From 111d5fbbb8dba6f43cf449ac9f422a228c6997e2 Mon Sep 17 00:00:00 2001 From: deeplook Date: Tue, 7 Nov 2023 11:32:16 +0100 Subject: [PATCH 1/7] Show schemas in streamlit (#690) --- dlt/helpers/streamlit_helper.py | 37 ++++++++--- .../test_streamlit_show_resources.py | 66 +++++++++++++++++++ 2 files changed, 94 insertions(+), 9 deletions(-) create mode 100644 tests/helpers/streamlit_tests/test_streamlit_show_resources.py diff --git a/dlt/helpers/streamlit_helper.py b/dlt/helpers/streamlit_helper.py index 082c1242da..d505ccaff3 100644 --- a/dlt/helpers/streamlit_helper.py +++ b/dlt/helpers/streamlit_helper.py @@ -2,12 +2,12 @@ from typing import Dict, List import humanize - from dlt.common import pendulum from dlt.common.typing import AnyFun from dlt.common.configuration.exceptions import ConfigFieldMissingException from dlt.common.exceptions import MissingDependencyException from dlt.common.destination.reference import WithStateSync +from dlt.common.utils import flatten_list_or_items from dlt.common.libs.pandas import pandas as pd from dlt.pipeline import Pipeline @@ -117,11 +117,13 @@ def _query_data_live(query: str, schema_name: str = None) -> pd.DataFrame: try: st.header("Pipeline info") credentials = pipeline.sql_client().credentials + schema_names = ", ".join(sorted(pipeline.schema_names)) st.markdown(f""" * pipeline name: **{pipeline.pipeline_name}** * destination: **{str(credentials)}** in **{pipeline.destination.__name__}** * dataset name: **{pipeline.dataset_name}** * default schema name: **{pipeline.default_schema_name}** + * all schema names: **{schema_names}** """) st.header("Last load info") @@ -225,16 +227,22 @@ def _query_data(query: str, chunk_size: int = None) -> pd.DataFrame: except SqlClientNotAvailable: st.error("Cannot load data - SqlClient not available") + st.header("Schemas and their tables") - if schema_name: - schema = pipeline.schemas[schema_name] - else: - schema = pipeline.default_schema - st.title(f"Available tables in {schema.name} schema") + num_schemas = len(pipeline.schema_names) + if num_schemas == 1: + schema_name = pipeline.schema_names[0] + selected_schema = pipeline.schemas.get(schema_name) + st.subheader(f"Schema: {schema_name}") + elif num_schemas > 1: + st.subheader("Schema:") + text = "Pick a schema name to see all its tables below" + selected_schema_name = st.selectbox(text, sorted(pipeline.schema_names)) + selected_schema = pipeline.schemas.get(selected_schema_name) - for table in schema.data_tables(): + for table in sorted(selected_schema.data_tables(), key=lambda table: table["name"]): table_name = table["name"] - st.header(table_name) + st.subheader(f"Table: {table_name}") if "description" in table: st.text(table["description"]) table_hints: List[str] = [] @@ -244,6 +252,17 @@ def _query_data(query: str, chunk_size: int = None) -> pd.DataFrame: table_hints.append("resource: **%s**" % table["resource"]) if "write_disposition" in table: table_hints.append("write disposition: **%s**" % table["write_disposition"]) + columns = table["columns"] + primary_keys = flatten_list_or_items([ + col_name for col_name in columns.keys() + if not col_name.startswith("_") and not columns[col_name].get("primary_key") is None + ]) + table_hints.append("primary key(s): **%s**" % ", ".join(primary_keys)) + merge_keys = flatten_list_or_items([ + col_name for col_name in columns.keys() + if not col_name.startswith("_") and not columns[col_name].get("merge_key") is None + ]) + table_hints.append("merge key(s): **%s**" % ", ".join(merge_keys)) st.markdown(" | ".join(table_hints)) @@ -264,7 +283,7 @@ def _query_data(query: str, chunk_size: int = None) -> pd.DataFrame: st.text(f"Top {rows_count} row(s)") st.dataframe(df) - st.title("Run your query") + st.header("Run your query") sql_query = st.text_area("Enter your SQL query", value=example_query) if st.button("Run Query"): if sql_query: diff --git a/tests/helpers/streamlit_tests/test_streamlit_show_resources.py b/tests/helpers/streamlit_tests/test_streamlit_show_resources.py new file mode 100644 index 0000000000..c550b45654 --- /dev/null +++ b/tests/helpers/streamlit_tests/test_streamlit_show_resources.py @@ -0,0 +1,66 @@ +""" +Create a pipeline with multiple resources for streamlit to show. + +Run streamlit showing this pipeline like this: + + dlt pipeline test_resources_pipeline show +""" + +import dlt + + +@dlt.source +def source1(nr): + def get_resource(nr): + for i in range(nr): + yield {"id": i, "column_1": f"abc_{i}"} + + resource = dlt.resource( + get_resource(nr), + name="One", + write_disposition="merge", + primary_key="column_1", + merge_key=["column_1"], + ) + yield resource + + +@dlt.source +def source2(nr): + def get_resource2(nr): + for i in range(nr): + yield {"id": i, "column_2": f"xyz_{i}"} + + def get_resource3(nr): + for i in range(nr): + yield {"id": i, "column_3": f"pqr_{i}", "column_4": f"pqrr_{i}"} + + yield dlt.resource( + get_resource2(nr), + name="Two", + write_disposition="merge", + primary_key="column_2", + merge_key=["column_2"], + ) + yield dlt.resource( + get_resource3(nr), + name="Three", + write_disposition="merge", + primary_key=["column_3", "column_4"], + merge_key=["column_3"], + ) + + +def test_multiple_resources_pipeline(): + pipeline = dlt.pipeline( + pipeline_name="test_resources_pipeline", destination="duckdb", dataset_name="rows_data2" + ) + load_info = pipeline.run([source1(10), source2(20)]) + + assert load_info.pipeline.schema_names == ["source2", "source1"] + assert load_info.pipeline.schemas.get("source1").data_tables()[0]["name"] == "one" + assert load_info.pipeline.schemas.get("source1").data_tables()[0]["columns"]["column_1"].get("primary_key") is True + assert load_info.pipeline.schemas.get("source1").data_tables()[0]["columns"]["column_1"].get("merge_key") is True + assert load_info.pipeline.schemas.get("source1").data_tables()[0]["write_disposition"] == "merge" + + # The rest should be inspected using the streamlit tool. From 7f2f042815fd548a35aa202629b7ddb443b8399b Mon Sep 17 00:00:00 2001 From: Anton Burnashev Date: Tue, 7 Nov 2023 15:37:35 +0100 Subject: [PATCH 2/7] Add a missing __init__.py, fix typing errors (#736) --- dlt/common/utils.py | 2 +- dlt/helpers/streamlit_helper.py | 4 ++-- tests/helpers/streamlit_tests/__init__.py | 0 .../test_streamlit_show_resources.py | 13 ++++++++----- 4 files changed, 11 insertions(+), 8 deletions(-) create mode 100644 tests/helpers/streamlit_tests/__init__.py diff --git a/dlt/common/utils.py b/dlt/common/utils.py index 692f8452c9..0214bc037a 100644 --- a/dlt/common/utils.py +++ b/dlt/common/utils.py @@ -144,7 +144,7 @@ def flatten_list_of_str_or_dicts(seq: Sequence[Union[StrAny, str]]) -> DictStrAn # return dicts -def flatten_list_or_items(_iter: Union[Iterator[TAny], Iterator[List[TAny]]]) -> Iterator[TAny]: +def flatten_list_or_items(_iter: Union[Iterable[TAny], Iterable[List[TAny]]]) -> Iterator[TAny]: for items in _iter: if isinstance(items, List): yield from items diff --git a/dlt/helpers/streamlit_helper.py b/dlt/helpers/streamlit_helper.py index d505ccaff3..7921e4e2e1 100644 --- a/dlt/helpers/streamlit_helper.py +++ b/dlt/helpers/streamlit_helper.py @@ -1,5 +1,5 @@ import sys -from typing import Dict, List +from typing import Dict, List, Iterator import humanize from dlt.common import pendulum @@ -253,7 +253,7 @@ def _query_data(query: str, chunk_size: int = None) -> pd.DataFrame: if "write_disposition" in table: table_hints.append("write disposition: **%s**" % table["write_disposition"]) columns = table["columns"] - primary_keys = flatten_list_or_items([ + primary_keys: Iterator[str] = flatten_list_or_items([ col_name for col_name in columns.keys() if not col_name.startswith("_") and not columns[col_name].get("primary_key") is None ]) diff --git a/tests/helpers/streamlit_tests/__init__.py b/tests/helpers/streamlit_tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/helpers/streamlit_tests/test_streamlit_show_resources.py b/tests/helpers/streamlit_tests/test_streamlit_show_resources.py index c550b45654..fcf232ea76 100644 --- a/tests/helpers/streamlit_tests/test_streamlit_show_resources.py +++ b/tests/helpers/streamlit_tests/test_streamlit_show_resources.py @@ -57,10 +57,13 @@ def test_multiple_resources_pipeline(): ) load_info = pipeline.run([source1(10), source2(20)]) - assert load_info.pipeline.schema_names == ["source2", "source1"] - assert load_info.pipeline.schemas.get("source1").data_tables()[0]["name"] == "one" - assert