Adaptation solutions for offline/weak network environments
The following optimization strategies can be used for the restricted network conditions:
- Local Model Deployment: Integrate open source models such as LLaMA, Falcon, etc. via HuggingFace (requires 8GB+ video memory), modify `configs/model_config.yaml` to specify the local endpoint.
- Caching mechanism utilization: When running in `-cache_only` mode, the system prioritizes reading previously cached results (stored in the `. /cache/` directory), and only requests for new queries are initiated.
- Streamline your search strategy: Configure `minimal_search=true` to limit the number of optimal results returned to a maximum of 3 per query to reduce the amount of data transferred.
- Segmented Execution Functions: Runs in stages via parameters such as `-stage=planning`, allowing only the search stage to be executed when the network is good and the writing process to take place offline.
Specific implementation:
- Install the local modeling service: `uv pip install transformers torch`
- Create offline configuration file `offline_mode.yaml` to disable cloud APIs
- Use the command: `python main.py -topic "local test" -offline -model=local/llama3`
- Complete projects can be packaged as Docker images for mobile use.
Options:
- Pre-download offline copies of knowledge bases such as Wikipedia (50GB+ storage required)
- Use RSS feeds instead of real-time search for updates
- Configure a local literature manager such as Zotero as an alternative source
This answer comes from the articleTogether Open Deep Research: Generating Indexed Deep Research ReportsThe
May not be reproduced without permission:AI productivity tools " How can I optimize my Open Deep Research experience in low network bandwidth environments?
