feat(ml): add qdrant ingestion

CHANGES.md

@@ -76,6 +76,7 @@
   ([#38139](https://github.com/apache/beam/issues/38139)).
 * (Python) Added type alias for with_exception_handling to be used for typehints. ([#38173](https://github.com/apache/beam/issues/38173)).
 * Added plugin mechanism to support different Lineage implementations (Java) ([#36790](https://github.com/apache/beam/issues/36790)).
+* (Python) Added [Qdrant](https://qdrant.tech/) VectorDatabaseWriteConfig implementation ([#38141](https://github.com/apache/beam/issues/38141)).
 
 ## Breaking Changes
 

sdks/python/apache_beam/ml/rag/ingestion/qdrant.py

@@ -0,0 +1,322 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any, Optional
+
+import grpc
+from objsize import get_deep_size
+
+try:
+  from qdrant_client import QdrantClient, models
+  from qdrant_client.common.client_exceptions import ResourceExhaustedResponse
+  from qdrant_client.http.exceptions import ResponseHandlingException, UnexpectedResponse
+except ImportError:
+  logging.warning("Qdrant client library is not installed.")
+
+import apache_beam as beam
+from apache_beam.ml.rag.ingestion.base import VectorDatabaseWriteConfig
+from apache_beam.ml.rag.types import EmbeddableItem
+
+DEFAULT_WRITE_BATCH_SIZE = 1000
+DEFAULT_MAX_BATCH_BYTE_SIZE = 4 << 20
+
+
+@dataclass
+class QdrantConnectionParameters:
+  """Configuration parameters for connecting to Qdrant service.
+
+  Either `location`, `url`, `host`, or `path` must be provided to establish
+  a connection.
+
+  Args:
+    location:
+      If `str` - use it as a `url` parameter.
+      If `None` - use default values for `host` and `port`.
+    url: either host or str of "<scheme>//<host>:<port>/<prefix>".
+      Default: `None`
+    port: Port of the REST API interface. Default: 6333
+    grpc_port: Port of the gRPC interface. Default: 6334
+    prefer_grpc: If `true` - use gPRC interface whenever possible.
+    https: If `true` - use HTTPS(SSL) protocol. Default: `None`
+    api_key: API key for authentication in Qdrant Cloud. Default: `None`
+    prefix:
+      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`
+    timeout:
+      Timeout for REST and gRPC API requests.
+      Default: 5 seconds for REST and unlimited for gRPC
+    host:
+      Host name of Qdrant service.
+      If url and host are None, set to 'localhost'.
+      Default: `None`
+    path: Persistence path for QdrantLocal. Default: `None`
+    **kwargs: Additional arguments passed directly into client initialization
+  """
+
+  location: Optional[str] = None
+  url: Optional[str] = None
+  port: Optional[int] = 6333
+  grpc_port: int = 6334
+  prefer_grpc: bool = False
+  https: Optional[bool] = None
+  api_key: Optional[str] = None
+  prefix: Optional[str] = None
+  timeout: Optional[int] = None
+  host: Optional[str] = None
+  path: Optional[str] = None
+  kwargs: dict[str, Any] = field(default_factory=dict)
+
+  def __post_init__(self):
+    if not (self.location or self.url or self.host or self.path):
+      raise ValueError(
+          "One of location, url, host, or path must be provided for Qdrant")
+
+  @classmethod
+  def for_cloud(
+      cls,
+      url: str,
+      api_key: str,
+      *,
+      prefer_grpc: bool = False,
+      timeout: Optional[int] = None,
+      **kwargs: Any,
+  ) -> "QdrantConnectionParameters":
+    """Connect to Qdrant Cloud. Requires the cluster URL and an API key."""
+    return cls(
+        url=url,
+        api_key=api_key,
+        https=True,
+        prefer_grpc=prefer_grpc,
+        timeout=timeout,
+        kwargs=kwargs,
+    )
+
+  @classmethod
+  def for_host(
+      cls,
+      host: str,
+      port: int = 6333,
+      *,
+      grpc_port: int = 6334,
+      prefer_grpc: bool = False,
+      https: bool = False,
+      api_key: Optional[str] = None,
+      timeout: Optional[int] = None,
+      **kwargs: Any,
+  ) -> "QdrantConnectionParameters":
+    """Connect to a self-hosted Qdrant instance by host and port."""
+    return cls(
+        host=host,
+        port=port,
+        grpc_port=grpc_port,
+        prefer_grpc=prefer_grpc,
+        https=https,
+        api_key=api_key,
+        timeout=timeout,
+        kwargs=kwargs,
+    )
+
+  @classmethod
+  def for_url(
+      cls,
+      url: str,
+      *,
+      api_key: Optional[str] = None,
+      prefer_grpc: bool = False,
+      timeout: Optional[int] = None,
+      **kwargs: Any,
+  ) -> "QdrantConnectionParameters":
+    """Connect using a full URL like 'https://my-qdrant.example.com:6333'."""
+    return cls(
+        url=url,
+        api_key=api_key,
+        prefer_grpc=prefer_grpc,
+        timeout=timeout,
+        kwargs=kwargs)
+
+  @classmethod
+  def local(cls, path: str) -> "QdrantConnectionParameters":
+    """Use an embedded Qdrant instance persisted to the given path."""
+    return cls(path=path)
+
+  @classmethod
+  def in_memory(cls) -> "QdrantConnectionParameters":
+    """Use an embedded in-memory Qdrant instance. Useful for tests."""
+    return cls(location=":memory:")
+
+
+@dataclass
+class QdrantWriteConfig(VectorDatabaseWriteConfig):
+  """Configuration for writing to Qdrant vector database.
+
