mlsanityData to 14 Oct 2026

Vextra: A Unified Middleware Abstraction for Heterogeneous Vector Database Systems

arXiv 2601.06727 ↗First posted 11 Jan 2026cs.DB cs.SEcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

The rapid integration of vector search into AI applications, particularly for Retrieval Augmented Generation (RAG), has catalyzed the emergence of a diverse ecosystem of specialized vector databases. While this innovation offers a rich choice of features and performance characteristics, it has simultaneously introduced a significant challenge: severe API fragmentation. Developers face a landscape of disparate, proprietary, and often volatile API contracts, which hinders application portability, increases maintenance overhead, and leads to vendor lock-in. This paper introduces Vextra, a novel middleware abstraction layer designed to address this fragmentation. Vextra presents a unified, high-level API for core database operations, including data upsertion, similarity search, and metadata filtering. It employs a pluggable adapter architecture to translate these unified API calls into the native protocols of various backend databases. We argue that such an abstraction layer is a critical step towards maturing the vector database ecosystem, fostering interoperability, and enabling higher-level query optimization, while imposing minimal performance overhead.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 8 of its 43 references that are in the map. First posted on arXiv 11 Jan 2026.

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8 of 43 references.

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Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 7

  1. 2024The Faiss librarycited by 376
  2. 2023Survey of Vector Database Management Systemscited by 39
  3. 2024When Large Language Models Meet Vector Databases: A Surveycited by 39
  4. 2023Vector database management systems: Fundamental concepts, use-cases, and current challengescited by 9
  5. 2018Performance evaluation for CRUD operations in asynchronously replicated document oriented databasecited by 3
  6. 2024Semantic Search and Recommendation Algorithmcited by 1
  7. 2025Beyond Relations: A Case for Elevating to the Entity-Relationship Abstractioncited by 1

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  2. 2025Towards Reliable Vector Database Management Systems: A Software Testing Roadmap for 2030 ↗
  3. 2025SPI: Query-Depth-Adaptive Indexing for Streaming RAG in Vector Databases
  4. 2026The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI
  5. 2025RAG-Stack: Co-Optimizing RAG Quality and Performance From the Vector Database Perspective
  6. 2026RAGPerf: An End-to-End Benchmarking Framework for Retrieval-Augmented Generation Systems
  7. 2026Beyond Similarity Search: A Unified Data Layer for Production RAG Systems
  8. 2025Building Scalable AI-Powered Applications with Cloud Databases: Architectures, Best Practices and Performance Considerations