load_info.pipeline.schemas.get("source1").data_tables()[0]["columns"]["column_1"].get("primary_key") is True - assert load_info.pipeline.schemas.get("source1").data_tables()[0]["columns"]["column_1"].get("merge_key") is True - assert load_info.pipeline.schemas.get("source1").data_tables()[0]["write_disposition"] == "merge" + source1_schema = load_info.pipeline.schemas.get("source1") # type: ignore[attr-defined] + + assert load_info.pipeline.schema_names == ["source2", "source1"] # type: ignore[attr-defined] + + assert source1_schema.data_tables()[0]["name"] == "one" + assert source1_schema.data_tables()[0]["columns"]["column_1"].get("primary_key") is True + assert source1_schema.data_tables()[0]["columns"]["column_1"].get("merge_key") is True + assert source1_schema.data_tables()[0]["write_disposition"] == "merge" # The rest should be inspected using the streamlit tool. From e92cf7554f4fc6876cc22f5a73e3b6b88dc350e1 Mon Sep 17 00:00:00 2001 From: Anush Date: Tue, 7 Nov 2023 22:04:01 +0530 Subject: [PATCH 3/7] feat: Qdrant destination support (#724) --- .github/workflows/test_destination_qdrant.yml | 82 + .gitignore | 5 +- dlt/destinations/qdrant/__init__.py | 53 + dlt/destinations/qdrant/configuration.py | 79 + dlt/destinations/qdrant/qdrant_adapter.py | 67 + dlt/destinations/qdrant/qdrant_client.py | 409 + .../docs/dlt-ecosystem/destinations/qdrant.md | 222 + docs/website/sidebars.js | 1 + poetry.lock | 7666 +++++++++-------- pyproject.toml | 4 +- tests/load/qdrant/__init__.py | 3 + tests/load/qdrant/test_pipeline.py | 348 + tests/load/qdrant/utils.py | 61 + tests/utils.py | 8 +- 14 files changed, 5230 insertions(+), 3778 deletions(-) create mode 100644 .github/workflows/test_destination_qdrant.yml create mode 100644 dlt/destinations/qdrant/__init__.py create mode 100644 dlt/destinations/qdrant/configuration.py create mode 100644 dlt/destinations/qdrant/qdrant_adapter.py create mode 100644 dlt/destinations/qdrant/qdrant_client.py create mode 100644 docs/website/docs/dlt-ecosystem/destinations/qdrant.md create mode 100644 tests/load/qdrant/__init__.py create mode 100644 tests/load/qdrant/test_pipeline.py create mode 100644 tests/load/qdrant/utils.py diff --git a/.github/workflows/test_destination_qdrant.yml b/.github/workflows/test_destination_qdrant.yml new file mode 100644 index 0000000000..09ded40f59 --- /dev/null +++ b/.github/workflows/test_destination_qdrant.yml @@ -0,0 +1,82 @@ +name: test Qdrant + +on: + pull_request: + branches: + - master + - devel + workflow_dispatch: + +env: + DESTINATION__QDRANT__CREDENTIALS__LOCATION: ${{ secrets.DESTINATION__QDRANT__CREDENTIALS__LOCATION }} + DESTINATION__QDRANT__CREDENTIALS__API_KEY: ${{ secrets.DESTINATION__QDRANT__CREDENTIALS__API_KEY }} + + RUNTIME__SENTRY_DSN: https://6f6f7b6f8e0f458a89be4187603b55fe@o1061158.ingest.sentry.io/4504819859914752 + RUNTIME__LOG_LEVEL: ERROR + + ACTIVE_DESTINATIONS: "[\"qdrant\"]" + ALL_FILESYSTEM_DRIVERS: "[\"memory\"]" + +jobs: + get_docs_changes: + uses: ./.github/workflows/get_docs_changes.yml + if: ${{ !github.event.pull_request.head.repo.fork }} + + run_loader: + name: Tests Qdrant loader + needs: get_docs_changes + if: needs.get_docs_changes.outputs.changes_outside_docs == 'true' + strategy: + fail-fast: false + matrix: + os: ["ubuntu-latest", "macos-latest", "windows-latest"] + defaults: + run: + shell: bash + runs-on: ${{ matrix.os }} + + steps: + - name: Check out + uses: actions/checkout@master + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: "3.10.x" + + - name: Install Poetry + uses: snok/install-poetry@v1.3.2 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + + - name: Load cached venv + id: cached-poetry-dependencies + uses: actions/cache@v3 + with: + path: .venv + key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}-gcp + + - name: Install dependencies + run: poetry install --no-interaction -E qdrant -E parquet + - run: | + poetry run pytest tests/load/ + if: runner.os != 'Windows' + name: Run tests Linux/MAC + - run: | + poetry run pytest tests/load/ + if: runner.os == 'Windows' + name: Run tests Windows + shell: cmd + + matrix_job_required_check: + name: Qdrant loader tests + needs: run_loader + runs-on: ubuntu-latest + if: always() + steps: + - name: Check matrix job results + if: contains(needs.*.result, 'failure') || contains(needs.*.result, 'cancelled') + run: | + echo "One or more matrix job tests failed or were cancelled. You may need to re-run them." && exit 1 diff --git a/.gitignore b/.gitignore index 3604d9b1bf..f26ea23d91 100644 --- a/.gitignore +++ b/.gitignore @@ -138,4 +138,7 @@ logs/ # temp tmp -**/tmp \ No newline at end of file +**/tmp + +# Qdrant embedding models cache +local_cache/ \ No newline at end of file diff --git a/dlt/destinations/qdrant/__init__.py b/dlt/destinations/qdrant/__init__.py new file mode 100644 index 0000000000..7a8619ffcd --- /dev/null +++ b/dlt/destinations/qdrant/__init__.py @@ -0,0 +1,53 @@ +from typing import Type + +from dlt.common.schema.schema import Schema +from dlt.common.configuration import with_config, known_sections +from dlt.common.configuration.accessors import config +from dlt.common.destination.reference import ( + JobClientBase, + DestinationClientConfiguration, +) +from dlt.common.destination import DestinationCapabilitiesContext +from dlt.destinations.qdrant.qdrant_adapter import qdrant_adapter + +from dlt.destinations.qdrant.configuration import QdrantClientConfiguration + + +@with_config( + spec=QdrantClientConfiguration, + sections=( + known_sections.DESTINATION, + "qdrant", + ), +) +def _configure( + config: QdrantClientConfiguration = config.value, +) -> QdrantClientConfiguration: + return config + + +def capabilities() -> DestinationCapabilitiesContext: + caps = DestinationCapabilitiesContext() + caps.preferred_loader_file_format = "jsonl" + caps.supported_loader_file_formats = ["jsonl"] + + caps.max_identifier_length = 200 + caps.max_column_identifier_length = 1024 + caps.max_query_length = 8 * 1024 * 1024 + caps.is_max_query_length_in_bytes = False + caps.max_text_data_type_length = 8 * 1024 * 1024 + caps.is_max_text_data_type_length_in_bytes = False + caps.supports_ddl_transactions = False + + return caps + + +def client( + schema: Schema, initial_config: DestinationClientConfiguration = config.value +) -> JobClientBase: + from dlt.destinations.qdrant.qdrant_client import QdrantClient + return QdrantClient(schema, _configure(initial_config)) # type: ignore + + +def spec() -> Type[QdrantClientConfiguration]: + return QdrantClientConfiguration diff --git a/dlt/destinations/qdrant/configuration.py b/dlt/destinations/qdrant/configuration.py new file mode 100644 index 0000000000..dc252e3b31 --- /dev/null +++ b/dlt/destinations/qdrant/configuration.py @@ -0,0 +1,79 @@ +from typing import Optional, Final + +from dlt.common.configuration import configspec +from dlt.common.configuration.specs.base_configuration import BaseConfiguration, CredentialsConfiguration +from dlt.common.destination.reference import DestinationClientDwhConfiguration + + +@configspec +class QdrantCredentials(CredentialsConfiguration): + # If `:memory:` - use in-memory Qdrant instance. + # If `str` - use it as a `url` parameter. + # If `None` - use default values for `host` and `port` + location: Optional[str] = None + # API key for authentication in Qdrant Cloud. Default: `None` + api_key: Optional[str] + + def __str__(self) -> str: + return self.location or "localhost" + + +@configspec +class QdrantClientOptions(BaseConfiguration): + # Port of the REST API interface. Default: 6333 + port: int = 6333 + # Port of the gRPC interface. Default: 6334 + grpc_port: int = 6334 + # If `true` - use gPRC interface whenever possible in custom methods + prefer_grpc: bool = False + # If `true` - use HTTPS(SSL) protocol. Default: `None` + https: bool = False + # If not `None` - add `prefix` to the REST URL path. + # Example: `service/v1` will result in `http://localhost:6333/service/v1/{qdrant-endpoint}` for REST API. + # Default: `None` + prefix: Optional[str] = None + # Timeout for REST and gRPC API requests. + # Default: 5.0 seconds for REST and unlimited for gRPC + timeout: Optional[int] = None + # Host name of Qdrant service. If url and host are None, set to 'localhost'. + # Default: `None` + host: Optional[str] = None + # Persistence path for QdrantLocal. Default: `None` + path: Optional[str] = None + + +@configspec +class