Three Scenarios for Optimizing Intelligent Body Performance
OxyGent provides a complete continuous optimization mechanism:
- parameter tuning: Adjust llm_params in HttpLLM (e.g. temperature=0.01 to reduce randomness) and semaphore control concurrency
- feedback loopBuilt-in evaluation engine automatically records execution logs, and the task decomposition process can be viewed through the MAS.monitor interface.
- data enhancement: the system automatically generates training data, developers can supplement the labeled data to the .env file
Take the financial risk control scenario as an example: first observe the decision path of the intelligent body through visual debugging (localhost:port/debug), then adjust the precision parameters of the mathematical tools, and finally use the historical transaction data to strengthen the training of the risk control intelligent body.
This answer comes from the articleOxyGent: Python open source framework for rapidly building intelligent systemsThe
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