0
Description: ![tr_logo_cmyk_tr_logo_cmyk](https://user-images.githubusercontent.com/10864294/29792093-382146cc-8c37-11e7-9e70-6f71b3d0800b.png)
        
        # LMDB Embeddings
        Query word vectors (embeddings) very quickly with very little querying time overhead and far less memory usage than gensim or other equivalent solutions. This is made possible by [Lightning Memory-Mapped Database](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database).
        
        Inspired by [Delft](https://github.com/kermitt2/delft). As explained in their readme, this approach permits us to have the pre-trained embeddings immediately "warm" (no load time), to free memory and to use any number of embeddings similtaneously with a very negligible impact on runtime when using SSD.
        
        For instance, in a traditional approach `glove-840B` takes around 2 minutes to load and 4GB in memory. Managed with LMDB, `glove-840B` can be accessed immediately and takes only a couple MB in memory, for a negligible impact on runtime (around 1% slower).
        
        ## Reading vectors
        
        ```python
        from lmdb_embeddings.reader import LmdbEmbeddingsReader
        from lmdb_embeddings.exceptions import MissingWordError
        
        embeddings = LmdbEmbeddingsReader('/path/to/word/vectors/eg/GoogleNews-vectors-negative300')
        
        try:
          vector = embeddings.get_word_vector('google')
        except MissingWordError:
          # 'google' is not in the database.
          pass
        ```
        
        ## Writing vectors
        An example to write an LMDB vector file from a gensim model. As any iterator that yields word and vector pairs is supported, if you have the vectors in an alternative format then it is just a matter of altering the `iter_embeddings` method below appropriately.
        
        I will be writing a CLI interface to convert standard formats soon.
        
        ```python
        from gensim.models.keyedvectors import KeyedVectors
        from lmdb_embeddings.writer import LmdbEmbeddingsWriter
        
        
        GOOGLE_NEWS_PATH = 'GoogleNews-vectors-negative300.bin.gz'
        OUTPUT_DATABASE_FOLDER = 'GoogleNews-vectors-negative300'
        
        
        print('Loading gensim model...')
        gensim_model = KeyedVectors.load_word2vec_format(GOOGLE_NEWS_PATH, binary = True)
        
        
        def iter_embeddings():
            for word in gensim_model.vocab.keys():
                yield word, gensim_model[word]
        
        print('Writing vectors to a LMDB database...')
        
        writer = LmdbEmbeddingsWriter(
            iter_embeddings()
        ).write(OUTPUT_DATABASE_FOLDER)
        
        # These vectors can now be loaded with the LmdbEmbeddingsReader.
        ```
        
        ## Customisation
        By default, LMDB Embeddings uses pickle to serialize the vectors to bytes (optimized and pickled with the highest available protocol). However, it is very easy to use an alternative approach - simply inject the serializer and unserializer as callables into the `LmdbEmbeddingsWriter` and `LmdbEmbeddingsReader`.
        
        A [msgpack](https://msgpack.org/index.html) serializer is included and can be used in the same way.
        
        ```python
        from lmdb_embeddings.writer import LmdbEmbeddingsWriter
        from lmdb_embeddings.serializers import MsgpackSerializer
        
        writer = LmdbEmbeddingsWriter(
            iter_embeddings(),
            serializer = MsgpackSerializer.serialize
        ).write(OUTPUT_DATABASE_FOLDER)
        ```
        
        ```python
        from lmdb_embeddings.reader import LmdbEmbeddingsReader
        from lmdb_embeddings.serializers import MsgpackSerializer
        
        reader = LmdbEmbeddingsReader(
            OUTPUT_DATABASE_FOLDER,
            unserializer = MsgpackSerializer.unserialize
        )
        ```
        
        ## Running tests
        ```
        pytest
        ```
        
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