Motivation

If you are doing a handful of different NLP tasks, or have a single NLP pipeline you keep tuning, you probably don't want to recompute embeddings. Hence, we cache them.

Quickstart

pip install embeddingcache

from embeddingcache.embeddingcache import get_embeddings
embeddings = get_embeddings(
            strs=["hi", "I love Berlin."],
            embedding_model="all-MiniLM-L6-v2",
            db_directory=Path("dbs/"),
            verbose=True,
        )

Design assumptions

We use SQLite3 to cache embeddings. [This could be adapted easily, since we use SQLAlchemy.]

We assume read-heavy loads, with one concurrent writer. (However, we retry on write failures.)

We shard SQLite3 into two databases: hashstring.db: hashstring table. Each row is a (unique, primary key) SHA512 hash to text (also unique). Both fields are indexed.

[embedding_model_name].db: embedding table. Each row is a (unique, primary key) SHA512 hash to a 1-dim numpy (float32) vector, which we serialize to the table as bytes.

Developer instructions

pre-commit install
pip install -e .
pytest

TODO

• Update pyproject.toml
• Add tests
• Consider other hash functions?
• float32 and float64 support
• Consider adding optional joblib for caching?
• Different ways of computing embeddings (e.g. using an API) rather than locally
• S3 backup and/or
• WAL
• LiteStream
• Retry on write errors
• Other DB backends
• Best practices: Give specific OpenAI version number.
• RocksDB / RocksDB-cloud?
• Include model name in DB for sanity check on slugify.
• Validate on numpy array size.
• Validate BLOB size for hashes.
• Add optional libraries like openai and sentence-transformers
  • Also consider other embedding providers, e.g. cohere
  • And libs just for devs
• Consider the max_length of each text to embed, warn if we exceed
• pdoc3 and/or sphinx
• Normalize embeddings by default, but add option
• Option to return torch tensors
• Consider reusing the same DB connection instead of creating it from scratch every time.
• Add batch_size parameter?
• Test check for collisions
• Use logging not verbose output.
• Rewrite using classes.
• Fix dependabot.
• Don't keep re-using DB session, store it in the class or global
• DRY.
• Suggest to use versioned OpenAI model
• Add device to sentence transformers
• Allow fast_sentence_transformers
• Test that things work if there are duplicate strings
• Remove DBs after test
• Do we have to have nested embedding.embedding for all calls?
• codecov and code quality shields