diff --git a/.github/workflows/python_check.yml b/.github/workflows/python_check.yml index 06eed3b..1dd9078 100644 --- a/.github/workflows/python_check.yml +++ b/.github/workflows/python_check.yml @@ -61,7 +61,7 @@ jobs: - name: Install Dependencies working-directory: bindings/python run: | - pdm sync -d + pdm sync -G :all # - name: Install Docker for macOS # if: matrix.os == 'macos-latest' diff --git a/bindings/python/README.md b/bindings/python/README.md index 5c2f10b..a8dd25d 100644 --- a/bindings/python/README.md +++ b/bindings/python/README.md @@ -1,9 +1,7 @@ -# Python bindings for pgvector.rs +# Python bindings for pgvecto.rs [![pdm-managed](https://img.shields.io/badge/pdm-managed-blueviolet)](https://pdm.fming.dev) -Currently supports [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy). - ## Usage Install from PyPI: @@ -11,15 +9,68 @@ Install from PyPI: pip install pgvecto_rs ``` -See the usage examples: -- [SQLAlchemy](#SQLAlchemy) +See the [usage of SDK](#sdk) + +Or use it as an extension of postgres clients: +- [SQLAlchemy](#sqlalchemy) - [psycopg3](#psycopg3) +### SDK + +Our SDK is designed to use the pgvecto.rs out-of-box. You can exploit the power of pgvecto.rs to do similarity search or retrieve with filters, without writing any SQL code. + +Install dependencies: +```bash +pip install "pgvecto_rs[sdk]" +``` + +A minimal example: + +```Python +from pgvecto_rs.sdk import PGVectoRs, Record + +# Create a client +client = PGVectoRs( + db_url="postgresql+psycopg://postgres:mysecretpassword@localhost:5432/postgres", + table_name="example", + dimension=3, +) + +try: + # Add some records + client.add_records( + [ + Record.from_text("hello 1", [1, 2, 3]), + Record.from_text("hello 2", [1, 2, 4]), + ] + ) + + # Search with default operator (sqrt_euclid). + # The results is sorted by distance + for rec, dis in client.search([1, 2, 5]): + print(rec.text) + print(dis) +finally: + # Clean up (i.e. drop the table) + client.drop() +``` + +Output: +``` +hello 2 +1.0 +hello 1 +4.0 +``` + +See [examples/sdk_example.py](examples/sdk_example.py) and [tests/test_sdk.py](tests/test_sdk.py) for more examples. + + ### SQLAlchemy -Install [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy) and [psycopg3](https://www.psycopg.org/psycopg3/docs/basic/install.html) +Install dependencies: ```bash -pip install "psycopg[binary]" sqlalchemy +pip install "pgvecto_rs[sqlalchemy]" ``` Then write your code. See [examples/sqlalchemy_example.py](examples/sqlalchemy_example.py) and [tests/test_sqlalchemy.py](tests/test_sqlalchemy.py) for example. @@ -31,9 +82,9 @@ All the operators include: ### psycopg3 -Install [psycopg3](https://www.psycopg.org/psycopg3/docs/basic/install.html) +Install dependencies: ```bash -pip install "psycopg[binary]" +pip install "pgvecto_rs[psycopg3]" ``` Then write your code. See [examples/psycopg_example.py](examples/psycopg_example.py) and [tests/test_psycopg.py](tests/test_psycopg.py) for example. diff --git a/bindings/python/examples/sdk_example.py b/bindings/python/examples/sdk_example.py new file mode 100644 index 0000000..8791eff --- /dev/null +++ b/bindings/python/examples/sdk_example.py @@ -0,0 +1,74 @@ +import os + +from openai import OpenAI + +from pgvecto_rs.sdk import PGVectoRs, Record, filters + +URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format( + port=os.getenv("DB_PORT", 