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diff --git a/litellm/proxy/_experimental/out/404/index.html b/litellm/proxy/_experimental/out/404/index.html
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deleted file mode 100644
index 1a966441d9a..00000000000
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deleted file mode 100644
index 5e7778e708d..00000000000
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+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/09kfqcp7rqvl7.js b/litellm/proxy/_experimental/out/_next/static/chunks/09kfqcp7rqvl7.js
deleted file mode 100644
index bd81119d2ae..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/09kfqcp7rqvl7.js
+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/09qlj1_ya5uqw.js b/litellm/proxy/_experimental/out/_next/static/chunks/09qlj1_ya5uqw.js
deleted file mode 100644
index 06d596c068f..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/09qlj1_ya5uqw.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0_8vcd9i7eo1r.js b/litellm/proxy/_experimental/out/_next/static/chunks/0_8vcd9i7eo1r.js
deleted file mode 100644
index d53e80e2dae..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0_8vcd9i7eo1r.js
+++ /dev/null
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Please select a model before sending a request.");console.log=function(){};let O=_||(0,r.getProxyBaseUrl)(),R=l(u,L),E=new t.default.OpenAI({apiKey:d,baseURL:O,dangerouslyAllowBrowser:!0,defaultHeaders:R});try{let t,r,s,l=Date.now(),i=!1,d=!1,u=e.map(e=>(Array.isArray(e.content),{role:e.role,content:e.content,type:"message"})),_=[];y&&y.length>0&&(y.includes("__all__")?_.push({type:"mcp",server_label:"litellm",server_url:`${O}/mcp`,require_approval:"never"}):y.forEach(e=>{if(e.startsWith("toolset:")){let t=e.slice(8),r=z?.find(e=>e.toolset_id===t),s=r?.toolset_name||t;_.push({type:"mcp",server_label:s,server_url:`${O}/mcp/${encodeURIComponent(s)}`,require_approval:"never"})}else{let t=T?.find(t=>t.server_id===e),r=t?.server_name||e,s=S?.[e]||[];_.push({type:"mcp",server_label:r,server_url:`${O}/mcp/${encodeURIComponent(r)}`,require_approval:"never",...s.length>0?{allowed_tools:s}:{}})}})),N&&_.push({type:"code_interpreter",container:{type:"auto"}});let L={model:c,input:u,litellm_trace_id:g,...j?{previous_response_id:j}:{},...f?{vector_store_ids:f}:{},...b?{guardrails:b}:{},...v?{policies:v}:{},..._.length>0?{tools:_,tool_choice:"auto"}:{}},R=M?await E.responses.create({...L,stream:!0},{signal:m}):await (async()=>{let e=await E.responses.create({...L,stream:!1},{signal:m}).withResponse();return d=null!==e.response.headers.get("x-litellm-cache-key"),e.data})(),B=M?R:(r=(t=R.output??[]).filter(e=>"message"===e.type).flatMap(e=>e.content??[]).filter(e=>"output_text"===e.type).map(e=>e.text??"").join(""),s=t.filter(e=>"reasoning"===e.type).flatMap(e=>e.summary??[]).map(e=>e.text??"").join(""),[...t.map(e=>({type:"response.output_item.done",item:e})),...s?[{type:"response.reasoning.delta",delta:s}]:[],...r?[{type:"response.output_text.delta",delta:r}]:[],{type:"response.completed",response:R}]),I="",$={code:"",containerId:""};for await(let e of B)if("object"==typeof e&&null!==e){if((e.type?.startsWith("response.mcp_")||"response.output_item.done"===e.type&&(e.item?.type==="mcp_list_tools"||e.item?.type==="mcp_call"))&&k){let t={type:e.type,sequence_number:e.sequence_number,output_index:e.output_index,item_id:e.item_id||e.item?.id,item:e.item,delta:e.delta,arguments:e.arguments,timestamp:Date.now()};k(t)}"response.output_item.done"===e.type&&e.item?.type==="mcp_call"&&e.item?.name&&(I=e.item.name),P=$;var P,H=$="response.output_item.done"===e.type&&e.item?.type==="code_interpreter_call"?{code:e.item.code||"",containerId:e.item.container_id||""}:P;if("response.output_item.done"===e.type&&e.item?.type==="message"&&e.item?.content&&C){for(let t of e.item.content)if("output_text"===t.type&&t.annotations){let e=t.annotations.filter(e=>"container_file_citation"===e.type);(e.length>0||H.code)&&C({code:H.code,containerId:H.containerId,annotations:e})}}if("response.role.delta"===e.type)continue;if("response.output_text.delta"===e.type&&"string"==typeof e.delta){let t=e.delta;if(t.length>0&&(a("assistant",t,c),!i)){i=!0;let e=Date.now()-l;x&&M&&x(e)}}if("response.reasoning.delta"===e.type&&"delta"in e){let t=e.delta;"string"==typeof t&&p&&p(t)}if("response.completed"===e.type&&"response"in e){let t=e.response,r=t.usage;if(t.id&&w&&w(t.id),r&&h){let e={completionTokens:r.output_tokens,promptTokens:r.input_tokens,totalTokens:r.total_tokens,...