QdrantClientConfiguration(DestinationClientDwhConfiguration): + destination_name: Final[str] = "qdrant" # type: ignore + # character for the dataset separator + dataset_separator: str = "_" + + # make it optional do empty dataset is allowed + dataset_name: Optional[str] = None # type: ignore + + # Batch size for generating embeddings + embedding_batch_size: int = 32 + # Number of parallel processes for generating embeddings + embedding_parallelism: int = 0 + + # Batch size for uploading embeddings + upload_batch_size: int = 64 + # Number of parallel processes for uploading embeddings + upload_parallelism: int = 1 + # Number of retries for uploading embeddings + upload_max_retries: int = 3 + + # Qdrant client options + options: QdrantClientOptions + + # Qdrant connection credentials + credentials: QdrantCredentials + + # FlagEmbedding model to use + # Find the list here. https://qdrant.github.io/fastembed/examples/Supported_Models/. + model: str = "BAAI/bge-small-en" + + def fingerprint(self) -> str: + """Returns a fingerprint of a connection string""" + + return self.credentials.location diff --git a/dlt/destinations/qdrant/qdrant_adapter.py b/dlt/destinations/qdrant/qdrant_adapter.py new file mode 100644 index 0000000000..ac51bd5f42 --- /dev/null +++ b/dlt/destinations/qdrant/qdrant_adapter.py @@ -0,0 +1,67 @@ +from typing import Any + +from dlt.common.schema.typing import TColumnNames, TTableSchemaColumns +from dlt.extract.decorators import resource as make_resource +from dlt.extract.source import DltResource + +VECTORIZE_HINT = "x-qdrant-embed" + +def qdrant_adapter( + data: Any, + embed: TColumnNames = None, +) -> DltResource: + """Prepares data for the Qdrant destination by specifying which columns + should be embedded. + + Args: + data (Any): The data to be transformed. It can be raw data or an instance + of DltResource. If raw data, the function wraps it into a DltResource + object. + embed (TColumnNames, optional): Specifies columns to generate embeddings for. + Can be a single column name as a string or a list of column names. + + Returns: + DltResource: A resource with applied qdrant-specific hints. + + Raises: + ValueError: If input for `embed` invalid or empty. + + Examples: + >>> data = [{"name": "Anush", "description": "Integrations Hacker"}] + >>> qdrant_adapter(data, embed="description") + [DltResource with hints applied] + """ + # wrap `data` in a resource if not an instance already + resource: DltResource + if not isinstance(data, DltResource): + resource_name: str = None + if not hasattr(data, "__name__"): + resource_name = "content" + resource = make_resource(data, name=resource_name) + else: + resource = data + + column_hints: TTableSchemaColumns = {} + + if embed: + if isinstance(embed, str): + embed = [embed] + if not isinstance(embed, list): + raise ValueError( + "embed must be a list of column names or a single " + "column name as a string" + ) + + for column_name in embed: + column_hints[column_name] = { + "name": column_name, + VECTORIZE_HINT: True, # type: ignore + } + + if not column_hints: + raise ValueError( + "A value for 'embed' must be specified.") + else: + resource.apply_hints(columns=column_hints) + + return resource diff --git a/dlt/destinations/qdrant/qdrant_client.py b/dlt/destinations/qdrant/qdrant_client.py new file mode 100644 index 0000000000..cba87e9528 --- /dev/null +++ b/dlt/destinations/qdrant/qdrant_client.py @@ -0,0 +1,409 @@ +from types import TracebackType +from typing import ClassVar, Optional, Sequence, List, Dict, Type, Iterable, Any, IO + +from dlt.common import json, pendulum, logger +from dlt.common.schema import Schema, TTableSchema, TSchemaTables +from dlt.common.schema.utils import get_columns_names_with_prop +from dlt.common.destination import DestinationCapabilitiesContext +from dlt.common.destination.reference import TLoadJobState, LoadJob, JobClientBase, WithStateSync +from dlt.common.storages import FileStorage + +from dlt.destinations.job_impl import EmptyLoadJob +from dlt.destinations.job_client_impl import StorageSchemaInfo, StateInfo + +from dlt.destinations.qdrant import capabilities +from dlt.destinations.qdrant.configuration import QdrantClientConfiguration +from dlt.destinations.qdrant.qdrant_adapter import VECTORIZE_HINT + +from qdrant_client import QdrantClient as QC, models +from qdrant_client.qdrant_fastembed import uuid +from qdrant_client.http.exceptions import UnexpectedResponse + +class LoadQdrantJob(LoadJob): + def __init__( + self, + table_schema: TTableSchema, + local_path: str, + db_client: QC, + client_config: QdrantClientConfiguration, + collection_name: str, + ) -> None: + file_name = FileStorage.get_file_name_from_file_path(local_path) + super().__init__(file_name) + self.db_client = db_client + self.collection_name = collection_name + self.embedding_fields = get_columns_names_with_prop( + table_schema, VECTORIZE_HINT) + self.unique_identifiers = self._list_unique_identifiers(table_schema) + self.config = client_config + + with FileStorage.open_zipsafe_ro(local_path) as f: + docs, payloads, ids = [], [], [] + + for line in f: + data = json.loads(line) + point_id = self._generate_uuid( + data, self.unique_identifiers, self.collection_name) if self.unique_identifiers else uuid.uuid4() + embedding_doc = self._get_embedding_doc(data) + payloads.append(data) + ids.append(point_id) + docs.append(embedding_doc) + + embedding_model = db_client._get_or_init_model( + db_client.embedding_model_name) + embeddings = list(embedding_model.embed( + docs, batch_size=self.config.embedding_batch_size, parallel=self.config.embedding_parallelism)) + vector_name = db_client.get_vector_field_name() + embeddings = [{vector_name: embedding.tolist()} for embedding in embeddings] + assert len(embeddings) == len(payloads) == len(ids) + + self._upload_data(vectors=embeddings, ids=ids, payloads=payloads) + + def _get_embedding_doc(self, data: Dict[str, Any]) -> str: + """Returns a document to generate embeddings for. + + Args: + data (Dict[str, Any]): A dictionary of data to be loaded. + + Returns: + str: A concatenated string of all the fields intended for embedding. + """ + doc = "\n".join(str(data[key]) for key in self.embedding_fields) + return doc + + def _list_unique_identifiers(self, table_schema: TTableSchema) -> Sequence[str]: + """Returns a list of unique identifiers for a table. + + Args: + table_schema (TTableSchema): a dlt table schema. + + Returns: + Sequence[str]: A list of unique column identifiers. + """ + if table_schema.get("write_disposition") == "merge": + primary_keys = get_columns_names_with_prop( + table_schema, "primary_key") + if primary_keys: + return primary_keys + return get_columns_names_with_prop(table_schema, "unique") + + def _upload_data(self, ids: Iterable[Any], vectors: Iterable[Any], payloads: Iterable[Any]) -> None: + """Uploads data to a Qdrant instance in a batch. Supports retries and parallelism. + + Args: + ids (Iterable[Any]): Point IDs to be uploaded to the collection + vectors (Iterable[Any]): Embeddings to be uploaded to the collection + payloads (Iterable[Any]): Payloads to be uploaded to the collection + """ + self.db_client.upload_collection( + self.collection_name, ids=ids, payload=payloads, vectors=vectors, parallel=self.config.upload_parallelism, batch_size=self.config.upload_batch_size, max_retries=self.config.upload_max_retries) + + def _generate_uuid( + self, data: Dict[str, Any], unique_identifiers: Sequence[str], collection_name: str + ) -> str: + """Generates deterministic UUID. Used for deduplication. + + Args: + data (Dict[str, Any]): Arbitrary data to generate UUID for. + unique_identifiers (Sequence[str]): A list of unique identifiers. + collection_name (str): Qdrant collection name. + + Returns: + str: A string representation of the genrated UUID + """ + data_id = "_".join(str(data[key]) for key in unique_identifiers) + return str(uuid.uuid5(uuid.NAMESPACE_DNS, collection_name + data_id)) + + def state(self) -> TLoadJobState: + return "completed" + + def exception(self) -> str: + raise NotImplementedError() + +class QdrantClient(JobClientBase, WithStateSync): + """Qdrant Destination Handler + """ + capabilities: ClassVar[DestinationCapabilitiesContext] = capabilities() + state_properties: ClassVar[List[str]] = [ + "version", "engine_version", "pipeline_name", "state", "created_at", "_dlt_load_id"] + + def __init__(self, schema: Schema, config: QdrantClientConfiguration) -> None: + super().