5432), + host=os.getenv("DB_HOST", "localhost"), + username=os.getenv("DB_USER", "postgres"), + password=os.getenv("DB_PASS", "mysecretpassword"), + db_name=os.getenv("DB_NAME", "postgres"), +) +embedding = OpenAI().embeddings + + +def embed(text: str): + return ( + embedding.create(input=text, model="text-embedding-ada-002").data[0].embedding + ) + + +texts = [ + "Hello world", + "Hello PostgreSQL", + "Hello pgvecto.rs!", +] +records1 = [Record.from_text(text, embed(text), {"src": "one"}) for text in texts] +records2 = [Record.from_text(text, embed(text), {"src": "two"}) for text in texts] +target = embed("Hello vector database!") + +# Create an empty client +client = PGVectoRs( + db_url=URL, + collection_name="example", + dimension=1536, +) +try: + # Add some records + client.insert(records1) + client.insert(records2) + + # Query (With a filter from the filters module) + print("#################### First Query ####################") + for record, dis in client.search( + target, filter=filters.meta_contains({"src": "one"}) + ): + print(f"DISTANCE SCORE: {dis}") + print(record) + + # Another Query (Equivalent to the first one, but with a lambda filter written by hand) + print("#################### Second Query ####################") + for record, dis in client.search( + target, filter=lambda r: r.meta.contains({"src": "one"}) + ): + print(f"DISTANCE SCORE: {dis}") + print(record) + + # Yet Another Query (With a more complex filter) + print("#################### Third Query ####################") + + def complex_filter(r: filters.FilterInput) -> filters.FilterOutput: + t1 = r.text.endswith("!") == False # noqa: E712 + t2 = r.meta.contains({"src": "two"}) + t = t1 & t2 + return t + + for record, dis in client.search(target, filter=complex_filter): + print(f"DISTANCE SCORE: {dis}") + print(record) + +finally: + # Clean up + client.drop() diff --git a/bindings/python/pdm.lock b/bindings/python/pdm.lock index 50cf351..4c5b0d4 100644 --- a/bindings/python/pdm.lock +++ b/bindings/python/pdm.lock @@ -2,11 +2,38 @@ # It is not intended for manual editing. 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b/bindings/python/pyproject.toml @@ -1,40 +1,58 @@ [project] -name = "pgvecto_rs" -version = "0.1.2" +name = "pgvecto-rs" +version = "0.1.3" description = "Python binding for pgvecto.rs" authors = [ - {name = "TensorChord", email = "envd-maintainers@tensorchord.ai"}, - {name = "盐粒 Yanli", email = "mail@yanli.one"}, + { name = "TensorChord", email = "envd-maintainers@tensorchord.ai" }, + { name = "盐粒 Yanli", email = "mail@yanli.one" }, ] dependencies = [ - "numpy>=1.23", - "toml>=0.10", + "numpy>=1.23", + "toml>=0.10", ] requires-python = ">=3.8" readme = "README.md" -license = {text = "Apache-2.0"} +license = { text = "Apache-2.0" } +classifiers = [ + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", +] [build-system] -requires = ["pdm-backend"] build-backend = "pdm.backend" +requires = [ + "pdm-backend", +] [project.optional-dependencies] -lint = [ - "black>=23.10.0", - "ruff>=0.1.1", +psycopg3 = [ + "psycopg[binary]>=3.1.12", +] +sdk = [ + "openai>=1.2.2", + "pgvecto_rs[sqlalchemy]", +] +sqlalchemy = [ + "pgvecto_rs[psycopg3]", + "SQLAlchemy>=2.0.23", ] - [tool.pdm.dev-dependencies] -dev = [ - "SQLAlchemy>=2", - "pytest>=7.4", - "psycopg[binary]>=3.1.12", -] +lint = ["ruff>=0.1.1"] +test = ["pytest>=7.4.3"] [tool.pdm.scripts] -test = "pytest tests/" -format = {composite = ["black .", "ruff check --fix ."]