(0,n.extractPromptCacheTokens)(r),...d?{servedFromResponseCache:!0}:{}},t=r.output_tokens_details?.reasoning_tokens??r.completion_tokens_details?.reasoning_tokens;t&&(e.reasoningTokens=t);let s=o(r.cost);void 0!==s&&(e.cost=s),h(e,I)}}}return A&&A(Date.now()-l),R}catch(e){throw m?.aborted||s.toast.fromError(`Error occurred while generating model response. Please try again. Error: ${e}`),e}}e.s(["buildPlaygroundHeaders",0,l,"customHeadersFromPairs",0,e=>Object.fromEntries(e.map(([e,t])=>[e.trim(),t]).filter(([e])=>""!==e)),"parseStoredHeaderPairs",0,e=>{if(!e)return[];try{let t=JSON.parse(e);return Array.isArray(t)?t.filter(a):[]}catch{return[]}},"withRequiredHeaders",0,(e,t)=>{let r=new Set(Object.keys(t).map(e=>e.toLowerCase()));return{...Object.fromEntries(Object.entries(e).filter(([e])=>!r.has(e.toLowerCase()))),...t}}],757625),e.s(["makeOpenAIResponsesRequest",0,i],459161)},441773,e=>{"use strict";let t=e=>"number"==typeof e&&Number.isFinite(e)&&e>0?e:void 0;e.s(["PROMPT_CACHE_CREATION_TOOLTIP",0,"Input tokens written to the LLM provider's prompt cache for reuse by later requests.","PROMPT_CACHE_READ_TOOLTIP",0,"Input tokens read from the LLM provider's prompt cache (e.g. Anthropic / OpenAI), billed at a discounted rate. Reported by the provider.","extractPromptCacheTokens",0,e=>{let r=e?.prompt_tokens_details??e?.input_tokens_details,s=t(e?.cache_read_input_tokens)??t(r?.cached_tokens),n=t(e?.cache_creation_input_tokens)??t(r?.cache_write_tokens);return{...void 0!==s&&{cacheReadTokens:s},...void 0!==n&&{cacheCreationTokens:n}}}])}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0axawyhd7z6bu.js b/litellm/proxy/_experimental/out/_next/static/chunks/0axawyhd7z6bu.js
deleted file mode 100644
index 35a0718a0f0..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0axawyhd7z6bu.js
+++ /dev/null
@@ -1,421 +0,0 @@
-(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,306228,e=>{"use strict";let t=(0,e.i(475254).default)("link-2",[["path",{d:"M9 17H7A5 5 0 0 1 7 7h2",key:"8i5ue5"}],["path",{d:"M15 7h2a5 5 0 1 1 0 10h-2",key:"1b9ql8"}],["line",{x1:"8",x2:"16",y1:"12",y2:"12",key:"1jonct"}]]);e.s(["Link2",0,t],306228)},455037,e=>{"use strict";var t=e.i(494144);e.s(["prism",()=>t.default])},157058,e=>{"use strict";var t=e.i(843476),a=e.i(934879),i=e.i(976883),r=e.i(135214),s=e.i(708347);e.s(["default",0,function(){let{accessToken:e,userRole:n,premiumUser:o}=(0,r.default)();return(0,s.isAdminRole)(n)?(0,t.jsx)(a.default,{accessToken:e,publicPage:!1,premiumUser:o,userRole:n}):(0,t.jsx)(i.default,{accessToken:e,isEmbedded:!0})}])},909947,e=>{"use strict";var t=e.i(865361);e.s(["generateCodeSnippet",0,e=>{let a,{apiKeySource:i,accessToken:r,apiKey:s,inputMessage:n,chatHistory:o,selectedTags:l,selectedVectorStores:p,selectedGuardrails:d,selectedPolicies:m,selectedVoice:u,endpointType:c,selectedModel:g,selectedSdk:f,proxySettings:h,customHeaders:x}=e,_="session"===i?r:s,b=window.location.origin,y=h?.LITELLM_UI_API_DOC_BASE_URL;y&&y.trim()?b=y:h?.PROXY_BASE_URL&&(b=h.PROXY_BASE_URL);let j=n||"Your prompt here",N=j.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),w=o.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),$={};l.length>0&&($.tags=l),p.length>0&&($.vector_stores=p),d.length>0&&($.guardrails=d),m.length>0&&($.policies=m);let v=g||"your-model-name",k=x&&Object.keys(x).length>0?`,
- default_headers=${JSON.stringify(x,null,2).replace(/\n/g,"\n ")}`:"",C="azure"===f?`import openai
-
-client = openai.AzureOpenAI(
- api_key="${_||"YOUR_LITELLM_API_KEY"}",
- azure_endpoint="${b}",
- api_version="2024-02-01"${k}
-)`:`import openai
-
-client = openai.OpenAI(
- api_key="${_||"YOUR_LITELLM_API_KEY"}",
- base_url="${b}"${k}
-)`;switch(c){case t.EndpointType.CHAT:{let e=Object.keys($).length>0,t="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`,
- extra_body=${e}`}let i=w.length>0?w:[{role:"user",content:j}];a=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.chat.completions.create(
- model="${v}",
- messages=${JSON.stringify(i,null,4)}${t}
-)
-
-print(response)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
-# response_with_file = client.chat.completions.create(
-# model="${v}",
-# messages=[
-# {