__init__(schema, config) + self.config: QdrantClientConfiguration = config + self.db_client: QC = QdrantClient._create_db_client(config) + self.model = config.model + + @property + def dataset_name(self) -> str: + return self.config.normalize_dataset_name(self.schema) + + @property + def sentinel_collection(self) -> str: + return self.dataset_name or "DltSentinelCollection" + + @staticmethod + def _create_db_client(config: QdrantClientConfiguration) -> QC: + """Generates a Qdrant client from the 'qdrant_client' package. + + Args: + config (QdrantClientConfiguration): Credentials and options for the Qdrant client. + + Returns: + QdrantClient: A Qdrant client instance. + """ + credentials = dict(config.credentials) + options = dict(config.options) + client = QC(**credentials, **options) + client.set_model(config.model) + return client + + def _make_qualified_collection_name(self, table_name: str) -> str: + """Generates a qualified collection name. + + Args: + table_name (str): Name of the table. + + Returns: + str: The dataset name and table name concatenated with a separator if dataset name is present. + """ + dataset_separator = self.config.dataset_separator + return f"{self.dataset_name}{dataset_separator}{table_name}" if self.dataset_name else table_name + + def _create_collection( + self, full_collection_name: str + ) -> None: + """Creates a collection in Qdrant. + + Args: + full_collection_name (str): The name of the collection to be created. + """ + + # Generates config for a named vector according to the selected model. + # Eg: vector_config={ + # "fast-bge-small-en": { + # "size": 364, + # "distance": "Cosine" + # }, + # } + vectors_config = self.db_client.get_fastembed_vector_params() + + self.db_client.create_collection( + collection_name=full_collection_name, vectors_config=vectors_config) + + def _create_point(self, obj: Dict[str, Any], collection_name: str) -> None: + """Inserts a point into a Qdrant collection without a vector. + + Args: + obj (Dict[str, Any]): The arbitrary data to be inserted as payload. + collection_name (str): The name of the collection to insert the point into. + """ + self.db_client.upsert(collection_name, points=[ + models.PointStruct( + id=str(uuid.uuid4()), + payload=obj, + vector={}, + )]) + + def drop_storage(self) -> None: + """Drop the dataset from the Qdrant instance. + + Deletes all collections in the dataset and all data associated. + Deletes the sentinel collection. + + If dataset name was not provided, it deletes all the tables in the current schema + """ + collections = self.db_client.get_collections().collections + collection_name_list = [collection.name + for collection in collections] + + if self.dataset_name: + prefix = f"{self.dataset_name}{self.config.dataset_separator}" + + for collection_name in collection_name_list: + if collection_name.startswith(prefix): + self.db_client.delete_collection(collection_name) + else: + for collection_name in self.schema.tables.keys(): + if collection_name in collection_name_list: + self.db_client.delete_collection(collection_name) + + self._delete_sentinel_collection() + + def initialize_storage(self, truncate_tables: Iterable[str] = None) -> None: + if not self.is_storage_initialized(): + self._create_sentinel_collection() + elif truncate_tables: + for table_name in truncate_tables: + qualified_table_name = self._make_qualified_collection_name( + table_name=table_name) + if self._collection_exists(qualified_table_name): + continue + + self.db_client.delete_collection(qualified_table_name) + self._create_collection(full_collection_name=qualified_table_name) + + def is_storage_initialized(self) -> bool: + return self._collection_exists(self.sentinel_collection, qualify_table_name=False) + + def _create_sentinel_collection(self) -> None: + """Create an empty collection to indicate that the storage is initialized.""" + self._create_collection(full_collection_name=self.sentinel_collection) + + def _delete_sentinel_collection(self) -> None: + """Delete the sentinel collection.""" + self.db_client.delete_collection(self.sentinel_collection) + + def update_stored_schema( + self, only_tables: Iterable[str] = None, expected_update: TSchemaTables = None + ) -> Optional[TSchemaTables]: + applied_update: TSchemaTables = {} + schema_info = self.get_stored_schema_by_hash( + self.schema.stored_version_hash) + if schema_info is None: + logger.info( + f"Schema with hash {self.schema.stored_version_hash} " + f"not found in the storage. upgrading" + ) + self._execute_schema_update(only_tables) + else: + logger.info( + f"Schema with hash {self.schema.stored_version_hash} " + f"inserted at {schema_info.inserted_at} found " + f"in storage, no upgrade required" + ) + return applied_update + + def get_stored_state(self, pipeline_name: str) -> Optional[StateInfo]: + """Loads compressed state from destination storage + By finding a load id that was completed + """ + limit = 10 + offset = None + while True: + try: + scroll_table_name = self._make_qualified_collection_name( + self.schema.state_table_name) + state_records, offset = self.db_client.scroll(scroll_table_name, with_payload=self.state_properties, scroll_filter=models.Filter(must=[ + models.FieldCondition( + key="pipeline_name", match=models.MatchValue(value=pipeline_name)) + ]), limit=limit, offset=offset) + if len(state_records) == 0: + return None + for state_record in state_records: + state = state_record.payload + load_id = state["_dlt_load_id"] + scroll_table_name = self._make_qualified_collection_name( + self.schema.loads_table_name) + load_records = self.db_client.count(scroll_table_name, exact=True, count_filter=models.Filter( + must=[models.FieldCondition( + key="load_id", match=models.MatchValue(value=load_id) + )] + )) + if load_records.count > 0: + state["dlt_load_id"] = state.pop("_dlt_load_id") + return StateInfo(**state) + except Exception: + return None + + def get_stored_schema(self) -> Optional[StorageSchemaInfo]: + """Retrieves newest schema from destination storage""" + try: + scroll_table_name = self._make_qualified_collection_name( + self.schema.version_table_name) + response = self.db_client.scroll(scroll_table_name, with_payload=True, scroll_filter=models.Filter( + must=[models.FieldCondition( + key="schema_name", + match=models.MatchValue(value=self.schema.name), + )] + ), limit=1) + record = response[0][0].payload + return StorageSchemaInfo(**record) + except Exception: + return None + + def get_stored_schema_by_hash(self, schema_hash: str) -> Optional[StorageSchemaInfo]: + try: + scroll_table_name = self._make_qualified_collection_name( + self.schema.version_table_name) + response = self.db_client.scroll(scroll_table_name, with_payload=True, scroll_filter=models.Filter( + must=[ + models.FieldCondition( + key="version_hash", match=models.MatchValue(value=schema_hash)) + ] + + ), limit=1) + record = response[0][0].payload + return StorageSchemaInfo(**record) + except Exception: + return None + + def start_file_load( + self, table: TTableSchema, file_path: str, load_id: str + ) -> LoadJob: + return LoadQdrantJob( + table, + file_path, + db_client=self.db_client, + client_config=self.config, + collection_name=self._make_qualified_collection_name(table["name"]), + ) + + def restore_file_load(self, file_path: str) -> LoadJob: + return EmptyLoadJob.from_file_path(file_path, "completed") + + def complete_load(self, load_id: str) -> None: + properties = { + "load_id": load_id, + "schema_name": self.schema.name, + "status": 0, + "inserted_at": str(pendulum.now()), + } + loads_table_name = self._make_qualified_collection_name( + self.schema.loads_table_name) + self._create_point(properties, loads_table_name) + + def __enter__(self) -> "QdrantClient": + return self + + def __exit__( + self, + exc_type: Type[BaseException], + exc_val: BaseException, + exc_tb: TracebackType, + ) -> None: + pass + + def _update_schema_in_storage(self, schema: Schema) -> None: + schema_str = json.dumps(schema.to_dict()) + properties = { + "version_hash": schema.stored_version_hash, + "schema_name": schema.name, + "version": schema.version, + "engine_version": schema.ENGINE_VERSION, + "inserted_at": str(pendulum.now()), + "schema": schema_str, + } + version_table_name = self._make_qualified_collection_name( + self.schema.version_table_name) + self._create_point(properties, version_table_name) + + def _execute_schema_update(self, only_tables: Iterable[str]) -> None: + for