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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Vector Databases and Embedding Stores for Pipeline Observability |
| Authors Name: | Devarsh Hemantbhai Patel |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206710
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| Published Paper Id: | IJRTI2509145 |
| Published In: | Volume 10 Issue 9, September-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i9.206710 |
| Abstract: | The rise of machine learning (ML) applications in production environments has driven the need for robust observability mechanisms that extend beyond traditional software metrics. This review paper explores the intersection of vector databases, embedding stores, and observability practices within ML pipelines. Vector databases have become essential for storing and retrieving high-dimensional embeddings used in semantic search, recommendation systems, and generative AI. However, their integration introduces new challenges related to data drift, versioning, indexing, and system-level diagnostics. Embedding stores, often operating in real-time microservices architectures, require end-to-end visibility across computation, storage, and inference layers. This paper systematically analyzes the unique observability requirements of embedding-centric architectures and highlights solutions for drift detection, traceability, semantic debugging, and AI-assisted monitoring. It also reviews recent innovations in telemetry collection, vector index behavior analysis, and compliance-aware observability in restricted execution environments. A unified observability framework is proposed to address the complexity and scalability demands of vectorized ML pipelines, providing a roadmap for reliable and interpretable deployment of ML systems. Additionally, this paper introduces the Semantic Health Score, a composite metric that captures embedding quality, drift stability, and vector index reliability in a single interpretable signal. To ensure observability systems remain scalable, the framework incorporates cost-aware optimizations such as sampling strategies, tiered telemetry storage, and edge aggregation. Furthermore, the observability design embeds governance and compliance strategies including provenance tracking, vector masking, jurisdiction-aware data routing, and access policy enforcement—ensuring that semantic observability remains both efficient and regulation-compliant in enterprise ML deployments. |
| Keywords: | Vector Databases, Observability, Embedding Stores, Machine Learning Pipeline |
| Cite Article: | "Vector Databases and Embedding Stores for Pipeline Observability ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b348-b359, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509145.pdf |
| Downloads: | 000270 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2509145
Registration ID:206710
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i9.206710
Page No: b348-b359 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509145 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509145 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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