feat(ml): add qdrant ingestion

[MichaelGruschke]

resolves #38141

This PR adds support for Qdrant vector database ingestion to Apache Beam's ML RAG pipeline.

Implementation details:

1. QdrantConnectionParameters

   • Dataclass for Qdrant connection configuration
   • Supports location, url, host, path for connection
   • Configurable port, grpc_port, api_key, https, timeout

2. QdrantWriteConfig

   • Extends VectorDatabaseWriteConfig
   • Supports both dense and sparse embeddings
   • Creates converter to models.PointStruct
   • Batched writes (default 1000 items/batch)

3. QdrantWriteTransform

   • Private PTransform wrapping _QdrantWriteFn
   • Batches elements and upserts to Qdrant collection
   • Handles client lifecycle (setup/teardown)

4. Dependency Changes

   • Added qdrant-client to ml_test extras_require

5.Tests

• Integration tests (qdrant_it_test.py) for end-to-end ingestion verification

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Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances Apache Beam's machine learning capabilities by integrating Qdrant, a popular vector database. It provides a complete framework for ingesting embedded data into Qdrant collections, allowing for scalable and efficient management of vector embeddings within RAG pipelines. The changes include new configuration options, data transformation logic, and thorough testing to ensure reliability and correctness.

Highlights

• Qdrant Integration: Added comprehensive support for Qdrant vector database ingestion within Apache Beam's ML RAG pipeline, enabling users to write EmbeddableItem objects to Qdrant collections.
• Configuration Classes: Introduced QdrantConnectionParameters for defining connection details (location, URL, host, port, API key, etc.) and QdrantWriteConfig for specifying write behavior (collection name, batch size, embedding keys).
• Data Transformation and Ingestion: Implemented _QdrantWriteTransform and _QdrantWriteFn to handle the conversion of EmbeddableItem objects into Qdrant's PointStruct format, supporting both dense and sparse embeddings, and performing batched upserts to the Qdrant collection.
• Dependency Management: Updated setup.py to include qdrant-client as a new dependency, ensuring it's available for ML-related tests and installations.
• Integration Tests: Provided robust integration tests (qdrant_it_test.py) to verify end-to-end Qdrant ingestion functionality, covering various scenarios like dense-only, sparse-only, hybrid embeddings, batching, and idempotent writes.

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