table_name in only_tables or self.schema.tables: + exists = self._collection_exists(table_name) + + if not exists: + self._create_collection( + full_collection_name=self._make_qualified_collection_name(table_name) + ) + self._update_schema_in_storage(self.schema) + + def _collection_exists(self, table_name: str, qualify_table_name: bool = True) -> bool: + try: + table_name = self._make_qualified_collection_name( + table_name) if qualify_table_name else table_name + self.db_client.get_collection(table_name) + return True + except UnexpectedResponse as e: + if e.status_code == 404: + return False + raise e \ No newline at end of file diff --git a/docs/website/docs/dlt-ecosystem/destinations/qdrant.md b/docs/website/docs/dlt-ecosystem/destinations/qdrant.md new file mode 100644 index 0000000000..519375cdac --- /dev/null +++ b/docs/website/docs/dlt-ecosystem/destinations/qdrant.md @@ -0,0 +1,222 @@ +--- +title: Qdrant +description: Qdrant is a high-performance vector search engine/database that can be used as a destination in dlt. +keywords: [qdrant, vector database, destination, dlt] +--- + +# Qdrant + +[Qdrant](https://qdrant.tech/) is an open-source, high-performance vector search engine/database. It deploys as an API service, providing a search for the nearest high-dimensional vectors. +This destination helps you load data into Qdrant from [dlt resources](../../general-usage/resource.md). + +## Setup Guide + +1. To use Qdrant as a destination, make sure `dlt` is installed with the `qdrant` extra: + +```bash +pip install dlt[qdrant] +``` + +2. Next, configure the destination in the dlt secrets file. The file is located at `~/.dlt/secrets.toml` by default. Add the following section to the secrets file: + +```toml +[destination.qdrant.credentials] +location = "https://your-qdrant-url" +api_key = "your-qdrant-api-key" +``` + +In this setup guide, we are using the [Qdrant Cloud](https://cloud.qdrant.io/) to get a hosted Qdrant instance and the [FastEmbed](https://github.com/qdrant/fastembed) package that is built into the [Qdrant client library](https://github.com/qdrant/qdrant-client) for generating embeddings. + +If no configuration options are provided, the default fallback will be `http://localhost:6333` with no API key. + +3. Define the source of the data. For starters, let's load some data from a simple data structure: + +```python +import dlt +from dlt.destinations.qdrant import qdrant_adapter + +movies = [ + { + "title": "Blade Runner", + "year": 1982, + }, + { + "title": "Ghost in the Shell", + "year": 1995, + }, + { + "title": "The Matrix", + "year": 1999, + } +] +``` + +4. Define the pipeline: + +```python +pipeline = dlt.pipeline( + pipeline_name="movies", + destination="qdrant", + dataset_name="MoviesDataset", +) +``` + +5. Run the pipeline: + +```python +info = pipeline.run( + qdrant_adapter( + movies, + embed="title", + ) +) +``` + +6. Check the results: + +```python +print(info) +``` + +The data is now loaded into Qdrant. + +To use vector search after the data has been loaded, you must specify which fields Qdrant needs to generate embeddings for. You do that by wrapping the data (or dlt resource) with the `qdrant_adapter` function. + +## qdrant_adapter + +The `qdrant_adapter` is a helper function that configures the resource for the Qdrant destination: + +```python +qdrant_adapter(data, embed) +``` + +It accepts the following arguments: + +- `data`: a dlt resource object or a Python data structure (e.g. a list of dictionaries). +- `embed`: a name of the field or a list of names to generate embeddings for. + +Returns: [DLT resource](../../general-usage/resource.md) object that you can pass to the `pipeline.run()`. + +Example: + +```python +qdrant_adapter( + resource, + embed=["title", "description"], +) +``` + +:::tip + +A more comprehensive pipeline would load data from some API or use one of dlt's [verified sources](../verified-sources/). + +::: + +## Write disposition + +A [write disposition](../../general-usage/incremental-loading.md#choosing-a-write-disposition) defines how the data should be written to the destination. All write dispositions are supported by the Qdrant destination. + +### Replace + +The [replace](../../general-usage/full-loading.md) disposition replaces the data in the destination with the data from the resource. It deletes all the classes and objects and recreates the schema before loading the data. + +In the movie example from the [setup guide](#setup-guide), we can use the `replace` disposition to reload the data every time we run the pipeline: + +```python +info = pipeline.run( + qdrant_adapter( + movies, + embed="title", + ), + write_disposition="replace", +) +``` + +### Merge + +The [merge](../../general-usage/incremental-loading.md) write disposition merges the data from the resource with the data at the destination. +For `merge` disposition, you would need to specify a `primary_key` for the resource: + +```python +info = pipeline.run( + qdrant_adapter( + movies, + embed="title", + ), + primary_key="document_id", + write_disposition="merge" +) +``` + +Internally, dlt will use `primary_key` (`document_id` in the example above) to generate a unique identifier (UUID) for each point in Qdrant. If the object with the same UUID already exists in Qdrant, it will be updated with the new data. Otherwise, a new point will be created. + +:::caution + +If you are using the merge write disposition, you must set it from the first run of your pipeline; otherwise, the data will be duplicated in the database on subsequent loads. + +::: + +### Append + +This is the default disposition. It will append the data to the existing data in the destination, ignoring the `primary_key` field. + +## Dataset name + +Qdrant uses collections to categorize and identify data. To avoid potential naming conflicts, especially when dealing with multiple datasets that might have overlapping table names, dlt includes the dataset name in the Qdrant collection name. This ensures a unique identifier for every collection. + +For example, if you have a dataset named `movies_dataset` and a table named `actors`, the Qdrant collection name would be `movies_dataset_actors` (the default separator is an underscore). + +However, if you prefer to have class names without the dataset prefix, skip `dataset_name` argument. + +For example: + +```python +pipeline = dlt.pipeline( + pipeline_name="movies", + destination="qdrant", +) +``` + +## Additional destination options + +- `embedding_batch_size`: (int) The batch size for embedding operations. The default value is 32. + +- `embedding_parallelism`: (int) The number of concurrent threads to run embedding operations. Defaults to the number of CPU cores. + +- `upload_batch_size`: (int) The batch size for data uploads. The default value is 64. + +- `upload_parallelism`: (int) The maximal number of concurrent threads to run data uploads. The default value is 1. + +- `upload_max_retries`: (int) The number of retries to upload data in case of failure. The default value is 3. + +- `options`: ([QdrantClientOptions](#qdrant-client-options)) An instance of the `QdrantClientOptions` class that holds various Qdrant client options. + +- `model`: (str) The name of the FlagEmbedding model to use. See the list of supported models at [Supported Models](https://qdrant.github.io/fastembed/examples/Supported_Models/). The default value is "BAAI/bge-small-en". + +### [Qdrant Client Options](#qdrant-client-options) + +The `QdrantClientOptions` class provides options for configuring the Qdrant client. + +- `port`: (int) The port of the REST API interface. The default value is 6333. + +- `grpc_port`: (int) The port of the gRPC interface. The default value is 6334. + +- `prefer_grpc`: (bool) If `true`, the client will prefer to use the gRPC interface whenever possible in custom methods. The default value is `false`. + +- `https`: (bool) If `true`, the client will use the HTTPS (SSL) protocol. The default value is `true` if an API Key is provided, else `false`. + +- `prefix`: (str) If set, it adds the specified `prefix` to the REST URL path. For example, setting it to "service/v1" will result in the REST API URL as `http://localhost:6333/service/v1/{qdrant-endpoint}`. Not set by default. + +- `timeout`: (int) The timeout for REST and gRPC API requests. The default value is 5.0 seconds for REST and unlimited for gRPC. + +- `host`: (str) The host name of the Qdrant service. If both