https://github.com/victorgoubet/techsage
Leverage the power of multi-agent AI to fuel your daily tech, programming, and architecture insights.
https://github.com/victorgoubet/techsage
crewai langchain llm multiagent multiagent-systems ollama openai python python3 tech technical-monitoring
Last synced: 6 months ago
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Leverage the power of multi-agent AI to fuel your daily tech, programming, and architecture insights.
- Host: GitHub
- URL: https://github.com/victorgoubet/techsage
- Owner: VictorGoubet
- License: mit
- Created: 2024-06-13T21:55:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-24T14:27:44.000Z (over 1 year ago)
- Last Synced: 2025-04-17T10:23:27.350Z (6 months ago)
- Topics: crewai, langchain, llm, multiagent, multiagent-systems, ollama, openai, python, python3, tech, technical-monitoring
- Language: Python
- Homepage:
- Size: 1.74 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
TechSage 🤖
TechSage is a multi-agent LLM platform delivering daily insights on technology, programming, cloud architecture, and more. Utilize OpenAI's LLMs or local models via Ollama, powered by CrewAI's multi-agent system, to stay ahead in the tech world.
Prerequisites •
Installation •
Configure •
Launch •
Docker
## Prerequisites 💡
- Python >= 3.10, <= 3.13
- `ollama` (if using a local model) [install here](https://ollama.com/download/)
- May need to install the c++ build tool if you don't already have it
## Installation 🛠️
To install TechSage, run:
```bash
pip install https://github.com/VictorGoubet/techsage/archive/refs/tags/v1.tar.gz
```
*Replace `v1` with the release you want to use.*
## Configure [optional] ⚙️
Execute this command only if you want to use the shell interface with specific configuration. For the Streamlit interface, you can configure everything directly within it.
```bash
configure-sage
```
### Configuration Options:
- `--model `: Name of the model to use (default: `llama3:8b`).
- `--model_url `: API URL of the model to use (default: `http://localhost:11434/v1`).
- `--verbose <1 or 0>`: Verbose level during configuration (default: 0).
- `--local `: Use a local model with Ollama or an OpenAI API model (default: True).
- `--openai_api_key `: Your OpenAI API key (required if local mode is disabled or using crew memory).
- `--google_search_api_key `: Delpha Google Search API key. If empty, a local Google search will be performed. Modify `api_google_search` method in `tools.py` to use another API. A DuckDuckGo tool is also available.
## Launch 🚀
After setting up, launch the script with admin rights. If no configuration is provided, the default configuration will be used:
```sh
launch-sage
```
**Note**: Be sure to have **ollama running** if you intend to use local models
### Launch Options:
- `--streamlit `: If `true`, the Streamlit interface will be used; otherwise, a shell interface will appear.
## Docker 🐋
Lazy to setup everything ? Just use the dedicated docker image and go to [http://localhost:8501](http://localhost:8501)
### CPU only
```bash
docker run -d -v ollama:/root/.ollama -p 8501:8501 victorgoubet/techsage:latest
```
### Nvidia GPU
First install GPU drivers for docker:
- **Linux**: [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation).
- **Windows**: [Nvidia Cuda on WSL](https://learn.microsoft.com/fr-fr/windows/ai/directml/gpu-cuda-in-wsl)
- **Mac**: *Not supported*
```bash
docker run -d --gpus=all -v ollama:/root/.ollama -p 8501:8501 victorgoubet/techsage:latest
```
*Note: GPU version not really stable*
## App preview
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