Prometheus RAG: A natural language interface for Prometheus metrics using RAG (Retrieval-Augmented Generation)

Prometheus RAG bridges the gap between natural language and Prometheus metrics by implementing a Retrieval-Augmented Generation (RAG) system. It allows users to query metrics using plain English, automatically finding relevant metrics and generating appropriate PromQL queries.

Key features:

• Natural language to PromQL translation

• Automatic metric metadata synchronization

• Vector similarity search for relevant metrics

• BERT-based encoding for semantic understanding

• Integration with vector database

This guide will help you set up and run Prometheus RAG locally for development.

Prerequisites

• Podman/Docker and Podman/Docker Compose
• Go 1.21+
• Access to an LLM server

Quick Start

1. Start VectorDB and Prometheus

   Launch the development Qdrant VectorDB and Prometheus server:

   # You can use podman or docker
   cd hack && docker compose up -d

2. Configure LLM Server

   Ensure you have a running LLM server and set these required environment variables:

   export PRAG_LLM_BASE_URL="your-llm-server-url"
   export PRAG_LLM_API_KEY="your-api-key"

3. Start RAG Server

   Launch the RAG application:

   go run main.go

4. Test the Service

   Send a test query:

   curl -X POST \
     http://localhost:8080/query \
     -H "Content-Type: application/json" \
     -d '{"query": "What is the total number of VMs?"}'

Configuration

The application can be configured using the following environment variables:

Server Configuration

Variable	Description	Default
PRAG_DEBUG	Enable debug logging	false
PRAG_HOST	Server host address	0.0.0.0
PRAG_PORT	Server port	8080

Prometheus Configuration

Variable	Description	Default
PRAG_PROMETHEUS_ADDRESS	Prometheus server URL	http://localhost:9090
PRAG_PROMETHEUS_REFRESH_RATE_MINUTES	Metadata refresh interval	10

VectorDB Configuration

Variable	Description	Default
PRAG_VECTORDB_HOST	Qdrant server host	localhost
PRAG_VECTORDB_PORT	Qdrant server port	6334
PRAG_VECTORDB_COLLECTION	Collection name	prag-metrics
PRAG_VECTORDB_ENCODER_DIR	Encoder models directory	./_models

OpenAI-compatible LLM Configuration

Variable	Description	Default
PRAG_LLM_BASE_URL	LLM server base URL	http://localhost:1234/v1/
PRAG_LLM_API_KEY	Authentication key
PRAG_LLM_MODEL	Model identifier	granite-3.1-8b-instruct