the URL and host are `None`, it is set to `localhost`. + +- `path`: (str) The persistence path for a local Qdrant instance. Not set by default. + +### Run Qdrant locally + +You can find the setup instructions to run Qdrant [here](https://qdrant.tech/documentation/quick-start/#download-and-run) + +### Syncing of `dlt` state + +Qdrant destination supports syncing of the `dlt` state. diff --git a/docs/website/sidebars.js b/docs/website/sidebars.js index 106162c50e..69fe9d2be6 100644 --- a/docs/website/sidebars.js +++ b/docs/website/sidebars.js @@ -81,6 +81,7 @@ const sidebars = { 'dlt-ecosystem/destinations/athena', 'dlt-ecosystem/destinations/motherduck', 'dlt-ecosystem/destinations/weaviate', + 'dlt-ecosystem/destinations/qdrant', ] }, ], diff --git a/poetry.lock b/poetry.lock index 15c257b607..72c24349c6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,18 +1,26 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + [[package]] name = "about-time" version = "4.2.1" description = "Easily measure timing and throughput of code blocks, with beautiful human friendly representations." -category = "dev" optional = false python-versions = ">=3.7, <4" +files = [ + {file = "about-time-4.2.1.tar.gz", hash = "sha256:6a538862d33ce67d997429d14998310e1dbfda6cb7d9bbfbf799c4709847fece"}, + {file = "about_time-4.2.1-py3-none-any.whl", hash = "sha256:8bbf4c75fe13cbd3d72f49a03b02c5c7dca32169b6d49117c257e7eb3eaee341"}, +] [[package]] name = "adlfs" version = "2023.8.0" description = "Access Azure Datalake Gen1 with fsspec and dask" -category = "main" optional = true python-versions = ">=3.8" +files = [ + {file = "adlfs-2023.8.0-py3-none-any.whl", hash = "sha256:3eb248a3c2a30b419f1147bd7676d156b5219f96ef7f11d47166afd2a3bdb07e"}, + {file = "adlfs-2023.8.0.tar.gz", hash = "sha256:07e804f6df4593acfcaf01025b162e30ac13e523d3570279c98b2d91a18026d9"}, +] [package.dependencies] aiohttp = ">=3.7.0" @@ -29,9 +37,12 @@ docs = ["furo", "myst-parser", "numpydoc", "sphinx"] name = "agate" version = "1.6.3" description = "A data analysis library that is optimized for humans 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Add analytics to your Python and Django apps, webapps and games" -category = "main" optional = true python-versions = "*" +files = [ + {file = "minimal-snowplow-tracker-0.0.2.tar.gz", hash = "sha256:acabf7572db0e7f5cbf6983d495eef54081f71be392330eb3aadb9ccb39daaa4"}, +] [package.dependencies] requests = ">=2.2.1,<3.0" @@ -2961,17 +4474,40 @@ six = ">=1.9.0,<2.0" name = "more-itertools" version = "10.1.0" description = "More routines for operating on iterables, beyond itertools" -category = "main" optional = true python-versions = ">=3.8" +files = [ + {file = "more-itertools-10.1.0.tar.gz", hash = "sha256:626c369fa0eb37bac0291bce8259b332fd59ac792fa5497b59837309cd5b114a"}, + {file = "more_itertools-10.1.0-py3-none-any.whl", hash = "sha256:64e0735fcfdc6f3464ea133afe8ea4483b1c5fe3a3d69852e6503b43a0b222e6"}, +] + +[[package]] +name = "mpmath" +version = "1.3.0" +description = "Python library for arbitrary-precision floating-point arithmetic" +optional = true +python-versions = "*" +files = [ + {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, + {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, +] + +[package.extras] +develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] +docs = ["sphinx"] +gmpy = ["gmpy2 (>=2.1.0a4)"] +tests = ["pytest (>=4.6)"] [[package]] name = "msal" version = "1.23.0" description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect." -category = "main" optional = true python-versions = "*" +files = [ + {file = "msal-1.23.0-py2.py3-none-any.whl", hash = "sha256:3342e0837a047007f9d479e814b559c3219767453d57920dc40a31986862048b"}, + {file = "msal-1.23.0.tar.gz", hash = "sha256:25c9a33acf84301f93d1fdbe9f1a9c60cd38af0d5fffdbfa378138fc7bc1e86b"}, +] [package.dependencies] cryptography = ">=0.6,<44" @@ -2985,9 +4521,12 @@ broker = ["pymsalruntime (>=0.13.2,<0.14)"] name = "msal-extensions" version = "1.0.0" description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism." -category = "main" optional = true python-versions = "*" +files = [ + {file = "msal-extensions-1.0.0.tar.gz", hash = "sha256:c676aba56b0cce3783de1b5c5ecfe828db998167875126ca4b47dc6436451354"}, + {file = "msal_extensions-1.0.0-py2.py3-none-any.whl", hash = "sha256:91e3db9620b822d0ed2b4d1850056a0f133cba04455e62f11612e40f5502f2ee"}, +] [package.dependencies] msal = ">=0.4.1,<2.0.0" @@ -3000,3348 +4539,9 @@ portalocker = [ name = "msgpack" version = "1.0.5" description = "MessagePack serializer" -category = "main" optional = true python-versions = "*" - -[[package]] -name = "multidict" -version = "6.0.4" -description = "multidict implementation" -category = "main" -optional = false -python-versions = ">=3.7" - -[[package]] -name = "mypy" -version = "1.6.1" -description = "Optional static typing for Python" -category = "dev" -optional = false -python-versions = ">=3.8" - -[package.dependencies] -mypy-extensions = ">=1.0.0" -tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} -typing-extensions = ">=4.1.0" - 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"zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"}, {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"}, ] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[extras] +athena = ["botocore", "pyarrow", "pyathena", "s3fs"] +az = ["adlfs"] +bigquery = ["gcsfs", "google-cloud-bigquery", "grpcio", "pyarrow"] +cli = ["cron-descriptor", "pipdeptree"] +dbt = ["dbt-athena-community", "dbt-bigquery", "dbt-core", "dbt-duckdb", "dbt-redshift", "dbt-snowflake"] +duckdb = ["duckdb"] +filesystem = ["botocore", "s3fs"] +gcp = ["gcsfs", "google-cloud-bigquery", "grpcio"] +gs = ["gcsfs"] +motherduck = ["duckdb", "pyarrow"] +mssql = ["pyodbc"] +parquet = ["pyarrow"] +postgres = ["psycopg2-binary", "psycopg2cffi"] +pydantic = ["pydantic"] +qdrant = ["qdrant-client"] +redshift = ["psycopg2-binary", "psycopg2cffi"] +s3 = ["botocore", "s3fs"] +snowflake = ["snowflake-connector-python"] +weaviate = ["weaviate-client"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<3.13" +content-hash = "2686fa0fe1e5f2e796b472f4be28af52e65b5d6bb0bcbbd8df784fd6bc63cf80" diff --git a/pyproject.toml b/pyproject.toml index 3795f0096b..dcfa51172b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ packages = [ ] [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.8.1,<3.13" requests = ">=2.26.0" pendulum = ">=2.1.2" simplejson = ">=3.17.5" @@ -76,6 +76,7 @@ weaviate-client = {version = ">=3.22", optional = true} pydantic = {version = ">=1.10,<2.0", optional = true} adlfs = {version = ">=2022.4.0", optional = true} pyodbc = {version = "^4.0.39", optional = true} +qdrant-client = {version = "^1.6.4", optional = true, extras = ["fastembed"]} [tool.poetry.extras] @@ -98,6 +99,7 @@ athena = ["pyathena", "pyarrow", "s3fs", "botocore"] weaviate = ["weaviate-client"] pydantic = ["pydantic"] mssql = ["pyodbc"] +qdrant = ["qdrant-client"] [tool.poetry.scripts] dlt = "dlt.cli._dlt:_main" diff --git a/tests/load/qdrant/__init__.py b/tests/load/qdrant/__init__.py new file mode 100644 index 0000000000..ca034e3c52 --- /dev/null +++ b/tests/load/qdrant/__init__.py @@ -0,0 +1,3 @@ +from tests.utils import skip_if_not_active + +skip_if_not_active("qdrant") diff --git a/tests/load/qdrant/test_pipeline.py b/tests/load/qdrant/test_pipeline.py new file mode 100644 index 0000000000..303a5de69f --- /dev/null +++ b/tests/load/qdrant/test_pipeline.py @@ -0,0 +1,348 @@ +import pytest +from typing import Iterator + +import dlt +from dlt.common import json +from dlt.common.utils import uniq_id + +from dlt.destinations.qdrant.qdrant_adapter import qdrant_adapter, VECTORIZE_HINT +from dlt.destinations.qdrant.qdrant_client import QdrantClient +from tests.pipeline.utils import assert_load_info +from tests.load.qdrant.utils import drop_active_pipeline_data, assert_collection + +@pytest.fixture(autouse=True) +def drop_qdrant_data() -> Iterator[None]: + yield + drop_active_pipeline_data() + + +def sequence_generator(): + count = 1 + while True: + yield [{"content": str(count + i)} for i in range(3)] + count += 3 + + +def test_adapter_and_hints() -> None: + generator_instance1 = sequence_generator() + + @dlt.resource(columns=[{"name": "content", "data_type": "text"}]) + def some_data(): + yield from next(generator_instance1) + + assert some_data.columns["content"] == {"name": "content", "data_type": "text"} # type: