{"id":458,"date":"2024-02-08T10:15:27","date_gmt":"2024-02-08T15:15:27","guid":{"rendered":"https:\/\/ozer.gt\/log\/?p=458"},"modified":"2024-02-08T11:46:42","modified_gmt":"2024-02-08T16:46:42","slug":"simplified-calls-for-llm-apis","status":"publish","type":"post","link":"https:\/\/ozer.gt\/log\/2024\/02\/08\/simplified-calls-for-llm-apis\/","title":{"rendered":"Simplified calls for LLM APIs"},"content":{"rendered":"<p>For a new project, I&#8217;ve been exploring options to develop a backend to query multiple large language models and just came across this great solution.<\/p>\n<p>It&#8217;s an open source project called LiteLLM and it provides a unified interface to call 100+ LLMs using the same input and output format, including OpenAI, Anthropic, models on Hugging Face, Azure etc.<\/p>\n<p>There is cost tracking and rate limits. To make things easier, there is even a user interface. What I found most useful is the ease of comparison and benchmarking between LLMs. Kudos to the developer team.<\/p>\n<p>I can see so many business use cases for integrations like this: rapid prototyping and experimentation, performance benchmarking and optimization, cost control&#8230;<\/p>\n<p><a href=\"https:\/\/litellm.vercel.app\/docs\/\">Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>For a new project, I&#8217;ve been exploring options to develop a backend to query multiple large language models and just came across this great solution. It&#8217;s an open source project called LiteLLM and it provides a unified interface to call 100+ LLMs using the same input and output format, including OpenAI, Anthropic, models on Hugging [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cybocfi_hide_featured_image":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-458","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/458","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/comments?post=458"}],"version-history":[{"count":1,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/458\/revisions"}],"predecessor-version":[{"id":459,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/458\/revisions\/459"}],"wp:attachment":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/media?parent=458"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/categories?post=458"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/tags?post=458"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}