https://github.com/saswatamcode/metricsgpt
Talk to your metrics.
https://github.com/saswatamcode/metricsgpt
llms metrics ollama prometheus python thanos
Last synced: 6 months ago
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Talk to your metrics.
- Host: GitHub
- URL: https://github.com/saswatamcode/metricsgpt
- Owner: saswatamcode
- License: apache-2.0
- Created: 2024-07-15T10:22:38.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-04T10:09:13.000Z (8 months ago)
- Last Synced: 2025-03-24T14:06:39.234Z (7 months ago)
- Topics: llms, metrics, ollama, prometheus, python, thanos
- Language: Python
- Homepage:
- Size: 6.48 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# metricsGPT
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.
## Installation
Ensure you have Python 3.12+ and Node v20+ locally .
By default this tool uses [`llama3`](https://ollama.com/library/llama3) and [`nomic-embed-text`](https://ollama.com/library/nomic-embed-text).
```bash
ollama pull llama3
ollama pull nomic-embed-text
```
Have 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/
```bash
pip3 install metricsgpt
metricsGPT --server --config=config.yaml
```
If building locally you can use Poetry,
```bash
poetry install
poetry run metricsGPT --server --config=config.yaml
```
and visit localhost:8081!
##Â Configuration
Edit [config.yaml](./config.yaml) to suit your own models/Prometheus/Thanos setups.
```yaml
# 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"
```
## TODOs:
- Much more efficient vectorDB ops
- Use other Prom HTTP APIs for more context
- Range queries
- Visualize
- Embed query results for better analysis
- Process alerts