ignore[index] + + qdrant_adapter( + some_data, + embed=["content"], + ) + + assert some_data.columns["content"] == {"name": "content", "data_type": "text", "x-qdrant-embed": True} # type: ignore[index] + + +def test_basic_state_and_schema() -> None: + generator_instance1 = sequence_generator() + + @dlt.resource + def some_data(): + yield from next(generator_instance1) + + qdrant_adapter( + some_data, + embed=["content"], + ) + + pipeline = dlt.pipeline( + pipeline_name="test_pipeline_append", + destination="qdrant", + dataset_name="test_pipeline_append_dataset" + uniq_id(), + ) + info = pipeline.run( + some_data(), + ) + assert_load_info(info) + + client: QdrantClient + with pipeline.destination_client() as client: # type: ignore[assignment] + # check if we can get a stored schema and state + schema = client.get_stored_schema() + print("Print dataset name", client.dataset_name) + assert schema + state = client.get_stored_state("test_pipeline_append") + assert state + + +def test_pipeline_append() -> None: + generator_instance1 = sequence_generator() + generator_instance2 = sequence_generator() + + @dlt.resource + def some_data(): + yield from next(generator_instance1) + + qdrant_adapter( + some_data, + embed=["content"], + ) + + pipeline = dlt.pipeline( + pipeline_name="test_pipeline_append", + destination="qdrant", + dataset_name="TestPipelineAppendDataset" + uniq_id(), + ) + info = pipeline.run( + some_data(), + ) + assert_load_info(info) + + data = next(generator_instance2) + assert_collection(pipeline, "some_data", items=data) + + info = pipeline.run( + some_data(), + ) + assert_load_info(info) + + data.extend(next(generator_instance2)) + assert_collection(pipeline, "some_data", items=data) + + +def test_explicit_append() -> None: + """Append should work even when primary key is specified.""" + data = [ + {"doc_id": 1, "content": "1"}, + {"doc_id": 2, "content": "2"}, + {"doc_id": 3, "content": "3"}, + ] + + @dlt.resource(primary_key="doc_id") + def some_data(): + yield data + + qdrant_adapter( + some_data, + embed=["content"], + ) + + pipeline = dlt.pipeline( + pipeline_name="test_pipeline_append", + destination="qdrant", + dataset_name="TestPipelineAppendDataset" + uniq_id(), + ) + info = pipeline.run( + some_data(), + ) + + assert_collection(pipeline, "some_data", items=data) + + info = pipeline.run( + some_data(), + write_disposition="append", + ) + assert_load_info(info) + + data.extend(data) + assert_collection(pipeline, "some_data", items=data) + + +def test_pipeline_replace() -> None: + + generator_instance1 = sequence_generator() + generator_instance2 = sequence_generator() + + @dlt.resource + def some_data(): + yield from next(generator_instance1) + + qdrant_adapter( + some_data, + embed=["content"], + ) + + uid = uniq_id() + + pipeline = dlt.pipeline( + pipeline_name="test_pipeline_replace", + destination="qdrant", + dataset_name="test_pipeline_replace_dataset" + uid, # Qdrant doesn't mandate any name normalization + ) + + info = pipeline.run( + some_data(), + write_disposition="replace", + ) + assert_load_info(info) + assert info.dataset_name == "test_pipeline_replace_dataset" + uid # Qdrant doesn't mandate any name normalization + + data = next(generator_instance2) + assert_collection(pipeline, "some_data", items=data) + + info = pipeline.run( + some_data(), + write_disposition="replace", + ) + assert_load_info(info) + + data = next(generator_instance2) + assert_collection(pipeline, "some_data", items=data) + + +def test_pipeline_merge() -> None: + data = [ + { + "doc_id": 1, + "title": "The Shawshank Redemption", + "description": ( + "Two imprisoned men find redemption through acts " + "of decency over the years." + ), + }, + { + "doc_id": 2, + "title": "The Godfather", + "description": ( + "A crime dynasty's aging patriarch transfers " + "control to his reluctant son." + ), + }, + { + "doc_id": 3, + "title": "The Dark Knight", + "description": ( + "The Joker wreaks havoc on Gotham, challenging " + "The Dark Knight's ability to fight injustice." + ), + }, + ] + + @dlt.resource(primary_key="doc_id") + def movies_data(): + yield data + + qdrant_adapter( + movies_data, + embed=["description"], + ) + + pipeline = dlt.pipeline( + pipeline_name="movies", + destination="qdrant", + dataset_name="TestPipelineAppendDataset" + uniq_id(), + ) + info = pipeline.run( + movies_data(), + write_disposition="merge", + dataset_name="MoviesDataset" + uniq_id() + ) + assert_load_info(info) + assert_collection(pipeline, "movies_data", items=data) + + # Change some data + data[0]["title"] = "The Shawshank Redemption 2" + + info = pipeline.run( + movies_data(), + write_disposition="merge", + ) + assert_load_info(info) + assert_collection(pipeline, "movies_data", items=data) + + +def test_pipeline_with_schema_evolution(): + data = [ + { + "doc_id": 1, + "content": "1", + }, + { + "doc_id": 2, + "content": "2", + }, + ] + + @dlt.resource() + def some_data(): + yield data + + qdrant_adapter(some_data, embed=["content"]) + + pipeline = dlt.pipeline( + pipeline_name="test_pipeline_append", + destination="qdrant", + dataset_name="TestSchemaEvolutionDataset" + uniq_id(), + ) + pipeline.run( + some_data(), + ) + + assert_collection(pipeline, "some_data", items=data) + + aggregated_data = data.copy() + + data = [ + { + "doc_id": 3, + "content": "3", + "new_column": "new", + }, + { + "doc_id": 4, + "content": "4", + "new_column": "new", + }, + ] + + pipeline.run( + some_data(), + ) + + table_schema = pipeline.default_schema.tables["some_data"] + assert "new_column" in table_schema["columns"] + + aggregated_data.extend(data) + + assert_collection(pipeline, "some_data", items=aggregated_data) + + +def test_merge_github_nested() -> None: + p = dlt.pipeline(destination="qdrant", dataset_name="github1", full_refresh=True) + assert p.dataset_name.startswith("github1_202") + + with open("tests/normalize/cases/github.issues.load_page_5_duck.json", "r", encoding="utf-8") as f: + data = json.load(f) + + info = p.run( + qdrant_adapter(data[:17], embed=["title", "body"]), + table_name="issues", + write_disposition="merge", + primary_key="id" + ) + assert_load_info(info) + # assert if schema contains tables with right names + print(p.default_schema.tables.keys()) + assert set(p.default_schema.tables.keys()) == {'_dlt_version', '_dlt_loads', 'issues', '_dlt_pipeline_state', 'issues__labels', 'issues__assignees'} + assert set([t["name"] for t in p.default_schema.data_tables()]) == {'issues', 'issues__labels', 'issues__assignees'} + assert set([t["name"] for t in p.default_schema.dlt_tables()]) == {'_dlt_version', '_dlt_loads', '_dlt_pipeline_state'} + issues = p.default_schema.tables["issues"] + assert issues["columns"]["id"]["primary_key"] is True + # make sure that vectorization is enabled for + assert issues["columns"]["title"][VECTORIZE_HINT] # type: ignore[literal-required] + assert issues["columns"]["body"][VECTORIZE_HINT] # type: ignore[literal-required] + assert VECTORIZE_HINT not in issues["columns"]["url"] + assert_collection(p, "issues", expected_items_count=17) + + +def test_empty_dataset_allowed() -> None: + # dataset_name is optional so dataset name won't be autogenerated when not explicitly passed + p = dlt.pipeline(destination="qdrant", full_refresh=True) + client: QdrantClient = p.destination_client() # type: ignore[assignment] + + assert p.dataset_name is None + info = p.run(qdrant_adapter(["context", "created", "not a stop word"], embed=["value"])) + # dataset in load info is empty + assert info.dataset_name is None + client = p.destination_client() # type: ignore[assignment] + assert client.dataset_name is None + assert client.sentinel_collection == "DltSentinelCollection" + assert_collection(p, "content", expected_items_count=3) + diff --git a/tests/load/qdrant/utils.py b/tests/load/qdrant/utils.py new file mode 100644 index 0000000000..96b582a28e --- /dev/null +++ b/tests/load/qdrant/utils.py @@ -0,0 +1,61 @@ +import dlt +from typing import Any, List + +import dlt +from dlt.common.pipeline import PipelineContext +from dlt.common.configuration.container import Container + +from dlt.destinations.qdrant.qdrant_client import QdrantClient + + +def assert_unordered_list_equal(list1: List[Any], list2: List[Any]) -> None: + assert len(list1) == len(list2), "Lists have different length" + for item in list1: + assert item in list2, f"Item {item} not found in list2" + + +def assert_collection( + pipeline: