Local Environment Optimization Program
To optimize the search speed of OpenDeepSearch in a local environment, you can start at several levels:
Base configuration optimization
- Make sure to use Python version 3.10+, older versions may affect performance
- Recommended to run in a virtual environment to avoid dependency conflicts
- Regularly update the dependency library:
pip install --upgrade -r requirements.txt
API and model tuning
- For simple queries, use lightweight models such as the
google/gemini-2.0-flash-001 - Reasonable configuration of API call timeout time to avoid waiting too long
- Reduce double-counting by caching frequently used query results locally
Code-Level Optimization
- Limiting the scope and depth of deep searches
- For batch queries, use the asynchronous processing mechanism
- Turn off unnecessary logging output to reduce I/O overhead
Key tools and techniques
- utilization
cProfileAnalyzing performance bottlenecks - Consider installing acceleration libraries such as
numbamaybenumpy - For long-running services, Docker containerized deployment can be used
By using the above methods, you can significantly increase the speed of local operation while maintaining the quality of smart search.
This answer comes from the articleOpenDeepSearch: an open source search tool that supports intelligent reasoningThe
May not be reproduced without permission:AI productivity tools " How to optimize OpenDeepSearch search speed in a local environment?
