✨ feat(Destination PGVector): new connector

airbyte-integrations/connectors/destination-pgvector/README.md

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+# 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.

airbyte-integrations/connectors/destination-pgvector/acceptance-test-config.yml

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+acceptance_tests:
+  spec:
+    tests:
+      - spec_path: integration_tests/spec.json
+connector_image: airbyte/destination-pgvector:dev

airbyte-integrations/connectors/destination-pgvector/bootstrap.md

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+# 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/.

airbyte-integrations/connectors/destination-pgvector/destination_pgvector/__init__.py

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+#
+# Copyright (c) 2023 Airbyte, Inc., all rights reserved.
+#
+
+
+from .destination import DestinationPGVector
+
+__all__ = ["DestinationPGVector"]

airbyte-integrations/connectors/destination-pgvector/destination_pgvector/common/catalog/catalog_providers.py

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+# 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