dlt.Pipeline, + collection_name: str, + expected_items_count: int = None, + items: List[Any] = None, +) -> None: + client: QdrantClient = pipeline.destination_client() # type: ignore[assignment] + + # Check if collection exists + exists = client._collection_exists(collection_name) + assert exists + + qualified_collection_name = client._make_qualified_collection_name(collection_name) + point_records, offset = client.db_client.scroll(qualified_collection_name, with_payload=True, limit=50) + + if expected_items_count is not None: + assert expected_items_count == len(point_records) + + if items is None: + return + + drop_keys = ["_dlt_id", "_dlt_load_id"] + objects_without_dlt_keys = [ + {k: v for k, v in point.payload.items() if k not in drop_keys} for point in point_records + ] + + assert_unordered_list_equal(objects_without_dlt_keys, items) + +def drop_active_pipeline_data() -> None: + print("Dropping active pipeline data for test") + def has_collections(client): + schema = client.db_client.get_collections().collections + return len(schema) > 0 + + if Container()[PipelineContext].is_active(): + # take existing pipeline + p = dlt.pipeline() + client: QdrantClient = p.destination_client() # type: ignore[assignment] + + if has_collections(client): + client.drop_storage() + + p._wipe_working_folder() + # deactivate context + Container()[PipelineContext].deactivate() diff --git a/tests/utils.py b/tests/utils.py index 2eba788542..823b1cca83 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -8,6 +8,8 @@ from typing import Iterator, List from unittest.mock import patch +from requests import Response + import dlt from dlt.common.configuration.container import Container from dlt.common.configuration.providers import DictionaryProvider @@ -27,8 +29,8 @@ # destination constants -IMPLEMENTED_DESTINATIONS = {"athena", "duckdb", "bigquery", "redshift", "postgres", "snowflake", "filesystem", "weaviate", "dummy", "motherduck", "mssql"} -NON_SQL_DESTINATIONS = {"filesystem", "weaviate", "dummy", "motherduck"} +IMPLEMENTED_DESTINATIONS = {"athena", "duckdb", "bigquery", "redshift", "postgres", "snowflake", "filesystem", "weaviate", "dummy", "motherduck", "mssql", "qdrant"} +NON_SQL_DESTINATIONS = {"filesystem", "weaviate", "dummy", "motherduck", "qdrant"} SQL_DESTINATIONS = IMPLEMENTED_DESTINATIONS - NON_SQL_DESTINATIONS # exclude destination configs (for now used for athena and athena iceberg separation) @@ -63,7 +65,7 @@ def TEST_DICT_CONFIG_PROVIDER(): providers_context.add_provider(provider) return provider -class MockHttpResponse(): +class MockHttpResponse(Response): def __init__(self, status_code: int) -> None: self.status_code = status_code From 46d65aece9879e0ff9c044f75b69110c809cfbf8 Mon Sep 17 00:00:00 2001 From: Marcin Rudolf Date: Wed, 8 Nov 2023 10:43:01 +0100 Subject: [PATCH 4/7] bumps to version 0.3.24 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index dcfa51172b..2c327ce3b9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "dlt" -version = "0.3.23" +version = "0.3.24" description = "dlt is an open-source python-first scalable data loading library that does not require any backend to run." authors = ["dltHub Inc. "] maintainers = [ "Marcin Rudolf ", "Adrian Brudaru ", "Ty Dunn "] From d1128046aa2be362fd77b7e956637efd5efdd351 Mon Sep 17 00:00:00 2001 From: Gregory DeVincentis Date: Wed, 8 Nov 2023 06:49:07 -0500 Subject: [PATCH 5/7] Adds authenticator to SnowflakeCredentials class (#734) --- dlt/destinations/snowflake/configuration.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/dlt/destinations/snowflake/configuration.py b/dlt/destinations/snowflake/configuration.py index 829b492127..66c2f82282 100644 --- a/dlt/destinations/snowflake/configuration.py +++ b/dlt/destinations/snowflake/configuration.py @@ -57,6 +57,7 @@ class SnowflakeCredentials(ConnectionStringCredentials): database: str = None warehouse: Optional[str] = None role: Optional[str] = None + authenticator: Optional[str] = None private_key: Optional[TSecretStrValue] = None private_key_passphrase: Optional[TSecretStrValue] = None @@ -96,6 +97,7 @@ def to_connector_params(self) -> Dict[str, Any]: warehouse=self.warehouse, role=self.role, private_key=private_key, + authenticator=self.authenticator, ) From 1f65adaa9d4451cd9ef8a932dbee03510d59e7fd Mon Sep 17 00:00:00 2001 From: Steinthor Palsson Date: Wed, 8 Nov 2023 06:51:21 -0500 Subject: [PATCH 6/7] Set port correctly in mssql connection string (#731) * Set port correctly in mssql connection string * Fix flaky sorting in arrow loading test --- dlt/destinations/mssql/configuration.py | 3 +-- tests/load/mssql/test_mssql_credentials.py | 24 ++++++++++++++++++++++ tests/load/pipeline/test_arrow_loading.py | 8 ++++++-- 3 files changed, 31 insertions(+), 4 deletions(-) create mode 100644 tests/load/mssql/test_mssql_credentials.py diff --git a/dlt/destinations/mssql/configuration.py b/dlt/destinations/mssql/configuration.py index 581da0f782..17f10ddff0 100644 --- a/dlt/destinations/mssql/configuration.py +++ b/dlt/destinations/mssql/configuration.py @@ -60,8 +60,7 @@ def _get_odbc_driver(self) -> str: def to_odbc_dsn(self) -> str: params = { "DRIVER": self.odbc_driver, - "SERVER": self.host, - "PORT": self.port, + "SERVER": f"{self.host},{self.port}", "DATABASE": self.database, "UID": self.username, "PWD": self.password, diff --git a/tests/load/mssql/test_mssql_credentials.py b/tests/load/mssql/test_mssql_credentials.py new file mode 100644 index 0000000000..9b57692bb2 --- /dev/null +++ b/tests/load/mssql/test_mssql_credentials.py @@ -0,0 +1,24 @@ +from dlt.common.configuration import resolve_configuration + +from dlt.destinations.mssql.configuration import MsSqlCredentials + + + +def test_to_odbc_dsn() -> None: + creds = resolve_configuration( + MsSqlCredentials("mssql://test_user:test_password@sql.example.com:12345/test_db?FOO=a&BAR=b") + ) + + dsn = creds.to_odbc_dsn() + + result = {k: v for k, v in (param.split('=') for param in dsn.split(";"))} + + assert result == { + 'DRIVER': 'ODBC Driver 18 for SQL Server', + 'SERVER': 'sql.example.com,12345', + 'DATABASE': 'test_db', + 'UID': 'test_user', + 'PWD': 'test_password', + 'FOO': 'a', + 'BAR': 'b' + } diff --git a/tests/load/pipeline/test_arrow_loading.py b/tests/load/pipeline/test_arrow_loading.py index b69ac5d2be..bd709e764d 100644 --- a/tests/load/pipeline/test_arrow_loading.py +++ b/tests/load/pipeline/test_arrow_loading.py @@ -46,7 +46,7 @@ def some_data(): assert some_table_columns["time"]["data_type"] == "time" qual_name = pipeline.sql_client().make_qualified_table_name("some_data") - rows = [list(row) for row in select_data(pipeline, f"SELECT * FROM {qual_name} ORDER BY 1")] + rows = [list(row) for row in select_data(pipeline, f"SELECT * FROM {qual_name}")] for row in rows: for i in range(len(row)): @@ -66,7 +66,7 @@ def some_data(): if isinstance(row[i], datetime): row[i] = pendulum.instance(row[i]) - expected = sorted([list(r.values()) for r in records], key=lambda x: x[0]) + expected = sorted([list(r.values()) for r in records]) for row in expected: for i in range(len(row)): @@ -75,6 +75,10 @@ def some_data(): load_id = load_info.loads_ids[0] + # Sort rows by all columns except _dlt_id/_dlt_load_id for deterministic comparison + rows = sorted(rows, key=lambda row: row[:-2]) + expected = sorted(expected) + for row, expected_row in zip(rows, expected): # Compare without _dlt_id/_dlt_load_id columns assert row[:-2] == expected_row From 517dc40196e6f9ad6495f68afa28879bc98e020a Mon Sep 17 00:00:00 2001 From: Adrian Date: Wed, 8 Nov 2023 16:05:08 +0100 Subject: [PATCH 7/7] fix canonical link --- docs/website/blog/2023-10-26-dlt-prefect.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/website/blog/2023-10-26-dlt-prefect.md b/docs/website/blog/2023-10-26-dlt-prefect.md index c78a47b7b5..8bd6321489 100644 --- a/docs/website/blog/2023-10-26-dlt-prefect.md +++ b/docs/website/blog/2023-10-26-dlt-prefect.md @@ -13,6 +13,10 @@ authors: tags: [dbt runner, dbt cloud runner, dbt core runner] --- + + + + This article is reposted from Prefect.io blog, and you can read the original [there](https://www.prefect.io/blog/building-resilient-data-pipelines-in-minutes-with-dlt-prefect). >The hardest part about writing a blog is getting started - writing the outline and filling out the first few key points. The same can be said for writing data pipelines: you need to inspect docs, determine data structures, write tests, etc.