-# "role": "user",
-# "content": [
-# {
-# "type": "text",
-# "text": "${N}"
-# },
-# {
-# "type": "image_url",
-# "image_url": {
-# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}
-# }
-# }
-# ]
-# }
-# ]${t}
-# )
-# print(response_with_file)
-`;break}case t.EndpointType.RESPONSES:{let e=Object.keys($).length>0,t="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`,
- extra_body=${e}`}let i=w.length>0?w:[{role:"user",content:j}];a=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.responses.create(
- model="${v}",
- input=${JSON.stringify(i,null,4)}${t}
-)
-
-print(response.output_text)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg") # or .pdf
-# response_with_file = client.responses.create(
-# model="${v}",
-# input=[
-# {
-# "role": "user",
-# "content": [
-# {"type": "input_text", "text": "${N}"},
-# {
-# "type": "input_image",
-# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}
-# },
-# ],
-# }
-# ]${t}
-# )
-# print(response_with_file.output_text)
-`;break}case t.EndpointType.IMAGE:a="azure"===f?`
-# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
-# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
-# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
-import os
-import requests
-import json
-import time
-from PIL import Image
-
-result = client.images.generate(
- model="${v}",
- prompt="${n}",
- n=1
-)
-
-json_response = json.loads(result.model_dump_json())
-
-# Set the directory for the stored image
-image_dir = os.path.join(os.curdir, 'images')
-
-# If the directory doesn't exist, create it
-if not os.path.isdir(image_dir):
- os.mkdir(image_dir)
-
-# Initialize the image path
-image_filename = f"generated_image_{int(time.time())}.png"
-image_path = os.path.join(image_dir, image_filename)
-
-try:
- # Retrieve the generated image
- if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
- image_url = json_response["data"][0]["url"]
- generated_image = requests.get(image_url).content
- with open(image_path, "wb") as image_file:
- image_file.write(generated_image)
-
- print(f"Image saved to {image_path}")
- # Display the image
- image = Image.open(image_path)
- image.show()
- else:
- print("Could not find image URL in response.")
- print("Full response:", json_response)
-except Exception as e:
- print(f"An error occurred: {e}")
- print("Full response:", json_response)
-`:`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
- # In a real implementation, this would upload the file to OpenAI
- # For this example, we'll just return a placeholder ID
- return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${v}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`;break;case t.EndpointType.IMAGE_EDITS:a="azure"===f?`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${v}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`:`
-import base64
-import os
-import time
-
-# Helper function to encode images to base64
-def encode_image(image_path):
- with open(image_path, "rb") as image_file:
- return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
- # In a real implementation, this would upload the file to OpenAI
- # For this example, we'll just return a placeholder ID
- return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${N}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
- model="${v}",
- input=[
- {
- "role": "user",
- "content": [
- {"type": "input_text", "text": prompt},
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image1}",
- },
- {
- "type": "input_image",
- "image_url": f"data:image/jpeg;base64,{base64_image2}",
- },
- {
- "type": "input_image",
- "file_id": file_id1,
- },
- {
- "type": "input_image",
- "file_id": file_id2,
- }
- ],
- }
- ],
- tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
- output
- for output in response.output
- if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
- image_base64 = image_data[0]
- image_filename = f"edited_image_{int(time.time())}.png"
- with open(image_filename, "wb") as f:
- f.write(base64.b64decode(image_base64))
- print(f"Image saved to {image_filename}")
-else:
- # If no image is generated, there might be a text response with an explanation
- text_response = [output.text for output in response.output if hasattr(output, 'text')]
- if text_response:
- print("No image generated. Model response:")
- print("\\n".join(text_response))
- else:
- print("No image data found in response.")
