Langroid provides two core optimization mechanisms:
- Response Cache: Store LLM responses via Redis or Momento to avoid repeated queries for the same content
- Tool Call: When LLM needs to perform a computation or query, it can do so through the
ToolMessageTrigger local functions instead of consuming tokens
When dealing with math problems, for example, the intelligence will prioritize calling Python computational tools instead of letting LLM perform the calculations. Combiningsingle_roundand other task control parameters can effectively reduce unnecessary API calls. Tests show that these optimizations reduce the operating costs of the 30%-50%.
This answer comes from the articleLangroid: Easily Navigating Large Language Models with Multi-Intelligent Body ProgrammingThe
May not be reproduced without permission:AI productivity tools " How does Langroid optimize the cost of API calls?
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