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Private LLM RAG system deployed on Kubernetes with full observability

By PulseAugur Editorial · [1 sources] ·

This article details how to set up a private Retrieval-Augmented Generation (RAG) system using LangGraph on Kubernetes for enhanced data privacy. The setup involves running local LLMs and utilizing an observability stack including OpenTelemetry, Prometheus, Grafana, Tempo, and Loki to monitor system performance, costs, and identify bottlenecks. The process covers data ingestion into a PostgreSQL database with pgvector, a LangGraph workflow for question answering, and deployment within a Kubernetes environment to ensure sensitive data never leaves the host. AI

IMPACT Enables secure, private LLM deployments for sensitive data, reducing reliance on third-party APIs.

RANK_REASON The article describes a technical implementation and setup guide for a specific software architecture.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Private LLM RAG system deployed on Kubernetes with full observability

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The article describes a technical implementation and setup guide for a specific software architecture.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Jmili Amine ·

    Private LangGraph RAG on Kubernetes: Running a Local LLM with Full Observability (OpenTelemetry, Prometheus, Grafana)

    <h2> The problem </h2> <p>Health records, payslips, identity documents, personal e-mails, private notes: this is the data a large language model (LLM) would be most useful on, and the data that should never be sent to a third-party API.</p> <p>Sending it to a hosted model means l…

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