- print("Full response for debugging:")
- print(response)
-`;break;case t.EndpointType.EMBEDDINGS:a=`
-response = client.embeddings.create(
- input="${n||"Your string here"}",
- model="${v}",
- encoding_format="base64" # or "float"
-)
-
-print(response.data[0].embedding)
-`;break;case t.EndpointType.TRANSCRIPTION:a=`
-# Open the audio file
-audio_file = open("path/to/your/audio/file.mp3", "rb")
-
-# Make the transcription request
-response = client.audio.transcriptions.create(
- model="${v}",
- file=audio_file${n?`,
- prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""}
-)
-
-print(response.text)
-`;break;case t.EndpointType.SPEECH:a=`
-# Make the text-to-speech request
-response = client.audio.speech.create(
- model="${v}",
- input="${n||"Your text to convert to speech here"}",
- voice="${u}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
-)
-
-# Save the audio to a file
-output_filename = "output_speech.mp3"
-response.stream_to_file(output_filename)
-print(f"Audio saved to {output_filename}")
-
-# Optional: Customize response format and speed
-# response = client.audio.speech.create(
-# model="${v}",
-# input="${n||"Your text to convert to speech here"}",
-# voice="alloy",
-# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm
-# speed=1.0 # Range: 0.25 to 4.0
-# )
-# response.stream_to_file("output_speech.mp3")
-`;break;default:a="\n# Code generation for this endpoint is not implemented yet."}return`${C}
-${a}`}])},652272,209261,e=>{"use strict";var t=e.i(843476),a=e.i(271645),i=e.i(871689),r=e.i(643531),s=e.i(174886),n=e.i(306228),o=e.i(196631);let l=/^[a-zA-Z0-9][a-zA-Z0-9._-]*(\/[a-zA-Z0-9][a-zA-Z0-9._-]*)*$/,p=e=>e.trim().replace(/\/+$/,""),d=/\.(md|markdown|txt|json|ya?ml|toml)$/i,m=/\.zip$/i,u=/^[0-9a-fA-F]{64}$/,c=/^\d{1,3}(\.\d{1,3}){3}$/,g=/^[A-Za-z0-9-]+$/,f=/^[A-Za-z0-9._-]+$/,h=/^https?:\/\//i,x="ssh://",_=/^([a-z0-9._-]+)@([^:/@]+):(?!\/)(.+)$/i,b=e=>e.pathname.split("/").filter(e=>""!==e),y=e=>{try{return new URL(e)}catch{return null}},j=e=>e.hostname.includes(".")&&!e.hostname.startsWith("[")&&!c.test(e.hostname),N=e=>{let t=e.split("/").filter(e=>""!==e);return t[t.length-1]??""},w=e=>e.toLowerCase().replace(/[^a-z0-9-]+/g,"-").replace(/-+/g,"-").replace(/^-+|-+$/g,""),$=(e,t,a,i)=>{let r=p(i??"");return""!==r?l.test(r)?{parsed:{source:"git-subdir",url:t,path:r},label:`${e} subdir — ${t} @ ${r}`,suggestedName:w(N(r))}:null:{parsed:{source:"url",url:t},label:`${e} repo — 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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0bks94633rs4s.js b/litellm/proxy/_experimental/out/_next/static/chunks/0bks94633rs4s.js
deleted file mode 100644
index 4b990f3a1f5..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0bks94633rs4s.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1kid9zr1--h6y.css b/litellm/proxy/_experimental/out/_next/static/chunks/0ckv5j_fni3xm.css
similarity index 96%
rename from litellm/proxy/_experimental/out/_next/static/chunks/1kid9zr1--h6y.css
rename to litellm/proxy/_experimental/out/_next/static/chunks/0ckv5j_fni3xm.css
index e0f11bcd0b6..124b1ed3803 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1kid9zr1--h6y.css
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index 00000000000..c94282b86ba
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deleted file mode 100644
index 39cbd65bf5c..00000000000
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+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0xsuy-8_q50ub.js b/litellm/proxy/_experimental/out/_next/static/chunks/0iv7jb6y4ocy_.js
similarity index 79%
rename from litellm/proxy/_experimental/out/_next/static/chunks/0xsuy-8_q50ub.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/0iv7jb6y4ocy_.js
index a7fb552b5a7..0398a3edb78 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0xsuy-8_q50ub.js
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@@ -1,7 +1,7 @@
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client = openai.OpenAI(
