Using embeddings from Python#
You can load an embedding model using its model ID or alias like this:
import llm
embedding_model = llm.get_embedding_model("ada-002")
To embed a string, returning a Python list of floating point numbers, use the .embed()
method:
vector = embedding_model.embed("my happy hound")
If the embedding model can handle binary input, you can call .embed()
with a byte string instead. You can check the supports_binary
property to see if this is supported:
if embedding_model.supports_binary:
vector = embedding_model.embed(open("my-image.jpg", "rb").read())
The embedding_model.supports_text
property indicates if the model supports text input.
Many embeddings models are more efficient when you embed multiple strings or binary strings at once. To embed multiple strings at once, use the .embed_multi()
method:
vectors = list(embedding_model.embed_multi(["my happy hound", "my dissatisfied cat"]))
This returns a generator that yields one embedding vector per string.
Embeddings are calculated in batches. By default all items will be processed in a single batch, unless the underlying embedding model has defined its own preferred batch size. You can pass a custom batch size using batch_size=N
, for example:
vectors = list(embedding_model.embed_multi(lines_from_file, batch_size=20))
Working with collections#
The llm.Collection
class can be used to work with collections of embeddings from Python code.
A collection is a named group of embedding vectors, each stored along with their IDs in a SQLite database table.
To work with embeddings in this way you will need an instance of a sqlite-utils Database object. You can then pass that to the llm.Collection
constructor along with the unique string name of the collection and the ID of the embedding model you will be using with that collection:
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)
If the collection already exists in the database you can omit the model
or model_id
argument - the model ID will be read from the collections
table.
To embed a single string and store it in the collection, use the embed()
method:
collection.embed("hound", "my happy hound")
This stores the embedding for the string “my happy hound” in the entries
collection under the key hound
.
Add store=True
to store the text content itself in the database table along with the embedding vector.
To attach additional metadata to an item, pass a JSON-compatible dictionary as the metadata=
argument:
collection.embed("hound", "my happy hound", metadata={"name": "Hound"}, store=True)
This additional metadata will be stored as JSON in the metadata
column of the embeddings database table.
Storing embeddings in bulk#
The collection.embed_multi()
method can be used to store embeddings for multiple items at once. This can be more efficient for some embedding models.
collection.embed_multi(
[
("hound", "my happy hound"),
("cat", "my dissatisfied cat"),
],
# Add this to store the strings in the content column:
store=True,
)
To include metadata to be stored with each item, call embed_multi_with_metadata()
:
collection.embed_multi_with_metadata(
[
("hound", "my happy hound", {"name": "Hound"}),
("cat", "my dissatisfied cat", {"name": "Cat"}),
],
# This can also take the store=True argument:
store=True,
)
The batch_size=
argument defaults to 100, and will be used unless the embedding model itself defines a lower batch size. You can adjust this if you are having trouble with memory while embedding large collections:
collection.embed_multi(
(
(i, line)
for i, line in enumerate(lines_in_file)
),
batch_size=10
)
Collection class reference#
A collection instance has the following properties and methods:
id
- the integer ID of the collection in the databasename
- the string name of the collection (unique in the database)model_id
- the string ID of the embedding model used for this collectionmodel()
- returns theEmbeddingModel
instance, based on thatmodel_id
count()
- returns the integer number of items in the collectionembed(id: str, text: str, metadata: dict=None, store: bool=False)
- embeds the given string and stores it in the collection under the given ID. Can optionally include metadata (stored as JSON) and store the text content itself in the database table.embed_multi(entries: Iterable, store: bool=False, batch_size: int=100)
- see aboveembed_multi_with_metadata(entries: Iterable, store: bool=False, batch_size: int=100)
- see abovesimilar(query: str, number: int=10)
- returns a list of entries that are most similar to the embedding of the given query stringsimilar_by_id(id: str, number: int=10)
- returns a list of entries that are most similar to the embedding of the item with the given IDsimilar_by_vector(vector: List[float], number: int=10, skip_id: str=None)
- returns a list of entries that are most similar to the given embedding vector, optionally skipping the entry with the given IDdelete()
- deletes the collection and its embeddings from the database
There is also a Collection.exists(db, name)
class method which returns a boolean value and can be used to determine if a collection exists or not in a database:
if Collection.exists(db, "entries"):
print("The entries collection exists")
Retrieving similar items#
Once you have populated a collection of embeddings you can retrieve the entries that are most similar to a given string using the similar()
method.
This method uses a brute force approach, calculating distance scores against every document. This is fine for small collections, but will not scale to large collections. See issue 216 for plans to add a more scalable approach via vector indexes provided by plugins.
for entry in collection.similar("hound"):
print(entry.id, entry.score)
The string will first by embedded using the model for the collection.
The entry
object returned is an object with the following properties:
id
- the string ID of the itemscore
- the floating point similarity score between the item and the query stringcontent
- the string text content of the item, if it was stored - orNone
metadata
- the dictionary (from JSON) metadata for the item, if it was stored - orNone
This defaults to returning the 10 most similar items. You can change this by passing a different number=
argument:
for entry in collection.similar("hound", number=5):
print(entry.id, entry.score)
The similar_by_id()
method takes the ID of another item in the collection and returns the most similar items to that one, based on the embedding that has already been stored for it:
for entry in collection.similar_by_id("cat"):
print(entry.id, entry.score)
The item itself is excluded from the results.
SQL schema#
Here’s the SQL schema used by the embeddings database:
CREATE TABLE [collections] (
[id] INTEGER PRIMARY KEY,
[name] TEXT,
[model] TEXT
)
CREATE TABLE "embeddings" (
[collection_id] INTEGER REFERENCES [collections]([id]),
[id] TEXT,
[embedding] BLOB,
[content] TEXT,
[content_blob] BLOB,
[content_hash] BLOB,
[metadata] TEXT,
[updated] INTEGER,
PRIMARY KEY ([collection_id], [id])
)