Detailed deployment guidance and technical preparation
The deployment process of the system follows a standard Python project structure and is divided into three main phases:
- environmental preparation: Requires Python 3.8+ runtime environment, recommended to use virtualenv to create an isolated environment. Key dependencies include LangChain 0.1.x, BeautifulSoup4, and other web parsing libraries. Graphics memory requirements depend on the size of the selected LLM (minimum 4GB).
- Configuration points::
- API keys need to be configured in the .env file after cloning the repository (OpenAI/Azure, etc.)
- Adjust parameters such as the number of agents, timeout settings, etc. via research_config.yaml
- Local documents should be stored in . /data/inputs directory support txt/pdf format
- execute a command: In addition to the basic main.py startup method, advanced users can use the
python advanced.py --streaming --agents=5Enable real-time streaming output and multi-agent enhancement mode. The system provides Docker-compose solution for one-click deployment.
Note: You need to download about 2GB of NLP model cache for the first run, so it is recommended to keep a stable internet connection.
This answer comes from the articleGPT Researcher: Generate comprehensive, detailed research reports utilizing local and web-based dataThe
May not be reproduced without permission:AI productivity tools " How to install and run GPT Researcher and what technical preparation is required?
