Bowl in 1994 took place on January 30th at 3:30 PM local time. Converting this to the specified format (%Y-%m-%dT%H:%M:%S.%fZ) results in: 1994-01-30T15:30:00.000Ztry:    print(fallback_4.invoke({"event": "the superbowl in 1994"}))except Exception as e:    print(f"Error: {e}")    1994-01-30 15:30:00PreviousComparing Chain OutputsNextLangSmithFallback for LLM API ErrorsFallback for SequencesFallback for Long InputsFallback to Better ModelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safeguard against these. That's why we've introduced the concept of fallbacks.
When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safeguard against these. That's why we've introduced the concept of fallbacks. ->: Bowl in 1994 took place on January 30th at 3:30 PM local time. Converting this to the specified format (%Y-%m-%dT%H:%M:%S.%fZ) results in: 1994-01-30T15:30:00.000Ztry:    print(fallback_4.invoke({"event": "the superbowl in 1994"}))except Exception as e:    print(f"Error: {e}")    1994-01-30 15:30:00PreviousComparing Chain OutputsNextLangSmithFallback for LLM API ErrorsFallback for SequencesFallback for Long InputsFallback to Better ModelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.