affine-vectordb 0.1.0

A unified interface for vector databases

affine

Affine is a Python library for providing a uniform and structured interface to various backing vector databases and approximate nearest neighbor libraries. It allows simple dataclass-like objects to describe collections together with a high-level query syntax for doing filtered vector search.

For vector databases, it currently supports:

• qdrant
• weaviate
• pinecone

For local mode, the following approximate nearest neighbor libraries are supported:

• FAISS
• annoy
• pynndescent
• scikit-learn KDTree
• naive/NumPy

Note: this project is very similar to vectordb-orm, which looks to be no longer maintained.

Installation

pip install affine-vectordb
# or `pip install "affine-vectordb[qdrant]"` for qdrant support
# `pip install "affine-vectordb[weaviate]"` for weaviate support
# `pip install "affine-vectordb[pinecone]"` for pinecone support

Basic Usage

from affine import Collection, Vector, Filter, Query

# Define a collection
class MyCollection(Collection):
    vec: Vector[3] # declare a 3-dimensional vector

    # support for additional fields for filtering
    a: int
    b: str

db = LocalEngine()

# Insert vectors
db.insert(MyCollection(vec=[0.1, 0.0, -0.5], a=1, b="foo"))
db.insert(MyCollection(vec=[1.3, 2.1, 3.6], a=2, b="bar"))
db.insert(MyCollection(vec=[-0.1, 0.2, 0.3], a=3, b="foo"))

# Query vectors
result: list[MyCollection] = (
    db.query(MyCollection)
    .filter(MyCollection.b == "foo")
    .similarity([2.8, 1.8, -4.5])
    .limit(1)
)

Engines

A fundamental notion of affine are Engine classes. All such classes conform to the same API for interchangeabillity (with the exception of a few engine-specific restrictions which are be mentioned below). There are two broad types of engines

1. LocalEngine: this does nearest neighbor search on the executing machine, and supports a variety of libraries for the backing nearest neighborsearch (these are called the backend of the local engine).

2. Vector database engines: these are engines that connect to a vector database service, such as QDrant, Weaviate, or Pinecone.

Vector Databases

The currently supported vector databases are:

Database	Class	Constructor arguments	Notes
Qdrant	affine.engine.QdrantEngine	host: str hostname to use port: int port to use	-
Weaviate	affine.engine.WeaviateEngine	host: str hostname to use port: int port to use	-
Pinecone	affine.engine.PineconeEngine	api_key: Union[str, None] pinecone API key. if not provided, it will be read from the environment variable PINECONE_API_KEY. spec: Union[ServerlessSpec, PodSpec, None] the PodSpec or ServerlessSpec object. If not provided, aServerlessSpec will be created from the environment variables PINECONE_CLOUD and PINECONE_REGION.	the Pinecone engine has the restriction that every collection must contain exactly one vector attribute.

Approximate Nearest Neighbor Libraries

The LocalEngine class provides an interface for doing nearest neighbor search on the executing machine, supporting a variety of libraries for the backing nearest neighborsearch. Which one is specified by the backend argument to the constructor. For example, to use annoy:

from affine.engine.local import LocalEngine, AnnoyBackend

db = LocalEngine(backend=AnnoyBackend(n_tress=10))

The options and settings for the various supported backends are as follows:

Library	Class	Constructor arguments	Notes
naive/numpy	affine.engine.local.NumPyBackend	-	-
scikit-learn KDTree	affine.engine.local.KDTreeBackend	keyword arguments that get passed directly to sklearn.neighbors.KDTree	-
annoy	affine.engine.local.AnnoyBackend	n_trees: int number of trees to use n_jobs: int defaults to -1	-
FAISS	affine.engine.local.FAISSBackend	index_factory_str: str	-
PyNNDescent	affine.engine.local.PyNNDescentBackend	keyword arguments that get passed directly to pynndescent.NNDescent	-