import sqlite_utils
import llm

# This collection will use an in-memory database that will be
# discarded when the Python process exits
collection = llm.Collection("entries", model_id="ada-002")

# Or you can persist the database to disk like this:
db = sqlite_utils.Database("my-embeddings.db")
collection = llm.Collection("entries", db, model_id="ada-002")

# You can pass a model directly using model= instead of model_id=
embedding_model = llm.get_embedding_model("ada-002")
collection = llm.Collection("entries", db, model=embedding_model)

# Store a string in the collection with an ID:
collection.embed("hound", "my happy hound")

# Or to store content and extra metadata:
collection.embed(
    "hound",
    "my happy hound",
    metadata={"name": "Hound"},
    store=True
)

# Or embed things in bulk:
collection.embed_multi(
    [
        ("hound", "my happy hound"),
        ("cat", "my dissatisfied cat"),
    ],
    # Add this to store the strings in the content column:
    store=True,
)