+  This class defines the parameters needed to write data to a qdrant collection,
+  including collection targeting, batching behavior, and operation timeouts.
+
+  Args:
+    connection_params: QdrantConnectionParameters with connection settings.
+    collection_name: Name of the Qdrant collection to write to.
+    timeout: Optional timeout for write operations in seconds. Default is None.
+    batch_size: Number of points to write in each batch. Default is 1000.
+    kwargs: Additional keyword arguments to pass to the client's upsert method.
+    dense_embedding_key: name for the dense vector in the qdrant collection.
+    sparse_embedding_key: name for the sparse vector in the qdrant collection.
+  """
+
+  connection_params: QdrantConnectionParameters
+  collection_name: str
+  timeout: Optional[int] = None
+  batch_size: int = DEFAULT_WRITE_BATCH_SIZE
+  max_batch_byte_size: int = DEFAULT_MAX_BATCH_BYTE_SIZE
+  kwargs: dict[str, Any] = field(default_factory=dict)
+  dense_embedding_key: str = "dense"
+  sparse_embedding_key: str = "sparse"
+
+  def __post_init__(self):
+    if not self.collection_name:
+      raise ValueError("Collection name must be provided")
+    if self.batch_size <= 0:
+      raise ValueError("Batch size must be a positive integer")
+
+  def create_write_transform(self) -> beam.PTransform[EmbeddableItem, Any]:
+    return _QdrantWriteTransform(self)
+
+  def create_converter(
+      self,
+  ) -> Callable[[EmbeddableItem], "models.PointStruct"]:
+    def convert(item: EmbeddableItem) -> "models.PointStruct":
+      if item.dense_embedding is None and item.sparse_embedding is None:
+        raise ValueError(
+            "EmbeddableItem must have at least one embedding (dense or sparse)")
+      vector = {}
+      if item.dense_embedding is not None:
+        vector[self.dense_embedding_key] = item.dense_embedding
+      if item.sparse_embedding is not None:
+        sparse_indices, sparse_values = item.sparse_embedding
+        vector[self.sparse_embedding_key] = models.SparseVector(
+            indices=sparse_indices,
+            values=sparse_values,
+        )
+      id = (
+          int(item.id)
+          if isinstance(item.id, str) and item.id.isdigit() else item.id)
+      return models.PointStruct(
+          id=id,
+          vector=vector,
+          payload=item.metadata if item.metadata else None,
+      )
+
+    return convert
+
+
+class _QdrantWriteTransform(beam.PTransform):
+  def __init__(self, config: QdrantWriteConfig):
+    self.config = config
+
+  def expand(self, input_or_inputs: beam.PCollection[EmbeddableItem]):
+    return (
+        input_or_inputs
+        | "Convert to Records" >> beam.Map(self.config.create_converter())
+        | beam.ParDo(_QdrantWriteFn(self.config)))
+
+
+class _QdrantWriteFn(beam.DoFn):
+  def __init__(self, config: QdrantWriteConfig):
+    self.config = config
+    self._client: "Optional[QdrantClient]" = None
+
+  def start_bundle(self):
+    self._batch = []
+    self._batch_byte_size = 0
+
+  def process(self, element, *args, **kwargs):
+    element_byte_size = get_deep_size(element)
+    new_batch_byte_size = self._batch_byte_size + element_byte_size
+
+    is_batch_full = len(self._batch) >= self.config.batch_size
+    is_batch_too_large = new_batch_byte_size > self.config.max_batch_byte_size
+    if (is_batch_full or is_batch_too_large):
+      self._flush()
+    self._batch.append(element)
+    self._batch_byte_size += element_byte_size
+
+  def setup(self):
+    params = self.config.connection_params
+    self._client = QdrantClient(
+        location=params.location,
+        url=params.url,
+        port=params.port,
+        grpc_port=params.grpc_port,
+        prefer_grpc=params.prefer_grpc,
+        https=params.https,
+        api_key=params.api_key,
+        prefix=params.prefix,
+        timeout=params.timeout,
+        host=params.host,
+        path=params.path,
+        check_compatibility=False,
+        **params.kwargs,
+    )
+
+  def teardown(self):
+    if self._client:
+      self._client.close()
+      self._client = None
+
+  def finish_bundle(self):
+    self._flush()
+
+  def _flush(self):
+    if not self._batch:
+      return
+    if not self._client:
+      raise RuntimeError("Qdrant client is not initialized")
+
+    max_retries = 3
+    attempt = 1
+    while True:
+      try:
+        self._client.upsert(
+            collection_name=self.config.collection_name,
+            points=self._batch,
+            timeout=self.config.timeout,
+            **self.config.kwargs,
+        )
+        break
+      except ResourceExhaustedResponse as e:
+        time.sleep(e.retry_after_s)
+        # don't count rate-limit against max_retries
+        continue
+      except (UnexpectedResponse, ResponseHandlingException,
+              grpc.RpcError) as e:
+        if attempt > max_retries:
+          raise
+        time.sleep(2**attempt)
+        attempt += 1
+    self._batch = []
+    self._batch_byte_size = 0
+
+  def display_data(self):
+    res = super().display_data()
+    res["collection"] = self.config.collection_name
+    res["batch_size"] = self.config.batch_size
+    res["max_batch_byte_size"] = self.config.max_batch_byte_size
+    return res