Talk to your metrics.
Note
This is a work in progress with no API guarantees. The current implementation needs work on scalability. Right now it will cause quite some load on your Prometheus API and take a while.
Ensure you have Python 3.12+ and Node v20+ locally .
By default this tool uses llama3 and nomic-embed-text.
ollama pull llama3
ollama pull nomic-embed-textHave some prometheus up and running. You can use make run-prom to get one running in docker that scrapes itself.
You can choose to grab the CLI from https://pypi.org/project/metricsgpt/
pip3 install metricsgpt
metricsGPT --server --config=config.yamlIf building locally you can use Poetry,
poetry install
poetry run metricsGPT --server --config=config.yamland visit localhost:8081!
## Configuration
Edit config.yaml to suit your own models/Prometheus/Thanos setups.
# Prometheus Configuration
prometheus_url: "http://localhost:9090"
# prometheus_auth:
# # Basic authentication
# basic_auth:
# username: "your_username"
# password: "your_password"
# # Or Bearer token
# bearer_token: "your_token"
# # Or custom headers
# custom_headers:
# Authorization: "Custom your_auth_header"
# X-Custom-Header: "custom_value"
# # TLS/SSL configuration
# tls:
# cert_file: "/path/to/cert.pem"
# key_file: "/path/to/key.pem"
# skip_verify: false # Set to true to skip
prom_external_url: null # Optional external URL for links in the UI
query_lookback_hours: 1.0
# Storage Configuration
vectordb_path: "./data.db"
series_cache_file: "./series_cache.json"
# Server Configuration
refresh_interval: 900 # VectorDB Refresh interval in seconds
server_host: "0.0.0.0"
server_port: 8081
# LLM Configuration
llm:
provider: "ollama"
model: "llama3.1"
embedding:
provider: "ollama" # or "openai"
model: "nomic-embed-text"
dimension: 768 # optional, defaults to this dimension
# For Azure OpenAI embeddings:
#embedding:
# provider: "azure"
# model: "text-embedding-ada-002"
# deployment_name: "your-embedding-deployment"
# api_key: "your-api-key"
# endpoint: "your-azure-endpoint"
# api_version: "2023-05-15"
# dimension: "dimensions of model"
# For Watson embeddings:
#embedding:
# provider: "watsonx"
# api_key: "your-api-key"
# project_id: "your-project-id"
# model_id: "google/flan-ul2" # optional, defaults to this model
# dimension: "dimensions of model"
# For OpenAI embeddings:
#embedding:
# provider: "openai"
# model: "text-embedding-ada-002"
# api_key: "your-api-key"
# dimension: "dimensions of model"
# Example configurations for different providers:
# For OpenAI:
#llm:
# provider: "openai"
# model: "gpt-4"
# api_key: "your-api-key"
# For Ollama:
#llm:
# provider: "ollama"
# model: "metricsGPT"
# timeout: 120.0
# For Azure:
#llm:
# provider: "azure"
# model: "gpt-4"
# deployment_name: "your-deployment"
# api_key: "your-api-key"
# endpoint: "your-azure-endpoint"
# For Gemini:
#llm:
# provider: "gemini"
# model: "gemini-pro"
# api_key: "your-api-key"
# For WatsonX:
#llm:
# provider: "watsonx"
# api_key: "your-api-key"
# project_id: "your-project-id"
# model_id: "your-model-id"- Much more efficient vectorDB ops
- Use other Prom HTTP APIs for more context
- Range queries
- Visualize
- Embed query results for better analysis
- Process alerts