API

vecs is a python client for managing and querying vector stores in PostgreSQL with the pgvector extension. This guide will help you get started with using vecs.

If you don't have a Postgres database with the pgvector ready, see hosting for easy options.

Installation#

Requires:

Python 3.7+

You can install vecs using pip:

pip install vecs

Usage#

Connecting#

Before you can interact with vecs, create the client to communicate with Postgres. If you haven't started a Postgres instance yet, see hosting.

import vecs
DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
# create vector store client
vx = vecs.create_client(DB_CONNECTION)

Get or Create a Collection#

You can get a collection (or create if it doesn't exist), specifying the collection's name and the number of dimensions for the vectors you intend to store.

docs = vx.get_or_create_collection(name="docs", dimension=3)

Upserting vectors#

vecs combines the concepts of "insert" and "update" into "upsert". Upserting records adds them to the collection if the id is not present, or updates the existing record if the id does exist.

# add records to the collection
docs.upsert(
    records=[
        (
         "vec0",           # the vector's identifier
         [0.1, 0.2, 0.3],  # the vector. list or np.array
         {"year": 1973}    # associated  metadata
        ),
        (
         "vec1",
         [0.7, 0.8, 0.9],
         {"year": 2012}
        )
    ]
)

Deleting vectors#

Deleting records removes them from the collection. To delete records, specify a list of ids to the delete method. The ids of the sucessfully deleted records are returned from the method. Note that attempting to delete non-existent records does not raise an error.

docs.delete(ids=["vec0", "vec1"])

Create an index#

Collections can be queried immediately after being created. However, for good throughput, the collection should be indexed after records have been upserted.

Only one index may exist per-collection. By default, creating an index will replace any existing index.

To create an index:

docs.create_index()

You may optionally provide a distance measure and index method.

Available options for distance measure are:

vecs.IndexMeasure.cosine_distance
vecs.IndexMeasure.l2_distance
vecs.IndexMeasure.max_inner_product

which correspond to different methods for comparing query vectors to the vectors in the database.

If you aren't sure which to use, the default of cosine_distance is the most widely compatible with off-the-shelf embedding methods.

Available options for index method are:

vecs.IndexMethod.auto
vecs.IndexMethod.hnsw
vecs.IndexMethod.ivfflat

Where auto selects the best available index method, hnsw uses the HNSW method and ivfflat uses IVFFlat.

When using IVFFlat indexes, the index must be created after the collection has been populated with records. Building an IVFFlat index on an empty collection will result in significantly reduced recall. You can continue upserting new documents after the index has been created, but should rebuild the index if the size of the collection more than doubles since the last index operation.

HNSW indexes can be created immediately after the collection without populating records.

To manually specify method and measure, ass them as arguments to create_index for example:

docs.create_index(
    method=IndexMethod.hnsw,
    measure=IndexMeasure.cosine_distance,
)

note

The time required to create an index grows with the number of records and size of vectors. For a few thousand records expect sub-minute a response in under a minute. It may take a few minutes for larger collections.

Query#

Given a collection docs with several records:

Basic#

The simplest form of search is to provide a query vector.

note

Indexes are essential for good performance. See creating an index for more info.

If you do not create an index, every query will return a warning

query does not have a covering index for cosine_similarity. See Collection.create_index

that incldues the IndexMeasure you should index.

docs.query(
    data=[0.4,0.5,0.6],          # required
    limit=5,                     # number of records to return
    filters={},                  # metadata filters
    measure="cosine_distance",   # distance measure to use
    include_value=False,         # should distance measure values be returned?
    include_metadata=False,      # should record metadata be returned?
)

Which returns a list of vector record ids.

Metadata Filtering#

The metadata that is associated with each record can also be filtered during a query.

As an example, {"year": {"$eq": 2005}} filters a year metadata key to be equal to 2005

In context:

docs.query(
    data=[0.4,0.5,0.6],
    filters={"year": {"$eq": 2012}}, # metadata filters
)

For a complete reference, see the metadata guide.

Disconnect#

When you're done with a collection, be sure to disconnect the client from the database.

vx.disconnect()

alternatively, use the client as a context manager and it will automatically close the connection on exit.

import vecs
DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
# create vector store client
with vecs.create_client(DB_CONNECTION) as vx:
    # do some work here
    pass
# connections are now closed

Adapters#

Adapters are an optional feature to transform data before adding to or querying from a collection. Adapters make it possible to interact with a collection using only your project's native data type (eg. just raw text), rather than manually handling vectors.

For a complete list of available adapters, see built-in adapters.

As an example, we'll create a collection with an adapter that chunks text into paragraphs and converts each chunk into an embedding vector using the all-MiniLM-L6-v2 model.

First, install vecs with optional dependencies for text embeddings:

pip install "vecs[text_embedding]"

Then create a collection with an adapter to chunk text into paragraphs and embed each paragraph using the all-MiniLM-L6-v2 384 dimensional text embedding model.

import vecs
from vecs.adapter import Adapter, ParagraphChunker, TextEmbedding
# create vector store client
vx = vecs.Client("postgresql://<user>:<password>@<host>:<port>/<db_name>")
# create a collection with an adapter
docs = vx.get_or_create_collection(
    name="docs",
    adapter=Adapter(
        [
            ParagraphChunker(skip_during_query=True),
            TextEmbedding(model='all-MiniLM-L6-v2'),
        ]
    )
)

With the adapter registered against the collection, we can upsert records into the collection passing in text rather than vectors.

# add records to the collection using text as the media type
docs.upsert(
    records=[
        (
         "vec0",
         "four score and ....", # <- note that we can now pass text here
         {"year": 1973}
        ),
        (
         "vec1",
         "hello, world!",
         {"year": "2012"}
        )
    ]
)

Similarly, we can query the collection using text.

# search by text
docs.query(data="foo bar")

Deprecated#

Create collection#

note

Deprecated: use get_or_create_collection

You can create a collection to store vectors specifying the collections name and the number of dimensions in the vectors you intend to store.

docs = vx.create_collection(name="docs", dimension=3)

Get an existing collection#

note

Deprecated: use get_or_create_collection

To access a previously created collection, use get_collection to retrieve it by name

docs = vx.get_collection(name="docs")

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