} -check = {composite = ["black --check .", "ruff check ."]} +test = "pytest tests/" +format = "ruff format ." +fix = "ruff --fix ." +check = { composite = ["ruff format . --check", "ruff ."] } + +[tool.ruff] +select = ["E", "F", "I", "TID"] +ignore = ["E731", "E501"] +src = ["src"] [tool.pytest.ini_options] addopts = "-r aR" diff --git a/bindings/python/src/pgvecto_rs/psycopg/__init__.py b/bindings/python/src/pgvecto_rs/psycopg/__init__.py index b56b9a7..7298d5c 100644 --- a/bindings/python/src/pgvecto_rs/psycopg/__init__.py +++ b/bindings/python/src/pgvecto_rs/psycopg/__init__.py @@ -4,7 +4,7 @@ from psycopg.adapt import Dumper, Loader from psycopg.pq import Format from psycopg.types import TypeInfo -from ..utils.serializer import from_db_str, to_db_str +from pgvecto_rs.utils.serializer import from_db_str, to_db_str __all__ = ["register_vector"] diff --git a/bindings/python/src/pgvecto_rs/sdk/__init__.py b/bindings/python/src/pgvecto_rs/sdk/__init__.py new file mode 100644 index 0000000..00a625f --- /dev/null +++ b/bindings/python/src/pgvecto_rs/sdk/__init__.py @@ -0,0 +1,5 @@ +from pgvecto_rs.sdk.client import PGVectoRs +from pgvecto_rs.sdk.filters import Filter +from pgvecto_rs.sdk.record import Record + +__all__ = ["PGVectoRs", "Record", "Filter"] diff --git a/bindings/python/src/pgvecto_rs/sdk/client.py b/bindings/python/src/pgvecto_rs/sdk/client.py new file mode 100644 index 0000000..3e51da6 --- /dev/null +++ b/bindings/python/src/pgvecto_rs/sdk/client.py @@ -0,0 +1,124 @@ +from typing import List, Literal, Optional, Tuple, Type, Union +from uuid import UUID + +from numpy import ndarray +from sqlalchemy import ColumnElement, Float, create_engine, delete, insert, select, text +from sqlalchemy.dialects import postgresql +from sqlalchemy.engine import Engine +from sqlalchemy.orm import Mapped, mapped_column +from sqlalchemy.orm.session import Session +from sqlalchemy.types import String + +from pgvecto_rs.sdk.filters import Filter +from pgvecto_rs.sdk.record import Record, RecordORM, RecordORMType +from pgvecto_rs.sqlalchemy import Vector + + +class PGVectoRs: + _engine: Engine + _table: Type[RecordORM] + dimension: int + + def __init__( + self, + db_url: str, + collection_name: str, + dimension: int, + ) -> None: + """Connect to an existing table or create a new empty one. + + Args: + db_url (str): url to the database. + table_name (str): name of the table. + dimension (int): dimension of the embeddings. + """ + + class _Table(RecordORM): + __tablename__ = f"collection_{collection_name}" + id: Mapped[UUID] = mapped_column( + postgresql.UUID(as_uuid=True), primary_key=True + ) + text: Mapped[str] = mapped_column(String) + meta: Mapped[dict] = mapped_column(postgresql.JSONB) + embedding: Mapped[ndarray] = mapped_column(Vector(dimension)) + + self._engine = create_engine(db_url) + with Session(self._engine) as session: + session.execute(text("CREATE EXTENSION IF NOT EXISTS vectors")) + session.commit() + self._table = _Table + self._table.