api_key="your_api_key",
- base_url="${r}" # LiteLLM Proxy is OpenAI compatible, Read More: https://docs.litellm.ai/docs/proxy/user_keys
+ base_url="${s}" # LiteLLM Proxy is OpenAI compatible, Read More: https://docs.litellm.ai/docs/proxy/user_keys
)
response = client.chat.completions.create(
@@ -14,7 +14,7 @@ response = client.chat.completions.create(
]
)
-print(response)`})}),(0,t.jsx)(s.TabsContent,{value:"llamaindex",keepMounted:!0,children:(0,t.jsx)(n.default,{language:"python",code:`import os, dotenv
+print(response)`})}),(0,t.jsx)(r.TabsContent,{value:"llamaindex",keepMounted:!0,children:(0,t.jsx)(n.default,{language:"python",code:`import os, dotenv
from llama_index.llms import AzureOpenAI
from llama_index.embeddings import AzureOpenAIEmbedding
@@ -23,15 +23,15 @@ from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
llm = AzureOpenAI(
engine="azure-gpt-3.5", # model_name on litellm proxy
temperature=0.0,
- azure_endpoint="${r}", # litellm proxy endpoint
- api_key="sk-1234", # litellm proxy API Key
+ azure_endpoint="${s}", # litellm proxy endpoint
+ api_key="", # litellm proxy API Key
api_version="2023-07-01-preview",
)
embed_model = AzureOpenAIEmbedding(
deployment_name="azure-embedding-model",
- azure_endpoint="${r}",
- api_key="sk-1234",
+ azure_endpoint="${s}",
+ api_key="",
api_version="2023-07-01-preview",
)
@@ -41,7 +41,7 @@ index = VectorStoreIndex.from_documents(documents, service_context=service_conte
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
-print(response)`})}),(0,t.jsx)(s.TabsContent,{value:"langchain",keepMounted:!0,children:(0,t.jsx)(n.default,{language:"python",code:`from langchain.chat_models import ChatOpenAI
+print(response)`})}),(0,t.jsx)(r.TabsContent,{value:"langchain",keepMounted:!0,children:(0,t.jsx)(n.default,{language:"python",code:`from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
@@ -50,7 +50,7 @@ from langchain.prompts.chat import (
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
- openai_api_base="${r}",
+ openai_api_base="${s}",
model = "gpt-3.5-turbo",
temperature=0.1
)
@@ -65,4 +65,4 @@ messages = [
]
response = chat(messages)
-print(response)`})})]})]})})};var l=e.i(541202),d=e.i(135214),m=e.i(592392);e.s(["default",0,()=>{let{accessToken:e}=(0,d.default)(),n=(0,m.default)(e);return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(l.DeprecationBanner,{featureName:"The API Reference tab"}),(0,t.jsx)(i,{proxySettings:n})]})}],191905)},541202,e=>{"use strict";var t=e.i(843476),n=e.i(271645),s=e.i(522016),r=e.i(952571),a=e.i(37727);e.s(["DeprecationBanner",0,({featureName:e})=>{let[o,i]=(0,n.useState)(!1);return o?null:(0,t.jsxs)("div",{role:"alert",className:"mb-4 flex items-start gap-3 rounded-lg border border-border bg-muted/50 px-4 py-3 text-sm",children:[(0,t.jsx)(r.Info,{className:"mt-0.5 size-4 shrink-0 text-muted-foreground"}),(0,t.jsxs)("div",{className:"min-w-0 flex-1",children:[(0,t.jsx)("p",{className:"font-medium",children:`${e} is on a draft deprecation list`}),(0,t.jsxs)("p",{className:"mt-1 break-words text-muted-foreground",children:[`${e} is one of several experimental features we're considering removing, potentially as early as September 1, 2026. This list is a draft and is not final. If you rely on this feature, please share feedback on the `,(0,t.jsx)(s.default,{href:"https://github.com/BerriAI/litellm/discussions/32090",target:"_blank",rel:"noopener noreferrer",className:"underline underline-offset-4",children:"deprecation discussion"}),"."]})]}),(0,t.jsx)("button",{type:"button","aria-label":"Close",onClick:()=>i(!0),className:"shrink-0 rounded-md p-0.5 text-muted-foreground transition-colors hover:text-foreground",children:(0,t.jsx)(a.X,{className:"size-4"})})]})}])}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0kyzypnkzoi9v.js b/litellm/proxy/_experimental/out/_next/static/chunks/0kyzypnkzoi9v.js
new file mode 100644
index 00000000000..7367ba149b9
--- /dev/null
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