__table__.create(self._engine) + self.dimension = dimension + + def insert(self, records: List[Record]) -> None: + with Session(self._engine) as session: + for record in records: + session.execute( + insert(self._table).values( + id=record.id, + text=record.text, + meta=record.meta, + embedding=record.embedding, + ) + ) + session.commit() + + def search( + self, + embedding: Union[ndarray, List[float]], + distance_op: Literal["<->", "<=>", "<#>"] = "<->", + top_k: int = 4, + filter: Optional[Filter] = None, + ) -> List[Tuple[Record, float]]: + """Search for the nearest records. + + Args: + embedding : Target embedding. + distance_op : Distance op. + top_k : Max records to return. Defaults to 4. + filter : Read our document. Defaults to None. + order_by_dis : Order by distance. Defaults to True. + + Returns: + List of records and coresponding distances. + + """ + with Session(self._engine) as session: + stmt = ( + select( + self._table, + self._table.embedding.op(distance_op, return_type=Float)( + embedding + ).label("distance"), + ) + .limit(top_k) + .order_by("distance") + ) + if filter is not None: + stmt = stmt.where(filter(self._table)) + res = session.execute(stmt) + return [(Record.from_orm(row[0]), row[1]) for row in res] + + # ================ Delete ================ + def delete(self, filter: Filter) -> None: + with Session(self._engine) as session: + session.execute(delete(self._table).where(filter(self._table))) + session.commit() + + def delete_all(self) -> None: + with Session(self._engine) as session: + session.execute(delete(self._table)) + session.commit() + + def delete_by_ids(self, ids: List[UUID]) -> None: + def filter(record: RecordORMType) -> ColumnElement[bool]: + return record.id.in_(ids) + + with Session(self._engine) as session: + session.execute(delete(self._table).where(filter(self._table))) + session.commit() + + # ================ Drop ================ + def drop(self) -> None: + """Drop the table which the client is connected to.""" + self._table.__table__.drop(self._engine) diff --git a/bindings/python/src/pgvecto_rs/sdk/filters.py b/bindings/python/src/pgvecto_rs/sdk/filters.py new file mode 100644 index 0000000..3e50a1f --- /dev/null +++ b/bindings/python/src/pgvecto_rs/sdk/filters.py @@ -0,0 +1,13 @@ +from typing import Callable, Type + +from sqlalchemy import ColumnElement + +from pgvecto_rs.sdk.record import RecordORMType + +Filter = Type[Callable[[RecordORMType], ColumnElement[bool]]] +FilterInput = RecordORMType +FilterOutput = ColumnElement[bool] + + +def meta_contains(meta_contains: dict) -> Filter: + return lambda r: r.meta.contains(meta_contains) diff --git a/bindings/python/src/pgvecto_rs/sdk/record.py b/bindings/python/src/pgvecto_rs/sdk/record.py new file mode 100644 index 0000000..d7e6497 --- /dev/null +++ b/bindings/python/src/pgvecto_rs/sdk/record.py @@ -0,0 +1,52 @@ +from typing import List, Optional, Type, Union +from uuid import UUID, uuid4 + +from numpy import array, float32, ndarray +from sqlalchemy.orm import DeclarativeBase, Mapped + + +class RecordORM(DeclarativeBase): + __tablename__: str + id: Mapped[UUID] + text: Mapped[str] + meta: Mapped[dict] + embedding: Mapped[ndarray] + + +RecordORMType = Type[RecordORM] + + +class Record: + id: UUID + text: str + meta: dict + embedding: ndarray + + def __init__(self, id: UUID, text: str, meta: dict, embedding: ndarray): + self.id = id + self.text = text + self.meta = meta + self.embedding = embedding + + def __repr__(self) -> str: + return f"""============= Record ============= +[id] : {self.id} +[text] : {self.text} +[meta] : {self.meta} +[embedding]: {self.embedding} +========== End of Record =========""" + + @classmethod + def from_orm(cls, orm: RecordORM): + return cls(orm.id, orm.text, orm.meta, orm.embedding) + + @classmethod + def from_text( + cls, + text: str, + embedding: Union[ndarray, List[float]], + meta: Optional[dict] = None, + ): + if isinstance(embedding, list): + embedding = array(embedding, dtype=float32) + return cls(uuid4(), text, meta or {}, embedding) diff --git a/bindings/python/src/pgvecto_rs/sqlalchemy/__init__.py b/bindings/python/src/pgvecto_rs/sqlalchemy/__init__.py index fb80a28..eae4f7e 100644 --- a/bindings/python/src/pgvecto_rs/sqlalchemy/__init__.py +++ b/bindings/python/src/pgvecto_rs/sqlalchemy/__init__.py @@ -1,6 +1,6 @@ import sqlalchemy.types as types -from ..utils import serializer +from pgvecto_rs.utils import serializer class Vector(types.UserDefinedType): diff --git a/bindings/python/tests/test_sdk.py b/bindings/python/tests/test_sdk.py new file mode 100644 index 0000000..6409e90 --- /dev/null +++ b/bindings/python/tests/test_sdk.py @@ -0,0 +1,96 @@ +from typing import List + +import numpy as np +import pytest + +from pgvecto_rs.sdk import Filter, PGVectoRs, Record, filters +from tests import ( + EXPECTED_NEG_COS_DIS, + EXPECTED_NEG_DOT_PROD_DIS, + EXPECTED_SQRT_EUCLID_DIS, + OP_NEG_COS_DIS, + OP_NEG_DOT_PROD_DIS, + OP_SQRT_EUCLID_DIS, + URL, + VECTORS, +) + +URL = URL.replace("postgresql", "postgresql+psycopg") +mockTexts = { + "text0": VECTORS[0], + "text1": VECTORS[1], + "text2": VECTORS[2], +} + + +class MockEmbedder: + def embed(self, text: str) -> np.ndarray: + if isinstance(mockTexts[text], list): + return np.array(mockTexts[text], dtype=np.float32) + return mockTexts[text] + + +@pytest.fixture(scope="module") +def client(): + client = PGVectoRs(db_url=URL, collection_name="empty", dimension=3) + try: + records1 = [ + Record.from_text(t, v, {"src": "src1"}) for t, v in mockTexts.items() + ] + records2 = [ + Record.from_text(t, v, {"src": "src2"}) for t, v in mockTexts.items() + ] + client.insert(records1) + client.insert(records2) + yield client + finally: + client.drop() + + +filter_src1 = filters.meta_contains({"src": "src1"}) +filter_src2: Filter = lambda r: r.meta.contains({"src": "src2"}) + + +@pytest.mark.parametrize("filter", [filter_src1, filter_src2]) +@pytest.mark.parametrize( + "dis_op, dis_oprand, dis_expected", + zip( + ["<->", "<#>", "<=>"], + [OP_SQRT_EUCLID_DIS, OP_NEG_DOT_PROD_DIS, OP_NEG_COS_DIS], + [EXPECTED_SQRT_EUCLID_DIS, EXPECTED_NEG_DOT_PROD_DIS, EXPECTED_NEG_COS_DIS], + ), +) +def test_search_filter_and_op( + client: PGVectoRs, + filter: Filter, + dis_op: str, + dis_oprand: List[float], + dis_expected: List[float], +): + for rec, dis in client.search(dis_oprand, dis_op, top_k=99, filter=filter): + cnt = None + for i in range(len(VECTORS)): + if np.allclose(rec.embedding, VECTORS[i]): + cnt = i + break + assert np.allclose(dis, dis_expected[cnt]) + + +@pytest.mark.parametrize( + "dis_op, dis_oprand, dis_expected", + zip( + ["<->", "<#>", "<=>"], + [OP_SQRT_EUCLID_DIS, OP_NEG_DOT_PROD_DIS, OP_NEG_COS_DIS], + [EXPECTED_SQRT_EUCLID_DIS, EXPECTED_NEG_DOT_PROD_DIS, EXPECTED_NEG_COS_DIS], + ), +) +def test_search_order_and_limit( + client: PGVectoRs, + dis_op: str, + dis_oprand: List[float], + dis_expected: List[float], +): + dis_expected = dis_expected.copy() + dis_expected.sort() + for i, (rec, dis) in enumerate(client.search(dis_oprand, dis_op, top_k=4)): + assert np.allclose(dis, dis_expected[i // 2])